An Agent-Based Simulation Study of Competition in the Victorian Gas Market

by Neale F. Taylor

B.Sc. Honours I (University of New South Wales) 1967 M.S. (Louisiana State University) 1970 M.B.A. (Macquarie University) 1975

A dissertation submitted in satisfaction of the requirements for the degree of

DOCTOR OF PHILOSOPHY

SWINBURNE UNIVERSITY of TECHNOLOGY Hawthorn, Victoria

An Agent-Based Simulation Study of Competition in the Victorian Gas Market.

This thesis may only be used or copied with the written permission of Neale Taylor and Swinburne University. Unpublished rights reserved under the copyright laws of Australia. Printed in Australia. Abstract

Agent-based simulation is used to model the deregulated Victorian gas market. The model characterises the full range of market participants: producers, transmission operators, underground storage operator, wholesale traders, market manager (regulator) of the daily balancing and gas supply price setting system, distributors, retailers, and customers of various types and sizes in numerous regions. The model is domain specific and characterises participants’ behaviour in their market trading and business terms. Participants have resources and capacities, set retail prices, hold and negotiate supply contracts, decide if and when to increase capacities, determine and make daily wholesale price and volume offers, and set retail prices to customers, who have the capacity to swap retailers. All decisions are made in terms of market regulations, contract terms, seasonal patterns, market shares, prices, costs and profits.

The model provides a data rich platform that allows market agents to consider the combined interactions of both micro- and macro-decisions on their overall performance in multiple sectors. The model has few control agents and relies only on the mild synchronous control of agent interactions to achieve dynamic changes in the market over short and long periods. The model relies on the diversity of agents’ interactions and simple mental models to make micro-level business decisions rather than on using learning, gaming or control techniques. The agents’ myriad of interactions generates emergent market and industry trends as well as outlooks for individual market agent’s profit performance.

The model provides a simulation framework that a market participant can use to gain strategic insights to future market competition and how to be successful in a competitive market. Participants can use the model to assess the strengths and weaknesses of their own competitive positions in the market and to consider alternative strategies by use of a look-ahead capability for testing a range of strategies to guide dynamic implementation of varying strategies and plans.

The model is classified as a "proof of concept" model and one of very few that has attempted and succeeded in modelling a multi-sector, complex, adaptive commodity market with micro-level characterisation. The model is broadly calibrated within the limits of available data and, while outputs have not been extensively or statistically validated, retrospective model predictions compare well to actual history and a number of longer term model predictions provide rationally bounded outlooks of key industry criteria such as supply prices and demand.

The model platform has been used by the CSIRO to build their similarly structured NEMSIM (electricity) model.

i Acknowledgements

I thank the following friends and organisations for their support: • My supervising Professors Miles Nicholls and Myles Harding; two very different people but a fully complementary pair to guide me. They are always gracious in manner and giving of their time and advice, •The Australian Research Council (ARC), who awarded a SPIRT Grant for this work, • Origin Energy, who supported my work by becoming an industry partner with Swinburne University in applying for a SPIRT Grant; Origin Energy jointly funded the $100,000 SPIRT Grant for the model development work to implement my model design and hypotheses. Mr. Jon Hare and Mr. Angus Guthrie deserve special mention. •Mr. Geoff Lewis, who was employed under the SPIRT Grant to conduct the model development work to my design and specifications. Geoff did an excellent job in implementing my domain-specific thesis concepts and model design into agent-based protocols using Java software, • Swinburne University, for awarding me a PhD Scholarship to undertake this research, •Dr. David Batten and Dr. George Grozev from the CSIRO’s Centre for Complex Systems Science, who saw the potential of my model and approach; they have applied the gas model platform to address a number of parallel challenges in the electricity sector in eastern Australia. The CSIRO continues to use this model platform, •Mr. Mark Linton-Smith, who provided guidance in the use of the Framemaker software, which has been used to prepare this thesis, •Mr. Peter Millard, who acted as a professional proofreader to screen out final typing mistakes and identify any other writing errors for the author to change; Peter has no experience in the subject of this thesis, and has followed the ASEP guideline (A2.7) in his proofreading, and •My wife, Prue, and my extended family, who still love me.

Neale Taylor, August 2009

ii Declaration

This thesis contains no material that has been accepted for award of any other degree or diploma. To the best of my knowledge, this thesis contains no material published by or written by another person except where due reference is made in the text. Where work is based on joint research or publications, the relative contributions of the respective authors or contributors are disclosed.

Signed: ...... Neale F. Taylor

Dated: ......

iii iv Table of Contents

List of Figures ...... vii

List of Tables...... ix Chapter 1: Purpose, Objectives & Findings 1 1.1 Purpose...... 1 1.2 The Deregulation Challenge ...... 2 1.3 Previous Research...... 9 1.4 Research Gap...... 10 1.5 Research Objectives...... 12 1.6 Timing and Nature of Research Work...... 13 1.7 Structure of this Research Thesis...... 15 1.8 Principal Findings...... 17 1.9 Research Contribution and Uniqueness ...... 18 Chapter 2: The Victorian Gas Market 21 2.1 Australian Energy Market...... 21 2.2 Victorian Gas Market ...... 26 2.3 Modelling Issues for the Victorian Gas Market ...... 38 Chapter 3: Energy & Economic Modelling Taxonomies 43 3.1 Labys’ Taxonomy of Energy Models...... 43 3.2 Complexity & Complexity Theory...... 46 3.3 The Application of Simulation to Social and Economic Issues ...... 51 3.4 Steps in Modelling...... 57 Chapter 4: Modelling Methodologies 63 4.1 Discrete Event Programming ...... 63 4.2 Agent-Oriented Programming (AOP) & Intelligent Agents ...... 64 4.3 Agent-Based Modelling...... 67 Chapter 5: Energy & Economic Modelling Examples 83 5.1 Examples of Early Australian Energy & Economic Modelling ...... 84 5.2 Early Market Models Leading in a New Direction ...... 87 5.3 More Recent Applications ...... 88 5.4 Post 2003 Energy Modelling Studies ...... 92 5.5 Recent Australian Energy Forecasts ...... 100 5.6 The Adopted Modelling Approach For VicGasSim ...... 102 Chapter 6: Prototypes # 1 & # 2 105 6.1 Staged Model Development...... 105 6.2 Prototype # 1...... 106 6.3 Prototype # 2...... 119 Chapter 7: Prototype # 3 & VicGasSim 131 7.1 Prototype # 3...... 131 7.2 Prototype # 4 (VicGasSim) ...... 136 7.3 Participants Modelled...... 147 7.4 Model Data & Variables ...... 167

v Chapter 8: Model Calibration & Validation 179 8.1 Calibration & Validation Concepts ...... 180 8.2 VicGasSim Model Calibration & Validation...... 192 8.3 Predictive Calibration & Predictive Output Validation ...... 200 8.4 Conclusions about Validity of VicGasSim and Its Outputs ...... 209 Chapter 9: Exploring VicGasSim’s Potential 211 9.1 The Need to Understand What Drives Emergent Outputs...... 211 9.2 With Static Strategies ...... 211 9.3 With Dynamic Strategies Using Look-aheads ...... 232 9.4 Data-Mining of Scenarios to Identify Strategic Insights ...... 238 9.5 Conclusions ...... 246 Chapter 10: Observations, Conclusion & Future Model Enhancements 251 10.1 Observations...... 251 10.2 Conclusion...... 258 10.3 Future Model Enhancements...... 260

Abbreviations ...... 263

Bibliography ...... 265

Appendix 1: Access to the VicGasSim Model 279 1.1 Website Download...... 279 1.2 Access Permission ...... 279

Appendix 2: Agent Descriptions & Model Input 281 2.1 Agent Descriptions...... 281 2.2 Main Input File ...... 281 2.3 Weather File ...... 281 2.4 Model Initialisation File ...... 281

Appendix 3: Model Output Examples 283 3.1 Opening Window for VicGasSim ...... 283 3.2 Bid Stacks & Pool Price ...... 284 3.3 Bid Stacks in Graphical Form ...... 285 3.4 Look-ahead Test Case Comparisons ...... 286 3.5 Report Summaries...... 287 3.6 Supply Contract Monitoring ...... 288 3.7 Demand Tracking ...... 289 3.8 Retailer Annual Profit and Profit Margin Summary Reports...... 290

Appendix 4: Industry Changes Since 2003 291 4.1 Overview...... 291 4.2 Corporate and Regulatory Changes – Participants ...... 292

Appendix 5: Multiple-Criteria Decision Making (MCDM) 297

Appendix 6: Author’s Publications 299

Appendix 7: Author’s Curriculum Vitae 301

vi List of Figures

Figure 2-1: Australia’s Energy Consumption: 1973/74-1997/98 ...... 21 Figure 2-2: Australian Gas Supply Sources Connecting Pipeline Networks ...... 22 Figure 2-3: East Coast Supply & Demand for Gas ...... 24 Figure 2-4: Victorian Gas Market ...... 27 Figure 2-5: Victoria’s Historical Gas Consumption...... 28 Figure 2-6: Distribution of Gas Consumption by Sector - Victoria...... 29 Figure 3-1: The Development of Contemporary Approaches to Simulation in the Social Sciences...... 53 Figure 3-2: Werker & Brenner’s Simulation Model Classification System...... 54 Figure 3-3: Classification of Five Types of Simulation Models...... 55 Figure 3-4: Categorisation of Five Different Simulation Approaches ...... 60 Figure 4-1: The Key Components of an Intelligent Agent System ...... 66 Figure 5-1: Overview Structure of the EMCAS Model...... 91 Figure 5-2: Overview Structure of CSIRO’s NEMSIM Model ...... 99 Figure 6-1: Model Evolution ...... 105 Figure 6-2: Model Design of Prototype # 1 ...... 107 Figure 6-3: Agent-generated Competition...... 112 Figure 6-4: Number of Retailers Needed to Reduce Average Market Price Volatility...... 115 Figure 6-5: Prototype # 2 Look-ahead Capability...... 123 Figure 6-6: Comparison of Retailers’ Performance...... 125 Figure 6-7: Comparison of Producers’ Performance...... 127 Figure 6-8: Market Demand & Retail Prices...... 128 Figure 7-1: Proposed Four-tiered Hierarchical Gas Industry Model Structure ...... 133 Figure 7-2: Agent Strategies & Plans at Different Hierarchical Levels ...... 135 Figure 7-3: Representation of the Dynamics of the Model Structure (as used in NEMSIM) ...... 138 Figure 7-4: Retailer’s Model View of Daily Bidding System ...... 142 Figure 7-5: Map Showing Customer Areas & Transmission Grid ...... 147 Figure 8-1: Withdrawal Patterns & Levels By Customer Size...... 194 Figure 8-2: Simulated Victorian Gas Demand Pattern for 2001...... 195 Figure 8-3: Comparison of Actual Vs Modelled Bid Stacks...... 196 Figure 8-4: Comparison of Actual Vs Simulated Daily Pool Prices for 2001...... 197 Figure 8-5: Simulated Average Retail Prices For Each Customer Size Type ...... 198 Figure 8-6: Retailer Market & Profit Performance ...... 199 Figure 8-7: Comparison of Old & Recent Demand Forecasts...... 201 Figure 8-8: Comparison of Old & Recent Gas Supply Price Forecasts ...... 203 Figure 8-9: Comparison of Retail Prices ...... 207 Figure 8-10: Comparison of Supply Sources ...... 208 Figure 9-1: Average Gas Supply Prices...... 213 Figure 9-2: Forecast Gas Production by Source into Victoria...... 215 Figure 9-3: Retailer Profits & Costs for Origin Energy...... 217 Figure 9-4: Performance Trends – Origin Energy...... 219 Figure 9-5: Profit per Customer Size...... 222 Figure 9-6: Comparison of Retailers’ Annual Profits as Forecast by VicGasSim...... 223 Figure 9-7: Average Retail Prices...... 224 Figure 9-8: Average Gas Supply Costs...... 225 Figure 9-9: Origin’s Profit & Costs Performance...... 228 Figure 9-10: TXU’s Profit & Costs Performance ...... 229 Figure 9-11: Pulse’s Profit & Costs Performance...... 230 Figure 9-12: Energex’s Profit & Costs Performance...... 231 Figure 9-13: Retailer Profits and Costs – TXU Performance...... 233 Figure 9-14: Pulse Forced to Contract Higher Priced Gas...... 235 Figure 9-15: Pulse’s Relative Performance...... 236

vii viii List of Tables

Table 3-1: Labys’ Taxonomy of Model Types ...... 45 Table 6-1: Agent’s Beliefs, Plans, & Messaging Capabilities ...... 108 Table 6-2: Prisoner’s Dilemma – Retailer’s Cumulative Profits For Given Strategy Choices ...... 116 Table 6-3: Prisoner’s Dilemma – Producer’s Cumulative Profits For Given Strategy Choices ...... 117 Table 6-4: Retailer Strategy Choices ...... 126 Table 6-5: Retailer Total Profits over 15 Years ...... 126 Table 6-6: Comparison of Cumulative Total Producer Profits ...... 128 Table 7-1: Simulation Engine ...... 139 Table 7-2: Discrete Events to Complete Daily System Balance & Set Pool Price ...... 140 Table 7-3: Look-ahead Process ...... 146 Table 7-4: MarketManager (Vencorp) ...... 151 Table 7-5: Retailer Bidding ...... 155 Table 7-6: Retailer Bidding - Strategy1BidHandler ...... 156 Table 7-7: Retailer Bidding - Strategy2BidHandler ...... 157 Table 7-8: Storage Replenishment - Replenishment1BidHandler ...... 158 Table 7-9: Retailer - Producer Supply Contract Negotiations ...... 159 Table 7-10: Customer Churn ...... 164 Table 7-11: Exogenous Variables ...... 169 Table 7-12: Endogenous Variables ...... 174 Table 8-1: Comparison of Numbers of Customers by Retailer ...... 206 Table 9-1: Retailer Profit Performance Comparison - Effect of Intelligence ...... 237 Table 9-2: Detailed Comparison of Pulse’s Performance in 2003 & 2008 ...... 240 Table 9-3: Detailed Comparison of TXU’s Performance in 2003 & 2008 ...... 242 Table 9-4: Detailed Comparison of Origin’s Performance in 2003 & 2008 ...... 244 Table A5-1: Decision Matrix for Selecting a Retailing Strategy ...... 298

ix x Chapter 1: Purpose, Objectives & Findings

1.1 Purpose This thesis sets out to show that a complex, commercial market such as the Victorian gas market can be simulated by mimicking the myriad, commercial transactions that take place in this market. Simulating the multiplicity of market transactions is accomplished in a model using an agent-based protocol. The modelled market interactions yield an emergent market behaviour, achieve a sustainable market and overcome many of the modelling weaknesses in more traditional methods. The model in its final form is known as VicGasSim.

The thesis presents:

1. A description of the unique issues facing the Victorian gas market prior to deregulation in the 1990’s,

2. A justification for selecting agent-based simulation as a suitable modelling approach to address identified issues after consideration of historical and more recent trends in energy and economic modelling,

3. A structure for an agent-based model (ABM)1 to study this market,

4. Character profiles for the model’s agents, which act autonomously in an hierarchical fashion, to simulate the complex, interactive nature of this market,

5. Model outcomes, which show that the market will be strongly influenced by the characteristics of the individual participants as well as their interactions in the market in which they exist,

6. An understanding of how the participants’ decentralised, local interactions generate the emergent, dynamic characteristics for the participants and the macroscopic, aggregated market, which still shows a rational sense of regularity despite not being in perfect equilibrium, and

7. A new approach for testing the success or failure of possible strategies that participants might use to pursue improved commercial performance in the Victorian gas market and for testing the

impact of policy changes such as more or less regulatory control or carbon dioxide (CO2) emission controls and carbon pricing.

1. Abbreviations and acronyms used in this thesis are defined in an Abbreviations section that follows Chapter 10. Australian standard spelling is used except in quotations, which use a different standard – for example, American spelling and use of "z" versus "s".

1 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

On commencing this research effort, the author set out to build a model of the combined gas and electricity market for all of eastern Australia. This goal was overly ambitious and not within the bounds of available resources and time; therefore, the author focused on exploring and modelling the issues facing the gas industry in Victoria.

The stimulus for this research came from the author’s involvement1 in the gas industry and, in particular, involvement in a number of corporate acquisitions requiring estimates of future gas prices in the south eastern Australian gas markets to value target acquisitions. The author then believed that competitive behaviour would determine such prices and that they would be higher than prevailing forecasts, which were mainly based on least-cost optimised development planning. The author held a firm view that there had to be a better way to capture how these competitive pressures between market participants would emerge over time to influence future gas prices.

Agent-based modelling technology was selected as well-suited to address the market types of issues, which are identified and described in 1.22 and 2.3. The major research challenge has been in designing and building such a model, calibrating it to the Victorian gas market as it existed in the 2000-01 period. This research project commenced in late 1998 with most of the model development work completed in 2002-03; the final model is calibrated to actual data available in the 2000-01 period, which is the period when the market commenced deregulation.

The completed work provides a "proof of concept" model that can simulate all the major participants in this industry and mimic their commercial interactions and behaviours with minimum use of model control agents. All of VicGasSim’s prediction runs or future scenarios, which are included in this thesis, were generated in 2003. Given that the thesis was written in 2008-09, there is a material time break between writing the thesis and when the simulation runs were recorded and documented in 2003. Therefore, literature references have been updated to reflect more recent research as well as predictions from third party models that were published after 2003; these updates put the VicGasSim in a more recent context and provide more yardsticks, by which to assess the merits of VicGasSim.

1.2 The Deregulation Challenge Numerous countries have chosen to deregulate their domestic energy markets as the most effective way to achieve changes in energy pricing, consumption, and investment as well as to prioritise the take-up of new technologies. Australia is one of these countries and the State of

1. The author was involved in the gas industry for an extended period both as an employee of Esso Australia and later as a consultant and director of a gas wholesaling company. Much of that experience is used as the source for the anecdotal industry commentaries included in this thesis and the business decision-making models incorporated into the agent-based simulation model, which is described in this thesis.The author’s curriculum vitae is presented in Appendix 7. 2. References to other numbered sections in this thesis are designated only by the number of the section.

2 Chapter 1 Purpose, Objectives & Findings

Victoria has substantially deregulated and restructured its gas market. Deregulation and restructuring introduced complexity to market behaviour. A lack of history or experience in dealing with this type of market complexity has led industry participants to seek new modelling approaches to consider the implications of deregulation and market changes.

Most historical energy and econometric modelling efforts have relied on the assumption that the future would reflect an extension of the past. In the past, Victorian gas market interactions took place in an industry that was a monolopy on the retail side and a monolopy on the supply side. The market was highly regulated and long-term supply contracts inhibited supply competition even if such contracts provided low priced gas.

Past modelling of this environment relied heavily on a number of inherent assumptions. For example, all participants understood their places in the market and followed or accepted a minimum-cost optimised development order for provision and contracting of future gas supplies.

This situation led to a 30 year stable period in what was a double-sided monopolistic gas market with one upsteam supplier, Esso-BHP1, with surplus capacity under a fixed long-term contract, to supply gas to one Government-owned entity, the Gas and Fuel Corporation (GFC), that controlled gas transmission, distribution and retailing. The situation was stable until growth in market consumption approached the level at which demand was forecast to reach and exceed contracted supply capacity – sometime from the late-1990’s onwards. The Government also knew that annual supply volumes under its historical long-term supply contracts would step down annually from the early 2000’s before expiring completely by about 2010-11. It also knew that Esso-BHP would be looking to capitalise on their then monopolistic supply position to push for higher gas prices to expand capacity and write new supply contracts.

The Government looked for a solution in the form of market deregulation. The Government held a number of inquiries in the early and mid-1990’s to consider how best to deregulate the market. It introduced a new system of staged deregulation to apply from the late 1990’s2.

Under deregulation, historical Government monolopies have been restructured, disaggregated, and privatised. Several domestic companies expanded rapidly by acquiring some of the disaggregated downsteam3 entities; numerous, large multi-national energy companies entered the Australian market place to acquire other parts of the former Government-owned system.

1. In the early part of this thesis, this group is referred to as “Esso-BHP”, which is how it was known at the time this research started and is used in various model designations in this thesis. Esso is now known as ExxonMobil and BHP is now known as BHP Billiton. In later chapters, when dealing with more recent history, the group and individual companies are referred to by their current names. 2. See Chapter 2 for background on Government inquiries and deregulation policies and programs. 3. The term "downstream" in this thesis refers to all market activities downstream of a Producers’ point of sale at the gate to its processing plant. In the reverse sense, the term "upstream" means all exploration, development, production and processing upstream of the same point of sale by Producers.

3 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

The Government divided up its remaining supply contract with Esso-BHP into four parts (3 large, 1 small) and allocated these rights to the newly disaggregated and privatised retailers.

Some new entrants left the market relatively quickly and ongoing old and new participants have altered their initial strategies as time has progressed. These changes suggest some participants’ initial strategies were inappropriate to the circumstances.

The expected tailing-off of historical Gippsland supply contracts provided a window for new supply sources to enter the market with alternative, competitive supplies. A number of new gas discoveries were made both offshore and onshore Victoria from the late 1990’s. In addition, new, entrepreneurial owners acquired and appraised earlier discoveries, which had been economically stranded until deregulation. These new supply sources provided the first real supply competition to Esso-BHP since the creation of this market in the late 1960’s.

Encouragement for this supply competition was reinforced by other factors such as the major explosion that damaged the Longford Gas Processing Plant in 1998. There was also an emerging realisation that Cooper Basin gas supplies to New South Wales (NSW), South Australia (SA) and Queensland would be more expensive than previously anticipated due to higher than expected development costs. It was believed that increasing gas demands in NSW, SA and Queensland might only be met if supplemented by increased supplies from Victoria and/or northern Australia or Papua New Guinea (PNG). These changing views provided pressure for the additional deregulation of interstate trade in both gas and electricity and expansion of interstate gas and electricity supply connections.

However, market penetration by new gas suppliers has been and continues to be influenced by the wider corporate strategies of both major and minor market participants as well as by the simple cost minimisation objectives of most consumers. The growing complexity of how these emerging forces might confront each other became increasingly apparent almost immediately after the introduction of deregulation.

When deregulation started in the late 1990’s, aspiring market participants had little Australian historical basis for considering the implications of such market changes on their future business outcomes or for considering how they might influence those outcomes. Some participants entered blindly and aggressively using experience from other markets. Some recognised the unique complexities but still felt uncomfortable that they knew how to choose or define their forward strategies or in which directions the various energy markets, in which they competed, could, should or would develop.

4 Chapter 1 Purpose, Objectives & Findings

International experience under deregulation and some early local experience suggested that new competitive behaviour patterns would emerge; new directions and surprise outcomes should be expected. This experience indicated deregulation was typically followed by periods of volatility, caused market disruptions, stimulated some restructuring, and required follow-up regulatory changes to improve market efficiencies.

While such international experience sheds some light on possible effects to consider in designing a market simulation model for the Victorian gas market, it occurred in vastly different settings and legislated structures. Because of the differences, such experience hints at possible issues for Victoria’s situation but the latter needs separate assessment to avoid wrong conclusions, strategies and policies.

Change has occurred quickly in the Victoria gas market. Early participants positioned differently for horizontal and vertical integration efficiencies. Transmission and distribution systems were interconnected within and between states and open access widened competition. New supply sources have been developed. Within 5 years of deregulation being introduced, growth in gas demands in SA, NSW and Tasmania were being met from expanded and diversified Victorian gas supply sources, although Esso-BHP remained the largest supplier.

The industry has undergone some early restructuring through acquisitions and divestments and such restructuring is expected to continue. Large international new entrants paid high prices to acquire assets from Governments and challenged established domestic competitors; some have already exited the market due to a lack of success in expanding and/or generating required profits. Merger and acquisition activity has been relatively high as a result of outcomes in Victoria as well as in energy markets in other states. Further new discoveries have been made and exploration activity remains relatively high. Governments have conducted follow-up reviews1 and regulation has also undergone changes but the general thrust remains to encourage more competitive, efficient and transparent markets.

International demand for liquified natural gas (LNG) has grown rapidly in the last few years, driving up LNG prices. Netback gas prices from LNG exports from the North West Shelf (NWS) of Western Australia (WA) have flowed quickly to affect domestic gas prices in WA as domestic supplies have tightened markedly in the last year or so. Domestic gas prices in WA have approximately tripled over the last one to two years. The short time it took for this change to take place has surprised most of the market participants in WA. It is possible that a similar phenomenon could occur in southern and eastern Australian markets given recent announcements of plans to build several LNG plants on the central Queensland coast based on development of

1. See Appendix 4 for background on recent inquiries and regulatory and participant changes.

5 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

gas supplies from Coal Seam Methane (CSM). Any early price pressures from LNG flow-on effects could be tempered by possible exponential growth in supply from the very large gas resources assigned to CSM in Queensland and NSW. It is possible that a long term surplus of CSM could flood east coast markets and exert downwards pressure on long term gas prices in east coast domestic markets. Which effect will dominate? How can a market participant attempt to answer this question?

Another wild card that could affect the demand for gas in south eastern Australia is the forecast growth in Gas-fired Power Generation (GPG). A number of GPG plants have and continue to be built to supply peak electricity markets in response to market deregulation. This new sector is adding to the growth in gas demand. It is expected to grow rapidly with green energy pressures. Climate change, emission controls/targets and carbon trading will bring a whole new dimension to legislation and market competition. A recent forecast for GPG to replace brown coal-fired power generation in Victoria is presented in Chapter 8 (see Figure 8-7); if correct, such growth should put upwards pressure on gas prices, countering possible abundant supply of CSM.

The above examples are but some of the changes that have and will continue to bear on the broader energy market, especially gas and electricity. Change will continue to characterise and influence energy market dynamics. Uncertainty remains high and will remain high for decades.

The regularity of surprises since the late 1990’s has led Governments to initiate several inquiries to consider further modifications to the deregulated energy systems and to the phalanx of regulators introduced to protect various community and market sector interests1. Regulators and policy investigators struggle with the uncertainties about the future directions of competition in the industry and whether to support requested price increases, how to assess caps on regulated prices and tariffs, and whether to support increased regulation or to ease regulation. The regulators have been criticised for causing market inefficiencies, inhibiting infrastructure investments, and not allowing the market to work freely. In the early days of deregulation, regulators confronted new investors by imposing regulated price caps and transmission tariffs that did not provide the rates of return such investors asssumed when they set the prices they paid to acquire related assets in the mid-1990’s; subsequently, there has been some relaxation by regulators of return expectations.

As the market has matured, all parties have developed better understandings of the markets and a relatively dynamic market has evolved. But there have been some corporate and community casualties and, while the market might be rated as remaining in dynamic balance in any overall assessment, the market remains highly uncertain for most participants in terms of survival and sustaining an ongoing profitable business.

1. See Appendix 4 for a commentary on recent changes to regulations and regulators as well industry participants.

6 Chapter 1 Purpose, Objectives & Findings

In response to this uncertainty, new modelling approaches have been explored by many of the participants, often with a focus on a particular segment of the market and often relying on analysing the past for indications of future market trends. While segmented models are of value in giving insights to segment behaviours, modelling efforts that focus on a single business segment or limited number of segments or issues are unlikely to identify the critical determinants for the emergent behaviour of the whole market. Segment specific models may lead participants to adopt a successful short-term segment strategy but hold an inappropriate global strategy that fails to grow their overall business against emerging and changing competition. That is: they might win the battle but lose the war.

What needs to be recognised more widely is that the emergent1 behaviour of the market will be strongly influenced by the integrated nature of all of the market participants and their dynamic interactions. These aspects are explored as a major theme of this thesis, which differs from many other market studies in that the emphasis is on developing a tool to assist participants rather than to help governments and regulators test and develop improved policies or market designs (rules).

As a result of the changes and uncertainty described above, many companies have searched for new approaches to identify possible alternative strategies and test them for robustness. Market participants have taken early stands as to likely successsful strategies:

1. AGL, Origin Energy and others pursued horizontal integration by establishing a retailing and power generating presence in all of the eastern states. Dual-fuel marketing of gas and electricity has become a common strategy. Maintaining indirect participation and minority control of transmission and distribution systems was seen as important early in the post-deregulation period but is now seen to be less important to energy retailers and such businesses are being divested to infrastructure-type investment companies.

2. Origin Energy differentiated itself from AGL and other retailers by also participating in the upstream gas supply segment2 and took stronger, early positions in GPG and green energy.

3. TXU introduced a new segment by developing an underground gas storage project in western Victoria to provide for a workable secondary gas market with trading of storage rights to cover growing peak loads in the Victorian gas market.

4. Duke Energy concentrated its market entry by building interconnecting pipelines between states; this positioning suggested that it would use its multiple positions for building a gas trading business but it backed away from this position and eventually sold out of the market.

1. The concept of emergence is described in some detail in 4.3.7. 2. However, AGL took a number of recent steps to emulate Origin by acquiring upstream interests in CSM but subsequently reversed this position by divesting the same interests.

7 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

5. Other major international new entrants specialised in gas and/or electricity, in generation, in retailing, in distribution and transmission, and they often limited initial activities to one state or one energy form but have subsequently sold out, purchased or swapped assets to gain a presence in multiple markets.

As alluded to above, some early strategies appear to have been flawed. In some cases, too much was paid upfront and large dollar losses flowed to bottom-lines and reduced balance sheet values within as little as 3-4 years.

Increased competition introduced market volatility. Following deregulation, the wholesale electricity price in eastern Australia, dropped to a low of $15 per kWH in 1997-98 (Australian Bureau of Agricultural and Resource Economics [ABARE] 2000) at which level it was believed to equal marginal operating costs. Prices recovered to approximately $25 per kWH in 1998-99 when participants realised no party had market dominance. Such pricing volatility deferred investment decisions and introduced the additional risk of future capacity shortfalls.

In contrast, the wholesale price of gas in Victoria was relatively stable at approximately $2.70 per GJ from 1998 until 2001-02 (Australian Energy Regulator [AER] 2007) due to the staged introduction of retail competition across regions and continued reliance solely on existing Gippsland supply contracts. Similarly, the volume of gas traded in the new secondary market administered by Vencorp was initially and consistently at a low 2% of the total Victorian gas market; this was the minimum level necessary to balance the market on a daily basis under deregulation. But beyond 2001-02, and following the introduction of full contestability, the secondary gas market regularly traded in the 4-10% range during the peak winter season. It now sits in the 10-20% range (AER 2007).

Peak loads consistently exceed available base load capacity in both summer and winter and rely on more expensive supply from underground storage, interstate supply sources, and on rare occasions, from re-vaporisation of stored LNG. Supply from the latter sources saw daily wholesale gas prices rise to levels between $3 and $4/GJ with spikes to $6/GJ in the post- deregulation period. On 16 April 2007, following introduction of intra-day pricing changes, gas prices peaked at $35/GJ during the last trading period of the day (AER 2007).

In the gas retail market, regulated price caps protected smaller residential customers until 2002. Beyond that date, customers could choose to take up unregulated prices but if they did so they could not take up future offers under the protected price system.

Under regulated prices, retailers made early losses1 in these smaller market segments, which required cross-subsidisation by larger customers. While regulators maintain price caps, price

1. Anecdotal advice to the author by an industry participant as part of the SPIRT Grant project, which was granted to support this work.

8 Chapter 1 Purpose, Objectives & Findings

differentiation and competition remain inhibited. Differentiation increasingly relies on dual-fuel marketing and the cutting of average customer administrative costs by growing customer numbers through acquisitions in the electricity sector and markets in adjoining states. Higher customer numbers can be used to share the high fixed cost of related administrative systems.

Some companies were well placed to capitalise on these situations; others were not. Access to suitable energy and strategically-oriented modelling tools would have helped some of these companies to improve their positions and/or avoid becoming an early market casualty.

1.3 Previous Research In Australia, the most commonly reported energy modelling studies in the lead up to deregulation were those that used MARKAL1-based models to look at high level interactions between various energy sectors and the national economy and to consider the impact of national policies such as the adoption of new technologies to meet greenhouse gas emission targets. Such models are macro- econometric in nature and useful for a high level representation of industry structures and their relation to the national and state economies. They typically assume least-cost optimisation, do not consider strategic positioning or profitability, and do not closely represent the discreteness of individual projects and the graininess or heterogeneity of the Australian energy market. Australian energy market structures are in stark contrast to the more homogeneous nature of much larger and diversified markets like those in the USA, UK and Europe. ABARE maintained one or more such models in conjunction with Monash University (see 5.1.3 for further background) at the time the research for this thesis work commenced.

At a more commercial level, there are a number of consultants that maintain and publish forecasts and analyses of Australia’s various energy markets (e.g. Australian Gas Association [AGA] and ACIL2). When the research for this thesis started, public forecasters tended to rely on multiple models and forecasting approaches: Linear Programming models (LP’s) to forecast supplies were based on details from specific project cash flow models and designed to find cost-minimisation solutions; demand functions were derived from models, such as MARKAL, to provide economic drivers; individual project models and price elasticity functions were used to simulate price effects on demand. Price forecasts from these models tended to reflect a market dominated by cost-plus behaviour by the gas producers.

1. MARKAL is a generic model tailored by the input data to represent the evolution over a period of usually 40 to 50 years of a specific energy system at the national, regional, state or province, or community level. MARKAL was developed in a co-operative multinational project over a period of almost two decades by the Energy Technology Systems Analysis Programme (ETSAP) of the International Energy Agency. A description of MARKAL is given in Fishbone and Abilock (1981). 2. ACIL is a major consulting group, which specialises in analyses of government policy in the areas of economics and energy.

9 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Using combinations of such models does allow focused consideration of numerous issues but does not specifically handle the dynamics of the market or adjust for the various interactions that take place between market participants or the way in which participants react to actions or plans by other market participants. Such models often iterate on data flows between various models to provide some level of dynamic interaction and adjustment of model outputs.

Historically applied models have weaknesses. Macro-economic models such as the MARKAL 1 (& E4cast ), which are classified as general equilibrium econometric models, are unable to reflect the graininess of the heterogeneous Australian energy market. Reliance on least-cost optimisation loses touch with the strategic, interactive, discontinuous and discrete events likely to characterise the evolution of this sector of the Australian economy.

In the same way, LP models, which contain the cost structures of the major supply, transmission and distribution systems, can be used to schedule projects and to predict future energy prices, which are based on costs, but this approach does not account for or recognise the profit motivation, strategic impact of vertical and horizontal integration and regulatory constraints on competition. Such approaches are prone to over or under-predict future gas prices. They are useful because they include important cost information but there are possibly better ways to use that information depending upon the investigator’s objectives.

Descriptions of several such models or groups of models are presented in 5.1.

1.4 Research Gap 1.4.1 Limitations of Existing Energy Models The key limitation of model approaches, which were in use when this research commenced in the late 1990’s, is that they did not lend themselves to be devolved to the level at which they can handle the complexity and large number of the different types of interactions between the market participants over the short, medium and long term.

Labys (1999) generalises that the issue of choosing the right model depends upon defining the problem or question to be addressed. This approach might be put in more mundane terms as you need “horses for courses” and when embarking on a new “course” you will likely need a new “horse”. A summary of Labys’ taxonomy of mineral and energy model types is presented in 3.1.

1. E4cast is a partial equilibrium model of the Australian energy sector. It is maintained and used by ABARE to project energy consumption by fuel type, by industry and by state or territory, on an annual basis; the main consumption drivers in E4cast are real incomes, industry output and fuel prices. E4cast is described in ABARE 2003: Australian energy: national and state projections to 2019-20.

10 Chapter 1 Purpose, Objectives & Findings

1.4.2 New Directions for Energy Models In the 1990’s, new modelling techniques developed from the wider acceptance of the object- oriented programming protocol in languages such as Java software. A specific protocol variation of this object-oriented style of programming is known as Agent-Oriented Programming (AOP) to represent agent-based modelling (ABM) systems1.

An agent is an autonomous entity that can interact with other agents. Individual agents are presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, and their knowledge is usually somewhat limited to their own environment. An ABM is a computational model for simulating the actions and interactions of autonomous individuals or agents in a network, with a view to assessing their effects on the system as a whole2.

A stream of literature now exists recording the growing use of agent-based systems to control robots, search engines on the internet, predator-prey systems, and simulation systems to characterise a wide range of technical and commercial systems. In essence, agent-based systems involve a collection of agents, who are assigned human-like behaviour to act in their domain and interact in an independent way with other agents and their environment. A fuller explanation of AOP and ABM is presented in Chapters 3 and 4. Such systems usually have a large population of differing agents that interact with each other directly or indirectly.

Adoption of AOP and ABM can assist in overcoming some of the model limitations addressed in 1.2, 1.3 and 1.4.1. However, agent-based systems still bring their own special set of modelling challenges, including how to maintain a dynamic, non-equilibrium market in such models, how to simulate commercial negotiations between agents, and choosing the right level of detail to address the problems or questions being explored without either undercharacterising the market or overburdening the model.

The project, which is the subject of this thesis, adopts ABM and uses a hierarchical market structure separating reactive (consumption) market activity from proactive operational activities and from strategic or decision-making activities that link them together. This separation of levels allows formulating and testing segment strategies and plans, including cross-checks on the effects of decisions in one segment on other segments and how the combined effects influence corporate or higher level profitability outcomes. Hierarchical modelling has been adopted by various researchers to separate model interactions in order to allow focused and numerous interactions at one level and a lesser number of broad interactions at a higher level (see 7.1.2).

1. Sometimes referred to as agent-based simulations. Often the term multiagent or multi-agent is used in place of agent. 2. These high level descriptions of agents and agent-based models (ABM) are from Wikipedia (20 July 2008).

11 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

A special feature of the model has been to develop a set of agents with domain-specific profiles and with a capability to make ongoing forward projections (referred to in this thesis as "look-ahead" capability, which can be used in much the same way a market participant would run sensitvity analyses to aid in making an impending business decision). This look-ahead capability can be invoked on a regular basis to aid each agent to assess how effective any of its own plans or strategies are likely to be when played out interactively against other agents. An agent’s capability to look-ahead is used before choosing a high- or low-level strategy to apply in a specific market segment. This capability can assist model agents to cope with a setting of ongoing dynamic interactions without superimposing omnipresent, omnipotent, enforcing control agents.

Such control agents are used in many energy or commodity market models to find optimum solutions. The latter approach relies heavily on the modeller’s views about what will determine an optimal solution and that the solution will be accepted willingly by all participants in the real market place. This an unrealistic view of how a real and complex market works.

In contrast to the imposed control agent approach, a market-motivated set of detailed interactions between autonomous agents can provide a basis for eliciting an emergent market behaviour that is generated by the synthesis of localised agent interactions. This approach has the potential to better reflect the competitive imperfections of a complex and heterogeneous market that is undergoing a series of discontinuous changes. As explained in Chapters 3 and 4, recent literature suggests ABMs with sufficient detail in describing the behaviour of the majority of participants can achieve a dynamic, non-equilibrium market without the need to superimpose control through macro- or micro-econometric model features. Such an outcome is one of the main objectives for this research project.

1.5 Research Objectives This thesis will explore the following research objectives, which were established at the start of the project:

1.5.1 Objective # 1 To characterise the future evolution of the post-deregulation Victorian gas market by designing and implementing a model that can reflect:

1. the immature and non-homogeneous nature of this market,

2. the effect of a series of past and future discrete events, including: deregulation and restructuring, expiry of major supply contracts, and entry of new supply sources and new market participants, and

3. the strong influence of the differing corporate strategies, financial capabilities and asset ownership of the major market participants.

12 Chapter 1 Purpose, Objectives & Findings

1.5.2 Objective # 2 To create an agent-based simulation model of the Victorian gas market to explore the market’s evolution and the issues, which are raised in Objective # 1.

1.5.3 Objective # 3 To populate the model with a wide array of model agents, whose market actions are behavioural in nature, and who are characterised with market resources, negotiating strategies, operating plans, and corporate decision-making capability to achieve an ongoing dynamic market without recourse to model control agents.

1.5.4 Objective # 4 To use the model to simulate various scenarios that can be used to explore and understand emergent market conditions, which are derived from the synthesis of the individual strategies and interactions of the market participants, and to use these scenarios to identify likely key determinants for corporate success in this market.

1.6 Timing and Nature of Research Work As stated earlier, this research and associated model development work was undertaken in the period from 1998-2003. This period covered the first few years of deregulated competition in the Victorian gas market.

A number of refereed papers were published over this period: Taylor & Harding (2000); Taylor, Harding, Lewis & Nicholls (2000); and Taylor, Harding, Lewis & Nicholls (2003)1. The work formed the basis of a $100,000 grant awarded to Swinburne University in January 2001 under the Strategic Partnerships With Industry Research and Training (SPIRT) scheme administered by the Australian Research Council2. Origin Energy was the 50% industry funding and advising partner. The SPIRT Grant funding covered the cost of the model development work under the design and direction of the author. The model concepts, design, mental models and input data were all provided by the author. Professor Myles Harding provided input to the development of a number of the agent-oriented protocols adopted in the model structure. Programming of the model to the author’s model specifications was completed by Mr. Geoff Lewis. The project was conducted under the administrative guidance of Professor Miles Nicholls.

Development and testing work on the model ceased in mid-2003 after funds from the SPIRT Grant had been fully applied to developing VicGasSim. VicGasSim was operational from late 2002 onwards and used to generate the scenarios presented in this thesis. All scenarios

1. Details of the author’s publications are presented in Appendix 6. 2. SPIRT Grant File No C00107404

13 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

documented in this thesis were run and documented before mid-2003; many of the displays are included in the paper by Taylor, Harding, Lewis & Nicholls (2003). This point is made because these 2003 scenarios are compared to recent forecasts by third party forecasters; these comparisons are presented in Chapter 8.

Key findings from this work were presented by the author to the Energy Markets Group of the Decision Technology Centre at the London Business School in January 2003. This group is widely acknowledged1 for its energy modelling work on the UK and European electricity sector; their work is described in 5.3.1 and 5.4.1. Dr. Derek Bunn, the head of this Centre, described the author’s model as "a rich platform for investigating energy markets"2.

In 2003, the author and Professor Harding advised the CSIRO’s Centre for Complex Science on research work that the CSIRO was pursuing in regard to modelling the deregulating electricity market for eastern Australia. This Centre subsequently adopted much of the gas model structure and platform to build a similar model (known as NEMSIM) for the eastern Australian electricity market. This work continues as part of the CSIRO’s Energy Transformed Flagship3.

More background on NEMSIM’s development and acknowledgement of adopting the VicGasSim model platform and design for NEMSIM is presented in 5.4.2.1. The post-2003 work by the CSIRO to develop NEMSIM supports the value and robustness of the author’s preceding work. The NEMSIM developers considered using Argonne National Laboratories’ EMCAS model, which is described in 5.3.3, but they preferred to use the VicGasSim platform.

The author became involved in a number of activities that saw him take an extended break from late- 2003 until mid-2008 that delayed completion and documentation of the research project. The author redirected his attention to completing the thesis in mid 2008. The passage of time allowed additional validation work to be completed by comparing VicGasSim forecasts, which were completed and documented in 2003, with history over the period from 2002 through 2007. Forecasts from 2002 to 2020, which were made in 2003, could also be compared to third party 2020+ forecasts, which were made in 2006-08, using additional history then available to these third parties.

Because the initial research work and model documentation is based on material available in the 1998-2003 period, the author has elected to record his work much as it was first prepared in that period and in terms of the then available literature. A number of more recent literature references are included at relevant points to give an updated perspective to this project.

1. See Batten & Grozev (2006), Marks (2006), Sun & Tesfatsion (2007). 2. Personal communication, London Business School, January 2003. 3. CSIRO’s Energy Transformed Flagship is a major program, which is made up of many sub-programs investigating energy use, innovation, efficiency and policy. NEMSIM was extended to GENERSYS via a Commonwealth funded joint project with Core Energy in 2006- 07. See CSIRO’s website for more background.

14 Chapter 1 Purpose, Objectives & Findings

Similarly, the model validation is based on matching model outputs for the year 2001 to actual market data for 2001 (available in 2002-03). The model has not been recalibrated for the intervening history from 2002 through 2007 but the simulated outputs from 2002 through 2007 have been used to provide a predictive validation against actual history over this period. In some ways, this thesis includes a 6-year review of its own continued relevance.

The author believes the thesis stands adequately explained by reference to the literature, model calibration against 2001 actual data, and to model output comparisons to intervening history and recent third party forecasts. The level of calibration and validation is assessed as sufficient to claim that the VicGasSim has established a "proof of concept" of the proposed design to achieve the set objectives – but the claim is limited to this level since the model still requires more testing and fine- tuning before the model is fully calibrated, validated and reliable for commercial purposes.

1.7 Structure of this Research Thesis The thesis structure is explained below in terms of the content of each chapter.

Chapter 1 – Purpose, Objectives & Findings provides an introduction to the topic, related issues, the research objectives and plan, and key findings.

Chapter 2 – The Victorian Gas Market describes this market in the context of the wider Australian energy market as it stood at the time of deregulation and isolates a series of issues to be addressed in this thesis. It is important to understand the scope of issues confronting the industry to appreciate why the model is defined at a level of detail that allows most of these issues to be considered by the model. Much of the industry detail, which is presented in Chapters 1 and 2, is reflected in the final model, VicGasSim.

Chapter 3 – Energy & Economic Modelling Taxonomies describes various published taxonomies to put energy and economic modelling in the context of available modelling options and identifies the most appropriate type of model to address a given issue or question. This chapter also introduces the steps in modelling, the concept of complexity, and the main ABM simulation model types. The reported taxonomies are used to select and justify the ABM approach used in this work.

Chapter 4 – Modelling Methodologies describes the ways in which simulation has been applied to study social sciences, including economies and industries. The chapter describes the basic concepts of AOP and expands on how these concepts are well suited to address complex adaptive systems and applied to investigate Agent-based Computational Economics (ACE). The referenced literature covers that published before and after the time when the model development work was completed in 2003. The concepts of emergence and non-equilibrium dynamic systems are also introduced. This chapter concludes by describing the basis adopted to design and implement VicGasSim.

15 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Chapter 5 – Modelling Examples describes past energy and economic modelling efforts in Australia and overseas and puts these efforts into the context of the taxonomies in Chapter 3 and the modelling methodologies in Chapter 4.

Chapter 6 – Prototypes # 1 and # 2 describes the two prototype models developed to arrive at a suitable model framework. The first prototype tested a commercially available agent-modelling language, JACK, with an imbedded agent protocol and established a simple model framework to test the planned style of communication between producer and retailer agents as well as a number of other basic model design concepts. The second prototype, which was written in the Java software, expanded the first model design and developed a model mechanism for giving model agents improved intelligence by designing a model rethreading mechanism to give agents a look- ahead capability to test alternative assumptions before making a decision based on seeing how competitors would likely react over both short and long terms.

Chapter 7 – Prototype # 3 and VicGasSim describes a third prototype, in which the earlier framework is materially scaled up to include agents to represent all the main participants in the Victorian gas market, to provide simple business decision-making mental models for each type of agent, and to calibrate the model to actual Victorian market data. The fourth and final prototype is presented; it is referred to as the VicGasSim model. VicGasSim incorporates look-ahead capability into a selection of the major strategic agents as well as upgrades agents’ accounting cababilities to handle the large amount of participant data that is generated and stored when simulating up to 20 years of forward market competition.

Chapter 8 – Model Calibration & Validation describes several concepts on commonly acknowledged calibration and validation techniques as a preamble to presenting a calibration analysis of VicGasSim and several but limited validation exercises using both descriptive output calibration and predictive output validation by way of comparisons to third party forecasts using different model types.

Chapter 9 – Exploring the Potential of VicGasSim presents examples of model-generated future scenarios of industry trends and market participants’ performances. These examples are used to show the capability of the model to aid participants in understanding possible emergent market dynamics, selecting successful strategies, and avoiding market pitfalls.

Chapter 10 – Observations, Conclusions, & Future Model Development presents the author’s observations and conclusions about the completed work, the VicGasSim model, the future of the industry, and some broad comments about how the current model can be upgraded and its application widened. The project outcomes are also compared to the research objectives, which are presented earlier in Chapter 1.

16 Chapter 1 Purpose, Objectives & Findings

1.8 Principal Findings Major findings from this research project are presented in Chapter 10 and summarised below.

1. An effective model has been created and meets the research objectives described in 1.5.

(i) It generates outputs that show the effects of market discontinuities and uncertainties.

(ii) It models the dynamic changes of state that occur over both short and long terms and yet remains rationally bounded in predicting aggregated market performance.

(iii)It is domain rich in its characterisation of participants and in collecting performance data on participants and the market.

(iv) It incorporates hierarchical structures that provide computational efficiencies.

(v) It shows emergent trends that can be linked to participants’ strategies, market regulations, resources and uncertainties as well as showing trends that vary from extrapolation trends common in other forecasts.

2. Some outlook scenarios match reasonably closely to history and to forecasts now being generated by industry experts. One pleasing outcome is the capability of VicGasSim to generate long term forecasts of wholesale gas supply prices and market demand. The model predicted an almost doubling of real gas supply prices from 2002 to 2020 and a significant flattening of market demand. Most third party projections, which were made co-incidentally in 2000-03, predicted flat real prices and continued gas demand growth. Since 2003, real gas prices have increased and recent forecasts by industry experts now show significant increases in long term real gas prices and flat demand (excluding the recent emergence of GPG as a new source of rapid demand growth, which is expected as a result of new policies imposing carbon reduction programs and an emission trading system).

3. The use of micro-level characterisation of agents and their business decision-making mental models/plans provides a simulation framework that produces rationally bounded dynamic market trends without the need to invoke other common techniques such as learning algorithms, game theory and macro-level control agents or statistical algorithms.

4. Model-generated outlooks for the commercial performances of individual market participants do not gain the smoothing effect from the averaging of the major aggregation that goes into formulating predictions for the whole market. Therefore, more care is needed in using predictions in relation to participant performance than the market as a whole. The completed simulation runs highlight that sustaining long term performance requires close attention to the factors that affect both short and long term outcomes. No single simulation output should be taken as a decision-making basis; multiple simulations are needed to provide an understand-

17 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

ing of the market dynamics; often short term actions can cause unanticipated later consequences in a different part of the market. For example, what looks like a good short-term wholesaling strategy may cause longer term adverse consequences when negotiating later supply contracts. Calibration becomes more important as the simulation purpose moves from predicting market trends to directing company strategies and plans.

5. The use of industry-friendly terms and measures to characterise model inputs and outputs makes the model suitable for use by market participants’ employees for training and analysis purposes.

6. When the model’s look-ahead capability is invoked for multiple agents to consider a number of strategy choices, the run time for simulations increases markedly and preliminary testing suggests extra intelligence benefits may be marginal versus the alternative approach of using simple sensitivity testing and improving the basic mental models used by each agent.

7. The benefits of the VicGasSim model design have been recognised by other modellers. The model structure and platform have been adopted as the basis for design and development of the CSIRO’s NEMSIM model, which is now being used commercially to investigate the eastern Australian electricity market.

1.9 Research Contribution and Uniqueness The uniqueness of this research lies in the design of a model that characterises all of the participants, in what is a major, complex energy market, with a purely and consistent behavioural representation of each model agent, and where the model relies on the depth of agent characterisation and diverse scope of agent interactions to simulate a dynamic, non-equilibrium market undergoing numerous discontinuous and discrete changes. The model design and reliance on behavioural characterisation challenges the traditional1 approaches, which use techniques such as macro-econometric and general equilibrium models, least-cost LP optimisation models, and functional algorithmic models with control functions or control agents to enforce an ongoing market equilibrium (even if irrational).

The emergent market outputs from the VicGasSim model represent the synthesis and aggregation of the interactions of the individual agents in the models and not the dictates of model control agents. Therefore, the model outputs can be explored by potential users to understand why the market trends and predicted participants’ profit performances have emerged the way they have. The outputs are understandable because they can be tracked through the behaviourial (business and consumer) decisions of the market participants. The author believes that the VicGasSim

1. "Traditional" in this context means: as used and published in the period before 2003, although most Australian models remain similar to those used pre-2003.

18 Chapter 1 Purpose, Objectives & Findings

model differs from the NEMSIM and EMCAS models in that VicGasSim has a greater commercial and strategic focus on participants rather than on operational efficiency. While the the electricity sector has a greater range of operational challenges, the author believes that long- term behaviour of participants in either market needs to focus more on the wider, strategic, and general interest issues than appears is the focus in each of these other two models. This belief is explored in the following chapters of this thesis.

The author selected ABM and BDI1 agents as the most suitable techniques and concepts for modelling the Victorian gas market with a behavioural emphasis for modelling market competition. The choices were influenced by the level of information available to characterise the market, participant’s agents, and their behavioural models. The author created all of the model concepts and specified all agents to be included in the model to achieve the desired level of market interactions to achieve a functioning market. The definitions and detailed specifications for each agent’s resources and all plans pursued by each agent in the model were based on the research of the author and prepared by the author. All of the detailed agent interactions to be achieved in the model and the scheduling of events or activities and message-passing between agents were defined by the author. All input data was sourced and specified in formats designed by the author. The specifications requiring the model to adopt a hierarchical structure and to incorporate a "look-ahead" capability were based on the author’s research. In addition much of the specifications reflect the author’s own knowledge of and experience in the gas industry2.

Beyond the author’s model design and agent specification work, all model development work and computer coding to meet the author’s specifications was conducted by Mr Geoff Lewis, who is very experienced in the use of object-oriented concepts and related computer languages, such as Java software, which was used to build the model’s computer-based platform. Mr Lewis’ work was funded by the SPIRT Grant, which was described earlier, and supervised by Professor Harding. Mr Lewis is recognised for his development work, which included much experimentation to find ways to achieve and synchronise the myriad of complex interactions required by the author’s design and specifications. Professor Harding contributed numerous ideas to assist Mr Lewis in his development implementation work on the computer model. One of Professor Harding’s key contributions was the concept of "rethreading" by which the model is frozen at a given time and a series of clones made for running the model ahead in time to test alternative strategies and decision-making. The model is then "rethreaded" and restarted from the freeze point; the model moves forward to the next specified decision point in time, when the rethreading cycle is repeated to test the same type of decision or other types of decisions. Multiple

1. BDI stands for "Belief, Desires, Intentions". See 4.2.1 for an explanation of BDI agents. 2. The author’s curriculum vitae is presented in Appendix 7.

19 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

types of business decisions can be screened in the same simulation run. Such processes can be very time and data storage consuming. Professor Harding developed a cloning method that is efficient in time and data storage. It achieves this by cloning selectively only those parts of the model required to undertake a specific "look-ahead" investigation.

The author conducted all of the model calibration and validation work as well as all of the data analyses and interpretations presented in this thesis.

20 Chapter 2: The Victorian Gas Market

The Victorian gas market is a reasonably independent market but it does sit in the wider setting of the Australian energy market. This total Australian energy market can influence the evolution of the Victorian gas market and sets the context for considering the Victorian gas market. Therefore, it is useful to present some background of the Australian energy market (as seen in the 1998-2000 period) in order to identify issues that were then perceived as likely to influence the future evolution of the Victorian gas market, before considering ways to study such issues.

2.1 Australian Energy Market Figure 2-1 shows the growth in Australia’s primary energy consumption from 1973-74 to 1997-98.

Figure 2-1: Australia’s Energy Consumption: 1973/74-1997/98

Australia's Primary Energy Consumption PJ By Fuel Type: 1973‐74 to 1997‐98 6000

5000 Natural Gas 4000 Black Coal 3000 Brown Coal 2000 Oil 1000 Renewables 0 1973‐74 1979‐80 1985‐86 1991‐92 1997‐98

1973‐74 1997‐98 6.6% 7.5% 5.9% 17.9%

Natural Gas

25.3% Black Coal Brown Coal 34.1% Oil Renewables 50.5% 29.0% 10.1% 13.1% Sources: ABARE 1999; AGA 1997, AGA 1999a, AGA 1999c, AGA 2001.

ABARE [cited in AGA (1999c)] notes that total energy consumption in Australia grew by an average rate of 2.6% per year from 1973-74 to 1997-98. Within this period, coal and natural gas

21 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

were the only energy sources to increase their shares of the primary energy market. Natural gas increased its market share by the greatest margin from 6.6% to 17.9%. Coal’s share increased from 25.3% to 28.1%. The average consumption of natural gas grew by an average of 6.9% per year from 1973-74 to 1997-98.

2.1.1 Supply Setting The map below shows the locations of Australia’s major gas production sources and the existing and possible pipelines that do or might connect these sources to the main consuming areas.

Figure 2-2: Australian Gas Supply Sources Connecting Pipeline Networks

Source: AGA 2003

As shown in Figure 2-2 (as seen in 2003), the Australian gas market is essentially divided into two main systems: an east coast market covering Queensland, NSW, Victoria and SA, and a west coast or WA market covering an area from north of Karartha to south of Perth and west to Kalgoolie. A much smaller market exists in northern Australia and is supplied by gas from Palm Valley and Amadeus1.

Figure 2-2 also shows the mains supply sources. Observation of the pipeline supply connections indicates that the various sub-systems can be linked by a limited number of interconnections that will allow new supply sources such as those in the Timor Sea, North West Shelf (NWS) and PNG

1. Northern Australia now receives gas from the Bayu Undan offshore development, which was mainly developed to supply LNG to export markets

22 Chapter 2 The Victorian Gas Market

to be connected to the various Australian markets, including southern and eastern markets. The timing for entry of these large supply sources has been and will be strongly influenced by the reserves, capacity and pricing of existing suppliers and a number of new discoveries in southern and eastern Australia. The uncertainty over pricing and timing of these very large potential gas supplies is an issue always under consideration by participants in the Victorian gas market. Recent activity, including the development of CSM in Queensland and NSW, and deferral of gas supply from the north to the south and east, is explored in later chapters.

2.1.1.1 Electricity An east coast electricity market overlaps the equivalent gas market. This strong overlap is encouraging growth in the use of GPG units at strategic locations, where GPG can provide competitive electricity supplies. Development of regional GPG is usually geared to meeting high- priced peak loads.

The percentage of Australia’s gas being used as a fuel for electricity generation was 8.5% in 1997-98 (AGA 1999c). It has grown since then and is expected to continue growing strongly, especially as climate change and emission control pressures build. Models that address the interactions between gas and electricity markets will become increasingly important to market participants, who are either only in one market segment or in both. For example, Origin Energy was the first company to adopt a green energy marketing campaign (albeit at premium prices) as well as build a number of the earliest GPG plants in various states in eastern Australia.

2.1.2 Gas Supply & Demand Uncertainty as Seen in the Late 1990’s In the late 1990’s, demand for gas in Victoria was growing slowly and future growth was seen as uncertain because of growing emergence of GPG. Uncertainty also surrounded supply and prices. Existing gas supply contracts would start to wind down in under 10 years and expire completely by 2011. Existing suppliers would be seeking price increases for further Gippsland supplies. Markets for gas in adjoining states were also looking at the need for new gas supplies. Additional supplies looked like they might be available from Victoria, SA and south west Queensland but at uncertain prices. Abundant gas could be supplied from the NWS, the Timor Sea or PNG but long lead times would be needed and higher prices would definitely be needed to encourage connection of these latters sources.

23 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Figure 2-3 shows the uncertainties in the southern and eastern gas markets as seen in the late 1990’s.

Figure 2-3: East Coast Supply & Demand for Gas

PJ/A 1200 of ars Ye & d 1000 0 nty eman 5-1 tai g ? D cer rin Un ctu 800 stru Re 600

400 ? New Supplies - Size, Timing & Prices 200 Contracted Supply 0 1995 2000 2005 2010 2015 2020 2025 2030

Source: AGA 1997 and the author’s own market knowledge.

The figure shows the then-perceived impending supply gap between the expected continued growth in gas demand and the wind-down in contracted supplies from offshore Gipplsand in Victoria and the Cooper Basin Producers in SA and south west Queensland. At this time, moderate levels of reserves and resources still existed in Gippsland but the Cooper Basin’s position looked like it would quickly become constrained by natural decline.

If production potential from both sources was maximised against demand in these regions, supply could probably have matched demand into the 2005-10 period but would have required large investments to cover a short peak supply period before the then expected decline would occur. Such investments would have been seen normally as uneconomic and would have required a large increase in gas prices to make them justifiable.

The then-existing producers probably were planning for reduced supply contracts with the market being topped up by new smaller discoveries in other regions. In some ways, existing producers wanted to be protected from uneconomic investments – given the political sensitivity to any major retail price increases so soon after deregulation. At the other extreme, they did not want to leave so much of the impending market gap open to competition that the perceived gap would encourage the entry of major supplies from the Timor Sea or PNG1.

1. At that time, CSM from Queensland and NSW was not seen as able to supply the required volumes on both technical and commercial grounds, although the potential of the resource base was recognised.

24 Chapter 2 The Victorian Gas Market

2.1.3 Commonwealth Government Policy Framework

2.1.3.1 Origin of Reform Historical monopolistic type situations in both the gas and electricity industries in most east coast states had reached their stable limits by the early 1990’s when the Federal Government published strategy papers for both gas – DPIE 1991, and electricity – DPIE 1992 in ABARE Research Report 93.12 (Dalziel, Noble & Ofei-Mensah 1993). These papers explored the requirements to promote greater competition, including connection of the various state transmission systems to improve supply competition. The Federal and State Governments then co-operated through the Australian and New Zealand Minerals and Energy Council and the Council of Australian Governments (COAG) to enter an agreement to allow free and fair trade between states. Subsequently, they formed the National Grid Management Council to establish new pricing and marketing arrangements that were to operate from 1 July 1995.

2.1.3.2 Victorian Government Policy Framework The Victorian Government was the first Australian Government to respond to the change in Federal Government policy, which set the requirement for states to open up interstate trade in gas and to encourage more competition, by restructuring (i.e. by disaggregating, corporatising and privatising) state energy monopolies, including GFC.

In the mid-1990’s, the Victorian Government disaggregated GFC into a number of corporatised entities: three retailers, three distributors and a single transmission system operator with a Government market manager to keep the system in "balance" and set daily pool prices. The three retailers were assigned roughly equal shares of GFC’s offtake rights under GFC’s supply contracts with Esso-BHP. The Government later tendered the sale of these privatised entities to publicly owned investors. By 2003, the three retailers were Origin Energy, TXU and Pulse, the three distributors were Envestra (part owned by Origin Energy), TXU, and United (linked to Pulse), and the main transmission operation was owned by GasNet. The Government subsequently auctioned a small volume from its original Esso-BHP supply contracts; the rights were taken up by the Queensland-based company, Energex. Energex paid what was then regarded as a premium price to facilitate its entry into the Victorian gas market.

Marketing and retail pricing were controlled by Government appointed regulators; in Victoria, these aspects were initially controlled by the Office of the Regulator-General. Interstate transmission tariffs were regulated by the Pipelines Tribunal. Gas supply by joint marketing was regulated by the ACCC, as was transmission pipeline access and tariffs. Vencorp was established as the Victorian Government’s market manager. More recent changes to these original arrangements are described in Appendix 4.

25 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

2.1.3.3 2002 Energy White Paper by the Department of Primary Industry and Energy In 2001, COAG commissioned an independent review of the strategic direction for energy market reform in Australia and to consider the impact of changes that had taken place under earlier deregulation steps and the possible constraints of excessive and fragmented monitoring and supervisory regulation.

The main findings of this 2001-02 review were published in 2002 (COAG 2002), and include:

1. The energy sector governance arrangements are confused and there are excessive regulation and perceptions of conflicts of interest.

2. There is insufficient generator competition to allow Australia’s gross system to work as intended.

3. Transmission investment and operation are flawed, and the current regions do not reflect the needs of the market.

4. The financial contracts market is extremely illiquid, in part reflecting large regulatory uncertainty.

5. There are many impediments to the demand side playing its true role in the market.

6. There is insufficient competition in the east coast gas market, and too much uncertainty surrounding new pipeline development.

7. Greenhouse responses so far are ad hoc and poorly targeted.

8. The NEM is currently disadvantaging some regions.

These same uncertainties and issues faced the Victorian gas market in 1998-2003 and reinforced the need for new approaches to study the complexities of the various gas and electricity markets in eastern and southern Australia. The WA market has similar complexities but also has the over-riding influence of significant exports of LNG. Historically, LNG was a marginal product, which relied on mega-volumes to generate commercial projects at a time when the domestic gas market was well- supplied. In the last few years, gas supplies to the domestic market have tightened significantly and growth in LNG demand in greater Asia (including China) and the USA has seen LNG become a premium-priced product. LNG netback prices in 2007-08 have created a new benchmark for domestic gas prices in WA and domestic WA prices have risen significantly over this very short period.

2.2 Victorian Gas Market The Victorian gas market is made up of a number of segments with characteristics as described in the following sections on Markets, Supply, Customers (demand), Transmission, Distribution, Retailers, Storage, Regulators and Pricing. Participants in each section can be characterised and represented as agents in any model. These agent representations are presented later in this thesis.1

1. Where a capital letter is used for a market participant, such as a Market or Retailer or Producer, it designates a reference to the agent representation of market participants in the VicGasSim model and its prototypes. Lower case lettering is used for real world references.

26 Chapter 2 The Victorian Gas Market

The market has traditionally been divided between the "upstream" and "downstream". Upstream is made up of the supply sources and their associated processing facilities to generate both sales- quality gas and gas liquids such as LPG, and condensate recovered from the raw produced natural gas. In Victoria, the major natural gas producers also produce and process large volumes of crude oil. The profitability of Gippsland oil and gas liquids production by Esso-BHP supported the sale of their natural gas at relatively low gas prices and favourable long-term escalation and price re-opener terms, which stimulated the rapid take-up of gas by consumers in Victoria.

The downstream part of the Victorian gas market consists of (i) one or more transmission systems to move gas at high pressure from the upstream processing facilities to major market areas, (ii) one or more distribution systems moving gas at lower pressures to consumers, and (iii) consumers, who are characterised by the different levels of their consumption and their location. Consuming areas fall into six regional markets: the large Melbourne market surrounded by the much smaller markets, Geelong1, Western, Ballarat, Northern, and Gippsland.

Figure 2-4 shows the major regional gas markets, the major transmission grid and transmission zones (TZ), supply sources, interstate connections and the corporate images of a number of the major market participants in the early 2000’s.

Figure 2-4: Victorian Gas Market

Source: VicGasSim model home screen 2003

1. The "Geelong" label is missing from the map display in Figure 2-4. The Geelong market is the light grey area shown immediately south of the Ballarat market shown with a darker grey shading. The Geelong market is connected to the main Melbourne market transmission loop shown in blue (and labelled TZ4) via the green transmission line from the TZ4 loop to Geelong. The Geelong market transmission link is TZ5, which is also missing from the map display in Figure 2-4.

27 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

2.2.1 Victorian Gas Demand Figure 2-5 highlights the impact of the discovery of Gippsland gas in the 1960’s on gas consumption in Victoria. Given Victoria’s moderate climate and the long availability of abundant relatively low-priced gas, there was a very high level of penetration by gas into the residential energy market. In other states, residential use is subsidiary to the commercial and industrial markets. For this reason, gas policy and pricing has been and remains a politically sensitive area in Victoria.

Figure 2-5: Victoria’s Historical Gas Consumption Victoria's Gas Consumption 1969‐70 to 1997‐98 PJ 300

250

200

150

100

0 1969‐70 1973‐74 1977‐78 1981‐82 1985‐86 1989‐90 1993‐94 1997‐98

Source: AGA 1999a

2.2.1.1 Customers Demand in the Victorian market is broken down by its major customer types: residential, commercial, industrial and powergen (gas used for electricity power generation). Each of these market sectors has its own characteristic seasonal demand pattern.

In the late 1990’s, there were approximately 1.5 million gas users in Victoria. The vast bulk of customers were residential or small business users, but they consumed less than half of the gas volume consumed in Victoria. The distribution of gas consumption in 1997-98 is shown in Figure 2-6 on the following page.

28 Chapter 2 The Victorian Gas Market

Figure 2-6: Distribution of Gas Consumption by Sector - Victoria Gas Consumption by Sector PJ 250 Other 200 Residential 150

100 Commercial

50 Manufacturing 0 19971 ‐98

Source: AGA 1999a

Because the residential and commercial sectors were supplied by a virtual Government monopoly until the late 1990’s, gas pricing to various classes of customers was regulated by Government policy. This political control acted to protect smaller residential and commercial customers from the effects of normal competitive pressures. This led to a situation where the smallest customers were heavily subsidised by larger customers. Regulated prices generated losses from smaller customers that had to be offset by making profits from larger customers.

Obviously, the new retailers wanted to increase prices to levels that replaced losses with profits. If prevented from doing so by regulators inhibiting price increases, they would want to shed as many of these loss-making customers as they could. In addition, they wanted to find ways to cut their costs to make such customers profitable even under regulated prices.

When the Government deregulated the market, it provided for the staged relaxation of pricing to smaller customers. Retailers were allowed to offer pricing packages outside regulated prices from 2002, but regulated prices were to remain in place for those consumers, who wanted to retain such pricing.

A limited number of larger commercial and industrial customers have historically contracted for their own supplies directly with producers and stayed outside the scheme of prevailing regulated prices. They have traditionally paid much lower prices for buying much larger volumes of gas on direct terms that included maximum amd minimum volumes of gas to be supplied, and, in some cases, terms provided for interruptability of supply.

29 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

2.2.1.2 Gas Supply Sources Gas has been historically supplied from the Gippsland offshore gas fields developed by Esso- BHP in the late 1960’s. Small amounts of gas have been supplied from gas fields in the onshore Otway Basin and periodically via the Culcairn two-way interstate connection in northern Victoria. Culcairn is linked to the adjoining gas transmission system in NSW, which has been predominantly supplied by gas from the SA Cooper Basin to NSW/Sydney.

Additional volumes were needed to meet expected growth in demand and offset the wind back in legacy volumes from Esso-BHP’s original contracts with GFC. The first new (small) contracts were put in place in 2002. Other contracts have been added on an irregular basis and more will need to be negotiated over the immediate and ongoing future to underpin supply to the Victorian, NSW, Tasmanian and SA markets.

Expansion of existing facilities and development of new supply sources have altered and will continue to alter the supply-demand balance in Victoria and other southern and eastern Australian markets. They include:

1. Expansion of the Esso-BHP Gippsland Basin system (including development of the Kipper and Turrum fields),

2. Development of a number of small onshore Otway Basin fields,

3. Development of the Yolla field in the Bass Basin (BassGas),

4. Development of the offshore Patricia and Baleen fields in the eastern part of the Gippsland Basin,

5. Development of the small offshore Minerva and Casino fields in the Otway Basin,

6. Development of the large offshore Thylocene and Geographe fields in the Otway Basin,

7. Continued infill development of the mature Cooper Basin fields, and

8. Development of CSM in Queensland and NSW.

The location of these general source areas relative to markets and pipeline networks is shown in Figure 2-2.

New supply contracts from these sources in southern and eastern Australia have deferred the early penetration of gas from northern Australian sources such as PNG, Timor Sea, NWS or CSM in Queensland and NSW. Some of these latter sources had a chance to displace the more traditional sources but failed to capitalise on a window of opportunity that existed from approximately 1998 to 2000 when it looked like traditional supply sources could not provide

30 Chapter 2 The Victorian Gas Market

enough gas to meet the Gippsland contract transition requirements adequately and quench supply security concerns following the Longford processing plant explosion and fire in 1998.

This uncertain situation changed dramatically with the discovery of the Thylocene and Geographe gas discoveries off the western Victorian coast in 2001, and more recently, the increasing success of CSM in establishing its credentials as a material supply source in Queensland. In addition, Gippsland has increased its prospects of additional gas supplies and upgraded its processing facilities. Esso-BHP now supplies gas to Tasmania, Victoria and NSW; Esso-BHP has also widened the marketing of its gas supplies as other sources have captured some of its historical market in Victoria.

2.2.1.3 Interconnections to Adjoining States With the growing prospect of development of new supply sources and Government policy freeing up interstate gas trade, a number of new interstate pipelines have been built. These pipelines are shown in Figure 2-2 and discussed below.

2.2.1.3.1 Eastern Pipeline This pipeline was commissioned in 2000 by Duke Energy, following the Government’s freeing up of interstate gas trade. Initial development was supported by BHP, which contracted for transmission of its own Gippsland gas to various BHP iron and steel plants in NSW as well as a number of BHP’s industrial customers in NSW.

This pipeline initially connected the Esso-BHP’s Longford gas processing facility to the NSW market via a route that closely follows the eastern coastline as shown on Figure 2-2. Pipeline flow was initially unidirectional to NSW. Throughput was later supplemented by gas supplies from Patricia Baleen. The interconnection point at Longford is now considered as a trading hub between the Eastern Pipeline and GasNet transmission systems.

In December 2002, Esso and BHP announced new contracts with the major NSW retailer, AGL, whose existing Cooper Basin supply contracts would wind down over the period from 2002 to 2006. Most of the gas under these Esso and BHP contracts is shipped to AGL’s NSW customers via Duke’s Eastern Pipeline.

2.2.1.3.2 Tasmanian Pipeline In 2003, Duke commissioned a new pipeline to transmit Gippsland gas to Tasmania. This development followed efforts by the Tasmanian Government to overcome impending energy shortages in that state due to capacity constraints on Tasmania’s hydro-electric power generation system. This development was tied to Tasmania’s separate commitment to allow a bi-directional electricity supply cable across Bass Strait between Tasmania and Victoria.

31 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Duke’s participation in both the Eastern Pipeline and Tasmanian Pipeline provided it with a substantial line pack volume of gas at a trading hub. It was possible for Duke to use its pipeline flexibility to become a gas trader in the major Victorian and NSW markets. However, Duke did not push in this direction and Duke concentrated on buying or building several other pipeline interests in Australia and which were not linked to their interconnected pipelines in Victoria, NSW and Tasmania. These new pipelines added little corporate synergy to underpin Duke’s future growth in Australia and Duke eventually sold its Australian operations in 2004.

No material trading yet takes place through the Longford hub. It is highly likely to develop in the future and introduce more trading and strategic flexiblity to the Victorian gas market.

2.2.1.3.3 SEA Gas Pipeline A consortium consisting of Origin Energy, TXU and Australian National Power (ANP), constructed a new pipeline to connect the Victorian gas system to Adelaide and other SA markets. Construction commenced in 2002 and first gas flowed in late 2003. A number of contracts have been entered into for supply of gas from Gippsland and from the new developments underway at Yolla in the Bass Strait and at Thylocene-Geographe in the offshore Otway Basin. Yolla (or BassGas) enters the Victorian transmission system at or near Drouin. The Thylocene-Geographe discoveries enter the Victorian and SEA Gas pipeline systems at or near Port Campbell. The interconnection of these two transmission systems creates the potential for another trading hub in Victoria. Origin benefits by being a supplier of gas from both Yolla and Thylocene-Geographe and having built GPG units in Adelaide and south-east SA; it recently announced plans to build a very large GPG plant in western Victoria to capitalise on access to its own gas resources and to the SEA Gas Pipeline. Access to the SEA Gas Pipeline should also prove a strategic advantage for TXU as retailers preferentially move gas from western Victoria to TXU’s underground storage facility near Port Campbell.

2.2.1.3.4 Culcairn In the late 1990’s, the Victorian based GasNet and Australian Pipeline Trust, the then owner of the NSW transmission system that connected the Cooper Basin to Canberra and Sydney as well as other NSW markets, agreed to connect their two transmission systems with an interconnecting link via Culcairn. The initial, low volume transmission pipeline, known as the Culcairn Interconnection, provided market participants, especially AGL and Origin Energy, some trading capacity between these markets as well as providing a minor level of emergency back-up supply to either state.

The capacity of this interconnection was upgraded after the Victorian supply disruption caused by the 1998 explosion and fire at the Esso-BHP Longford Gas Processing Plant for supply of

32 Chapter 2 The Victorian Gas Market

Gippsland gas to Victoria. The Culcairn Interconnection is bi-directional and can be switched to flow gas from either state to cover shortfalls in the other. A lead time of several days is required to switch flow direction at Culcairn to allow Cooper Basin gas to be supplied into the Victorian market or Gippsland gas into the NSW market. Swapping direction usually occurs on a seasonal basis.

2.2.2 Retailers Customers were historically provided with their gas by a monopolistic GFC. After the disaggregation of the GFC, retailing activities were divided into three separate regions, which were roughly equal in size and number of customers.

Rights to sell gas to each group of customers were franchised, corporatised and finally privatised by tendered sales to a number of companies. These privatised franchises provided for a certain period of non-contestability of the customers in each region in order to provide a minimum return to the buyers. Legislation provided a schedule for the introduction of contestability for customers in each company’s region. The original retail franchise buyers were TXU, Pulse Energy (a joint venture investment by United Energy, Hastings Fund Management and AMP), and Origin Energy. They were allocated rights to share the old GFC supply contracts in order to meet their retailing needs in fixed proportion to the number of customers in their franchised areas at that time.

The sales profiles of each retailer varied: TXU had the smallest number of customers with a higher proportion of commercial and industrial customers in the more industrial western part of Melbourne and closer to its planned underground storage scheme. Pulse had the most central area, the highest number of customers and the highest proportion of residential and small commercial customers. Origin held the eastern area with an intermediate mix of customers that stretched east all the way to and past Longford.

Because of their different franchise locations and different customer mixes and load requirements, retailers incur different transmission and distribution tariffs. These differences then result in differing approved maximum retail prices to regulated gas customers known as Tariff V1 customers versus the larger and unregulated Tariff D2 customers.

In addition to jointly selling gas under their legacy contract that was made with GFC, Esso and BHP separately market gas to a limited number of industrial customers in Victoria and NSW. They also have the potential to offer any surplus gas supply into the daily wholesale market, which is administered by Vencorp.

1. Tariff V customers are those that are volume oriented, are generally smaller in size such as households and small commercial enterprises, and their prices are regulated. They incur different distribution and transmission tariffs to Tariff D customers. 2. Tariff D customers are those that are demand oriented, are generally larger in size such as large commercial and industrial enterprises, and their prices are unregulated.

33 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

2.2.3 Distributors When the Government disaggregated GFC’s distribution system into three main sub-systems, each distribution franchise was stapled to one of the retail franchises when they were offered for sale. But no stapled pair included over-lapping franchise areas. So initially, each retailer relied on another entity to distribute gas to its customers and it distributed its competitors’ gas supplies through its own distribution network. This situation would change slowly with growing retail contestability and customer swapping or churning.

Distribution in Victoria was regulated by the Office of the Regulator General (ORG), which set system tariffs for five-year periods, following reviews involving submissions from all market participants and ORG’s own analyses. ORG’s role was later changed to another regulator as described in Appendix 4.

At the time of calibrating the VicGasSim model, tariffs were set until 2007, when the next review would occur. Tariffs are usually set by reference to rate of return on the written-down replacement value of a distributor’s assets. Distribution tariffs can be broadly considered as specified in terms of a fixed and variable (or quantity) charge.

A key question or issue with distribution assets is whether owning a strategic group of high value assets would provide a corporate advantage that can be used to boost general interest profit levels. Each major retailer originally accepted the approach to owning distribution assets in Victoria but appeared to move to a different view over time. Their current positions are described in Appendix 4.

2.2.4 Transmission System There is one major Victorian transmission system, the Principal Transmission System (PTS), which links the major supply sources to the major distribution systems, which take the gas the last step to customers in each regional market. There are several minor regional transmission systems linking several regional centres to gas from the PTS.

The PTS network is shown in Figures 2-4. The network is divided into 14 different transmission zones (TZ’s); each TZ has its own tariff to move gas from the central hub at TZ4 to distribution offtake points in each of the regional TZ’s. There is also an injection charge for suppliers putting gas into the system. The injection charge depends upon the Injection Point (IP), of which there were three in 2002: Longford, Port Campbell and Culcairn. An additional IP now accommodates connection of new supplies from Yolla to a point near Drouin. Other new sources will likely enter at Longford or Port Campbell.

34 Chapter 2 The Victorian Gas Market

Transmission and injection tariffs (like distribution tariffs) can be broadly described as made up of a fixed and variable (or quantity) charge. Transmission charges are regulated by the ACCC in much the same way as distribution tariffs, except they are reviewed every two years. The PTS is owned by GasNet.

The PTS is considered to be a carriage type transmission system where no party holds contracted capacity but rather the PTS responds to directions from the Victorian Government’s transmission and market manager, Vencorp, to expand the system capacity based on Vencorp’s annual system review. Parties deliver gas into the system to meet their expected needs under a wholesale supply bidding system, which is also managed by Vencorp and is explained in 2.2.7.

2.2.5 Storage Facilities

2.2.5.1 Western Underground Storage (WUGS) Under a tender managed by the Victorian Government at the same time that it deregulated the Victorian gas market, TXU won the right to redevelop a Government-controlled but depleted gas field near Port Campbell to create an underground storage facility with the ability to store approximately 11 PJ of gas. Gas is injected in the summer months and withdrawn in winter months to cover peaks in market demand that cannot be covered by the existing supply system. The storage capacity of 11 PJ equals about 5% of one year’s gas consumption (~250PJ) in Victoria. However, the maximum daily withdrawal capacity of about 270 TJ/D is equivalent to about 25% of peak daily demand of about 1100 TJ/D in winter and can sustain this top-up capacity for several months. Daily injection is limited to about 80 TJ/D, which means it can take many months to build up storage if it is materially drawn down over winter.

TXU contracts storage and deliverability capacity to market participants: Origin, TXU (Retail) and Pulse. These parties wearing their retailer hats use their Gippsland supply contracts and other supply contracts to partly or completely fill their allocated share of storage capacity each summer at their contracted supply price. Then they bid to supply gas from storage into the wholesale market at high prices during the winter peak market season.

TXU charges users a tariff for use of this facility. The tariff can be considered as made up of fixed and variable component. The gas supplied from storage to the PTS costs retailers more than direct supply because of the associated storage tariffs, which are in addition to the transmission tariffs required to move gas to and from WUGS.

2.2.5.2 Dandenong LNG Storage The Victorian Government developed an LNG storage facility at Dandenong, under their original ownership of GFC. This facility liquefies gas from the PTS and stores it at high pressure for

35 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

delivery back into the PTS during "needle" peak demands or emergencies. The system is owned by GasNet and was operated by the liquid gas specialist company, BOG, for GasNet. GasNet is now owned by the APA Group.

The LNG storage tank has a capacity of 655TJ, which is equivalent to about 60% of peak day demand (~1,100TJ). Stored gas can be supplied at a rate of about 150TJ/D (or just under 15% of peak day demand) and is used to meet demand spikes.

The cost of operating the LNG system is high and it is the current last resort source of gas to the PTS. While the major retailers each hold storage and deliverability rights for trading purposes, the system operator is required to retain a certain amount of the capacity for emergency use at the direction of Vencorp.

While it can be drawn down in as little as 2-4 days, it can take up to three months to refill once depleted. Gas is injected at the direction of the retailers after they withdraw stored gas and typically they do this as quickly as possible given the refill time.

GasNet contracts storage and deliverability capacity to market participants: Origin, TXU and Pulse. These parties, wearing their retailer hats, use various supply contracts to fill their allocated share of storage capacity. They bid to supply gas from storage into the wholesale market during peak market conditions.

GasNet charges users a tariff for use of this facility. The tariff consists of fixed and variable components.

2.2.6 Retail Prices A prescribed set of customer prices is regulated in Victoria. The regulations set a maximum chargeable price for various size classes of customers. Since 2002, retailers have been able to offer small consumers the opportunity to move out from under the umbrella of regulated prices to take up an unregulated contract for the supply of gas. Prescribed customers are free to choose between offers from each retailer or to stay with regulated prices – but if they take up an unregulated offer, they cannot revert to take up future offers under the protected price system.

Large commercial and industrial customers typically negotiate directly with retailers or producers on an unregulated basis.

2.2.6.1 Regulated Retail Prices ORG requests submissions from retailers and other interested groups on a regular basis. Retailer submissions outline changes in their costs and changes in competing fuel prices to seek increases in the approved maximum retail prices. Retailers try to avoid getting locked into a cost-plus

36 Chapter 2 The Victorian Gas Market

regime based around a consensus on allowed rates of return. However, reviews do tend to get into cost details and comparisons. Following review of these submissions and its own analyses, ORG issues regulations that set maximum allowed prices, which each retailer can apply to sales to customers, who continue to receive gas from the same retailer without entering a contestable contract. The last group of residential customers became contestable in September 2002. Customers are not compelled to move to a formal contractual basis and can stay under the regulated prices and minimum service standards, which are administered by ORG.

2.2.6.2 Unregulated Retail Prices For those customers, who elect to enter a formal contract with a retailer, the related prices then fall outside the control of ORG. The majority of industrial customers have traditionally operated under individually negotiated, competitive contracts with retailers or directly with producers for very large volumes.

Contracted gas supply services are usually priced to fit the needs of individual customers and will be priced differently depending upon maximum and minimum volume factors and the length of the contract, which can often cover 5-10+ years, and whether the contract allows for interruption to supply when daily total supply is short of total demand on any given day. Larger contracts usually obtain discounted prices. Commonly, price is specified as a margin, say, 5%, that adds to the retailer’s total supply costs to deliver the contracted gas.

2.2.7 Wholesale Market Manager - Vencorp Vencorp was established by the Victorian Government in 1997 to act as the independent system planner and manager for the Victorian gas market and to act as system planner for the Victorian electricity sector but where system management then rested with the National Electricity Market (NEM). Vencorp manages the wholesale gas market operations and spot trading system to set a daily gas price and to administer daily metering of injections and withdrawals from the system. Vencorp determines the daily price by a price bidding system whereby Vencorp receives volume bids at varying prices and stacks them in price order. The bid that sits at that level of the bid stack where the actual total daily withdrawal falls sets the daily price for settling all net transactions into and out of the system on that day. Vencorp then settles monies owing between the injection and withdrawal parties at the daily pool price.

Market participants develop methods for estimating their daily needs and strategies for setting prices for the various tranches of gas that they are willing to inject into or withdraw from the PTS at various price levels. Each participant typically lodges a series of bids for volumes at various prices. Each set of bids will relate to that bidder’s estimated available supply in terms of its contracted peak day availability, where the bidder sits in regard to cumulative purchases under its

37 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

supply contracts versus its maximum annual purchase rights. The bidder also needs to consider any take or pay (TorP) provisions in its supply contracts; it needs to consider where it sits in building or drawing down its rights under its underground and LNG storage contracts. If it is smart enough, each bidder will try to track its competitors’ bids and contract positions to estimate their strategic position on any day. Bidding needs to consider expected sales to customers and the effect of predicted weather on demand levels.

Overall, deciding on a bid set requires consideration of a complex system of constraints. The application of game theory or learning algorithms to daily bidding can be helpful but such approaches might miss important over-riding contractual limitations that can bear strongly on outcomes over a whole year. Seasonal and contractual limits need to be averaged out against uncertain future pool prices. For example, a retailer may over-sell gas early in the year to make trading profits from spare daily capacity but then become exposed to the need to buy gas to meet the needs of its customers later in the year after it runs out of its annual contracted gas volume. This situation would be quickly picked up and taken advantage of by others competing in the pricing pool. Methods for structuring bid curves are described in 7.3.2 and 8.2.2.2.

The pool trading system also provides price transparency and lowers the barriers to entry by new participants. The market has worked reasonably effectively since operations commenced in 1998 but there have been recent changes, such as the introduction of intra-day re-bidding. These changes are discussed in Appendix 4.

2.3 Modelling Issues for the Victorian Gas Market In the lead up to and during the early stages of privatisation and deregulation of the Victorian gas market, debate and analyses tended to focus on the structural issues that would achieve both highest prices for asset sales by the Victorian Government as well as establishing a durable and appropriate level of competition (with a certain vagueness about the criteria for assessing what would be “appropriate”). Advocates argued for various levels of disaggregation to establish an adequate level of intra-industry downstream competition between retailers, distributors and transmission operators. Some focused on the most appropriate wholesale pricing model. Others pushed for shorter or longer periods before allowing customer contestability between retailers, who had acquired disaggregated customer bases at considerable cost. The downstream participants also argued that the proposed downstream reforms were pointless until the ACCC cancelled Trade Practices Act exemptions that allowed joint marketing by the historical monopolistic upstream producers supplying gas from the Gippsland and Cooper Basins to Victoria, NSW, SA and Queensland.

38 Chapter 2 The Victorian Gas Market

On other fronts, financiers and regulators argued about pricing models for valuing disaggregated businesses that made up the state-owned GFC and what rates of returns should be used for setting maximum regulated retail prices and the transmission and distribution tariffs. Uncertainty existed as to how long it would be before a functioning competitive market was working effectively to the satisfaction of appointed regulatory agencies. How quickly would they relax regulation?

These debates, inquiries and related analyses used an array of model types1 to analyse the issues of interest to specific parties. In hindsight many of these early studies and analyses failed to deal very well with the types of issues, uncertainties and challenges faced by the industry and reflected by the author’s various objectives in 1.5.1 to 1.5.4.

A list of identified issues, uncertainties and challenges follows:

1. Extended times may be needed to aggregate sufficient contracted volumes from disaggregated markets to justify the minimum needs for a large upstream or transmission commitment. Entry of large supply sources such as PNG or NWS or CSM to southern markets could require a lot of simultaneous negotiations that lead to mis-timed market penetration by these sources.

2. Existing models do not consider the development time lags and co-ordination issues of making decisions about expanding capacity.

3. Companies that hold controlling or frustrating minority interests in different joint ventures (JV) could favour developments that advance their net corporate positions at the expense of other companies in the same JV.

4. Regulation of investment returns can attract or deter timely expansion of existing transmission and distribution systems.

5. Producers may or may not entertain shorter contract periods or regular contract price reviews to allow retailers to manage the risks of lower priced alternative gas supplies being available in the future. Who will have the greater leverage to force shorter or longer supply contracts? Produc- ers or retailers? What forces will create a leverage advantage for one participant over another?

6. Less than optimal pipeline interconnections could introduce high priced gas, which then delays entry of cheaper sources until market growth opens up new supply windows.

7. Disaggregated downstream markets might prove inefficient due to the limited size of participants; some aggregations might be needed to increase horizontal and vertical integration. Will growth in customer numbers reduce average customer administrative costs enough to provide an advantage from aggregation? Will such cost reductions counter competitors’ advantages in other areas such as supply or distribution or storage alignments?

1. A taxonomy of model types and descriptions of some examples are provided in Chapters 3 and 5.

39 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

8. How much customer churn will occur as a result of dual-fuel marketing or as a result of marketing green energy? Will the historical price elasticity of demand for gas and electricity change from historical elasticity levels derived from data for what was a relatively stable period of energy pricing?

9. Are regional differences in each retailer’s cost elements important in sustaining a competitive retail position in the market?

10.What effect will pricing changes in the electricity market have on the gas market? And vice versa? Or what effect will changes in the level of export LNG demand and prices have on domestic gas prices?

11.Could the evolution of a secondary trading market become the market price-setter as some believe it has done in international crude oil markets?

12.How will regulators react to requests to change gas pricing structures for smaller regulated customers to make them more profitable to service without cross-subsidisation by larger customers?

13.How will participants’ behaviours and interactions change at the micro-level when they react to the above issues and uncertainties?

14.New gas discoveries in close proximity to existing markets could introduce lower or higher priced gas depending on their development costs and minimum entry volumes to justify development.

15.Exploration could slow or stop in periods of over-supply; future shortages could follow.

16.Delays due to Native Title disputes or extended supply negotiations could cause market tight- ness and higher prices that encourage alternative supplies.

17.Entry of PNG or NWS or CSM gas in anticipation of growth in GPG could be supported by policies encouraging greater controls over greenhouse gas emissions; wrong policies might not favour gas as much as expected.

Some answers are clearer than others. For example, and while not surprising in hindsight, it is now clear that strategic integration of assets in the southern and eastern Australian gas and electricity markets will be critically important to success in these markets. Integration and diversification are needed to spread risks, to lower average unit customer costs and to provide a base for dual-fuel marketing to differentiate energy retail offers in what remains a partly regulated price market. This example, and the issues/questions above, leads one to ask how a market participant can explore the strategic nature of competing in this market and what market aspects will prove to be more important than others in the long-run for each participant? Some of these events are highly likely to occur. Some will emerge at a pace that participants can manage, some will happen quickly and there will be winners and losers when such events happen. How participants position and structure their businesses will govern their overall long-term success or failure.

40 Chapter 2 The Victorian Gas Market

To date very little effort has been directed at designing models to explore the types of questions and issues listed previously. To understand possible options, it is helpful to see these questions in the context of what types of energy modelling have been applied in the past and continue to be applied, and what issues such models are capable of examining. This topic is taken up in Chapters 3, 4 and 5 in terms of model types, modelling methodologies, and examples of models being used in the 1990’s and early 2000’s; an update on what is being used more recently is also provided in Chapter 5 to give an updated context to the model design, which was chosen in 2001 for developing VicGasSim.

41 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

42 Chapter 3: Energy & Economic Modelling Taxonomies

Investigating an energy market typically considers both the energy and economic dynamics of that market. There are separate and overlapping streams of literature that present past thinking in each of these areas.

This chapter presents a number of such perspectives on how to characterise and differentiate both energy and economic models and what type of modelling approach is best suited to investigating a particular issue or set of issues. A number of taxonomies are presented; these include: (i) earlier work by Labys (1999), who shows how model types, including simulation models, have been developed to address specific and differing questions of interest at a macro- and micro-scale, (ii) a more recent and specific description of simulation model types to address different social issues (Gilbert & Troitzsch, 2005), and (iii) an introduction to agent-based simulation model types (Brenner & Werker, 2007) and their growing use to address economic issues and industry dynamics in complex adaptive systems, which are commonly in ongoing states of disequilibrium.

3.1 Labys’ Taxonomy of Energy Models Labys (1999) in his book "Modeling Minerals and Energy Markets" sets out a taxonomy for classifying the main types of energy and related economic models. He states that "the initial step in considering any model is to determine the appropriateness of the detail, theory, and implementation methods in relation to the purposes for which the model is intended". He proposes a method for moving from domain analysis to selecting a modelling method, to model design and development, to model validation, and finally to the use of the completed model for examining an issue or predicting some outcome.

He starts by categorising models as either normative or predictive. Models usually contain elements of both approaches but one is likely to be dominant.

Normative models are descriptive type models that are used to test the effect of changing the exogenous or independent elements of a system; for example, they can deal with how an energy system should develop given an objective. Normative models tend to deal with technical and engineering process concepts. The validation of such models focuses on the realism of the description of the system and the accuracy of the input parameters.

43 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Predictive models seek to forecast demand or supply and attendant effects over a period of time. Validation includes evaluation of both the model’s logical structure and its predictive power. Such models assess the direction of a response to a perturbing factor, the relative magnitude of such a response, and predict the perturbing factor itself, although the last is not usually an imperative for validation. Predictive models are usually behavioural in nature and usually oriented to forecasting and often involve economic or econometric modelling to explore the dynamics of complex adaptive systems. Some economic and energy models might claim to be predictive but their predictions are likely to be invalid since such models strive to forecast outputs for complex adaptive systems by using neo-classical, econometric, general equilibrium models.

Most energy models are formulated using a combination of theoretical and analytical methods from various disciplines including engineering, economics, econometrics, time series analyses, operations research, and management science. Labys (1999) points out that those models using economics, econometrics and management sciences will usually emphasise the behavioural aspects of decisions to produce or use energy.

With growing complexity of the energy issues in modern markets, Labys (1999) notes that "recent modeling efforts evidence a trend towards combining the behavioral and process approaches to mineral and energy modeling in order to provide a more comprehensive framework in which to forecast the conditions of future markets under alternative assumptions concerning the emergence of new production, conversion, and utilization technologies. In part this trend is the result of recognizing that formulating and evaluating alternative natural resource policies and strategies requires an explicit recognition of the technical constraints. It is possible to construct multi-objective criteria as some weighted combination of objectives and indeed, some objectives such as environmental control can be expressed through special constraint equations in the model. Nevertheless, the validity of this technique as a predictive tool depends on its ability to capture and represent the objective of the players in various sectors of the mineral or energy system and in those sectors of the national or regional economy that affect those sectors. The technique is normative in that it determines optimal strategies to achieve a specific strategy with a given set of constraints".

Labys (1999) indicates that developing and applying a mineral or energy model normally requires a number of components, which reflect various aspects of demand, supply, trade and price determination. Each component has its own economic behaviour or process. What gives a mineral or energy model a particular configuration is the kind of market system that it attempts to emulate. Each model must describe aspects of mineral or energy behaviour, which are peculiar to the system of interest.

44 Chapter 3 Energy & Economic Modelling Taxonomies

Labys’ (1999) concluding comment is "Finally, it goes without saying that in times of mineral and energy market crises, greater attention becomes directed towards modeling and particularly modeling market risk and uncertainty. Advances in the professional field of mineral and energy modeling should not depend on such crises. The models and findings which have come before us should be used to provide new advancements so that this field of economic modeling will continue to improve and grow".

The early part of Chapter 5 presents descriptions of a number of older energy and economic models to show that even in the early 2000’s, many failed to make the jump from normative to predictive modelling and their focus remains on how to improve the system or outcomes or efficiency rather than predict how the market might actually behave. They leave themselves exposed to criticism when they actually use their models to make long term predictions. The latter part of Chapter 5 presents descriptions of a number of more recent models, which have adopted programming techniques such as object-oriented programming (OOP) (see 4.1 and 4.2) to advance the development of new modelling techniques for investigating economic and energy industry questions that invoke emergent behaviour over time. Much of this new thrust highlights Labys’ (1999) recognition of the trend to combine "the behavioral and process approaches to mineral and energy modeling in order to provide a more comprehensive framework in which to forecast the conditions of future markets under alternative assumptions concerning the emergence of new production, conversion, and utilization technologies".

3.1.1 Taxonomy of Mineral & Energy Model Types Labys (1999) presents a taxonomy of model types closely linked to their different characteristics and purposes. This model classification is presented below.

Table 3-1: Labys’ Taxonomy of Model Types

Model Type Characteristics 1 Economic sector models Include time series models, which describe the supply, demand or prices for specific minerals, fuels or energy forms 2 Econometric market or industry Include supply, demand and price aspects of market adjustments for individual models or related mineral and energy commodities 3 Spatial equilibrium and Apply programming algorithms to describe the distribution of demand and programming models supply in a spatial or process context 4 Resource exhaustion forms of Describe how a firm or industry cartel might establish prices to achieve optimal optimisation models resource allocation over time 5 Input-output models Explain the flows of mineral or energy goods and services among industries in establishing outputs and demands 6 Economy interaction models Link the mineral and energy sectors with the overall economy, sometimes integrating two or more modelling methods 7 Resource balance methods Yield an empirical view of future energy demand and supply equilibria; includes time series forms 8 Transition models Constructed to explain the transition of mineral markets from planned to competitive systems in the eastern Europe nations

45 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

According to Labys’ (1999) model types and purposes, the Transition Model fits well with the situation of the Victorian gas market as it undergoes deregulation. The transition from regulation to deregulation has many similar characteristics to those considered by Labys as requiring a unique type of model. Market variables go from being planned or fixed to being flexible; externalities start to enter the consumption and production functions. Labys states that the modelling challenge is to be able to model what he terms the disequilibrium adjustments1. He also points to the challenge of finding sufficient data of adequate quality, which can provide the basis for quantifying the equations in the designed model structures for spatial and process oriented models. In models that attempt to project such conditions into the medium and long term, Labys points to the use of simulation as the best way to consider risks and uncertainties through "what if" and sensitivity testing, including "shocking" certain variables to observe results that might represent some form of market intervention or market shock (such as discovery of a new gas resource in a gas market capture area or a change in the market rules). He points to model analyses that use expert systems to investigate operational flexiblity, consider prices and costs as well as incorporate investment decision rules. This theme is taken up in more recent ABM research and literature, which is described later in this chapter.

Labys (1999) stops short of recognising the related sciences of complex systems and ABM’s, which were just starting to take hold when he published his reference book in 1999. There is much similarity between his commentary on trends for the future of energy and economic modelling and the later development of the concepts of complex systems and ABM’s.

3.2 Complexity & Complexity Theory Waldrop (1992) states that "the science of complexity studies how single elements, such as a species or a stock, spontaneously organize into complicated structures like ecosystems and economies; stars become galaxies, and snowflakes avalanches almost as if these systems were obeying a hidden yearning for order". Studying complex systems involves developing general results about non- linear systems that are systemic, self-inferential, and multi-level in nature, or in simple terms: where everything affects everything else. Waldrop characterises complex systems as "having a dynamism that is different to the unpredictable gyrations know as chaos. Chaos theory, with its very simple dynamical rules, cannot explain the structures, the coherence, the self-organizing coherence of complex systems, where chaos and order are brought into a special type of balance", which Waldrop calls the "edge of chaos, ... where components never quite lock into place and never quite dissolve into turbulence, either .... Complex systems are spontaneous, adaptive and alive".

1. This term conveys the same types of dynamic concepts that are discussed by Tesfatsion (2006) in regard to her descriptions of complex adaptive systems and what the author calls "dynamic, non-equilibrium". These non-equilibrium concepts in relation to complex adaptive systems are described in 3.2 and explored further in 4.3.4.

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Batten (2000) provides a philosophical commentary of how to think about complexity as it exists in complex settings such as economies, commodity markets and natural environments. He starts by pointing out that most twentieth-century economics have been reductionist in character. Reductionism tries to break down complex economies or markets into simpler parts, like industries and households, and those parts, in turn, into even simpler ones, like jobs and persons – but usually the characterisations are by functional definition. Although successful in some ways, this approach has left a void in explanation and understanding of the underlying driving forces. Reductionism can never tell us how an economy really works.

To find this out, Batten (2000) suggests that we must combine our knowledge of the smallest parts, the individual "agents"1, with our knowledge of their interactions to build up a behavioural picture of the whole economy. To date macro-economics has not devised a convincing way of doing this. Batten presents numerous examples to make these points and to support the use of agent characterisation at the lowest level to build up a framework for looking at complex systems. He refers to the use of modern computing power to simulate the interactions of agents at the lowest relevant levels to explore what these interactions generate as an emergent behaviour. Batten presents a number of applications where this approach has been used; they range from extinction of human civilisations, cellular congestion, stock markets and urban growth. Batten is a strong advocate of agent-based simulation to address complex systems and to generate what he calls artificial economies following the terminology of Casti (1998), whose work on a number of artifical worlds is described in Chapter 5.

An important dimension, when considering complex systems, is time. Over time, most complex systems will show adaptive reponses by the agents that populate a given system and these individual reponses over time are likely to change the way a system looks and feels. It will evolve over time and change its form and characteristics. A good simulation will be able to predict or forecast or reproduce such dynamic changes.

Gilbert and Troitzsch (2005) emphasise the relevance of simulation techniques to best address and explore many of the points raised by Labys (1999). Gilbert and Troitzsch state that "simulation introduces the possibility of a new way of thinking about social and economic processes, based on ideas about the emergence of complex behaviour from relatively simple activities (Simon 1996). These ideas, which are gaining currency not only in the social sciences but also in physics and biology, go under the name of complexity theory".

1. The concept of an agent was introduced in 1.4.2 and is developed further in 4.2.

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3.2.1 Complex Adaptive Systems Conventional philosophy of social science has often made too ready a connection between explanation and prediction. It tends to assume that the test of a theory is that it will predict successfully. This is not a criterion that is appropriate for non-linear theories, at least at the micro- scale. Complexity theory shows that even if we had a complete understanding of the factors affecting individual actions, this would still not be sufficient to predict group or institutional behaviour. The message is even stronger if we make the plausible assumption that it is not only social action that is complex in this sense, but also individual cognition (Conte and Castelfranchi 1995).

Tesfatsion (2006) suggests that a system is typically defined to be complex if it is composed of interacting units and the system exhibits emergent properties, that is, properties arising from the interactions of the units that are not properties of the individual units themselves. In extending this concept of complex systems to complex adaptive systems, Tesfatsion suggests three definitions to characterise subtle differences in emphasis of the different ways of thinking about such systems.

Definition 1: A complex adaptive system is a complex system that includes reactive units, i.e. units capable of exhibiting systematically different attributes in reaction to changed environmental conditions.

Definition 2: A complex adaptive system is a complex system that includes goal-directed units, i.e. units that are reactive and that direct at least some of their reactions towards the achievement of built-in (or evolved) goals.

Definition 3: A complex adaptive system is a complex system that includes planner units, i.e. units that are goal-directed and that attempt to exert some degree of control over their environment to facilitate achievement of these goals.

Casti (1998) uses the term “complex adaptive systems” to describe situations, where agents regularly review their beliefs and goals in order to adapt their strategies to changing circumstances and to predict the likely direction of emergent behaviour of such a system.

3.2.2 Complex Science for a Complex World Perez and Batten (2006a) draw together in the book, "Complex Science for a Complex World", a collection (or what they call an "ouvrage") of writings that explore the science of complexity theory and how new tools and methods, such as ABM, can provide a better understanding of complex and adaptive environments. This collection of works presents a wide-ranging background to the rapid growth in the application of ABM across a number of sectors, including energy. It is a good all round reference to the thinking on both the philosophy and application of ABM to consider complex systems. It is interesting because (i) it presents a selection of these

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works to show the main types of applications and the styles of model designs with variations on how agents behave or are encouraged to behave, (ii) it provides some philosophical background to justify the usefulness of ABM’s, and (iii) it comments on advances in model design with an increasing emphasis on incorporating behaviour patterns into such models to achieve outcomes that closely reflect reality and are useful for studying emergent behaviour.

Perez and Batten (2006b) focus on human ecosystems as complex adaptive systems reflecting the interactions of people, entities and the environment (including resources), as well as their co- evolution over time. They identify that, while the future state of human ecosystems has always been unpredictable, human beings are the critical players and it is their human reasoning and idiosyncracies that govern their adaptive behaviour over time and will influence the way in which the future will emerge.

Perez (2006) draws on the philosophy of the mind and semiotics to suggest that such contextual cognitive processes have deceived attempts to develop a predictive modelling formalism. He argues that the Artificial Intelligence paradigm needs more robust approaches and that many research efforts use meta-level controllers that supercede the intentional and rational systems of the agent. This point can be interpreted to mean that agents need to remain simple and learn, without understanding, but by re-inforcing or enforcing beliefs, whether they are good for an individual agent or not. He draws attention to the consumat theory of Jager and Janssen (2003), whose paradigm supports the creation of simpler models compared to earlier trends.

The consumat approach considers basic human needs and environmental uncertainties as the driving factors behind decision-making processes. Agents engage in different cognitive processes, including social imitation and comparison, according to their own perceptions of individual needs and environmental threat. Experience pre-empts cognition, and social rationality is directly built into intentional processes. Such human systems rely on relatively simple rules that can be validated against experimental economic settings.

Perez (2006) also explores how to design well-balanced models and draw attention to the best way to define simple sets of human behaviour rules for use in simulating interactive behaviour. He points to the work of Granath (1991), who introduced the concept of collective design in industry to define a process by which all actors involved in the production, diffusion, or consumption of a product are considered equal experts and invited to participate in the design of the product. Collective design typically faces three problems: barriers to understanding outside one’s own discipline, differing levels of knowledge, and dissonant communication between design contributors.

49 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Collective design in its own right is a challenge with an emphasis on mediation of design inputs to build what is a social construct about agent interactions. Work such as that by the Collectif ComMod group (2005) is pursuing approaches to faciliate how to capture different stakeholders’ viewpoints and the subjective referents used by the different stakeholders, and to integrate this knowledge into simulation models that in themselves serve as the mediating agents.

Perez and Batten (2006b) present a range of examples that make the points that the representation of agents’ profiles and how they deal with each other are considerably more subjective and challenging than simply looking for least-cost or optimal economic solutions. They see great value in constructing agent-based investigative models and applying them to broader ecosystems. They point to a diverse range of applications such as dealing with interactions between communities and their environments, planning housing developments, pest and disease management in agriculture, control of illicit drug markets, forest and marine management, warfare and reduction of greenhouse emissions. Behavioural interaction is a strong influencing determinant on future outcomes in such systems.

Perez and Batten (2006b) reinforce the concept that effective modelling of human (including business) behaviour needs to focus more on mimicking human inductive behaviour than on optimising deductive behaviour with scientific and technical theories, by recognising that human decisions involve pattern formation and recognition by intuition and creativity, and that humans differ in how well they do either or both. Some like to experiment and change; some like to follow and resist change under almost all circumstances. Perez and Batten liken the two types to explorers and sheep and that the world is made up of a mixture of both types, and that the nature of this mixture bears on the outcomes of interactions between such groups or individuals. Emergent human behaviour will reflect the balance of behaviours by the differing types within a community or between communities; it will also be influenced by the speed of reactions and different cognitive processes adopted by each group in learning from rapid successes or slow failures and/or extinction. Superimposition of external intelligence that either exceeds or under- represents agents’ cognitive processes will fail to show the possible direction of emergent behaviour of the environment in which they exist.

Perez and Batten (2006b) also point out that in simulation systems, the intrinsic structure of the system being simulated can have direct influence on the ways in which the human agents are likely to behave, i.e. react and adapt over time. The situations, in which the forecasts are being made by agents, serve to create the very world they are trying to forecast. Such co-evolutionary systems have been called reflexive and are generally self-reinforcing. Decisions of the type such as to act or not to act depends upon what everyone else is doing. The best thing any agent can do is to apply the strategy or mental model that has worked best so far. The mix of possible strategies

50 Chapter 3 Energy & Economic Modelling Taxonomies

is co-evolving incessantly over time, so much so that the choices made by individual agents seem to have little impact on collective outcomes. Sometimes, collective populations of agents exhibit emergent regularities; at other times, they display different types of complex behaviour.

Batten (2006) points out that sometimes self-referential systems can be susceptible to becoming self-defeating systems where self-referential behaviour can have adverse consequences on a group of agents. A benefit of agent-based simulation is to test what is the best way to understand possible outcomes ahead of implementing rule changes that could have a negative influence on future outcomes. He summarises an agent-based study, which shows how and why a community ended up over-fishing natural fisheries to their own eventual detriment and shows how to develop more appropriate fishing practices.

3.3 The Application of Simulation to Social and Economic Issues Gilbert and Troitzsch (2005) also point to the use of simulation as a suitable technique for examining social and economic issues: "In the social sciences, the target is always a dynamic entity, changing over time and reacting to its environment. It has both structure and behaviour. This means that the model must also be dynamic. We can represent the model itself as a specification, a mathematical equation, a logical statement or a computer program – but to learn something from the specification, we need to examine how the behaviour of the model develops over time. One way of doing this is using an analytical method. This entails deriving the model’s future structure from the specification by reasoning, perhaps using logic or more often by using mathematics. For example, we might have a model of the relationships between a set of macroeconomic variables and use algebra to derive the outcome if one of those variables changes over time. With complex models, especially if the specification is non-linear, such analytical reasoning can be very difficult or impossible. In these cases, simulation is often the only way. Simulation means ‘running’ the model forward through (simulated) time and watching what happens. Whether one uses an analytical technique or simulation, the initial conditions, that is, the state in which the model starts, are always important. Often, the dynamics are very different depending on the precise initial conditions used".

Gilbert and Troitzsch (2005) point to the differences between past statistical1 type modelling and more recent simulation2 modelling, why change has occurred, and the importance of data. Statistical models are used to predict the values of dependent variables, and simulation models, which embody some plausible assumptions, set out to see what happens and compare the behaviour of the model/program to observed patterns. They point to the key difference, in these

1. Akin to Labys’ normative modelling. 2. Akin to Labys’ predictive modelling.

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two types of modelling approaches, is the "reliance in the statistical approach ... to explain correlations between variables measured at one single point in time ... and ... reproduce the pattern of correlations among measured variables, but rarely will it be modelling the mechanisms that underlie these relationships ... whereas simulation models ... are concerned with processes ... and ... explicit representation of the processes, which are thought to be at work in the social world".

Given statistical models are based on equations derived from large historical databases and correlations rather than underlying processes, they cannot adapt to social systems, which are always dynamic entities. To give effective representation to dynamic systems, the model used must be dynamic as well.

Some users of each approach will emphasise different desires: statistical modellers will emphasise the need for making predictions and simulation modellers will emphasise the desire for understanding, although all simulation models will also be capable of making predictions.

There are numerous other reasons for preferring simulation over mathematics to characterise social systems. First, programming languages are more expressive and less abstract and more user-friendly for use by non-specialists. Second, simulation deals more easily with parallel processes and processes without a well-defined order of actions than do mathematical equations. Simulation programs are typically modular so that changes can be made in one area without causing flow-on effects in the design of the rest of the program or model design. Simulation easily accommodates interactions between heterogeneous agents with different objectives, frames of reference, and different capabilities. While these points are major strengths favouring simulation, a weakness in using simulation for experimental examination of a social system is the difficulty in proving the uniqueness of a predicted behaviour that shows emergent patterns that differ from past patterns. This weakness is addressed in Chapter 8, which deals with model calibration and validation.

3.3.1 The Application of ABM to Simulation of Social and Economic Issues Social simulation received its big boost with the application of agent technology and distributed application of this technology to replicate simple forms of artificial intelligence for the entities in many social and economic systems. This advance was followed by the extension of distributed agent technology to style agent behaviour so that agents could learn from their experience and hence provide a capability to simulate how societies or economies might adapt over time to new circumstances. Model learning first focussed on machine learning but has moved on to the modelling of social behaviour and social learning, which is typically more subtle and subject to greater uncertainties.

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Gilbert and Troitzsch (2005) have prepared a chart that shows the progession and evolution of modelling techniques from the use of differential equations, which were first described in the early 1700’s to ABM’s from the late 1980’s with rapidly expanded application in the 1990’s and early 2000’s.

This chart also puts into context various models covered by Labys (1999) and a number of the model examples presented in Chapter 5. Gilbert and Troitzsch’s (2005) chart is reproduced in Figure 3-1.

Figure 3-1: The Development of Contemporary Approaches to Simulation in the Social Sciences

1700 Differential Equations

Stochastic Processes

1900

Game Theory 1940

Queuing Cellular 1950 Models Automata Artiﬁcial MSM System Dynamics Intelligence 1960 DYNAMO Naive Synergetics 1970 World Dynamics Physics

1980 STELLA Multilevel QSIM Workﬂow Modelling Multi 1990 MICSIM Management, (MIMOSE) sCA Agent World Dynamics II UMDBS Business Models Process Modelling Legend: grey shaded area: equation based models; white area: object, event or agent based models; ‘sCA’ means cellular automata used for social science simulation; the other names of tools are explained in the respective chapters

Source: Gilbert and Troitzsch (2nd edition, 2005), published by Open University Press, see Figure 1.2 (page 7); reproduced with the kind permission of Open University Press.

This chart highlights the gradual but distinct movement over time from statistical approaches to the use of ABM simulation and puts ABM in the context of other modern modelling tools.

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Simulation also has its own sub-systems that need to be considered given each has a context, where it is best applied. The next section looks at various simulation model types and their relevant contexts.

3.3.2 Brenner & Werker’s Taxonomy of Economic Simulation Model Types Brenner and Werker (2007) present a Taxonomy of Simulation Models (or alternatively a Taxonomy of Inference). Their classification is based on two defining characteristics. The first is the degree of generalisation of the simulation model; that is, whether a model is very specific or very general or somewhere in between. The second is the degree of theoretical and empirical generalisation of the simulation model. A schematic diagram of their classification framework is shown in Figure 3-2.

Figure 3-2: Werker & Brenner’s Simulation Model Classification System

Source: Brenner and Werker (2007), Figure 1; reproduced with the kind permission of the authors.

Brenner and Werker (2007) offer the general rule with respect to the level of generalisation that "the more general a model, the more it is able to cover a larger variety of economic processes. This implies that a more general approach, covering more different model specifications, is likely to adequately represent a specific dynamic in the real world with a particular specification". With respect to the use of empirical data, they provide the general rule that "the more complex and complicated the economic processes captured by the model are, the more empirical data needs to be used to adequately restrict the model parameters".

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Brenner and Werker (2007) point out that models above the diagonal (from hypothetical, general to empirical, specific) are not possible because this would imply completely fixing parameters according to empirical data and, nevertheless, examining all different parameter and model settings. This is a contradiction because examining different settings means that they are not fixed. Hence, a completely empirical and completely general approach does not exist. Only approaches below the diagonal are feasible.

Brenner and Werker (2007) state that in presenting their classification framework, their main interest was to "address the question of what role should and can data play in simulation models. The role of data in different types of simulation models, including agent-based models and microsimulations etc, affects the way the model is set up, the runs are conducted and the results interpreted". They identify five types of simulation models, which they place in their classification/taxonomy framework. Their five simulation model types are shown in Figure 3-3 and their descriptions in the following paragraphs.

Figure 3-3: Classification of Five Types of Simulation Models

Source: Brenner and Werker (2007), see Figure 2; reproduced with the kind permission of the authors.

1. "Conventional simulation approach – The assumptions are based on theoretical considerations, which result from axioms, ad-hoc modelling or stylized facts. They mostly rely on common sense and the impression of the scholar using them. The problem with stylized facts is that they "fall from heaven" and often remain unmotivated. It is not usually possible to determine if the stylized facts are the only structural elements of economic processes or whether

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they partly mirror noise in the form of chance elements. The major principle of inference used in this kind of model is deduction1 because the focus lies on theoretical considerations and the analysis of their implications. Usually they include some level of induction2 because computational economists often check their models in the light of common, (usually empirical) knowledge about some features of reality. The idea is to find a simulation model that shows a behaviour that corresponds to the expectations based on common knowledge. Conventional simulation models typically fall along the lower left edge of the classification framework as shown in Figure 3-3.

2. History-friendly models – They are specific models that use case study data. They rely on detailed empirical knowledge about real historical processes and try to find a model that leads to processes with the same characteristics. These models focus mostly on induction, because the case study is the core of the analysis. The case study is used in two ways: it provides some knowledge about the underlying processes and it provides information about the realistic dynamics and uses quite a lot of empirical data. Once a model is found that describes the historical case adequately, simulations are run to study various aspects of interest. This is a deductive endeavour, for which a specific setting is chosen as the basic model. This approach is located in the lower middle of Figure 3-3.

3. Micro-simulations – They are specific models, which are based on comprehensive empirical data. In these models, the dynamics of the real system is thoroughly examined. Two requirements have to be satisfied when carrying out micro-simulations. First, the dynamics need to be simple to be sufficiently analyzed. Second, adequate empirical data has to be available to examine the underlying mechanisms. Given that these requirements are satisfied, the obtained simulation model can be used to describe or predict economic processes. The crucial theoretical assumption is that, in the future, the same mechanisms and dynamics take place as in the past. This is not necessarily the case. In Figure 3-3 this approach is located in the lower right edge because a highly specialized model is developed using a large amount of empirical data. Micro-simulations use induction extensively to set up the simulation model, while deduction is used subsequently to make predictions and facilitate counter-factual analysis with the help of multiple simulations.

4. Bayesian simulations – This approach uses general models, which incorporate empirical data by systematically comparing the simulation results to larger sets of empirical observations. This approach starts from the assumption that little is known about the model’s parameters and often also about the relationships between the variables of the model. The large set

1. See 4.3.2 for an explanation of deduction. 2. See 4.3.2 for an explanation of induction.

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of empirical observations, which is typically used, enables the modeller to thoroughly test the different specifications of the simulation model. In Bayesian simulations usually a lot of empirical data is available for the phenomenon but little is known about the processes that cause the phenomenon. Therefore a very general model is developed that includes all plausible processes that could cause the phenomenon, so that the initial simulation model is located at the upper edge in Figure 3-3. The intention of Bayesian simulations is to inductively obtain an adequate model specification. The model specification obtained might then be used in further deductive analyses, but this is not the major part of this approach.

5. Abductive simulations – This approach uses simulations in which empirical data is used as much as possible to specify the model, while keeping the model as general as necessary. In this kind of model, one major aim is to develop the model that adequately represents the economic processes, which are supposed to be analyzed by the model. Abductive simulation models start, similar to Bayesian simulations, with the set up of a very good model that should represent all mechanisms and processes that might play a role for the considered research question. In contrast to Bayesian simulations, empirical knowledge about the modelled mechanisms and processes is then immediately used to reduce the generality of the model. How- ever, it is crucial in this approach that the generality is reduced only as far as the empirical data allows. For example, if empirical evidence is available that shows that certain parameters fall into certain ranges, these ranges are used to restrict the model parameters. As a consequence, the simulations are started with a model based on some empirical knowledge but still quite general and therefore located in the middle of Figure 3-3. This approach combines induction and deduction when setting up the model and when carrying out runs. It takes an additional step by using empirical data about specific systems to reduce the generality of the model and by employing the resulting set of model specifications to generate knowledge about the kind of systems that are studied. Abductive simulation models include as much data as possible when setting up the assumptions and when testing the implications of the model. Assumptions are either specified by using empirical data or by including all logically possible model specifications. One needs to be aware of the trade-off between using empirical data and analyzing a lot of model specifications."

3.4 Steps in Modelling 3.4.1 By Labys Labys (1999) describes the following essential steps for mineral and energy modelling. Development normally begins with identification of the modelling problem. The modelling purpose must be clearly established at the outset. Modelling purpose then leads to selection of the modelling technology that will most effectively solve the problem at hand. Once a technology is

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selected, the model must be specified following the procedures of the chosen technology. This requires a process, which represents an interaction between selecting appropriate economic and engineering theories and establishing a database for model implementation and application. In the context of minerals and energy models, parameters derived from econometric models depend on statistical methodologies, while parameters employed in engineering systems normally depend on engineering experiments or on judgemental observations. In business applications, this includes compilation and manipulation of public information.

The next step is model validation and refinement. This step requires two properties: firstly, that the model’s parameters are demonstrably in accord with the underlying theory or principles and, secondly, that the model’s output replicates reality closely. This procedure depends on the use of parametric and non-parametric validation tests. The final step of modelling involves applying the model to solve or investigate the problem at hand. This process normally consists of explaining market history, of analysing selected policies, or of forecasting future paths of the model’s dependent variables. The final step can provide feedback to improve the model and solutions or forecasts.

3.4.2 By Brenner and Werker Brenner and Werker (2007) state that most simulation approaches can be separated into three steps, which are usually conducted consecutively. First, building the simulation model by defining the assumptions (here described as including all model restrictions, definitions, and settings). Second, they conduct the simulation runs after determining the parameter values. Finally, they analyse the results in order to describe, explain, understand, and/or predict economic processes. If the simulation results are used to adapt the model (as per Brenner and Werker’s abduction modelling technique), then they suggest this should be formally acknowledged as a fourth step.

Setting up the model requires the assumptions to be determined; they can come from ad hoc judgements, theoretical considerations, or empirical data. Brenner and Werker (2007) observe that ad hoc judgements are difficult to evaluate, agreed assumptions stemming from theoretical considerations are rare, and therefore they argue for use of as much empirical data as possible. The use of more data will allow the model to more reliably describe, explain and predict economic processes.

Empirical data can be available in the components of a simulation model, i.e. the assumptions that are used for building the simulation model and the implications that are studied as the outcomes of the simulation runs. They are different data sets. Empirical data is often either not available or difficult to obtain. Micro-simulations are not feasible in most cases because of the workload in

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collecting required empirical data or because of limited public access, so there is a need for incorporation of the previously described alternative approaches at least in parts of a model.

The approach, which is selected to run model simulations, depends upon what design approach is used and how the simulation results are to be analysed. Micro-simulations are empirically data rich and require a very large effort to collect the input data to generate simulation runs that are capable of accurate predictions; less testing is needed than for more general model approaches. In a completely general approach all parameters can be unrestricted and by using a Monte-Carlo approach the modeller can be quite sure of identifying an adequate parameter set. However, general models need more runs and testing because there are more possible data sets and only one of these runs might be adequate for answering the research question and we do not know which run has this characteristic. Therefore, depending upon the approach used in setting up the model, a model’s location in the spectrum of being totally general to totally empirical will govern the level of test running before moving to the next phase of using the model to answer the research question.

Brenner and Werker (2007) identify three types of such questions: examining the characteristics of economic processes modelled (including counter-factual analyses), predicting economic processes, and comparing simulation results with empirical data. In line with these different research aims, they distinguish three typical ways of inferring scientific insights from the results of simulation models, which use a combination of induction and deduction, and a fourth way that explicitly uses abduction as the inference principle by categorising simulation model specifications into sets of models.

In an effective simulation model, the simulation results are used to assess the characteristics of the system under study or to understand the relationships between variables and/or parameters or to conduct counter-factual analyses. In this case, the simulation results are treated in a similar way to empirical data. The aim is to obtain a detailed knowledge about the system described by the simulation model. Such analyses are typical of general and history-friendly models but can be used in micro-simulation models by way of counter-factual analyses and in abduction models to explore parameter relationships and system characteristics.

Predicting future developments employs deduction and its quality depends crucially upon the quality of the model. Most commonly, predictions are mainly carried out by micro-simulations because they are built on the basis of a comprehensive database and such predictions can be assumed to be quite accurate. Bayesian and abductive models that include a similar level of empirical data can provide predictions of the same quality.

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It is quite common to compare the simulation results with empirical data about the system’s dynamics and characteristics. The aim is to identify the underlying causal relationships to explain known phenomena. Potential explanations are formulated as assumptions in the set up of the simulation model and are tested by comparing their implications (simulation results) with empirical data. Many approaches are adopted for this purpose and are considered more extensively in Chapter 8, which covers model calibration and validation. One way is to simply use stylised facts to check whether the simulation results are plausible. The aim is to identify model settings for which the simulation results are in line with the empirical data.

Brenner and Werker (2007) point to the staged process of using Monte-Carlo approaches to obtain different sets of specifications for a real system and by comparison with empirical data to narrow the range of data specifications. Alternatively, if empirical data is available to test results for different real systems, the characterisation of the model systems can be improved. The testing of simulation models with empirical data and testing of model implications with empirical data shifts the location of each of the five different simulation approaches, which are shown by the ellipses at their typical initial locations, and in which direction they develop if the model is modified during the simulation approach. They all move to the lower right side of Figure 3-4 as is shown by the directions of the arrows.

Figure 3-4: Categorisation of Five Different Simulation Approaches

Source: Brenner and Werker 2007, see Figure 3; reproduced with the kind permission of the authors.

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In Bayesian and abductive models, empirical data on the implications of the simulation model are extensively used to specify the model further. For the model with more data we can estimate the parameters of the model and define quite small ranges for them. It is very likely that the adequate values of the parameters, i.e. the values that best represent the real world captured by this model, are within these small ranges. If we want to achieve a similar likelihood for including the adequate values for the model with less data we need to keep the ranges of the parameters much bigger. This, however, implies that many runs must be conducted to investigate the implications for the whole parameter range.

Brenner and Werker (2007) make two main conclusions about all kinds of simulation models: first, they argue that empirical data should be used more often where it is available, because this leads to much better founded simulation models and increases the soundness of the results. Second, simulation models seem to be the perfect tool to transfer (empirical) knowledge from the level of assumptions to the level of implications and vice versa. This means that in a simulation approach one can go back and forth between the assumptions and implications in the simulation model and identify the characteristics and dynamics of the economic processes modelled. Empirical data used for setting up assumptions and for testing implications improve the comprehension of the economic processes modelled.

The above steps advocated by both Labys (1999) and Brenner and Werker (2007) are broadly followed in developing VicGasSim and are described in detail in Chapters 6 through 9; these later chapters describe the modelling methodology selected, staged model development, and the level of calibration and validation.

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62 Chapter 4: Modelling Methodologies

The emergence of powerful yet low cost computers has offered the opportunity to use modern computing power to develop new ways of looking at and simulating complex systems, including many classic econometric problems as well as a wide range of social issues. These increased modelling capabilities have allowed development of simulation and ABM as specific subsets of simulation to investigate complex systems. This chapter explores the development of ABM from a combination of discrete event modelling, object-oriented programming and use of agent protocols to characterise the participants in complex systems.

One of the earliest protocols developed to aid the simulation of complex, dynamic systems was the Discrete Event System Specification (DEVS) promoted by Zeigler (1976, 1984), and later adopted to object-oriented programming and simulation as described by Nidumolu, Menon, and Zeigler (1998) and Zeigler, Kim & Praehofer (2000). DEVS provides the abstract descriptions of a universal formalism for modelling discrete event modelling. DEVS is described in the next section.

4.1 Discrete Event Programming According to Zeigler (1976, 19984), DEVS is a formalism that allows combined modelling of interacting discrete and continuous trajectories. DEVS can be viewed as a short-hand way to specify systems whose input, state and output trajectories are piecewise constant with step-like transitions identified as discrete events in the trajectories. The DEVS and its extensions offer an expressive framework for modelling, design, analysis and simulation of autonomous and hybrid systems. DEVS can be used to formulate and synthesise supervisory level controllers and DEVS has its origins in job shop scheduling simulations in operations research. DEVS can be used to design a Discrete Event Controller to manage the transition trajectory and behaviour of controlled objects as they change states. In later work, Nidumolu, Menon, and Zeigler (1998) point to the combination of DEVS with use of the object-oriented programming (OOP) and modelling of complex business processes. OOP offers comprehensiveness, ability to be understood, changeability, adaptability and re-usability of objects in a model framework; DEVS offers a methodology for managing their hybrid interactions over time. Their combined use became know as OO/DEVS modelling and is best exemplified by the work of Bunn and a number of colleagues, who have studied the electricity sector in the UK and Europe and whose work is described in 5.3.1 and 5.4.1.

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Zeigler’s (1976 1984) objective was to adopt a method that could preserve the benefits of the earlier system dynamics approach for strategic modelling but at the same time allow modularity and flexibility to integrate with other modules performing functions such as detailed optimisation and spread-sheet-based financial planning. In addition, the adoption of OO/DEVS aimed to address some of the methodological criticisms of system dynamics, related to modelling generalisation and aggregation relationships, lack of modelling detail, lack of reusable model components, and inappropriate time representation due to continuous time steps rather than discrete time steps. OO/DEVS was a precursor to Agent-Oriented Programming (AOP).

4.2 Agent-Oriented Programming (AOP) & Intelligent Agents AOP is a refinement of the OO/DEVS approach and uses intelligent agents to model the main interactions between parties or model objects. An AOP approach allows close representation of the actual decisions faced by participants and allows testing of specific decisions, projects and Government policies. The methodology appeals to participants because it details a model structure in terms of the participants’ actual strategies and the participants’ commercial decision- making rules. This protocol extends the message passing and discrete event signalling capabilities of previous object-oriented programming concepts.

There is a wide base of literature on the use of AOP and agent-based simulation in many situations from operations research, to predator-prey survival models of species, civilisations and environments, to simulating scientific behaviour such as the characterisation of turbulent flow. Study of this literature raises a number of modelling issues to be considered. Such issues include the nature of the agents in each system and how they interact or negotiate with each other. Agents can be co-operative or competitive and autonomous; they might be willing to share informatiom or might not.

Other questions need to be addressed: How intelligent is each group of agents? How do you build intelligence into agents? What are the resources available to them? How do they communicate? And with whom do they communicate? Or with whom do they not communicate? These questions lead us into considering more about the nature of agent theory and the wide range of negotiating styles open for agents to co-operate or compete.

AOP is a system that provides a representation of rational, human-like behaviour in an agent’s respective problem domains. This approach has the potential to overcome the limitations of traditional systems when attempting to operate in real time and changing environments. An agent-oriented model encapsulates the desired behaviour in modular units, which contain all the definitions and structures required for them to operate independently.

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Agents extend the concept of encapsulation1 to include a representation of behaviour at a higher level than object-oriented approaches. One early formalism of this approach is the BDI model, which is explained in the following section.

4.2.1 The BDI Protocol for Describing Agent Behaviour Rao and Georgeff (1995) outlined the Belief, Desires, Intentions (or BDI) concept or model for controlling and co-ordinating agent behaviour. Their work follows that of Bratman (1987), Hughes & Creswell (1968), and Konolige (1986). Brafman and Tennenholz (1997) describe a similar notional framework for mental state modelling recognising (i) beliefs and perceptions about the agent’s world, (ii) agent preferences or goals, and (iii) agent decision criteria or a method of choosing actions under uncertainty.

Agent-oriented systems consist of a collection of autonomous software agents, each of which responds to events generated by other agents and by the environment. Each agent continually receives perceptual input and, based on its internal state, responds to the environment by taking certain actions that in turn, affect the environment. AOP achieves dynamic interactions in a similar sense to system dynamics but in a modular and discrete fashion. An agent is a software component that can exhibit reasoning behaviour under both proactive (goal-directed) and reactive (event-driven) stimuli.

Each agent has:

1. a set of beliefs about the world (its own private and public data set),

2. a set of events to which it responds,

3. a set of goals that it may desire to achieve (either at the request of an external agent, as a consequence of an event that arises, or when one or more of its beliefs change),

4. a set of plans that describe how it can handle the goals or events that may arise, and

5. a capability to encapsulate a set of beliefs, events and plans for re-use in different settings or applications.

The key characteristics that make intelligent agents attractive are:

1. the ability to act autonomously and focus on goals, not methods,

2. a high-level representation of behaviour at a level above object-oriented constructs,

3. flexibility, combining pro-active and reactive behavioural characteristics,

4. real time context sensitivity to specified maintenance conditions,

1. From Wikipedia (25 July 2009): Encapsulation is described as the process of compartmentalising the elements of an abstraction that constitute its structure and behaviour; it includes behaviour in the same container as data and it allows control information to pass through a layered model such as to include data from an upper layer protocol in a lower layer.

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5. suitability to distributed applications,

6. concurrent or multi-threaded activity streams allowing agents to prioritise between goals and events or multi-task them, and

7. the ability to work co-operatively in teams if their environment permits this approach.

The basic scheme of an agent system is shown in Figure 4-1. This figure shows the key elements of the BDI model: an agent, which has or uses, beliefs (data), plans, events, and encapsulated capabilities to interact with other agents by either synchronous or asynchronous communication most commonly triggered by certain events.

Figure 4-1: The Key Components of an Intelligent Agent System

Event

Agent Plan

Database Event

Capability Plan

“has” Database “uses”

See footnote below for explanation of the legend1 on "has" and "uses".

This BDI agent model is defined in various other forms in subsequent literature on agent-based modelling but the basic features of all such descriptions are essentially the same. Zlotkin and Rosenschein (1996) use the term “logical axiomatization” to describe the approach of capturing rational behaviour using formalised agents, such as BDI agents, to precisely characterise rational

1. "has" means "is part of the agent and its environment". For example, an agent has a database that defines its beliefs; an agent has a set of plans that describe how it can handle the goals or events that may arise; an agent has a set of events to which it responds; and an agent has high level capabilities that encapsulate a set of beliefs, events and plans for re-use in different settings or application, certain capabilities might have or rely on other capabilities. "uses" means "is an action taken by an agent triggered by an event or a change in its database (environment or belief system)". For example, an agent uses a set plan that uses (or is triggered by) either a specific event signal or a specific change in its database (environment or belief system); an agent’s database uses both internal and external events to record changes in the database.

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behaviour with the objective to impose such behaviour on an automated agent. They point to the benefit of this approach, which lends itself to characterising agents in terms, which are consistent with the real-world domain of such agents.

All such agent-based approaches can be combined with “decision-theoretic”1 reasoning processes to optimise the value of computation under uncertainty and varying resource limitations (Horvitz, 1998). In multi-agent systems, an agent can be modelled to act rationally but expressed in decision-theoretic terms. Axioms can define different types of rationality in response to various uncertainties and consequences and lead agents to make particular risk-weighted choices of action. Possible combinations of these concepts are described in the next section.

4.3 Agent-Based Modelling 4.3.1 Definitions Wikipedia2 defines an agent-based model (ABM) as "a computational model for simulating the actions and interactions of autonomous individuals with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness.

The models simulate the simultaneous operations of multiple agents, in an attempt to re-create and predict the actions of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioural rules generate complex behaviour, that the whole is greater than the sum of the parts. Individual agents are typically characterised as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.

Most agent-based models are comprised of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics;(3) learning rules or adaptive processes; (4) an interaction topology; and (5) a non-agent environment3".

1. In a decision-theoretic framework, probability is used to model uncertainty about the domain, utility is used to model an agent’s objectives, and the goal is to design a strategy for the agent that maximises its expected utility. Decision theory can be viewed as a definition of rationality. The maximum expected utility principle states that a rational agent is one that chooses its strategy so as to maximise its expected utility. The decision-theoretic framework plays two roles: the first, and most important, is to provide a precise and concrete formulation of the problem we wish to solve; the second is to provide a guideline and method for designing an agent that solves the problem. 2. From: http://en.wikipedia.org/wiki/Agent-based-model, on 11 July 2009 3. This description shows how the BDI model still underpins most forms of agent interactions in ABM’s. Various forms of DEVS are still used for control of discrete events in ABM’s.

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Fagiolo, Moneta and Windrum (2007) provide an alternative description of agent-based modelling for economic systems. They set out the following summary:

"It is useful to introduce the main (but by no means all necessary) ingredients that tend to characterise most economics AB models.

1. A bottom-up perspective A satisfactory account of a decentralised economy is to be addressed using bottom-up perspective because aggregate properties are the outcome of micro-dynamics involving basic entities (agents) (Tesfatsion 2002). This contrasts with the top-down nature of traditional neoclassical models, where the bottom level typically comprises a representative individual and is constrained by strong consistency requirements associated with equilibrium and hyper-rationality. Conversely, AB models study economics that may be persistently out of equilibrium (if any) or fluctuating around some meta-stable states.

2. Heterogeneity Agents are (or might be) heterogeneous in almost all of their characteristics. These can range from initial endowments and other agents’ properties, all the way through to behavioural rules, competencies, rationality, and computational skills.

3. Bounded rationality The environment in which real-world economic agents live is too complex for the hyper-rationality to be a viable simplifying assumption (Dosi, Marengo, & Fagiolo 2005). It is suggested that one can, at most, impute to agents some local and partial (both in time and space) principles of rationality (e.g. myopic optimisation rules). More generally, agents are assumed to behave as bounded rational entities with adaptive expectations. Moreover, since they are not initially endowed with a full understanding of the underlying structure of the environment in which they operate, they engage in open-ended searches wherein the nature of learning is at odds with Bayesian decision rules assumed by neoclassical economies (Dosi et al. 2005).

4. Networked direct interactions Interactions among economic agents in AB models are direct and inherently non-linear (Fagiolo 1998; Windrum and Birchenhall 1998; Silverberg, Dosi & Orsenigo 1988). Agents interact directly because current decisions directly depend, through adaptive expectations, on the past choices made by other agents in the population (i.e. a widespread presence of externalities). These may contain structures, such as sub-groups of agents or local networks. In such structures, members of the population are in some sense closer to certain individuals in the socio-economic space than others. These interactions and structures may themselves endogenously change over time, since agents can strategically decide with whom they interact

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according to the expected payoffs. When combined with heterogeneity and bounded rationality, it is likely that the aggregation processes are non-trivial and, sometimes, generate the emergence of structurally new objects (Lane 1993 a, b)."

These characteristics again highlight many of the differences between ABM’s and neoclasscal models. Numeous differences were outlined in the discussion of several model taxonomies, which are described in Chapter 3, and in the discussion of various model examples, which are described in Chapter 5. Similarly, the nature of the agents, as described above for economics- oriented ABM’s, is similar to the nature of the BDI agents as described by Rao and Georgeff (1995) and presented in section 4.2.1. Fagiolo, Moneta and Windrum (2007) note that one of the driving reasons for the increasing use of ABM’s to address economic issues was the breakdown of the general equilibrium approach adopted in neoclassical models. Fagiolo et al. note that "AB models reject the aprioristic commitment of neoclassical models to individual hyper-rationality, continuous equilibrium and representative agents". They make the points that (i) neoclassical models rely on assumptions that every object in the world data set can be known and understood, whereas in ABM’s the set is unknown and agents must engage in an open-ended search for new objects, and (ii) ABM’s other differences include the use innovative learning, considered adaptation, defined bounded rationality, recognition of heterogeneity between agents, and the interaction between individual agents.

4.3.2 Agent Inference Techniques Brenner and Werker (2007), in explaining differences in simulation approaches, point out that all simulation models contain two common components: implications and assumptions. Assumptions1 contain premises and definitions and set the boundaries for the model, e.g. the region or time period/s to be covered by the simulation model. Independent from the approach taken, simulation always provides the tool to derive implications from assumptions in an analytical and logical way. Brenner and Werker refer to three forms of inference2 as typical of the tools used to derive model implications; these forms of inference are induction, deduction and abduction. Brenner and Werner characterise these forms as follows:

1. "Deduction is often summarised as inferring from general to particular. Deduction sustains information already contained in the assumptions but does not create a new one. If A=B and B=C, (assumptions) then A=C. (implication) It is claimed that only models using deductions are true, but in this sense true models do not exist in economics, because there are virtually no assumptions that are unanimously accepted.

1. "Assumptions" can be correlated to "beliefs (data), events, and encapsulated capabilities" in the previously described BDI agent model. 2. The tool of "inference" can be correlated with "plans" in the previously described BDI agent model.

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2. Induction is often summarised as inferring from particular to general. It is often seen as creating information – however doubtful. The inference in induction states something that is not contained in the original observations. If the inference arguments are strong, it is probable that the claims made about the conclusions hold. Inductive inference is based on data. However, even if the number of observations in the data set is large, it is, in principle, impossible to have all observations available because future events cannot be observed. This means that the implications derived from data are uncertain.

3. Abduction – sometimes called retroduction – categorises particular events into general patterns. Abduction starts by collecting detailed information and from this data the underlying driving forces are identified. The mechanisms to infer from assumptions to implications are derived from the collected information. Abduction allows us to identify underlying structural elements that explain observations, and to develop a theory of the world we are investigating.

Many modern economic agent-based simulation models use all three inference techniques within the same model. Induction is used where the model designer has a reasonable knowledge of the economy or industry under study and can stylise the main rules governing agent interactions and has access to a diverse data set to initialise a model. Deduction can play a part to make simple comparisons and choices. Abduction can be used to sift or mine the data sets generated by the model in order to discern patterns being adopted by certain objects or classes of objects and to anticipate the future effect of such patterns, if maintained.

Axelrod and Tesfatsion (2006) provide a similar explanation that "scientists use deduction to derive theorems from assumptions, and induction to find patterns in empirical data. Simulation, like deduction, starts with a set of explicit assumptions. But unlike deduction, simulation does not prove theorems with generality. Instead, simulation generates data suitable for analysis by induction. Nevertheless, unlike typical induction, the simulated data from a rigourously specified set of assumptions regarding an actual or proposed system of interest rather than direct measurements of the real world. Consequently, simulation differs from standard deduction and induction in both its implementation and its goals. Simulation permits increased understanding of systems through controlled computational experiments". Axelrod and Tesfatsion also point to four specific goals that are typically pursued by ABM researchers: empirical understanding, normative understanding, heurisitic systems, and methodological advancement – these goals are described below.

Researchers look for empirical understanding and ask "Why have particular large scale regularities evolved and persisted, even when there is little top-down control?" When seen in the real world, can models be derived that can reliably generate observed global regularities?

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Normative understanding is sought by using ABM’s to discover good designs such as design of auction systems or power bidding systems (e.g. the failure of early power bidding system in California in early 2000’s due to poor design). The key issue is to determine if a design is efficient, fair and orderly despite likely efforts by privately motivated agents to gain maximum advantage through strategic behaviour.

Heuristic systems can provide greater insights as to the causal mechanics if the level of agent characterisation is widespread at all levels of a model design. The assumptions can be kept simple so long as there is wide representation of the majority of agent interactions that typify a market. The consequences can be far from obvious when there are many agents interacting. The large scale effects of the interacting agents are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction. Axelrod and Tesfatsion (2006) use, as an example, the much earlier work of Schelling (1971), who used a simple agent-based model to simulate residential segregation (or "tipping"), to show how segregation patterns would emerge in neighbourhoods as a result of individual resident choices and simple preferences, even in fairly tolerant communities. The resulting patterns are far from obvious and far from the logical patterns that a "normal" person might expect to see evolve.

Methodological advancement is being provided by the use of ABM’s. ABM’s allow researchers to explore a variety of ways to address, consider and visualise study of all types of social systems. The diversity allowed creates a new problem for third parties trying to reproduce researchers results if a different model is used. This challenge is discussed further in Chapter 8, which deals with the calibration and validation of ABM’s.

4.3.3 Agent-based Computational Economics (ACE) In more recent years, economists have recognised the importance and capability of agent-based simulation models to address many aspects of modelling economies from small-scale to large- scale. A new field of research has emerged and is known as Agent-based Computational Economics (ACE). A major expose of recent advances in this area is presented in the Handbook of Computational Economics, Volume 2: Agent-based Computational Economics edited by Tesfatsion and Judd (2006). Similarly, a special issue of Computational Economics, vol. 30, no. 3, 2007 provides various summaries of model classifications and the need for improved calibration and validation methods. Much of what is addressed in these two references relates to this thesis and the project of building a gas industry model in the sense of attempting to simulate the market dynamics of what is a whole industry and essentially a mini-economy. The ability to model the Victoriann gas industry is strongly influenced by the significant volume of available data and ability to formulate simple behavioural or mental models for the main industry participants.

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Tesfatsion (2006) describes ACE methodology as a culture-dish approach to the study of economic systems viewed as complex adaptive systems, which is described as a concept in 3.2. The ACE modeller starts by computationally constructing an economic world comprising multiple interacting agents (units). The modeller then stands back to observe the development of this world over time.

One of the key time-related issues that has historically confronted economic modellers is that of whether an economic system is in static equilibrium or some state of dynamic transition where the concept of equilibrium becomes fuzzy and not applicable. The question then becomes: How does one address the state of complex economic and social systems that are in a dynamic state of non-equilibirum or disequilibrium? Tesfatsion (2006) suggests the best way to address complex adaptive systems is through dynamically complete simulation models with an emphasis on the agent information procurement and processing rather than on meeting equilibrium rules or criteria. She provides the following supporting comments: "One key departure of ACE modeling from more standard approaches is that events are driven solely by agent interactions once initial conditions have been specified. Thus, rather than focusing on the equilibrium states of the system, the idea is to watch and see if some form of equilibrium develops over time. The objective is to acquire a better understanding of a system’s entire phase portrait, i.e., all possible equilibria together with corresponding basins of attraction. An advantage of this focus on process rather than on equilibrium is that modeling can proceed even if equilibria are computationally intractable or non-existent".

4.3.4 State Capability of ACE As already pointed out traditional or neoclassical economic models relied on designs that maintained macro-economic general equilibriums by use of special control agents or mechanisms. Tesfatsion (2006) points to the ongoing reliance of many economists on the Walrasian equilibrium model, which was devised by nineteenth century economist, Leon Walras, as a fundamental paradigm for thinking about competitive models of co-ordination success. Tesfatsion points to the work of Katzner (1989) and Takayama (1985) and summarises their description of a Walrasian equilibrium in modern day form as "a precisely formulated set of conditions under which feasible allocations of goods and services can be price-supported in an economic system organized on the basis of decentalized markets with private ownership of productive resources. These conditions postulate the existence of a finite number of price-taking profit-maximizing firms who produce goods and services of known type and quality, a finite number of consumers with exogenously determined preferences who maximize their utility of consumption taking prices and dividend payments as given, and a "Walrasian auctioneer" (or equivalent clearing house construct) that determines prices to ensure each market clears.

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Assuming consumer non-satiation, the first Welfare Theorem guarantees that every Walrasian equilibium allocation is Pareto efficient. In its strongest form, a Walrasian Equilibrium is achieved when the total of excess supply is zero; that is, the total value of all demands for a product or service equals the total value of all supplies of the same product or services.

The most salient structural characteristics of Walrasian equilibrium is its strong dependence on the Walrasian auctioneer pricing mechanism, a co-ordination device that eliminates the possiblity of strategic behaviour. All agents are passively mediated through payment systems; face-to-face personal interactions are not permitted. Consumers and firms both face price- taking, their decisions reduce to simple optimization problems with no perceived dependence on the actions of the agents. The market clearing conditions to achieve equilibrium are imposed through the Walrasian Auctioneer pricing mechanism. While Walrasian equilibrium is an elegant way to achieve efficient allocations, it does not address (and was not designed to) how production, pricing, and trade actually take place in real-world economies through various forms of procurement processes. Customers and suppliers must identify what goods and services they wish to buy and sell, in what volume, and at what prices. Potential trade partners must be identified, offers to buy and sell must be prepared and transmitted, and received offers must be compared and evaluated. Specific trade partners must be selected, possibly with further negotiation to determine contract provisions, and transactions and payment processing must be carried out. Finally, customer and supplier relationships involving longer-term commitments must be managed. A Walrasian equilibrium can be a reasonable assumption when the procurement processes do not affect the system’s long-run behaviour. But for economic systems without a globally stable equilibrium, procurement processes determine how the dynamics of the system play out over time from any initial starting conditions. If the modeller removes an assumed Walrasian Auctioneer from a model, the modeller must come to grips with the challenging issues such as asymmetric information, strategic interactions, expectation formation on the basis of limited information, mutual learning, social norms, transaction costs, externalities, market power, predation, collusion, and the possiblity of co-ordination failure (convergence to Pareto-dominated equilibrium1). Over time, increasingly sophisticated tools are permitting economic modellers to incorporate procurement processes in increasingly compelling ways. Some of these tools involve advances in logical deduction and some involve advances in computational power. Agent-based Computational Economics is one direction of such development. Agents can range from active data-gathering decision-makers with sophisticated learning capabilities to passive world features with no cognitive functioning. The agents in an ACE model can include economic entities as well as social, biological, and physical entities (e.g.

1. See definition in 8.1.1.

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families, crops, and weather). Each agent is an encapsulated piece of software that includes data together with behavioural methods that act on these data. Some of these data and methods are designated as publicly accessible to all other agents, and some are designated as private and hence not accessible by any other agents, and some are designated as protected from access by all but a specified subset of other agents. Agents can communicate with each other through their public or protected methods. The ACE modeler specifies the initial state of an economic system by specifying each agent’s initial data and behavioural methods and degree of accessibility of these data and methods to other agents. An agent’s data might include its type attribute (e.g. world, market, firm, consumer), its structural attributes (e.g. geography, design, cost function, utility function), and information about the attributes of other agents (e.g. addresses). An agent’s methods can include socially instituted behavioural methods (e.g. antitrust laws, market protocols) as well as private behavioural methods. Examples of the latter include production and pricing strategies, and methods for changing methods (e.g. methods for switching from one learning algorithm to another). The resulting ACE model must be complete and able to develop over time based solely on agent interactions, without further interventions from the modeler".

The focus of social science simulation models on behaviour and processes extends to thinking about how businesses behave. Some relevant concepts are described in the next section.

4.3.5 Modelling of Competitive Enterprises Stalk, Evans and Shulman (1992) describe market competition as a “wave of movement” in which “success depends on the anticipation of market trends and the quick response to changing customer needs”. They set the scene for the application of agent modelling to competitive business enterprises.

Lin, Tan and Shaw (1998) expand this concept to describe “business processes as the key building blocks of corporate strategy not products and markets”. They also state that “the competitive success of an enterprise depends upon transforming a company’s key processes into strategic capabilities that consistently provide superior values to the customer”. They see “business rules as the focus for running the business and containing the goals and techniques to optimise the enterprise’s mission, emphasising profit, quality, customer satisfaction, stability and some other factors”. Based on these principles, Lin, Tan and Shaw use agent-modelling to replicate the internal decision making processes of enterprises. Their emphasis extends itself to modelling the business negotiating processes that create whole services and commodity markets. Such processes are often hierarchically structured.

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4.3.6 Hierarchical Model Structures for Competitive Enterprises & Industries Shoham, Goldszmidt and Boutilier (1999) suggest that when moving from a singular focus to a wider or global focus, there is a need for agent-modelling to incorporate both decision theoretics and economic methods to manage and co-ordinate a large collection of agents. They identify the need to segment model design so that agents interact with one another in two distinct ways: firstly, micro-interactions that take place among a small number of agents reflecting single agent decision techniques and beliefs while reacting explicitly to other agents’ beliefs and actions; secondly, macro-interactions take place among large numbers of agents and are based on the economic notion of a market mechanism or exchange.

For example, Casti (1998) splits his insurance Would-Be-World (see 5.3.2) into (i) a micro-level, where insurance company agents possess goals for business and profit growth, pricing structures for insurance risks, and strategies to compete with other insurance companies, and (ii) a macro- level, where companies deal with insurance customers, competitors, insurance claims, disaster outcomes, and make decisions about levels of re-insurance and financial reserves.

Hierarchical differentiation can also be used to reflect different decision-making styles and behaviour (Alami & Chatila 1998, Pinson, Louçã & Moraitis 1997, Moulaek 1997). Hierarchical model designs improve the run-time efficiency of models (D’Ambrosio 1996) by avoiding detailed agents at one level and avoiding focused short-term behaviour carrying over to decision- making at a higher level or longer term behaviour. In VicGasSim, hierarchical agent-modelling is used to differentiate strategic decision-making from operational decision-making in order to reduce the number of total transactions between the agents in the model.

The modelling goal is not to let the breadth of interactions at the macro-level of agent definition encumber the micro-interactions at the level, where key participants interact to stimulate and balance market needs. Individual agents focus on decision-making and maintaining their own belief system about their environment, other agents, and markets of direct relevance. Market clearing behaviour centres on the broader economic notion of a market mechanism or exchange that the agents create through their combined interactions.

4.3.7 Emergence

A formal notion of emergence is one of the most important ideas to come from complexity theory. The basic concept is described below.

The philosopher, George Lewes, first used the term in 1875, when critiquing Hume's theory of causation to distinguish between two types of effects: resultants and emergents (Bunge 2001). Emergence is now an important concept in complex adaptive systems theory, which studies "the emergence of complex large scale behaviours from the aggregate interactions of less complex agents" (Holland 1995).

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Ueda (2001) puts it more simply when describing the related concept of synthesis: putting together the parts so as to form a whole, or the combination of separate elements of thought into a whole, as of simple into complex conceptions, specific into general, and individual propositions into systems. Ueda suggests emergence related approaches, such as symbolic artificial intelligence and knowledge-based learning present feasible ways for offering efficient robust and adaptive solutions to the problem of synthesising the parts of a system to form a complex whole. He describes evolutionary computation, self-organisation, behaviour-based methods, reinforcement learning, and multi-agent systems as more suitable techniques for addressing such issues than the traditional, analytical, deterministic approaches based on top-down problem decomposition used in operations research and econometric modelling.

Numerous complex social and economic systems have been modelled with multi-agent system technologies to simulate emergence patterns. For example, Rauch (2002) describes a multi-agent simulation to explain the rise and fall of habitation in the Long House Valley by the ancient Anasazi people over a period of 550 years. The growth of the settlement and final abandonment of civilisation in the valley are a function of the capabilities of individual families as farmers and the influence of prevailing weather patterns. This agent-based simulation was moderately successful in locating ancient population centres in the valley as a function of the geography of water and fertile lands as well as topography and social interactions.

Emergent behaviour in modern commodity markets is driven by similar competitive behaviour for survival. A number of studies have made relevant observations about competing businesses and how their behaviour influences market outcomes. Some of these observations are relevant to modelling complex and uncertain energy markets.

Gilbert and Troitzsch (2005) state that "emergence occurs when interactions among objects at one level give rise to different types of objects at another level. More precisely, a phenomenon is emergent if its description requires specification of new categories, which are not required to describe the behaviour of the underlying components. For example, temperature is an emergent property of the motion of atoms. An individual atom has no temperature, but a collection of them does. That the idea of emergence in the social sciences is not obvious is attested by the considerable debate among sociologists, starting with Durkheim (1895), about the relationship between individual characteristics and social phenomena. Durkheim, in his less cautious moments, alleged that social phenomena are external to individuals, while methodological individualists argued that there is no such thing as society (for example, Watkins 1955). Both sides of this debate were confused because they did not fully understand the idea of emergence. Recent social theorists (Kontopoulos 1993; Archer 1995; Sawyer 2001) are now beginning to

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refine the idea and work through the implications. Simulations can provide a powerful metaphor for such theoretical investigations.

There is one important caveat in applying complexity theory to social phenomena. It appears to leave human organizations and institutions as little different in principle from animal societies such as ants’ nests (Drogoul and Ferber 1994) or even piles of sand. They can all be said to emerge from the actions of individuals. The difference is that, while we assume, for instance, ants have no ability to reason – they just follow instinct and in doing so construct a nest – people do have the ability to recognize, reason about and react to human institutions, that is, to emergent features. The institutions that result from behaviour that takes into account such emergent features are characteristic of human societies (for example, governments, churches and business organizations). The emergence of such reflexive institutions is called ‘second-order emergence’ and might be one of the defining characteristics of human societies, distinguishing them from animal societies (Gilbert 1995). It is what makes sociology different from ethology. Not only can we as social scientists distinguish patterns of collective action, but the agents themselves can also do so and therefore their actions can be affected by the existence of these patterns. A theoretical approach that was originally developed within biology, but which is becoming increasingly influential because it takes this reflexive character of human interaction seriously, is known as autopoietic or self organization theory (Varela, Thompson and Rosch 1991; Maturana and Varela 1992). Autopoietic theory focuses on organisms or units that are ‘self-producing’ and self-maintaining. An autopoietic system is one that consists of a network of processes that create components that through their interactions continuously regenerate the network of processes that produced them. Social institutions and cognitive systems have both been analyzed in these terms by Maturana and Varela (see also Winograd and Flores 1986). The emphasis is on process and on the relations between components, both of which can be examined by means of simulation, accounts for the developing link between this theoretical perspective and simulation research. Simulation can also usefully be applied to theories involving spatial location and rationality, two topics that have often been neglected in social science, but which are increasingly recognized to have profound implications. Geographical effects can be modelled by locating agents on a simulated landscape, faithfully reproducing an actual terrain – see, for example, Lansing’s (1991) simulation of the irrigation system in Bali – or on the regular grid of cells used with a cellular automata model. Rationality (Elster 1986) can be modelled using the artificial intelligence techniques described in Chapters 8 and 9, but often the main concern is not to model general cognitive capability, but to investigate the consequences of bounded rationality. For example, some theories about markets assume that traders have perfect information about all other traders and all transactions and are able to maximize their own profits by calculating their

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optimum strategy on the basis of all this information. In large markets, this is obviously unrealistic. What are the consequences for markets reaching equilibrium if the traders have limited information and limited capacity to process that information? Epstein and Axtell (1996: Chapter 4) describe a model they constructed to study the effect of decentralized markets where traders possess only local information and bounded rationality".

Axelrod and Tesfatsion (2006) describe emergence in ABM’s as when the ABM system exhibits emergent properties, that is properties arising from the interactions of the agents that cannot be deduced simply by aggregating the properties of the agents. Simulations generate histories (predictions) that can reveal the dynamic consequences of the assumptions that are used to design and initialise the ABM.

4.3.8 Agent Learning In pursuing an AOP approach, there remains the broader question of "Can agents improve the way they negotiate with each other, compete with each other or attempt to outmanouvre each other to achieve either short term or long term success according to some goal or yardstick of success?" Can agents learn from past behaviour by competitors or past changes in their local or global setting to improve their decisions? Or do they adopt a production line mentality that their states change as their environment changes but the rules governing these changes remain the same? These questions are particularly important as a model becomes more complex and dynamic and involves multi-level bargaining or negotiations between agents. Obviously, those agents that can learn from their experience should be able to do better than agents, who do not have a capacity to learn.

Much has been written about programmed learning behaviour based on the success or failure of previously applied strategies and reactions to the behaviour of other agents’ success or failure. There is a large literature base and recent summaries are provided by Gilbert and Troitzsch (2005) and Brenner (2006).

Gilbert and Troitzsch (2005) point out that learning techniques in ABM’s take two forms: firstly, where the relative priorities of rules are modified, so that the conditions of high priority rules that apply are examined before low priority rules, and, secondly, the rules themselves evolve using a form of genetic algorithm1.

1. Genetic algorithms are a specific form of evolutionary computation used to find optimal solutions such as the shortest route between two locations. Evolutionary computation is used where an analogue exists whereby reproduction of individual members with offspring inheriting "genetic" material from their parents. In the case of the shortest route quest, routes are generated and compared and the more efficient routes are carried forward for future comparisons. New routes are generated that inherit parts of one efficient route and parts of other efficient routes to find even more efficient routes. Successive generations include better and better outcomes. The inher- itance blending speeds up the process of finding the best route from among both parent and offspring routes.

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In most economies or industries, each firm must operate under significant behavioural and structural uncertainty and the way they deal with this is based on their levels of rational expectations and how firms or other entities learn to process available information. At one level, it can be assumed they have perfect knowledge and share that knowledge to find optimal arrangements between them or at a weaker level, their expectations will align on average with available information. There is a need to prioritise the rules to be used in dealing with uncertainty.

Tesfatsion (2006) suggests the greater weakness comes from behavioural uncertainty or the uncertainty as to what actions other agents will take. Resolving choices involves a trade-off between "Information Exploitation" and "Information Exploration" in successive periods. An agent’s ability to resolve trade-offs will govern their long-term fate: survival or insolvency. If they survive, how profitable will they be in the short-term and long-term? Can they learn to improve the way they process information to deal with uncertainty?

Firm’s or agent’s ability to learn from available information will have a strong influence on their ability to process information and survive or not. The real world will not see a perfect clearing balance between supply and demand as in the imposed Walrasian solution. There will be dynamic shortfalls and excesses. Consumers and firms will need capabilities to ration and store, to reallocate resources and productive capacities, invest in or shut-down facilities and prioritise offers. Models of systems that face possible excess supply or demand will require support systems of public and private methods for such matters as rationing, inventory management, debt, insolvency determination, price discovery, and profit allocation. Real world economies or industries have numerous organisations and networks to address co-ordination problems to deal with short and long term dynamic disequilibrium in a market.

Tesfatsion (2006) suggests that ACE frameworks can incorporate realistically rendered institutional aspects with relative ease. Consequently, ACE researchers are increasingly focusing on the role of conventions and organisations in relation to economic performance. The challenge in designing an ACE model is dealing with what at first glance can appear an intractable level of interagent communication – publicly and privately. Everything seems to depend upon everything else. One way to overcome intractability is to relax the level of description of an agent’s information and methods and provide them with some plasticity to adjust their positon and methods over time, i.e. provide them with some simple learning capabilities such as recursive testing or more sophisticated capabilities to make decisions in the face of uncertainty (but the latter use more model time and usually lose transparency as to what drives a decision).

Shared information can be a major benefit to learning but co-operation and information sharing approaches are usually prevented by regulations such as the Trade Practices Act (1990), which

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prohibits such behaviour in a competitive market. Co-operation conflicts with the deregulation objective of open and unfettered competition1. In addition, in a dynamic market there are risks in sharing information with a party in one setting when you might regret such sharing in a future and/or different setting.

In business process models, there is a need to find alternative methods to widespread sharing of information between agents and to the use of optimising control agents. Pragmatic realism and public uncertainty need to prevail as the main model design criteria of such models. That is not to say that participants cannot use publicly available data and learning models to improve their positions in markets. Much has been written about the types of learning models that can be used in business process models, especially in regard to industry auction systems such as exist in regulated public markets for energy; much has been written about learning and gaming applications in wholesale electricity bidding markets.

Zlotkin and Rosenschein (1996) also summarise various approaches that have been advocated for handling distributed negotiations and achieving satisfactory outcomes. These include use of Smith’s contract net (Smith 1980) for handling the distribution of tasks and sub-tasks between a network of agents based on cost bidding to handle each task, use of interagent collaboration agents (Durfee & Lesser 1987) which exchange partial solutions at detail levels to arrive at a group consensus for a global solution, and Gasser’s (1991, 1993) incorporation of the social aspects of agents and the capability of agents to develop, modify and codify their own interactions to handle a continuously changing environment. Game theory is another approach often considered for use in market models to facilitate achieving the best agent outcomes. This approach typically uses various theoretical solutions for making decisions under uncertainty. These solutions include the maximin, minimax regret, and indifference methods to solve simple, static decision games on a repetitive basis. Brafman and Tennenholz (1997) introduce the need for game theory to be extended to deal with the more complex and dynamic agent worlds where the agents’ contexts and beliefs change over time.

A single strategy election may prove inadequate to meet an agent's long-term goal. Brafman and Tennenholz (1997) suggest this problem can be addressed by explicitly modelling the relationship between the states of an agent at different time points. They introduce the concept of using agent models to make predictions or what they call “runs” to allow the agent to make a single choice among possible protocols (policies or strategies) and choose between sequences of states not just between single, static states. In order to make such complicated choices, Brafman and Tennenholz

1. The Trade Practices Act does provide for exemptions to be granted in special circumstances where other community benefits might be derived from co-operation between market participants. For example, there are numerous cases where joint marketing of gas by several parties in a single production joint venture is permitted by official exemption. Exemptions are granted on a case-by-case basis.

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suggest decisions can be made by using the methods of economics, game theory and Bayesian analysis, where beliefs are modelled using probability distributions, preferences are modelled using utility functions, and decisions are made on the basis of expected utility maximisation1.

4.3.8.1 How to choose the right learning model Brenner (2006) provides advice to ACE modellers on how to choose an adequate learning model for their simulations. Most learning models are derived from psychological literature. Early researchers tended to the view that learning converges towards optimal behaviour in equilibrium, at least in the long-term. Brenner focuses not so much on critiquing this approach versus alternative models, but rather under what conditions do the various existing models work best. He groups learning processes into three forms: non-conscious learning, routine-based learning and belief-based learning. The matter of choice, of which learning model to apply, is described as a two-step process: first, decide which type of learning fits the given context, and second, choose a model that can apply to that type of learning.

Non-conscious learning is, in general, mainly relevant if conscious learning does not take place. Conscious learning requires the individual to be aware and reflect upon behaviour. The choice between the two kinds of conscious learning (routine-learning and belief learning) is more difficult. Routine learning is usually based on empirical or experimental observations of learning and without relying on any undertsanding of the real world processes or situation. The question of whether to choose routine-based learning or belief-based learning is related to the choice between using a complex and realistic model or whether to use a simple, approximating model. Routine- based modelling has been most commonly used due to the complexity of modelling the processes of the brain in more complex situations. Brenner (2006) describes using experimental or pyschological knowledge as the basis for creating a model that best describes a real learning process. A simpler and commonly used technique in economic simulation models is to formulate a learning model that leads to an outcome, which corresponds to known stylised facts. They can be as simple or as complex or realistic as needed to obtain the correct outcome. Some researchers search for learning models that converge to equilibrium, as under neo-classical theory, which in general (and as discussed earlier in 4.3.8) have major practical weaknesses but might apply in special cases. Other researchers strive for clever or even optimal learning models amongst possible alternative behaviours.

Brenner (2006) points out that yet another group of researchers argue that the choice of learning model is not in many circumstances the main aim of the research; the main aim is often to analyse a certain complex situation and learning is only included to represent the basic dynamics of

1. Appendix 5 presents a description and example of the Multiple-Criteria Decision Making process.

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behaviour. The strength of this argument only holds if different learning models predict a similar behaviour. Rappaport, Seale and Winter (2000) state "that the simplest model should be tried first, and that models postulating higher levels of cognitive sophistication should only be employed as the first one fails". They suggest three selection criteria: experimental evidence, pyschological knowledge, and simplicity.

A number of energy and economic model examples are described in Chapter 5. These examples give additional insight to the possible design approaches, which can be used to meet differing types of objectives. The choice of a modelling approach for VicGasSim is addressed at the end of Chapter 5.

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The early sections of this chapter describe a series of energy and economic models that were considered when this research project first commenced in the late 1990’s and early 2000’s. These models reflect one or more approaches to energy modelling (including several Australian forecasting models), economic modelling, and modelling industries or social sciences. The examples highlight a number of weaknesses and limitations typical of some model types or applications.

The later sections of this chapter describe a number of recent models used to address complex systems and which were developed in or reported in the post 2003 period and hence post date the author’s model development work. As shown later, VicGasSim’s forecasts, which were made in 2002-03, hold up well against more recent forecasts.

An important example is the CSIRO’s NEMSIM model, which is based on important aspects of the author’s research project; NEMSIM is described in 5.4.2.1. The CSIRO’s Energy Transformed Flagship Project1, which is now looking to link ABM’s such as NEMSIM to macro-economic models such as MARKEL2, which are maintained by the Government through ABARE.

The trend in model development, as shown in this chapter, reflects the growth in power of low cost personal computers and the related ability to use this modern, user-friendly computing power to develop new ways of looking at complex systems, including many classic econometric problems. Labys (1999) recognised the potential for using such computing power for incorporation of embedded "what if" testing into models and extending this concept by greater use of expert systems or artifical intelligence in models to address the dynamic uncertainties in adaptive complex systems. The use of ABM simulation remains at the forefront of tools for exploring energy market issues.

The chapter ends with a broad description of the modelling approach, which was adopted for VicGasSim.

1. The CSIRO’s Energy Transformed Flagship Project is a major Government initiative to improve the efficiency of Australia’s energy production and supply with an emphasis on reducing greenhouse emissions. 2. Described in 5.1.3.

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5.1 Examples of Early Australian Energy & Economic Modelling A selection of models from the late 1990’s and early 2000’s is described and shows the style of modelling in use at the time VicGasSim was developed.

5.1.1 RISC RISC is an Australian energy consulting group that maintains an LP model of the Australian gas sector (GasAdvice & RISC 1998). The model incorporates a number of regional markets and a transmission grid with average tariffs for current and probable interconnection links. Development and operating costs are developed in stand-alone cash-flow models and converted to cost/ probability curves that are used by the LP model. The model searches for a best-fit solution to a mixed function of supply costs and delivered, alternative energy prices. The model allows for some dynamic behaviour by considering the effect of delivered prices on demand but handles project interactions on a least-cost solution basis. It has limited flexibility to model price cyclicity or project decision points and tends to smooth out potential market volatility and its effects.

5.1.2 ACIL (now ACIL Tasman) ACIL is an energy/economic consulting group that maintains a series of economic and energy models, including a gas sector model. This latter model is modularised into segments along similar lines to the RISC model. It is supported by a very large database maintained by ACIL on all of the major gas projects and markets in Australia. ACIL also maintains a number of cash flow models for specific projects. The gas model balances supply and demand via a central LP clearing model. ACIL (1998) developed two scenarios for two assumed, fixed input prices. These prices then set the project development and demand trends. The model schedules new project entries on the basis of relative costs. It is a useful model for testing various demand and supply assumptions but it is limited in its representation of dynamic market interactions. An update on ACIL Tasman’s current modelling capacity is presented in Chapter 9.

The ACIL (1998) and RISC (1998) models are primarily predictive in nature and fall mostly into Labys’ (1999) economic sector model class. They are useful for portraying certain fundamentals about the supply-demand balance for a commodity over a medium to long term period. They rely on input drivers/assumptions for future economic activity levels, future energy prices, historical price elasticities and knowledge about the reserves and costs of various supply sources. Beyond that they assume that participants will abide by the logic that such participants know enough about each other’s business costs that they will act in a fashion that minimises costs. Such an approach ignores other competitive dimensions of the market place. For example, some downstream participants own interests in some upstream assets, some own interests in transmission pipelines or distribution systems, some own holdings in multiple sectors, and some

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suppliers and retailers might price goods and services to penetrate or impede market entry by competitors because of, or through, their cross-holdings.

Do these latter aspects matter? It depends upon the issue to be addressed. If you are seeking a view about the long term demand for gas in Victoria, likely supply sources, the sensitivity of demand levels to prices, or the effect of the powergen market or gas demand, they can be useful global level tools. But they will not be useful for exploring the interaction between market participants or in exploring how these interactions might influence market dynamics and outcomes. They also can be wrong in predicting the future supply mix because the minimum costs supplier might not always end up being the next supplier, due to other strategic dynamics.

5.1.3 ABARE In the 1990’s and early 2000’s, ABARE used three models to study Australian gas markets:

MENSA (Naughten, Melanie & Dlugosz 1997), MEGABARE (ABARE 1996) and E4cast (ABARE 2003). MENSA is an intertemporal, optimising, LP model of the Australian energy market and fashioned after the International Energy Agency’s MARKAL model (Fishbone and Abilock 1981). The MENSA model includes regional representation of consumption, supply, transportation, distribution, and refining. MENSA forecasts supply on a generalised least-cost optimisation basis. MEGABARE is a macro-economic, computational general equilibrium model that links investment and labour force growth to the economy, as a function of time. The model provides outlooks for energy demand and consumption in a global setting and is useful for long term macro-economic testing. The two models operate with high level representation of the industry structure and do not closely represent the discreteness of individual project constraints and the effects of uncertainties on participants. E4cast is a partial equilibrium model of the Australian energy sector. It is maintained and used by ABARE to project energy consumption by fuel type, by industry and by state or territory, on an annual basis; the main consumption drivers in E4cast are real incomes, industry output and fuel prices, which are sourced from other models such as MENSA and MEGABARE. These models are more commonly used for investigation of the effects of policy on national or regional economies and energy demands.

5.1.4 AGA AGA publishes industry outlooks (for example, see AGA 1997). These have been developed in conjunction with ABARE, which provided the AGA with gas demand profiles from ABARE’s modelling work. In AGA 1997, AGA modified ABARE’s demand to reflect specific scenarios for their own view on announced future GPG plants, supply from existing gas sources and estimates of future gas discoveries. Supply scenarios are used to meet demand scenarios by assuming east coast sources of supply are used to fill the east coast markets on a priority basis. As

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with the RISC (1998) and ACIL (1998) models, there is only limited modelling of dynamic interaction between the various upstream and downstream industry sectors. The lack of dynamics limits examination of the effects of such interactions on the various models’ forecasts.

5.1.5 Weaknesses In Early Australian Energy Models The above models do not attempt to set up a dynamic capability to handle discrete and uncertain events such as the following:

1. discrete events and discontinuous arrangements due to deregulation,

2. horizontal and vertical integration,

3. general interest or corporate integration effects,

4. new entrants,

5. ability to defray central overheads and high debt loads,

6. supply pool bidding,

7. retail pricing strategies,

8. conversion of loss-making customer groups,

9. contracting strategies for locking-in new supplies on competitive terms, and

10.coping with uncertain outcomes.

Existing models work at a high level of analysis and cannot adequately and dynamically represent the types of issues raised above and in Chapter 2. They cannot estimate the period and amplitude of market cycles or the effects of timing shifts against which decision-makers want to test the robustness of projects and/or market positions.

These models typically assume smoothed changes in equilibrium conditions or solve for optimised least-cost solutions or, at best, have limited, but not discrete, interactive capability. Most are limited to testing changes by generating sensitivities for the changed conditions which, in themselves, are fixed for the duration of the scenario. Some iterative cycling of data between models is sometimes attempted. Existing models are limited by not incorporating or reflecting the behavioural and decision-making nature of those making the critical choices in the types of situations described above.

The decisions and uncertainties confronting the Australian gas industry today are daunting to many participants and they are searching for better ways to develop their strategies and positions. Participants want to test their positions in a far more dynamic fashion.

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5.2 Early Market Models Leading in a New Direction 5.2.1 USDOE The US Department of Energy (USDOE) was one of the first groups to respond to the early oil price shocks in the 1970’s. The USDOE spent considerable time and effort in examining how it might deal with discontinuities that came with these price shocks. The discontinuities undermined confidence in relying on past market behaviour or experience or data to forecast or, even more importantly, to understand what dynamics are critical to developing a view about likely emerging patterns that results from such discontinuities or discordant system changes.

5.2.1.1 Fossil 2 In the 1970’s, the USDOE (1980) commissioned a model using the system dynamics modelling paradigm (such as used in the STELLA software package). System dynamics uses the principle of engineering control theory to provide feedback logic loops to influence the rate of change in other primary model functions. For example, as the price of oil went up, this would encourage increased exploration after a lag in time; subsequently, the model would forecast oil supply to increase and prices to fall. The feedback generates a degree of long-period cyclicity; long-term equilibriums may or may not occur. The approach was used extensively to address dynamic energy modelling needs during the period of dramatic oil price shocks in the 1970’s and 1980’s. The method of solution involves the use of differential calculus requiring the characterisation of industry activity as a number of mathematical functions. This approach, while useful on a national and global scale, lacks flexibility to handle changing structural features and analyses closer to a project level. It was a stepping stone to later efforts by USDOE to incorporate greater flexibility and detail into their modelling capability.

5.2.1.2 NEMS The Energy Information Administration (EIA), a part of USDOE, developed a very large integrated energy model in the early 1990’s. This model is known as the “National Energy Modeling System (NEMS) (USDOE 1998). It is used to generate the EIA’s Annual Energy Outlook for the domestic US energy market (USDOE 1999).

NEMS evolved from several interim models following on from Fossil 2, these included the Project Independence Evaluation System (PIES), the Intermediate Future Forecasting System (IFFS), and the Long Term Analysis Package (LEAP). These models used various combinations of LP and general equilibrium models to optimise least-cost solutions of supply and transportation to meet end-use demand. While these models introduced increasing detail and sub- sectors, their optimisation approach ignored the real world sub-optimal behaviour due to changing circumstances.

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NEMS is organised and implemented as a modular system. It provides for operational independence of its components, which pass information to each other through a global database structure. An integrating module controls execution of each of the component modules. Individual modules take different forms using macro-economic, micro-economic and LP models. Some system dynamics features are retained. The NEMS model includes the features of a segmented model and provides for some dynamic interaction between broad market participants and between market segments.

However, NEMS operates at a level above that useful for simulation and testing of participant strategies. It does not include any profit calculations and serves a national purpose of testing the effects of changes in costs and prices on the interactions between market segments and the US economy in general. It does not attempt to model the myriad commercial transactions that underlie overall national economic activity. This approach is appropriate when looking at all sectors of a large economy such as the US and looking to test global changes, including oil price shocks.

The extended model development work by USDOE provides a useful reference in highlighting the use of model modularity to cover very large markets and the use of model dynamics to account for ongoing cross-effects between sectors.

5.3 More Recent Applications 5.3.1 LBS’s OO/DEVS Modelling of the UK Electricity Sector The London Business School’s (LBS) Centre for Decision Sciences has worked on a number of electricity deregulation and pricing studies since the late 1980’s. Their work has focused on industry-wide outlooks addressing specific participant activity and behaviour.

Bunn and Larsen (1992), Bunn, Larsen and Vlahos (1993) and Bunn and Vlahos (1989) combined the use of an optimising LP with a system dynamics model to study issues related to the transfer of ownership under privatisation, capital structures, shareholder returns and the competitive structure of the UK national electricity industry, including market incentives to invest, regulation effects, uncertainty and competitive strategies.

Uncertainty was introduced to the model by use of random errors in the expected demand. The study characterised the potential for market price and margin volatility if the participants did not exchange foresight or information or were not regulated in this direction. These studies also looked at the probable sustainable strategies of participants and likely changes in their market shares. The dynamic model proved useful for this more interactive modelling type of study and provided an industry balance and market equilibrium.

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These initial models lacked modularity and a new modelling platform was adopted by Ninios, Vlahos and Bunn (1995). This approach used the Object Orientation and the Discrete Event Specification Formalism, or OO/DEVS, based on the work of Zeigler (1976, 1984, 1998, 2000). Zeigler’s objective was to adopt a method that could preserve the benefits of an earlier system dynamics approach for strategic modelling but at the same time allow modularity and flexibility to integrate with other modules performing functions such as detailed optimisation and spread- sheet-based financial planning. In addition, the adoption of OO/DEVS aimed to address some of the methodological criticisms of system dynamics, related to modelling generalisation and aggregation relationships, lack of modelling detail, lack of reusable model components, and inappropriate time representation due to continuous time steps rather than discrete time steps.

Bunn and his various colleagues believe strategic simulation should provide the core methodological focus when attempting to integrate a number of model concepts yet retain an overall industry context where evolutionary changes are occurring. The need for a platform that allows re-specification of the industry structure is seen as crucial.

Bunn and Silverman (1995) extended their modelling approach from OO/DEVS to new techniques using Knowledge-Based-Decision-Aids (KBDA’s) and Artificial Intelligence or AI- enhanced simulation modelling. These decision technologies provide decision makers with a methodology that is intuitive, advisory, explainable and readily communicable with information organised in line with industry experience. This type of approach also aligns with Labys’ (1999) view about the desirability of using embedded expert systems for “what if” testing and decision- making in the face of uncertainty.

Bunn and Day (1998) tested this latter perception by replicating the earlier OO/DEVS work in an agent-based simulation framework for studying UK electricity pricing pools. The agent-based simulation approach produced results more characteristic of systems with less than perfect competition and discrete rather than continuous supply pricing functions. They concluded that this approach offered the potential to more realistically model situations with respect to observed discontinuous supply functions, non-linear elasticity, and asymmetric ownership structures in the industry.

The work of Bunn and his various colleagues has a general leaning towards policy type investigation versus providing participants with tools to improve profit performance. Therefore, the models they developed through the 1990’s tend to be limited to market characterisation within specific sectors. It seems fair to state that much of their work appears more like that of an investigator for the regulators than for participants trying to identify how to build a comparative advantage. However, participants would certainly benefit by considering the issues raised and explored by Bunn and his various colleagues.

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5.3.2 Casti’s Would-Be-Worlds Another early and practical example to focus on characterising commercial behaviour in a market place as the main method to formulate a simulation model of that market was the work of Casti (1998). He used agent-based simulations to model the risk insurance market in regions susceptible to disasters such as hurricanes or earthquakes. The model has proved useful for showing participants how to avoid bankruptcy and being under-funded when disasters hit unexpectedly and how to adapt their forward marketing strategies, margins, re-insurance levels, and financial reserves to ensure long-term survival as well as profitable growth. This work is interesting in that it is one the few reported studies that search for the best long-term outcomes in terms of profits and avoiding economic ruin. It uses the concepts of profits and losses, and balance sheet strengths or weaknesses. It is a model for users not regulators. Casti refers to his artificial economies as "Would-Be-Worlds".

5.3.3 EMCAS Argonne National Laboratory (at the University of Chicago and working in conjunction with the US Department of Energy) developed the Electricity Market Clearing Systems (EMCAS), which is an electricity market model, to study the US electricity market (North et al. 2002, Vesolka et al. 2002). The model has been commercially available since 2003 for adaptation to other real world markets that use similar system rules to those used in the USA. The final EMCAS model extended concepts in several earlier models (VanKuiken et al. 1994; Veselka, et al. 1994). The final model development work took place at much the same time as the work on the VicGasSim model. The underlying structure of EMCAS is that of a time continuum ranging from hours to decades. Modelling over this range of time scales is necessary to understand the complex operation of electricity networks and their market places.

The focus of agent rules in EMCAS varies to match the time continuum. Over longer time scales, human economic decisions dominate. Over shorter time scales, physical laws dominate.

On the scale of decades, the focus is long-term human decisions constrained by economics. On the scale of years, the focus is short-term human economic decisions constrained by economics. On the scale of months, days, and hours, the focus is short-term human economic decisions constrained by both economics and physical laws. On the scale of minutes, the focus is on physical laws that govern energy consumption and distribution. In EMCAS, time scales equate to decision levels. There are six decision levels, which are implemented in the model; Level 1 represents the smallest time resolution, that is, the hourly or real-time dispatch. Level 6 is where agents perform their long-term, multi-year planning.

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Figure 5-1: Overview Structure of the EMCAS Model

Source: Argonne National Laboratories, managed and operated by UChicago, Argonne, LLC, for the US Department of Energy under contract No. DE-AC02-06CH11357; reproduced with the kind permission of Argonne National Laboratories.

Figure 5-1 shows the broad structure of the EMCAS model, which includes a large number of different agents to model the full range of time scales. Many EMCAS agents are relatively complex compared to the agents that are adopted in VicGasSim; this is driven by the different objectives of the two models. EMCAS agents are highly specialised to perform diverse tasks ranging from acting as generation companies to modelling complex network transmission lines. To support specialisation, EMCAS agents include large numbers of highly specific rules. Users can define new strategies (within the provided protocol) to be used by EMCAS agents and then users can examine the market place consequences of these strategies. EMCAS is a very sophisticated model but it is designed for universal use and while it includes a profit orientation, the author sees this usage as focused on achieving operational efficiences; it has a different emphasis to VicGasSim. VicGasSim is more oriented to modelling the implementation of strategic competition and focusing on the overall profit performance of participants rather than operational efficiency. Nevertheless, EMCAS has similar structural design features to VicGasSim. Some additional comments on EMCAS are referred to in 5.4.2.1, which presents background on the NEMSIM model and makes reference to why the developers of NEMSIM chose the VicGasSim platform rather than EMCAS.

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5.4 Post 2003 Energy Modelling Studies Application of agent-based modelling to investigate issues in the electricity sector has been a very prolific area of research since 2003. There has been a significant number of papers written about both specific and general issues facing the electricity sector in many countries, where deregulation has occurred. A selection of these papers is described below to give an idea of the range of modelling undertaken and the issues studied.

5.4.1 Bunn and Associates Bunn and his various colleagues in the Department of Decision Science at the London Business School have continued their energy modelling work, as described earlier in 5.3.1. They continue to use the object-oriented and agent-based model frameworks already described. They have used their various model platforms to study specific issues, mainly in the UK and European Union (EU) electricity sectors.

Bunn and Oliveira (2003) applied their model in a co-ordination game context to analyse the possiblity of market power abuse in a competitive market. Agent-based modelling was applied to consider whether two participants in the England and Wales electricity market could profitably influence wholesale prices after these two companies refused to sign a market abuse licence condition on the basis that they were not big enough to exert a significant influence on market prices.

The agent-based model represents generators and wholesale buyers from the centralised New Electricity Trading Arrangement (NETA). The agent participants are given various gaming strategies to test if they can influence pricing when they are unconstrained. The study is a good example of testing a specific rule change. This project uses a learning algorithm to explore if an evolutionary collusion process is possible and if this process might open the door to implicit collusion. Bunn & Oliveira (2003) do point out that the learning process occurs in a stable model environment, where the agents can iteratively adapt their behaviour and where illicit, strategic co- ordination becomes possible. They also point out that in the real market, the participants have to learn in an environment where demand, fuel costs and transmission constraints reshape the system continuously. These changes make behaviour co-ordination (and implicit collusion) harder to achieve in reality; they acknowledge that participants actions will be strongly influenced by what is going on around them and how they react to those uncertainties. Their study is useful but their acknowledgement of the weakness in their approach gives support to the approach adopted in VicGasSim, where model structure relies on modelling the participants’ behaviour and goals in terms that link to changes in their environment.

Bunn and Karakatsani (2003) review the main issues and research on modelling and forecasting electricity prices and suggest the majority of then current work was based on stochastic and time

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series methods with varying sophistication. The authors point to the vulnerability of such forecasts and to the issues that come with deregulation, regulatory interference, changing competitive forces and cost structures. They point to the need to require time series and econometric specifications to be consistent with higher level political and strategic models, which they state will be of a quite different and more subjective character to those already included in their model. They also suggest that the underlying energy market issues are important to pick up and they tend to be subjective and challenging to model. They do not link this issue to the earlier thematic work, by Bunn and his various colleagues, using agent-based modelling, but they do suggest there are vulnerabilities in models, which are too focused and too stochastic.

Bunn and Ruperez-Micola (2004) describe a study using agent-based modelling to set up a double-sided auction market to study a duopoly in a generalised wholesale market, which links n producers (sellers) to m end-use suppliers (buyers) – somewhat like Prototypes # 2 & # 3 in this thesis work. They vary n and m to test the effects of market power concentration as well as making single or multiple agents more aware of market knowledge than other agents; they also use the same model to test the effects of cross-holdings, which provide extra market knowledge. Bunn and Ruperez-Micola use their generalised results to hypothesise about the benefits or weaknesses in the existing ownership structures and information transparency in the real European electricity market – without attempting to model this market. They offer some generalised conclusions: transparency leads to lower prices, the value of small cross-holdings is greater in transparent markets, and more downstream competition reduces the influence of upstream co-ordination and improves downstream performance. Their ability to look more specifically at the real market would require the effort to collect sufficient data to convert the generalised model to a real market simulation, from which more specific conclusions might be made. This can be a large task but rewarding in providing a market model capable of considerable specific, rather than general, insights to the European market. It would require collaboration with sufficent domain experts to characterise all of the major agents or groups of agents in such a market model.

Bunn and Ruperez-Micola (2008) extend their earlier work to better define the relationships between market prices and market information, cross-holdings and concentration of power. They suggest that EU energy regulators have underestimated the relationship between market power and "small" cross-holdings. The authors recognise the weaknesses of using this type of analysis to generalise about behaviour given the widely different peculiarities of each market in the EU. They provide general insights only, but they do point to the benefit of using an agent-based simulation model to disentangle the different factors that influence strategic behaviour.

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As an aside, it is interesting to speculate here about Origin Energy in the Australian energy market and about its relatively successful performance in the southern and eastern Australian gas and electricity markets. Does Origin’s success relate in part to their greater number of cross- holdings and operatorships across the various markets? They are numerous: Yolla gas production, Thylocene-Geographe gas production, SA and Queensland Cooper Basin gas production, Envestra’s gas distribution businesses in several states, GPG units in Victoria, SA and Queensland. Origin also markets gas and electricity in Victoria, SA, NSW and Queensland. Its various, sometimes small, cross-holdings may have given it a significant advantage in both flexiblity and insights not available to competitors.

5.4.2 Other Energy Market Studies Sueyoshi and Tadiparthi (2005) describe an agent-based model to investigate the US wholesale electricity power generation and supply market. They have created a market representing groups of producers interacting with buyers in a trading market with two components: a day-ahead market and a real-time market. They generalise certain characteristics into series of demand and supply algorithms to provide key inputs to agents, who have machine learning capabilites. They use this model to explore which of the day-ahead or real-time market is more important for generators. They seek to explore what type of risk-taking is important for generators. To focus on the identified issue, their model excludes the wider strategies of wholesalers, because wholesalers’ decisions are often influenced by external factors such as temperature changes and seasonal effects. While the researchers do make a number of important conclusions about the simulated market, they go on to list the future work needed to enhance their model. They include the need to be able to predict/simulate real-time demand fluctuations, especially for weather/ temperature, since they can dramatically affect prices. They need to incorporate an appropriate wholesale bidding market, improved cost information on supply, transmission, and regional and geographic constraints as they also affect prices. Further work is planned along the lines of the VicGasSim model, which includes agent representation of each of these factors, as well as others.

Sueyoshi and Tadiparthi (2007) describe an updated model that overcomes a number of the weaknesses identified in their 2005 paper. They apply the upgraded model to match daily trading patterns in the California electricity trading market during the Californian electricity crisis by studying this market over the 1998-2001 period. Their work differs from the VicGasSim model in that it uses more global representations of the participants’ profiles and places an emphasis on machine learning from stored historical trading data. It does not look at linking its trading analyses to other factors that govern participant profitability in the overall market or to making capacity investment decisions. While the model has been updated, it still does not address a number of the weaknesses noted in their earlier work.

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Walter and Gomide (2008) provide further support through use of agent-based simulation to look at electricity trading markets with a focus on market co-ordination and short-term pricing. They provide numerous references to similar research efforts. These referenced papers have a common theme of modelling autonomous agents but their focus is on activating them with clever learning and bidding strategies; they tend not to isolate differences in the agents’ profiles and tend not to look at how such differences might influence other aspects of their performance.

With most studies having a short-term focus, it is hard for more peripheral constraints of a longer term nature to show an influence on short-term matters, such as daily bidding strategies. But over time, related matters are highly likely to cause re-alignment of generating capacities and contracts, and other changes that alter a participant’s underlying costs structure. It might take several years for such structural changes to be realised in daily bidding strategies but their influence will emerge over time.

Such models can be useful to guide participant’s short-term behaviour, but all strategies need to be updated for peripheral information changes on a regular basis to avoid any of them distorting or biasing the predicted outcomes. VicGasSim provides such adjustments automatically by having the capacity to run over very long time horizons and by allowing other strategies to change agents’ cost bases over time.

As a further comment about the need to consider peripheral issues, it is worth noting the work of Stern (2004) who has studied the vulnerability of the UK gas market to the security of imported gas supplies. Stern uses his analyses to identify near-term issues in shortages of peaking capacity as a factor that could make the UK energy sector susceptible to security risks in or from Europe or from further east where major gas supplies originate in Russia and central Asia. He points out that while domestic gas supplies meet average UK demand, they are not able to meet UK peak electricity needs and that the electricity power generation sector will require changes in import patterns of gas to alter supply sources to get around peak transmission network constraints. Given some of these constraints may be difficult to change, the UK could possibly require imports of LNG as well. Changes in both supply sources would change the gas and electricty supply conditions and pool prices. This study presents another example where wider supply-demand and capacity issues need to be programmed into any study of a gas or electricity market that sets out to make any conclusions other than how to get the lowest price on a given day. VicGasSim includes detail down to the level of daily and seaonal peaking to ensure it picks up these types of issues and then uses a mix of short and long-term testing to show the consequences of any such anomalies in the system.

There are two other common areas of recent study in relation to energy markets, again with a pre- dominant emphasis on the electricity market. These study areas are: locating new decentralised

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power generation plants and finding the optimal locations from which to supply power into a transmission network supplying multiple regional markets on a real time basis. Several examples are described below.

Kok, Warmer and Kamphuis (2005) present an agent-based model that is being developed with a market control concept for supply and demand matching in electricity networks. The work follows an anticipated rapid growth in the installation of, what they term distributed generation (DG), "behind the meter" so that it can directly supply some or all of an owner’s needs and at times provide electricity into the public grid. In a traditional network system, supply and information flowed one way, and money flowed the other way. However, growth in DG will see supply, information and money flowing both ways. Complexity levels will increase. The applied control concept uses a network of networks within networks and supply-demand market controllers working at various levels with the global network. Commercial and pricing transactions are negotiated at each level of generation, including DG, and local usage to keep sub- networks in balance. The expectation is that this control concept can smooth the profile of imports and exports of electricity to and from a given network and reduce overall peak demand on the system. This is very much an optimisation model but provides insights into how model designs might need to be adapted to handle more complex system balancing. A modular design should make it easier to adopt such changes.

Jiaming et al. (2007) address the same challenge of capturing the most efficient use of DG. They point out a number of problems with the type of approach described above. They use a similar agent-based representation of the market, but enforce a supply cap on the power drawn from the grid to simplify the system administration, reduce volatility of wholesale pool prices and to provide a less administratively demanding way of dealing with network constraints during summer and winter peaks.

The last two studies present examples whereby agent-based modelling is applied to a complex system and used to test alternative rules or regulations that may or may not improve or simplify how a new regulation might be implemented. As such rules are refined and adopted they can be built into more diversified models, such as VicGasSim. Models such as VicGasSim can be used to test such rule changes and to check if and how they might influence wider emergent market behaviour.

Another area where agent-based energy systems can be of value is to consider CO2 emissions. Miyamoto et al. (2008) developed a model of the trading interface between an operation such as a factory and electricity supply, where both sides face CO2 emission limits and pricing impacts that need to be built into their product prices. The model shows an emphasis from a plant operations perspective and incorporates emission trading rights into the negotiating system between parties.

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It should be relatively easy to tag the relevant agents in any agent-based model to incorporate

CO2 emission levels into the profiles for such agents. If CO2 requires processing, related costs can be easily incorporated into agent profiles. If CO2 or emission rights are established and traded, a market for such rights can be added to existing markets with pricing and costing behaviours dealt with in the same way that other products are traded. Most existing energy market models, because of their modular designs, should be well placed to include CO2 or emission requirements into such models.

5.4.2.1 NEMSIM Batten & Grozev (2006) present an example of a more ambitious agent-based model (NEMSIM) that adopts a wider market perspective than others identified. NEMSIM represents a simulation model of Australia’s National Electricity Market (NEM). This model uses the broad design concepts used to design VicGasSim but replaces VicGasSim’s gas market characteristics with a design of Australia’s east coast electricity market (NEM). The CSIRO’s adoption of the VicGasSim platform and design features follows consultation between the author, Swinburne University and the CSIRO and execution of an agreement between the parties to use the VicGasSim model platform as the basis for formulating NEMSIM.

NEMSIM has an emphasis on the generation, pool trading and distribution sectors. It has additional dimensions such as accommodating hedging and includes the tracking of the emissions levels of different generating plants as well as the costs of handling these emissions. These additions lead to changes in cost structures and in the bidding behaviours for distributed generators at locations spread over a very large and complex network. The stucture has been designed to study pool-bidding and location of GPG units as well as to test the effects of possible new CO2 emissions systems that are expected to be introduced in the very near future. A contemporaneous description and understanding of the NEM is provided by Moran (2006).

The modular capability of the NEMSIM model is used to incorporate emission and thermal efficiency performance profiles for each generating unit agent and to generate a forecast of greenhouse gas (GHG) emissions for each model scenario. This allows examination of the effect of varying the pattern of investment in new powergen plants of differing GHG emission performance on trading rights and future energy prices. GHG from electricity generation accounted for 33% of Australia’s net CO2 emissions in 2002 (Australian Greenhouse Office 2004). Estimates of GHG were previously derived by CSIRO’s Electricity Market Model (Graham and Williams 2003) that is similar to the bottom-up, optimisation, econometric models such as MARKAL. The weaknesses of MARKAL type of energy-econometric models were described in 1.3 and 5.1.3.

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In deciding on the structure to adopt for their NEMSIM model, Batten and Grozev (2006) considered the agent-based work of Bunn and his various colleagues at the London Business School as well as the EMCAS model. The latter model is commercially available for adaptation to markets such as the NEM. Batten and Grozev describe both the EMCAS and LBS simulation models as electronic laboratories that probe the possible effects of market rules by simulating strategic behaviour of the participants. This description is consistent with the author’s comments at the end of 5.3.3.

The EMCAS model is recognised as arguably the most sophisticated agent-based electricity model developed to date, but it differs from VicGasSim in that it uses numerous genetic algorithms to drive adaptive learning of some agents. Batten and Grozev (2006) recognised certain limitations to the application of this model to simulate the NEM. For example, they refer to the work of Nicholaison, Petrov & Tesfatsion (2001), who hold the view that the precise form of learning behaviour assumed in the example of a double auction system on price and volume may be irrelevant to negotiating processes. This view is consistent with the views of Brenner (2006) and Rappaport, Seale & Winter (2000) as reported in 4.3.8.1 and to some early VicGasSim testing intelligence levels in VicGasSim agents (see 9.3.3). These views suggest use of more micro-level agents with simple plans is preferred to an emphasis on the use of more sophisticted learning and negotiating processes; the choice is strongly influenced by the availability of public and private data.

As a result of their model screening, Batten and Grozev (2006) comment on the work of Taylor et al. (2003) at Swinburne University in regard to the latter’s work on an agent-based model to simulate the Victorian gas market. Batten and Grozev suggest that the model design and platform designed for this work can address many of the limitations faced by alternative approaches. Batten & Grozev made the following comments:

“Taylor et al. (2003) developed an agent-based model to simulate the complexity of the large-scale Victorian gas market in south-eastern Australia. The model can be used to elicit possible emergent behaviour that could not be elicited otherwise under an uncertain future of deregulation and restructuring. Like an electricity market, the complexity in the gas market derives from uncertain effects of a multiplicity of possible participant interactions in numerous segments, such as production, storage, transmission, distribution, retailing, service differentiation, wholesale trading, power generation and risk management. The agent-oriented programming platform devised for this work has the potential to overcome the limitations of traditional approaches (discussed earlier) when attempting to operate in changing environments. A similar platform has been adopted for our National Electricity Market Simulator.”

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Batten and Grozev (2006) describe the hierarchical structure of the NEMSIM model. Its structure shows much similarity to those published by Taylor et al. (2003), which are described in this thesis [both structures have much in common with the EMCAS structure but each differs in its objectives and emphasis]. The NEMSIM structural overview diagram, which is reproduced in Figure 5-2, is an excellent representation of the structural design in the VicGasSim model, which is to be expected given that they have a common heritage.

Figure 5-2: Overview Structure of CSIRO’s NEMSIM Model

Source: Batten, D & Grozev, G (2006), see Figure 11.5; reproduced with the kind permission of ANU E Press and the authors.

While very similar, the NEMSIM and VicGasSim models differ in some fundamental aspects. NEMSIM concentrates on pool pricing, market clearing and scheduling, and market pricing which in some ways are far more complex systems than their equivalents in the Victorian gas market. VicGasSim on the other hand takes a much wider view of the market and looks at retail competition and cross-holding effects on total corporate profit contributions from participant involvement in multiple sectors. One is more directed to supply and generation issues, the other is directed at overall strategy and improving overall profits. Both have some steps to go before they can achieve every aspect conveyed in Figure 5-2.

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5.4.2.2 Agent-based Modelling of Electricity Systems (AMES) Sun and Tesfatsion (2007) report on the status of development work of an open source ACE framework for study of the Wholesale Power Market Platform (WPMP), which was proposed in 2003 by the US Federal Energy Regulatory Commssion for common adoption by all US wholesale power markets. This WPMP market design was proposed following the melt down of the Californian wholesale electricity market in 2000. Tesfatsion & Judd refer to their test framework as Agent-based Modeling of Electricity Systems (AMES); it is designed to permit computational study of the WPMP design in different regional settings. AMES is an open source equivalent to the commercial EMCAS model but differs in a number of ways. Ames has moved further in the same direction as VicGasSim in representing most functions in terms of profit and costs functions but as of 2007, AMES lacked bilateral trading by longer term contracting capability and lacked calibration, validation and much of the downstream segmentation detail included in EMCAS and VicGasSim.

5.5 Recent Australian Energy Forecasts 5.5.1 ABARE (2007): Australian energy: national and state projections to 2029-30 ABARE (2007) presented a further extensive set of energy projections to 2029-30 in their research report 07.24. ABARE’s projections are developed by use of their E4cast model, which was described in 1.3 and 5.1.3. Of most interest to this thesis, are their projections that growth in total energy consumption in Australia will slow and average less than 2% pa over the period to 2029-30. In contrast, production of natural gas is forecast to grow at an average rate of 5.4% pa; at this level, it is forecast to be the fastest growing of the major energy production sources in Australia.

5.5.2 Australian Energy Regulator (2007): State of the energy market 2007 AER (2007) provides a very comprehensive report on the history of deregulation of Australia’s energy markets from genesis through intervening changes in market participation, regulation and regulators, as well as providing a good source of historical data and recently developed long-term forecasts. The report ends by presenting summaries of institutional arrangements and greenhouse gas policy.

AER’s 2007 report on historical data for retail gas prices between 2000 and 2007 and AER’s recent long term forecasts are presented in 8.3.4 and compared to outlooks generated by VicGasSim in 2002-03. Appendix 4 describes the reported changes regulations and regulators.

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5.5.3 ACIL Tasman (2008): The impact of an ETS1 on the energy supply industry – Modelling the impacts of an emissions trading scheme on the NEM and SWIS2, prepared for the Energy Supply Association of Australia (22 July 2008) ACIL Tasman’s (2008) report provides outlooks for energy in both the eastern and western Australian markets, with an emphasis on electricity and gas and the impact of emission controls and policies on these energy sectors. This report builds on work conducted initially by ACIL in the early 2000’s, as reported in 5.1.2.

The 2008 report provides an integrated outlook for CO2 emissions, pricing of carbon emissions, the impact of carbon cost to force replacement of high emission power generators with lower emission technologies, of which the largest medium term beneficiary will be growth in GPG and gas demand. The ability to support this growth in gas demand is based on a forecast of projected available gas resources, including CSM.

An outlook for the future east coast gas price in each of the major cities is presented. The report provides a rich database for making comparisons with outputs generated by VicGasSim, even though the latter were generated in 2002-03 and have not been updated for policy changes and their likely impact in stimulating increased growth in GPG.

It is worth noting how ACIL Tasman generate their outlooks. In the report they describe the sequential use of three separate models. Firstly, a general equilibrium model (Tasman Global) of the Australian economy to generate industry demand for energy by type. Output from this model is fed to an electricity sector model capable of translating the regional electricity demands from Tasman Global into electricity prices based on operation of a modelled pool price/distribution model (PowerMark). This model has been modified to generate forecasts of CO2 emissions and related costs and the impact on forcing replacment of high CO2 emission (coal-fired) plants.

PowerMark generates demands for gas to fuel existing and new GPG plants with lower CO2 emissions. Gas demand forecasts from PowerMark provide input to ACIL Tasmans’ gas market model (GasMark) to generate supply scenarios to match the overall gas demand, including the increased gas for GPG. The model also generates a forecast of future gas prices. Their description implies that GasMark works on a lowest-regional-cost supply basis.

ACIL Tasman’s work is backed by incorporation of a wealth of industry data collected from work on various studies over a long period. It includes reliable estimates of available resources, current prices in all segments, operating costs, and future development costs. However, the modelling relies on a number of simplifying assumptions or procedural steps. A number of these were

1. ETS: Energy Trading System 2. SWIS: South West Interconnected System; the name of the interconnected power system in WA.

101 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

critiqued in 1.3 and 5.1.2 in the review of ACIL’s earlier versions of the same types of models used by ACIL to generate forecasts published in the early 2000’s.

The series of models contains numerous predictive algorithms, allows limited effects for competitive interactions and tends to still reflect lowest cost solutions. In this case, ACIL Tasman (2008) acknowledge that the model assumptions and Government targets force the rate of change in GPG to meet desired emission levels without due regard to competitive forces or processes and interactions with Government by the groups that are directly affected.

As with many works, ACIL Tasman’s approach is extremely useful for early identification of trends and issues, but their procedures may have a tendency to over-predict early GPG gas demand and as a result over-predict future gas supply prices. ACIL Tasman’s 2008 forecast of gas supply prices is compared to VicGasSim’s gas price forecasts as well as the ACIL’s forecasts made in 2003; this comparison is presented in 8.3.2.

5.6 The Adopted Modelling Approach For VicGasSim Most of the literature described to date, suggests that most agent-based simulation studies of specific industries that consider evolutionary changes are of the "history-friendly" type and are used to gain micro-founded insights into the structure of industry evolution (Dawid 2007).

Brenner and Werker (2007) and Tesfatsion (2006) both point to the value of micro-simulation models, if enough information is available and the model designer has access to personnel, who can provide simple behavioural or mental models to make each agent’s business decisions. In the case of the Victorian gas market, considerable data and information as well as industry structural elements were published for the Victorian gas industry during the 1990’s and early 2000’s in preparation for deregulation of this industry. This data on both the industry and the participants provides a rich base on which to build a micro-level model of virtually all of the participants. The author also has a working knowledge of the industry from his technical and commercial experience in this industry1. Therefore, a very-detailed micro-level of specification level has been selected for VicGasSim.

Malerba et al. (1999) and Malerba and Orsenigo (2002) argue these type of models should be capable of reproducing the main facts in the historical development of the industry. The idea is to start with verbal descriptions of the actual structure of an industry and then translate the verbal descriptions into a formal model. The use of agent-based simulation is an effective methodology to capture this industry characterisation of a set of heterogeneous agents to represent market participants. The greater the level of detail, the greater will be the concretisation of the model and

1. See author’s curriculum vitae in Appendix 7.

102 Chapter 5 Energy & Economic Modelling Examples

the greater the number of mechanisms available to explain and understand observed phenomena and causalities. The greater the number of model levels or processes then the simpler the process descriptions can be.

The challenge is to keep the level of mental models relatively simple as the number of agents and interaction increases. In the case of the participants in the Victorian gas market, the number of customers is very large but they fall into a modest number of types in a modest number of regions. Similarly, the number of transmission entities, distributors, retailers, producers is relatively few in each case. However the number of interactions is extremely large when all levels of market interaction are simulated in the one model; therefore keeping agents’ mental models simple acts to reduce the computing load generated by such model designs.

The VicGasSim model follows the broad approach of Brafman and Tennenholz (1997), Shoham, Goldszmidt and Boutilier (1999), Casti (1998) and Tesfatsion (2006) in using logical axiomatisation through business styled mental or process models. These mental models are described, in business terms of production, sales, market shares, prices, costs, and profits, to replicate sufficient rational behaviour in order to achieve some semblance of a market (model) equilibrium without recourse to game theory, or macro-equilibrium control modules. The author judges that, according to Brenner and Werker’s (2007) taxonomy of simulation models, the final VicGasSim model is best classified as a history-friendly simulation model that tends strongly towards a micro-simulation model.

In designing the VicGasSim model, the author has provided the characterisation of each sector and strived to maintain the balance in the design constructs for each agent. The author has acted as the design participant and contributor on behalf of each sector using his own knowledge of the industry, research, case studies of a specific sector, and advice obtained from participants. He is not biased towards any sector and he has not had to negotiate with others to get the right balance between differing proposals. Therefore, the agent representations in VicGasSim are at similar, simple levels of cognition to allow regular and consistent communication as such agents do in their common market place.

This VicGasSim model was developed in four stages; advanced stages use a repeated look-ahead capability to improve agents’ abilities to make rational choices over time rather than repetitive learning, game theory or other theories to improve agent intelligence and adaptivity.

The final VicGasSim model, whose structure and characterisation are described in Chapters 6 and 7, attempts to address many of the modelling issues, which have been raised and discussed in this Chapter. It also includes most of the market detail described in Chapter 2.

103 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

104 Chapter 6: Prototypes # 1 & # 2

6.1 Staged Model Development A series of agent-based prototype models was created to develop a model structure for simulating industry behaviour in the Victorian gas market. This model evolution is shown diagrammatically in Figure 6-1:

Figure 6-1: Model Evolution

The model was developed in four stages through a series of prototype models with increasing capabilities and detail. This development work took two years to complete.

Prototype # 1 established a conceptual market model with a very simple gas-market transactional capability between a limited number of agents. Prototype # 2 enhanced selected agents’ strategic intelligence by incorporating a look-ahead capability to test how alternative strategies might play out in the market place against competitors, who are making similar strategy choices. Prototype # 3 significantly expanded the scope of the conceptual framework of Prototypes # 1 and # 2 to

105 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

incorporate a wide range of agents to adequately represent all of the major participants in the real Victorian gas market and input data reflecting available information on this market. Prototype # 4 combined the enhanced strategic intelligence (look-ahead capability) developed in Prototype # 2 to the expanded market scope of Prototype # 3. Progress on Prototypes # 1, # 2 and # 3 was publicly reported and published in Taylor and Harding (2000), Taylor et al. (2000), and Taylor, et al. (2003).

This staged model development is presented in the following sections of this chapter and in Chapter 7. The model design for and results from each Prototype are described in order, moving from the very simple to very complex in just four stages.

6.2 Prototype # 1 The initial model was developed using the agent-oriented language, JACK Intelligent Agents, designed by and reported by Agent Oriented Software (2000). JACK provides a framework in Java software for multi-agent system development. JACK agents are modified versions of the BDI agents, which are described in 4.2.1. JACK provides a set of syntactical additions to its host language: key words for the main components of an agent (such as agent, plan and event); a set of statements for declaration of strongly typed attributes and characteristics of agent components (such as the information contained in beliefs or carried by events); a set of statements for the definition of static relationships (for example, which plans can be adopted to react to a certain event); and a set of statements for the manipulation of an agent’s state (for example, additions of new goals or sub-goals to be achieved, changes to beliefs, and with which agents interactions are possible). The JACK framework provides for additional Java software code to be incorporated into an agent’s components. It comes with a compiler that provides the correct semantics of the BDI architecture and a set of classes to provide run time support for concurrency management, default behaviour, and agent communication. Further background on the Jack Intelligent Agents software is provided by Busetta, Ronnquist, Hodgson & Lucas (1999).

The model design is relatively simple and the early development work concentrated on creating and testing an agent-based model infrastructure for a gas market. The model protocol is described in axiomatic terms representing agents’ beliefs about their gas resources, supply contracts, fixed and variable cost structures, profit margins and market shares.

Agents possess a range of defined strategies and plans to handle varying market conditions and for deciding the prices and volumes of gas bought and sold between parties. The agents are goal driven to achieve increased market shares and profit margins.

The first step was to use the BDI model and JACK software to create a very simple concept prototype model to simulate competition in a simple gas market. The first prototype was used to

106 Chapter 6 Prototypes # 1 & # 2

gain experience in applying this modelling technique and the JACK software by creating and testing a few basic model structural features, such as asynchronous negotiations, and to determine how the model would handle real world market timing. The first prototype incorporated agents representing two Joint Venture Producers (JVP) supplying gas to two Joint Venture Retailers (JVR) meeting the demand needs of one gas Market as shown in Figure 6-2.

Figure 6-2: Model Design of Prototype # 1

<< Agent >> Timer

<< Plan >> Year Timer

<< Agent >> << Agent >> << Agent >> Producer R e ta iler Market

<< Database >> << Database >> << Database >> B e lie fs B e lie fs Beliefs

<< Plan >> << Plan >> << Plan >> Plan Plan Plan

See footnote for explanation of linking bars and arrows1.

Agents hold specified beliefs about their resources, costs structures, profit margins and market shares and they possess a range of defined strategies and plans to handle both varying market conditions and for setting prices and volumes bought and sold between parties. They are goal- driven to achieve increased market share and profit margins. Several of these representations are encapsulated as agent capabilities for use in different settings and plans. The details of each agent, its beliefs, plans, message passing capability and event signals are shown in the Table 6-1.

1. Linking bars and arrows have same meanings of "has" and "uses" as explained for Figure 4-1. The usage of links and arrows in Figure 6-1 is simplified and not complete and the direction of the arrows between Producers, Retailers and the Market to their individual Plans are shown in the wrong direction - they should point in the opposite direction to that shown; for a detailed explanation, see Figure 4-1 and its description for a clearer explanation of the meaning of the linking bars and arrows.

107 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 6-1: Agent’s Beliefs, Plans, & Messaging Capabilities

BELIEFS Message Event AGENTS -Databases PLANS Passing Capability Signals TimerAgent JavaGregorianClock YearTimerPlan StartOfSimulation (1) ClockDilationFactor CreateOtherAgents StartOfYear/Epochs TimeToWake GoToSleep JVProducerAgent Estimated Reserves JVPContractingPlan SupplyGasToJVR’s StartOfYear/Epochs (2-8) FixedCost SteadyPricingPlan OfferNewContracts Wait/ VariableCosts VolatilePricingPlan (Vols/Prices) SleepForAllOffers InitialContracts JVPOperatingPlan UpdateContractDatabase TimeToWake (min & max volumes, price) JVPMarketShare JVPProfits JVRetailerAgent Fixed Cost JVRContractingPlan AllocateSalesToJVP’s StartOfYear/Epochs (2-8) Variable Costs SteadyPricingPlan SeekNewContractOffers Wait/ Initial Contracts VolatilePricingPlan AcceptBestOffers SleepForAllOffers (min & max volumes, JVROperatingPlan TimeToWake price) JVRMarketShare JVRProfits MarketAgent InitialMarket (Period1) NextYearsMarket AllocateCurrentYrMarketToJVR’s StartOfYear/Epochs (1) MarketGrowthRate UpdateThisYearsNeeds PriceElasticity CalcNextYearsMarketOutlook 6.2.1 Model Methodology The design is relatively simple and development work concentrated on creating and testing an agent-based model infrastructure, which can mimic the axiomatic, commercial type of behaviours that characterise the gas industry. The design was made capable of expansion to include a greater number of market participants and for inclusion of more detailed market representation in later stages of model development work.

The model does not invoke any central control agents to maintain model equilibrium. There is no use of blackboards, model managers or agents to arbitrate outcomes, contract nets, co-operation methodologies or general equilibrium modules. The model is designed simply to maintain a market where supply and demand are driven by the rationally bounded interactions between the agents in the model and daily balances are usually met by adjusting prices, utilising storage facilities or curtailing demand to customers, who hold contracts that permit such cut backs. There is no mechanism to generate a general equilibrium at all times but rather the model is in a state of ongoing dynamic disequilibrium from which emerges an impression of statistical equilibrium, in which the aggregate equilibrium is compatible with individual disequilibrium. The equilibrium of a system no longer requires that every single element be in equilibrium by itself, but rather that the statistical distributions describing aggregate phenomena be stable, i.e. in “a state of macroscopic equilibrium maintained by a large number of transitions in opposite directions” (Feller, 1957).

108 Chapter 6 Prototypes # 1 & # 2

Design work focused on establishing the protocol for the use of a clock timer agent to schedule the major commercial cycles, which are typical of a gas market in which both asynchronous and concurrent activities and message signalling take place. Agents, once created, implement their annual plans concurrently in separate threads within a series of time epochs in each year. They are woken by the timer agent at the start of each epoch and go to sleep when their tasks and message sending to other agents are completed. Within an epoch, each agent is given enough time to complete their tasks. Real elapsed time within each epoch is controlled by the time dilation factor on the computer system clock.

The sequencing of model steps is outlined below to introduce the model design and agent interactions that mimic the commercial behaviour of participants in a typical gas market. This sequence should be read with reference to Table 6-1.

1. Model initialisation

(i) Create agents

(ii) Read agent beliefs (input data)

(iii)Start clock

(iv) Call YearTimerPlan to schedule time events from Startyear to Endyear and to schedule time Epochs and associated event messages between agents.

2. Start Epoch 1 (Epochs are time periods within a yearly cycle of events)

(i) Market calls plan to assess current year’s gas needs based upon growth expectation reflecting demand elasticity to prices over preceding two years.

(ii) Market sends message notifying all Retailers of estimated current and following year’s gas needs based upon market growth expectations and market prices in prior two years.

3. Start Epoch 2

(i) Each Retailer calls plans to estimate its market share and volume in the current year and the volume that it will target based upon its past market share trend.

(ii) Each Retailer calls plans to estimate a price for supplying the above volume based upon existing supply contracts, its own fixed and variable costs, plus a profit margin based upon its assumed pricing strategy to gain market share and increase profit.

(iii)Retailers send messages to the Market with offer terms.

109 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

4. Start Epoch 3

(i) Market uses plans to prioritise offers in terms of prices, maximises take from lowest offers and minimises take from other offers if additional gas is needed beyond lower priced offers. Market modifies expected usage for price elasticity1 effects of actual prices in offers.

(ii) Market sends message to Retailers notifying them of those offers accepted and actual volumes to be supplied.

(iii)Retailers send messages to Producers notifying the producers of the volumes the Retailers will take under each existing contract, maximising volumes from lowest priced contracts and minimising volumes from remaining contractual obligations. Retailers must pay for supply of minimum obligations even if they lose market share and take under their minimum obligations.

(iv) Producers update their beliefs for remaining, uncommitted reserves available for following year commitments.

(v) An internal accounting agent is called by each agent to calculate and store related data on usage, prices, average costs, market shares, and profits.

5. Start Epoch 4

(i) Retailers use plans to assess the share of the market that they will target next year. This volume is based upon expected total market growth posted by the market agent, the Retailer’s past market share trend, a contingency margin allowing for market churn rates and the competitive strategy adopted. Retailers determine incremental contract volumes they will seek from Producers in order to add to their existing contracted supply. Retailers who are closer to minimum offtake levels may decide they do not need any new contracts and cover growth from existing contracts; this reduces their exposure from increasing their commitments to Producers when facing an uncertain marketing outcome in the following year.

(ii) Retailers notify all Producers of required amount for a new contract volume for remaining years of model.

(iii)Producers assess total volume obligations based upon the assumption that Retailers will take the maximum volumes available from existing contracted obligations plus all newly requested contract volumes. This total volume is used to determine average supply cost

1. In Prototypes # 1 and # 2, price elasticity refers to elasticity of gas demand solely to changes in gas price. Later in VicGasSim, the price elasticity of gas demand is driven by both the price of both gas and electricity. A gas price elasticity factor and an electricity price elasticity factor are both provided as input data. The gas and electricity price elasticity factors are sourced from AGA (1996) and based on work done by ABARE for AGA; see also AGA 1999b, which provides information on price elasticities. Gas prices are provided dynamically by the model; electricity prices are provided as input in the form of a forecast of growth in annual electricity prices.

110 Chapter 6 Prototypes # 1 & # 2

(based on fixed and variable costs) and the Producer adds a profit margin adjusted annually for past trends in market share and an assumed competitive price setting strategy. This price is set as the price for new incremental supply/contract offers

(iv) Producers notify Retailers of price response to requests.

(v) Retailers prioritise responses, choose lower priced contract and notify Producers

(vi) Producers and Retailers update beliefs database by adding new contracts to a contracts database.

6. Start Next Year

(i) Advance Year Plan by one year and restart epochs.

7. End Year

(i) End model run.

111 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

6.2.2 Results Figure 6-3 shows output for a case of two Producers supplying two Retailers with gas for consumption in a single Market. Each Retailer follows a different type of strategy or plan to the other Retailer. One follows a steady pricing plan, the other follows a more volatile plan. Each Producer follows a different type of strategy or plan in the same way Retailers do.

Figure 6-3: Agent-generated Competition

Strategies Adopted: Retailer 1 - Steady price competition Retailer 2 - Volatile price com petition Producer 1 - Steady price competition Producer 2 - Volatile price competition

Avg M arket Price/Unit 300 2.00

250 1.50

200 1.00

150 Market Volume 0.50

100 0.00

100 Market Share Split, % R & P R 80 2 2 2 Following R Volatile Strategies 1 60

R 1 & P1 Following 40 Steady Strategies P 2 20 P 1

0 Supply Prices / Unit

2.00

1.00

0.00

Profit M argins / U nit 0.50

Total Profit 0.00 R = 50 Avg Costs / Unit 1 R = 200 -0.50 2

P = 290 -1.00 1 P 2 = 125 -1.50

-2.00

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Years

The data for related performance measures are compared vertically over the same 10 year time period. This alignment allows tracking of the interactive nature of the competing strategies pursued by the individual Retailers and Producers. The peaks and troughs reflect the courseness of this model and agent details but it does suffice to show the potential for using this type of approach to focus on

112 Chapter 6 Prototypes # 1 & # 2

participant behaviour to forecast market outcomes. While some peaks and troughs appear to align, they are actually changing due to changes in prior periods, as explained in following paragraphs. A number of emergent trends are clearly evident for market and participant performance.

The Producers are initialised with surplus reserves and identical cost structures. Retailers are similarly initialised with identical cost structures. Each Retailer is assigned an initial contract with an assigned Producer. These contracts contain identical minimum and maximum offtake volumes and a gas price fixed for the remaining periods of the simulation. Retailers are required to pay for gas not taken below the minimum and this gas is returned to the Producers’ uncommitted reserves inventory. Initial maximum offtake volumes are sufficient to allow each Retailer to gain approximately 60% of the initial market. Producers are limited to setting the prices for incremental supply contracts to cover the next year’s expected growth. In contrast, Retailers can reset the average price each year for all gas they sell to the market. The Producers and Retailers can differ from their sector counterpart in the competitive strategies they elect to adopt for setting Retailers’ average prices or Producers’ incremental prices.

One Retailer follows a “Steady” strategy of reducing its profit margin in a steady and more conservative (relative to the other Retailer) fashion until it gains market share and then holds this profit margin constant relative to its average costs. The other Retailer adopts a “Volatile” strategy to alter its profit margin both up or down in greater proportions than the first Retailer.

Confronted with equal retail price and volume offers at the time of initialisation, the market randomly chooses one of the Retailers (R1) from which to maximise its purchase and one (R2) to minimise its initial offtake. The Retailers therefore start with markedly different market shares (61%/39%). This immediately gives R1 (and, by contractual association, Producer, P1) a volume advantage that reduces their average costs and increases their profit margin. What they do with this advantage, and how R2 and P2 react, is then governed by their pricing strategy reactions to changes in their market shares.

Figure 6-3 shows the agents’ pricing strategies kick in more clearly after 2003 when sufficient price history is created. R2, which adopts a volatile strategy, pulls down its price in 2003, and undercuts

R1’s prices and as a result gains a first sizeable increase in market share and increased volume. This gain reduces R2’s average costs by the spreading of fixed costs over a larger volume. Having gained share, R2 invokes its more volatile strategy to boost profit margin and hence profits in the next period. This change more than offsets the associated volume loss because R1 is too conservative in its price change. R2 gains a big jump in profits versus R1.

1 This cycle is repeated. R2’s performance is more volatile but it still reduces its price trend more over time than R1. Even though R2 pulls down its price, it maintains its average (even if volatile) profit margin and boosts profit by gaining volume and reducing average costs.

1. Downwards sloping trend line or downwards sloping band of oscillating prices, which as a band is lower than band of prices for R1.

113 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

R1’s steady price reductions are never enough to catch up with the volatile but reducing pricing trend of

R2 which capitalises on its reducing average cost base. Thus the market holds R1 at minimum offtake levels and R1suffers the secondary burdens of volume effects on average costs and forward pricing. Overall, this first prototype model shows how Retailers are likely to choose (in this model run) the volatile pricing strategy to gain market share and profits over choosing a more steady pricing strategy.

The positions of Producers, P1 and P2, show less sensitivity than the Retailers because a large proportion of their pricing is fixed within long term contracts. P1 starts with a large market share because it is contractually linked at initialisation to R1 and hence starts with a volume and average costs advantage like R1. While P2 adopts volatile structure like R2, P2 never adjusts its incremental contract prices sufficiently to ever underprice P1, and P2 never gains significant new contracts from the more effective Retailer, R1.

In this scenario, volatility wins out at the retail level where the prices on total sales can be altered. However, the same strategy is unsuccessful at the Producer level because price changes can only be invoked on incremental contracts. P2 would have to increase the range on its volatility to have a greater chance of gaining against P1.

There is nothing special about these model outcomes, per se. They are illustrative of one simulation scenario over a 10-year period with a given set of data assumptions on costs, initial contract terms and the degree of profit margin adjustment allowed under each strategy.

The importance lies in recognising the model’s potential and flexibility:

1. To allow industry participants to test corporate alignment strategies across upstream and downstream sectors, pricing levels, effects of cost reduction programmes on future performance, impacts of new projects and both competitor and consumer price reactions,

2. In using axiomatic plans as defined and used by market participants to simulate and test the multi-faceted behaviours of individual participants,

3. In providing outputs in terms the participants recognise and deal with in their own planning: market shares, prices, costs, and profits, and

4. To allow more agents, more elaborate plans and expanded belief systems to be developed and introduced into the model through its modular structure.

Flexibility is important because once participants see value in the approach, it is possible they can be enticed to financially support enhancements to this type of modelling approach.

114 Chapter 6 Prototypes # 1 & # 2

6.2.3 Effect of the Number of Agents on Price Volatility Another use of this type of model was to look at changes in the structure of a sector within the model. For example, the level of competition in the model was raised by increasing the number of Retailers from 2 to 4, 6, and 8 (and where all but one of the Retailers continue to follow a volatile pricing strategy). The model showed that the market entry of an increased number of Retailers, with similar cost structures, reduced pricing volatility but only mildly reduced the average market price.

Figure 6-4 shows that increasing the number of Retailers from 2 to 8 can completely remove volatility from the market, as previously shown in Figure 6-3, where only two Retailers are competing. While the changes in retail numbers are discrete, the changes in model outputs follow a regular pattern and the derived volatility index has been presented as a continuous or smoothed function. Volatility is not significantly affected until the number of retailers (in this simple model) exceeds 61 and is completely eliminated above 8. The example shows the potential for this style of analysis by Governments to assess a suitable level of disaggregation to provide stable yet competitive market places.

Figure 6-4: Number of Retailers Needed to Reduce Average Market Price Volatility

Maximum Volatility 1 ai Dev imum Max Volatility Index

0 2345678 No. of Retailers

6.2.4 Relevance of the Prisoner’s Dilemma to Strategy Choice When Producer and Retailer agents elect to choose between alternative strategies, to maximise their competitive position and performance, they face the Prisoner’s Dilemma (Tucker 1950). Prisoner’s Dilemma deals with the class of situations in which there is a fundamental conflict between what is a rational choice for an individual member of a group and the group as a whole.

1. There is nothing unique about the number 6; it is only relevant to this hypothetical market and is not to be taken as indicative of the minimum number of Retailers, which might suit the Victorian gas market. The point being made is in the value of the capability of this tool and analysis for such purposes in models, which are calibrated to a specific market.

115 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 6-2 shows the profit payoff pairs for participants from pursuing alternative strategies and competing agents choose the same strategies or opposing strategies. The payoff matrix tables follow the convention described by Dixit and Skeath (2004).

Table 6-2: Prisoner’s Dilemma – Retailer’s Cumulative Profits For Given Strategy Choices

Retailer 2

Steady Volatile Strategy Steady (174, 9) (47, 210)

Volatile (223, 46) (133, 75) Retailer 1

In reality, the problem domains are different from Tucker’s classic Prisoner’s Dilemma model. Tucker described a situation where only two parties make a single choice. The gas prototype model is different in that it simulates the effects of choices over time and allows secondary effects to come into play. In addition, individual parties are linked to choices by other parties. Nevertheless, it is useful to consider a “payoff table” to see if there are dominant strategy equilibriums.

For Retailers, the volatile strategy is dominant, as shown in Table 6-2. A Retailer will maximise profits by following a volatile strategy, if its competitor adopts a steady strategy. If both adopt the most individually beneficial strategy they do not do as well as they individually might have hoped as described above. If they choose the steady strategy, they individually risk worst outcomes. For the Retailers, their possible profit payoffs are as follows:

If,

R1 chooses volatile, then it faces the profit payoff pairs: (223, 46) or (133, 75), or if

R1 chooses steady, then it faces the profit payoff pairs: (174, 9) or (47, 210).

Under any assumption of R1’s risk averseness to poor outcomes, it prefers the volatile strategy.

If,

R2 chooses volatile, then it faces the profit payoff pairs: (47, 210) or (133, 75), or if

R2 chooses steady, then it faces the profit payoff pairs: (174, 9) or (223, 46).

Under any assumption of R2’s risk averseness to poor outcomes, it prefers the volatile strategy.

The Nash (1950, 1953) equilibrium1 solution for Retailers is {Volatile, Volatile}.

These preferences are not influenced by one retailer getting a starting advantage imparted by the Market randomly allocating initial market dominance.

1. A Nash equilibrium is described in the footnote on the next page

116 Chapter 6 Prototypes # 1 & # 2

The totals show that this dominant strategy will not yield the same level of combined payoff as when they co-operate and agree to follow different strategies. However, a half share of the higher total is only moderate and far less than the upside temptation of the dominant strategy. Under co- operation, Retailers fear the situation where one of them defects from a commitment causing loss of profit potential to the other.

For Producers, the choice is not as clear cut, as shown in Table 6-3.

Table 6-3: Prisoner’s Dilemma – Producer’s Cumulative Profits For Given Strategy Choices

Producer 2

Steady Volatile Strategy Steady (346, 71) (290, 126)

Volatile (141, 282) (154, 233) Producer 1

The Producers’ possible profit payoffs are as follows:

If,

P1 chooses volatile, then it faces the profit payoff pairs: (141, 282) or (154, 233), or if

P1 chooses steady, then it faces the profit payoff pairs: (346, 71) or (290, 126).

Under any assumption of P1’s risk averseness, it prefers the steady strategy. If,

P2 chooses volatile, then it faces the profit payoff pairs: (290, 126) or (154, 233), or if

P2 chooses steady, then it faces the profit payoff pairs: (346, 71) or (141, 282).

Under any assumption of P2’s risk averseness to poor outcomes, it prefers the volatile strategy.

The Nash equilibrium1 solution for Producers is {Steady, Volatile}.

P1 and P2 face quite different payoff risks because of the underlying initial and inflexible contractual alignment between each Producer and each Retailer and the random market selection of R1 over R2. In making its choice before the simulation starts, P1 should recognise it could equally be in P2’s position.

1. From Wikipedia (30 July 2009): In game theory, Nash equilibrium (named after John Forbes Nash, who proposed it) is a solution concept of a game involving two or more players, in which each player is assumed to know the equilibrium strategies of the other players, and no player has anything to gain by changing only his or her own strategy unilaterally. If each player has chosen a strategy and no player can benefit by changing his or her strategy while the other players keep theirs unchanged, then the current set of strategy choices and the corresponding payoffs constitute a Nash equilibrium. Stated simply, Amy and Bill are in Nash equilibrium if Amy is making the best decision she can, taking into account Bill's decision, and Bill is making the best decision he can, taking into account Amy's decision. Likewise, many players are in Nash equilibrium if each one is making the best decision that he or she can, taking into account the decisions of the others. However, Nash equilibrium does not necessarily mean the best cumulative payoff for all the players involved; in many cases all the players might improve their payoffs if they could somehow agree on strategies different from the Nash equilibrium (e.g. competing businessmen forming a cartel in order to increase their profits).

117 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

If a risked weighted assessment is made from running this strategy choice over a larger number of simulations and where initial alignments could be reversed half the time, the steady strategy will become the dominant choice. However, this expectation could be over-ridden if the lowest possible outcome of 71 is below the risk averse threshold of one or both Producers.

The model will be designed for use by individual industry participants to develop long term strategies and test investment decisions and corporate positioning. The simulation design will focus on representation of the competing pressures from possible upstream supply cost increases, regulated transmission interconnections, and decreases from increased downstream market competition due to deregulation. Uncertainty will be handled by providing each agent with the capability to recursively test plans against model-generated views about future gas developments, transmission interconnections, project costs, price elasticities of market demand sectors, and competitors’ past responses. This recursive approach will be used to formulate an envelope of scenarios to develop the most robust strategy or investment choices for each agent.

The timer protocol and framework established in the prototype will be expanded to handle the increased number of time linked and discrete events created by this complex range of competitive interactions. Time epochs will be broken down into months and days to allow representation of seasonal peaking conditions, which have a strong influence on pricing and installed capacities.

6.2.5 Conclusions from Development Work on Prototype # 1 The following conclusions were drawn from the development work on the first prototype:

1. Multi-agent simulation using an axiomatic representation of gas industry participants’ behaviour has generated dynamic participant outcomes beyond the capability of existing industry models.

2. The model design has the modularity and flexibility to allow expansion for handling the industry level design described above and which will allow focus on the issues and uncertainties confronting industry participants.

3. The results provided confidence for application of the design concepts to an expanded model.

4. The epoch timing methodology to manage discrete, asynchronous and concurrent activities provided an effective approach for agent co-ordination.

5. Interpreting simulation scenarios provides a learning process that has inherent advantages to industry participants to identify durable strategies. This learning is in terms of measuring their own behaviour and performance against those of their peers in terms with which they deal every day at many levels in their organisations.

118 Chapter 6 Prototypes # 1 & # 2

6. AOP provides a program design technique which is easily understandable by domain experts and allows an interactive development process that is most useful when trying to establish an appropriate infrastructure to achieve the program’s main objectives.

7. Experience with the JACK Intelligent Agent Software showed that significant sections of Java software code still had to be written to characterise agents and their plans; the prescribed JACK framework inhibited a number of protocols sought for the current work and a number of changes were adopted to over-ride the prescribed JACK protocols. It was concluded that rather than force-fitting the model design into JACK by making major code additions, it was simpler and more beneficial to design an original set of protocols in Java software and which are specific to this research project. The JACK software was discarded at the end of work on Prototype #1. The new Java-coded protocols still use the BDI agent concept and use a new model engine, which is described further in 7.2.1; agent plans and other agent components have been designed, developed and coded as part of this project.

6.3 Prototype # 2 In Chapters 3 and 4, literature was presented to outline trends in dealing with decision-making by agents facing uncertainty, particularly when the uncertainty was dynamic in time. In Prototype #1 the agents had limited intelligence and looked backwards for information and experience to make strategy choices – this form of intelligence in a dynamic market relies on regular updating to provide ongoing relevance to prevailing circumstances and does not anticipate emergent changes in such circumstances but reacts to them (risking the consequences of events that occur in the intervening reaction time).

Prototype # 2 adopts and incorporates the broad “complex adaptive systems” approach of Brafman and Tennenholz (1997), Casti (1998), Shoham, Goldszmidt and Boutilier (1999), and Tesfatsion (2006), whereby agents regularly review their beliefs and goals so as to adapt their strategies to changing circumstances and predict the likely direction of emergent behaviour of such a system. Agent models are used to make predictions about such complex systems. The concept is extended by making “look-ahead" runs to allow an agent to make a single choice among possible plans or strategies and choose between sequences of states not just between single, static states. In order to make such complicated choices, Brafman and Tennenholz suggest decisions can be made by using the methods of economics and games theory where beliefs are modelled using probability distributions, preferences are modelled using utility functions, and the decisions are made on the basis of expected utility maximisation. A simpler choice is adopted here but only because of the development of an efficient model re-threading technique that is available for all agents to regularly test multiple strategies over both short and long-terms. Agents

119 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

can regularly test the potential impact of altering the strategies or plans or quantums applied. Here the data richness of the look-aheads is seen as more than adequate to replace the need to provide agents with games theory methods, learning algorithms, probabilities based on history, and utility functions reflecting only a generalised measure of risk averseness that might or might not be applicable to the emergent behaviour.

Prototype # 2 consists of an enhanced version of the gas model as developed in Prototype # 1 but the program was rewritten and expanded in Java software. The model has the same limited number of Producers, Retailers and single market structure (without transmission and distribution sectors). But the model is enhanced with more agent detail and capabilities to let agents make a wider range of forward commitments (as described below). The Producer and Retailer agents are endowed with adaptive look-ahead capabilities to vary and test alternative pricing and contracting strategies and to consider the possible effects of such variations on their future market share and profit growth. The upgraded agent framework is described below.

6.3.1 Producers Producers’ agent profiles were styled along the following lines:

1. Upstream gas Producers possess potential to expand capacity with attendant changes in fixed and variable costs as well as requiring specified lead-times to bring projects on-line.

2. Producers annually test requests from Retailers for both short and long term supply contracts against potential future capacity and associated fixed costs in order to provide price offers for new gas contracts.

3. Price offers are based on amortised fixed costs and variable operating costs, assuming forecast outcomes on future usage.

4. Producers pursue a profit margin goal and seek to maximise long-term profits by modifying prices and profit margins based upon changes in market share.

5. Producers make supply/price offers and test acceptances against minimum commitment volumes required to build each capacity expansion.

6. Producers can elect to pursue one of two possible pricing strategies to stimulate market share growth: “Steady” designed to avoid exposures to large losses from unsuccessful underpricing; “Volatile” designed to win market share quickly in order to gain volume and amortise relatively high fixed costs.

7. Producers have no adaptive capability and pursue an assigned pricing strategy.

8. Producers offer an additional discount to Retailers who want to enter into longer-term contracts versus short-term contracts.

120 Chapter 6 Prototypes # 1 & # 2

9. Contracts contain maximum and minimum quantities for each year plus a price for each year of the contract.

10.Producers supply gas each year under existing contracts as requested or required by Retailers.

11.Producers only have knowledge of movements in their own market share. Producers and Retailers do not share information about their prices or individual market shares.

6.3.2 Retailers Retailers’ agent profiles were styled along the following lines:

1. Retailers supply a single market and base their price offers on their own cost structure and gas supply contracts and a profit margin.

2. Retailers have a market price goal to recover a target margin over their average unit cost. Their average cost reflects the weighted-sum of their average supply costs, amortisation of their own fixed costs, and their variable costs (which can be considered to also cover their transmission and distribution costs).

3. Retailers can elect to pursue one of two possible pricing strategies: “Steady” designed to avoid exposures to large losses from unsuccessful underpricing to stimulate market share growth; “Volatile” designed to win market share more quickly and amortise fixed costs.

4. Retailers alter their price target to reflect their views about their relative market share and their chosen pricing strategies.

5. Each year Retailers seek new contracts from all Producers. Retailers estimate future trends in the market and their market share in order to identify how much additional gas they desire to hold under contracts at fixed prices.

6. Retailers can select from two contracting strategies: short term and long term depending upon how they see their future competitive position. Retailers take up supply offers based upon the differences in total gas supply costs over the period they nominated.

7. Retailers have access to movements in Producer gas prices and movements in their own market shares but not to the prices and market shares of other individual Retailers.

8. Each Retailer is endowed with the capacity to make look-ahead “runs” or predictions or probes of the future. The Retailers use these look-ahead runs to make their annual strategy choices reflecting their then prevailing beliefs of the future. These look-ahead runs cover a nominated period (of 5 years in this analysis) and are repeated each year giving the agent the opportunity to modify or adapt his strategy choices to predicted market conditions.

121 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

6.3.3 The Market Market’s agent profile was styled along the following lines:

1. The Market seeks its annual requirements of gas each year.

2. The Market receives price offers and volume limits from each Retailer to supply gas.

3. The Market estimates the total volume of gas that it will take, based upon its beliefs about underlying economic growth and the effect of current offer prices on that economic activity.

4. The Market allocates its needs to the various Retailers based upon the lowest price offers and nominated volume limits. The Market is not required to enter contracts with Retailers and can alter its Retailer choice each year.

6.3.4 Model Methodology The model has been tested for a case of two Producers supplying four Retailers selling gas to a single Market. Individual performances are shown in Figures 6-6 for each Retailer in terms of profiles for their market shares and their annual cash flow per unit of gas sold and made up of their annual unit costs1 plus unit profits2. The unit costs and profits are in real terms with no inflation assumed. Figure 6-7 shows similar performance outputs for the Producers. These two figures are discussed later.

Retailer unit costs include two components: (1) the unit cost of gas supplies (the average unit price at which Retailers buy gas from Producers), and (2) their own costs or overheads (made up of (a) their fixed annual costs amortised over their market sales for that year) plus (b) a variable unit cost. As Retailers sell more gas, their unit costs will drop because of their ability to amortise their fixed cost over a greater volume of gas sales.

6.3.5 Look-ahead Forecasting by “Endowed” Retailers The model is constructed such that at the start of each year the model makes a "clone" of its then- standing database and all agents. It then runs on to completion with one of an assigned array of strategy combinations open to be nominated by one Retailer. At the completion of this run, the model stores that Retailer’s cumulative profits from that run. The model then restores the original model condition ("clone") prior to making the predictive run. The model repeats this cycle for each possible strategy combination nominated for that Retailer. During these look-ahead runs for

1. Unit costs refer to costs per unit; a unit might be a unit of volume or thermal capacity, such as a GJ, or a Customer; unit costs might be quoted in $/GJ or $/Customer. They differ from "costs" which mean the level of applicable costs quoted in $’s; costs are considered by sector or in total. 2. Unit profits refer to profits per unit; a unit might be a unit of volume or thermal capacity, such as a GJ, or a Customer; unit profits might be quoted in $/GJ or $/Customer. They differ from "profits" which mean the level of applicable profits quoted in $’s; profits are considered by sector or in total. Profit margin is the proportion of profit to total costs; it is quoted as a percentage, say, 5%, which means a profit margin of 5% of the total cost base is applied to the item under consideration.

122 Chapter 6 Prototypes # 1 & # 2

a given Retailer, all other agents are assumed to retain their last chosen strategy options but they also alter in each time period so they are not static in their long-term behaviour patterns.

At the completion of all nominated strategy look-aheads the agent searches the stored cumulative profit outcomes of its possible strategy combinations and chooses the most profitable strategy elections. It sets these as its actual choices for the next real simulation period.

Each other Retailer is allowed to test its options and revise its positions prior to making the actual run for the next period. When all elections are in place, the model moves forward one period and any new contracts, prices, market share changes and plant expansion decisions are locked in place. In this process, a Retailer might change from steady pricing behaviour to volatile pricing. It might also write some long-term contracts after previously entering short-term contracts.

Figure 6-5 shows numerous 5-year look-ahead profit projections (shown in different colours) as seen by Retailer 1 at the start of each year. The thick black line shows the actual profits achieved versus possible options seen by the look-heads. The differences between the final outcomes (thick black line) and look-ahead options (coloured lines) show that Retailer 1 made some poor choices early (black line falls below most of look-aheads in the 2003-07 period) but improved to make significant profits in the late life of the simulation (black line falls above most of look- aheads in this the 2010-2014 period) and by regularly changing strategies the Retailer avoided following what eventually turned into poor performance scenarios. It is possible further work on strategy definition could improve early period performance.

Figure 6-5: Prototype # 2 Look-ahead Capability

Annual Profits 60

0 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 -10 Years -20

It is interesting to note that Retailer volatility appears each time supply pricing undergoes some level of discontinuous change (see Figure 6-5) such as in the early and late life of this scenario. In

123 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

the middle life, Retailer 1’s look-aheads indicate a period of relative stability; in these circumstances Retailer 1 performed worse than during periods of perceived pricing volatility.

The real power of this model lies in these look-ahead capabilities. The model can assist industry participants to project and test possible strategies against intelligent, adaptive responses from consumers, suppliers and competitors. Participants see model outputs in units and forms they understand. Participants can explore the possible forces that will shape market outcomes. They can identify strategies that will influence the market and their place in it. The market participants’ behaviours and interactions actually generate a dynamic market that can show aggregated patterns that might show an emergent pattern that differs from past patterns or trends.

The resulting market is unlikely to be a least cost optimised equilibrium solution as generated by econometric and LP modelling. The outputs are not driven by statistical assumptions and are not controlled by macro-economic or mathematical algorithms. Also, strategy selection is not encumbered by following any game theory, estimating probabilities, using complex negotiating rules or by learning. The author follows the views of Brenner (2006), Malerba et al. (1999) and Tesfatsion (2006) in stating that market models, which have micro-level designs such as that outlined in this thesis, will come closest to predicting future market trends, particularly, when such markets face significant and ongoing change as is typical of complex adaptive systems. The requirement for success is availability and access to data and a knowledge of market participant behavioural rules.

In describing the market, the challenge is to work closely with diverse industry participants to incorporate sufficient characterisation of the key issues and participant behaviour. Keeping agent characterisation at a consistent and relatively simple level is the key to success. On the modelling side, the challenge is to extend the negotiating structure to improve the efficiency of negotiating mechanisms that consider time dependent states, multiple uncertainties, multiple strategies for handling uncertainties, and participants’ varying risk averseness.

The outcomes of the 4 Retailers’ competitive behaviour are shown and compared in Figure 6-6.

124 Chapter 6 Prototypes # 1 & # 2

Figure 6-6: Comparison of Retailers’ Performance

Years Retailer 1

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Figure 6-6 shows the Retailers all start with similar market shares close to 25% and similar costs and profit margins. The first two years of the figure represent historical performance and provide small differences that lead the various Retailers to adopt different initial plans for Year 3.

125 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

The Retailers’ market prices have not been shown on the figures to avoid cluttering the figures with excessive lines. A Retailer’s prices can be interpreted as the summation of the Retailer’s total unit costs and unit profit or loss and is usually shown by the value at the top of the bars. Where a Retailer makes a loss, as does Retailer 2 in the late life period of these outlooks, the price is no longer shown by the top of the bars but by the net summation of the components, including the loss component below the base line.

Retailer 1 triggers the first changes by way of holding slightly different starting costs and market shares that lead it to choose the volatile pricing strategy and the long term supply contracting strategy. The other Retailers opt for the alternative strategies. The Retailers’ different strategy choices are shown in Table 6-4.

Table 6-4: Retailer Strategy Choices

Retail erPricing Strategies Years =1234 5 6 78 9 10 11 12 13 14 15 1 Vol atile / Steady V S V SSSSSSV V V V S 2 Vol atile / Steady SSSSSSSSSSSSSS 3 Vol atile / Steady VSSSSVVVSSVVVS 4 Vol atile / Steady SVSSSSSSSSSVVS

Contracting StrategiesYears =123456789101112131415 1 Short / Long SS LSS LLSSSSSSS 2 Short / Long SSSSSS LLLLLLSS 3 Short / Long SSSS LSSSSSSSSS 4 Short / Long SSS LLLLLSSSSSS

Retailer 1’s initial strategy choices allow it to grow market share for a sustained period, as does Retailer 4 who makes greater early profits. In retaliation, Retailers 2 and 3 cut prices to attempt to regain market share at the expense of profits. These out of phase cycles continue over the life of the outlook. While it is not possible to identify a clear “winning” set of strategy choices, Retailers 1 and 3 do better over this 15-year period as shown by the comparison of total profits in Table 6-5.

Table 6-5: Retailer Total Profits over 15 Years

Retailer No Total Profits 1282 2 40 3206 4 88

Figure 6-6 shows that Retailer 1 does very well in Years 11-15 by trading off market share dominance and increasing profits, thus “creaming the market” over this period. Retailer 1 achieves this result by choosing volatile pricing and writing short-term contracts just as Producer 1 starts to cut the price of his supplies, see Figure 6-7. The combined result is a growth in profit margins while still pricing well above his competitors. This combination has a limited life and

126 Chapter 6 Prototypes # 1 & # 2

Retailer 1 will soon pay the price for this combination of strategies unless they are changed again to counter its loss of market share.

In contrast to Retailer 1 and 3, Retailer 4 has held prices low unsuccessfully at the expense of sustained reduced profits but eventually gains market share dominance in Years 12-15, just as Producer 1 is offering low priced supplies, see Figure 6-7. Retailer 4 appears well positioned for the following period. Retailer 2 on the other hand appears to choose the wrong combination of strategies in terms of its actual market shares and prices and eventually incurs sustained losses late in the simulation. Producer type performance is shown in Figure 6-7.

Figure 6-7: Comparison of Producers’ Performance

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In this model, Producers are constrained by their annual fixed costs of supply and future expansions, which are shown at the bottom of Figure 6-7. If a Producer can generate a small price

127 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

advantage that allows the Producer to typically gain the Retailers’ favour then that Producer can leverage its profits from the effect of volume gains on the amortisation rate of fixed costs.

Both Producers start with 50% of the market, but Producer 2’s initial fixed cost advantage allows it to soon underprice Producer 1 and gain a market share advantage. This success continues until Producer 2 has to expand capacity that increases its fixed costs. This change is followed quickly by Producer 1 being able to invoke a series of fixed cost reductions from Year 12 onwards. These fixed cost reductions could represent Producer 1 gaining access to lower priced gas from new discoveries made in the late life period. The end result is that these fixed cost changes lead Producer 1 to regain market share dominance late in the simulation period. But Producer 1’s pricing strategy is not smart enough to translate this advantage into large profit improvements. Producer 1’s pricing strategies let this economic rent slip through to the Retailers.

Producers total profits over 15 years are shown in Table 6-6.

Table 6-6: Comparison of Cumulative Total Producer Profits

Producer Total Profits 1312 2224

Figure 6-8 shows the Market’s demand profile, indicating retail pricing volatility flows to demand. In this model, there is minimum lag built into this effect. Also, the permanent shift in Producer supply prices late in the life of the model does not over-ride the Retailers’ competitive behaviour at that time. In fact, several Retailers make large profits by driving up prices despite falling supply prices. The volume and price outlooks indicate the potential of this type of model to be used for forecasting total market supply and demand balances as well as prices.

Figure 6-8: Market Demand & Retail Prices

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The objective of this prototype has been to establish a framework where a market is created by the participants’ competitive behaviours and the considered application of a series of adaptive

128 Chapter 6 Prototypes # 1 & # 2

commercial decisions. In this model, this process occurs, firstly, by Producers deciding when to expand capacity, and secondly, by price-competing Retailers looking to gain market share.

The author believes that strategy choices should be flexible and responsive to the market under study. This view is consistent with Zlotkin and Rosenschein (1996), who state that “logical axiomatization”1 using rules that best fit the situation under study should govern selected negotiating protocols.

6.3.6 Conclusions from Development Work on Prototype #2 Even in simplified form, the proposed design mechanism for an agent-based gas market model shows powerful advantages over traditional econometric and LP forecasting techniques. The following conclusions were made from the development work on Prototype #2:

1. The model represents a “synthesis” of both the behavioural and physical representations of the real market it portrays.

2. This behavioural and physical representation of model outputs is a distillation of the "collective wisdom"2 of its participants and is not a captive of historical and statistical bases where behaviour and physical discreteness are lost in various averaging processes and which cannot handle discontinuities in future behaviour and industry cost structures.

3. The model includes a unique forecasting capability to provide a learning capability: it includes “soothsayer” agents that can generate prognostications regarding future data and the environment, assessing their own likely behaviour and making decisions based on their own and differing outlooks of the future. Thus, the agents not only predict their emergent behaviour and market trends for demand, supply and prices but actually drive the changes in these directions.

4. The model’s flexible and modular structure allows it to be expanded to include other energy forms, such as the closely linked market for electricity, and linked to econometric modules to test the interactions between the energy market and the other markets that make a national economy.

1. See section 4.2.1 for previous reference to and fuller description of "logical axiomatization". 2. "Collective wisdom" is used here in a figurative sense; individual agents have only limited "wisdom" in terms of their assigned goals, resources and assigned plans to deal with certain events, including changes in their environment; the term is used to convey that the model outputs reflect the aggregated effects of the localised interactions between the myriad of agent transaction that occur in the model. To the extent that some new emergent aggregated response might occur, this can appear (incor- rectly) to some observers like the agents possess and show some wisdom in causing the change to occur. In reality, the aggregated changes reflect the combined responses of individual agents making simple low-level decisions in responses to localised events or changes in their individual environments.

129 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

5. The market analysis can be expanded to reflect behaviour at any chosen level of detail. It can incorporate more sophisticated decision rhetoric such as introducing probabilistic risk aver- sion and expected utility maximisation, or by rules that the participants believe will reflect their more pragmatic approach to decision-making. The latter rules might include the commercial decision criteria such as those related to net present values of proposed projects or decisions, shareholder objectives on dividends, profits and losses, return on assets, and price/ earning ratios, and agents agreeing to buy and sell or merge assets or projects. The author sees significant potential to explore the latter and believes such capabilities are now within the reach of an agent-based model.

The unique model infrastructure and re-threading capability allows the model to manage the multitude of discrete and linked events and allows the model to systematically clone itself then run forward, rewind, and reset itself. This process is used to assess the performance of alternative strategies as applied at each decision point in the model. This look-ahead approach and inclusion of the full supply-to-market chain1 reflecting the influence of both company ownership and regulator controls/reviews are keys for defining possible emergent market behaviour.

Results to date indicate the approach is capable of formulating regional and national supply- demand and price outlooks without reverting to inclusion of arbitrating agents, LP cost minimisation or macro-economic control solutions. The look-ahead design mechanism indicates potential to be a far more powerful predictive tool than traditional forecasting methods.

The number of interactions considered in the model so far shows that a real-world market model will cover a level of complexity well beyond that of current modelling efforts. Importantly, the model design in Prototype #2 has built-up a model design form that can be expanded to accommodate this complexity and do it in terms used by and views held by the individual participants in this market. These "views" cover the types of decisions they individually make, either at an operational level or at a corporate level, or as influenced externally by competitive changes in supply, resources, or policy changes by regulators. The possible emergent behaviour of a complex market is beyond the comprehension of corporate managements without the aid of models of this nature.

1. "Full supply-to-market chain" includes development, production, transmission, storage, distribution, retailing, consumption and regulation.

130 Chapter 7: Prototype # 3 & VicGasSim

7.1 Prototype # 3 Prototype # 3 materially expanded on the concept models developed in Prototypes # 1 and # 21 and calibrates the expanded model to available data on the Victorian gas market. Prototype # 3 includes a set of diverse interactions that typify the Victorian gas market at a micro-and macro-level. The expanded framework includes a wider array of Retailers and Producers with more detailed attributes, transmission and distribution systems with multi-dimensional tariff systems tied to delivery locations as well as average and peak throughputs, system capacities, supply contracts, a wholesale trading market to maintain daily market dynamics, and the main types of customers, which have different demand characteristics and pay different prices. All data was closely set to match or replicate real market data; all agent plans and strategies closely follow simple behavioural and decision-making rules typical for the industry.

Prototype # 3 focuses on the major step of scaling up the transaction mechanisms to reflect the "real" Victorian gas market and accommodates the exponential growth in transactions to be modelled and data to be stored and managed. The development objective for Prototype #3 was to accomodate the scale up of transactions and to collect and provide the input data to characterise the differing profiles of agents in the model.

Importantly, Prototype # 3 did not include or provide look-ahead capability to any agents. This was to be one of the final development steps to achieve Prototype #4 in order to arrive at a working Victorian gas market simulator. Incorporation of look-aheads was saved to follow establishment of the ability to generate reasonable characterisations of participant’s prices, market shares and profits, among other market criteria.

Prototype # 3 was developed to assess if a reasonable level of calibration could be achieved against a number of key market criteria and patterns such as for demand profiles by customer types, daily pool prices, and long term supply prices. Most importantly, the prototype needed to yield commercial and profit outcomes that looked reasonable for guiding corporate strategic planning.

1. The earlier prototypes, which are described in Chapter 6, are conceptual in nature, not calibrated to the Victorian gas market, and they established the chosen simulation framework for the main types of agents and associated behaviours.

131 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

A major goal of Prototype # 3 was to translate the resulting effects of all the localised interactions into derived profits for each of the Retailers represented in the model. Retailers were chosen as the focus for this prototype because they are involved in more types of transactions than any of the other agents in the model.

The complexity of this expanded Prototype # 3 can be contemplated by considering the number of involved agents and number of localised, interactive transactions taking place daily, seasonally and annually: five Retailers are bidding gas into the market each day from 14 contracts supplying gas at three injection points; a wholesale market is clearing weather-based random imbalances between inputs and outputs by way of a pool price and linepack; Retailers incur rate, geographic and seasonal dependent transmission and distribution tariffs for delivery to 14 withdrawal zones; profits are based on tracking accumulated costs for up to 200 differing types of Customers, which can have separate prices (some regulated and some unregulated); Retailers are also supplying gas to less model-specified markets in the adjoining states of NSW, SA and Tasmania. To understand their positions, Retailers have accountants, who track volumes, costs and prices to isolate their profits for the main customer groupings, and, when aggregated, the accountants track the Retailers’ overall profit performances. This information will be used in Prototype # 4 to identify, develop and vary participants’ forward pricing and cost strategies over time.

Prototype # 3 can be considered as a static calibration and validation model in that it considers only one historical year of market activity, 2001, although it had the capacity to run for up to 30 years. Outputs are used to test how the various inputs and assumptions combine to yield detailed profit information and how the latter compares to what is known about the more commercial aspects of the Victorian gas market. Only after this step, is the model time horizon extended to look at competition over the longer term; this is done in Prototype #4.

7.1.1 Model Structure Prototype # 3 incorporates all of the main participants in the Victorian gas market. This includes those agent types already included in Prototypes #2 and #3, namely, Producers, Retailers, Markets and Customers. The agent profiles for each of these agent types have been materially restyled to mimic each real participant in the Market. For example, Retailers have supply contracts with TorP, MDQ, ACQ, contract lengths and prices, they have their own overheads, they have regulated and unregulated retail prices for each of their Customer types by region, they hold specific numbers of each Customer type in each market region and they know the Transmission and Distribution tariffs to get gas to each Customer type. They hold gas storage contracts. They have profit margin targets for each type of Customer. They have a series of plans for making and revising retail price offers to gain new Customers. They have plans to negotiate

132 Chapter 7 Prototype # 3 & VicGasSim

new supply contracts with Producers to fit their forward needs. They have plans to assess their forward gas needs and the terms and conditions they desire for new supply contracts. They have plans to bid their gas into the market pool each day. The terms and conditions on each contract, tariffs and prices are closely matched to what is known about each participant’s position in the market. Their numerous plans are typical of the plans that each participant would follow in competing in the Victorian gas market.

Producers and Customers have similarly expanded agent profiles. In addition, Prototype # 3 also includes detailed agent representation of the Transmission and Distribution sectors because they represent significant investment sectors in themselves. Owners of these sectors follow passive investment and return strategies. They represent major cost sources for Retailers and, in cases where Retailer parent companies hold interests on such sectors, they can contribute to Corporate profits.

The wholesale pricing pool manager, Vencorp, is also represented as the MarketManager. Vencorp balances the gas injections and withdrawals each day and sets the daily pool price according to the market clearing rules.

7.1.2 Hierarchical Structure and Layered Strategies The interactions of all of these agents is structured in an hierarchical fashion as shown in Figure 7-1.

Figure 7-1: Proposed Four-tiered Hierarchical Gas Industry Model Structure

AGENT HIERARCHY :

Environmental/Control Timer

Strategic Companies Governments

Exploration Production Transmission Retail Distribution Operational Joint Ventures Joint Ventures Joint Ventures Joint Ventures Joint Ventures

Markets Economy

Reactive Residential Commercial Industrial

Electricity

133 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Prototype # 3 adopts a four-tiered, hierarchically structured organisation of multiple agents:

1. At the highest level, the model itself is controlled by a timer agent.

2. At the next level down are agents, which focus on making strategic decisions, namely companies making decisions about investments in the various operational joint ventures (at lower operational levels), and Governments setting policies on issues such as taxes, royalties, the rate of releasing gas exploration acreage, transmission and distribution tariff regulation, investment allowances, and greenhouse gas emissions. At this stage, companies are passive with their only plan being to recognise profit contributions from holding equity in different market segments and recognising any such holdings in negotiations between, say, Retailer and Producer agents when the company holds equity on both sides of such negotiations; the plan provides for preference to be provided to aligned interests over non-aligned interests; it is an area for future enhancement.

3. At the next level down are agents exercising operational type plans for exploration, production, pipeline transmission, retailing and distribution. In Australia, most of these operations are run under joint venture structures with equity held by two or more companies. Negotiating upstream and downstream supply agreements and scheduling new or expanded joint venture developments are key features at this level. Development and operating costs plus capacity expansion planning are also important determinants of future profits. All of these agents, except for explorers, are active in the model.

4. At the lowest level are the consumers of the gas produced and supplied; they react to offers by altering consumption and/or swapping Retailers. Agents at this level also incorporate one or more plans to reflect consumer’s demand sensitivity to the general economy, alternative competing electricity, and gas prices.

In future models, more detail can be added to represent the electricity sector with much the same structure as outlined for the gas industry. This approach will allow closer scrutiny of strategies for those companies pursuing participation in both markets as well as regional competition and optimised placement of GPGs to meet regional electricity needs, taking advantage of differences in gas supply locations and both gas and electricity transmission grids.

Similarly, in future models, more detail can be added to endow greater intelligence to Government and Regulator agents so that they also play more interactive roles than provided in the development models described in this thesis. Explorers can be included to generate higher or lower exploration activity when the market is perceived to be short or long in gas supplies. Transmission operators and Distributors can also be given wider capabilities to expand capacities and change their tariff structures.

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The model is designed to model gas markets in adjoining states. The current model assumes a rather simple set of agents to represent these markets buying gas from and selling gas to Victoria. This is required to achieve a more accurate timing estimate for the drawdown of Victoria’s gas reserves and the external pressure that might influence capacity changes in gas supply from Victorian sources. These factors become important in the 2010-15 period when northern supplies could press to enter southern and eastern markets.

Based around the generalised hierarchical structure in Figure 7-1, Figure 7-2 shows another representation of this structure but with comments included to show the major types of participants modelled and the main strategies, plans and activities undertaken by each agent type in the Victorian gas model. Each of these areas has an agent or plan or event or message- communication representation in the model.

Figure 7-2: Agent Strategies & Plans at Different Hierarchical Levels

Arbitrators Companies - Strategies Regulators • Resets contract prices – 3 yrly • General interest focus • Resets tariffs - 5 yrly • Multiple commercial criteria • Alter strategic criteria • Reviews retail price caps • Takes a longer-term view • Multiple commercial criteria

Retailers - Strategies Transmission/Distrib’n Gas Markets (Res/Com/Ind/Powergen) • Wholesale bidding • Complex tariff structures • 250+ segments • Market pricing • Line pack Region • Supply contracting Wholesale Market • Supply contracting Transmission zone Distribution zone • New entrants • Market clearing • New entrants Distributor mechanism Customer type Tariff type, • Sets pool pricing Customer size Other Considerations Producers - Strategies • Daily bidding system • Seasonal demand • Electricity pricing • Wholesale bidding • Price elasticity Peaking Storage • Interstate markets • Supply Contracting • Churn/inertia • High priced UGS & LNG • New entrants • Accountants • Manage market risks • Fixed & variable cost structures of participants

A number of these agents are inactive or remain passive in the current model, namely, (i) the arbitrators, who periodically review contract prices, where arbitration clauses are written into such contracts, and (ii) the tariff Regulators, who are not given any capability to increase regulated gas prices, and transmission and distribution tariffs. No system capacity increases are assumed in this model and tariffs are assumed to remain at current real levels; this is an area for further enhancement.

Contestability was assumed to slowly move small Customers out of the regulated regime; the final model design imposes a maximum level of churn of the number of customers, who can swap Retailers each year. The model does not provide for an adjustment mechanism to regulated retail prices. As discussed in later chapters, which deal with model predictions, the assumed churn level

135 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

and lack of review of regulated prices and tariffs puts an inappropriate squeeze on predicted profits due to predictions of rising supply costs; these assumptions need to be changed in future versions of the model.

Figure 7-2 gives view to the market complexity, which comes from both the number of participants and the uncertain effects of the multiplicity of participant interactions in and between the numerous segments, such as production, transmission, storage, distribution, retailing, service and price differentiation, wholesale trading, power generation, and risk management. The modelling of the full commercial chain of the diverse operational and strategic interactions from production all the way to consumption is unique to this thesis. A fuller description of how the model mechansims work is provided in 7.2, which describes Prototype #4.

7.2 Prototype # 4 (VicGasSim) Prototype # 4 provides the last step by activating the model’s look-ahead capability and including additional accounting and data storage capability. This final prototype is referred to as VicGasSim.

Other model development features in VicGasSim include:

1. An efficient time compression and threading approach to manage the scheduling of a multitude of discrete events,

2. A user-friendly XML data input interface that allows users to add participants and/or modify participants’ plans or decision-making criteria,

3. Efficient data storage management to allow participants to develop strategic criteria about their own private and surrounding public environments, given the large number of look- aheads that can be undertaken by interacting participants, and

4. Dynamic graphic displays that allow users to watch a series of displays in real (model) time.

The individual adaptive agents are linked by sharing certain public data on the wholesale trading market, transmission and distribution systems, weather and sales. The composite market outcomes in turn feed back into the determinations of the localised and more global interactions between market participants, particularly in terms of negotiating short and/or long-term supply contracts.

The hierarchical structure of VicGasSim means that decision-making, which is conducted at one level for operating type agents, such as Retailers, Producers, the Wholesale Market Manager, the Transmission operator and Distributors, can be separated from the decision-making of higher level agents, such the corporate decision-making of Companies, which hold interests in multiple sectors, Regulators controlling tariffs and gas prices. Customers, who are mostly reactive over short-term time frames, also operate at a higher level when they annually consider new retail prices.

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Key operating decisions take account of general or corporate interest effects such as where Retailers, who are participants in the upstream supply market can consider how best to use upstream profits: they can retain them in corporate profits or they can indicate to their Retail groups that they want them to take a more aggressive pricing stand in the Market. More aggressive pricing strategies could gain market share, boost use of related distribution assets and lead to improved overall corporate profits.

In the reverse sense, they might boost retail gas prices, lose market share but free up upstream gas supplies, which they sell to other Retailers, who are short on supplies. Company-level agents can use their higher-level view to test taking such corporate stands before advising related operating segments of the strategic approach that they want them to pursue.

Figure 7-3 presents a view of a similar model structure. This shows a cartoon representation of the model structure for the CSIRO’s equivalent electricity model, NEMSIM, which was based on the VicGasSim design and structure, and developed in conjunction with the author and Swinburne University. Replace the word electricity by gas, generation by production and the figure essentially conveys the structure of VicGasSim. However, VicGasSim includes additional sets of agents to allow a closer representation of Customers such that the model includes agent representation of market Retailers and retail level competition. VicGasSim also includes a greater accounting emphasis on collecting sufficient revenue and costs data to allow participants to calculate corporate level and segment profits and losses.

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Figure 7-3: Representation of the Dynamics of the Model Structure (as used in NEMSIM)

Source: Batten & Grozev (2006), see Figure 11.5; reproduced with the kind permission of ANU E Press and the authors.

The following sections give a more detailed description of the model and the agents in the model. Some sections include pseudocode1 descriptions as examples of components of the model.

7.2.1 The Simulation Engine & Discrete Event Manager The main control agent driving the model is its simulation engine and discrete event manager. Consideration was given to providing the model with the flexibility to use the ability of object-oriented programming to consider multiple, synchronous processing threads of communication between agents but this approach was rejected on the basis that such approaches can find it more difficult to manage the critical synchronisation steps where parts of the model or agent decision-making must wait for completion of tasks by other agents to ensure data alignment. The other weakness of multiple,

1. Wikipedia describes pseudocode as a high-level description of a computer programming algorithm (or program) that uses the structural conventions of some programming language, but it is intended for human reading rather then machine reading. Pseudocode typically omits details that are not essential for human understanding of the algorithm, such as variable declarations, system-specific code and sub-routines. The programming language is augmented with natural language descriptions, where convenient, or with compact mathematical notation. The purpose of pseudocode is that it is easier for humans to understand than conventional programming language code, and that it is a compact and environment-independent description of the key principles of an algorithm or program or part of a program.

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synchronous processing threads is the greater challenge of debugging and tracking through the unsynchronised sections of the model output to find the source of coding errors.

Given the development funds for building the model were limited, the safer option of using a single stream discrete event manager was chosen.

Table 7-1 presents a set of pseudocode for the simulation engine that drives the discrete event manager. Table 7-1: Simulation Engine

Simulation Engine Component

// Initialise Create gas model from XML configuration to create "context".

// Resolve FOR each simulation component in the "context" Resolve references to other components in the "context".

Create "event list" with "start time" and "end time".

// Activate FOR each simulation component in the "context" Schedule the component's first event in the "event list".

// Execute CALL RunContext with the "context" and "event list".

// Close Prepare results and perform any cleanup.

START RunContext with "context" and "event list" WHILE there are events in the "event list" AND current time is less than the "end time". Remove the next event from the "event list". EXECUTE the event on its simulation component. Optionally reschedule the simulation component with a new event. END

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Table 7-2 presents an example of the time control agent’s schedule of communications that must take place with other agents at the start of each day so agents can reconcile and record their daily volumes, revenue and costs. The schedule gives an idea of the level of transactions tracked in the model each day, each year and for the total run period that can extend to 30 years.

Table 7-2: Discrete Events to Complete Daily System Balance & Set Pool Price Timing of Description Events 07:52:00 MarketManager: Event = reconcile daily accounts. MarketManager determines difference between actual gas injected and gas used by each Retailer for the prior day, including gas used from line pack to meet daily balance; Vencorp revises preliminary price to final pool price for the day; collects or pays Retailers for net gas injected or used as calculated at the daily pool price. Payments recorded by each Retailer into its Profit/Loss account. MarketManager determines daily gas inputs recorded against each supply and storage contract held between a Retailer and Producer or Retailer and Storage System owner, and adjusts Retailers ownership of gas in the system line pack. Producer Accountants record gas used from each Retailer’s numerous contracts often in different regions. Storage Accountants determine and record various input, output and capacity charges applying to each Retailer using the storage facilities on that day. Charges recorded as income by StorageAccountants and as costs by RetailerAccountants. 07:53:00 LinePackManager determines (annually) a net charge for changes in gas held in line pack by each Retailer; LinePackManager collects and pays relevant Retailers. The MarketManager on behalf of each Retailer determines how much gas was used by regulated and unregulated Customers; this data is shared with the Transmission and Distribution agents so the latter can assess the relevant transmission and distribution tariffs applying to gas provided to each Retailer’s customers. RegulatedAccountant determines the monies owing for each Customer’s consumption by applying the regulated tariff for each Retailers regulated Customers, depending upon their size and location and Distributor. Retailer charges its Customers for gas used and records same into its profit/loss account. The InjectionAccountant for the Transmission system calculates the annual injection charge applying at each injection point for each Retailer injecting gas into the Transmission system (as directed by Vencorp under the daily wholesale market bidding system) at that Injection Point.

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Table 7-2: Discrete Events to Complete Daily System Balance & Set Pool Price (Continued) The DeliveryAccountant for the Transmission system calculates the annual Peak Volume, Peak Demand Day and Anytime charges applying to each Retailer’s gas offtake into each Transmission Zone as calculated by applicable tariffs. 07:54:00 Usage of each SupplyContract is tallied and checked against contract terms to see if it terminates or continues in the current year; checks for any unused gas below the minimum TorP volume and if any such gas exists then extends contract by one year to allow Retailer to recovery this gas for which it has paid but not taken/used. 07:55:00 Retailers adjust their forecasting algorithm based on assumed growth in overall base load. Customers adjust the number of gas consumers in each area based on the assumed economic growth applying in that area and based on the elasticity of demand to recent gas and electricity prices. 07:57:00 At start of each year, Customers compare new prices offered by Retailers and determine if they are better than their existing Retailer. An allowed percentage of Customers are allowed to swap if lower priced gas is offered. Customer numbers in each class in each region are redetermined to influence the future level of consumption in a given class in a given region. 07:58:00 At start of each year, Retailers forecast the future demand by all of their Customers over a set number of years into the future. Negotiations commence with Producers for supply of gas under new contracts. Retailers determine their predicted need and compare that with what they have contractually available in each of the forecast future years. They identify their uncontracted need in each year under consideration. They seek offers from all Producers. New Producers or new supply sources determine the first available year that they can supply gas if agreement reached now; Producers calculate a price for this gas based on development and operating costs plus a target profit margin. Each Producer also provides criteria for adjusting these base prices if Retailers seek alternative TorP, MDQ or contract length terms to those preferred by the Producer. Producers determine prices for supply from existing supply sources based on prior price offers and the status of capacity utilisation from those sources. Producers alter price without regard to costs based on the principle that they favour capacity maximisation to keep competitors out of the market.

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Table 7-2: Discrete Events to Complete Daily System Balance & Set Pool Price (Continued) Retailers adjust Producer price offers for their individual TorP, MDQ and Contract Length preferences and put them in a seriatim of price order and nominate those offers they seek to accept. Producers accept those offers that existing supply or future minimum commitment needs allow; any unsatisfied demand is treated by another round of new offers from Producers. The process is repeated until all Retailers’ needs are met. 08:00:00 Vencorp directs Producers to inject gas on behalf of Retailer’s nominations that Vencorp accepted for that day. 08:01:00 Vencorp sets preliminary pool price for the day. (There is a process for making later adjustments if supply needs to be topped up.)

7.2.2 Example of Model Transaction Detail To give another view of the transaction complexity, Figure 7-4 shows the main transactions involved in one sector, namely the daily wholesale gas bidding system, whereby Retailers purchase and deliver gas to Customers, and track the costs for these activities. The chart shows those activities, as modelled over a 12-month period, that require action by a Retailer or influence its decisions and outcomes.

Figure 7-4: Retailer’s Model View of Daily Bidding System

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The figure is drawn from the viewpoint of those Retailers active in the short-term market. The main flow of these transactions is (i) to forecast and supply daily gas needs of Customers, (ii) to bid supply into the wholesale market to meet forecasts of their own needs plus make a trading profit if possible from selling gas to other Retailers, and (iii) to track use of the transmission and distribution systems to a Retailer’s various price and cost classes of Customers at different locations so that the costs of supplying each Customer type and each location can be tracked versus prices offered to gain or lose market share to enhance overall profit performance at a corporate level. A key objective is to accurately tag and track costs and prices to allow assessment of the volumes, market shares and profit margins by region, tariff and Customer class.

Similar detailed processes and interactions take place for each type of transaction in the model, For example, sequences exist for the longer-term transactions undertaken annually by Retailers and Producers to negotiate gas supply contracts to replace gas consumed during the year and to extend a Retailer’s coverage of future gas sales to its customers. Such negotiations also require a Retailer to implement strategies and plans to forecast its future gas needs to meet demand. The Retailer also implements plans and strategies to estimate future demand and costs in order to make price offers for Customers to buy and consume gas in the context of pricing competition from other Retailers. Producers have similar sets of plans to maximise their supply contracting outcomes as they trade surplus gas in the wholesale market.

Further appreciation of the model and the depth of characterisation is provided in the following sections, which include descriptions of the nature of each of the agents represented in the VicGasSim model. Their resources, plans, strategies, nature or behaviour, and even their names are represented very closely to the way that they are described in the following sections.

Because of the adopted real-world naming protocol, it might often appear that the author is talking about the real market participants but the descriptions and commentary are most typically describing aspects of the participants as modelled and how they or their behaviour or plans are modelled. From here to the end of Chapter 10, the vast bulk of references to market participants by name will be references to their model agent counterparts. Virtually all of what is described in this thesis is represented in the model by quantified input or model design characterisation of each agent’s resources, capacities, constraints, plans, rules and objectives; much of this information is representative of the real world values that apply to that market participant and its model agent representation. Therefore, some statements about a model agent’s characteristics will be close to reality but they are still to be taken as model-derived values or statements about model-derived values or positions. Some further explanation of the model’s design, agents and artefacts, and details are given in online documentation available via the website that provides access to download and use the model.

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A copy of this document is presented in the file, origin.html, which is stored on the CD in Appendix 2. The file can be opened by an internet browser, such as Internet Explorer or Mozilla Firefox. The document defines the agents and the exogenous and endogenous variables, which are required to run the VicGasSim model and record its dynamic outcomes. Appendix 2 also presents an example input file, o30.xml, which is stored on the same CD in Appendix 2. The file can be opened by an internet browser, such as Internet Explorer or Mozilla Firefox.

In the model, participants are often described for their typical group behaviour or for the specific behaviour of an individual agent participant. For example, Esso-BHP, Origin, TXU, Pulse, Vencorp, GasNet’s PTS, and most other participants in the Victorian gas market are all characterised specifically, including their names. Often, this naming protocol needs to be remembered because the model and model output descriptions can seem as if they are referring to real behaviour in the real market.

Additional perceptions of the model and its capability are conveyed in Appendix 3, which shows a number of screen image displays from the model, while it is running. The displays show just a small sample of the detailed transactions undertaken by agents in their daily, weekly, monthly and annual interactions.

7.2.3 Look-ahead Enhancement Agent learning in VicGasSim is undertaken by use of the model’s look-ahead capability, which is described in 6.3.5. Agents at various levels in the model can use this look-ahead capability to help them make a range of short, medium and long-term decisions. The look-ahead concept allows nominated agents to test a range of differing strategic plans over a set period to test which strategy will achieve the most profitable outcome over that period. While the look-ahead tests assume that the competing agents keep their strategies constant over the period, the test cases are reassessed on a regular basis within the overall test period. For example, a Retailer might test three differing long term supply strategies over, say, a look-ahead for nine years. It gets the opportunity to overcome any poor choices by rebidding for other supplies each year within the nine years, as do other Retailers also testing look-aheads; the objective is that the multiple sets of tests by multiple Retailers provides a comprehensive testing of the forward period with regular adjustment opportunities. While still heuristic and dependent upon the modeller to design an appropriate set of test cases or strategies for each type of decision, the benefit comes in that there has been no compromise in moving away from defining this simple learning process in business-like terms and mental models versus the alternative approaches such as using genetic algorithms or game theory, which are not as easy for users to understand in terms of final outcomes over time.

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Agents operating at higher levels make their decisions less frequently and take more factors into consideration in making strategic decisions, which involve a moderate to high degree of risk and where reaction times might extend for years. Operational agents usually focus on daily decisions especially for setting the wholesale bid-stack offers. In the case of Reatilers’ supply contract management, they make different decisions over time horizons that vary from meeting daily supply needs of consumers to scheduling seasonal drawdown and build up of peaking storage over 3-6 month periods. They reconsider retail prices annually. They take on a more strategic nature to look 3-10+ years forward for contracting new gas supplies to cover expiring contracts and changes in demand; they need to consider a wider array of influences and can benefit by use of greater intelligence to test a range of strategies or plans in each case.

Companies are recognised in the model as having cross-holdings in different segments reflecting their higher level strategic focus but corporate look-aheads are not yet provided. However, Retailers have an ability to consider their company’s general interest across sectors such as interests in upstream supply projects and storage projects as well as pool trading profits. They record and recognise these supplementary profits in making decisions where they can make a choice between buying from or selling gas to related corporate affiliates. However, at this time, the model does not use this data to make more global corporate assessments of its total interest position nor does the model currently allow companies to adopt sub-optimal strategies in one segment to maximise total corporate profits in either the short or long term. The model was designed to allow it to be developed further to address such broader goals in a future version of the model.

The model can be run over periods of up to 30 years. Participants test ranges of strategies over varying periods relevant to the sector or decision under consideration at a given time; this is done in part to reduce computational loads on the model and in part to reflect typical time frames considered by market participants when considering specific sector decisions. For example:

1. Customers test their Retailer selection over a 1-3 years look-ahead period.

2. Retailers test wholesale prices over one day at a time with a one-week look-ahead and they test setting Customer retail prices over a 1-3 year period.

3. Producers and Retailers test new supply contract prices over 3-10 year periods.

It was intended (but not completed) that companies test their integrated corporate strategies annually in terms of overall profit performance over 10-20 year time frames, and that Regulators would test community benefits and industry price needs every two years as well as Distribution and Transmission needs every five years over 5-10 year forward test periods in both cases.

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Currently available model Test Cases are:

1. a SupplyTestCase for use by both Retailers and Producers to choose the "best" contract terms for writing new supply contracts each year,

2. a RetailPriceTestCase for use by Retailers to choose the "best" retailing price strategy to use for setting its retail prices for the next 12 months, and

3. a BiddingTestCase for use by Retailers/Wholesalers to choose the "best" bidding strategy to use for setting and structuring its daily wholesale bid offers for the next week or month.

The pseudocode form of the model context for running the Look-ahead process is shown in Table 7-3. A description of each test case and how it works is given in Table 7-11, which presents the main exogenous variables in the model.

Table 7-3: Look-ahead Process Look-ahead process

A LookAhead is a simulation component, scheduled to EXECUTE at a regular interval, that makes a number of copies of the "context" at the time of execution and runs the copies for a fixed period. Each copy is used to evaluate a different test case. At the end of the LookAhead, the results of the copies are compared and the best test case is chosen to be applied to the original "context" in the main execution phase.

EXECUTE LookAhead event FOR each test case in the LookAhead Create a copy of the main "content" and its "event list". Modify the copy "context" using the test case settings. Schedule the "end time" of the LookAhead in the copy "event list".

CALL RunContext with the copy "context" and copy "event list".

IF the copy "context" ran successfully Calculate the "result" from the final state of the copy "context".

Compare results of all successful test cases. Apply test case with best result to the original "context". Reschedule LookAhead in the main "event list".

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7.3 Participants Modelled The main entry screen to the VicGasSim model is shown in Figure 7-5.

Figure 7-5: Map Showing Customer Areas & Transmission Grid

Figure 7-5 identifies the participants and geographic distribution of regional markets and the 14 withdrawal zones within the overall Principal Transmission System (PTS) network linking the three Injection Points (IP’s) to the regional markets, which are shown as shaded areas on the map.

The simulation model includes multiple Producers, a Transmission network, a wholesale Market to balance daily inputs from three injection points against distribution offtakes to 14 regional withdrawals zones, from which three regional distributors carry gas to 1.5 million Customers, which are classified into eight consumption and 15 price ranges. An agent-based approach allows representation of the multiplicity of related interactive transactions between the above participants. The model is used to elicit possible emergent behaviour that could not otherwise be formulated under an uncertain future of deregulation and restructuring.

The logos on the map indicate the main players represented in the model of the Victorian gas market:

Vencorp: is owned by the Victorian Government and manages the wholesale gas trading system and the PTS.

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GasNet: owns and operates the PTS on a regulated tariff basis and makes a fixed rate of return on the PTS assets. GasNet also owns a peaking LNG storage scheme on the outskirts of Melbourne.

Origin Energy, Pulse Energy, and TXU: are the three major Retailers in Victoria.

AGL and Energex: are minor Retailers in Victoria at the start time of model simulation from 1 January 2002 but have major gas and electricity interests in other southern and eastern Australia markets. AGL recently acquired Pulse Energy but their role at the time of model development was as a minor Retailer/wholesaler with gas available through the Culcairn IP.

Envestra (part owned and managed by Origin), TXU and United Energy (linked to Pulse): own and operate the three distribution networks.

Esso and BHP: are major Gippsland producers jointly holding the major supply contracts to the main Victorian gas retailers (originally held through GFC). Esso and BHP each hold additional supply sources in adjoining states or offshore as in BHP’s major interest in the small offshore Otway Minerva gas field.

Origin Energy and AGL: possess minor supply contracts for supply of gas from NSW and SA via the Culcairn IP from NSW; Origin Energy holds small contracts for the supply of onshore gas from the Otway Basin in western Victoria via the Port Campbell IP. Origin holds interests in the Yolla and Thylocene-Geographe discoveries made offshore Tasmania and Otway in the time period when this model was being finalised. Origin owned an interest in the SEA Gas pipeline to SA when this pipeline was first built.

TXU: owns and operates the Western Underground Storage System (WUGS) on a contracted service basis to Origin Energy, Pulse and itself. This system is used to provide peaking supplies during winter and is replenished each summer. TXU owned an interest in the SEA Gas pipeline to SA when this pipeline was first built.

Shell, Woodside, OMV and Santos: are Producers waiting to enter the market at the time this model was developed. Woodside holds a major interest in Thylocene-Geographe. Santos supplies small volumes of gas from the onshore Otway Basin in western Victoria via the Port Campbell IP and holds an interest in the Casino discovery made offshore Otway in the time period when this model was being finalised. Shell held an interest in the Kipper gas field.

Duke Energy: owns pipelines from the Gippsland IP to NSW and Tasmania and is capable of acting as a trading agent between markets utilising spare capacity contracted for interstate markets and its substantial inventory of gas stored in its major pipelines. Duke also built a GPG plant at Bairnsdale between the Longford and Patricia Baleen gas supply sources and adjoining its pipeline network.

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ACCC and ORG (not shown): When the deregulated market commenced, pricing and tariffs were administered by the Australian Competition and Consumer Commission (ACCC) and the Office of the Regulator General (ORG). They monitor and regulate prices and transmission and distribution tariffs. Regulated prices are reviewed every three years and tariffs every five years. Maximum prices will remain regulated in the near-term as the competitive unregulated market is established by Retailers. These caps make it difficult for Retailers to establish profitable sales to all segments since profit margins in the smaller residential markets are already believed1 to be negative and subsidised by positive profits from larger Customers.

The model also includes details of each participant’s interests, costs, contracted supply capacity, contract terms on minimum and maximum amounts of daily quantities, and financial information. More details on specific agents and their activities are described in the following sections. While this detail was described in general market terms, the model replicated these relationships very much as described in the following sections and was numerically calibrated to what was publicly known about these relationships, be they contractual or as posted. Where no public data is available, the author has derived his own estimates based on anecdotal information or best guess assumptions, and configured them in a form that is typical of how the participants would consider such matters. All agents have plans for how they assess their various market positions and how they alter their plans based on their competitive environment in each relevant time period.

Details on each Participant or type of agent in VicGasSim are described in the following sections.

7.3.1 Vencorp – Market Manager Vencorp acts as the MarketManager and regulator of the (Victorian) PTS and the wholesale market, which is used to clear the daily gas market. Vencorp administers the daily wholesale bidding market and determines the daily pool price depending upon submitted bids and the actual daily demand or withdrawals from the PTS. Vencorp also manages the level of linepack2 between operational limits on PTS pipeline pressures.

As described in 2.2.7, Retailers usually offer, or bid, gas they hold under supply contracts with one or more Producers and storage operators. They bid to supply their gas via a series of tranches of gas volumes, which they are willing to supply at increasing prices. Retailers will alter the way they structure their bid-stacks3 to meet their changing objectives. Retailers usually structure their bid-

1. Personal and anecdotal advice from a market participant. 2. Linepack is the volume of gas stored in a pipeline under high pressure. Operating pressures can vary within certain limits and this alters the amount of linepack. The pipeline operator can use this operating flexibility to source gas from linepack to help in balancing market needs. Independent pipeline operators, such as Duke, can use available linepack to trade gas in the market. 3. Retailer Bid-stacks or bidding curves are formulated each day and provided to Vencorp, who uses them to set the daily pool price. Bid stacks provide a series of volume tranches of gas that a Retailer is willing to sell that day at a set price. See Model plan 4. in 7.3.2 for more background on how Reatilers shape bidding curves; Table 7-7 also provides information.

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stacks to hedge, or price-protect, their own expected daily withdrawal requirements at or close to their contract costs. Withdrawals are estimated to cover Customer demands as well as storage replenishment needs. Retailers also offer estimated surplus gas to others at increasing premiums to make a potential trading profit from any Retailer, who poorly forecasts/bids its own needs, or, who is short of peaking storage capacity on peak summer and winter days. The way this hedging is pursued will depend on what intelligence is available about competitors’ prevailing supply positions.

Vencorp also manages the daily financial balancing between Retailers, who inject nominated gas volumes and whose Customers withdraw actual volumes gas from the PTS. While Vencorp charges Retailers a fee for operating in the market, in the model, this fee is deemed to be handled in each Retailer’s assigned fixed and variable overhead costs. While Vencorp acts as the MarketManager, this is a relatively passive agent role in the model. Other MarketManagers are created in the model to manage supply of gas into NSW, SA and Tasmania.

Model plans used by a Vencorp, as the Victorian MarketManager, are as follows:

1. Vencorp receives and orders bid stack elements from all Retailers, who desire to bid gas supplies into the PTS on a given day.

2. Vencorp forecasts the given day weather and estimates daily total demand.

3. Vencorp selects the bid level that will meet the forecast daily demand; sets the daily pool price at the level of that supply bid.

4. Vencorp directs Retailers to arrange for their bidded supply offers to be injected into the PTS up to the level of the bid that set the pool price and that will match forecast demand.

5. Vencorp receives recorded actual demand, which is generated by the Weather agent sampling from its EED file for a given day with a random variation applied to the EDD-based value for that day and a total model-based "actual" market consumption determined for that day.

6. Vencorp makes adjustments to linepack to cover the difference between forecast demand and end-of-day actual (in the model) demand or, if insufficient linepack capacity, resets the pool price during the day to a higher or lower bid offer and orders additional or reduced injections to match revised expectations of demand or withdrawals from the PTS. These arrangements are similar to the real world ability of the MarketManager to use line pack to balance injections against withdrawals or to order more or fewer injections.

7. If supply is still insufficient, Vencorp uses is emergency/reserved LNG supplies and/or orders Retailers to curtail Customers.

8. Vencorp conducts end-of-day accounting and arranges balancing payments between agents, who participated in the market on the given day.

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Table 7-4 provides a pseudocode summary of how the MarketManager (Vencorp) manages the wholesale market, manages the gas in and out of the system to ensure it remains in balance each day, and sets the daily pool price to clear the market each day.

Table 7-4: MarketManager (Vencorp)

MarketManager Component

Retailers submit daily volume bids to the MarketManager by the "bid submission deadline", after which the MarketManager calculates the market price based on forecast demand. At the daily "reconcile" time, the actual consumption is known and the accounts with the Retailers are reconciled.

EXECUTE MarketManager daily "bid submission deadline" event FOR each Storage component Allocate bid volumes from storage (if any).

Calculate the forecast "demand volume". Adjust the "demand volume" to maintain linepack at target level.

Find the marginal bid to meet the "demand volume". Set "market price" from marginal bid. Notify Retailers up to the marginal bid to inject gas.

EXECUTE MarketManager daily "reconcile" event // First Pass: Calculate actual consumption and provisional injected volume FOR each Retailer Sum "gas used" by Retailer's customers. Sum "gas injected" from Retailer's bids.

// Check linepack constraints IF difference between "gas injected" and "gas used" breaks linepack min/max constraints Calculate "linepack adjustment" to bring level within constraints.

Update "demand volume" using minimum of "linepack adjustment" and "unallocated gas" from bids.

Recalculate marginal bid using adjusted "demand volume". Recalculate "market price" using new marginal bid.

// Second Pass: Check if usage has to be reduced to prevent linepack from falling below zero FOR each Retailer Calculate "net gas transfer" (difference between gas injected and gas used).

IF "net gas transfer" is positive Increment linepack by "net gas transfer". ELSE Remember negative "net gas transfer" for Retailer.

IF there are negative "net gas transfers" // Proposed gas consumption will deplete linepack. FOR each Retailer with a negative "net gas transfer" Proportionally reduce the Retailer's "gas used". FOR each of the Retailer's customers Proportionally reduce the Customer's consumption.

Notify the Retailers of the final gas injection.

151 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

7.3.2 Retailers The three major incumbent Retailers and two minor new entrants hold a number of supply contracts from one or more supply sources. Retailers inject gas into the PTS at one or more of the three IP’s. Retailers are required to supply their gas into the market via the wholesale market described in 7.3.1. This wholesale market sets the daily wholesale cost for each Retailer’s supply of gas.

Retailers incur daily costs for gas supply (which is indirectly linked to their long-term supply and storage contracts), wholesale trading, and transmission and distribution. In addition, Retailers have their own costs typically incurred in servicing their Customers; these costs take on the form of high fixed costs with low variable costs.

Retailers set prices, within regulated price caps, across regional and customer size groupings for both residential and most commercial (Tariff D) Customers, assuming the risks on their input costs. For larger unregulated commercial and industrial (Tariff V) Customers, Retailers typically enter risk-sharing contracts whereby they pass-through all of the actual supply, transmission and distribution costs (and associated risks) for a low target Retailer profit margin.

Under full contestability, Retailers seek to alter prices in the various segments, which are differentiated by Customer classes and transmission/distribution tariffs. Retailers will hold or can create cost and profit-margin advantages that they will use to gain market share in relevant segments. Increasing total Customer numbers allows better amortisation of their own considerable fixed costs.

The model is initialised with six active Retailers:

1. Origin,

2. TXU,

3. Pulse,

4. Energex,

5. Esso-BHP, who are treated as one entity for simplicity, and

6. SA Cooper Basin Suppliers.

The big three Retailers have assigned numbers of initial Customers of each size type and in each market region. These numbers are approximately calibrated to published information on the total number of Customers that were devolved to each company at the time of disaggregation of the Gas and Fuel Corporation. The distribution of each Retailer’s Customers has been developed by consideration of the geographic, demographic and industrial distribution of Customers in each of the original franchised regions assigned at the time of disaggregation and prior to the start of deregulation.

152 Chapter 7 Prototype # 3 & VicGasSim

Each Retailer holds one or more supply contracts to cover gas sales to its Customers. Major Retailers hold contracts to store and recover gas from WUGS to cover winter demand peaks. Some major and minor Retailers hold contracts for supply of low levels of interstate gas available via the Culcairn IP. Major Retailers also hold contracts to use a very limited storage facility for gas in the form of LNG held at Dandenong (on the outskirts of Melbourne) as an expensive last resort to cover extreme seasonal peaks or to cover operational or emergency downtimes.

Retailers sell gas to their Customers by arranging for daily delivery by the relevant local distribution systems, which they or competitors may own. Retailers charge their Customers for the gas consumed by charging a tariff which they post and adjust every year. Retailers arrange for supply of gas each day into Victoria’s central transmission system such that gas is available to their Distributors or delivery to their Customers.

Model plans used by a Retailer are as follows:

1. Daily: Forecast daily demand based on weather outlook.

2. Daily: Assess WUGS and LNG storage needs against seasonal peaking expectations, current state of storage, days remaining for drawdown or build up of storage; develop estimate of daily storage usage or build-up needs. In both cases Retailers arrange shipment and storage of gas in summer months or periods of low demand and call for its supply at times of peak market demand when contracted gas supplies reach their contractual peaks. They assess the best days on which to build their storage inventory to minimise the price of their stored gas. Table 7-8 presents pseudocode for this process.

3. Daily: Repeat 2. for linepack storage drawdown or buildup.

4. Daily: Shape and segment individual bid curves for each supply contract that reflects Price, TorP, ACQ and MDQ terms of each contract against cumulative annual drawdown of each contract and seasonal patterns; adjusting bidding strategies to avoid paying for any unused gas paid for but not taken if their Customers demand drops below the TorP level on one or more contracts; adjust bid curves for storage availability or needs. Bid structures are designed to meet their own sales needs at a price as close as possible to the costs of supply under their own contracts but to apply an increasingly high premium above this level to make trading profits from any Retailers, who might mismanage its bidding strategy on the day or gets caught short on peaking/storage/supply capacity on that day, for example, due to not storing enough gas on low demand days. However, they do not want to overcommit supply at their contract prices as they will pass on their advantage to other Retailers, who may be short of gas on that day. Similarly, the MDQ on some contracts is artificially lowered when structuring bidding stacks so as to avoid over-bidding on lower priced contracts and under-bidding from

153 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

higher priced contracts such that a Retailer has to pay for gas it has not used but is contractually bound to take to certain minimum levels. A Retailer can be assigned one of several bidding strategies for shaping their bidding curves; pseudocode summaries for a BidHandler implementation plan as well as various BidStrategyHandlers (Strategy1BidHandler and Strategy2BidHandler) are shown in Tables 7-5, 7-6 and 7-7. A Retailer combines segmented individual bid curves into one segmented, price-ordered bid stack and lodges this daily bid stack with MarketManager just before start of the day.

5. Daily: Receive daily injection orders from the MarketManager and request contracted suppliers to deliver bid-level gas supplies from each contract into the PTS for delivery to own Cus- tomers and into nominated storage facilities and/or linepack.

6. Daily: Receive or pay daily financial adjustments between Retailers as directed by the Mar- ketManager based on the final pool price. Allocate trading costs between each region and each Customer type.

7. Seasonally: Assess likelihood of overbidding supplies against ACQs of each contract; when within specified limit, scale back ACQ used to formulate daily bid curve for that supply contract.

8. Seasonally and Daily: Manage storage of gas in an underground storage facility known as the Western Underground Storage System (WUGS) and the Dandenong Liquefied Natural Gas (LNG) facility. One of the StorageReplenishmentBidHandler plans (Replenishment1BidHandler) is shown in Table 7-8.

9. Monthly: Prepare and send accounts to all Customers; receive payments.

10.Monthly: Receive accounts from Transmission operator and Distributors; make payments and record transmission and distribution costs between each region and Customer type.

11.Monthly: Receive accounts from Producers and storage operators; make payments and allocate production and storage costs between each region and Customer type.

12.Monthly: Pay overhead costs; allocate overhead costs between each region and Customer type.

13.Annually: Determine and record annual financial performance by region and each Customer type and in total; adjust for equity levels in each business segment and any upstream profits/ losses in operating area.

14.Annually: Make revised retail offers to all Customers; set target prices by applying a profit margin to the accumulated costs for purchasing gas and getting the gas to each Customer type at each location. Adjust target prices after assessing recent changes in market share in each

154 Chapter 7 Prototype # 3 & VicGasSim

market segment in each region. If market share moves up or down by specified percentages, then adjust target retail price up or down according to assigned plan or rule. Receive acceptances from responding Customers and arrange to deliver gas and charge Customers.

15.Annually: Seek new supply offers at lowest cost offered by Producers; make forecasts of demand growth over a specified forward period, forecast decline in supply due to expiring contracts over same period, calculate difference between expected demand and contracted supply, post additional volumes sought over specified period together with preferred TorP, MDQ and contract length. Seek offers from Producers. Receive supply offers from Producers, stack in price order and nominate acceptances. If requirements not fully met, re-post outstand- ing needs and solicit new offers; repeat process until needs met or set limiting number of iterations. [Producers might offer alternative contract terms on contract length, TorP, ACQ, MDQ, and price; both Retailers and Producers have plans for iterating to reach agreement or defer taking up new contracts until a subsequent period and rely on the wholesale market to cover any interim shortfalls on contracted supply. A pseudocode summary of this process is shown in Table 7-9.

The goals of Retailers include:

1. seeking to pass on the direct and indirect costs of supplying each class of Customer to preserve their target margin for each class of Customer,

2. seeking to make a trading profit on unused supply capacity on any given day,

3. seeking to ensure they obtain contracted new supplies at the minimum available price, and

4. seeking to avoid over-contracting supply in case market demand turns down.

Table 7-5: Retailer Bidding

Retail Bidding Component

The Retailer bidding strategy is encapsulated in a BidHandler implementation.

EXECUTE Retailer daily "bid submission" event FOR each Market in which the Retailer participates Calculate forecast "demand volume" by the Retailer's Customers in the Market.

Generate bids according to some strategy. Submit the bids to the MarketManager.

EXECUTE Retailer annual "post prices" event Notify each Customer Tariff group of the Retailer's average price for the past year.

155 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 7-6: Retailer Bidding - Strategy1BidHandler Strategy1BidHandler

This is a simple bid handler.

START GenerateBids Get the Retailer's forecast gas usage for the day.

// Nominate take-or-pay portion at zero price. FOR each contract with unallocated gas. Calculate the average daily take-or-pay amount divided by 2. Restrict the amount to the remaining contract volume. Restrict the amount to the contract's MDQ. Nominate the amount at zero price. Subtract the amount from the remaining forecast usage.

// Bid remainder up to MDQ. FOR each contract with unallocated gas. Get the unallocated amount of the contract's MDQ. Restrict the amount to the remaining contract volume.

IF the unallocated amount is less than the remaining forecast usage. Bid the amount at the contract price. Subtract the amount from the remaining forecast usage.

ELSE

IF there remains unsatisfied forecast usage. Restrict the forecast usage to the remaining contract volume. Bid the forecast usage at the contract price. Subtract the forecast usage from the unallocated amount. Set the remaining forecast usage to zero.

IF there is still an unallocated amount. Calculate the price premium factor. Bid the remaining amount at the contract price times the premium factor.

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Table 7-7: Retailer Bidding - Strategy2BidHandler Strategy2BidHandler

This is the main supply bid handler.

START GenerateBids Given: take-or-pay factor topFactor between 0 and 1.

Get the Retailer's forecast gas usage for the day.

Construct a stack of supply and storage contract amounts initialised to MDQ, ordered by increasing price.

// 1. Dispose of out-of-date contract supplies first. FOR each out-of-date contract with unused gas. Restrict the amount to remaining forecast usage. Nominate the amount at zero price. Subtract the amount from forecast usage.

// 2. Nominate some portion of take-or-pay volume at zero price. Calculate take-or-pay volume: topV = usage * topFactor Sum the amounts from all contracts with take-or-pay amounts: totalCV

FOR each contract with unallocated take-or-pay gas, in price order. Calculate the contract's portion of the take-or-pay volume, topV: top_amount = (contract_amount / totalCV) * topV

Nominate the take-or-pay amount at zero price. Subtract the amount from the forecast usage.

// 3. Bid the remainder, either at contract price or using a price function. IF the strategy has no price function. FOR each contract with unallocated gas, in price order. Get the contract's unallocated amount. Bid the amount at the contract price. Subtract the amount from the forecast usage.

ELSE Given a price function. FOR each price, P1, in the stack of contracts, in order of increasing price. Get the next higher price, P2, or, if P1 is the last price, then P2 = P1 x some final premium factor. Sum the unallocated amounts of all contracts at the price. FOR each contract at the price. Get the contract's unallocated amount. Divide the amount into volume increments. FOR each volume increment. Calculate the price by interpolating between P1 and P2 using the price function and some premium factor.

Bid the volume increment at the interpolated price.

157 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 7-8: Storage Replenishment - Replenishment1BidHandler Replenishment1BidHandler

Attempts to replenish storage volume from supply contracts.

START GenerateBids Sums the remaining gas on all the retailer's supply contracts.

FOR each replenishable storage contract. Get the space available in storage for the contract.

Calculate the target replenishment volume as the minimum of the available space and the allowable daily replenishment volume.

Attempt to meet target replenishment volume from unallocated gas from the retailer's supply contracts.

// Attempt to meet remaining target by removing premium supply bids. IF there is still unmet target replenishment volume. FOR each existing premium supply bid. Redirect the necessary portion of the bid volume, up to the total bid volume, to the replenishment bid.

IF all the supply bid volume was used. Remove the supply bid.

Subtract the redirected amount from the replenishment target.

158 Chapter 7 Prototype # 3 & VicGasSim

Table 7-9: Retailer - Producer Supply Contract Negotiations SupplyMarket component

Manages the negotiation of new contracts between Retailers and Producers

EXECUTE SupplyMarket annual "contracting" event Find the "maximum contract length" required from amongst all Retailers.

FOR each year up to the "maximum contract length" WHILE negotiation is incomplete Get supply stacks from all Producers, sorted in order of increasing tranche price.

CALL Retailer. SubmitSupplyBids

IF no supply bids submitted End negotiation for this year. ELSE FOR each supply tranche Offer supply to one or more bidding Retailers. IF offer accepted Make a supply contract between Producer and Retailer.

START Retailer. SubmitSupplyBids for a given future year Calculate the total available volume in existing contracts for the year. Predict the Retailer's demand in the year.

Calculate the "unallocated demand" as the difference between the predicted demand and the available supply.

IF the "unallocated demand" is less than or equals zero No gas required in the year, submit no bids. ELSE IF the Retailer does not favour short contracts Reject short duration supply tranches.

IF the Retailer and supply tranche Producer share an owner Company Favour the supply tranche by adjusting the price downwards.

WHILE the "unallocated demand" is greater than zero and there are supply tranches with available gas Bid for supply from the cheapest tranche and reduce the "unallocated demand" by the volume available in the tranche. END

159 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

7.3.3 Producers and Gas Supply Sources, including Storage Systems The following Producer Groups are represented in the model:

1. Esso-BHP Gippsland Venture,

2. SA Cooper Basin Venture,

3. Thylocene-Geographe Offshore Otway Venture,

4. Patricia Baleen Offshore Gippsland Venture,

5. Kipper Offshore Gippsland Venture,

6. Minerva Offshore Otway Venture,

7. Casino Offshore Otway Venture,

8. Yolla Offshore Bass Basin Venture,

9. Timor Sea Venture, and

10.PNG Venture.

The main Producer group is the Esso-BHP Gippsland group. Their legacy contracts, which were put in place with GFC in the late 1960’s, will expire over the period from about 2000 to 2011. New Esso-BHP Gippsland sources are being developed to supply the market in competition with other sources, who are negotiating or marketing their gas for supply to the market. A number of such contracts had been put in place by the early 2000’s, including some by Esso-BHP. There are 14 initial contracts modelled between relevant Producers and Retailers, including terms for the period of the contracts, minimum annual volumes (below which Retailers must pay for gas not taken), maximum daily quantities, maximum annual contract volumes, and prices.

Model plans used by a Producer are as follows:

1. Daily: Assess total capacity against total contracted volumes and daily conditions. Determine spare capacity and act as a Retailer to bid spare capacity at premium prices into wholesale market in competition with high priced WUGS or LNG storage gas.

2. Daily: Receive directions from the MarketManager to deliver a specified volume of contracted gas to nominated injection points on behalf of Retailers, whose bids were accepted. Record volumes for billing to Retailers.

3. Monthly: Determine cumulative volumes delivered to each Retailer over the month; determine value and invoice Retailers; receive payments and record.

4. Monthly: Compile and pay operating costs for the month and record.

5. Annually: Compile financial performance for the year and record.

160 Chapter 7 Prototype # 3 & VicGasSim

6. Annually: Receive requests from Retailers for offers to contract new supplies of gas under long term contracts. Producers are profiled with reserves, operating costs, existing capacities, and development costs, lead times and entry volume hurdles to develop a new source of capacity. They are also given the capability (rules) by which they can agree or seek to alter the length of and other terms for the contracts they execute with Retailers.

(i) For new contracts from existing spare capacity: Producers set the initial price for contracting spare gas by targeting a profit margin over their amortised development and operating costs. Producers monitor the annual utilisation of their existing supply commitments against capacity and alter new contract pricing up or down depending upon whether capacity is close to maximum in which case a premium will be sought, or whether capacity is under-utilised, in which case prices will be discounted to increase utilisation in order to keep other sources out of the market and reduce the amortised fixed operating unit cost. If Retailers seek contract terms that differ from the Producers favoured terms for ACQ, MDQ and TorP, the Producer offers Retailer’s requested terms with price adjustments versus the Producer’s base target pricing.

(ii) For new contracts from undeveloped capacity: Producers set the initial price for contracting spare gas by targeting a margin over their forecast development and operating costs relative to the total gas volume requested from this new source. Producer advises of offer terms, including lead-time to develop new capacity. Producer receives tentative acceptances; if total acceptances for supply from this new source are less than the threshold volume needed to justify the development, then offers are cancelled and Retailers revert to their next best offers. An iterative process is followed until Retailers needs are met or a specified number of iterations is exceeded.

The goals of Producers include:

1. seeking to contract gas at highest possible prices over longest possible periods with MDQ and TorP terms that reflect the nature of their projects and costs,

2. seeking to cover development and operating costs and a target profit margin for each supply source,

3. responding to competition by reducing target offer prices and terms on new contracts so that reserves are not "stranded" for long period; or alternatively, increasing target offer prices if there is no competition or facilities are close to capacity, and

4. tracking reserves to avoid over-contracting.

161 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

7.3.4 Consumption Markets & Customers (Demand) The Victorian gas market consists of six major regional markets (Melbourne, Ballarat, Geelong, Gippsland, Northern, and Western). The 1.5 million Victorian consumers generically fall into four groups based on their usage levels and differing load factors: (i) residential Customers who individually use low volumes with a high seasonal variation with high usage in winter; (ii) commercial Customers who have low to moderate consumption levels with moderate seasonal variation; (iii) industrial Customers who have a consistent large load; and (iv) gas-fired electricity power generation, whose demand is governed by irregular shortfalls during periods of peak demand in the electricity market.

Eight classes of Customers are included in the model and they are segmented by their general level of offtake rates; the classes are further tagged as to whether they are regulated Tariff V Customers or unregulated Tariff D Customers. The low volume classes are predominantly residential, the middle classes are predominantly commercial classes, and the high volume classes are predominantly unregulated industrial Customers. These subdivisions are broadly consistent with the size classifications adopted by the pricing regulator, who periodically reviews and resets the regulated price caps applying to each regulated class.

Customers are cross-tagged or divided into sub-classes depending on the geographic delivery zone into which they fall for determination of Transmission and Distribution delivery tariffs. Customer prices and costs are accounted for by the relevant tagging.

Each class of Customer has a prescribed demand function (or plan to use gas) that has a minimum summer usage and a variability of usage depending upon changes in the daily and seasonal weather outcomes. The fixed and variable parts of this function are escalated each year as a function of the prescribed annual economic growth for that class of Customer and the relativity of the model’s gas prices to assumed electricity prices, which are included as an input variable open to testing1.

Model plans used by a Customer are as follows:

1. Daily and annually: Consume gas according to their demand needs; planning related to demand is based on a series of assigned demand functions for each Customer type depending upon the weather and day of the week for residential and small commercial Customers. Industrial Cus- tomers’ demand functions are essentially flat with random variation within assigned limits and sensitive to the day of the week. GPG Customers have seasonal (quarterly) demand functions that include a random variation within assigned limits and sensitive to the day of the week.

1. The use of exogenous electricity prices can be upgraded by expanding the model concept to include a parallel simulated dynamic electricity market.

162 Chapter 7 Prototype # 3 & VicGasSim

2. Annually: Receive and assess new offers for following year. Customers compare newly offered unregulated prices to each other and to regulated prices if a Customer is currently holding a regulated price offer and a Customer takes up the lowest priced offer received or stays with its existing regulated arrangement. The model allows a maximum churn rate that limits the percentage of Customers in any group at any location swapping to a new Retailer; this cap1 on the churn rate can be varied as an input variable.

Table 7-10 presents a pseudocode summary of a Customer’s decision plan to swap Retailers or not (and how an over-riding churning cap is imposed).

Each Customer has two simple goals:

1. to buy and consume gas at the lowest possible price from Retailers, who make offers to sell the Customer gas, and

2. to alter its annual consumption in response to the retail price of gas and relativity of the gas price to the comparable electricity price.

1. In the model runs, the maximum churn rate was set at a fixed 5% and never altered. In the validation phase, it became apparent that not providing for interim adjustments of regulated prices and limiting the maximum annual Customer churn rates to 5% per annum has had a constraining effect on longer term Retail profits as they are squeezed by material increases in unconstrained supply prices for gas from Producers. Retailers are forced to cover costs by increasing prices to large commercial and industrial Customers disproportion- ately. Outputs showing the effects of these constraints are presented and discussed in Chapter 8.

163 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 7-10: Customer Churn

Customer component

A Customer belongs to a Retailer in a Market/TransmissionZone/Distributor hierarchy. Each Customer is divided by ConsumptionRange and can be further divided by Tariff.

EXECUTE Customer annual "churn" event FOR each customer consumption range and tariff CALL CustomerChurn

START CustomerChurn for a customer consumption range Find the cheapest price posted by any Retailers in the customer's Market during the Retailers' "post prices" event.

IF the Customer's current Retailer is amongst the cheapest Exclude the Customer's current Retailer.

IF the cheapest price is less than the Customer's current price Calculate the churn factor using: churnFactor = churnRate * (currentPrice - cheapestPrice)

IF the churn factor exceeds some maximum churn factor Set the churn factor to the maximum churn factor.

Distribute the churn factor proportionally amongst the cheapest Retailers. (That is, divide the churn factor by the number of cheapest Retailers.)

FOR each of the cheapest Retailers Calculate the number of Customers who will churn using: churnCount = churnFactor * customerCount

Remove the churning Customers from the current Retailer.

Add the churning Customers to the chosen Retailer. END

7.3.5 Weather and Electricity Market Effects on Demand Consumption in the residential Market and the smaller end of the commercial Market is strongly influenced by the weather. Historical data shows a regular seasonal pattern of demand in these sectors as a function of the temperature, wind and chill factors. In addition, there is a material chance that rare peak loads can occur in either summer (as a result of air-conditioning loads on very hot days) or winter on extremely cold and windy days. Vencorp compiles an index of the weather; this index is known as the Effective Degree Day or EDD; the EDD index for each day of the year 2001 is presented in Appendix 2.

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Historical data has been used to derive a series of fixed and variable type functions to estimate demands within minimum and maximum limits for the smaller residential and commercial Customers in each of the approximately 200 geographic and Customer size segments in the model; these estimated demands are linked to the forecast EDD on each day. Demand from the larger commercial and industrial Customers is reasonably constant with a regular fall-off pattern over Fridays, Saturdays and Sundays as business related activity winds down. Demands are generated in a similar fashion to the residential and smaller commercial Customer groups but the various functions show far less volatility to the weather.

The GPG market for gas is more irregular than other markets. Demand is a function of the availability of base-load electricity generating units to cover peak loads in the electricity market in peak periods and when one or more units are offline, then GPG units will typically enter the market and sell electricity at high prices. The associated gas demand for GPG units to cover such gaps is represented in terms of the historical probabilities of certain loads occurring within each quarter.

All demand functions include a random variation within set limits to reflect weather and demand uncertainty. Agents forecast demand using weather and probability-based demand functions, and use the following plans:

1. Daily: Forecast EDD for a given day based on historical function with a growth factor to adjust annually for effect of economic activity on demand; adjust for day of week. Post estimated demands for Retailers.

2. Daily: Forecast GPG demand for a given day based on historical probability function with a growth factor to adjust annually for effect of economic activity on demand; adjust for day of week. Post estimated demands for Retailers.

7.3.6 Transmission The Principal Transmission System or PTS is a high pressure system, which is owned and operated by GasNet under the direction of the MarketManager, Vencorp. GasNet tracks injected volumes and charges each PTS user (Retailer) a tariff for gas injected at various injection points around the state and charges a set of tariffs for gas withdrawn and transferred into the numerous low pressure Distribution grids around the State. In this model, GasNet is treated as a relatively passive participant, who tracks volumes and charges, and who follows directions from Vencorp to accept injected gas volumes.

Gas is moved through the PTS to each of the regional markets as shown on the map in Figure 7-5. The PTS is broken down into 14 Transmission Zones (TZ) for delivery of gas and determination of tariffs charged for use of the PTS. The PTS is a monopoly system and tariffs remain regulated. Tariffs apply to gas withdrawn in each of the 14 TZ withdrawal areas shown on the map.

165 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Transmission tariffs are charged to each Retailer for the gas they put through the PTS. Tariffs are made up of three components: an Injection charge applied to all gas, which each Retailer arranges to be injected into the system at each injection or input point; an Anytime delivery charge applied to each unit of gas moved to a specific TZ; and a Peak delivery charge based on the amount of gas injected into the system on the five peak days of the year. Tariffs are regulated and indexed for annual increases and reviewed every five years, reviews are based on investment and regulated rates of return. In addition, the PTS owner applies a different level of tariff structure to small users versus large users (Tariff V and D Customers). Tariffs are included in the input data, which is presented in Section 2.2 of Appendix 2.

Model plans used by a Transmission agent are as follows:

1. Daily: Measure gas delivered by each Retailer at each IP, gas moved to a specific TZ, and the amount of gas injected into the system on the five peak days of the year; determine relevant Transmission tariffs for each Retailer’s injected and delivered volumes.

2. Monthly: Invoice Retailers for total of all Transmission tariffs; receive payments.

3. Monthly: Pay overhead costs.

4. Annually: Determine and record annual financial performance against each Retailer, region and Customer type, and in total.

7.3.7 Distribution For most Customers, gas is withdrawn from the PTS and distributed to them via one of three low- pressure distribution networks, which cover the six regional markets. Some large Customers invest in a direct connection to the PTS and avoid distribution tariffs, thus lowering their delivered gas prices.

Distributors apply tariff structures similar to those for the PTS. Distribution tariffs are significantly greater than transmission tariffs because of the greater extent and value of the assets involved. Distributors charge a structured array of tariffs depending upon the class of Customer, geographic location, capital invested and a regulated rate of return. Distributors are regulated in a similar way to GasNet and are treated as passive participants in the model.

Model plans used by a Distribution agent are as follows:

1. Daily: Measure gas delivered to each Customer of each Retailer in each Distribution region; tag volumes by Customer type and determine relevant Distribution tariffs for each Customer; record tariffs.

2. Monthly: Invoice Retailers for total of all distribution tariffs; receive payments.

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3. Monthly: Pay overhead costs.

4. Annually: Determine and record annual financial performance against each Retailer, in each region, by Customer type, and in total.

7.3.8 Companies In most cases, Companies have involved themselves to differing levels of participation in the major related segments. Cross-involvements include holding interests in parallel electricity markets and/or participation in adjoining states. Some companies own interests in segments on a sole basis while some participate on a part-interest basis via joint ventures or jointly owned companies. No two companies are involved in the same mix of segments in the same way or at the same levels. This means that each company will have different corporate advantages or burdens compared to their competitors.

Companies have no active corporate plans in the model other than to accumulate their profits from each sector. However, Companies’ Retailer agents take credit for any upstream benefits when making their decisions.

7.3.9 Regulators Regulators have no active plans in the model other than to post the initial regulated retail prices by Customer types and regions and to post initial Transmission and Distribution tariffs for each TZ and region respectively.

7.4 Model Data & Variables 7.4.1 Data Sources Most data was sourced from the following reports, studies and publications:

1. Dalziel, Noble and Ofei-Mensah: The Economics of Interconnection: Electricity Grids and Gas Pipelines in Australia (1993)

2. AGA: Gas Distribution Industry Performance Indicators (1995)

3. AGA: Price Elasticities of Australian Energy Demand (1996)

4. AGA: Industrial Gas Demand: Growth and Prospects (1997)

5. AGA: Gas Supply and Demand Study 1997 (1997)

6. Naughten, Melanie & Dlugosz: Modelling ‘supports' to the electricity sector in Australia (1997)

7. AGA: Gas Distribution and Retailing: Responding to New Opportunities (1998)

8. AGA: Gas Transmission Pipelines: Development and Economics (1998)

9. Dept. of Treasury & Finance: Victoria’s Electricity Supply Industry - Towards 2000 (1997)

10.Dept. of Treasury & Finance: Victoria’s Gas Industry - Implementing a Competitive Structure (1998)

167 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

11.GasAdvice & RISC: Victorian Gas Market Study 1998-2020: A comprehensive evaluation of the current and future supply and demand for natural gas in Victoria and its impact on an integrated S-E Australian gas market (1998)

12.AGA: Natural Gas Consumption in Australia to 2015 (1999)

13.ORG: Gas Industry Comparative Performance Report (2000)

14.Trowbridge Consulting: Risk Quantification Review of Victorian Gas Market Arrangements (2000)

15.Vencorp: Review of Victorian Gas Market Arrangements for LinePack Management (2000)

16.Vencorp: Access Arrangement Review Issues Paper (2001)

17.Vencorp: Annual Gas Planning Reviews 2002 to 2006 (2001)

18.Vencorp: Annual Report (2001)

19.Vencorp: Guide to the Gas Market (2001)

20.Vencorp: Information on the Transfer of Authorised Maximum Daily Quantity (2001)

21.Vencorp: Pricing Variation of 14, 18 and 19 June 2001 (2001)

22.Vencorp: Victorian Gas Industry Market and System Rules (2001)

23.AGA: Gas Statistics Australia 2001 (2001)

24.Grimwade: The Victorian Gas Market: Debunking the Myths (2001)

25.MultiNet: MultiNet Gas Distribution (2001)

26.GasNet: Tariff Explanation (2002)

27.GasNet: Tariff Calculation Examples (2002)

28.MultiNet: Schedule of Distribution Tariffs for 2002 (2002)

29.Stratus Networks: Schedule of Distribution Tariffs for 2002 (2002)

30.TXU Networks: Schedule of Distribution Tariffs for 2002 (2002)

Public domain data is not generally provided at the level of detail represented in this model. Therefore, a series of assumptions has been made by the author (based on his experience and other anecdotal advice) to divide up available data for initial calibration purposes. Sufficient calibration has been attempted to establish that the main model interactions generate patterns typical of the Victorian gas market and that the derived profits resulting from the multitude of modelled interactions are at least close to the anecdotal evidence that is available about the profit margins achieved from various classes of Customers and about the total profits achieved by the Retailers commercially represented in this prototype. Differences between actual data and currently simulated data are discussed when calibration comparisons are presented in Chapter 8.

168 Chapter 7 Prototype # 3 & VicGasSim

7.4.2 Model Variables The model, which is classified as a history-friendly cum micro-simulation model, has a large set of variables that are used to initialise the model and set up the underlying layer of assumptions about agent interactions and mechanisms and agent plans. The simulation uses these mechanisms and plans to transform the assumptions, via deductive steps, into dynamic outputs of the whole model. The characteristics of the assumptions and mechanisms can be considered as exogenous variables. In contrast, the variables defined to record the resulting dynamic outputs can be considered as the model’s endogenous variables. The following two sections describe the majority of exogenous and endogenous variables used and recorded in the model.

7.4.2.1 Exogenous Variables Table 7-11 lists the exogenous variables that are used to initialise the model and to set the bounds on agent interactions and mechanisms that are used within the dynamics of the model.

Table 7-11: Exogenous Variables

Variable Name Description of (input) variables set at initialisation Title Name to describe model Start Start time for model run; 07:59 01-Jan-2002 End End time for model run; 07:59 01-Jan-2021 LogManager File File Manager to create a log file for storing intervening detailed variable data (in addition to reported output variables) for checking integrity of model or debugging; Log can be set at three levels from sparse to detailed AccountManager Defines date and frequency for completing accounting by various agents; sets accounting types to be completed on that day: invoicing, growth, churn, retailPrice, contracting ScheduleManager Sets times of day for agent accounting activities: 0752 Vencorp daily reconciliation, 0753 Annual invoicing submission time, 0754 Annual accounting time, 0755 Annual growth time, 0756 Annual post retail prices, 0757 Annual customer churn time, 0758 Annual supply contracting time, 0759 (Annual, monthly, daily) Look-ahead time, 0800 Retailer daily bid time, 0801 Vencorp publish market price Map Geographic description (map outline) to display area containing market being simulated (Victoria and adjoining state interfaces) Region Geographic description (map outline) to display region containing a regional market being simulated: sets geographic limits for each Market (region) Market A Retail Market defined by name, region and by weather model applicable to each Market, which is tagged for applicable region, Transmission Zone (TZ), Distributors within each TZ, Retailers using each Distribution system within each TZ, applicable Distribution Tariff V ($/GJ), applicable Distribution Tariff D ($/GJ) Customer Customer agent of a Retail Market; consumes gas sold to it by a Retailer; defined by CustomerType, churnRate, maxGrowthPriceFactor, with elements defined for Consumption [ConsumptionRange, Customer’s applicable distribution Tariff (V or D), number of customers in a given consumption range by Tariff type], Contract margin Contract (Margin) (Initial) Margin (decimal fraction) that is applied to the cost of gas supplied under a tariff to get the target supply price, which is paid by the Customer

169 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 7-11: Exogenous Variables (Continued)

Variable Name Description of (input) variables set at initialisation ORG Office of the Regulator General; defined by name and elements that set initial regulated prices (Tariffs) in terms of a peak volume charge ($/GJ) and anytime charge f($/GJ) or each Retailer, by each Market; by each Customer Name, by each Consumption range Pipeline Segment of pipeline Network; characterised by a name for each TZ with an initial peak demand charge, a peak volume Charge and an anytime charge ($/GJ) and elements that can link to a specified injection point MarketManager Sets name of one or more MarketManagers (regulators), (e.g. Vencorp); sets reconciliation and bid price determination times; key elements are a Forecast Manager to estimate daily demand for managing bidding process, an Account Manager to invoice wholesalers (Retailer and Producers selling gas as wholesalers), and Linepack (key elements are minimum, target and maximum capacity bounds, and capacity available to each Retailer). Forecast Manager requires nomination of Simple Demand Forecaster (constant daily demand), SeasonalForecaster (Used for Powergen market – randomly sampled daily gas volume sampled from demand ranges for each of four seasons and a probability that daily demand will fall in that range, or EDDForecaster (daily demand forecast is based on a weather model that forecasts an Effective Degree Day (EDD) reading for each day based on history (requires a regional Weather EDD Id nominally provided by each customer, a base or minimum demand, a variable addition depending upon day or the year, and limits for a random variation about the base plus variable forecast, and units in TJ or GJ, daily offsets to alter demands on set days such as Fridays, Saturdays and Sundays and an optional GrowthManager to grow the forecast annual base and variable demand elements with adjustment factors for annual changes in gas and electricity prices. Company Sets names of Companies operating in the Market and defines each company’s equity level in each Retailer, Producer, Storage operator. Distributor, Transmission operator, and/or a Powergen operator. Retailer Defines each Retailer (company) Agent by name. A Retailer obtains gas from producers and sells to Customers in Markets. The transfer of gas is done via a wholesale market, which is managed by a Market Manager: Each Retailer has a name, a schedule Id for the bidding phase, a fixed cost and a unit variable cost. Key elements for each Retailer include: an optional LookAhead Manager (for controlling DailyLookAhead, WeeklyLookAhead and AnnualLookAhead testing) – see later separate descriptions; a MarketManager (for a Market in which the Retailer has Customers (each MarketManager has a Forecaster (with similar options to those discussed for the (Regulator) MarketManager, and one or more BidHandlers); an AccountManager with sub-accountants for tracking simple or internal costs and posted retail prices, costs of gas purchased under contracts from Producers, injection costs into the PTS, delivery costs, storage capacity reservation charges ($/ TJ/D), Tariff V and Tariff D costs, regulated prices and PTS net bidding adjustments tied to each Regulator.

170 Chapter 7 Prototype # 3 & VicGasSim

Table 7-11: Exogenous Variables (Continued)

Variable Name Description of (input) variables set at initialisation Producer Defines each Producer (company) Agent by name. A Producer supplies gas under contract to a Retailer. The daily transfer of gas is done via the wholesale market, which is managed by a (real) Market Manager. Each Producer has a name; key elements are: schedule Id for the bidding phase, an Id for each supply source, an annual capacity for each supply source, a remaining ultimate gas reserve, a fixed cost and a unit variable cost, lead-times to develop one or more capacity expansions for each source (including undeveloped sources), the threshold annual volume that must be accumulated before a commitment to an undeveloped or inactive supply source will be made; low and high utilisation price factors that change price offers if forecast supply is below or above current low and high capacity limits set by prescribed limit factors; initial price margin for supply from developed or undeveloped sources, nominated duration for a standard contract from each supply source (a set discount is offered if a Retailer will take a longer contract and premium if the Retailer wants a shorter contract; similar adjustment factors apply to Producer specified standard contract MDQ’s and TOP’s). Other key elements for each Producer include: a PTS injection point for each supply source; an optional LookAheadManager; a MarketManager (for a Market in which the (Producer) Retailer has Customers (each MarketManager has a Forecaster and one or more BidHandlers); and an AccountManager with sub-accountants for tracking simple or internal costs and posted retail prices, Costs of gas purchased under contracts from producers injection costs into the PTS, linepack usage costs, delivery costs, Tariff V and Tariff D costs, regulated prices. Storage Described in terms of Storage type (compressed LNG, underground); seasonal storage limits Distributor A Distributor owns one or more regional (medium pressure) Distribution (pipeline) Systems that take gas on behalf of each Retailer in the Distribution area from a Transmission system to Customers (low pressure). Each Distributor is characterised by a name. Each Customer is tagged to a Distributor based on location; each Distributor has an AccountManager to determine tariffs due to the Distributor and invoices Retailers. (Tariffs are tagged to each Customer). Transmission A Transmission operator owns one or more (high pressure) Transmission (pipeline) systems that transport gas from Producers’ gas supply sources to the various Distribution systems. A Transmission system may have more than one injection point (with its own injection charge) plus various nodes and branches taking gas to regional Distribution systems. A Transmission System is broken into various Transmission Zones (TZ), each of which has a different Tariff structure. Each Transmission operator is characterised by a name. Each Customer is tagged to a TZ and Tariff Structure is based on location; each Transmission operator has an AccountManager to determine tariffs due to the Transmission operator and invoices Retailers. (Tariffs are tagged to each Customer.) LookAheadManager Each Retailer has an optional LookAheadManager for controlling DailyLookAhead, WeeklyLookAhead and AnnualLookAhead testing. Invoked in input data; a model user can test and compare how various alternative strategies over one day ahead, say for wholesale bidding, or over a week ahead, say to check wholesale bidding for more fundamental consistency that might appear more clearly over a number of days than over one day. Three test cases have been defined for testing under the model’s LookAhead capability: SupplyContractTestCase, RetailPriceTestCase and BiddingTestCase; each is described below SupplyTestCase An annual test case for conducting supply contracting negotiations by a Retailer and multiple Producers. Three contracting attributes can be set and varied between test cases: Contract length, TOP, MDQ. Multiple test cases are defined for one or more Retailers who then use the model’s LookAhead capability to run each test case singularly for a nominated period (3, 6 or 9 years) of the model run time and to assess the cumulative total profit for each test case over the nominated period; when all test cases are completed the Retailer chooses the best test case outcome and negotiates the contract terms using the test case parameters. The following year, the process is repeated and each Retailer has an opportunity to try to offset any poor choices made the previous year

171 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Table 7-11: Exogenous Variables (Continued)

Variable Name Description of (input) variables set at initialisation RetailPriceTestCase An annual test case for conducting retail pricing negotiations by a Retailer with its various Customers. A single attribute can be set: a price margin adjustment – either positive or negative. Multiple test cases are defined for one or more Retailers who then use the model’s LookAhead capability to run each test case singularly for a nominated period (3, 6 or 9 years) of the model run time and to assess the cumulative total profit for each test case over the nominated period; when all test cases are completed the Retailer chooses the best test case outcome and sets the Retail price using the price margin from the chosen test case parameters. The following year, the process is repeated and each Retailer has an opportunity to try to offset any poor choices made the previous year – possibly due to unforecast pricing behaviour by a competitor. BiddingTestCase A daily or weekly test case for developing a Retailer’s strategy for setting daily wholesale bidding offers to supply gas into the wholesale market. Each set of test cases must be tagged to a MarketManager (to recognise gas is supplied to multiple (including interstate) markets with their own MarketManager. Five bidding strategies have been defined and are available for testing: SatelliteMarketBidHandler, ReplenishmentBidHandler, Supply1BidHandler, Strategy2BidHandler, and Strategy1BidHandler. Multiple test cases can be set for a selected Bidding Strategy. The SatelliteMarketBidHandler bids a limited amount of gas to a satellite market. This is a reasonably simple bid manager to account for Retailers or Producers (acting as Retailers) who sell gas interstate and allocates gas to that market so that it is not available to the Victorian gas market, which is the primary focus of VicGasSim. A fixed price is nominated for each offer to a Satellite Market; each bid can tag a favoured Producer as preferential supplier otherwise a Retailer bids from the first available (lowest-priced) contract. This accommodates where a Producer is selling (acting as a Retailer) directly into another market, say NSW, SA or Tasmania. The ReplenishmentBidHandler bids gas into or out of storage each day based on a Retailer forward expectation of season needs to build or use gas storage against current and YE contract constraints on ACQs, MDQ, and TOP positions. Each TestCase sets a volume to be bid each day. The Supply1BidHandler bids gas for use in the Victorian gas wholesale market. Test cases allow testing of three attributes to the Supply1BidStrategy mechanism, which fits a bidding curve around expected demand: a percentage premium on price for bids beyond the expected demand; a random premium percentage price variation; a price volatility factor. The Strategy1BidHandler bids gas be used to bid gas into the Victorian gas wholesale market. Test cases allow testing of two attributes to the Strategy1Bid Strategy mechanism, which fits a bidding curve around expected demand: a percentage premium on price for bids beyond the expected demand; a random premium percentage price variation. This bidding Strategy also allows setting what type of algorithm is used to vary the bidding curve shape between the set contract and demand points: exponential (with variable values controlling the steepness of the curve fit).This is the most sophisticated and generally adopted bidding strategy used by all Retailers in the model and test cases are compared on the basis of testing variations in the way the bidding curve is shaped and divided up but where the overall curve is shaped primarily by each Retailer’s long term gas supply contract constraints and expected demand by each Retailer’s Customers on each day. The Strategy2BidHandler bids based on using a particular function to interpolate prices between adjacent supply and storage contracts. Contract volumes are treated in a price ordered/stacked or cumulative fashion with the ability to vary the following parameters that shape the bidding curve and volume increments of each price offer tranche: topFactor (Proportion of TOP gas offered at zero price to ensure it gets into the market if it must be paid for even if not delivered), premium (a premium factor applied to all contract prices for surplus gas) lastPremium (a premium applied to the price of the last (most expensive) contract in order to set an upper limit to the price curve; standardIncrement (volume of a standard bid volume to divide up bidding offer tranches, fineIncrement (the volume of a fine bid about the expected demand bid to give best chance of matching own expected demand within own contracted supply prices and obtaining premiums above this level from Retailers who might end up short of gas on that day), and fineRange ( the range of volumes about the expected demand point within which the fineIncrement is used for making bids).

172 Chapter 7 Prototype # 3 & VicGasSim

Table 7-11: Exogenous Variables (Continued)

Variable Name Description of (input) variables set at initialisation Contracts Each Retailer is assigned a base set of contracting preferences: topFactor (the TOP used by the Retailer when negotiating new supply contracts); mdqFactor (the MDQ used by the Retailer when negotiating new supply contracts); contractLength (the contract length used by the Retailer when negotiating new supply contracts); acceptShortContracts (an election to vary (or not) contract lengths) if a price favoured supply tranche on offer isn’t long enough to meet the desired contract length, allows acceptance of a shortened, tranche-length contract instead, if so elected. If not so elected, all contracts must meet the Retailer’s set contract length and other non-conforming bids are rejected; predMargin (multiplier for the predicted annual volume, used to inflate annual ACQ); diffMargin (multiplier for the difference between the predicted annual volume and the total available volume for the year) - used to ensure forecast modest gaps covered by a safety margin to avoid supply shortfalls but can lead to over-contracted supply and need to possibly sell at discounted prices into the wholesale market to other Retailers who might hold higher-priced contracts or the surplus could prove a bonus if other Retailer’s end up short of gas and this surplus can be onsold via the wholesale market at a premium. SupplyContract Existing gas supply contracts at initialisation. Key elements are: name of Producer who is contracted to supply under this contract; TorP (the TOP pay factor for this contract); ACQ ( the Annual Contract Quantity that can be purchased under this contract; usedGas ( the cumulative quantity of gas already used under this contract; gas used and committed are tracked against gas reserves from a given supply source); mdqHi (maximum daily quantity for the high demand season); mdqSh (maximum daily quantity for the shoulder season); mdqLow (maximum daily quantity for the low season). The use of three MDQ factors rather than a single MDQ factor, as will exist in the actual contracts, is to ensure that supply does reach the ACQ limit well before the end of each/any year. StorageContract Existing contracts for use of the WUGS storage system. A contract has the following key elements: a gas source, a start year, duration, and an optional price. Usage of the system under a contract will be defined by: an amount of gas that can be stored under the contract, initial amount stored at time zero, maximum daily quantity that can be produced from stored volumes; fixed annual cost ElectricityMarket A pseudo electricity market to generate electricity prices (or a price index) in each year. Key elements are initial price index, year of initial index; an annual price growth factor to be applied to increase the price index. The price index is used by Customers in judging their consumption preference for gas over electricity; it is combined with the price elasticities for gas and electricity to determine each Customer’s annual increase or decrease in demand as both gas and electricity prices increase or decrease.

A more detailed description of all input (exogenous) variables is provided in files referred to in Sections 2.1 and 2.2 of Appendix 2. Sections 2.3 and 2.4 of Appendix 2 provide the model files that are used as the base for the EDD weather forecasting plan and model initialisation file.

7.4.2.2 Endogenous Variables Table 7-12 lists the endogenous variables that are published annually by the model for each Retailer; there are numerous other variables that are used within the model dynamics but they are only observable by requesting a log file of a simulation run. Such log files can be set to record and display virtually every transaction in the model (and each intervening bid in a series of bid rounds or negotiations before two agents reach agreement) but are of a similar commercial form to or variation of those listed in Table 7-12.

173 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

. Table 7-12: Endogenous Variables

Variable Name Description No of Customers Forecast number of each Customer type for each Retailer by each consumption range for each year in a model run [the same data is also available for each market region] and the total number of Customers for each Retailer Market Share Forecast market share for each Retailer by Customer type for each consumption range and on a total Market basis for each year. Average Daily Average daily consumption as segmented above. Consumption Total Consumption Total annual consumption as segmented above. Average Price Average retail gas price segmented as above. The average represents the weighted price for gas sales at the regulated and unregulated prices. Regulated Price Regulated retail price segmented as above – the model does not currently redetermine these prices every 5 years as is done by the real market regulator (ORG). Unregulated Price Unregulated retail price segmented as above. Redetermined each year by the model. Revenue Annual revenue received by each Retailer segmented as above. Costs (Total) Total annual costs incurred by each Retailer segmented as above. Total costs represent the summation of costs billed for Supply Costs [made up of Producers for gas supply, Storage costs for injecting and withdrawing gas to storage to assist in meeting peak demands, Trading costs (losses or profits) from active participation in the wholesale market], Transmission Charges, Distribution Charges, and Overhead Costs. Supply (Total) Total annual supply costs incurred by each Retailer segmented as above. Total supply costs represent the summation of costs billed from Producers for gas supply, Storage costs for injecting and withdrawing gas to storage to assist in meeting peak demands, and Trading costs (losses or profits) from active participation in the wholesale market. Production (Costs) The total annual cost of gas supplied under contract from multiple Producers – as segmented above. Storage (Costs) The total annual cost of gas injected, withdrawn and stored in the WUGS or LNG storage systems under contract with the storage operators – as segmented above. Trading (Costs) The total annual Trading costs (outcomes can be profits or losses but in VicGasSim are treated as a positive or negative costs) from active participation in the wholesale market. A cost occurs if a Retailer ends up buying a net amount of gas (i.e. above what it can take or ends up taking under its contracted supplies) over the year from the market via the wholesale market. A profit (or negative cost) occurs if a Retailer has surplus gas available under supply contracts and is able to sell some of this surplus via the wholesale market to other Retailers at a premium price because these other Retailers end-up under-contracted or consistently adopt poor wholesale bidding strategies. Transmission (Costs) The total annual cost of gas injected into and transported through one or more of the Transmission systems in the model – as segmented above. In the model, Transmission Tariffs (per unit) remain as set by input data at time zero. In reality, they are redetermined by the relevant regulator on a periodic basis after consideration of changes in investment in transmission facilities and any changes in return on investment that might be required to boost future investment. This is an area for future enhancement in VicGasSim.

174 Chapter 7 Prototype # 3 & VicGasSim

Table 7-12: Endogenous Variables (Continued)

Variable Name Description Distribution (Costs) The total annual cost of gas injected into and transported through one or more of the Distribution systems in the model – as segmented above. In the model, Distribution Tariffs (per unit) remain as set by input data at time zero. In reality, they are redetermined by the relevant regulator on a periodic basis after consideration of changes in investment in distribution facilities and any changes in return on investment that might be required to boost future investment. This an area for future enhancement in VicGasSim Overhead (Costs) The total annual overhead costs for each Retailer; defined by a large fixed annual cost and small variable unit cost the allocated unit overhead costs to each market segment and on a total basis can be reduced by boosting gas sales in each market segment and the total market. Gross Profit Total annual profit for each Retailer – as segmented above. Average Profit Total annual profit achieved for each Customer by each Retailer – as segmented above.

The model has embedded capability to search or "mine" much of the model generated information that characterises virtually all commercial quantification of each interaction between agents. Other endogenously calculated variables are used to derive dynamic visual displays while the model is running or on completion of each run. Other calculated data can be considered by manually compiling information generated by the model’s detailed logging and storing capability (log files at fine, medium or course detail levels are possible) of the vast majority of transactions conducted within the model at a detailed level.

According to the definition of emergence in 4.3.7, most of the endogenous variables record emergent properties in relation to the market as a whole and the performance of individual market participants. The endogenous properties are different from those used to initialise agent participants. A good example is the outlook for future price of gas supplies, future supply costs, future profits, new contract properties, entry timing for new gas developments or sources of supply, such as gas from the Timor Sea. These endogenous outputs, which rely on dynamic agent behaviours, indicate VicGasSim fits Labys’ predictive class of model, which can be said for only a few of the other models reviewed in this thesis.

Appendix 3 presents a series of screen snapshots that show the output form of many of these simulated outputs. The screen snapshot in Section 3.2 of Appendix 3 presents the integrated bid stack of all modelled offers from all Retailers and highlights where Vencorp has set the pool price at the expected demand on that day. This bid stack and the daily pool price display can be monitored dynamically as the model is running. The display can be changed to a graphical form.

The screen snapshot in Section 3.3 of Appendix 3 presents a comparison of the submitted bid stack by each Retailer (in graphical form) versus the $2.68/GJ pool price set by Vencorp for the day based on the integrated stacking of all Retailers bid offers as shown in the screen shot in

175 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Section 3.2 (although for a different day). It is interesting to note how each Retailer’s bid stack differs in shape and placement relative to the pool price for the day. The bid stacks for the three main Retailers, Origin, TXU and Pulse, are all very similar with differences reflecting small differences in their supply contracts and demands; the step nature of the bid stacks reflects that they hold a series of differing contracts with different terms. The bid stacks for the Producers acting as Retailers to sell surplus supply capacity into the spot market reflects their opportunistic positioning of their bid-stacks to only sell at high pool prices when Retailers are caught short; the Producers’ minimum prices are well above the actual pool price in this case, which is very close to the pool price anticipated by the Retailers on the day shown in the display.

These bid stacks and the daily pool price display can be monitored dynamically as the model is running. The graphic display in the screen shot in Section 3.3 of Appendix 3 also shows the results of a look-ahead comparison for TXU of three test cases run over a weekly test period. Each test case would have used a different set of criteria to shape its bid curve over the test period. Test Case 1 and Test Case 2 have generated very similar outcomes, and Test Case 2 has failed (the reasons would require investigation of the detailed log file and inputs could be varied to achieve a successful Test Case for comparison purposes). While little sensitivity testing has been completed at the time of documenting this proof of concept model design, the author’s general observations are that profit differences achieved between alternative test cases are generally very small – suggesting the impact of trying to find optimal bidding strategies might not be of significant importance over time but it is an area where certain goals must be met to broadly balance contracted supply against the daily demands of a Retailer’s own Customers; failure to meet the basics of this requirement could prove extremely costly. The main objective is to ensure the bulk of the expected daily demand from a Retailer’s own Customers is met from its own contracted gas supply contracts. Shaping the upper portion to capture benefits from random daily or seasonal market anomalies does not appear a dominant area for strategic attention in terms of long term positioning and model attention.

The screen snapshot in Section 3.6 of Appendix 3 presents a set of endogenous variables that characterise all new (those added to supplement those set in place at initialisation of the model) contracts generated by the modelled annual negotiations between each Retailer and various Producers. All new contracts are listed and show the start and end year for each contract, the contracted gas price for each contract together with the negotiated MDQ, ACQ and TOP for each contract. It also records for each contract the volume of gas used by that year-end and any unused gas that carries over to extend the contract period.

There is a very large array of lesser but more detailed endogenous variables that are represented in the model or can be isolated and defined by the model for storage and reporting. Examples are

176 Chapter 7 Prototype # 3 & VicGasSim

shown in Chapter 8. These include the forecast annual gas supply volume from each supply area as shown in Figure 9-2. Figure 9-1 shows the average gas supply price by gas supply area.

Other endogenous variables that are of interest, but not yet compiled and stored, are the average contract length, average MDQ, average ACQ and average TOP for new contracts over time to allow investigation of any possible future trends to change the style of contract terms as the Market evolves over time. For exanple, with the rapid change in contract arrangements needed as historical EssoBHP contracts wind down, will Retailers look to shorten contract lengths to deal with greater competition or will they opt for maximum length contracts if they believe future contracts will see ever increasing prices? How will these uncertainties be affected by the impact of higher real prices on Customer demand? VicGasSim opens a platform to consider and qualify possible dynamic, interacting influences and behavioural responses.

7.4.3 Conclusions from Development of Prototype # 3 & VicGasSim The following conclusions were made from the development work on Prototype # 3 and VicGasSim:

1. A significantly expanded set of industry-type protocols has been successfully incorporated into the model and linked through an expanded range of autonomous agents with localised interactions.

2. The performance capability and calibration of the model can be judged by simulated outputs, across a range of widely differing characteristics of the market, including Retailer bid-stacks, and seasonal market demand profiles. Comparisons of model outputs to actual data are presented in Chapters 8 and 9.

3. The model is able to maintain a dynamic, non-equilibrium market across a wide range of industry conditions without introduction of any over-arching control agents and whose aggregated behaviour presents as being in "statistical equilibrium1".

4. The model is able to estimate Retailer profits and a wide range of performance criteria on a total basis and across a range of Customer types. This ability to generate and track corporate type performance points to the significant assistance this tool can provide to market participants. It can aid them in identifying successful strategies in terms that they can understand and use daily

1. Foley (1999) states "the conceptualization of the market as a probability field over transactions implies statistical fluctuations in the experience of identical traders entering the market at any time and in the experience of any individual trader over time. Thus the market can be in statisitical equilibrium without requiring that each individual trader is individually in equilbrium, as Walras and Marshall presume) ... Competition among traders gives rise to the emergence of an average price and a regular equilibrium probability distribution of transaction prices, but that distribution implies that some traders are “harder” or less likely to occur than others, reflecting the liquidity of the market and the requirement for each trade to find a counterpart".

177 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

178 Chapter 8: Model Calibration & Validation

An ABM of an economic system, such as a regional industry model for natural gas, invokes many variables if the designer seeks to build a detailed micro-simulation type model. In the case of VicGasSim, the model design is hierarchical and includes multiple sectors and multiple agents. There are numerous agents representing production, transmission, storage, distribution, wholesaling, retailing, consumption by market types, and regulators. Agents have corporate strategies that consider synergies across multiple sectors and segments. The more agents, variables, objects, strategies and interactions there are in the model, and the fewer control agents, then the greater is the challenge of deciding if the outputs from model simulation runs reflect possible or even likely future behaviour by individual agents.

Fagiolo, Monetta and Windrum (2007) point to a perceived lack of robustness in AB modelling and identify four key problem areas that have inhibited wider application and adoption of AB modelling. They are:

1. The diversity and poor understanding of model types versus the high level of standardisation of neoclassical economic theories and models,

2. A lack of comparability between models; models are viewed in isolation and designed for specific purposes with many degrees of freedom,

3. A lack of standard procedures and techniques for constructing and analysing ABM’s, and

4. The problematical relationship between ABM’s and empirical data; different techniques are adopted for conducting empirical validation given the wide diversity of classes of empirically observed objects that can be replicated and validated.

The first part of this chapter looks at suitable characteristics for such models. The second part looks more specifically at model calibration and validation.

179 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

8.1 Calibration & Validation Concepts 8.1.1 Evaluation Criteria The effectiveness of the design mechanism developed for the Victorian gas market model should meet the recognised conditions for such models. It is useful to consider the types of model evaluation criteria that prevailed before ABM’s came into common usage to address social and economic issues in complex adaptive systems, which are subject to possible emergence of new characteristics over time. These evaluation criteria are well-represented by those of Zlotkin and Rosenschein (1996); they are repeated below:

1. Efficiency: Agents should not squander resources or waste utility. Agreements should satisfy the requirements of Pareto Optimality1 or Global Optimality2.

2. Stability: Agents should not deviate from agreed upon or assigned strategies.

3. Simplicity: Agent communication overheads and computational requirements should remain simple and low.

4. Distribution: Systems must avoid a central decision-maker to avoid bottlenecks and failures of overloaded nodes.

5. Symmetry: Agents must play single roles in interactions.

Sandholm (1999) provided an alternative assessment method and tabled a view that the choice of protocol for a negotiating mechanism would depend on what properties the protocol designer wanted the overall system to have but could be evaluated against the following six criteria:

1. Social Welfare: This is the sum of agents’ payoffs or utilities in a given solution. It measures the global good of the agents but is somewhat arbitrary because it requires comparisons of individual agent’s utilities.

1. From Wikipedia (30 July 2009): Pareto efficiency, or Pareto optimality, is an important concept in economics with broad applications in game theory, engineering and the social sciences. The term is named after Vilfredo Pareto, an Italian economist who used the concept in his studies of economic efficiency and income distribution. Informally, Pareto efficient situations are those in which any change to make any person better off would make someone else worse off. Given a set of alternative allocations of, say, goods or income for a set of individuals, a change from one allocation to another that can make at least one individual better off without making any other individual worse off is called a Pareto improvement. An allocation is defined as Pareto efficient or Pareto optimal when no further Pareto improvements can be made. Such an allocation is often called a strong Pareto optimum (SPO) by way of setting it apart from mere "weak Pareto optima" as defined below. Formally, a (strong/weak) Pareto optimum is a maximal element for the partial order relation of Pareto improvement/strict Pareto improvement: it is an allocation such that no other allocation is "better" in the sense of the order relation. Pareto efficiency does not necessarily result in a socially desirable distribution of resources, as it makes no statement about equality or the overall well-being of a society. 2. From Wikipedia (30 July 2009): In mathematics, a global optimum is a selection from a given domain which yields either the highest value or lowest value (depending on the objective), when a specific function is applied. Co-operative optimisation is based on a system of multiple agents cooperating to solve a hard optimisation problem. The problem is divided into a number of manageable sub-problems which are each assigned to an agent. The co-operation is achieved by asking the agents to pass their solutions to their neighbors and to compromise their solutions if they conflict. When all conflicts are resolved, the system has found a consensus solution as the solution to the original problem. For a certain class of co-operative optimisation algorithms, a consensus solution must be the global optimal one, guaranteed by theory.

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2. Pareto Efficiency: The sum of the agents’ payoffs is maximised. Social welfare maximising solutions are a subset of Pareto efficient solutions.

3. Individual Rationality: An agent’s payoff for participation is no less than it would receive for not participating. Self-interested rational agents must see an equal or greater payoff from participation over non-participation.

4. Stability: Mechanisms should be designed to be stable and not able to be manipulated, i.e. they should motivate each agent to behave in the desired manner. This can be achieved when there are dominant strategies. However, when an agent's strategies depend upon strategies chosen by other agents then other stability criteria such as the Nash equilibrium are more applicable. Agents are in Nash equilibrium when each agent chooses its best strategic response to the other agents’ strategies.

5. Computational Efficiency: Mechanisms with lower computational requirements are desirable but this must be measured against the solution quality and domain solutions that satisfy some of the evaluation criteria.

6. Distribution and Communication Efficiency: Distributed protocols are preferred to minimise the level of agent communication required to converge on a global solution and to avoid single point failures and processing performance bottlenecks.

Both Zlotkin & Rosenschein (1996) and Sandholm (1999) suggest it is not necessary, as designers of games or negotiation rules, to achieve any more than the reasonably weak form of equilibrium as defined by Nash (1950, 1953), who suggested that, as a minimum, a system only be required to implement internal efficiency. Zlotkin and Rosenschein restate this minimum requirement in the terms that it is an adequate test if a system maintains "coherence” or a consistent balance of the whole system. Both sets of criteria reflect the focus of thinking by some researchers in the 1990’s on maintaining models in an equilibrium and optimised state. These criteria have less relevance in the context on dynamic, non-equilibrium models as discussed in 4.3.4.

However, the VicGasSim model does satisfy some of these criteria:

1. Efficiency, Social Welfare, Pareto Efficiency and Individual Rationality: VicGasSim does not set out to meet these requirements in that is does not look to optimise the utilisation of resources; VicGasSim’s main objective is to provide a plaform for participants to test various strategies not to predict the "best" or "correct" answer. VicGasSim is a strategy exploration type tool to allow improved understanding of possible market outcomes. But that is not to say that VicGasSim cannot give indications of future market behaviour or directional outcomes.

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2. Stability: VicGasSim agents are limited to use the strategies and plans, which are assigned to them, and they use their capabilities to decide on the best strategy to apply or best way to apply the strategy without knowing how other agents might act in the future. VicGasSim appears to meet the stability criteria.

3. Simplicity and Computational Efficiency: the VicGasSim design has opted to use a range of very simple mental models for each agent to make its decisions; this is possible because of the large amount of data available to calibrate the model. While this choice reduced computational levels by comparison to using more sophisticted mental models or learning and behavioural models, it still invokes great number of interactions. There is a another trade-off that must be assessed against other modelling objectives; in this thesis, the model outputs are useful to industry users since they are preserved in terms used and understood by participants.

4. Symmetry, Distribution and Communication Efficiency: VicGasSim uses a minimum number of centralised decision-makers or control agents; decision-making is decentralised by use of autonomous, decision-making agents, which represent each participant in the market; agents have single roles in each market segment with separate or parent or corporate agents to advise their individual segment agents.

The current model’s lack of focus in the areas of Efficiency, Social Welfare, Pareto Efficiency and Individual Rationality is claimed as one of its virtues in that it does not set out to find an optimal solution for the whole market. The model finds negotiated solutions for each participant; the combined outcomes reflect a pragmatic outcome given the market circumstances and recognises that the market is a real market not a perfect market. Some participants might seek an "optimal" goal but other competitors will likely strongly resent this solution and prefer an alternative outcome favourable to them. There will be winners and losers in making the market work. The Victorian gas market is heterogeneous, grainy, of moderate transparency, with some residual barriers to entry, and is susceptible to discontinuous changes – it is far from being a perfect market, where optimal and coherent solutions are both predictable and achievable over time.

Market participants will never share all information to achieve a perfect market because in many cases it is against the Trades Practices Act (depending upon the objective) and in other ways they will rely on their own strategies for pursuing competition and growth. It would require an independent (autocratic and dictating) regulator to find an optimised answer for a gas industry in a free market – assuming the Regulator knew what the right answer was.

The older style of evaluation criteria, which are discussed above are less relevant when considered in the context of recent literature on ABM’s, which are used in dynamic, non-equilibrium

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situations. The relevance and effectiveness of VicGasSim can be better considered in the context of recent literature on ABM calibration and validation, which is described in 8.1.2 to 8.1.5.

8.1.2 Need for Confidence in Model Outputs Is a model capable of predicting believable emergent industry trends? Judging whether simulated market-wide changes are important factors in themselves depends upon the calibration and validation of the model used to simulate future outcomes.

To a degree the level of confidence required will depend upon the use of the model. For making public claims about the future, for providing commercial advice on the future, and for use as the basis of providing an expert opinion in a court case or a legal arbitration, any model needs to be tested, calibrated and validated to a high level of confidence.

For use as a training tool for companies to give staff and management an understanding of market dynamics and interactions, a high level of testing, calibration and validation is required if the training is to be realistic and meaningful but not necessarily accurate or all-encompassing in the interactions and behaviours modelled. For acceptance of a "proof of the model concept", as is claimed for VicGasSim in this thesis, a considerably lesser level of testing, calibration and validation is required.

This chapter presents initially a number of views on calibration and validation of ABM’s (and other similar or related model types) and the view that a satisfactory approach depends upon the type of model used and use for which it is to be used. Later in the chapter, various inputs to and outputs from VicGasSim are presented to convey the level of calibration and validation completed in support of publishing this thesis in regard to the VicGasSim model design and the claims about the model that are made in Chapter 10, which presents a number of observations, conclusions and recommendations for development of further model enhancements, testing, calibration and validation.

8.1.3 Why Is Even Minimum Calibration and Validation Needed? In designing VicGasSim, a choice was made to move in the direction of micro-simulation – in the direction of detail, multi-agents, multi layers, multi-segments and many simple business rule based decision-making mental models for most agents. The number of agents and number interactions without imposed central control agents suggests the model is open to the risk of generating unrealistic simulation outcomes, especially over long periods of projection for an industry that fits the definition a complex adaptive system and in which dynamic changes have occurred and are expected to continue over coming decades.

Even though most agents’ mental models have been kept simple; they are typically allowed to operate within a set of outer bounds to their individual behaviours. How wide can these bounds be made? Widening the bounds of agent’s rationality may provide more room for the model to

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accommodate changes, it could also allow a wide array of possible future scenarios. At some point of relaxation, some scenarios are likely to approach a random walk outcome and be of little value in explaining how a market might develop and change over time. It becomes clear quickly that a model designer needs to consider how to minimise the chance of random walk outcomes and how to maximise the chance of reliable market enlightenment. As a designer moves from the general, hypothetical level to the specific, empirical level of micro-simulation, he is faced with the challenge of the trade off between over-parametisation and loss of model control versus striving for the detail to move closer to modelling reality.

The author and designer of VicGasSim elected to move towards micro-simulation and detailed characterisation at the risk of overparametisation and loss of model control. Therefore, validity of the model design, data, agent capabilites and ouputs need at least a modest level of calibration and validation even to support a claim of "proof of concept" for the multi-sector industry model design and the business-like agent characterisation attempted in VicGasSim.

Before giving consideration to VicGasSim’s calibration and validity against the author’s objectives, it is useful to consider what has been written in the literature about calibration and validation of ABM’s and like-models. While the rewards of using ABM’s are now widely recognised, so are the inherent risks. As a result, the specific growth in the use of ABM and ACE by economists has led to a growing set of literature about the empirical validation of ABM’s. This has become an area of study in itself as the value of ABM’s to study economies and industries has become more widely used and critiqued. This next several sections set out a number of recent perspectives on empirical calibration and validation of ABM’s.

8.1.4 Introduction to Empirical Calibration & Validation of ABM’s Fagiolo, Birchenhall and Windrum (2007) (in their introduction to a special issue1 of Computational Economics dealing with the Empirical Validation in Agent-based Models) present their view that "the expression empirical validation (of an ABM) typically means the procedure through which the modeller assesses the extent to which the model’s outputs approximate reality, typically described by one or more "stylized facts" drawn from empirical research. More generally, however, to "empirically validate" a given ABM can involve the appraisal of how realistic is the set of model assumptions (e.g. the behavioural rules employed by the agents in the model), or the evaluation of the impact of alternative market designs and/or policy measures".

Fagiolo, Birchenhall and Windrum (2007) point out four key problems that have been recognised in using ABM’s to study issues in the social sciences: a lack of understanding of models developed

1. Computational Economics, vol. 30, no. 3, 2007 - a special issue containing a series of articles on calibration and validation; most of these articles are referred to in this chapter

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to date (i.e. few outside the model designers and users know what is inside such models given their complexity; a lack of consistency between model designs (i.e. typically, they are all uniquely designed for the issue under investigation); the previous deficiencies lead to a lack of standard techniques for constructing and analysing AB models that in many cases look to explore complex adaptive systems that have many aspects that are not in static or dynamic equilibrium; and finally, the problematic relationship between ABM’s and empirical data or the difficulty of validation of such models. The first three problems re-inforce the need for greater attention to validation.

Fagiolo, Monetta and Windrum (2007) paint a broad framework for empirical validation. They set out that economic and other types of models "isolate some features of an actual phenomen, in order to understand it and to predict its future status under novel conditions. These features are usually described in terms of causal relations, and it is usually assumed that some causal mechanisim (deterministic or stoichastic) has generated the data. We call this causal mechanism the real-world data generating process (rwDGP). A model approximates portions of the rwDGP by means of a model data generating process (mDGP). The mDGP must be simpler than the rwDGP and, in simulation models, generates a set of simulated outputs. The extent to which the mDGP is a good representation of the rwDGP is evaluated by comparing the simulated outputs of mDGP to the real-world observations of the rwDGP."

Fagiolo, Monetta and Windrum (2007) point to the issues of the trade-off between concretisation versus isolation, instrumentation versus realism, choice of pluralist or aprioristic methodology, and the under-determination or identification problem. As a modeller moves to greater concretisation, the modeller moves closer to one-one mapping of the mDGP to rwDGP. There must be a limit to the level of concretisation (detail and definition in the model description). Fagiolo et al. point out that economists usually agree there is a need to isolate the main causal mechanisms to overcome the hurdle of analytical intractability caused by descriptive accuracy and detail. The challenge is to decide if the level of abstraction adopted in defining certain processes for certain entities will have an impact on the phenomemon being examined. Neoclassical approaches have traditionally placed importance on analytical tractability. ABM’s and computational power in even laptop computers have opened up the potential to move in the direction of greater descriptive accuracy.

Neoclassical modellers typically reflect a realism view that economic theorems exist in reality and are in fact real-world entities and can be assumed independent of enquiry, representation and measurement. The instrumentalism view is that theoretical entities are not true descriptions of the world and have limits in their application to making predictions. Instrumentalists are more willing to play with the assumptions and parameters of a model to get better predictions and ones that do not force conditions such as equilibriums.

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Apriorism in modelling is a commitment to a set of a priori assumptions, which most commonly relate to general equilibrium conditions and perfect rationality by entities in such models. This approach is well grounded in research in the sciences, where there may be strong grounds for assuming a set of hard core assumptions and relationships. However, it can lead to weaknesses if such assumptions are questionable and never tested by empirical validation. In contrast, pluralism addresses the complexity of the subject of economic investigation by allowing different levels of analysis and testing of different kinds of assumptions that allow the model to be altered in the face of anomalies and to explain why anomalies occur rather than the common a priori approach of claiming ad hoc excuses to explain anomalies.

Under-determination can occur when different models are shown to be consistent with the observed data. Which model or hypothesis can be identified as right? Or more correct? This is also known as the problem of identification (Haavelmo 1944). Under-determination can be reduced in those ABM designs that include multiple or auxiliary hypotheses; models in which the auxiliary hypotheses are also matched will establish more grounds for claiming the uniqueness of the model’s capability to match observed data and claim confidence in using the model to explain behaviour in the system under investigation.

Given there are different issues depending upon the type of model, the type of issue being explored and the availability of data, there are different views on how to test or validate claims about the usefulness or applicability of an ABM. The next section presents a number of approaches that have been advocated for consideration; their differences reflect the range of ABM’s that have been described in the literature to date. A consensus is still building on how to standardise the testing of ABM’s.

Fagiolo, Moneta and Windrum (2007) argue that validation must address two main factors: the complexity of ABM’s (which usually contain non-linearities, stochastic dynamics, non-trivial interaction structures and micro- and macro-feedbacks that preclude examination under static equilibrium conditions) and their heterogeneity. In contrast, neoclassical approaches generally rely on statistical inference to overcome the issues of validating modelled heterogeneity.

The accuracy of how well mDGP represents rwDGP depends upon a range of preliminary model- related factors, ranging from the quality of micro- and macro-parameters to the set of initial micro- and macro-conditions as proxies for the initial real-world conditions. The problems of model representation are compounded in models that deal with discrete events over time and where the real-world system appears to reflect non-linearities, randomness in individual agent behaviour (decision rules) and network communications, complicated micro- amd macro-stochastic processes that govern micro- and macro-variables, feedbacks where macro-level variables affect micro-level

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agent perceptions and adaptive expectations. Fagiolo, Moneta and Windrum (2007) regroup these issues into (i) the need to consider the nature of the objects under study, (ii) the goal of the analysis, (iii) the modelling assumptions and (iv) the need for appropriate sensitivity analysis.

In regard to the rwDGP, usually only a little is known about a system; what is and will be available is a unique but limited set of time series of observed data. In contrast the mGDP will typically have a set of initial conditions defined for an isolated set of what is assumed to be the main causal micro- and macro-parameters and relationships. The simulation model has the capacity to use these defined parameters and relationships to generate multiple time series of stylised traces of data to compare to the equivalent observable real-world data time series. Each time series can be rerun to explore the variability because the entities’ behaviour may or may not be ergodic and variations can compound over time, especially if the imposed bounds on model behaviour are too wide to maintain rational behaviour. These multiple run data sets can be analysed externally to generate a set of statistics on the micro- and macro-time series generated by the simulation model to see how ergodic or non-ergodic each variable might be. To investiage this risk, the model can be rerun to test variations in the limits of parameters, which govern each agent’s behaviour. The various sets of generated data can be used to generate Monte Carlo distributions for each statistic in the sense of its consistency under various assumed limits to its bounded rationality or initial conditions. The sets of statistics can be tested for differences between calculated moments. The greater the level of non-linearity and stochastic elements in the mDGP, the looser is the mapping between initial conditions, micro- and macro-parameters, and the moments of the distributions for the mean and variances of parameteers under investigation. This is a very analytical and rigourous approach and typically will involve considerable time and effort. It is similar to the approach advocated by Marks (2007) as decsribed in 8.1.5.5. As model design moves to be more empirical and specific, as in micro-simulation, and the objective moves from prediction to understanding, then some greater flexibility may be possible.

8.1.5 Various Approaches to Empirical Validation This section describes a number of different validation approaches to highlight different emphases for different types of ABM’s as well as to highlight the high level of commonality in what to consider, and the order in which it might be considered to validate the usefulness of an ABM. These descriptions, with the exception of the Marks’ (2007) approach in 8.1.5.5, are taken from the overviews provided by Fagiolo, Monetta and Windum (2007).

8.1.5.1 The Indirect Calibration Approach The indirect calibration approach is pragmatic and has been used where a detailed understanding of the inner workings of an economic system are not known. This approach first performs

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validation and then indirectly calibrates the model by focusing on changing parameters to generate results that are consistent with output validation. It is commonly used for models that keep the available micro-economic description as close as possible to empirical and experimental evidence about behaviour and interactions. Stylised facts typically are at and concern the macro-level and cross-sectional level of relationships of an economic system. The available data is used to set the parameters that will be modelled, the model is designed around these macro-parameters and cross- sectional relationships; the empirical evidence on these stylised parameters is used to restrict the space of these parameters, and the initial conditions if the model turns out to be non-ergodic. The calibration is achieved by varying the selected key parameters to match known, real-world outputs. The results can be improved by exploring statistical analysis of subspace of auxiliary parameters or stylised facts/results. Fresh implications can be used to alter the model and data.

This approach is criticised on two fronts: no attempt is made to calibrate the micro- and macro- parameters using their real-world equivalents and there are limits on exploring the understanding and plausibility of feedback influences if there is not a reasonable level of representation of the feedbacks between macro- and micro-levels of auxiliary entities within a subspace of interest in the model.

8.1.5.2 The Werker-Brenner Approach The Werker-Brenner approach (Werker and Brenner 2004; Brenner and Werker 2007) is a three-step procedure for empirical calibration and follows similar steps to the first two steps in the Indirect calibration approach. Step 1 uses existing empirical knowledge to calibrate initial conditions and where data is not available, Werker and Brenner advocate the model should be left general with wide ranges for parameters. Step 2 uses Bayesian inference procedures to reduce the plausible set of dimensions within the initial dimension space to validate each model specification derived from Step 1. Step 3 uses what Werker and Brenner call "methodological abduction" to invoke a further round of model calibration by testing alternative models to derive the most appropriate model structure. Like other validation-calibration techniques the adopted set of models does not help identify a "true" or real-world model if it is in fact unknown or not clearly understood but relies on the Bayesian analysis to select a model to represent the real-world; it can still be a false model and susceptible to bad results. These approaches also encourage model designs where calibration can be based on readily available data. Lack of cross-functional insights into how an economic system or market works at the participant level can influence model design if the modeller does not have or cannot gain such insights without a disproportionate effort relative to the modeller’s own expertise and goals. For example, agents’ mental models are an important component in many economic ABM’s (including VicGasSim), yet the mental models used by real-world agents tend to be unobservable to those outside the subspace in which such agents operate in practice. This hurdle can be described by holding workshops with industry practitioners (Sun & Tesfatsion 2007) to define in

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simple terms their key decisions and how they make them, insights to industry costs and profit goals, and where market rules are well defined in public regulations, which govern a specific market. The lack of mental models induces the modeller to work at a more abstract level and rely on statistical, Bayesian and abduction procedures to establish some reasonable level of confidence in validation completed using model outputs to explore an economic or market issue.

A related issue is that effective calibration requires a wealth of high quality data. The Werker- Brenner validation approach can invoke a need for a high level of data even if mental models are generalised due to its two step calibration process, where data is needed to support each model tested in their abduction stage.

In cases where the rwGDP is non-ergodic and evolves or changes over time, the initial conditions become not only very important but it may be necessary to regress over past history to calibrate the model. How far does a model need to regress? And how many macro- and micro-subspaces need to be accurately reproduced for a model to be deemed calibrated?

If observed parameters are time dependent rather than static then the modeller needs to consider how to model those parameters that are fast-moving from those that are slow moving. These differences need to be accommodated in the model and model calibration and validation procedures.

Counterfactual testing of predictions can also be used to test how a model copes with input or derived data that fall outside current history or expectations or how it copes with imposed or exogenous economic shocks. Are such predictions still understandable and rational? Can agents alter their behaviours adequately or is the model constrained by an unchanged or unchanging underlying model structure? Fagiolo, Monetta and Windum (2007) claim that the closer a model is to reproducing the micro-level fundamentals, including production functions and decision- influencing utility functions, the greater will be its capability to avoid mispredicting the consequences of an exogenously or endogenously imposed shock to the system.

8.1.5.3 The History-Friendly Approach Fagiolo, Moneta and Windrum (2007) point out that the history-friendly approach seeks to overcome the problem of over-parametrisation and bring modelling more in line with empirical evidence and reduce the dimensionality of a model. The history-friendly approach has been used more commonly in validating micro-economic transients (industrial paths of development). A "good" model is described as one that can generate multiple stylised facts observed in an industry. The approach follows the work of Malebra et al. (1999), and Malebra and Orsenigo (2002). Models can be built around a mix of available data, detailed empirical studies, industry case studies, and anecdotal evidence about the history of the industry under investigation. The wide and diverse range of data is used to design the model and specify the agent behaviours, decision

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rules, and interactions as well as the environment in which they operate. The data should guide the selection of the key variables likely to generate the observed history. Data is used to empirically validate the model by comparing its simulated stylised trace outputs with the actual history of the industry. Modellers use backward induction to arrive at a satisfactory approximation of structural assumptions. The high dependency of stylised designs on selected examples within the demand and supply side of any industry model restricts their universal application. The level of available data and case studies or access to industry experts will govern the level of stylised assumptions and mental models for the various agent types.

Such micro-focused models can be very powerful but a common weakness in their use to date has been the limited sensitivity testing, particularly cross-run variability to validate such models. An individual model trace may not be typical and many combinations of parameters might be able to produce similar output traces. Counterfactual testing also suffers because such models are stochastic, non-ergodic and structually evolve over time. The key to future success with these types of models is access to a level of high quality data; therefore, taking the time to build such databases can be an important part of model design as well as validation.

8.1.5.4 The Methodological Approach Tesfatsion (2006)1 notes the importance of what she describes as the main methodological aspects of validation of computational models. Bianchi, Cirillo, Gallegati and Vagliasindi (2007) summarised them under the following three techniques:

1. Input validation: ensuring that the fundamental structural, behavioural and institutional conditions incorporated in the model reproduce the main aspects of the actual system. This is called ex ante validation: the researcher, in fact, tries to introduce the correct parameters in the model before running it. The information about parameters can be obtained by analyzing actual data, thanks to the common empirical analysis. Input validation is a necessary step one has to take before calibrating the model.

2. Descriptive output validation: matching computationally generated output against already available data. This kind of validation procedure is probably the most intuitive one and it represents a fundamental step towards a good model’s calibration.

3. Predictive output validation: matching computationally generated output against yet-to-be- acquired data. Obviously, the main problem concerning this procedure is essentially due to the delay between the simulation results and the final comparison with actual data. This may cause some difficulties when trying to study long-time phenomena. Anyway, since prediction should be the real aim of every model, predictive output validation must be considered an essential tool for an exhaustive analysis of a model meant to reproduce reality.

1. Tesfatsion maintains a website that has an extensive section dealing with empirical validation: see http://www.econ.iastate.edu/tesfatsi/ empvalid.htm

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8.1.5.5 The Marks Approach to Sensitivity Testing and Analysis Marks (2007) points to two issues that have limited wider adoption of ABM’s. Firstly, model sufficency but not necessity, and, secondly the difficulties in verifying and validating such models due, perhaps, to their complexity.

8.1.5.5.1 Sufficiency and Necessity Marks (2007) describes how models can reflect the characteristics of sufficiency, whereby, and as has already been pointed out in 8.1.4, several models might be able to simulate a desired behaviour but how is a choice made as to the preferred model? The models reflect that necessity are those that can exclude non-acceptable solutions. Without a clear understanding of the range of possible models, it is difficult to generalise about those simulation models that might meet the test of necessity. Only when the set of necessary models is known to be small is it reasonably easy to use simulation to derive or meet necessity.

8.1.5.5.2 Validation Marks (2007) asks the question: what is a good simulation? And answers: one that achieves its aims. A simulation might attempt to explain a phenomenon, or to predict the outcome of a phenomenon, or to explore a phenomenon, i.e. to play with the model to understand the interactions of elements of the structure that produces the phenomenon. Marks presents a formalism for validation that is characterised in terms of model completeness (or incompleteness) and model accuracy (or inaccuracy) and combines these measures into a definition of a validity measure, which weights averages of what he calls (from statistics) the Type I error of inaccuracy and the Type II error of incompleteness. This formalism can be used to explore conditions when it might be more appropriate to settle for a more general and inaccurate model when the amount of prior observations is small. Inversely, an incomplete but detailed micro-model might be more appropriate for exploring model dynamics as changes are made to underlying assumptions.

8.1.5.6 Differences in Approach The approaches, which have been described so far, differ in the way they are applied. Indirect calibration and the Werker-Brenner approaches can be applied to both micro- and macro-ABM’s (from firms to industries to countries). The history-friendly approach only addresses micro- dynamics. and can use causal as well as anecdotal evidence. All approaches use data for model design and validation purposes but indirect calibration approach does not use data to calibrate initial conditions and parameters. The Werker-Brenner and history-friendly approaches perform model calibration first and then validation. Indirect calibration performs validation first and then indirectly calibrates by focusing on the parameters that are consistent with output validation. Tesfatsion (2006) settles for advocating alternative forms that share much in common with the

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history-friendly approach but also introduces the potential for using the benefit of elapsed time to check predictions against subsequently available data.

Whichever approach is adopted the major challenge is to define a sufficiently strong empiricial test or series of tests for an ABM and which match the purpose and nature of the model. Sensitivity testing has to be conducted before the validity of a model can be claimed and its predictions believed or accepted as basis for building a confident understanding of how an economic or market system might evolve over time.

8.2 VicGasSim Model Calibration & Validation The model is calibrated and validated in four ways. Firstly, the model is initialised with a vast array of actual (input) data,which was publicly available at model time zero (2000-01), plus a set of simple business decision-making mental models, which follow basic business rules that fit the Victorian gas industry in particular or business in general. Secondly, the model has been used to simulate the first full year (2001) of model-generated outputs for comparison with various sets of actual 2001 industry data, which were available in 2003 when these simulation runs were made; these short term outlooks are generated before the model dynamics and complex agent interactions can significantly influence industry behaviour. Thirdly, VicGasSim predictions, which were made in 2003, were compared to actual historical trends of key historical trends from 2002 to 2008, which was available at the time of finalising this thesis. Fourthly, VicGasSim post 2008 long term predictions, which were made in 2003, are compared to more recently published post 2006-08 predictions from other commercially available models.

8.2.1 Input Data Calibration VicGasSim’s design calls for the model to be initialised with a significant level of industry data and characterisation. For example, market participants by name, ownership, project participation, initial supply contract volumes; gas reserves, production rates, capacities, development plans and costs; transmission systems, transmission zones and charges, injection points and charges; distribution systems and charges; customers, demand, demand functions by customer type and season, reportable ranges of consumption, regulated retail prices; historical weather and day-of- the week patterns and their correlations to gas demand, price elasticity of gas demand to the price of gas and the price of electricity. Values for the vast majority of this information is available in a wide array of industry and Government documents (see 7.4.1) and has been collected and used to substantiate the various agents of a relevant model and to initiate the settings of the various public and private model data sets for the main agents in this market.

In numerous cases, the author has had to use his judgement to allocate actual data to market segments and sub-segments. For example, in the year 2000, gas demand and the number of

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customers is available for total Victoria; these totals have been allocated to regions and per Retailer, using other statistics such as number of post codes in a region or by public, anecdotal descriptions of the make-up of each Retailer’s mix of customers: for example, TXU starts with a dominant position to the west of Melbourne; this is predominantly industrial area and a higher proportion of industrial customers have been allocated into this area; the location of major customers such as GPG plants and these have been allocated directly to each area.

The author, as part of his industry experience, has also collected a lot of anecdotal evidence about Retailer and Producer overheads, the distribution of net retail profits per customer type, fixed and variable costs for Retailers and Producers. The author has also used his own exposure to the industry to design a set of very simple business decision-making models for use by the main agent participants in the model. All of these mental models are defined in terms of making decisions based on current prices or trends in prices, costs, profits, market shares, levels of under-utilisation of plant capacity, MDQ’s, ACQ’s, TOP’s, lead times for supply expansions and development costs, and other commonly used industry measures. There are only a few instances, where general algorithms or assumptions are used in the model. Some examples include: annual economic growth; annual indexation of electricty prices, price elasticities, churn assumptions, interstate gas demand supplied from Victorian supply sources; these are areas that can be improved in future enhancements of the model but are outside the focus of testing and validating the initial design concept.

Table 7-11 lists the exogenous (input) variables that are initialised in the model; Appendix 2 contains detailed description of the model variables and an example of an actual input data file.

Numerous runs were performed initially to fine-tune the model settings to generate rationally bounded forward behaviour for each agent. For example, the bounds on rates of change in altering wholesale bidding strategies and the variables governing the rate of change of prices or profit margins were tested. Once the fine-tuning was completed, various simulation runs were made to generate a number of long term outlooks.

8.2.2 Calibration by Short-term "Descriptive Validation" The following sections present the model’s forecast outputs over the year 2001. This follows the "descriptive validation" approach described by Bianchi et al. (2007) in 8.1.5.4. In some areas, outcome profiles were judged by their nature or characteristics if there was no available actual market data against which to compare the model data. Where such market data was available, it was compared to the model outputs over the same period. These comparisons were made to check that input details for each agent and the agents modelled behaviour and programmed interactions would generate model outcomes that mimicked market outcomes closely but no statistical determination has been made for the closeness of the fit (given a proof of concept is being claimed).

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8.2.2.1 Consumer Markets In the model, the consumption of gas is characterised as falling into eight Customer size ranges. Forecast demands for these size segments are shown in Figure 8-1.

Figure 8-1: Withdrawal Patterns & Levels By Customer Size

2001 Withdrawals by Customer Size Range Model Results 1000 Customer Size Range, TJ/D 900 0 - 0.1 800

0.1 - 0.2 700

0.2 - 1.4 600

1.4 - 5 500

400 5 - 10

300 10 - 100 Withdrawals, TJ/D Withdrawals,

200 100 -1000

100 1000+

0 1-Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec Days of Year

Each customer size range is specified by differing demand characteristics, whereby the smaller residential Customers (at the top of the plot) show greater seasonal volatility than do larger commercial and industrial Customers (at the bottom). The patterns are as expected but no public domain data has been located to make direct comparisons at this level. Generation of these detailed forecasts required best-guess allocations1 of the number of Customers into the 200 odd market segments in the model and generation of differing seasonal load factors for each Customer segment. However, the patterns and relationships match expectations and can be better judged by comparing the composite model demand for the whole state, by aggregating all of the individual sector demands, versus the actual Victorian demand data profile, which was published for 2001. The comparison is shown in Figure 8-2.

1. See 8.2.1 for explanation of how year 2000 input data was allocated.

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Figure 8-2: Simulated Victorian Gas Demand Pattern for 2001

TJ Simulated Actual 1000

800

600

400

200

Days of Year

The simulated demand for the total of the 200 odd simulated segments making up the composite statewide-demand broadly matches the overall seasonal demand pattern and market volatility. But there are a number of differences that could be improved; (i) the overall demand level needs to be raised, and (ii) there is a lack of volatility over the summer period (Dec-Mar). The minimum limits for a selection of the 200 odd demand segments should be increased and the volatility in the summer period should be increased to improve the overall match.

Given the objective and emphasis of this initial research to establish a working model with a heuristic emphasis on wide use of simple business based mental models rather than learning and genetic algorithms, the current match is assumed sufficiently adequate for judging the model’s ability to tag and track the profit effects of varying load factors by regions, tariffs and Customer size groups. These regional and seasonal variations affect the calculation of each Retailer’s cost of gas purchases, pool prices, gas trading costs, tariff costs, and derived industrial market prices. These latter calculated and accumulated data plus other commercial measures allow assessment of profits per Customer type and total Retailer profits.

8.2.2.2 Wholesale supply market Each day, Retailers formulate a bid-stack, which they provide to Vencorp. Vencorp formulates an integrated price-ordered bid-stack, which it uses to decide which sources of gas and quantities of gas from each source are to be produced each day. A daily pool price is set each day at the lowest priced bid that clears the market. The pool price is used to settle daily net trades between the participants putting gas into and withdrawing gas from the PTS. Typical simulated bid-stacks for the three major Retailers in winter are shown versus published data for bid-stacks for 18 June 2001.

195 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Figure 8-3: Comparison of Actual Vs Modelled Bid Stacks Retailer Bid Stacks Simulated Vs. Actual (18 June 2001)

9 Simulated R1 8 Simulated R2 Simu;lated R3 7 Actual R1 Actual R2 6 Actual R3

3 Prices, A$/GJ

0 0 100 200 300 400 500 600 Volumes, TJ

In Figure 8-3, the character of the modelled bid-stacks shows a similarity to the character of the real profiles selected. They have a lot of overlap around the $2.7-$3.20/GJ price level which is at or just above the cost level of major Retailers’ main supply contracts. The higher bid prices at and above $3.5/GJ represent the prices to cover supply from more expensive interstate sources and for gas from the WUGS and LNG storage facilities.

The profiles differ in their low and high hedge sections. Retailers hedge their base volumes by pricing them at low or zero prices to ensure they are accepted into the PTS so they can meet their own expected demands at a guaranteed cost (their own contract costs) and/or to ensure they lift the minimum volumes required under their various supply contracts. Retailers hedge their surplus or peaking capacity to ensure they can supply themselves on peak days with supply from their own higher cost sources.

They can also take positions to sell gas at or below break-even prices to ensure they meet contractual offtake requirements or to take a risk position by pricing gas high to make a profit from other Retailers, who may have poorly forecast their needs or who might be short of stored gas on peak days. Priced too high, this opportunity will be lost to other parties.

Hedge positions need close monitoring of one’s own expected demands and supply contract positions as well as those of competitors. Hedging strategies will change over time as parties gain or lose ground in forecasting demand and managing their various supply and storage contracts.

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Prototype # 3’s ability to simulate participants daily bidding strategies and ability to replicate the daily pool price outcomes over a full trading year is shown in Figure 8-4. This figure compares simulated pool prices with actual daily pool prices as set by Vencorp over the year 2001. The variation in daily pool price reflects the interaction of weather and demand variations; forecasting accuracy; contract terms including the cost of gas, minimum and maximum rates; IP and pipeline and storage capacities; and bidding strategies.

The character of the simulated pool price profile, which is shown in Figure 8-4, is judged adequate for testing the model’s capability to replicate the related interactions that drive wholesale trading costs or profits over an annual seasonal cycle. As the market grows and existing supplies tighten in their ability to meet demand, price volatility is expected to increase, at least until new supply sources enter the market when the whole price framework should stabilise but at a generally higher (or lower) level.

Figure 8-4: Comparison of Actual Vs Simulated Daily Pool Prices for 2001 4

3.8

3.6

3.4

3.2

3 Simulated

2.8 Actual Price, A$/GJ 2.6

2.4

2.2

2 1- 1- 1- 1- 1- 1- 1- 1- 1- 1- 1- 1- Jan Feb Ma r Apr Ma y Jun Jul Aug Sep Oct Nov Dec Days of Year

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8.2.2.3 Retail Gas Prices for Each Consumer Size Group The simulated average retail gas prices as they stood in 2001 for regulated and derived prices across the various markets are shown in Figure 8-5. For small Customers the prices represent the average of actual regulated prices in those ranges. For higher offtake Customers the average prices include an increasing percentage of Tariff D Customers with unregulated prices, which are simulated by negotiations within the model.

Figure 8-5: Simulated Average Retail Pricesy For Each Customer Size Type

8 Major Retailer #1 Major Retailer #2 6 Major Retailer #3

Price A$/GJ 4 Minor Retailer #1 Minor Retailer #2 2

0 0 -0.1 GJ/ D 0.1-0.2 0.2-1.4 1.4-5 GJ/ D 5-10 GJ/ D 10-100 100-1000 1000+ GJ/ D Average GJ / D GJ / D GJ / D GJ / D Customer Size

The price differences mainly reflect the differing costs of each Retailer in supplying each market segment. For 1998, AGA (2001) reported the average residential gas price in Victoria was $8.28/ GJ and that the average commercial and industrial price was $4.35/GJ. The 1998 values compare well with the range of 2001 prices generated by the model as shown in Figure 8-5. Some market price escalation occurred between 1998 and 2001.

The price differences within each Customer size range indicate the relative hurdles some Retailers will need to overcome in certain market segments to improve their competitive ability in those markets by changing their supply mix and/or reducing various costs. Alternatively, they indicate the natural advantages that some competitors will use to improve their positions.

8.2.2.4 Retailer Profitability Over the year, the model calculates and accumulates the costs by each Retailer in supplying gas to its average Customer in each geographic, distribution and pricing segment. This includes the cost of gas supplied by Producers to the Retailers from contracted and uncontracted sources, cost of storing and reproducing gas for peak periods, trading costs or profits to settle daily market imbalances, the transmission tariff to get gas into the PTS at each injection point and daily and peak transmission charges to each customer type in the various TZ’s in each market region, the local distribution charges, and each Retailer’s own fixed and variable administrative charges allocated to each segment.

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Each Retailer has a series of model accountants that track all these costs or credits on a tagged basis as they vary each day. The Retailer subtracts the total annual cost to supply the average customer in each classification to the total relevant revenue under regulated or newly contracted prices. The model can determine each Retailer’s annual average profit margin for each Customer type. Retailers use this data to assess their forward pricing strategy.

The model currently outputs an accounting summary for each of the three major and two minor Retailers represented in the model. The main objective at this current stage of development has been to check that the derived profit margins across the various classes of Customers follow expected patterns and that total Retailer profits come out at the author’s expected order of magnitude and the expected directional relativity between Customers. Closer calibration needs access to confidential records of the companies, which are unlikely to provide such data unless they are using the model for their own purposes. It is hoped this will occur in the future.

The estimated profit distribution by customer size classifications and total year 2001 profit for one of the major Retailers are shown in Figure 8-6.

Figure 8-6: Retailer Market & Profit Performance T yp ical M ajor R etailer

Profit/Loss 400 O verhead 400000 D is tribution Transm ission Trading 300 Storage 300000 P roduc tion Cum Sales Pofit Margin 200 No of Custom ers 200000

10 0 100000 Cumulative Sales, TJ Costs/Profits, A$ milions Profit Margin, A$/Customer No of Customers, Thousands 0 0 0-0.1 0.1- 0.2- 1.4 - 5 5-10 10 - 10 0 10 0 - 1000+ Total GJ/D 0.2 1.4 GJ/D GJ/D GJ/D 1000 GJ/D GJ/D GJ/D GJ/D

-100 -100000

The figure shows the interplay of Customer numbers, costs and pricing in each Customer group. It also shows the expected cross-subsidisation of losses on the smaller residential and commercial customers by the profits on larger commercial and industrial customers. For this Retailer, a loss is incurred on the smallest customer group but this are more than offset by profits on the fewer but larger and more profitable commercial Customer groups. The range of profit margins broadly matches anecdotal advice about those achieved in the real market. Similar information is assessed for all competing Retailers in the model.

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The total annual profit of A$30 million shown in Figure 8-6 for this simulated Retailer is in line with the author’s expected order of magnitude for a major Retailer operating in this market in 2001. Some major Retailers are expected to be above this and some below.

To gain more confidence in the total retail profit estimate would require working closely with one or more Retailers to gain improved insight to the assumptions made. More time can be spent on compiling public information from participant submissions to inquiries by the various regulators; this type of effort should allow improvements in the model outlooks.

Study of the breakdown of total annual costs versus annual profits shows that just small changes in costs in the larger cost segments could have large effects on profits. This detailed division of costs and profits in the model will allow future pricing strategies to be linked to cost advantages or disadvantages in each Market segment. Retailers considering full contestability in every possible Market segment will be able to assess, via a range of strategies, how to price gas in each of approximately 200 possible Market segments to improve overall Retailer profitability over time.

8.3 Predictive Calibration & Predictive Output Validation VicGasSim output traces for various market characteristics can now be compared to actual history for the period from 2002 through 2007 and, in which period, actual history shows material changes in the direction of data trends for average industry demand and gas supply prices. This comparison follows the "predictive calibration" approach described by Bianchi et al. (2007) in 8.1.5.4. Longer term predictions post 2007 are compared to a number of third party forecasts made in 2007 and 2008 and where the latter forecasts have had the benefit of being able to "match" history up to 2007-08. This comparison aligns with the "predictive output validation" approach also described by Bianchi et al. but in this case relying on comparisons to other forecasts rather than waiting for further actual history to accumulate.

Comparisons are shown in the following sections; the extent of these comparisons is quite limited and any validation conclusions must be limited to only stating that comparsions are encouraging and sufficient only for establishing a proof of concept of the proposed model design.

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8.3.1 Comparison of Old and New Scenarios and Actual History (2002-2007)

8.3.1.1 Demand Figure 8-7 shows a comparison between a demand outlook developed in 2007 by Vencorp (2007) (which includes actual history for 2002-07) and the outlook prepared with VicGasSim, where the latter outlook is based on the modelled market emergence reflecting the character of the agent participants and their resources as they were seen considered to be in the 2000-02 period.

Figure 8-7: Comparison of Old & Recent Demand Forecasts

The VicGasSim outlook, which was developed in the early 2000’s, comes very close to matching actual data from 2002 to 2007. VicGasSim’s longer-term outlook still matched reasonably well to Vencorp’s 2007 forecast of system and GPG demand in the period to 2018 (excluding GPG demand, which is due to the more recently planned introduction of carbon trading).

At the same time (2002-03) that the above VicGasSim prediction was generated, Vencorp (2001) had just published a range (high, medium and low) of demand forecasts for both system and GPG demands, the forecasts have been extrapolated over the period to 2020 (Vencop’s system demand went to 2016 and its GPG forecast only went to 2006. Vencorp’s earlier range of forecasts are compared to both the 2002-03 VicGasSim model forecast and to Vencorp’s updated 2007 forecasts for system and GPG demands as well as a new forecast for the extra GPG demand to be stimulated by the introduction of carbon trading.

Vencorp’s 2001 forecasts (Vencorp 2001) were prepared by the National Institute of Economic and Industry Research (NIEIR). NIEIR used a series of econometric models, which are not well suited to forecasts where the industry is about to undergo major structural change and deregulation. These

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models are driven by projections of Gross State Product, projections of state industry and population growth as well as forecast gas prices. Independent input was developed for special areas such as cogeneration and air-conditioning effects which were seen as important at the time. Instrumental in generating the large spread between the high and low gas demand forecasts are the assumed high (3% pa) and low (1% pa) assumptions on economic growth. These assumptions are the primary drivers of the spread of NIEIR’s demand forecasts. The difference between compounding growth by 3% pa versus 1% pa accounts for virtually all of the difference between NIEIR’s low and high forecasts.

While NIEIR explain in some detail the bases of their economic forecast, they say nothing about how they forecast gas prices, which they show as averaging -0.7% pa from 2001 to 2007 and which they held constant in real terms from 2007 to 2016. As a result, their forecasts show no deviation over time due to prices, which have actually turned out to increase materially and where ACIL Tasman (2008) is now predicting continuing increases in real prices, as will be discussed in 8.3.2.

The forecast generated by the VicGasSim model, which simulates increases in real gas prices and accomodates the price elasticity effect of those increasing gas prices, shows a gentle rollover of system and normal GPG demand from 2002 to 2008 with a continuing gentle decline in demand out to 2020. Vencorp’s 2007 forecast shows a very similar pattern to that of the VicGasSim model with the rollover occurring a little earlier and essentially flattening over the longer-term.

Both actual history (2002-07) and the Vencorp 2007 forecast assume higher economic growth than does the VicGasSim model scenario. If VicGasSim’s assumed growth was increased, its demand outlook would probably lift above actuals but this relativity would be offset by the fact that VicGasSim has used an old price elasticity, which was derived from an historical period when gas prices had been relatively stable under the Government controlled retail monopoly. While recent price elasticity data has not been analysed, it is highly likely that it has changed since the time that the VicGasSim model scenario was generated.

The VicGasSim model appears to have done a better job than Vencorp/NIEIR of simulating this aspect of the market place – due to its ability to forecast the increase in gas prices over time. It would appear that the VicGasSim model gains its extra insight by being capable of forecasting the increase in gas supply prices that has taken place and is now similarly forecast by third parties to continue (see 8.3.2).

Few early 2000 forecasts (except VicGasSim) were predicting wholesale price increases of the magnitude that has occurred. By forecasting these wholesale price increases and allowing for price- demand elasticity, the VicGasSim model forecasts a demand outlook of the total Victoria system plus GPG demand (excluding effects of emission controls and recently proposed GHG policies) that has come close to matching actual demand to 2007. VicGasSim’s forecast over the remaining outlook period to 2020 also compares well with the latest 2008-2020 forecast by Vencorp (2007).

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8.3.2 Gas Supply Prices A comparison of gas supply prices predicted by various groups is presented below in Figure 8-8. The figure shows nine forecasts (for each source) from VicGasSim, three (specifically defined high, mid and low averages for Victoria) from ACIL, and one (average Victoria) from NIEIR. The data is presented in 2008$/GJ to allow current relativity to be appreciated. Where required, forecasts have been CPI-adjusted to present them on a consistent 2008$/GJ basis.

Figure 8-8: Comparison of Old & Recent Gas Supply Price Forecasts

2001 VicGasSim Model Vs Other Forecasts in 2001 & 2008 2008$/GJ 7.00

6.50

6.00

5.50

5.00

4.50

4.00

3.50

3.00

Cooper Basin Gippsland Basin Onshore Otway - Origin Onshore Otway - Santos Patricia Baleen Bass Gas Minerva Thylocene-Geographe Timor Sea ACILTasman(2008)-VicAvg ACIL(2001)VicAvgConstrained ACIL(2001)VicAvgCompetitive NIEIR/Vencorp(2001)

The comparisons show that in 2001 neither ACIL nor NIEIR was able to accurately forecast future gas prices. Why? They use lowest cost-based methods for estimating what producers would charge for their gas. Their forecasts suggest they assumed that competitive buying pressure would keep prices low and close to the then existing Gippsland gas that was contracted in the late 1960’s. It also reflects a belief that Gippsland producers had spare capacity and would not allow other producers to get a foothold in the market and would keep their prices low.

The VicGasSim model was the first model to forecast increasing supply prices. This was so because it was the first of these models to allow for a balancing of forces between and amongst both multiple Producers and multiple Retailers and to incorporate an outlook for increased development costs for future gas supplies. Producers in VicGasSim discerned that Cooper Basin Producers needed material price increases to develop extra capacity to meet SA and NSW needs, let alone marginal Victorian demand. In reality, one of the Gippsland producers held minority

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interests in the Cooper Basin fields and would have been aware of this situation and would have been looking to place their surplus capacity into these adjoining markets at higher prices to match SA prices in SA and NSW. They would have wanted to use these benchmark prices to influence (higher) Victorian gas prices. They also did not want to face expanding their onshore processing capacity at high cost for a short production peak. They might have considered the trade-off of allowing new Producers to enter the market while they waited for higher prices to fill the perceived, impending supply gap. VicGasSim allows this interplay of forces to be played out in terms of the various agents’ differing resources and market positions and risks.

Figure 8-8 shows that, in 2001, NIEIR appear to have fallen into the trap of assuming lowest cost- based pricing, which tracks then existing Gippsland contract prices, would not increase. NIEIR failed to pick that future prices would jump according to emerging competitive forces and cost increases. The figure also shows that from 2011 Gippsland prices are now forecast by ACIL Tasman (2008) to jump markedly in real terms.

ACIL’s 2001 Competitive scenario fell into a similar trap of assuming Gippsland would hold prices down. Although it did pick that prices would jump in 2011 but not as much as forecast by VicGasSim or by the more recent 2008 forecast by ACILTasman.

A number of new Producers entered the market post 2001. It is possible the first several gas sales contracts might have been low-priced to gain first entry and were possibly influenced by Origin’s participation on both sides of the bargaining table in terms of first contracts with the Yolla JV. A similar logic might have applied to OMV bringing gas from the Patricia Baleen JV into the market early. OMV may have kept the initial sale price relatively low to get into the market before the then current gap was filled by others. Awareness of these low prices might have influenced NIEIR and ACIL’s 2001 forecasts.

Other larger discoveries that followed in offshore Otway were closer to the SA market, which was facing price increases from the SA Cooper Basin Producers. The Longford explosion/fire also changed the pricing dynamics to support a quick step up in pricing of new Gippsland supplies. While VicGasSim participants did not know all aspects of their competitors closely, the model dynamics provide a good feel for future directions and understanding of who held more leverage than others. Similarly, Retailers in the model talk to all potential old and new suppliers. All could soon see that the real price benchmark was in matching future Cooper Basin supply prices into SA and NSW; both old and new Victorian gas suppliers could ship gas to both SA and NSW against these higher prices, but they had to allow for the extra transmission costs to SA and NSW.

Because Retailers held declining legacy contracts from Gippsland, they could never contract for the large entry volumes that Producers were seeking to underwrite the costs for the new offshore developments. Therefore, most new developments included contracts that shipped volumes into

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SA or NSW or Tasmania as well as filling the growing gap in Victoria. This dynamic cycle reduced the Retailers’ leverage to constrain prices for gas supply into the Victorian market.

These dynamics are not hard-wired into VicGasSim but rather VicGasSim’s agent participants quickly gain an "understanding" of some or all of the market elements described above; this ability to "understand" is built into the agents’ profiles and communication methods. All of the data used to characterise each participant came from published information or from the author’s general domain knowledge that was no more or less than others working in the industry at that time. It is this domain characterisation of participants’ cost structures, profit goals and reactions to losing or gaining market share that influenced the pricing pattern shown in Figure 8-8. The levels of characterisation are very domain-like but modelled in relatively simple terms that broadly mimic the way participants think and talk to each other when negotiating or making decisions. It is confidence-building that the VicGasSim forecast, which was generated in 2000-03 and published in 2003, has proved more accurate than others made at the same time. One example where the model was not close was the timing when Minerva comes on stream. The model forecast this to be much later than it did in reality; this probably reflects its relatively small size and the model must have overcosted its development relative to other developments. Model improvements are possible and these are explored in Chapter 10.

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8.3.3 Number of Customers Table 8-1 compares the actual growth or decline in the number of Customers serviced by each Retailer over the period from 2002 to 2005-06 to that predicted by the model and to the model estimates adjusted from model assumed economic growth of 1% pa to actual economic growth of 3% pa.

Table 8-1: Comparison of Numbers of Customers by Retailer

Number of Customers

2005-06 Model 1998 Estimatesa 2002 Modelb 2005-06 Actual 2005-06 Model Adjustedc Pulse 504,000 535,327 516,796 566,554 608,252 TXU 398,000 418,152 448,628 425,456 456,770 Origin 501,000 544,869 561,604 536,889 576,403 Energex & 9,121 103,794 13,343 14,325 Others Total 1,403,000 1,507,469 1,630,822 1,542,242 1,655,750 a. Estimates for 1998 from "Victoria’s Gas Industry – Implementing a competitive structure" (Energy Projects Division, Department of Treasury and Finance, April 1998) b. Adjusted for ~1% pa actual growth over period from 1998 to 2002 c. Adjusted for 3% pa actual growth versus 1% pa assumed model growth over 3.5 years

Source: AER (2007, p. 290)

The key points to note from the data in Table 8-1 are:

1. The model gets the right trends for both Origin and TXU and, when adjusted for economic growth, comes reasonably close to actuals (with the exception of the new entrant, Energex).

2. The model does poorly in estimating the performance of Pulse; the model forecasts that Pulse will win new customers when in reality it gains very few Customers.

3. Energex and other minor entrants have done reasonably well in winning customers from the major retailers. The model did not predict this outcome.

Why has the model not done so well in forecasting the number of customers? There are several reasons. As already explained, the model did not allow swapping of Tariff V customers (only the larger business and industrial customers) and churn was capped well below what has turned out to have occurred. Pulse with the largest number of residential and small commercial customers was most susceptible to customers swapping to Energex.

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8.3.4 Retail Gas Prices Figure 8-9 shows a comparison of actual gas prices for households against forecasts generated by the model. Actual data is taken from AER (2007, p. 300) and is based on ABS records; the actual data is presented as an index of inflation adjusted average gas price for households (small users) in Melbourne. These are compared to the average gas price for all customers as forecast by the model for each Retailer and CPI-adjusted to the same index basis.

Figure 8-9: Comparison of Retail Prices

Source: AER (2007, p. 300) for actual data from 2000 to 2006

The comparison shows that Origin starts with a higher average price as a result of its higher regulator-approved Tariff V prices in 2000; these higher approved prices reflect that Origin’s different geographic market and distribution network required higher prices to cover higher costs.

The model forecast that retail prices would start to increase in real terms in 2006-08, whereas actual data shows a real prices increase starting to occur as early as 2004. This difference reflects the model’s weakness in not adequately modelling early contestability. This difference can be addressed by fine-tuning the characterisation of each Retailer’s cost base, adjusting for higher economic growth and allowing price adjustments to occur for smaller Customers rather than requiring the model to take up this cost pressure via high prices to commercial and industrial Customers. However, there is still a strong correlation between the trend of actual, recent retail price history and the model forecasts.

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8.3.5 Production Figure 8-10 shows a comparison of the model forecast of supply sources as a percentage of market demand in 2008 versus data from Vencorp’s 2008 Annual Planning Review (Vencorp 2008). The comparison should only be taken as an indicative comparison since Vencorp only presents a summary distribution of total 2008 supply from all Victorian sources, including gas that moves interstate to SA, Tasmania and NSW. Vencorp indicates a total of 375 PJ of gas supply would be available in 2008 versus Victorian system and GPG demand of about 230 PJ. Vencorp’s supply data has been normalised to percentages of the gas actually consumed in Victoria. The model data is also expressed as a percentage distribution to compare to the Vencorp data.

Figure 8-10: Comparison of Supply Sources ComparisonComparsion of 2008 of Supply 2008 Sources Supply Sources 100% Thylocene‐ 90% Geographe ia 80% r Bass Gas to ic 70% V m 60% Patricia Baleen o fr 50% n io 40% Otway & Other ct u d 30% ro Gippsland P 20% 10% Cooper Basin 0%

Vencorp(2008 ) Model

The key point to note is that the distributions are broadly aligned and show that the model outlook made in 2002 generated a very similar distribution to what has actually happened in the market. The projects that have entered the market are similar to those as projected back in 2002. No one source has cornered a dominant position in capturing volumes, as the old Gippsland contracts expire and decline.

AER (2007) makes a comment in its 2007 State of Energy report that contract lengths have tended to become shorter. Observations of model data shows the model contract transactions in the period to 2007 are shorter than is traditional – most probably to accommodate the rate of change in supply as the existing Gippsland contracts wind down.

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8.4 Conclusions about Validity of VicGasSim and Its Outputs VicGasSim has been shown to be initialised with a significant proportion of its input variables tied to actual industry data. Its design meets the broad design evaluation criteria described by Zlotkin and Rosenschein (1996) and follows the trend to more micro-simulation designs, as used by Sun & Tesfatsion (2007) for the AMES model. VicGasSim is founded on a set of many simple mental models reflecting basic business rules and the author’s industry experience. The model has generated a set of first year (2001) simulation outputs that closely match the actual historical traces for numerous industry data sets as well as generate profit outlooks by segments and total that reflect anecdotal comments provided to the author by various industry contacts, who were consulted during the development work on the model. A limited set of medium term outlooks for various industry parameters overlap available history from 2002 to 2007. A similar limited set of long term outlooks from 2008 to 2020 overlap more recent predictions generated by commercial forecasters with the benefit of far more recent history, early emerging trends following deregulation, and entry of a number of new supply sources.

The limit to making greater claims for VicGasSim lies in the lack of rigourous follow-up testing such as proposed by Marks (2007) and described in 8.1.5.5. More sensitivity testing is needed to test replication ability, eliminate scenario failures where the model moves outside the rational bounds of agent behavioural patterns, test variations in the assigned bounds of rational behaviour, and conduct extensive statistical analyses of multiple model runs.

Now that a workable model exists and can be demonstrated to industry participants, further efforts can be made to elicit assistance from such participants to better calibrate what are some of the more commercially sensitive parts of the model, such as Customer distributions and participant cost structures.

Overall, the relatively simple calibration and validation exercises, which have been completed, are judged to provide sufficient support to claim a "proof of concept". This claim relates to the VicGasSim model, its design and the understanding that can come from a model that is predominantly framed in business terms with a long term strategic focus on all sectors/segments of the industry – not just one or two as is typical of many of the industry model designs, which have been reviewed in this thesis.

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210 Chapter 9 Exploring VicGasSim’s Potential

Chapter 9: Exploring VicGasSim’s Potential

9.1 The Need to Understand What Drives Emergent Outputs A number of long term outlooks have been generated, analysed and compared in order to explore agent behaviour and to consider the effect of providing agents with intelligence via the model’s look-ahead capabilities, which were described in 6.3.5, 7.2.3 and 7.4.2.1. Outputs are presented on the basis of agents applying both static1 and dynamic (look-ahead) strategies. Both approaches show the potential of the model to help market participants understand how their industry might evolve over time, what might be the key factors or trends that could influence their own future, and how they might position themselves to best respond to possible changes. Understanding how and why scenarios differ may assist in identifying dominant influences or underlying market forces that pre-ordain that certain strategies or market participants, who use such strategies, are more likely than others to succeed or fail.

The following sections present a series of outlooks, which are generated by the model when agents use, firstly, static strategies and then dynamic strategies selected by agents using the model’s look-ahead capability.

9.2 With Static Strategies The key behaviour patterns adopted under static strategy scenarios are:

1. All agents retain their assigned strategies but they can vary their settings for implementing each strategy or plan in relation to prevailing relevant circumstances.

2. Retailers and Producers do not alter their individual contract negotiating strategies but they retain their preferences and plans for contracting TorP, MDQ, ACQ, contract lengths, and contracting lead times and rules for altering these preferred terms.

3. Retailers maintain the same wholesale bidding plan in terms of the way they shape their bidding curves such that they only vary offers in response to the prevailing state of their supply and storage contracts and obligations as well as their expected Customers’ demands each day. Plan implementation is altered depending upon daily, seasonal and annual circumstances.

1. The term "static" means application of assigned strategies without use of the model’s look-ahead capability. Assigned static strategies remain in place for an entire model run. Under a static strategy, the implementation takes account of prevailing circumstances at each point in time and the implementation will still vary the action taken or decision made from previous choices. By comparison, when the model runs in look-ahead mode, a number of strategies are available for use in a specific area and the agent uses the look-ahead capability to choose which strategy to apply in each time frame and the strategy choices can be varied over time; look-aheads provide more strategic variability and choice is based on assessing potential future impacts versus static strategies, which determine choices based on one strategy approach, which is assessed on past or prevailing circumstances.

211 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

4. Retailers set their initial retail price offers to supply unregulated Tariff D Customers (i.e. the larger commercial and industrial Customers) by adding an assigned profit margin to their own changing total supply cost base and altering this margin based on their immediate past competitive position.

5. Retail margins are altered in reaction to changes in past period market shares.

6. Producers alter their prices to Retailers for new supply contracts; they base this on their own cost structure and the level of utilisation of any existing or on-line production or processing facility. As above, they alter their profit margin based on existing circumstances.

7. Distribution and Transmission tariffs remain fixed over the outlook period; they are currently highly regulated and regulation is expected to prevail for some time.

8. The Regulator does not change the regulated prices in the future despite changing Market conditions1. This means that if Retailers’ costs rise they will be forced to recover costs from the unregulated sectors in the Market; inversely, if costs drop, Retailers will be allowed to retain additional profits from all sectors.

The following sections present a series of model outputs that characterise Retailers’ profit performances when they apply single, unchanged strategies and plans, whereby they react to changes in their circumstances using their basic business mental models to consider past and current information (but they do not consider future changes); they can adjust the way they apply each strategy or plan within the allocated bounds of rational behaviour for such a plan. Explanations are given for various aspects of the model’s predictions and future scenarios.

1. This assumption has been previously acknowledged as unrealistic but model development work ceased before the model design could be altered to allow for periodic review and modification of regulated retail prices to reflect each Retailer’s changing cost structure. Such a change needs to be made in any future model development work.

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9.2.1 Gas Supply Prices Figure 9-1 shows model forecasts of gas supply prices.

Figure 9-1: Average Gas Supply Prices.

5.50 Cooper Basin

5.00 Gippsland Basin

Onshore Otway - Origin 4.50 Onshore Otway - Santos

4.00 Patricia Baleen

3.50 Bass Gas

Minerva

Gas Price, 2000$/GJ 3.00 Thylocene-Geographe 2.50 Timor Sea 0 02 04 06 08 1 16 18 20 0 0 0 2 20 20 20 20 2012 2014 2 2 20 Years

The prices shown are the modelled average prices obtained by each supply source in each year. The price is the weighted sum of the prices for all contracts written to supply gas for a given year from that production source to all Retailers. Individual contract prices vary significantly depending upon when each contract was written. For example, the early average Gippsland price reflects contracts, which were written in the sixties and expire in 2011. In 2012, the average Gippsland price jumps discontinuously, reflecting the model’s assigned price structure for future Gippsland gas supply, which will require sizeable new investments, and whose owners will try to push up prices more in line with other competing sources as shown in Figure 9-1.

This pricing outlook is influenced by the Producers’ pricing strategies, competition and Retailer flexibility. Producers’ strategies are similar, in that each Producer sets a goal for the profit margin (say, 30%), which it desires to recover on its average supply cost (which is based upon assigned fixed and variable cost components to supply Retailers and where the fixed component is scaled to allow for amortisation of capital costs over different volumes). These profit margins are varied depending upon the annual status of each Producer’s assessment of its capacity utilisation. If under-utilised and below a set minimum limit, it reduces its profit margin goal when setting prices for new contracts in future years. If close to capacity (above a set maximum utilisation limit), the Producer raises its profit margin goal in setting prices. The effectiveness of this supply/ price competition is seen in the reasonably uniform price growth and convergence between average prices from Gippsland and BassGas and to a lesser degree from the Cooper Basin. Average prices from the other major supply source, Thylocene-Geographe, remain relatively

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constant over a long time reflecting that in the process of making an initial commitment to a major new offshore development, these Producers sought commitments from Retailers to buy large initial contract volumes over a long contract period given market prices were still at moderate levels. The combination of long commitments with moderate prices was required to make the large development commercial. This position is similar to that governing Gippsland’s constant prices pre-2011. Retailers, who lock-in such contracts, hold a long-term advantage if, as forecast in this model scenario, the cost of gas from other later sources is higher in real terms.

There are a number of price discontinuities apparent in the graph. These price steps occur when a Producer’s contracted current supply facilities reach their then-prevailing capacity. Typically, each of the existing Producers has undeveloped reserves that can be contracted for development on specified lead times and for minimum threshold capacities. In most cases, such Producers must move to develop smaller fields or more expensive fields, often with a need to expand processing capacity. This translates to higher supply costs for new contracts – often discontinuously higher. Such events can be seen for Gippsland, BassGas and Patricia Baleen. The timing of such expansion and related supply price increases is a function of the future outlook for Retailers’ demands, expiry dates for existing supply contracts, and the lead times to develop new capacity.

Overall, a reasonable level of competition is seen between the Producers, prices do converge slowly and prices trend upwards reflecting costs, robust competition and good agent planning to avoid Producers getting stuck with under-utilised capacity or stranded reserves. The apparent convergence of prices suggests that competition pushes Producers towards a common level of prices in negotiations with Retailers. While there is some convergence in supply prices, the continuing differences and discontinuities indicate that the forecast production mix is not based on a least-cost set of outcomes but rather shows that additional competitive forces and goals are also influencing the end outcomes. The results also suggest the market dynamics require a long period of time for supply prices to converge.

The end result is an outlook where prices from Producers will rise in real terms from the early 2000’s level of about $2.70/GJ to approximately $4.50/GJ by 2020, an increase of almost 70%. This is the principal change driving the increase in the total cost of supply by Retailers and the retail prices to Customers (after adding the additional costs for storage, trading, transmission and distribution, which remain reasonably constant over this same period, and a profit or loss margin). The forecast retail price increase, which is shown in Figure 9-7, is strongly influenced by the assigned cost structures in this model scenario and of the pricing and capacity utilisation strategies assigned to the Producers. If these are changed, the supply prices will change up or down. This model’s great strength is that it lets different parties test their own cost structure and strategies relative to what they guess are those of their competitors.

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While it might be seem self-evident that increasing costs will drive increasing prices, what is of value is VicGasSim’s ability to point to the likelihood of an ongoing spread in supply prices that slowly reduces from about $1/GJ to $0.50/GJ (and it takes a considerable time for the spread to reduce). Such a spread can have a strong influence on the competitive positions of Retailers, who buy gas on the opposite ends of that spread. Understanding the timing of possible price changes by individual Producers and pre-positioning are critical planning steps for both Retailers and Producers.

9.2.2 Gas Supply Analysis Figure 9-2 shows the model’s forecast annual supply from each production source into the Victorian market.

Figure 9-2: Forecast Gas Production by Source into Victoria.

250 Timor Sea Thylocene-Gepgraphe 200 J Minerva

150 Bass Gas Patricia Baleen 100 Onshore Otway - Santos Onshore Otway - Origin Production, P 50 Gippsland Cooper Basin 0

2 4 6 8 0 2 4 0 0 0 0 1 1 1 16 18 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2020 Years

Several observations can be made about the modelled demand dynamics in this static strategy scenario:

1. The forecast increases in retail prices (see Figure 9-7) tend to flatten the whole market over the period to 2020. [This demand forecast does not include the effect of recent policies changes that are now expected to stimulate rapid growth in GPG demand in Victoria as previously seen in Figure 8-7.]

2. The existing Esso-BHP Gippsland contracts held by the three major Victorian Retailers continue to dominate the market outlook until 2011 when they expire. Esso-BHP have considerable additional reserves to maintain a strong presence in the market but do not increase their total production level. They pace the entry of new supply contracts to generate competition and achieve supply price consistency with other suppliers then entering the market with

215 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

projects that require high development costs. In this outlook, Esso-BHP do not act to increase their maximum supply capacity and allow supplies into Victoria to decline gradually while they build exports of gas to Tasmania, NSW and SA.

3. New supply sources and Producer groups enter the market but in virtually all cases these entries are in conjunction with contracts written with gas buyers, who have complementary gas and GPG markets in adjoining states. Each of these complementary markets has opened because of the removal of barriers to interstate trade in gas and development of pipeline connection between Victoria and SA (2004), NSW (1999 and 2000), and Tasmania (2004). Each of the major Retailers entered new supply contracts in the 2001-03 period and the bulk of the early supplies from these contracts go interstate. These interstate contracts are included in the model and provide some flexibility for meeting peak and marginal needs in Victoria. While developed for interstate markets, they have facilitated a gradual change in the Victorian market over the period to 2007. If these supply sources had not been developed for markets in adjoining states, the Victorian market may have required some form of intervention to accommodate the need to dedicate a large share of the Victorian market to a major new supply project such as Thylocene-Geographe or BassGas to justify its development. Such intervention might have caused a major market distortion.

4. Supply volumes to Victoria from each new supply source are forecast to remain minor until Gippsland contracts start to step down materially post 2007. The one exception is Patricia Baleen, whose initial supply is contracted to Energex, whose smaller Gippsland contract expires much earlier than those of the three major Retailers.

5. Beyond 2007, the most recently committed offshore developments at Yolla (BassGas) and Thylocene-Geographe are able to fill the dominant share of the supply gap left by the expiring Gippsland contracts. Cooper Basin gas expands its presence once it becomes more price competitive as prices rise to accommodate incoming sources post 2010. Price competition aspects are discussed in the next section.

6. The Timor Sea supply source enters the market in 2018 in this model scenario. This timing reflects its assigned cost structures and threshold commitment volumes. This entry timing will be strongly influenced by market entry of such gas first into Queensland and NSW; such entry could then lead to adoption of marginal pricing strategies to enter southern markets. Such impacts require modelling of the wider south eastern Australian gas markets and possible future interconnections between northern Australia, Queensland, NSW, SA and Victoria. Timor Sea’s entry could also be delayed by rapid growth of CSM, which is not modelled as a supply source available to Victoria in this model scenario.

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9.2.3 Predicted Long-Term Commercial Performance for Origin Energy This section presents a detailed look at one Retailer’s (Origin Energy) predicted performance from this model run in order to show the model’s capability to generate micro-level detail for understanding such performance and in business terms typical of those used to describe such performance. In later sections, Origin’s1 performance is compared to those of the other major Retailers and the comparisons used to draw out observations about those factors that can have a major influence on market outcomes as a whole and for individual Retailers.

9.2.3.1 Profits and Costs Figure 9-3 shows an example of a set of simulated retail financial performance for Origin Energy over the 2002-2020 period. The performance outlook encapsulates the effects of a myriad of complex interactions over this extended period. Some high level observations can be made about these model outcomes:

Figure 9-3: Retailer Profits & Costs for Origin Energy. Origin Energy Performance

550 Gross Profit 500 450 Distribution 400 350 Transmission 300 250 Wholesale 200 Trading 150 Contract Supply Profits & Costs, $M 100 50 Storage 0 -50 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Overhead Years

1. Overhead: This remains relatively constant reflecting an assigned high fixed cost and no model ability to reduce costs through cost cutting or mergers or acquisitions to amortise its fixed costs across a greater number of Customers.

2. Storage: Origin makes regular use of these services to supplement its own contracted supply sources to provide daily, seasonal and annual flexibility.

1. The use of real world names from here until end of Chapter 10 is almost universally talking about the model equivalents of such entities.

217 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

3. Contract Supply: The cost of contracted gas supply falls through 2007 as Origin is able to lower its cost of gas supply from Producers (this is due to Origin contracting gas supplies at reduced prices from the Yolla JV, in which Origin has a majority and controlling interest; it is one of the few, new supply contracts that are negotiated and agreed at or below the prices of the legacy Gippsland contracts). Beyond 2007, Origin’s Gippsland low cost gas supply contracts are assumed to step down until 2011, when they are assumed to expire. The cost of new supplies post 2007 drives up the cost of supply. The level of this increase reflects the assigned cost structures, pricing strategies and goals of Producers able to supply gas at those times. The cost of gas supply continues to rise slowly over the period to 2020, reflecting entry of increasing supplies of more expensive gas sources. The effect of increased volumes is discussed 9.2.3.2.

4. Wholesale Trading: The model forecasts Origin to make material use of the secondary or trading market to source gas outside of its contracted supply sources. Origin either generates an ability to purchase such gas within the TorP limits of its existing contracts and at lower prices than such contracts or it undercontracts actual peaking capacity in these years and is forced into the secondary market on a regular basis during the peak winter season to buy gas at higher prices than its contracted supply; the latter scenario is consistent with the assumed Yolla JV supply contract, which has an assigned high TorP level. In the 2011-2013 period, when the average cost of supply first steps up, Origin’s profits decline. It appears forced to enter the wholesale or secondary market to meet its needs from other Retailers; this action appears cheaper than entering new supply contracts. The same comments apply to the 2018-2020 period.

5. Transmission & Distribution: There are no noticeable changes in the forward pattern of total Transmission and Distribution costs. Given they are fixed on a per unit basis, the only factors which drive the allocation of these tariffs for each Retailer is the growth in that Retailer’s Customer base in each Tariff zone. Retailers will work to gain Customers in those zones where they hold relatively higher prices and lower regulated tariffs. The minor changes in the forward pattern of these costs suggest that the level of Customer churning between Retailers within zones is relatively minor. Again, this is strongly influenced by the fixed regulated retail prices and the low churn limit, which restricts the number of regulated Tariff V Customers, who take up unregulated offers.

6. Profit: appears to be robust out to 2008 but then steadily declines; the most obvious correlation with this performance is the increase in supply costs from 2008. This profit squeeze occurs because of the assumption that regulated retail prices are fixed at 2000 levels. There is a strong correlation between the step in supply costs and steady decline in gross profits.

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9.2.3.2 Performance Trends The driving forces behind Origin’s overall performance can be further explained by converting its performance into a series of unit measures such as cost per GJ of gas sold and average retail price and looking at trends in the number of its Customers and its Market share. These trends are shown in Figure 9-4.

Figure 9-4: Performance Trends – Origin Energy.

120 8 Market Share, %

7 100 Gas Sales, PJ 6 80 5 Avg Retail Price, $/GJ 60 4 Avg Profit, $/GJ 3 40

Gas Sales, PJ Gas Sales, Avg Unit Cost, Marjet Share, % Marjet Share, 2 20 $/GJ 1 No of Customers x 100,000 Customers No of No of Cust 0 0 x100,000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Avg Retail Price, Cost & Profit, $/GJ

Additional performance comments on this data follow:

1. Average Unit Cost (from all sources, including supply, storage, trading, transmission and distribution): This is the dominant area of change apparent in this forward scenario. The assigned cost structure and pricing strategies of incoming Producer supply sources and facility expansions is such that the contracted new supplies to Origin drive up its average cost of supply markedly post 2008 (as its existing major legacy Gippsland contract expires). More background on the cost of Producer supply is described later. This model scenario forecasts an increase in average total (real $) costs of approximately 35% over the period to 2020, from just below $6/GJ to about $8/GJ.

2. Average Retail Price: Retailers increase retail prices to unregulated commercial and industrial customers, using a contracted profit margin, which has an assigned target of 5%1 of the total operating costs, to deliver gas to individual Customers; the effect is that real Retail prices for these types of Customers can be more volatile depending upon the annual increases (or

1. Such Customers usually contract their gas on the basis of sharing the risk of future price increases or decreases and in return the Retailers seek only a reasonably low profit margin, here assumed to be 5%.

219 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

decreases in costs); in return for this low profit margin, Customers grant the Retailer the right to curtail supply on a limited number of days each year. In contrast, the Regulator is assumed to hold constant all regulated retail prices to residential and smaller commercial Customers in each zone. The end effect is an increasing weighted-average retail price, which mimics the increase in supply costs but on a dampened scale because of the "freeze" imposed on regulated retail prices, the 5% pa churn limit and the 5% target margin for commercial and industrial unregulated Customers. The model result is that Origin’s average retail price is forecast to increase from approximately $6.60/GJ to just over $8/GJ, a total real increase of about 21%. Most of this increase comes post 2010 and the actual rate of real price increase reaches a level of about 2% pa.

3. Average Unit Profit: The diminishing gap between the average retail price and average unit supply cost leads to a steady decline in the average unit profit. Average unit profit is maintained flat at about $1.50/GJ out to 2010 but then falls steadily until reaching a level of less than $0.50/GJ in 2020. The implication is that Retailer’s profitability will come under serious erosion post 2010 unless the model’s assumptions about Regulators persevering with fixed regulated prices and tariffs are changed.

4. Number of Customers: The number of Customers supplied by Origin generally increases steadily over the period. The one exception is the 2004 entry effect of Energex, who wins Customers from Retailers, including Origin, in the period immediately after commencement of the model1. Beyond 2003, the number of Origin’s Customers increases steadily reflecting the assumed growth in economic activity. This growth is distributed in the form of both Cus- tomer numbers and consumption levels.

5. Gas Sales: Origin’s gas sales peak in around 2008 at approximately 75 PJ/A, up from about 65PJ/A at the start of the model. Origin’s increasing retail prices cause some dampening of its customers usage and, once the price steps up markedly post 2008, Origin’s gas sales start to decline such that by 2020 sales are back to the same levels that prevailed at the start of the model run. During this total period compound economic growth is about 20%. The model’s outputs reflect reduced gas usage by Customers despite the underlying growth in economic activity. Since an assumed increase in electricity prices is less than that for gas, some Custom- ers can be assumed to be moving to alternative cheaper energy sources, reducing discretionary usage or finding more efficient ways to use their gas. The model does not include any mechanisms that consider these usage and technology sensitivities other than by using cross-price elasticity and a forecast of electricity prices to alter gas demand. Care is needed to ensure the

1. Energex has a much lower overhead structure and no regulated Customers but despite paying a premium for its Gippsland supply contract, it can still post competitive retail prices; these benefits allow Energex to quickly capture unregulated Customers.

220 Chapter 9 Exploring VicGasSim’s Potential

model responses do not deviate outside reasonable expectations or inversely are inhibited by reliance on inappropriate historical statistical relationships. It is also worth noting that the price elasticities used in the model are based on historical data, which mostly relates to the monopolistic stable gas price period before deregulation. Price elasticity post deregulation could differ from the historical data. An expanded VicGasSim should consider these aspects in a more discrete fashion as is done for the other market interactions in the model.

6. Market Share: The 2004 entry of Energex reduces Origin’s market share in the following two to three years of the model but after that Origin’s market share remains relatively constant with only a minor fall over the remaining period of the model run to 2020.

7. Other Observations:

(i) Figure 9-4 shows a close correlation between profit and supply costs; it also shows that the model outputs remain rationally bounded over an extended market period despite the major policy constraints placed on some agent behaviour. As costs go up, prices rise, demand rolls over and profit margin comes down. The model maintains a dynamic and yet rationally behaving Market, despite the relatively simple strategy and plan assumptions for each of the active model agents and despite the impact of certain adverse constraints and assumptions.

(ii) It is highly likely such a profit outlook would become commercially unsustainable for Retailers after about 2010, indicating the assumed or imposed fixed regulatory regime is inappropriate (as previously noted).

(iii)Annual profit movements are relatively small despite some material market changes. Understanding how such changes are offset is important to implementing such plans in the real market in order to minimise profit volatility.

(iv) The predictions show an active wholesale market and it is possible small wholesalers and/or retailers could attempt to enter the real wholesale market.

(v) Retailers could achieve a relevant effect on their annual profits by reducing their unit overhead costs (other than by selling more gas to some Customers). This objective is not built into the current model design but could be accommodated by upgrading corporate level agents to have plans to lower unit overhead costs by representing Retailers’ involvement in electricity markets, markets in adjoining states, and to make or accept takeover offers for other agent’s sections of the market place. Such plans could consider upstream integration to take a wider corporate view of where and how profits are made and to allow greater internal cross-subsidisation and risk diversification.

221 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

9.2.3.3 Profit Distribution Across Customer Types As explained in Chapter 7, the model recognises Customers in eight size-related groups. The model collects prices and costs for each Retailer in relation to each Customer group such that Origin’s profits can be distributed or tagged to each Customer group as shown below.

Figure 9-5: Profit per Customer Size.

Profit per Customer Size

70 60 50 40 M 30 20 2002 2010 2020 10 Profit, $ 0 -10 l .1 .2 .4 0 ta -0 0 1 10 000 o 2- 5-10 - 1 T -20 .1- . 1.4-5 - 1000- 0 0 10 0 -30 10 Consumption Range, GJ/D

The smallest group, which is predominantly made up of residential Customers, starts at a loss and the loss increases over time as costs increase against fixed, regulated prices. The next three groups are made of larger residential and smaller commercial Customers with a decreasing ratio of regulated customers to unregulated Customers. While all of these groups start with a profit because of the inclusion of more profitable larger commercial Customers, the profit declines over time because of the assumed price controls, low assigned maximum churn rates and target profit margins. The groups consuming above 5 TJ/D are large commercial or industrial users and the derived Retailer profits are controlled by price increases offset by declining usage; the profits are relatively flat for these large size Customer groups.

While profit made from individual Customers generally increases with Customer size (see Table 9-4) such that the largest industrial Customers are by far the most profitable Customers on a unit basis, their numbers are few and their total profit contribution is minor by comparison to the Customers in the 1-5 GJ/D range where Retailers make the vast bulk of their profits. If this distribution is proven by further validation work, then the Customers in the 1-5 GJ/D group should be the main short-term target for competition as they will be more commercially astute than residential and small commercial Customers and will more readily respond to active competition.

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In the longer-term, if Regulators allow Retailers to convert losses in the residential and small commercial sectors to profits, such a change should allow a material change in the outlook for total profits. In the real world, this will require a reduction in the regulatory pressure in these politically sensitive markets; in the model, it will require allowing the model regulator to review and change retail prices in line with each Retailer’s change in costs.

9.2.4 Relative Performance of Origin to the Other Retailers Figure 9-6 shows the relativity of the forecast profit performances of the other Retailers to that already presented for Origin.

Figure 9-6: Comparison of Retailers’ Annual Profits as Forecast by VicGasSim.

Annual Profit 70 60 Energex 50 40 Origin Energy $M 30 20

10 Pulse 0 -10 2002 2007 2012 2017 TXU -20 -30

Origin starts with higher profits1 than Pulse and TXU due its higher regulated prices and assigned lower total supply costs (~60% of revenue versus ~65-70% for Origin’s competitors as shown in the Figures 9-9, 9-10, 9-11 and 9-12). In the model, Origin is also able to recognise credits from its upstream profits to subsidise and strengthen its Retail profit position. Origin’s total annual profits show minor increases to 2007 at rates consistent with the growth Customer numbers and its gas sales volume to 2007; beyond 2007, annual profits fall, as do gas sales and, more importantly, as profit margins get squeezed by assumed fixed regulated retail prices, a low maximum churn rate and increasing gas supply costs

All major Retailers follow similar annual total profit patterns but Pulse outperforms Origin, TXU and Energex from 2002 to 2010; the reasons for these differences are explained in 9.2.5. Pulse, TXU and Energex all suffer the same fall in profits due to increasing costs once their Gippsland contracts expire in 2011 and the profit squeeze due to the previously described adverse policy

1. Origin’s higher regulated retail prices reflect a higher cost base than those of the other main Retailers; therefore, if an asset base were included in Origin’s model data base, Origin’s higher profits may not translate into a higher return on assets than those of other Retail- ers.

223 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

constraints/assumptions that are allowed to prevail in the model. TXU gets no advantage from its greater access to underground storage capacity nor does Origin obtain any apparent supply cost advantage by utilising its own gas supply from the Yolla JV, Thylocene-Geographe, Onshore Otway and Cooper Basin, which require high development costs to increase supplies.

Pulse’s profits fall at a slower rate than those of the other Retailers and Pulse obtains a late-life advantage by gaining the first major (but short-period) contract awarded by the Timor Sea supply source. This contract significantly lowers its average gas price relative to its competitors and boosts profits against the trend over a short period from 2016-19.

TXU’s retail business falls into a loss-making situation just before 2020, followed closely by Pulse when its 3-year, low-priced Timor Sea contract expires.

Some aspects of this scenario are pushing the bounds1 of how participants would act but the outlooks do show the potential for use of the model to explore such matters and make changes to strategies, plans and for model users to test and make changes to model constraints and assumptions.

Figure 9-7: Average Retail Prices.

Average Prices ENGX Total 10 10 OE Total 9 9 PLS Total 8 8 TXU Total 7 7 $/GJ ENGX Unreg 6 6 OE Unreg 5 5

4 4 PLS Unreg

3 3 TXU Unreg 2002 2007 2012 2017

Figure 9-7 shows forecast trends in each Retailer’s average (total weighted) retail price and unregulated price. Regulated prices are not plotted since they are assumed not to change over the forecast period [Origin’s average regulated price is $9.09/GJ, Pulse’s average regulated price is $8.10/GJ, and TXU’s average regulated price is $8.57/GJ (see Tables 9-2, 9-3 and 9-4) – these price differences reflect differences in the Regulator recognised costs for each Retailer to deliver

1. It is highly unlikely Retailers would continue to market gas at declining profits and profit margins in the face of no review of regulated retail prices. It is also unlikely that Timor Sea gas Producers would write an initial 3-year contract to supply gas to Victoria unless its carriage from the Timor Sea was already underwritten by large sales into Queensland and NSW.

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gas in its region (each region has a different size and customer mix)]. When the regulated prices, which are in the range $8-9/GJ, are weighted with the unregulated prices, which are shown to be in the inital range of $3-4/GJ for industrial customers, the weighted average of both retail prices comes out in the range of $6-7/GJ and increases as unregulated prices increase and there is some limited churn of Customers in response to unregulated offers. Energex starts from a very low base and achieves good performance because of its much lower costs and ability to capture profitable unregulated Customers.

Figure 9-8: Average Gas Supply Costs.

Avg Supply Cost

9 Energex

8 Origin Energy 7 $/GJ 6 Pulse

5 TXU 4

3 2002 2007 2012 2017

Figure 9-8 shows a comparison of forecast average total supply costs (including upstream supply, trading, storage, transmission and distribution costs). All three major Retailers start with similar average supply costs and, through 2008, they obtain new contracts on terms that preserve their average supply costs. Post 2008, all face upward costs pressure but Pulse gets a “lucky” short- term supply break and runs against the trend for the three years before facing first upwards pressure and then gaining the advantage of the forecast first entry of Timor Sea gas into Victoria. Origin’s position is influenced by its negotiating strategy, which favours supply offers from the Yolla JV and Thylocene-Geographe JV sources where it corporately holds interests and can make extra corporate profits by such sales, even if such supply sources require higher supply prices for development or expansions.

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9.2.5 Detailed View of Retailer Performance Characteristics The following four figures show the model-generated performance outputs for each Retailer. They show more detail than in the previous charts and show the model dynamics annually up to 2020. The figures can be studied in depth by observation and comparisons; a number of broad comments about these forecasts, which reflect use of "static strategies", follow:

1. Origin, TXU and Pulse all grow their Customer numbers consistently over the period to 2020 in line with assumed economic growth.

2. Origin, TXU and Pulse hold their market shares broadly at the original levels; there is a small swap of 1-2 percentage points of market share from Origin mostly to Pulse due to Origin increasing its average retail price more than the other parties.

3. Origin drives up its average retail price to almost $8/GJ by 2020 versus only $7/GJ by TXU and Pulse reflecting its higher long term supply costs.

4. Origin’s average supply costs grow more rapidly (from an earlier time) and to a higher level than those of TXU and Pulse; Origin’s higher supply costs drive it to charge higher retail prices. The reason for Origin’s higher costs relates to Origin owning interests in a number of gas supply sources and giving preference to purchasing supply from these sources earlier than other parties. It does this in the model because Origin recognises that higher sales from such sources will contribute more than other sources to its overall profit position, even if they are more costly than alternative sources. The model allocates a joint interest benefit from Origin’s upstream profits to its Retail profits, which are reported here. So while Origin’s supply costs go up more than other parties’ supply costs, and while it increases its retail prices at the cost of losing gas sales, it is able to maintain a profit longer than the other parties due to the upstream profit that Origin gains from these supply sales and because the model allocates these credits to Origin’s profit results, which are shown in Figure 9-9.

5. Origin, TXU and Pulse grow their sales volume consistently over the period to about 2010 and then the volume growth slows or declines due to their increasing retail prices; Origin’s sales fall more than those of the other parties due to Origin’s higher average retail prices (biased by upstream profit effects).

6. Energex also suffers significant losses in retail sales due to incurring large increases in its average retail prices relative to its initial average price.

7. Retailers use the wholesale trading market to different extents in different periods; this reflects the discontinuous nature of blending supply from old and new contracts. Several Retailers make periodic profits from wholesale trading and such trading becomes a major commercial feature of supply to TXU and Pulse late in the prediction period, just as larger gas

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supplies enter from the north (Timor Sea in this forecast). This combination of outcomes is consistent with tightening local supplies and supports the view that the market invokes consistent reactions across multiple sectors of the market.

8. The Retailers have different distributions of customers across the various size segments of the market and these differences lead to different profitability mixes across Customer types.

It should be kept in mind that these results reflect a scenario that is based on the assigned set of input data, strategies/plans, strategy/plan bounds, and key assumptions. The results should only be taken as reflecting the depth of the model’s capability to track agent interactions and show market outcomes in terms much as they are used in the real market and by real market participants; they should not be taken as indicative of expected actual results. More work is needed in calibrating and upgrading some aspects of the model design before the outcomes could be relied on for decision-making purposes by participants.

227 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Figure 9-9: Origin’s Profit & Costs Performance.

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228 Chapter 9 Exploring VicGasSim’s Potential

Figure 9-10: TXU’s Profit & Costs Performance

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229 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

Figure 9-11: Pulse’s Profit & Costs Performance.

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230 Chapter 9 Exploring VicGasSim’s Potential

Figure 9-12: Energex’s Profit & Costs Performance.

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231 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

9.3 With Dynamic Strategies Using Look-aheads The model has an enhanced mode, which comes into play when agents are assigned a higher level of intelligence via the look-ahead capabilities built into the model. This section explores a number of scenarios generated when one or more agents are assigned look-ahead capabilities for both sensitivity testing and improved decision-making. An example, in which one or more Retailers use an array of wholesale bidding strategies to alter key bidding criteria in order to outperform their competitors, is presented. Retailers select and apply one of a range of bidding strategies for short or extended periods to meet competition. This process is repeated and strategies are changed and updated on a regular basis. One or more agents use the look-ahead capability to choose what wholesale bidding strategy to apply each day to determine their bid- stacks to be lodged each day. While strategy choices are made daily, scenarios are generated over 20 year periods to check if any benefits are more short term and/or long term in nature. The objective is to explore how much more efficient or profitable specific agents might become by invoking their look-ahead capability, and if the use of improved intelligence alters market outcomes significantly.

The first test was to look at giving enhanced intelligence to just one Retailer, TXU. Comparisons were generated when TXU operates without and then with enhanced look-ahead capability; the results are presented in the next section.

9.3.1 Introducing One Intelligent Retailer to the Wholesale Market Figure 9-13 shows model-generated forecasts of TXU’s market performance for two scenarios: one where all Retailers apply the same wholesale bidding strategy with no look-ahead testing of alternatives and the other where TXU has a look-ahead capability to test 18 alternative bidding strategies. It applies selected strategies over a coming week and varies that choice as seasonal and other market conditions change and it adopts new supply contracts over time. A strategy varies over time in the way the derived bidding curves are structured in relation to expected demand, available gas, and prices of gas. Changing the shape of the bidding curve (i.e. the rate of change over certain sections of the curve) makes the stepped bids more or less aggressive. As an example, if a Retailer finds that it is sitting on spare gas due to being under its TorP contract levels, it will tend to adopt a more aggressive strategy to sell gas for which it has already paid a Producer.

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Figure 9-13: Retailer Profits and Costs – TXU Performance.

TXU Performance All Retailers using 1 wholesale bidding strategy 550 500 Gross Profit 450 Distribution 400 350 Transmission 300 250 Wholesale Trading 200 Profits & Costs, $M & Costs, Profits 150 Contract Supply 100 Storage 50 0 Overhead -50 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Years 2002-2020 Cumulative Profits = $256 Million TXU Performance TXU testing 18 whoelsale bididng strategies Vs 1 by other Retailers 550 500 Gross Profit 450 Distribution 400 350 Transmission 300 250 Wholesale Trading 200

Profits & Costs, $M & Costs, Profits Contract Supply 150

100 Storage 50 0 Overhead -50 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Years 2002-2020 Cumulative Profits = $258 Million

What we see is that TXU uses this look-ahead ability to make greater use of wholesale trading (see changes in the light yellow section of each vertical bar in the lower section of Figure 9-13) in the early years versus the model-generated scenario when TXU only pursues one fixed strategy to set its daily bidding stack (see upper section of Figure 9-13).

While TXU is only testing alternative bidding strategies over a week look-ahead, it does it consistently and, if successful, saves its own contracted gas that it can use later when future gas supplies might require higher prices. TXU makes most savings in the medium term, despite incurring higher total supply costs in the early years. The savings from flow-on effects or outcomes are not by long term design but by the consequence of TXU’s short-term actions from the simple strategy of maximising next week’s profits from its bidding behaviour in the wholesale market segment.

233 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

There is nothing super-intelligent about this behaviour, but it is likely to be close to the way Retailers participate in this market today. They will test a series of strategies over the conditions for the next day, or week, against predicted weather sensitive demand and to the underlying state of their contract positions for both gas supplies and storage/peaking. A participant such as TXU might be expected to most commonly test possible strategies against its competitors’ last known strategy positions. In this model case by retesting every week, we allow TXU to have a relatively better chance than otherwise of changing the selected strategy as market and contract conditions change. As a matter of principle, it seems reasonable to expect that TXU will do better by such testing if its competitors follow fixed strategies and where the latter do not change their strategies relative to their contract and market conditions.

However, in this case, the profit impact over time is not great. The overall outcome is as follows: TXU improves its 2002-2020 cumulative profits from $256M to $258M. What is somewhat surprising (given the attention of the Government through its various legislation, regulations and market manager, Vencorp), is that TXU’s retail profit performance improves by less than 1%. This reflects that improving intelligence in just the wholesale trading segment itself may not be enough intelligence to make large improvements in corporate performance. Other constraints and choices such as gas supply contract terms for contract lengths, TorPs, ACQs and MDQ’s appear to limit daily flexibilty to improve long term profits1.

The wholesale trading market serves a main purpose in providing a daily market clearing and balancing system, and it fills a major operational service in managing the matching of supply with expected daily demand. But there is a hint in the above comparison that individual Retailers are unlikely to generate a significant benefit over the long term by their efforts to optimise their positions in the wholesale markets. However, there is always the risk of losses if not enough attention is paid to any area: "every profit dollar counts" and Retailers do need to compete in all sectors. But the effort dedicated to this area needs to be assessed versus other profit sensitivities, some of which have been explained earlier in this chapter. There are other risks such as short- term actions, which cause unanticipated long term consequences for the enhanced intelligent agent or for one or more other agents. Surprises can occur.

1. It is interesting to note that while TXU has achieved similar cumulative profits over the test case period, it can be seen that it has materially reduced the level of its total costs over the same period (probably by causing changes in long term supply contracts). It is likely that in the model, this saving will flow into lower retail prices. The market benefits more than TXU does. This point is made only as an observation that points to the need to fully explore and cross-correlate output traces to understand what factors are influencing other factors. If the SupplyTestCase was run in parallel, it might change this predicted effect.

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9.3.2 Surprises Can Occur Figure 9-14 shows the forecast profit outcomes for Pulse in the same cases when TXU’s bidding intelligence is varied or not varied.

Figure 9-14: Pulse Forced to Contract Higher Priced Gas

Pulse Performance All Retailers using 1 wholesale bidding strategy 550 500 Gross Profit 450 Distribution 400 350 Transmission 300 250 Wholesale Trading 200 Contract Supply Profits & Costs, $M & Costs, Profits 150 100 Storage 50 0 Overhead -50 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Years 2002-2020 Cumulative Profits = $436 Million

Pulse Performance TXU testing 18 whoelsale bididng strategies Vs 1 by other Retailers 550 500 Gross Profit 450 Distribution 400 350 Transmission 300 250 Wholesale Trading 200

Profits & Costs, $M & Costs, Profits 150 Contract Supply 100 Storage 50 0 Overhead -50 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Years 2002-2020 Cumulative Profits = $214 Million

What we see is that Pulse suffers an unforeseen outcome as a result of TXU’s improved wholesale bidding behaviour and suffers a major change in its long term profit-making capability.

When all Retailers follow just one bidding strategy, Pulse outperforms TXU. Pulse is able to effectively use the wholesale market in 2008 and 2009 to supply its marginal gas needs over what appears1 to be a critical contracting period for Pulse. Pulse appears able to save the last cheap gas from Pulse’s main existing Gippsland contract which scales down from about 2007. This unused

1. The word "appears" is used since not enough data has been saved from these model runs to track changes in supply contracts. The comments are based on the author’s best guess of what is causing the observed model outcomes.

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gas allows Pulse to bridge to more competitive new supply contracts after the expiry of existing Gippsland contracts at the end of 2010. In the alternative scenario, competitors change their wholesale bidding behaviour and Pulse’s ability to position itself adequately for new longer-term supply contracts is altered. Its supply costs step up markedly in 2011 and it quickly moves to a loss-making position from 2014, whereas in the other scenario it can generate profits to 2019.

When TXU improves its capability to alter its wholesale bidding strategy, it changes Pulse’s ability to capitalise on its Gippsland contract gas and places Pulse in a poor position to avoid taking up higher priced gas than it does in the other scenario. In addition, Pulse attempts to recover by cutting retail prices to gain market share but is unsuccessful. The relative effect on Pulses’ performance is to see profits turn into losses from 2015. The overall outcome is that Pulse’s 2002-2020 cumulative profits drop materially from $436M to $214M..

Figure 9-15 shows the relative difference in Pulse’s long-term profits in the two scenarios and correlates this to the way Pulse was forced into taking up higher priced gas, which flowed into higher prices but insufficient to avoid losses from 2015.

Figure 9-15: Pulse’s Relative Performance. Pulse's Relative Performace

60 10 M

40 8

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-40 0 Avg Unreg Prices, $/GJ Gross Annual Profits, $ Years Pulse1-TXU1 Profits Pulse1-TXU18 Profits Pulse1-TXU1 Prices Pulse1-TXU18 Prices

TXU’s use of improved bidding intelligence does not directly lead TXU to use the trading market in the same 2008-2009 period that is important to Pulse. The outcomes do show how indirect effects of short-term strategy influences, when compounded over time, can cause major differences to occur in other areas. The point is not to overly focus on one strategic area such as daily wholesale bidding at the cost of not examining other strategic influences.

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Pulse’s problem in these model scenarios has less to do with relative performance in applying bidding strategies in the wholesale market but more about how a Retailer contracts new supplies and sets prices for retail gas – both longer term matters. In this case, Pulse’s assigned daily bidding strategy leads it into poor supply contracting positions in one or more critical periods.

If the model were run with Pulse invoking look-ahead test cases to consider alternative supply then Pulse might have predicted the above exposure and made alternative supply arrangements before the expiry of the legacy Gippsland supply contracts. Such changes might have achieved a substantially better long term outcome when dealing with enhanced Retailers such as TXU in this example.

9.3.3 All Retailers Intelligent In the Wholesale Market The model was further changed to provide all four Retailers with a similar level of wholesale bidding intelligence to that tested already by TXU. A comparison is tabulated below in Table 9-1 to show the effect on each Retailer’s profit performance. The comparison shows the relativity of all Retailers with fixed strategies, then only TXU with look-ahead capability, then all Retailers with look-ahead capability. The table shows cumulative, total profits over the period from 2002 to 2020.

Table 9-1: Retailer Profit Performance Comparison - Effect of Intelligence

2002-2020 Cum Profits Energex TXU Pulse Origin Energy All – single fixed strategy 47.2 256 496 734 TXU only – 18 strategies 48.6 258 214 726 All – 18 strategies 46.5 234 115 732

The model forecasts show that Pulse did well in the base case (all major Retailers apply a single fixed strategy), in which Pulse gains artificially from a series of special outcomes. It is able to take advantage of the wholesale market to delay taking up a greater proportion of the higher price supply sources without being exposed to the adverse consequences already described. It appears Pulse is able to use the wholesale market and take up a greater proportion of shorter contracts to achieve this delay as well as positioning to take up the first entry (then lower priced) contract for gas from the Timor Sea. Combined together, these advantages provide Pulse with a supply cost advantage, which is, however, achieved because of a rather tenuous access to the wholesale trading market. These outcomes are not a direct reflection of the Retailer’s performance in the wholesale market (by optimising its bidding strategies) but rather show the indirect influence of daily wholesale trading on future positioning for long term supply contracts. It would not be a sound plan to rely on uncertain, daily wholesale trading over an extended period to achieve long term supply contracting goals.

Even a small change in Pulse’s circumstances, when TXU is given greater market insights and intelligence, causes Pulse to do poorly in the long-term. When Pulse is given comparable insights

237 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

and intelligence, it cannot do any better and in fact makes significantly fewer profits. This inability to improve its long term performance suggests there might be something intrinsically weak in Pulse’s resources, its customer mix and market location. The most logical explanation is that it has the highest number and greatest proportions of residential and commercial Customers with regulated prices. This situation causes Pulse to suffer greater losses on such Customers than its peer Retailers. Pulse would likely do considerably better if regulated prices were allowed to increase in the model and Pulse might then continue to out-perform its peers.

In contrast, increasing intelligence to all Retailers does not materially affect the performance of the remaining Retailers. While TXU’s, Origin’s and Energex’s cumulative profits are reduced very marginally, when they all have increased intelligence, the provision of this sophisticated intelligence does not much improve the level of competition by lowering retail prices and their cumulative profits. This insensitivity suggests that the model generates a strong level of competition between Origin, TXU and Energex and they remain robust competitors until 2020, even without enhanced look-ahead capability and despite major changes in market outcomes. This comparison suggests that the model’s base design, which uses significant micro-level definition, provides representataion of the market complexity at a level that achieves a high level of competition and rational model predictions without achieving significant benefits from increased agent intelligence in the wholesale market sector. The conclusion is rather tenuous based on the major sensitivity identified in predicting Pulse’s long term profit performance and requires more extensive testing of other look-ahead test cases1. However, it is possible that further model design enhancements of each agent’s base strategies and implementation plans might be a more effective direction for model improvements than finding ways to reduce the long run times when the model invokes multiple look-aheads for multiple agents. The direction of this conclusion is similar to the views of Brenner (2006), Malerba et al. (1999) and Tesfatsion (2006) (see 4.3.8.1 and 6.3.5), who believe that model designs that include many agents with simple mental models at all levels in a system, and are based on abundant empirical data, are preferable to more general system definitions with fewer agents and less empirical data but where the model relies on more sophisticated learning techniques to make decisions and predictions.

9.4 Data-Mining of Scenarios to Identify Strategic Insights The model’s data collection system is one of its strong features. This data collection is done through a series of accountants, who act for individual agents to collect, record and accumulate data on all aspects of agent behaviour and performance.

1. Additional testing was planned to apply look-ahead capability in the areas of retail pricing and supply contracting, but this work was not undertaken before the end of the current research period and model development funding.

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A selection of data is presented for each of the main three initial Retailer agents, Pulse, Origin and TXU, to provide a basis for developing some understanding of the value of this style of agent rich and data rich platform to explore participant performance. The agents’ performances are a function of the way the model has been designed and the profiles defined for each of these Retailers. While possibly indicative of some aspects of their possible performance over this period, the model outputs should not be seen so much as reliable forecasts of market outcomes but of expected behaviour patterns and of the robustness or weakness of certain participants because of the resources and strategies assigned in each model run.

The performances of Pulse, TXU and Origin are studied in terms of a set of model outputs for each of these Retailers for the two years: 2003 and 2008. These years were selected to provide a comparison before the appearance of anomalous distortions due to the effects of fixed regulated retail prices which cause the subsequent unrealistic squeeze on modelled Retailer profits. The Retailer’s individual results are analysed in terms of changes in their individual performances over this 5-year period. These comparisons start on the next page.

239 An Agent-Based Simulation Study of Competition in the Victorian Gas Market

9.4.1 Pulse’s Performance from 2003 to 2008

Table 9-2: Detailed Comparison of Pulse’s Performance in 2003 & 2008

2003 - LAH5 Market Division Units -0.1 GJ/D 0.1-0.2 GJ/D 0.2-1.4 GJ/D 1.4-5 GJ/D 5-10 GJ/D 10-100 GJ/D 100-1000 GJ/D 1000- GJ/D Total No. Customers 293802 164104 64794 14993 753 257 79 35 538817 Market Share % 37.64 34.31 30.57 35.14 35.02 26.36 15.4 34.65 35.51 Avg. Consumption GJ/D/C 0.03 0.13 0.48 2.28 7.5 44.59 482.28 1288.51 0.36 Total Consumption PJ 3.22 7.79 11.35 12.48 2.06 4.18 13.91 16.46 70.80 Avg. Price $/GJ 9.99 8.26 8.27 7.94 4.02 3.9 3.46 3.45 5.85 Regulated $/GJ 9.99 8.26 8.31 8.02 4.43 4.41 0 0 8.11 Unregulated $/GJ 0 0 4.48 3.77 3.78 3.65 3.46 3.45 3.49 Revenue $M 30.36 63.93 94.53 99.03 8.28 16.31 48.14 56.71 417.3 Costs $M 50.19 63.4 74.24 74.29 8.66 16.96 43.71 51.51 382.97 Supply $M 8.71 22.19 32.76 35.76 5.91 11.99 39.87 47.2 204.39 Producers $M 8.6 21.91 32.35 35.31 5.84 11.84 39.37 46.61 201.83 Storage $M 0.71 1.8 2.66 2.9 0.48 0.97 3.23 3.83 16.57 Trading $M -0.6 -1.52 -2.24 -2.45 -0.4 -0.82 -2.73 -3.23 -14.01 Transmission $M 0.79 1.57 2.69 2.72 0.37 0.76 2.37 2.86 14.13 Distribution $M 15.91 25.32 32.47 33.56 2.15 3.84 0.35 0.14 113.74 Overhead $M 24.78 14.32 6.33 2.25 0.23 0.36 1.12 1.32 50.7 Gross Profit $M -19.83 0.53 20.29 24.74 -0.38 -0.64 4.43 5.2 34.33 Avg. Profit $/C -67.49 3.24 313.13 1650.06 -508.13 -2508.31 56019.9 148554.99 63.71 Gas Supply Price $/GJ 2.89

2008 - LAH5 Market Division Units -0.1 GJ/D 0.1-0.2 GJ/D 0.2-1.4 GJ/D 1.4-5 GJ/D 5-10 GJ/D 10-100 GJ/D 100-1000 GJ/D 1000- GJ/D Total 2008 vs 2003 No. Customers 314956 177614 66687 16456 780 255 86 36 576870 7.06% Market Share % 39.06 35.86 30.42 36.49 34.12 25.15 13.76 34.62 36.74 3.46% Avg. Consumption GJ/D/C 0.03 0.13 0.49 2.37 8 46.25 451.9 1304.86 0.36 0.00% Total Consumption PJ 3.45 8.43 11.93 14.24 2.28 4.30 14.19 17.15 75.80 7.06% Avg. Price $/GJ 10 8.24 8.26 7.92 3.6 3.46 2.83 2.81 5.61 -4.10% Regulated $/GJ 10 8.24 8.3 8 4.43 4.41 0 0 8.1 -0.12% Unregulated $/GJ 0 0 3.72 3.08 3.08 2.98 2.83 2.81 2.85 -18.34% Increase in Unreg price '% Revenue $M 32.53 71.56 98.88 113.22 8.23 14.93 40.25 48.35 427.97 2.56% Costs $M 50.3 64.73 70.71 77.28 8.45 15.2 36.89 44.36 367.91 -3.93% Supply $M 7.54 20.12 27.75 33.15 5.3 10.01 32.97 39.85 176.69 -13.55% Producers $M 7.63 20.37 28.09 33.55 5.36 10.13 33.37 40.33 178.82 -11.40% Storage $M 0.73 1.96 2.7 3.23 0.52 0.97 3.21 3.88 17.19 3.74% Trading $M -0.82 -2.2 -3.04 -3.63 -0.58 -1.09 -3.61 -4.36 -19.33 37.97% Transmission $M 0.84 1.75 2.81 3.09 0.41 0.79 2.42 2.98 15.09 6.79% Distribution $M 17.09 28.31 33.99 38.61 2.5 4.04 0.36 0.14 125.04 9.93% Overhead $M 24.83 14.55 6.16 2.43 0.24 0.37 1.14 1.38 51.1 0.79% Gross Profit $M -17.76 6.83 28.17 35.95 -0.22 -0.26 3.36 4 60.06 74.95% Avg. Profit $/C -56.4 38.45 422.46 2184.34 -276.06 -1038.48 39075.45 110997.85 104.11 63.41% Increase in Avg. Profit % -16.43% 1086.73% 34.92% 32.38% -45.67% -58.60% -30.25% -25.28% 63.41% Gas Supply Price $/GJ 2.36 -18.28% Incremental Profit $'000000 2.07 6.3 7.88 11.21 0.16 0.38 -1.07 -1.2 25.73

In Table 9-2, the forecast results show Pulse does very well between 2003 and 2008. It has increased its total annual downstream profit from $34m to $60M, a real increase of 75%. The key aspects of Pulse’s apparent success over this period are summarised below:

1. The dominant effect comes from Pulse reducing the average price of the gas it buys from Pro- ducers from $2.85/GJ to $$2.36/GJ and this has contributed to lowering its total supply cost (from Producers and traders) by over 11%, or $23M. This is consistent with Pulse sourcing gas from the Yolla (BassGas) JV, a single field supply source, which has an assigned lower price due to having less peaking (MDQ) flexibility than, say, Gippsland contracts. A smaller alternative supply source, which was developed at the same time, is the Basker Manta JV development. In the model, the Yolla and Basker Manta JVs might also have dropped their prices even lower than the initially assigned prices to force their way into the market before other Producer groups, which were planning new developments at roughly the same time as the Yolla JV.

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2. Pulse has also sourced wholesale trading profits in both 2003 and 2008; the only major Retailer to do so; again this reflects access to lower priced gas supplies, which Pulse is able to on sell into the wholesale market at a profit (shown in table as negative supply costs).

3. These supply benefits have been offset to a minor degree by passing some of this advantage to its downstream Customers by reducing its unregulated retail gas prices by 18.5% and average price by 4%.

4. Pulse’s retail pricing changes have gained Customers and sales volumes in the mid-size residential and commercial Markets while essentially holding a static position with larger commercial and industrial Customers.

5. Pulse started with the largest market share and increases its market share.

6. By boosting its Customers numbers, Pulse lowers it average Customer administration charge (overhead) from $94 per Customer to $89 per Customer.

7. Due to the low maximum churn rate assumed for the Market, Pulse “creams” the residential and smaller commercial Customers by holding high levels of Customers at regulated prices due to lowering its supply costs to these Customers; this gain in the controlled market offsets the effect of lowering prices to larger commercial and industrial Customers even though supply costs are lowered.

8. In this model comparison, Pulse could have chosen not to cut its retail prices quite as much as it did and forsaken some of its growth in market share. The dilemma is that Pulse can only make its profit from its production and trading cost advantage if it takes the gas and onsells it. That is, it has to move it by selling it, to turn potential profit into achieved profit. In this model scenario, Pulse opts to cut unregulated retail prices to move its contracted gas and "creams" the smaller end of the regulated Market in the process.

9. The pattern of profit per Customer type or size shows a general trend for more profit per Cus- tomer as Customer size grows but there are some irregularities in this general trend (similar irregularities occur for the other Retailers). The irregularities fall amongst the mid-size, or mostly commercial, Customers. These irregularities suggest the allocations of Customers between size groups and regions is not as well calibrated as it should be; to improve calibration of such data needs access to data that is only likely to be available from the Retailers. Some adjustments can be made in future runs to find a distribution that achieves a smooth gra- dation of profits as Customer size grows. [This comment applies to the data for all Retailers.]

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9.4.2 TXU’s Performance from 2003 to 2008

Table 9-3: Detailed Comparison of TXU’s Performance in 2003 & 2008

2003 - LAH5 Market Division Units -0.1 GJ/D 0.1-0.2 GJ/D 0.2-1.4 GJ/D 1.4-5 GJ/D 5-10 GJ/D 10-100 GJ/D 100-1000 GJ/D 1000- GJ/D Total No. Customers 197662 134162 75988 12114 356 283 305 25 420895 Market Share % 25.32 28.05 35.85 28.39 16.56 29.03 59.45 24.75 27.74 Avg. Consumption GJ/D/C 0.03 0.13 0.48 2.27 7.5 44.56 157.86 1288.26 0.43 Total Consumption PJ 2.16 6.37 13.31 10.04 0.97 4.60 17.57 11.76 66.06 Avg. Price $/GJ 10.02 8.41 8.55 8.43 4.42 3.88 3.86 3.73 6.1 Regulated $/GJ 10.02 8.41 8.57 8.48 5.98 4.39 0 0 8.57 Unregulated $/GJ 0 0 5.07 4.1 3.92 3.88 3.86 3.73 3.82 Revenue $M 20.49 53.25 114.66 84.72 4.31 17.86 67.89 43.89 407.08 Costs $M 36.18 56.2 96.84 67.27 4.23 17.45 64.14 43.04 385.36 Supply $M 6 18.58 39.33 29.5 2.86 13.51 51.57 34.49 195.83 Producers $M 5.14 15.91 33.69 25.27 2.45 11.57 44.16 29.54 167.72 Storage $M 0.51 1.59 3.36 2.52 0.24 1.15 4.4 2.94 16.72 Trading $M 0.35 1.08 2.29 1.72 0.17 0.79 3 2.01 11.39 Transmission $M 1.6 3.6 9.33 7.61 0.54 3.08 10.15 7.15 43.07 Distribution $M 10.98 21.53 40.02 27.99 0.7 0.33 0.46 0.1 102.12 Overhead $M 17.6 12.49 8.15 2.17 0.14 0.53 1.96 1.3 44.34 Gross Profit $M -15.7 -2.95 17.82 17.45 0.07 0.41 3.75 0.85 21.72 Avg. Profit $/C -79.41 -21.96 234.55 1440.61 204.03 1446.96 12306.86 33879.92 51.6 Gas Supply Price $/GJ 2.96

2008 - LAH5 Market Division Units -0.1 GJ/D 0.1-0.2 GJ/D 0.2-1.4 GJ/D 1.4-5 GJ/D 5-10 GJ/D 10-100 GJ/D 100-1000 GJ/D 1000- GJ/D Total 2008 vs 2003 No. Customers 203883 137871 77511 12643 374 297 362 26 432967 2.87% Market Share % 25.28 27.84 35.36 28.04 16.36 29.29 57.92 25 27.58 -0.58% Avg. Consumption GJ/D/C 0.03 0.13 0.49 2.37 8 46.19 141.22 1305.11 0.45 4.65% Total Consumption PJ 2.23 6.54 13.86 10.94 1.09 5.01 18.66 12.39 71.11 7.65% Avg. Price $/GJ 10.03 8.4 8.55 8.42 4.48 3.98 3.96 3.82 6.14 0.66% Regulated $/GJ 10.03 8.4 8.56 8.47 5.94 4.39 0 0 8.56 -0.12% Unregulated $/GJ 0 0 5.11 4.19 4.02 3.97 3.96 3.82 3.92 2.62% Increase in Unreg price % 0.79% 2.20% 2.55% 2.32% 2.59% 2.41% 2.62% Revenue $M 21.14 56.59 119.09 92.37 4.91 19.97 74.1 47.41 435.57 7.00% Costs $M 36.99 59.45 101.34 74.1 4.84 19.4 69.87 46.45 412.44 7.03% Supply $M 6.35 20.31 41.96 33.04 3.3 15.13 56.37 37.42 213.88 9.22% Producers $M 5.63 18.01 37.21 29.3 2.93 13.41 49.99 33.18 189.67 13.09% Storage $M 0.52 1.66 3.44 2.71 0.27 1.24 4.62 3.07 17.53 4.84% Trading $M 0.2 0.63 1.31 1.03 0.1 0.47 1.76 1.17 6.68 -41.35% Transmission $M 1.65 3.83 9.71 8.29 0.61 3.34 10.92 7.55 45.9 6.57% Distribution $M 11.34 22.79 41.51 30.48 0.78 0.36 0.49 0.11 107.86 5.62% Overhead $M 17.66 12.52 8.16 2.29 0.15 0.58 2.09 1.37 44.81 1.06% Gross Profit $M -15.85 -2.86 17.75 18.27 0.07 0.56 4.23 0.96 23.13 6.49% Avg. Profit $/C -77.75 -20.75 229 1444.91 176.2 1896.86 11686.81 36909.04 53.41 3.51% Increase in Avg. Profit % -2.09% -5.51% -2.37% 0.30% -13.64% 31.09% -5.04% 8.94% 3.51% Gas Supply Price $/GJ 3.01 1.45% Incremental Profit $'000000 -0.15 0.09 -0.07 0.82 0 0.15 0.48 0.11 1.41

In Table 9-3, the model-generated results show TXU does relatively poorly between 2003 and 2008 with total annual downstream profit only increasing from $22m to $23M. The key aspects of TXU’s apparent failure to grow over this 5-year period are summarised below:

1. TXU has not positioned itself, as did Pulse, to gain attractive new supply contracts with Pro- ducers to gain production supply price advantages; its producers supply cost actually rises from $2.96/GJ to $3.01/GJ by comparison to Pulse and Origin, whose average supply prices drop.

2. TXU increases its gas sales volume by 7.65% slightly higher than Pulse’s 7.05% but in line with overall economic growth.

3. The natural economic growth in residential and smaller commercial segments, which remain subject to regulated prices, provides just sufficient revenue increases in these segments to cover increased costs and to hold profits at 2003 levels

4. TXU increases its prices in contestable markets by 2.6% in line with supply cost increases and market share drops very marginally.

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5. Overall, TXU has the lowest market share and will require less supply contract flexibility than its peers and this might account for it not seeking to take up lower-priced new supply contracts as do Pulse and Origin. TXU, like Pulse, does not have any upstream involvement as does Origin, who gets a profit credit from these activities in the model. As a result, TXU does nothing startlingly new or different over this 5-year period and ends up with flat profits and under-performing versus Pulse and Origin.

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9.4.3 Origin’s Performance from 2003 to 2008

Table 9-4: Detailed Comparison of Origin’s Performance in 2003 & 2008

2003 - LAH5 Market Division Units -0.1 GJ/D 0.1-0.2 GJ/D 0.2-1.4 GJ/D 1.4-5 GJ/D 5-10 GJ/D 10-100 GJ/D 100-1000 GJ/D 1000- GJ/D Total No. Customers 285812 177650 69434 14414 690 278 108 32 548418 Market Share % 36.61 37.14 32.76 33.78 32.09 28.51 21.05 31.68 36.14 Avg. Consumption GJ/D/C 0.03 0.13 0.49 2.28 7.5 44.57 265.61 1288.11 0.34 Total Consumption PJ 3.13 8.43 12.42 12.00 1.89 4.52 10.47 15.05 68.06 Avg. Price $/GJ 10.65 9.15 8.96 9.05 4.19 4.23 3.58 3.39 6.55 Regulated $/GJ 10.65 9.15 8.98 9.15 5.81 6.17 0 0 9.09 Unregulated $/GJ 0 0 4.48 3.88 3.72 3.64 3.58 3.39 3.5 Revenue $M 31.48 76.68 110.18 108.44 7.91 19.14 37.45 51.01 442.28 Costs $M 46.73 69.02 83 74.61 7.6 18.17 34.97 49.69 383.8 Supply $M 8.62 24.44 35.89 34.97 5.51 13.19 30.54 43.89 197.05 Producers $M 7.78 22.04 32.37 31.54 4.97 11.9 27.55 39.59 177.75 Storage $M 0.68 1.92 2.82 2.75 0.43 1.04 2.4 3.45 15.48 Trading $M 0.17 0.47 0.7 0.68 0.11 0.26 0.59 0.85 3.82 Transmission $M 1.18 2.55 4.07 4.22 0.61 1.53 3.11 4.16 21.43 Distribution $M 15.78 28.24 36.75 33.18 1.24 2.98 0.26 0.13 118.56 Overhead $M 21.14 13.79 6.29 2.25 0.24 0.47 1.05 1.51 46.75 Gross Profit $M -15.25 7.66 27.18 33.82 0.3 0.96 2.48 1.32 58.48 Avg. Profit $/C -53.36 43.11 391.43 2346.54 440.91 3466.6 22991.2 41191.66 106.63 Gas Supply Price $/GJ 2.90

2008 - LAH5 Market Division Units -0.1 GJ/D 0.1-0.2 GJ/D 0.2-1.4 GJ/D 1.4-5 GJ/D 5-10 GJ/D 10-100 GJ/D 100-1000 GJ/D 1000- GJ/D Total 2008 vs 2003 No. Customers 284160 177279 69125 14783 728 291 131 34 546531 -0.34% Market Share % 35.24 35.8 31.54 32.78 31.85 28.7 20.96 32.69 34.81 -3.68% Avg. Consumption GJ/D/C 0.03 0.13 0.49 2.37 8 46.19 228.06 1305.08 0.36 5.88% Total Consumption PJ 3.11 8.41 12.36 12.79 2.13 4.91 10.90 16.20 71.81 5.52% Avg. Price $/GJ 10.68 9.17 8.97 9.06 3.93 3.93 3.22 3.05 6.34 -3.21% Regulated $/GJ 10.68 9.17 8.99 9.17 5.96 6.17 0 0 9.1 0.11% Unregulated $/GJ 0 0 4.06 3.48 3.34 3.26 3.22 3.05 3.15 -10.00% Increase in Unreg price % -9.38% -10.31% -10.22% -10.44% -10.06% -10.03% -10.00% Revenue $M 31.39 79.43 111.79 116.3 8.37 19.35 35.16 49.53 451.32 2.04% Costs $M 45.63 68.04 80.01 75.74 7.91 18.14 33.17 48.6 377.22 -1.71% Supply $M 7.64 22.51 32.4 33.37 5.54 12.79 28.42 42.22 184.9 -6.17% Producers $M 6.42 18.91 27.21 28.03 4.65 10.74 23.87 35.45 155.28 -12.64% Storage $M 0.66 1.95 2.81 2.89 0.48 1.11 2.46 3.66 16.01 3.42% Trading $M 0.56 1.66 2.38 2.45 0.41 0.94 2.09 3.11 13.6 256.02% Transmission $M 1.19 2.66 4.14 4.52 0.69 1.65 3.36 4.62 22.83 6.53% Distribution $M 15.7 29.02 37.16 35.47 1.41 3.19 0.28 0.14 122.38 3.22% Overhead $M 21.09 13.84 6.31 2.37 0.27 0.51 1.1 1.63 47.11 0.77% Gross Profit $M -14.24 11.39 31.79 40.57 0.46 1.21 1.99 0.93 74.09 26.69% Avg. Profit $/C -50.1 64.24 459.84 2744.08 628.93 4148.95 15208.71 27386.21 135.57 27.14% Increase in Avg. Profit % -6.11% 49.01% 17.48% 16.94% 42.64% 19.68% -33.85% -33.52% 27.14% Gas Supply Price $/GJ 2.57 -11.07% Incremental Profit $'000000 1.01 3.73 4.61 6.75 0.16 0.25 -0.49 -0.39 15.61

In Table 9-4, the model-generated results show Origin does moderately well between 2003 and 2008. It has increased its total annual downstream profit from $58m to $74M, an increase of $15M or 27%. The relative increase is not as good as Pulse’s 75% increase in profits.

The key aspects of Origin’s moderate success over this 5-year model period are summarised below:

1. Origin has cut the average supply price of the gas from $2.85/GJ to $2.36/GJ for a total supply cost reduction of $12M; this gain comes by lowering its supply cost from Producers by over $22M but incurring $10M greater costs in trading to meet its total supply needs. Origin appears to have sourced the same cheaper gas as does Pulse but Origin appears short on peaking capacity, forcing Origin to buy gas from the wholesale market.

2. Origin maintains a consistent use of storage capacity – further suggesting it is tighter than other Retailers in peaking capacity and uses storage as well as the wholesale market to meet its peaking requirements.

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3. Like Pulse, Origin needs to move its gas to Customers to realise gains from its new lower- priced gas supply contracts. Origin does this by dropping its unregulated retail prices by 10% versus Pulse cutting prices by 18%. This pricing difference reflects that Pulse has reduced its costs more than Origin due to Pulse’s success in the wholesale trading market versus Origin and despite any upstream credits flowing to Origin.

4. Origin gains a 5.5% increase in sales volume by lowering its unregulated prices to commercial and industrial Customers.

5. Origin’s market share drops from 36.14% to 34. 81% but because of inertia amongst residential and smaller regulated commercial Customers with fixed retail prices, Origin “creams” these markets and increases its profit recoveries in all customer classes.

6. Origin does not achieve the trading profit that Pulse has been able to squeeze out of its gas supply position. As stated above, this suggests that Origin may have less contracted gas from less flexible supply sources.

7. Origin holds its overhead costs flat at $47M with its average Customer administrative cost increasing from $85 per Customer to $86 per Customer.

Pulse and Origin have clearly outperformed TXU due to gaining access to lower priced supplies between 2003 and 2008 but they have followed different pricing policies to unregulated Customers. Pulse has cut prices, which were subsidised by success in wholesale trading, and has gained customer numbers and boosted overall profits. Origin has decreased prices to a lesser degree than Pulse and given up some Customers to preserve and increase the profit per Customer advantage it has over Pulse, possibly to facilitate higher sales of gas from its upstream interests.

Figure 9-6, which shows the annual pattern of changes over this 5 year period, shows there are individual years when each of these Retailers suffers short-term setbacks compared to the others. Care is needed to look at all available information before trying to draw any conclusions and given the "proof of concept" state of VicGasSim, conclusions should be considered indicative only.

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9.5 Conclusions 9.5.1 About the Model 1. The model design provides a rich platform from which to project and explore participant behaviour over time.

2. Model-generated market behaviour and outcomes tend to reflect the dominant features of the model and resources and strategies allocated to or available to major model participants.

3. It is important in the model design stage to identify the key sectors and transaction types that drive the way a whole market works and to work out what level of primary, secondary and possibly even tertiary and quarternary transactions that need to be reflected by the model structure to simulate a market that remains rationally bounded over time even if in a state of dynamic, non-equilibrium.

4. The model design, which incorporates all of the main participants and market sectors, only requires a reasonably low or relatively simple level of characterisation of each participant’s resources, transactions and behaviour patterns in each market sector to achieve dynamic but rationally bounded simulation outputs.

5. Identification of the key transactions and behaviours needs input from one or more domain experts to (i) design or to participate in designing the market model, (ii) characterise participants by providing knowledgeable and anecdotal input to initialise each agent when actual data or published case studies are not available, (iii) define various sets of forward strategies in simple industry terms and concepts, and (iv) describe how such agents might reflect on various alternative scenarios to make their choices.

6. The model generates huge amounts of participant and market oriented data; this data can be accumulated, stored and analysed in much the same way real participants would do using internally generated and externally available data to conduct varied analyses in each market sector. In the model, data generation and collection are done by allocating multiple accounting agents responsible to each participant. They collect and accumulate all relevant data (both private and public) for use by the agent participants.

7. Agents act asynchronously and overall model control is provided by a time clock that controls certain sequences of transactions. Most transactions can occur asynchronously but to keep a rational sequence of transactions over a long period, transactions and communications must be accomplished within set time slots within a day or a week, a month or a year, when accounting agents record the completed asynchronous transactions within that period. It is the

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capability of these accounting agents to record and store data that facilitates the use of look- ahead intelligence to update possible forward scenarios based on generating their own forward assumptions about strategy options versus competitors.

8. Look-aheads do help clarify what choices a participant should make at each time step. The most effective and more likely scenarios will be generated when the maximum number of participants possess and use the maximum number of look-aheads across as many of their key decision areas as possible.

9. Look-ahead capabilities improve the quality of model outcomes but their extensive use across multiple agents and multiple sectors requires materially increased running times for the model and "tuning" input data for participants resources to accommodate multiple scenarios with a wider range of outputs. Testing over wide ranges of market scenarios can exceed the limits of participants’ resources or their rational bounds of behaviour; for example, some test cases needed access to more gas than other cases and input data needed to be adjusted to provide access to more (alternative) gas sources than initially provided.

10.Model weaknesses & solutions:

(i) With all look-ahead switches turned to “ON” or maximum look-aheads activated, the Vic- GasSim model did start to overload with data management issues, slowed down and periodically moved outside its operational bounds. Such issues can be improved by using improved computing power, decentralised computers to represent individual participants interacting with each other, adjustment of resources made available to agents or inclusion of additional agents to enter the market to help fill demand or supply gaps, including CSM and Explorers to generate new longer term supplies, and more active Regulators to more frequently review and change tariffs and retail prices.

(ii) There is a suggestion that look-aheads may only provide marginal improvements when an ABM of a complex market includes well-characterised behaviour models at the micro- level and when this is done for all agents in the modelled market.

(iii)Micro-level modelling requires access to significant data and to domain experts with a sufficiently wide appreciation of all sectors of a market place such as the upstream and downstream gas industry to characterise all of the agents at sufficient and similar levels of detail; effective design using multiple domain experts needs clever project management to maintain a consistent level of design detail in each sector or decision-making area of the model.

(iv) Domain experts are not typically well-versed in object-oriented programming concepts and this can hamper debugging stages when errors need to be tracked through model programming of transaction sequences and steps.

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9.5.2 About the Victorian Gas Market It must be remembered that VicGasSim is only rated to being a proof of concept model and requires improved calibration and validation testing before any simulation results can be used with confidence to draw conclusions about possible future market trends or successful strategies for participants. But given this caveat, the following observations are made about the future of the Victorian gas market as "seen" by VicGasSim. These observations are based on the model- generated forecasts reported in this thesis and, where appropriate, are related to what is known about the market as it exists today and about the participants’ positions in that market today.

1. Upstream repositioning for long term supply contracts appears likely to have the strongest influence on the Victorian gas market. Those Retailers that gain access to the lowest priced gas from Producers will prove most successful.

2. The simulated market does not work to a least cost solution; a wide array of supply sources will prevail over the period to 2020 and lead to an ongoing spread of supply prices, which will only converge somewhat over this same period.

3. Gippsland producers hold a considerable surplus of both developed and undeveloped gas but they opt to stage market entry of this gas to set the price of their gas supplies up to the next alternative price level in the future rather than to accept lower prices and preserve their historical, monopolistic position. They allow their production levels to decline to ensure market entry by higher cost producers.

4. Upstream involvement may improve a Retailer’s long term participation in the retail market.

5. Retailers who entered contracts with the Yolla JV (BassGas) and, to a lesser degree, the Basker Manta JV, which were the first developments to enter the market after deregulation, gained access to the only gas priced at lower prices than those prevailing pre-deregulation. In the model, these events prove a major benefit to those Retailers, who took such contracts.

6. The development of the large Thylocene-Geographe discoveries in the offshore Otway Basin required a larger commitment than did smaller discoveries such as the Yolla and Basker Manta JVs. Thylocene-Geographe Producers required a higher price over longer periods in order to achieve the volume commitments that they needed (in the model) to cover their large investment. While those Retailers, who negotiated access to this still relatively low-priced gas, paid more than those, who accessed early gas supplies from Yolla and Basker Manta, they do better in the market over the long term because of the larger size and longer supply period of such contracts. These larger and longer term contracts protect such Retailers from later, higher priced gas from other sources.

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7. Other Producers bringing onstream smaller fields appear to have more flexibility and are able to move their gas supply prices up reasonably quickly, once Gippsland supplies drop away.

8. Pulse starts as the largest Retailer and holds the largest initial allocation of Gippsland gas. In the model, it appears to take up early, lower priced Yolla and Basker Manta supplies and uses these contracts to advantage over short periods to 2008 to make extra profits in the trading market – selling gas to other Retailers – such as to Origin for peaking.

9. In the model, both Origin and Pulse "cream" the residential and small commercial sectors in periods when they leverage their supply position and boost profits against fixed, regulated prices to the bulk of their Customers, who show inertia during the early years of staged contestability.

10.TXU starts as the smallest of the big three Retailers and with a lower proportion of regulated Customers and higher proportion of unregulated commercial and industrial Customers. TXU fails to position its supply position as do Pulse and Origin, who leverage their medium term positions with lower supply costs into the regulated price market.

11.Market prices are forecast to increase only moderately until 2008-2010 and then the rate of increase picks up as expensive new developments start to come on line. ExxonMobil-BHP Billiton’s 2008 announcement of a $1.5G development cost for bring the Turrum gas field on line with first material gas by 2015 are indicators of the cost pressures that will hit the east coast market in the next five to eight years. In the model, Timor Sea gas is predicted to enter the Market between 2015 and 2020 at prices that will reflect the high transmission costs from the Timor Sea.

12.The influences, which dominate Retailers performance in the considered model scenarios, are: (i) how well Retailers position themselves to gain competitively priced supplies of gas, and that they do not let themselves get locked into or locked out of supply at the wrong and right prices, and (ii) how well they capture Customers in the 0.2-5 TJ/D markets, which generate the largest contributions to Retailers’ long term profits.

13.Long distance transmission costs from sources such as the Cooper Basin and the Timor Sea will set the long term price targets for local mature sources such as Turrum and other Gippsland, BassGas and Otway discoveries, which might be made over the next five to ten years.

14.While the model comparisons show that Origin appears to be the most robust retail participant in the Victorian gas market, this is due to the upstream credits used to cross-subsidise Origin’s retailing. If Origin did remain the only Retailer with an upstream presence in this area then it might come to dominate the Victorian gas market in the downstream segment. However, the model’s weakness in not allowing price increases to flow to regulated Customers has inhibited Pulse’s ability to leverage its position as the largest Retailer in the Victorian gas market.

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These model trends are being overtaken by the movement of Retailers to build complementary holding in gas and electricity markets within Victoria and adjoining states. The model needs to be expanded in scope to cover both gas and electricity and all eastern state markets before meaningful conclusions about long term market dominance are possible.

15.In the model scenarios considered, Origin’s peer competitors need to achieve a quantum shift in the market dynamics to claw back the advantage that Origin established by participating in exploration in Bass Strait. They could buy out existing upstream players or look to lower Cus- tomer processing costs by acquisitions to become dual-fuel marketers; all have moved to dual-fuel marketing since the model testing was undertaken in the early 2000’s. The next round of opportunities to claw back Origin’s advantage could come with Government stimulated growth in decentralised GPG, and carbon trading.

Whatever the circumstances or changes in the market environment (such as occurrence of new discoveries, supply of cheap CSM gas from Queensland or, inversely, the flow-on of high LNG export net back gas pricing to domestic gas, initially in Queensland, as it has done in WA), Retailers will be looking to optimise their positions as intermediaries between Producers and Customers. With growing uncertainty in a complex and dynamic market, they will need increasing capability to test their positions against Producers and their Retailer competitors. Models that allow testing of multi-segment involvement will have an increasingly important role. Such models will be more useful if they are user-friendly to participants’ employees so that employees can test their plans and market understanding. This model takes a step in that direction by specifying the vast bulk of its input parameters at a micro-level and in domain jargon and criteria.

250 Chapter 10 Observations, Conclusion & Future Model Enhancements

Chapter 10: Observations, Conclusion & Future Model Enhancements

The following observations are made in regard to this research project, the VicGasSim model, its development, its calibration and validation, and application to the Victorian gas market. These are followed by a number of conclusions, including an assessment of the outcomes of the research project versus the original research objectives, and an overall assessment of the project. The thesis concludes with some suggestions on how the VicGasSim model can be improved.

10.1 Observations 10.1.1 About the Overall Project 1. Initial literature screening indicated ABM and discrete event modelling was the best approach for addressing the market dynamics that would follow the deregulation of the Victorian gas market.

2. The model development work was staged through four prototypes; the first two prototypes were used to set up and test the most fundamental interactions of the type, which characterise a gas market, and developed a rethreading/cloning method for conducting look-ahead (intelligent) screening of the future. The latter two prototypes tested the scale up of the initial framework to a representation of the Victorian gas market.

3. Look-aheads can increase agent intelligence but need to be selectively used until further computing efficiency can be developed for the model.

4. The commercial agent package JACK was used for the first prototype. The model’s design emphasis on characterising participants’ interactions in terms very similar to reality required significant coding of these interactions outside of the basic JACK code and the structure. JACK was jettisoned in favour of developing all code and agent protocols from scratch in Java software. This avoided JACK’s over-riding structural arrangements and methods, which constrained the model design.

5. Access to industry participants, such as Origin Energy, who was a joint sponsor of the SPIRT Grant that financed the model development work, provided a number of market insights that filled gaps in the author’s knowledge and experience and other helpful information.

6. The delay in completing the documentation of the thesis has provided time to accumulate six years of actual data, which has been used to compare to forecasts, which were generated by VicGasSim in 2002-03. This has allowed application of the "predictive calibration" technique to support the claim of "proof of concept" for the VicGasSim model design.

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7. The analysis of the outputs, which were generated by VicGasSim, has provided insights into how VicGasSim can be enhanced (see 10.3).

10.1.2 About the Model Th following observations are made in relation to the working version of VicGasSim:

1. The VicGasSim model proved to be reasonably stable given it had very few controls to keep it within the rational bounds of a market in an ongoing dynamic, non-equilibrium. This outcome reflects achievement of a sufficient level of characterisation of major participants and their interactions; this approach has materially reduced and almost eliminated the use of control agents in the model. The model’s market rationality came from characterising each participant’s behaviour with sufficient capability to make decisions in regard to each of the key elements in this market. While no capability was overly sophisticated, the number and type of transactions covered the spread of key transactions that exist in the supply chain from Producer through the transmission and distribution systems, with due regard to weather patterns, demand, pool pricing/trading, storage, short and long-term contract negotiations, retail pricing, and customer churning, and where each commercial participant’s main goals are to make both short and long- term profits. The model favoured diversity of participant characterisation of their plans and their interactions rather than trying to optimise each market outcome directly. The outcome is not an optimal market solution but rather a vision of what an actual or real world market might look like over time and what forces might appear to most influence the market.

2. The model design is weak in not providing for 5 yearly reviews of regulated retail prices. This led to an unrealistic squeeze of Retailer profits after about 2010. This has not hindered the working of the model but has dampened the outlook for long term retail prices and Retailer profits. The effect of this design weakness on the model outputs and the ability of the model’s ability to show the consequences of this inappropriate imposition highlight the model’s ability to test policy alternatives. Nevertheless, the current model would benefit by making the regulators more active participants in the model’s market and require them to assess and adjust tariffs and prices on a regular basis.

3. The assumed maximum annual churn rate of 5% pa has proved to be well below actual Cus- tomer churn levels, which reached over 60% between 2002 and 2006. This assumption should be increased in future model runs; the algorithm should be re-considered and upgraded in the light of experience under deregulation. This has not hindered the working of the model but has dampened the outlook for long term retail prices and Retailer profits.

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4. Similarly, it could be beneficial if the transmission and distribution owners were given greater capability to plan on expansion of system capacities and seek tariff increases to cover required investments. This has not been an issue in the reported model runs since the gas demand remains flat over the run time to 2020. If the GPG demand is updated to reflect recent policy changes and undergoes rapid and material growth, extra transmission capacity will be needed in the model. Currently, Producer agents are the only participants required to work within existing capacity constraints and given the ability to decide when and by how to expand capacity as well as how to update prices to recover new investments.

5. The links to the adjoining state gas markets and the parallel Victorian electricity market are represented in the model and adequate for the current objectives. However, they will become increasingly important in the future and the model should be upgraded for better representation of energy substitution between gas and electricity and energy trade between states.

6. Emission trading could become an important determinant of future trends in energy demand and pricing and needs to be reflected into any future model development.

10.1.3 About Gas Supply Price Predictions Model predictions indicate that:

1. Gas supply prices will increase markedly in real terms over the period to 2020.

2. The dominant influence on the immediate post-deregulation market evolution will be the rapid wind-down of historical Gippsland gas supply contracts and entry of new supply sources putting upwards pressure on gas supply prices.

3. A marked increase in wholesale gas prices well above the levels contemplated by third parties at the same time that the model work was first completed in 2002-03. The model outcomes reflect (i) assumed development costs for new projects or plant capacity expansions and the timing of expansions, (ii) levels of capacity utilisation, (iii) the level of competition between Producers, and (iv) the relative positions of Retailers’ past supply contracts and demand expectations.

4. The model outputs show an ongoing spread of supply costs that will dynamically influence Retailers’ relative profit performances over time. This spread is forecast to show some convergence over the period to 2020.

10.1.4 Observations on Predicted Retailer Performance Model predictions indicate that:

1. Some marginal changes in Retailers’ competitive relativity occur (see 3. below) but the market remains within rational bounds of expected behaviour. These observations are valid up to about 2007-08 when the modelled Market becomes unrealistic due to the continuation of fixed regu-

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lated retail prices (with no 5-yearly reviews by the regulator as actually occurs in the real market) and the assumed low cap on annual churn rate imposed on the model predictions. Beyond 2008, Retailers’ annual profits drop rapidly as result of inability to recover increasing gas supply costs from Producers as they bring new supply sources and capacity expansions on line.

2. The imposition of inappropriate polices (no increase in regulated prices despite gas supply price increases) or inappropriate assumptions (a low annual churn cap of 5% pa in the model versus actual levels above 30% pa)1 quickly causes a loss of profits that influences behaviour in the Retail market. The model predicts this rapid profit deterioration hence showing the capability of the model for use in testing the impact of policy designs or participants’ assumptions about some new and highly uncertain outcome. These model runs show the model’s ability to test churn assumptions in response to differing price offers; earlier discussion shows how the model can test poor policy design such as the frequency of regulated price reviews.

3. Extensive discussion of and general observations about each Retailer’s performance are presented in 9.4 and 9.5 and are not reproduced here; they are specific to the assumptions of the individual model runs. However, these descriptions of example performance outputs point to the value of VicGasSim for use in providing market participants with a platform for testing strategies that they might implement in the future to outperform competitors. The user- friendly form of the model outputs also shows how the model can be used for training participant’s employees and providing an understanding of the dynamics of competition and how certain strategy positions can influence other business sectors in unanticipated ways.

10.1.5 On Comparisons to Third Party Predictions Comparisons to third party predictions indicate:

1. The model in its 2002-03 working form has proved effective by forecasting a long-term increase in long term gas supply prices; third party forecasts, which were made in 2001-02 suggested prices would remain relatively flat. The model’s success lies in forecasting the flow on effects of Producers’ cost increases and in accommodating the changing leverage between the upstream and downstream participants. The simulated gas supply price outlook is somewhere between a cost-plus solution and the alternative of market bearable solutions. The predicted co-existence in the model of supply contracts at different prices in the same delivery year indicates that dynamic competitive forces would generate a wide range of prices being offered among parties and that the differences would vary over time as contracts were settled.

1. The low churn cap would not be a strong influence in the model predictions while modelled regulated retail prices are fixed without 5 yearly reviews. If regulated retail prices do not increase in the model, modelled residential Customers will always want to retain the low regulated prices versus what will be predominantly higher unregulated prices over time. Some churn will occur in the model as Retail- ers have an ability to reduce unregulated prices in an attempt to gain customer numbers to lower their per unit overhead cost allocation to each customer.

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2. Most third party forecasts are still being generated by neoclassical style models and these forecasts could prove unreliable in the future as the market further changes, such as when higher GPG demand and CSM supply emerge.

10.1.6 About the Victorian Gas Market in 2008 The following observations are made about the Victorian gas market in 2008:

1. A greater number of potential suppliers exist than were assumed in the model. New discoveries at Longtom and Casino and a number of smaller onshore fields have occurred and are not reflected in the model. If the model was rerun with these potential sources included, they could lower near term gas supply prices below those shown in Figure 9-1, but it is believed that their effects would be small due to the relatively small size of these discoveries. However, most of the longer term sources predicted to come on stream have done so or been committed; therefore, any long term changes in future scenarios should be marginal. The gas supply price outlook in Figure 9-1 could be influenced by the potential effects of abundant CSM gas supplies; these potential effects are addressed in points 5 and 6 below.

2. The effects of planned introduction of trading carbon emission rights is already being built into recent forecasts, such as that by Vencorp (2007) for Victoria, and which shows forecast gas demand growing by up to 60% by 2020. This increase reflects GPG replacing the far less efficient brown coal-fired power generation plants. Such demands will be replicated to a lesser extent in other states, which use black coal for power generation. Under such pressure, Victorian gas prices could rise even higher than shown in Figure 9-1.

3. The rapid growth in development of CSM supplies and resources in central Queensland could mean a new abundant supply source in Queensland that could move “surplus” gas supplies south and dampen gas supply price pressures below those in Figure 9-1.

4. Offsetting this downwards pressure, potential commitments to build a number of LNG plants on the coast of central eastern Queensland will absorb a lot of planned CSM development and supply growth. Even if CSM supplies can match all of these needs, LNG prices from exports based on CSM supplies will create higher gas pricing benchmarks for the eastern and potentially southern Australian gas markets, as they have done in WA.

5. Pricing dynamics will be different in each set of circumstances – be it new supply developments and GPG demand growth in Victoria or CSM supply in Queensland – and large uncertainties exist as to where the likely balance will fall. The current model has been shown to have the potential to isolate where these balances of forces might settle and how long it will take.

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10.1.7 Outcomes versus Research Objectives

10.1.7.1 Objective #1 To characterise the future evolution of the post-deregulation Victorian gas market by designing and implementing a model that can reflect:

1. the immature and non-homogeneous nature of this market,

2. the effect of a series of past and future discrete events, including: deregulation and restructuring, expiry of major supply contracts, and entry of new supply sources and new market participants, and

3. the strong influence of the differing corporate strategies, financial capabilities and assets ownership of the major market participants.

Outcome: The limited scenarios generated by VicGasSim characterise the post-deregulation of the Victorian gas market from 2002 to 2020. The model is populated with agents representing virtually all of the main participants in the market or anticipated to enter the market over the medium term future. While the total number of agents is sufficiently large to reflect a complex adaptive system, the number is not so great that the market is homogeneous; the model preserves the actual heterogeneity of the real market. The late 1990’s restructuring of the market has been built into the model as key agent assumptions about the many market allocations prescribed by the Government in the corporatisation and privatisation process implemented in the late 1990’s (such as allocations to each Government created retailer, distributor and transmission operator such as customers, supply contract volumes and terms, initial regulated retail prices, distribution and transmission tariffs, and regulated market rules) – all are initialised in the model as publicly prescribed or on the basis of anecdotal evidence. The model is also initialised with a wide range of simple agent strategies that are based almost solely on business/financial concepts of prices, costs, market shares, profits/losses and cross-ownerships and joint holdings. The model outputs allow participants to test various strategies to consider the expiry of contracts and how to structure new contracts to deal with the uncertainty about timing of new supply contracts or other competitive actions by other agents active in the modelled market.

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10.1.7.2 Objective #2 To create an agent-based simulation model of the Victorian gas market to explore the market’s evolution and the issues, which are raised in Objective #1.

Outcome: The author has created a model that is a mix of Werker and Brenner’s history-friendly and micro-simulation ABM’s. The VicGasSim model is initialised with significant actual data at a micro-level. This data has allowed the model to match numerous market performance criteria in the year 2001. The model has generated a number of future scenarios with significant data outputs that allow detailed analyses and explanation of these scenarios.

10.1.8 Objective #3 To populate the model with a wide array of model agents, whose market actions are behavioural in nature, and who are characterised with market resources, negotiating strategies, operating plans, and corporate decision-making capability to achieve an ongoing dynamic market without recourse to model control agents.

Outcome: The author has followed the modelling steps advocated by Labys and Brenner and Werker, to consider the level of data available in setting up the model design. In the case of the deregulating Victorian gas market, there was lot of data published in association with the public consultation process in the lead up to deregulation and in subsequent Government reporting to monitor that the system put in place is working as planned or to determine if it requires modifications. This wide supply of information has provided the basis to opt for a micro-level simulation type ABM that allows the greatest level of model characterisation of empirical inputs and empirical outputs in data forms that the industry actually tracks; this detailed level of tracking provides a high level of understanding about the market dynamics that the model predicts to occur.

10.1.9 Objective #4 To use the model to simulate various scenarios that can be used to explore and understand emergent market conditions, which are derived from the synthesis of the individual strategies and interactions of the market participants, and to use these scenarios to identify likely key determinants for corporate success in this market.

Outcome: The limited scenarios generated by VicGasSim characterise possible trends in the period following deregulation of the Victorian gas market. The scenarios show a set of emergent trends of increasing gas supply price and a flattening of residential, commercial and industrial demand (with the exception of GPG, which is now expected to grow rapidly as a result of stimulation by recent changes in Government policy and despite increasing gas supply prices). The spread of forecast gas supply prices suggests that the market will be discontinuous on an irregular frequency where timing is being driven by the competitive interactions between market

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participants. This spread does reduce over the period to 2020 but it never closes to zero; this also suggests the agent interactions remain in competition and subject to irregular discontinuous but negotiated outcomes. A number of generated scenarios show how a focus on short-term versus long term objectives can radically alter an agent participant’s profit performance by not anticipating important and discontinuous change in supply sources. VicGasSim’s abundant output recording and storage system means that model users can look through these output data sets to explore unexpected outcomes to identify the likely cause, and test alternative model runs to see what alternative positioning or strategies might have generated better outcomes. The keys to success in this market are (i) paying close attention to the timing and sizes on committing to new gas supply contracts: sometimes shorter contracts will be more appropriate and, at other times, longer contracts will be more appropriate; timing is the key to be positioned to access gas that is lower priced than that held by competitors or competitors might face if they get timing wrong, (ii) deciding whether to use any advantage for "creaming" the market by holding or increasing retail prices or by passing some or all of the benefit to customers to boost customer numbers (a Retailer’s choice will be a function that Retailer’s customer mix), and (iii) risk diversification into adjoining market segments to reduce per customer overheads and to provide ability to sell gas from owned upstream supplies. Wholesale trading and storage management are important operational areas to get right and to avoid flow-on effects that could negatively affect gas supply positioning but do not appear to be major sources of profit in and of themselves.

10.2 Conclusion The completed research shows that VicGasSim represents a well chosen model design to simulate a dynamic, non-equilibrium, complex, adaptive Victorian gas market. VicGasSim is capable of generating rationally bounded future scenarios and to make predictions of the future emergent behaviour for this market. The model needs further calibration and validation before it can claim confidence in such predictions; predictions remain dependant upon the model assumptions set for each model run.

The model’s greatest strength is its ability to provide an understanding of how the future market dynamics might work over time and what might be the principal drivers of emergent trends. The completed project can claim proof of concept for the model design, which has placed a priority on representing all of the market sectors from resource development, through production, transmission, wholesale market clearing, storage, distribution to demand by diverse types of customers sensitive to weather and competing energy forms. Achieving this level of representation has been made possible by giving agents simple decision-making plans to use in each sector and across sectors; simplicity is offset by diversity and multiplicity and exclusion of sophisticated, time-consuming methodologies, which are more commonly used in single sector

258 Chapter 10 Observations, Conclusion & Future Model Enhancements

energy models or limited sector models. Agent mental models are all defined in axiomatic business terms, which are commonly used in the industry; this facilitates the user-friendly appeal to potential participant users of VicGasSim. Other model features assist in its use: a wide range of dynamic graphic displays and logging of detailed transaction level outputs, which is possible because of the breadth of accountant agents used in the model design. Look-aheads provide an innovative approach to giving more future-oriented intelligence to agents but the limited testing gives a hint that this might not add much value to long term modelling given the myriad interactions provided even in cases without the use of look-aheads. Multiplicity of hierarchical, interactive transactions might be more important over long periods than applying sophisticated learning or decison-making techniques; more work is needed to explore this tentative finding.

Few, if any, other models, which have been considered in this thesis, have attempted to build such a domain-rich and participant-oriented platform across all sectors of a diverse commodity market as has been attempted in this research. In this sense, this research has achieved much of what the author set out to explore in 1998.

What is also rewarding is that scenarios from some segments of the model show similar trends to actual market performance from 2002 through 2007 and that VicGasSim’s 2020 demand and gas supply price outlooks, which were generated in 2003, show similar trends to third party long term forecasts being generated in the 2006-08 period with the benefit of intervening history. Similarly rewarding is the continued use of the VicGasSim model design and platform in the CSIRO’s NEMSIM model.

The model is worthy of further enhancement and a number of suggestions are provided in the following section.

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10.3 Future Model Enhancements To improve and recalibrate the VicGasSim model, the following suggestions are made:

1. Provide Regulators access to sufficient data so that they can collect, review and periodically change regulated retail prices for each Retailer.

2. Increase maximum allowed churn levels; this is an existing input but the model churning plan would benefit by upgrading to a more behavioural-based plan, which makes decisions on other factors as well as price.

3. Recalibrate all participant data bases.

4. Provide Transmission operators and Distributors with sufficient intelligence to periodically expand their capacity and increase or decrease tariffs in the various regions of both systems. This would require an ability to monitor and project capacity needs plus some simple agent plans to assess likely capacity steps, timing, costs and how to vary tariffs to achieve a rate of return goal.

5. Improve the representations of the current simple agents that simulate related markets such as gas sales to SA, NSW and Tasmania.

6. Add similar simple agents to act as representations for electricity markets in parallel areas, including Customer numbers such that Reatilers can share administrative costs across markets.

7. A hedging market could be added to allow more interactive and competitive pricing behaviour and to provide further transparency whereby model participants can look for clearer pricing signals or indicators.

8. Decision criteria could be extended to include future investments in look-ahead cash flows that would allow decisions based on consideration of discounted cash flows, return on investments, utility attitudes and risk averseness to making losses rather than the simple cumulative profit comparisons now used.

9. Decision processes could include higher level agents representing companies holding vertically and horizontally integrated interests in multi-party joint ventures for supply, transmission, distribution, retailing and the GPG. This would almost become an imperative if the model was extended to cover all of eastern and southern Australia as well as a parallel electricity market.

10.Company agent profiles and plans should be further developed to improve the top level and strategic level of the hierarchical structure. Company agents should be able to make estimates of corporate profits from all sectors so that the effectiveness of strategy diversification or specialisation can be better assessed and recorded. Company agents should have plans to test buying and selling sectors to improve overall performance. This will require including bal-

260 Chapter 10 Observations, Conclusion & Future Model Enhancements

ance sheet type profiles, including debt carrying capacity, for each sector or the Company’s overall business. Similarly, Company plans should ensure they monitor for insolvency and plans to survive or be terminated by winding up or being taken over.

11. Improved negotiating and/or bargaining methodologies could use multi-dimensional and multi-period negotiations including risk attitudes to probability based expectations. Such an example is shown in Appendix 5. However, such a change would move away from the current design emphasis on using a multiplicity of agents with simple plans rather than fewer agents with sophisticated plans; putting that more simply: modelling with simple multiplicity and diversity appear (tentatively) better than using less multipicty with focused sophisticated methodologies.

12.An emission trading system could be added.

13.A new GPG demand function is needed to reflect recent policy initiatives that will drive rapid growth in this sector.

14.There is potential to use decentralised computing to facilitate co-ordinating a set of similar models for each state and each energy form, VicGasSim could act as the model design for state gas markets (NSWGasSim, SAGasSim, and TasGasSim, QldGasSim – each would need to be based on a similar level of input data to that collected for VicGasSim), NEMSIM could act as the basis of the east coast electricity system. Interconnecting rules would govern trade between states and new behavioural models would be needed to handle inter-energy competition – a major project and major challenge, but potentially very powerful.

To handle the increased transaction levels implied in many of the changes suggested above, it might be necessary to redesign parts of the model and possibly consider moving to decentralised computing, where separate models of different parts of the overall eastern Australian energy markets are spread over interacting computers.

Moving in the other direction, it is possible to scale down the wide coverage of the market and focus on a specific sector to address more specific issues such as are being addressed by the CSIRO’s NEMSIM model in looking at trading strategies. Emission controls and costs, and locating GPG plants at advantageous positions in the transmission network are examples for such consideration.

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262 Abbreviations

ABARE ...... Australian Bureau of Agricultural and Resource Economics ABM...... Agent Based Modelling ACCC...... Australian Competition and Consumer Commission ACE...... Agent-based Computational Economics ACQ ...... Annual Contract Quantity AER...... Australian Energy Regulator AGA ...... Australian Gas Association AHP...... Analytic Hierarchy Process AI...... Artificial Intelligence AMES...... Agent-based Modelling of Electricity Systems ANP...... Australian National Power AOP...... Agent-Oriented Programming ARC ...... Australian Research Council BDI...... Beliefs, Desires, Intentions COAG ...... Council of Australian Governments CSM ...... Coal Seam Methane CV ...... Curriculum Vitae DG ...... Distributed Generation Dept...... Department DOE ...... Department of Energy (US) $ ...... Australian Dollar DPIE...... Department of Primary Industry and Energy ECAM...... Extended Content Addressable Memory EIA...... Energy Information Administration (US) EMCAS...... Electricty Market Complex Adaptive System ETSAP ...... Energy Technology Systems Analysis Programme GFC ...... Gas and Fuel Corporation G ...... Billion GJ ...... Gigajoule GHG ...... Greenhouse Gas GPG ...... Gas-fired Power Generation IFFS ...... Intermediate Future Forecasting System (US) IP ...... Injection Point JV ...... Joint Venture KBDA’s ...... Knowledge-Based-Decision-Aids LAH ...... Look-ahead

263 LBS...... London Business School LEAP ...... Long Term Analysis Package LNG ...... Liquefied Natural Gas LP...... Linear Programming M ...... Million MCDM...... Multiple-Criteria Decision Making MDQ ...... Maximum Daily Quantity NEM...... National Electricity Market NEMS...... National Energy Modeling System (US) NEMSIM ...... National Electricity Market Simulation NIEIR ...... National Institute of Economics and Industry Research NSW ...... New South Wales NWS ...... North West Shelf OMV ...... Österreichische Mineralölverwaltung, Austria's largest petroleum company OO/DEVS ...... Object Orientation/Discrete Event Specification Formalism ORG ...... Office of the Regulator General ΦΚΦ ...... Phi Kappa Phi ΠΕΤ ...... Pi Epsilon Tau PIES ...... Project Independent Evaluation System PJ...... Petajoule PNG...... Papua New Guinea PTS ...... Principal Transmission System RISC...... Resource Investment Strategy Consultants SA ...... South Australia SEA ...... South East Australian TorP...... Take or Pay TZ...... Transmission Zones UGS...... UnderGround Storage USDOE ...... US Department of Energy WA ...... Western Australia WPMP ...... Wholesale Power Market Platform WUGS...... Western UnderGround Storage

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278 Appendix 1: Access to the VicGasSim Model

1.1 Website Download An operating version of the model can be downloaded. The website is: http://www.mphcon.com.au/gas/index.html

1.2 Access Permission The website’s home page is open for inspection but does not permit access to the VicGasSim model. To download the VicGasSim program from the website, potential users must obtain permission and a password from the author and Professor Myles Harding. To obtain permission to download the program, contact Neale Taylor (email: [email protected]) or Professor Myles Harding (email: [email protected]).

279 280 Appendix 2: Agent Descriptions & Model Input

2.1 Agent Descriptions The model has an online document describing all of the agents and artefacts in the model. This document also defines the input required to run the VicGasSim model. A copy of this document is presented in the file, origin.html, which is stored on the CD included at the end of this thesis. The file can be opened by an internet browser, such as Internet Explorer or Mozilla Firefox.

2.2 Main Input File An example input file can be inspected by opening the file, o30.xml, which is stored on the CD included at the end of this thesis. The file can be opened by an internet browser, such as Internet Explorer or Mozilla Firefox.

This file shows a set of data for running the model in conjunction with the other two data files, which are described below. The data can be altered to allow users to prepare and/or alter the model initialisation data and extensive data input file. Markets can be defined and added; new participants of most types can be defined and added.

2.3 Weather File The model can accommodate separate files of daily weather data typical of the seasonal pattern and daily variability within each geographic area modelled. Each data file is a set of the historical weather indices. The index is known as the Effective Degree Day or EDD (which reflects the combined human effect of temperature plus wind) and is collected for each day in a given year. Each area can have its own weather pattern. The model’s simulated weather is generated by sampling the weather index from a few days either side of the same day in the historical data file and randomly varying the index within a set band around the historical weather input profile.

An example input file for the year 2001 can be inspected by opening the file, edd_txt.pdf, which is stored on the CD included at the end of this thesis.

2.4 Model Initialisation File The model requires an initialisation file to get the model started and calibrated to basic information such as the start and end dates for a model run and the name and location of the input data files to be used for the model run. An example input file can be inspected by opening the file, gas_txt.pdf, which is stored on the CD included at the end of this thesis.

281 282 Appendix 3: Model Output Examples

The model makes summary reports and charts available via the model screen when the model is running or has completed a test run. A series of screen images is presented below and on the following pages to show the model’s graphics capability. The model also stores a series of output reports as csv files for offline data analyses in programs such as Microsoft Excel.

3.1 Opening Window for VicGasSim

283 3.2 Bid Stacks & Pool Price

284 3.3 Bid Stacks in Graphical Form

285 3.4 Look-ahead Test Case Comparisons

286 3.5 Report Summaries

287 3.6 Supply Contract Monitoring

288 3.7 Demand Tracking

289 3.8 Retailer Annual Profit and Profit Margin Summary Reports

290 Appendix 4: Industry Changes Since 2003

4.1 Overview This thesis was initiated in 1998-99 with the model design completed in 2002-03. A number of publications have recorded recent changes in the Victorian and Australian gas markets. These include:

4.1.1 ABARE (2003): “Australian Gas Markets – moving towards maturity” ABARE’s report concludes the Victorian spot market trading pool is working effectively without need of a market maker and there is value in posting the economic value of gas on a daily basis. ABARE reports the volume of gas traded (outside contracted volumes to cover own needs) each day had grown to be in the range of 10-20% by 2003 and provided a sufficient margin to entice development of smaller fields to enter the market without need to obtain a long-term contract as might apply to larger developments. They also report that the spot market has also helped the entry of a number of smaller retailers into the market.

The report notes that by 2003, most regulatory impediments have been removed and interstate trade is allowed, but the market is still seen as lacking liquidity and transparency due to the limited number of market participants. ABARE recognises that trading changes will occur dependent upon new discoveries bringing new supply sources to the market and will be influenced by the creation of more trading hubs (where transmission network pipelines meet). Both changes will allow trading of both gas and transport rights to manage short-term price risk exposures. ABARE advocated creation of a transport rights trading market, which does not exist. ABARE doubted there would be any effect from cancelling Trade Practices Act exemptions that allow joint-marketing of gas by producers. They do not see a need for the introduction of trading in gas derivative products until greater market liquidity develops.

4.1.2 Australian Energy Regulator (2007): State of the energy market 2007 This AER report provides a very comprehensive description of the history of deregulation of Australia’s energy markets from genesis through intervening changes in market participation, regulation and regulators, as well as providing a good source of historical data and recently developed long-term forecasts. It ends by presenting summaries of institutional arrangements and greenhouse gas policy. AER notes a number of changes in regulations and regulators; these changes are described in 4.2 of this Appendix. Historical data between 2000 and 2007-08 and recent forecasts from AER’s report are presented in Chapter 8, see 8.3.3, 8.3.4, Table 8-1 and Figure 8-9, and compared to scenario outlooks generated in 2002-03 by VicGasSim.

291 4.1.3 ACIL Tasman (2008): The impact of an ETS1 on the energy supply industry – Modelling the impacts of an emissions trading scheme on the NEM and SWIS, prepared for the Energy Supply Association of Australia (22 July 2008) ACIL Tasman’s recent and comprehensive report covers outlooks for energy in both the eastern and western Australian markets, with an emphasis on electricity and gas and the impact of emission controls and policies on these energy sectors. This report builds on work conducted initially by ACIL in the early 2000’s, as reported in 5.1.2.

An outlook for the future east coast gas price in each of the major cities is presented; ACIL Tasman’s forecast of the long term average Victorian gas supply price is compared to a range of gas supply prices, which were generated by VicGasSim, see 8.3.2 and Figure 8-8. The report provides a rich data base for making comparisons with outputs generated by VicGasSim, even though the latter were generated in 2002-03 and have not been updated for policy changes and their likely impact in stimulating increased growth in GPG.

4.2 Corporate and Regulatory Changes – Participants The industry has undergone a significant level of change in participants since the VicGasSim model and forecasts were developed in 2002-03. The area of most change has been ownership of the downstream participants in the transmission, distribution and retailing sectors.

TXU (owned by TXU Corporation and SP Australia) was acquired by Singapore Power in 2004 and combined with the generating assets then held by Singapore Power. CLP Asia Power subsequently acquired the company in 2005 and renamed it TRUenergy. CLP Asia Power has acquired other energy assets, which it has injected into TRUenergy with a focus on dual-fuel marketing and power generation in Victoria, SA and NSW. TRUenergy has swapped certain assets with AGL whereby both companies have improved the synergy across their respective asset portfolios. TRUenergy has subsequently disposed of its interest in the SEA Gas Pipeline between western Victoria and SA. This is not a full description of TRUenergy’s current profile but it suffices to indicate that the original TXU has undergone significant restructuring by several rounds of ownership changes and has strengthened the mix of its gas and electricity assets in southern and eastern Australia. Two effects are to reduce its average customer administrative costs due to improved economies of scale by increasing customer numbers and to provide a basis for dual-fuel marketing in multiple states.

1. ETS: Energy Trading System

292 Pulse was acquired by AGL in 2002 after AGL had missed gaining a downstream gas retail position in the first award of retail licences by the Victorian Government in the 1990’s. Subsequently, AGL came under corporate attack by Alinta and in 2006 both groups reached agreement to divide their respective asset portfolios such that AGL’s portfolio moved to focus on power generation and retailing and Alinta focused on the infrastructure assets. AGL ended up well placed with high customer numbers and an ability to offer dual-fuel marketing.

Origin is the only original Retailer operating with its original ownership structure intact. However, Origin has continued to broaden its strategy. In 2008, it announced plans to build a very large GPG plant in western Victoria. It has another GPG plant nearby in south-eastern SA. Origin is the major party in the Yolla joint venture, that discovered the Yolla gas field and led the effort to bring the field on production in 2006 by supplying Origin (Retail) in Victoria as the joint venture’s initial customer. Origin has also participated in the development of the Thylocene- Geographe discoveries offshore in the Otway Basin and these fields came onstream in 2008 supplying Victoria and SA.

Origin has built a large position in Queensland in coal seam methane resources and is rapidly building a position as a major power generator as well as a gas and electricity retailer and producer. Origin sold out of the distributor, Envestra, in 2008 as well as selling its interest in the SEA Gas pipeline.

Obviously, through the structural changes that have taken place, TRUenergy (incorporating TXU) and AGL (incorporating Pulse) have altered their capacity to compete in Victoria and enhanced their robustness. What the new owners have done are good examples of the type of strategy changes that can be well tested in models such as VicGasSim.

The Australian Energy Regulator (2007) notes there are some common threads in the changing ownership landscape, including a tendency towards greater specialisation. Most entities have been shifting their primary focus either towards network infrastructure or the non-network (production, generation and retail) sectors. The trend appears to be driven by capital markets and may reflect an assessment of limited efficiency benefits from integration across the network and non-network sectors. At the same time, there is increasing integration within each sector.

TRUenergy, Origin and AGL, the new gentailers (retailers that own generation plant), have absorbed most of the mass market electricity and gas retail businesses sold in Victoria, South Australia and Queensland. So far, there has been no comparable change in retail ownership in New South Wales. but this will change if, as planned, the NSW Government moves to further deregulate the electricity market in NSW.

293 There has been a similar concentration of ownership in the gas and electricity distribution and transmission sectors. Such assets are now held by companies that concentrate on holding utility or infrastructure type assets. Such companies have been developed to provide reasonably stable and secure rates of returns for investors seeking stable dividends. The early periods of ownership by companies, such as TRUenergy, Origin and AGL, probably gave them sufficient insights in how these sectors work to justify the sale of related assets to release funds for building up their positions in generating and retailing.

The number of producers and new gas fields brought into the PTS remains in line with early 2000 expectations. Gas supply has moved from one major supplier (Esso-BHP) to the following sources: Yolla, Patricia Baleen, Thyolcene-Geographe, Minerva, and Casino. Other recent or old discoveries have been developed or are likely to be developed in the medium term; these include: Basker-Manta, Longtom, Kipper and Turrum. These should all be supplying varying volumes of gas into the market by 2015.

Concerns about the state of the Victorian gas market have moved from Victoria facing declining supplies, to security post the major explosion/fire at the Longford processing plant, to the current position where there are abundant medium term supply sources in different locations. There are now even moderate volumes of gas being exported to NSW, SA and Tasmania via an increased number of interconnecting pipelines. This change has occurred in just five to ten years and has been strongly encouraged by deregulation. The changes have effectively deferred any reliance on major gas sources in PNG or the Timor Sea or CSM until past 2020. The length of this deferral could be shortened if there is a surge in the use of gas in Victoria by GPG to substitute for existing brown coal-fired power generators. A new connecting pipeline is mooted to connect Wallumbilla (central Queensland to Newcastle) in anticipation of shipping CSM to NSW and releasing other southern gas for Victoria.

In just the last one to two years, this increased GPG scenario has taken on a sense of reality. AER, ACIL Tasman, and ETSA have presented emissions outlooks for the electricity generation industry. Key points made in these studies include: (i) introduction of carbon-trading will add to electricity prices, and (ii) generators will place an early emphasis on replacing the least-efficient and highest emission coal fired plants (particularly using brown coal from the La Trobe Valley) with GPG plants.

Vencorp included in their 2007 Annual Planning Report a revised outlook for marked increase in the use of GPG in Victoria; see Figure 8-7, which shows Vencorp’s forecast of rapid growth in gas consumed by this sector in Victoria. Vencorp’s forecast shows an approximate 20% increase in gas consumption in Victoria by 2011, stepping up to +35% in 2014 and +60% by 2017 (see

294 Figure 8-7). Such changes would have a dramatic effect on the local supply position that would see accelerated capacity development and likely higher wholesale gas prices than the VicGasSim forecasts which were generated in 2002-03 and more than recent forecasts by ACILTasman, which are shown in Figure 8-8.

There have been changes in the regulatory structure with more co-ordination and centralisation in energy regulation. The Australian Energy Regulator (AER) was formed in 2005 to centralise control over the monitoring and enforcing of national legislation governing the electricity and gas industries as well as economic regulation of electricity and gas transmission in eastern and southern Australia. The AER’s authorities will extend to energy distribution and non-price aspects of energy retailing. This has required states to transfer some powers to the Commonwealth Government. State regulators, such as Vencorp in Victoria, remain in place to manage gas trading and the price-setting pool in Victoria. The Allen Consulting Group (2006) conducted a statutory review of Vencorp for the Minister for Energy Industries. In 2007, Vencorp assumed control of the Queensland gas market as well. NEMMCO remains in place to manage the similar system for the electricity pool in eastern Australia. The ACCC retains its oversight of competitive behaviour and reviews mergers and various energy related authorisations under the Trade Practices Act. The ACCC has ceded some of its monitoring and oversight roles to the AER.

A strong futures market has developed for electricity derivatives and has grown rapidly since 2005 but no such market exists for natural gas, because gas systems have more flexiblity due to the stored gas in the transmission and distribution systems. In addition, the existing gas pooling arrangements appear to have worked reasonably effectively to date; this could change with the expected rapid growth in gas usage by GPG units in Victoria, where the gas market could soon take on more of the characteristics of the electricity market. Vencorp is moving to improve the effectiveness and responsiveness of its gas pooling system. Vencorp has introduced ex ante pricing, within day rescheduling and rebidding, and prices are reviewed every four hours up to 10pm. These changes add flexibility and provide clearer and more certain pricing signals; they will also bring trading in the gas and electricity markets into closer alignment. Additional reforms may be implemented in the Victorian market in the future. One example under consideration is the introduction of "transmission rights" to encourage investment in the transmission system.

There has been a re-alignment of generation and retailing interests under the more centralised control of both electricity and gas across all of eastern and southern Australia. Dual-fuel marketing has become a critical strategy to reduce average customer administrative costs and to provide greater flexiblity in designing dual-fuel offers to attract customers. This dual-fuel marketing is rapidly expanding to include marketing of green energy from hydro, wind and solar sources.

295 The move to full retail gas competition in 2002 and the industry restructuring that commenced around the same time have stimulated a growing emphasis on dual-fuel marketing and customers swapping retailers. AER (2007, p. 296) report that since 2002, the level of churning by customers, often seeking the best dual-fuel offers, has risen significantly with churning of residential and business customers increasing from 6% in 2002-03 to 18% in 2005-06 and 11% in the first-half of 2007. Cumulative churn since 2002-03 to mid 2007 has been 62%. Competition would appear to be strong and active. A 2006 report by the Finnish-based Utility Customer Switching Research Project described Victoria and South Australia as among the "hottest" (most active) retail markets in the world.1

This level of competition and churning has encouraged the entry of a number of new retailers to the gas market in Victoria. While ABARE (2003) suggested the market (in 2003) was lacking liquidity and transparency due to the limited number of market participants, there are 14 gas retailers nationally of which 10 retailer licences are currently on issue that relate to selling gas to small customer markets in Victoria. However, only six retailers are active participants in the Victorian market place. Besides Origin Energy, AGL, and TRUenergy, more recent new entrants include Australian Power & Gas, Energy Australia and Victoria Energy. The style of analyses presented in 6.2.3 which investigated the effect of the number of retailers on pricing volatility for the simple Prototype #2 model, could be replicated in VicGasSim to see if the presence of six retailers should make for an efficient, stable market with low volatility or some greater or lesser number is sufficient.

1. First Data Utilities and Vaasa EMG, Utility customer switching research project, World retail energy market rankings, 2006

296 Appendix 5: Multiple-Criteria Decision Making (MCDM)

The agents in VicGasSim have relatively simple methods (mental models) for making their decisions; as stated in the final observations in Chapter 10, this approach has been one of the reasons for the success in implementing VicGasSim across a wide range of integrated sectors. It is tentatively suggested that improved methods might not add to the efficiency of predicting emergent market properties since the large number of interactive complex transactions is over- riding the importance of improved methodologies. Nevertheless, in contemplating enhancements to the model there could be a need to add methods for exercising more subjective judgements in some areas. New techniques will be required.

Improved negotiating and/or bargaining methodologies could use multi-dimensional and multi- period negotiations, including risk attitudes to probability based expectations. Beer et al. (1998) suggest that in systems composed of multiple autonomous agents, negotiation is the key form of interaction that enables agents to arrive at a mutual agreement regarding some belief, goal or plan. The negotiations may be of many different forms such as auctions, protocols in the style of a contract net, and argumentation, but it is unclear just how sophisticated the agents or the protocol must be for successful negotiation in different contexts.

A future research goal would be to explore how to blend the complex adaptive framework of the current model with dynamic negotiating systems without losing the value of the base simplicity and reasonable outcomes already achieved by relying on the myriad interactions to set up what is believed to be a more appropriate framework for making decisions in a complex adaptive system. The current work has given each participant at least a reasonable and efficient ability to judge how to make its various strategic selections on a regular and ongoing basis. As always, there are ways to improve the current approach. One approach would be for companies and operating units to use multiple criteria decision-making (MCDM) to select and change strategies. For example, a Retailer could assess multiple strategies by rating look-ahead outcomes on a weighted basis of changes in market shares, total profits, and customer profitability. Regulators could test tariff and price caps based on the rate of change in market prices, profit margins, and market shares. Arbitrators could look at profits on both sides of a contract as well as at prevailing market prices. Companies could test strategies based on assuming more or less aggressive changes to the strategic criteria of its operating groups. To select which look-ahead option to apply in the coming period, each agent could use a simple form of MCDM to assess the merits of the forecast outcomes under a given range of strategic options. In this way each agent can use a wider judgement as to the possible benefits of one strategy over another.

297 This MCDM approach allows a weighted judgement using criteria rated in different units such as market shares, profit margins, and total profits. The method converts outcomes in dimensional units into ordered dimensionless units that allows cross weighting of criteria in different unit dimensions.

To give some idea of how this approach could be applied in VicGasSim, an example is provided for a Retailer deciding what retail pricing strategy to adopt. The possible MCDM matrix is shown in Table A5-1.

Table A5-1: Decision Matrix for Selecting a Retailing Strategy

Proportional Change in Proportional Change in Proportional Change in Market Share after 3 Customer Profitability Total Retail Profitability years after 3 years after 3 years Criteria 40% Criteria 40% Criteria 20% Weighting = Weighting = Weighting = Criteria Real Price Change % Change Rating Hi(5) to Lo(1) RatingWeighted Contribution % Change Rating Hi(5) to Lo(1) RatingWeighted Contribution % Change Rating Hi(5) to Lo(1) RatingWeighted Contribution Total Rating Aggressive Price Decrease -10% 5% 5 2.00 -5% 1 0.40 5% 4 0.80 3.20 Aggressive Price Increase +10% 2% 1 0.40 15% 5 2.00 15% 5 1.00 3.40 Moderate Price Decrease -3% 3% 2.5 1.00 -1% 2 0.80 -1% 1 0.20 2.00 Moderate Price Increase +3% 4% 4 1.60 2% 3 1.20 2% 2 0.40 3.20 Unchanged Prices 0% 3% 2.5 1.00 3% 4 1.60 3% 3 0.60 3.20

In the example above, the relevant participant would choose an aggressive price increase to apply in the coming period or until circumstances alter this choice in future periods. The criteria will vary for each type of participant and each type of decision to be made. Triantaphyllou (2000) reports possible weaknesses in the adoption of the Analytic Hierarchy Process (AHP)1 as a relatively simple form of the MCDM method; however, he indicates it can be an acceptable approach if the number of objectives and attributes under consideration is low as it is in the case outlined above. If a more complicated or extensive sets of criteria are used, logic errors are possible and more sophisticated techniques could be warranted.

1. AHP is a structured technique for helping people deal with complex decisions.

298 Appendix 6: Author’s Publications

299 300 Appendix 7: Author’s Curriculum Vitae

Neale Taylor was born in Sydney, NSW, in January 1943. He was educated at various state schools in NSW and matriculated to univerisity by passing the 1963 entrance examination, which was then conducted by the University of Sydney.

He graduated from the UNSW with a first class honours degree in Applied Geology in 1967 and commenced work for Esso Australia as a reservoir engineer working on the development of the Gippsland Basin oil and gas discoveries, which were being made at that time. In 1968, Esso sponsored two years of graduate study at Louisiana State University (LSU) for obtaining a Masters Degree in Science (in Petroleum Engineering). While at LSU he was invested as a member of the ΦΚΦ and ΠΕΤ honour societies. He was awarded the Society of Petroleum Engineers’ National Graduate Prize for Best Paper based on his thesis work (a simple simulation model of how to restart gelled oil in a pipeline – a possible major issue for oil discoveries then being developed in northern Alaska). From 1972 to 1975, he studied part-time at Macquarie Graduate School of Management to obtain a Masters Degree in Business Administration.

In his career at Esso, he filled a number of management positions in engineering, planning and commercial roles, including leading Esso’s acquisition of Delhi Petroleum in 1987 for approximately A$900 million (in 1987 dollars). At the time, this acquisition was the largest acquisition undertaken by Exxon anywhere in the world. Subsequent work on Delhi’s oil and gas interests in South Australia and south west Queensland and selling gas to Adelaide and NSW, introduced the author to many of the issues that characterise the south east Australian gas industry. The author retired from Esso in 1998 and acted as a consultant advising various companies on the south east Australian gas market. In late 1998, he joined the Board of the Terra Gas Trader Limited (TGT), which was a wholesale gas trader in South Australia and owned by the SA Government and which held the rights to various gas supply agreements with the SA and SWQ gas suppliers and the SA gas transmission pipeline owner. TGT was corporatised and underwent privatisation, while the author was a director, a position he held until 2002.

In 2003, the author became involved with various companies that explored for petroleum in central Asia (principally the Kyrgyz Republic); the author was the CEO and director of Cambrian Oil and Gas plc, when it listed on AIM in London in 2005. He remains a director of several companies in the group that currently retains a reduced interest in exploration interests in the Kyrgyz Republic, plus other interests in petroleum development in Turkey and the Julia Creek oil shale deposit. The author is the Chairman of the Board of Tap Oil Limited, an ASX-listed company, which produces oil and gas in Western Australia and is involved in gas marketing.

301 The author is an active member of his local church in Bairnsdale, a member of Bishop-in-Council for the Gippsland Diocese of the Anglican Church in Australia. He is a director of Gippsland Anglican Aged Care Pty Limited, which runs several retirement villages in the Gippsland community.

The author is considering further work to continue development, testing and application of VicGasSim, or variations thereof, to facilitate greater understanding of the south east Australian energy market and how it might be influenced by various events such as the expansion of CSM production in Queensland and NSW, east coast LNG exports and policy impacts such as carbon reduction schemes and energy trading systems.

302 01-Jan-01 1 0.0 02-Jan-01 2 0.0 03-Jan-01 3 0.0 04-Jan-01 4 0.0 05-Jan-01 5 0.0 06-Jan-01 6 0.0 07-Jan-01 7 0.0 08-Jan-01 8 0.0 09-Jan-01 9 0.0 10-Jan-01 10 0.0 11-Jan-01 11 0.0 12-Jan-01 12 0.0 13-Jan-01 13 0.0 14-Jan-01 14 0.0 15-Jan-01 15 0.0 16-Jan-01 16 0.0 17-Jan-01 17 0.0 18-Jan-01 18 0.0 19-Jan-01 19 0.0 20-Jan-01 20 0.0 21-Jan-01 21 0.0 22-Jan-01 22 0.0 23-Jan-01 23 0.0 24-Jan-01 24 0.0 25-Jan-01 25 0.0 26-Jan-01 26 0.0 27-Jan-01 27 0.0 28-Jan-01 28 0.0 29-Jan-01 29 0.0 30-Jan-01 30 0.0 31-Jan-01 31 0.0 01-Feb-01 32 0.0 02-Feb-01 33 0.0 03-Feb-01 34 0.0 04-Feb-01 35 0.0 05-Feb-01 36 0.0 06-Feb-01 37 0.0 07-Feb-01 38 0.0 08-Feb-01 39 0.0 09-Feb-01 40 0.0 10-Feb-01 41 0.0 11-Feb-01 42 0.0 12-Feb-01 43 0.0 13-Feb-01 44 0.0 14-Feb-01 45 0.0 15-Feb-01 46 0.0 16-Feb-01 47 0.0 17-Feb-01 48 0.0 18-Feb-01 49 0.0 19-Feb-01 50 0.0 20-Feb-01 51 0.0 21-Feb-01 52 0.0 22-Feb-01 53 0.0 23-Feb-01 54 0.0 24-Feb-01 55 0.0 25-Feb-01 56 0.0 26-Feb-01 57 0.0 27-Feb-01 58 0.0 28-Feb-01 59 0.0 01-Mar-01 60 0.0 02-Mar-01 61 0.0 03-Mar-01 62 0.0 04-Mar-01 63 0.0 05-Mar-01 64 0.0 06-Mar-01 65 0.0 07-Mar-01 66 0.0 08-Mar-01 67 0.0 09-Mar-01 68 0.0 10-Mar-01 69 0.0 11-Mar-01 70 0.0 12-Mar-01 71 0.4 13-Mar-01 72 0.0 14-Mar-01 73 0.0 15-Mar-01 74 0.0 16-Mar-01 75 0.0 17-Mar-01 76 2.2 18-Mar-01 77 0.9 19-Mar-01 78 0.0 20-Mar-01 79 0.0 21-Mar-01 80 0.0 22-Mar-01 81 4.6 23-Mar-01 82 0.0 24-Mar-01 83 0.0 25-Mar-01 84 0.0 26-Mar-01 85 0.0 27-Mar-01 86 2.1 28-Mar-01 87 4.0 29-Mar-01 88 2.3 30-Mar-01 89 0.0 31-Mar-01 90 0.0 01-Apr-01 91 0.2 02-Apr-01 92 2.3 03-Apr-01 93 1.1 04-Apr-01 94 0.0 05-Apr-01 95 0.0 06-Apr-01 96 0.0 07-Apr-01 97 0.0 08-Apr-01 98 0.0 09-Apr-01 99 2.9 10-Apr-01 100 2.3 11-Apr-01 101 4.3 12-Apr-01 102 2.9 13-Apr-01 103 2.3 14-Apr-01 104 0.5 15-Apr-01 105 0.0 16-Apr-01 106 0.0 17-Apr-01 107 0.0 18-Apr-01 108 0.0 19-Apr-01 109 1.3 20-Apr-01 110 4.3 21-Apr-01 111 4.7 22-Apr-01 112 5.5 23-Apr-01 113 5.2 24-Apr-01 114 3.2 25-Apr-01 115 4.4 26-Apr-01 116 0.0 27-Apr-01 117 4.5 28-Apr-01 118 5.6 29-Apr-01 119 3.3 30-Apr-01 120 3.8 01-May-01 121 3.7 02-May-01 122 5.8 03-May-01 123 3.6 04-May-01 124 3.4 05-May-01 125 4.8 06-May-01 126 3.3 07-May-01 127 4.7 08-May-01 128 5.2 09-May-01 129 5.5 10-May-01 130 3.6 11-May-01 131 6.6 12-May-01 132 7.6 13-May-01 133 3.7 14-May-01 134 2.8 15-May-01 135 0.5 16-May-01 136 3.3 17-May-01 137 7.0 18-May-01 138 6.2 19-May-01 139 6.1 20-May-01 140 6.0 21-May-01 141 6.5 22-May-01 142 6.6 23-May-01 143 7.5 24-May-01 144 8.1 25-May-01 145 7.0 26-May-01 146 7.6 27-May-01 147 6.8 28-May-01 148 7.4 29-May-01 149 7.6 30-May-01 150 7.7 31-May-01 151 7.7 01/06/01 152 9.2 02/06/01 153 6.4 03/06/01 154 4.6 04/06/01 155 4.7 05/06/01 156 4.0 06/06/01 157 3.0 07/06/01 158 0.7 08/06/01 159 4.0 09/06/01 160 5.0 10/06/01 161 5.0 11/06/01 162 8.4 12/06/01 163 6.9 13/06/01 164 5.3 14/06/01 165 13.2 15/06/01 166 12.4 16/06/01 167 9.8 17/06/01 168 9.6 18/06/01 169 9.9 19/06/01 170 11.6 20/06/01 171 10.2 21/06/01 172 8.5 22/06/01 173 6.5 23/06/01 174 6.7 24/06/01 175 6.9 25/06/01 176 6.5 26/06/01 177 7.2 27/06/01 178 10.7 28/06/01 179 10.3 29/06/01 180 8.9 30/06/01 181 9.0 01/07/01 182 7.8 02/07/01 183 9.3 03/07/01 184 12.0 04/07/01 185 9.5 05/07/01 186 9.4 06/07/01 187 10.0 07/07/01 188 8.8 08/07/01 189 7.8 09/07/01 190 7.3 10/07/01 191 6.3 11/07/01 192 9.1 12/07/01 193 8.6 13/07/01 194 7.1 14/07/01 195 9.0 15/07/01 196 8.2 16/07/01 197 9.2 17/07/01 198 9.9 18/07/01 199 10.0 19/07/01 200 9.0 20/07/01 201 8.4 21/07/01 202 8.5 22/07/01 203 11.1 23/07/01 204 10.5 24/07/01 205 10.8 25/07/01 206 11.5 26/07/01 207 9.7 27/07/01 208 9.6 28/07/01 209 9.9 29/07/01 210 8.9 30/07/01 211 6.8 31/07/01 212 7.8 01/08/01 213 9.9 02/08/01 214 9.3 03/08/01 215 10.1 04/08/01 216 8.5 05/08/01 217 9.4 06/08/01 218 9.5 07/08/01 219 10.4 08/08/01 220 10.3 09/08/01 221 6.2 10/08/01 222 4.0 11/08/01 223 6.6 12/08/01 224 4.8 13/08/01 225 6.9 14/08/01 226 5.7 15/08/01 227 5.3 16/08/01 228 5.6 17/08/01 229 11.9 18/08/01 230 11.3 19/08/01 231 10.4 20/08/01 232 12.0 21/08/01 233 12.5 22/08/01 234 12.8 23/08/01 235 10.2 24/08/01 236 10.4 25/08/01 237 7.8 26/08/01 238 8.7 27/08/01 239 9.7 28/08/01 240 9.4 29/08/01 241 6.9 30/08/01 242 7.4 31/08/01 243 6.2 01-Sep-01 244 7.3 02-Sep-01 245 6.5 03-Sep-01 246 4.5 04-Sep-01 247 7.5 05-Sep-01 248 7.4 06-Sep-01 249 7.2 07-Sep-01 250 4.7 08-Sep-01 251 9.4 09-Sep-01 252 7.2 10-Sep-01 253 4.3 11-Sep-01 254 3.7 12-Sep-01 255 3.7 13-Sep-01 256 4.7 14-Sep-01 257 4.2 15-Sep-01 258 3.2 16-Sep-01 259 0.7 17-Sep-01 260 5.7 18-Sep-01 261 4.2 19-Sep-01 262 0.0 20-Sep-01 263 4.6 21-Sep-01 264 5.0 22-Sep-01 265 0.0 23-Sep-01 266 0.1 24-Sep-01 267 0.8 25-Sep-01 268 6.6 26-Sep-01 269 7.7 27-Sep-01 270 4.5 28-Sep-01 271 2.2 29-Sep-01 272 0.0 30-Sep-01 273 0.1 01-Oct-01 274 0.0 02-Oct-01 275 2.8 03-Oct-01 276 4.5 04-Oct-01 277 6.2 05-Oct-01 278 4.8 06-Oct-01 279 6.4 07-Oct-01 280 6.4 08-Oct-01 281 4.3 09-Oct-01 282 3.8 10-Oct-01 283 0.8 11-Oct-01 284 2.8 12-Oct-01 285 5.0 13-Oct-01 286 4.7 14-Oct-01 287 5.2 15-Oct-01 288 3.0 16-Oct-01 289 3.3 17-Oct-01 290 2.0 18-Oct-01 291 5.1 19-Oct-01 292 8.2 20-Oct-01 293 5.7 21-Oct-01 294 0.9 22-Oct-01 295 0.0 23-Oct-01 296 0.0 24-Oct-01 297 0.0 25-Oct-01 298 0.0 26-Oct-01 299 3.8 27-Oct-01 300 4.3 28-Oct-01 301 2.9 29-Oct-01 302 3.1 30-Oct-01 303 5.3 31-Oct-01 304 3.4 01-Nov-01 305 2.9 02-Nov-01 306 3.2 03-Nov-01 307 0.0 04-Nov-01 308 0.0 05-Nov-01 309 0.0 06-Nov-01 310 2.7 07-Nov-01 311 4.0 08-Nov-01 312 2.2 09-Nov-01 313 0.0 10-Nov-01 314 4.9 11-Nov-01 315 7.8 12-Nov-01 316 6.2 13-Nov-01 317 3.8 14-Nov-01 318 1.8 15-Nov-01 319 0.2 16-Nov-01 320 0.0 17-Nov-01 321 0.0 18-Nov-01 322 4.2 19-Nov-01 323 2.8 20-Nov-01 324 0.6 21-Nov-01 325 0.0 22-Nov-01 326 0.0 23-Nov-01 327 0.0 24-Nov-01 328 0.0 25-Nov-01 329 0.0 26-Nov-01 330 0.0 27-Nov-01 331 0.8 28-Nov-01 332 0.4 29-Nov-01 333 0.0 30-Nov-01 334 0.0 01-Dec-01 335 0.0 02-Dec-01 336 2.5 03-Dec-01 337 2.0 04-Dec-01 338 3.6 05-Dec-01 339 0.0 06-Dec-01 340 2.6 07-Dec-01 341 1.9 08-Dec-01 342 0.6 09-Dec-01 343 0.0 10-Dec-01 344 1.3 11-Dec-01 345 4.1 12-Dec-01 346 1.4 13-Dec-01 347 0.0 14-Dec-01 348 0.0 15-Dec-01 349 0.0 16-Dec-01 350 0.0 17-Dec-01 351 0.0 18-Dec-01 352 0.0 19-Dec-01 353 0.0 20-Dec-01 354 0.0 21-Dec-01 355 0.0 22-Dec-01 356 0.0 23-Dec-01 357 0.0 24-Dec-01 358 0.0 25-Dec-01 359 0.2 26-Dec-01 360 4.2 27-Dec-01 361 2.2 28-Dec-01 362 0.0 29-Dec-01 363 0.0 30-Dec-01 364 0.0 31-Dec-01 365 0.0 # Victorian Gas Market Simulator - Release 0.4.20 # Revision: $Id: Gas.properties,v 1.39 2004/04/02 04:59:14 gsl Exp $

# Initial display resolution (defaults to 1024x768). Should be equal to or # smaller than the screen resolution. screen.width = 1024 screen.height = 768

# Configuration to load by default config.file = o30.xml

# XSLT transformation file for formatting the configuration (should be in # the "config" directory). config.transform = sty.xsl

#config.class = SimpleConfiguration #config.class = XercesConfiguration

# Set the default display increment which is a positive multiple of the # "display.units" described below. Default: 60 #display.increment = 1

# Define the default units used by the display increment. Possible values # are "second", "minute", "hour", "day" and "week". Default: second #display.units = week

#show.loading = true editable = false detail.level = high

# Log file # Levels: SEVERE, WARNING, INFO, CONFIG, FINE, FINER, FINEST log.level = CONFIG log.file = log.txt

#################### SIMULATION COMPONENT PROPERTIES #################### # These properties are used to configure components of the simulation. The # format of a simulation component property is: # . = # Note that there can be no periods ('.') in the portion. ######################################################################### # ModelContext properties # Validate the XML configuration against the Schema #swin.sim.simulation.configuration.Configuration.applySchema = false

# ControlBar properties # Controls whether the memory usage is shown in the control bar. # [true, false] Default: true swin.sim.ControlBar.showMemory = true swin.sim.ControlBar.memoryInterval = 5.0

#swin.util.Configurator.defaultPkg = gas.actives. #swin.sim.DisplayPanel.background = white #swin.sim.DisplayPanel.foreground = black #swin.sim.simulation.EventList.bagClass = swin.sim.simulation.EventBagList #swin.sim.simulation.EventList.bagClass = swin.sim.simulation.EventBagTreeMap

# Pipeline properties # Controls whether the distribution pipelines are shown at start-up. # [true, false] Default: true gas.actives.Pipeline.showPipes = true

# Vencorp properties # Number of days of injections, withdrawals and linepack that are graphed. # Default: 50, Max: 1000, Min: 10 gas.actives.Vencorp.historySize = 200

# Supply properties # The undeveloped pass limit determines how many passes of a single year's # negotiation phase an undeveloped Supply will participate in before # withdrawing. Default: 10, Min: 1, Max: SupplyMarket.PASS_LIMIT gas.actives.Supply.undevelopedPassLimit = 25 # SupplyMarket properties # The number of passes in a single year's negotiation the SupplyMarket will # attempt before failing. Default: 100, Min: 10, Max: 1000 gas.actives.SupplyMarket.passLimit = 100

# HistoryManager properties # Default: 1, Max: 100, Min: 1 gas.actives.history.HistoryManager.yearsOfHistory = 2

# History properties # Number of data points to be stored. Default: 1000, Max: 1000, Min: 0 # If set to zero, storage is disabled gas.actives.history.History.maxPoints = 400

# Cache graphical points in each history object. If not cached, the points # are recreated every time a graph that requires them is redrawn. # [true, false] Default: true gas.actives.history.History.cachePoints = false

# Customer properties # Number of days of historical data display. # Default: 200 Min: 10 Max: 365 #gas.actives.customer.Customer.daysDisplayed = 200 # Maximum percentage of customers that can churn in any given year. # Default: 0.05 Min: 0.0 Max: 1.0 gas.actives.customer.Customer.maxChurnFactor = 0.05

# SupplyContract properties # The following 5 properties control the ACQ management procedure. They # are common to all SupplyContracts. To disable use of this procedure, set # the "mdqAdjustActivates" property to 1.0.

# The factor by which the contract's actual used volume must exceed the # target calculated volume before MDQ adjustment is applied. This factor # also controls when adjustment deactivates. If set to 1.0, this # disables the MDQ adjustment process. # Default: 1.1 Min: 1.0 Max: 5.0 #gas.actives.contract.SupplyContract.mdqAdjustActivates = 1.0 gas.actives.contract.SupplyContract.mdqAdjustActivates = 1.001

# The number of days the actual used volume must stay above the target # calculated volume before the "mdqAdjustIncrement" is applied. # Default: 7 Min: 1 Max: 366 gas.actives.contract.SupplyContract.mdqExcessCountLimit = 1

# The initial percentage adjustment applied to the MDQ once the need for # adjustment has been detected, based on the "mdqAdjustActivates" factor. # Default: 10 Min: 0 Max: 50 gas.actives.contract.SupplyContract.mdqAdjustInitial = 50

# The incremental percentage applied to the adjustment percentage whenever # the contract has remained adjusted for "mdqExcessCountLimit" days. # Default: 5 Min: 0 Max: 50 gas.actives.contract.SupplyContract.mdqAdjustIncrement = 10

# The maximum percentage adjustment that can be applied to the MDQ. # Default: 100 Min: 10 Max: 100 gas.actives.contract.SupplyContract.mdqAdjustMax = 100 swin.sim.DisplayPanel.background = white C:\Documents1 - 2...\o30.xml 28/06/2010 12:31:16 PM 2 6 7 8 9 10 14 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 1 C:\Documents67 < andPoint Settings\sbesselaar\Desktop\Theses x="439" y="242"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 2 C:\Documents133 < andPoint Settings\sbesselaar\Desktop\Theses x="306" y="421"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 198 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 3 C:\Documents199 < andPoint Settings\sbesselaar\Desktop\Theses x="260" y="380"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 4 C:\Documents265 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 5 C:\Documents331 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 6 C:\Documents397 and - 2...\o30.xml 28/06/2010 12:31:16 PM 398 399 400 401 402 403 404 405 406 407 408 409 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 7 C:\Documents463 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 8 C:\Documents529 and 2...\o30.xml 28/06/2010 12:31:16 PM 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 9 C:\Documents595 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 10 C:\Documents661 < andPoint Settings\sbesselaar\Desktop\Theses x="365" y="381"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 11 C:\Documents727 and - 2...\o30.xml 28/06/2010 12:31:16 PM 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 12 C:\Documents793 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 13 C:\Documents859 and - 2...\o30.xml 28/06/2010 12:31:16 PM 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 14 C:\Documents925 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 15 C:\Documents991 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 16 C:\Documents1057 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 17 C:\Documents1123 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 18 C:\Documents1189 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 19 C:\Documents1255 < andPoint Settings\sbesselaar\Desktop\Theses x="221" y="353"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 20 C:\Documents1321 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 21 C:\Documents1387 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 22 C:\Documents1453 - 2...\o30.xml 28/06/2010 12:31:16 PM 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 23 C:\Documents1519 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 24 C:\Documents1585 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 25 C:\Documents1651 < andPoint Settings\sbesselaar\Desktop\Theses x="25" y="387"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 26 C:\Documents1717 < andPoint Settings\sbesselaar\Desktop\Theses x="681" y="387"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 27 C:\Documents1783 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 28 C:\Documents1849 and - 2...\o30.xml 28/06/2010 12:31:16 PM 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 29 C:\Documents1915 and 2...\o30.xml 28/06/2010 12:31:16 PM 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 30 C:\Documents1981 and 28/06/2010 12:31:16 PM 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 31 C:\Documents2047 and 28/06/2010 12:31:16 PM 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 32 C:\Documents2113 and - 2...\o30.xml 28/06/2010 12:31:16 PM 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 33 C:\Documents2179 < andMarket Settings\sbesselaar\Desktop\Theses id="Melbourne"> - 2...\o30.xml 28/06/2010 12:31:16 PM 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 34 C:\Documents2245 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 35 C:\Documents2311 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2337 2338 2339 2340 2341 2342 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2371 2372 2373 2374 2375 2376 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 36 C:\Documents2377 < andPoint Settings\sbesselaar\Desktop\Theses x="312" y="340"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 2378 2379 2380 2381 2382 2383 2385 2386 2387 2388 2389 2390 2391 2393 2394 2395 2396 2397 2398 2399 2401 2402 2403 2404 2405 2406 2407 2408 2410 2411 2412 2413 2414 2415 2416 2417 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 37 C:\Documents2443 < andPoint Settings\sbesselaar\Desktop\Theses x="250" y="339"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 2444 2445 2446 2447 2448 2450 2451 2452 2453 2454 2455 2456 2457 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2482 2483 2484 2485 2486 2487 2489 2490 2491 2492 2493 2494 2496 2497 2498 2499 2500 2502 2503 2504 2505 2506 2507 2508 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 38 C:\Documents2509 2511 2513 2514 2515 2517 2518 2519 2520 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2571 2572 2573 2574 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 39 C:\Documents2575 < andVisual Settings\sbesselaar\Desktop\Theses type="image" name="images/origin.gif - 2...\o30.xml" x="400" y="20" 28/06/2010 12:31:16 PM 2576 width="100" height="50"/> 2577 2578 2579 2580 2581 2582 2583 2584 2585 2588 2589 2590 2592 2593 2596 2597 2598 2600 2601 2602 2603 2604 2605 2607 2608 2609 2610 2612 2613 2614 2615 2616 2617 2618 2620 2621 2622 2623 2625 2626 2627 2628 2629 2630 2631 2632 2634 2635 2636 2637 2638 2639 2640 - 2...\o30.xml 28/06/2010 12:31:16 PM 2642 2643 2644 2645 2646 2647 2652 2655 2658 2659 2662 2665 2671 2672 2674 2675 2676 2677 2678 2679 2680 2682 2683 2684 2686 2687 2688 2689 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 41 C:\Documents2707 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 2708 2709 2710 2711 2712 2713 2715 2716 2717 2718 2719 2720 2721 2723 2726 2729 2732 2735 2738 2741 2742 2743 2744 2745 2746 2747 2748 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 42 C:\Documents2773 and - 2...\o30.xml 28/06/2010 12:31:16 PM 2774 2775 2776 2777 2778 2779 2780 2782 2785 2788 2789 2790 2791 2792 2793 2794 2795 2796 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2830 2833 2836 2837 2838 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 43 C:\Documents2839 and Settings\sbesselaar\Desktop\Theses - 2...\o30.xml 28/06/2010 12:31:16 PM 2840 2842 2851 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2984 2985 2986 2988 2989 2990 2991 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3017 3018 3019 3020 3021 3022 3023 3024 3025 3027 3028 3031 3034 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 46 C:\Documents3037 and< SupplyContractSettings\sbesselaar\Desktop\Theses acq="3.0" duration - 2...\o30.xml="2" source="GippslandBasin" supply="S2" 28/06/2010 12:31:16 PM 3038 mdqHi="10" mdqLow="7" mdqSh="8" price="3.05" top="2.0" 3039 used="0.0" year="2006"/> 3040 3043 3046 3047 3050 3053 3056 3057 3059 3060 3061 3063 3064 3065 3066 3067 3068 3070 3071 3084 3085 3096 3210 3211 3212 3213 3215 3216 3217 3218 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 49 C:\Documents3235 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 3236 3237 3238 3239 3240 3241 3242 3244 3245 3246 3247 3248 3249 3250 3251 3253 3256 3259 3262 3263 3266 3267 3269 3270 3271 3272 3273 3275 3276 3279 3290 3299 3413 3414 3415 3417 3418 3419 3420 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 52 C:\Documents3433 and 28/06/2010 12:31:16 PM 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3446 3447 3448 3449 3450 3451 3452 3453 3455 3458 3461 3464 3465 3468 3469 3472 3473 3476 3479 3480 3481 3482 3483 3486 3487 3489 3490 3491 3492 3493 3494 3496 3497 3498 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 53 C:\Documents3499 28/06/2010 12:31:16 PM 3500 3502 3503 3504 3505 3506 3508 3509 3510 3512 3513 3514 3515 3516 3517 3518 3519 3520 3523 3524 3525 3528 3529 3530 3533 3534 3535 3536 3537 3538 3539 3540 3541 3544 3545 3546 3549 3550 3551 3552 3553 3554 3555 3556 3557 3560 3561 3562 3563 3564 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 54 C:\Documents3565 < andProducerAccountant Settings\sbesselaar\Desktop\Theses/> - 2...\o30.xml 28/06/2010 12:31:16 PM 3566 3567 3570 3571 3572 3573 3574 3575 3576 3577 3580 3581 3582 3584 3585 3586 3587 3588 3589 3590 3591 3595 3596 3597 3600 3601 3602 3605 3606 3607 3608 3609 3610 3611 3612 3615 3616 3617 3618 3619 3620 3621 3622 3625 3626 3627 3630 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 55 C:\Documents3631 - 2...\o30.xml 28/06/2010 12:31:16 PM 3632 3633 3634 3635 3636 3637 3640 3641 3642 3645 3646 3647 3650 3651 3652 3653 3654 3655 3656 3657 3658 3661 3662 3663 3666 3667 3668 3671 3672 3673 3676 3677 3678 3679 3680 3681 3682 3683 3685 3687 3688 3690 3691 3692 3693 3695 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 56 C:\Documents3697 < andAccountManager Settings\sbesselaar\Desktop\Theses> - 2...\o30.xml 28/06/2010 12:31:16 PM 3698 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 57 C:\Documents3763 < andPipeline Settings\sbesselaar\Desktop\Theses ref="TZ9"/> - 2...\o30.xml 28/06/2010 12:31:16 PM 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3812 3813 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 58 C:\Documents3828 and 28/06/2010 12:31:16 PM 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3842 3843 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3872 3873 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 59 C:\Documents3894 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 3895 3896 3897 3898 3899 3900 3902 3903 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3936 3937 3938 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 60 C:\Documents3960 and - 2...\o30.xml 28/06/2010 12:31:16 PM 3961 3962 3963 3964 3965 3967 3968 3969 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3998 3999 4000 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 61 C:\Documents4026 and - 2...\o30.xml 28/06/2010 12:31:16 PM 4027 4029 4030 4031 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4064 4065 4066 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 62 C:\Documents4092 and - 2...\o30.xml 28/06/2010 12:31:16 PM 4093 4095 4096 4097 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 ©1998-2010 Altova GmbH http://www.altova.comRegistered to Sian Besselaar (Swinburne University of Technology) Page 63 C:\Documents4157 and Settings\sbesselaar\Desktop\Theses 28/06/2010 12:31:16 PM 4158 4159 4160 4161 4162 4163 4164 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194

Global Element Definitions gasmodel Visual Map Region Market Customer Pipeline Regulator Tariff MarketManager Linepack GrowthManager ForecastManager Forecaster SimpleDemandForecaster SeasonForecaster EDDForecaster Company Storage SeasonalStorage Producer Supply InjectionPoint Distributor Transmission Retailer LookAheadManager LookAhead DailyLookAhead WeeklyLookAhead AnnualLookAhead TestCase SupplyContractTestCase RetailPriceTestCase BiddingTestCase BidManager BidHandler SatelliteMarketBidHandler Replenishment1BidHandler Supply1BidHandler Strategy2BidHandler Strategy1BidHandler AccountManager Contracts SupplyContract StorageContract Weather CustomerTypes ConsumptionRanges ElectricityMarket ScheduleManager SimLog Global Type Definitions TariffRange Network CompanyElement StorageType MonthSet LAHType IOAccountant RefElement Element Groups Attribute Groups contractAttr storageAttr Data Types simYear TimeOfDay SeasonType C:/…/origin.html 1/21 28/06/2010 Schema WeekDay LogLevel TariffType MonthType

Global Element Definitions

gasmodel

Attributes Name Type Use Default Description title xs:string required start xs:string required end xs:string required width xs:decimal required height xs:decimal required

Elements:

1. LogManager Optional

Attributes Name Type Use Default Description level LogLevel optional The maximum log level file xs:string optional Name of the log file

Elements:

LogAll Optional

If present, this element indicates that all simulation components capable of logging their execution data should do so. Note that is used at high (verbose) log levels, this can produce very large log files.

SimLog Zero or more

Registers a simulation component with the execution log manager at the configured log level.

Attributes Name Type Use Default Description name xs:string required Name of the simulation component

2. AccountManager Optional

Defines global accounting parameters such as the date on which annual accounting is performed and the Schedule ids of various accounting tasks.

Attributes Name Type Use Default Description date xs:string required The date of the first accounting period in the year. freq xs:integer optional The number of accounting periods in a year. accountingId xs:NMTOKEN optional Id of the Schedule defining the time of day at which accounting occurs invoicingId xs:NMTOKEN optional Id of the Schedule defining the time of day at which invoicing occurs Id of the Schedule defining the time of day on the annual accounting date at growthId xs:NMTOKEN optional which growth is calculated Id of the Schedule defining the time of day on the annual accounting date at churnId xs:NMTOKEN optional which the customers churn. Id of the Schedule defining the time of day on the annual accounting date at retailPriceId xs:NMTOKEN optional which the retailers post their prices to all the customers in the markets in which they participate. Id of the Schedule defining the time of day on the annual accounting date at contractingId xs:NMTOKEN optional which the SupplyMarket commences contract negotiation between retailers and producers.

3. ScheduleManager Optional 4. Map Optional 5. Market Zero or more 6. Regulator Zero or more 7. Pipeline Zero or more 8. MarketManager One or more C:/…/origin.html 2/21 28/06/2010 Schema 9. Company Zero or more

10. Zero or more

Retailer Distributor Transmission Producer Storage SeasonalStorage InjectionPoint 11. Weather One or more 12. ConsumptionRanges Optional 13. CustomerTypes Optional 14. ElectricityMarket Optional

Visual

Attributes Name Type Use Default Description box, circle, arc, type required The type of rendering used to depict the component image, custom For type="image", provides the path to the image resource (in the Visible name xs:string optional component's package) For type="custom", name of the custom renderer for this Visible component to renderer xs:string optional be found in the ".renderers" package x xs:decimal required y xs:decimal required width xs:decimal required height xs:decimal required

Map

Graphical representation of the geographic region whose gas industry activities are being modelled.

Attributes Name Type Use Default Description name xs:string required color xs:string optional

Elements:

1. Array Zero or more

Elements:

1. Point Zero or more

Attributes Name Type Use Default Description x xs:decimal required y xs:decimal required

Region

Graphical representation of a sub-area within a Map.

Attributes Name Type Use Default Description color xs:string optional titlex xs:decimal optional titley xs:decimal optional

Elements:

1. Point Zero or more

Attributes

C:/…/origin.html 3/21 28/06/2010 Schema Name Type Use Default Description x xs:decimal required y xs:decimal required

Market

A retail market. Contains customers that consume gas. The hierarchy of the market is TransmissionZone -> Distributor -> Retailer -> Customer.

Attributes Name Type Use Default Description name xs:NMTOKEN required The name of the retail market weatherId xs:NMTOKEN required The weather model that affects the market

Elements:

1. Region Mandatory

2. Zero or more

TransmissionZone

Attributes Name Type Use Default Description id xs:NMTOKEN required The id of the transmission zone

Elements:

Zero or more

1. Distributor

Attributes Name Type Use Default Description id xs:NMTOKEN required The id of the distributor

Elements:

1. Zero or more

Retailer

Attributes Name Type Use Default Description id xs:NMTOKEN required The id of the retailer

Elements:

1. Customer Zero or more

2. TariffV Optional

Extended from TariffRange.

Attributes Name Type Use Default Description fdc xs:decimal required

3. TariffD Optional

Customer

A customer agent of a retail market. Consumes gas sold to it by a retailer.

Attributes Name Type Use Default Description name xs:string required Refers to a defined CustomerType. The rate of price-dependent customer churn. A number from 0 to 1, churnRate xs:decimal optional by default takes the churnRate of the associated CustomerType. The maximum annual growth in customer numbers that can be maxGrowthPriceFactor xs:decimal optional achieved after adjusting for price and electricity effects. By default

C:/…/origin.html 4/21 28/06/2010 Schema takes the max factor from the associated CustomerType.

Elements:

1. Consumption Zero or more

Defines a particular consumption range for this type of customer.

Attributes Name Type Use Default Description id xs:string required Refers to a defined ConsumptionRange The customer's distribution tariff type. Defaults to "V". Assume no tariff TariffType optional V sub-elements. The number of customers in the given consumption range. Assume no number xs:integer optional sub-elements.

Elements:

Tariff Zero or more

If there are customers in this consumption range of more than one tariff type, then they are defined in separate Tariff elements.

Attributes Name Type Use Default Description id TariffType required The tariff id number xs:integer required The number of customers of this type, consumption range and tariff

Elements:

1. Contract Optional

Defines the margin that is applied to the cost of gas supplied under this tariff to get the price paid by the customer.

Attributes Name Type Use Default Description margin xs:decimal required The tariff margin (may be positive or negative)

Contract Optional

Defines the margin that is applied to the cost of gas supplied in the sole tariff of this consumption range to get the price paid by the customer.

Attributes Name Type Use Default Description margin xs:decimal required The tariff margin (may be positive or negative)

Pipeline

Extended from Network.

Attributes Name Type Use Default Description name xs:string required color xs:string optional titlex xs:decimal optional titley xs:decimal optional Td xs:decimal optional 0.0 peak demand charge ($/GJ) Tv xs:decimal optional 0.0 peak volume charge ($/GJ) Ta xs:decimal optional 0.0 anytime volume charge ($/GJ)

Optional

1. Connect

Elements:

Zero or more

Pipeline InjectionPoint

C:/…/origin.html 5/21 28/06/2010 Schema

Regulator

Attributes Name Type Use Default Description name xs:NMTOKEN required

Elements:

1. Retailer Zero or more

Attributes Name Type Use Default Description id xs:NMTOKEN required

Elements:

1. Market Zero or more

Attributes Name Type Use Default Description id xs:NMTOKEN required

Elements:

1. Customer Zero or more

Attributes Name Type Use Default Description name xs:string required

Elements:

Tariff Optional Consumption Zero or more

Attributes Name Type Use Default Description id xs:string required

Elements:

Mandatory

1. Tariff

Tariff

Attributes Name Type Use Default Description supply xs:decimal required off xs:decimal optional peak xs:decimal optional rate xs:decimal optional

MarketManager

Attributes Name Type Use Default Description name xs:NMTOKEN required reconcileId xs:NMTOKEN optional Id of the Schedule that defines the reconciliation time priceId xs:NMTOKEN optional Id of the Schedule that defines the daily price calculation time The price to be returned if no average price can be calculated (typically defaultAveragePrice xs:decimal optional 2.0 because the config is stuffed and the pool price is always zero). dumpBidStack xs:boolean optional false If true, the bid stack is written to file on a daily basis.

Elements:

1. Visual Mandatory C:/…/origin.html 6/21 28/06/2010 Schema 2. SimLog Optional 3. ForecastManager Optional 4. AccountManager Optional 5. Linepack Optional 6. Market Zero or more

Refers to the name of a Market component to be managed by this MarketManager

7. Storage Zero or more

Linepack

Attributes Name Type Use Default Description max xs:decimal required min xs:decimal required target xs:decimal required

Elements:

1. Retailer Zero or more

Attributes Name Type Use Default Description id xs:NMTOKEN required volume xs:decimal optional

GrowthManager

Attributes Name Type Use Default Description Proportion of growth that is applied to customer number. Default is 1.0 customer xs:decimal optional (100%) Proportion of growth that is applied to base offtake volume. Default is (1.0 - offtake xs:decimal optional customer growth factor). The adjustment factor applied to the overall growth factor due to change in gasFactor xs:decimal optional gas price The adjustment factor applied to the overall growth factor due to change in electricityFactor xs:decimal optional electricity price

Elements:

1. Growth One or more

Attributes Name Type Use Default Description year xs:integer required The year for which the growth factor is applied factor xs:decimal required The growth factor the be applied

ForecastManager

Elements:

Optional

Forecaster

Placeholder element that is substitutable by:

SimpleDemandForecaster SeasonForecaster EDDForecaster

SimpleDemandForecaster

C:/…/origin.html 7/21 28/06/2010 Schema Basic forecaster that produces the same value forecast every day.

Substitutable for Forecaster.

Attributes Name Type Use Default Description amount xs:decimal required The volume in TJs that is forecast every day

Elements:

1. GrowthManager Optional

SeasonForecaster

A forecaster that produces a random gas volume forecast that differs depending on the season. It requires that 4 seasons be defined. Each seasons take a set of ranges with an associated probability that the forecast will lie in that range during the season. Probabilities are not normalized so they should sum to 1.0 for a given season.

Substitutable for Forecaster.

Elements:

[4..4]

1. Season

Attributes Name Type Use Default Description id SeasonType required The id of the southern hemisphere season

Elements:

One or more

1. Range

Attributes Name Type Use Default Description low xs:integer required The low end of the range high xs:integer required The high end of the range prob xs:decimal required The probability that the forecast lies in this range

EDDForecaster

Forecast based on a weather model that produces EDD factors for each day. Random variability to the forecast volume can be achieved. Forecast offsets can be applied for particular days of the week.

Substitutable for Forecaster.

Attributes Name Type Use Default Description The id of the weather model to be used. If no model is provied by the weatherId xs:NMTOKEN optional forecaster, it is expected the the object doing the forecasting (eg., Customer) provides a weather model to use. base xs:decimal required The base gas volume used, independent of the weather The weather factor that when multiplied by the EDD for the day provides the factor xs:decimal required weather-dependent gas volume var xs:decimal required The random variation about the base/factor forecast volume units TJ, tj, GJ, gj optional The units of the gas volume used in the forecast (TJ or GJ)

Elements:

1. DailyOffset [0..7]

Attributes Name Type Use Default Description day WeekDay required The day of the week to apply the offset amount xs:decimal required The amount, in the forecaster units, by which to offset the forecast

2. GrowthManager Optional C:/…/origin.html 8/21 28/06/2010 Schema

Company

Attributes Name Type Use Default Description name xs:NMTOKEN required

Elements:

1. Visual Mandatory

2. Zero or more

Retailer Producer Storage Distributor Transmission PowerGen

Storage

Derived from StorageType.

SeasonalStorage

Extended from StorageType.

1. Input Optional 2. Output Optional

Producer

Attributes Name Type Use Default Description name xs:NMTOKEN required

Elements:

1. AccountManager Optional 2. Supply Zero or more

Supply

Attributes Name Type Use Default Description id xs:NMTOKEN required Supply identifier, unique with the parent Producer capacity xs:decimal required The annual capacity of gas that can be supplied in PJs The total size of the supply. Default units is PJs but appending an reserve xs:string optional 0 'E' makes it exajoules, appending a 'P' makes it petajoules. A size of zero indicates an inexhaustible supply. fixedCost xs:decimal required The annual fixed cost ($) associated with this supply variableCost xs:decimal required The variable cost of supply in $/GJ leadTime xs:integer optional 0 The number of years it takes to bring the supply online The percentage discount (0 to 1.0) to be applied to the base price lowUtilizationFactor xs:decimal optional 0.05 (average wholesale price) if the volume allocated from this supply as a proportion of the capacity is below the "lowUtilizationLimit". The percentage premium (0 to 1.0) to be applied to the base price (average wholesale price) if the volume allocated from this highUtilizationFactor xs:decimal optional 0.1 supply as a proportion of the capacity is above the "highUtilizationLimit". The proportion of capacity below which a discount is applied to lowUtilizationLimit xs:decimal optional 0.3 the base price. The proportion of capacity above which a premium is applied to highUtilizationLimit xs:decimal optional 0.85 the base price. The percentage margin (0 to 1.0) on top of the average cost of C:/…/origin.html 9/21 28/06/2010 Schema initialMargin xs:decimal optional 0.3 gas from this supply (calculated from the fixed and variable costs) used to calculated the base price when utilization is zero. The duration in years of the standard contract. Contracts longer than the standard length are discounted, contracts shorter than the standardLength xs:integer optional 6 standard length pay a premium. The severity of the discount or premium depends on the "standardLengthAdjustment" attribute. The offset applied for each year of difference between the standardLengthAdjustment xs:decimal optional 0.03 requested contract length and the Supply's standard contract length. standardMDQAdjustment xs:decimal optional 1.0 Multiplier adjustment applied to the raw MDQ factor. standardTOPAdjustment xs:decimal optional 1.0 Multiplier adjustment applied to the raw TOP factor. maxLength xs:integer optional 9 The maximum length in years of a supply contract The standard proportion of a contract ACQ that must be taken or standardTOP xs:decimal optional 0.7 paid for The standard multiplier factor that is applied to the average daily standardMDQFactor xs:decimal optional 3.0 contract volume (ACQ/365) to get the contract's MDQ The volume of bids that must accumulate in a given year before an commitThresholdVolume xs:decimal optional inactive Supply can start development. The default is 25% of capacity.

Elements:

1. InjectionPoint Mandatory

InjectionPoint

Attributes Name Type Use Default Description name xs:NMTOKEN required tariff xs:decimal required

Elements:

1. Visual Mandatory 2. Pipeline Zero or more

Extended from RefElement.

Attributes Name Type Use Default Description p xs:decimal optional

Distributor

Attributes Name Type Use Default Description name xs:NMTOKEN required

Elements:

1. AccountManager Optional

Transmission

Attributes Name Type Use Default Description name xs:NMTOKEN required desc xs:string optional

Elements:

1. AccountManager Optional

2. Zero or more

InjectionPoint

3. Zero or more

C:/…/origin.html 10/21 28/06/2010 Schema Pipeline

Retailer

A retailer company agent. Obtains gas from producers and sells to customers in markets. The transfer of gas is done via a wholesale market which is managed by a MarketManager.

Attributes Name Type Use Default Description name xs:NMTOKEN required The unique name of the retailer biddingId xs:NMTOKEN optional The Schedule id of the bidding phase fixedCost xs:decimal optional The retailer's fixed cost varCost xs:decimal optional The retailer's variable cost

Elements:

1. LookAheadManager Optional 2. MarketManager One or more

Refers to a wholesale MarketManager of a market in which the retailer has customers and supplies gas. A retailer can operate in more than one wholesale market. However, due to limitations of the bidding system, one market must be the "main" market and all other wholesale markets are considered "satellite" markets and only receive a limited allocation of gas.

Attributes Name Type Use Default Description id xs:NMTOKEN required The id of this wholesale MarketManager

Elements:

1. Forecaster Mandatory

2. One or more

BidHandler

3. AccountManager Optional 4. Contracts Optional

LookAheadManager

Elements:

Zero or more

LookAhead

Placeholder element that is substitutable by:

DailyLookAhead WeeklyLookAhead AnnualLookAhead

DailyLookAhead

A lookahead that is run each day.

Derived from LAHType.

Substitutable for LookAhead.

WeeklyLookAhead

A lookahead that is run each week.

Derived from LAHType.

C:/…/origin.html 11/21 28/06/2010 Schema Substitutable for LookAhead.

AnnualLookAhead

A lookahead that is run each year.

Derived from LAHType.

Substitutable for LookAhead.

TestCase

Placeholder element that is substitutable by:

SupplyContractTestCase RetailPriceTestCase BiddingTestCase

SupplyContractTestCase

Test case for conducting supply contract negotiations by the retailer with the producers. Three contracting attributes can be set; contract length, TOP and MDQ. Leaving out an attribute or setting it to -1 means use the prevailing attribute during the test case.

Substitutable for TestCase.

Attributes Name Type Use Default Description id xs:string optional Test case identifier contractLength xs:integer optional -1 The contract length to be used during contract negotiations by this test case topFactor xs:decimal optional -1 The TOP factor be used during contract negotiations by this test case mdqFactor xs:decimal optional -1 The MDQ multiplier to be used during contract negotiations by this test case logLevel LogLevel optional The log level to be this testcase. Set to "true" if you want to keep each log file generated for this testcase. By keepLogs xs:boolean optional default only the last is kept.

RetailPriceTestCase

Retailer test case for testing the retail pricing strategies.

Substitutable for TestCase.

Attributes Name Type Use Default Description id xs:string optional Test case identifier The percentage adjustment (0 to 1.0) to be applied to all customer retail marginAdjustment xs:decimal required contract margins by this test case.

BiddingTestCase

Retailer test case for modifying the daily wholesale market bidding strategy.

Substitutable for TestCase.

Attributes Name Type Use Default Description id xs:string optional The optional id for the test case The id of the market manager whose bid handlers this test case modifies. If marketManager xs:NMTOKEN optional undefined, the first of the retailer's market managers is used.

Elements:

1. ANY ELEMENT Zero or more

BidManager

C:/…/origin.html 12/21 28/06/2010 Schema Elements:

Zero or more

BidHandler

Placeholder element that is substitutable by:

SatelliteMarketBidHandler Replenishment1BidHandler Supply1BidHandler Strategy2BidHandler Strategy1BidHandler

SatelliteMarketBidHandler

Bids a limited volume of gas to be exported to a satellite market. Any retailer's MarketManager that is using the SatelliteMarketBidHandler should be defined before the main market (which is using the conventional bid handlers) in order for there to be unallocated contract gas that can be allocated to export.

Substitutable for BidHandler.

Attributes Name Type Use Default Description price xs:decimal optional 0.0 The price applied to all bids, or $0 is absent

Elements:

1. Producer Zero or more

List of producers that are favoured as the source for gas to export. If no list is defined or if there are no contracts with preferred producers, the retailer bids from the first available contract.

Attributes Name Type Use Default Description id xs:NMTOKEN required The id of the producer whose contracts are preferred as a source of bid gas

Replenishment1BidHandler

Substitutable for BidHandler.

Attributes Name Type Use Default Description vol xs:decimal optional Volume to replenish daily.

Supply1BidHandler

Substitutable for BidHandler.

Attributes Name Type Use Default Description y xs:decimal required Percentage premium on price for bids beyond the expected demand v xs:decimal required Random premium percentage variation pv xs:decimal optional 0.0 Price volatility

Strategy2BidHandler

Retailer wholesale bidding strategy that structures the retailer's daily bids using a particular function to interpolate prices between adjacent supply or storage contracts.

Substitutable for BidHandler.

Attributes Name Type Use Default Description topFactor xs:decimal optional 0.0 Proportion of the contract volume that is allocated at $0

C:/…/origin.html 13/21 28/06/2010 Schema premium xs:decimal optional 0.0 The premium factor applied to all contract prices (range 0 to 1) The premium applied to the price of the last (most expensive) contract in lastPremium xs:decimal optional 0 order to get a price curve (range 0 to 1) standardIncrement xs:decimal optional 25.0 The volume (TJ) of a standard bid fineIncrement xs:decimal optional 10.0 The volume (TJ) of a fine bid about the expected demand point The range (+/-) of volumes (TJ) about the expected demand point within fineRange xs:decimal optional 20.0 which the fineIncrement is used for bids

Elements:

Optional

ExponentialFunction

Attributes Name Type Use Default Description factor xs:decimal optional 1.0 Controls the steepness of the curve (high number means steeper)

LinearFunction NullFunction

Strategy1BidHandler

Substitutable for BidHandler.

Attributes Name Type Use Default Description y xs:decimal required Percentage premium on price for bids beyond the expected demand v xs:decimal required Random premium percentage variation

AccountManager

Defines a list of accountants to be used by the parent simulation component.

Elements:

Zero or more

SimpleAccountant InjectionAccountant DeliveryAccountant TariffVAccountant TariffDAccountant RegulatedAccountant

Attributes Name Type Use Default Description regulator xs:NMTOKEN required

ProducerAccountant LinepackAccountant StorageAccountant

Extended from IOAccountant.

Attributes Name Type Use Default Description capacityTariff xs:decimal required Supply capacity tariff in $ per TJ/day

Contracts

Attributes Name Type Use Default Description The TOP factor used by the retailer when negotiating new supply topFactor xs:decimal optional 0.7 contracts The MDQ multipler used by the retailer when negotiating new supply mdqFactor xs:decimal optional 3.0 contracts The contract length used by the retailer when negotiating new supply contractLength xs:integer optional 3 contracts C:/…/origin.html 14/21 28/06/2010 Schema If a favoured supply tranche isn't long enough to meet the desired contract length, accept a shortened, tranche-length contract instead, if acceptShortContracts xs:boolean optional true true. If false, all contracts must meet the contract length so short tranches are rejected. predMargin xs:decimal optional 1.0 Multiplier for the the predicted annual volume. Used to inflate the ACQ. Multiplier for the difference between the predicted annual volume and diffMargin xs:decimal optional 1.0 the total available volume for the year. Pretty useless.

Elements:

Zero or more

SupplyContract StorageContract

SupplyContract

Supply contract for gas from a producer's injection point

Attributes Name Type Use Default Description supply xs:NMTOKEN required The id of the producer's source from supplies this contract top xs:decimal required Take or pay (PJ) acq xs:decimal required Annual contract quantity (PJ) used xs:decimal required Amount of gas used from this contract. Required for simulation initialisation. mdqHi xs:decimal required Maximum daily quantity for high season, TJs per day mdqSh xs:decimal required Maximum daily quantity for shoulder season, TJs per day mdqLow xs:decimal required Maximum daily quantity for low season, TJs per day

Includes attributes from:

contractAttr

StorageContract

Storage contract for gas with WUGS

Includes attributes from:

contractAttr storageAttr

Weather

Elements:

1. One or more

SimpleWeatherModel

Attributes Name Type Use Default Description id xs:string required

EDDWeatherModel

Attributes Name Type Use Default Description id xs:string required file xs:string required

CustomerTypes

Elements:

One or more

1. CustomerType

C:/…/origin.html 15/21 28/06/2010 Schema Attributes Name Type Use Default Description Name of the customer type that must be referenced by all Customer name xs:string required objects of that type The percent rate of churn of all customers of this type (unless churnRate xs:decimal optional 0.1 overridden by local churn rate). A number from 0 to 1.0 The maximum growth in customer numbers for this type of customer maxGrowthPriceFactor xs:decimal optional 3.0 due to price and electricity effets.

Elements:

Sequence of:

ForecastManager Optional GrowthManager Optional Consumption Zero or more

Attributes Name Type Use Default Description id xs:string required

Elements:

1. ForecastManager Optional 2. GrowthManager Optional

ConsumptionRanges

Elements:

Zero or more

1. Consumption

Attributes Name Type Use Default Description id xs:string required min xs:decimal required max xs:decimal optional paysDist xs:decimal optional

ElectricityMarket

Attributes Name Type Use Default Description index xs:decimal optional 1.0 The initial price index year xs:integer optional The year for which the initial price index applies growth xs:decimal required The annual fixed growth factor to be applied to the electricity price index

ScheduleManager

Elements:

Zero or more

Schedule

Attributes Name Type Use Default Description time TimeOfDay required id xs:NMTOKEN required desc xs:string optional

SimLog

Locally enables execution logging for a simulation component.

C:/…/origin.html 16/21 28/06/2010 Schema Attributes Name Type Use Default Description level xs:string optional The log level file xs:string optional

Global Type Definitions

TariffRange

Elements:

Zero or more

1. Range

Attributes Name Type Use Default Description prop xs:decimal required tariff xs:decimal required

Network

Elements:

Zero or more

Point

Attributes Name Type Use Default Description x xs:decimal required y xs:decimal required

Branch Close

CompanyElement

Attributes Name Type Use Default Description Reference to the name of the operational unit in which the company has a stake ref xs:NMTOKEN required of ownership share xs:decimal optional 1.0 Indicates the proportion of the operational unit owned by the company

StorageType

Attributes Name Type Use Default Description name xs:NMTOKEN required desc xs:string required volume xs:decimal required Total capacity of the storage facility in TJs mdq xs:decimal required Maximum daily withdrawal from storage (TJ) input xs:decimal required Maximum daily input to storage (TJ)

Elements:

1. Visual Mandatory 2. AccountManager Optional 3. InjectionPoint Optional

MonthSet

Elements:

C:/…/origin.html 17/21 28/06/2010 Schema 1. Month Zero or more

Attributes Name Type Use Default Description name MonthType required

LAHType

Common lookahead type.

Attributes Name Type Use Default Description The duration of the lookahead period. A sequence of one or more numbers followed by tokens. Valid tokens are "s" (seconds), "m" (minutes), "h" (hours), duration xs:string required "d" (days), "w" (weeks), "M" (months) and "y" years). Eg., "1w2d" is 1 week and 2 days. logLevel LogLevel optional The log level to be used by all testcases.

Elements:

One or more

TestCase

IOAccountant

Attributes Name Type Use Default Description inputTariff xs:decimal required Input volume tariff in $ per GJ outputTariff xs:decimal required Output volume tariff in $ per GJ

RefElement

Attributes Name Type Use Default Description ref xs:NMTOKEN required

Element Groups

Attribute Groups

contractAttr

Attributes Name Type Use Default Description source xs:NMTOKEN required Gas source (producer or storage). year simYear required Year in which the contract comes into effect duration xs:integer required Duration of the contract in years price xs:decimal optional $ per GJ

storageAttr

Attributes Name Type Use Default Description capacity xs:decimal required Amount of gas that can be stored by the contract (PJ) inventory xs:decimal required Initial amount of stored gas (PJ) mdq xs:decimal required Maximum daily quantity (TJ) annualCost xs:decimal optional Fixed annual cost ($)

C:/…/origin.html 18/21 28/06/2010 Schema Data Types

xs:string

Plain text of arbitrary length. A single character, word, sentence, paragraph, etc.

xs:NMTOKEN

A single word (token) of plain text.

xs:boolean

A Boolean value. Possible values:

true false

xs:integer

A positive or negative integer value.

xs:positiveInteger

A positive integer value, not including zero (0).

xs:nonNegativeInteger

A positive integer value, including zero (0).

xs:decimal

A positive or negative decimal value.

xs:date

A date value of the form dd-MMM-yyy (eg., 20-Jun-2001).

simYear

Year of the simulation. Assumed to be 1 Jan

Base data type is xs:integer.

Minimum value is 2000 inclusive.

Maximum value is 2050 inclusive.

TimeOfDay

Base data type is xs:integer.

Value must match the pattern ([0-1][0-9]|2[0-3])[0-5][0-9].

SeasonType

Base data type is xs:NMTOKEN.

Possible values:

summer autumn winter C:/…/origin.html 19/21 28/06/2010 Schema spring

WeekDay

Base data type is xs:NMTOKEN.

Possible values:

Monday monday Tuesday tuesday Wednesday wednesday Thursday thursday Friday friday Saturday saturday Sunday sunday

LogLevel

Base data type is xs:NMTOKEN.

Possible values:

SEVERE WARNING INFO CONFIG FINE FINER FINEST severe warning info config fine finer finest

TariffType

Base data type is xs:NMTOKEN.

Possible values:

D V d v

MonthType

Base data type is xs:NMTOKEN.

Possible values:

january february march april may june july august september

C:/…/origin.html 20/21 28/06/2010 Schema october november december

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