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

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An Agent-Based Simulation Study of Competition in the Victorian Gas Market 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 August, 2009 Copyright © 2009 Neale F. Taylor and Swinburne University. All rights reserved. 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 Unpublished rights reserved under the copyright laws of Australia.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.........................................................................................
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