A Study of Wind Energy, Power System Balancing and Its Effects on Carbon Emissions in the Australian NEM

A study of wind energy, power system balancing and its effects on carbon emissions in the Australian NEM

Masters of Renewable Energy Dissertation School of Engineering and Science Murdoch University Selina Lyons BE(Hons) PostGradDip(Energy Studies) MIEAust CPEng RPEQ

Supervisors: Dr Jonathan Whale, Dr Justin Wood October 2014

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Declaration

I declare that all work undertaken in this research topic, and presented in this dissertation is my own work, and that where data, research and conclusions from others have been used to support my findings, that these have been fairly referenced and acknowledged.

Abstract

With the increasing installation of wind power around the world the questions surrounding its benefits and issues are also growing at the same rate. This paper analyses the wind energy in the Australian National Electricity Market (NEM) using actual data from 2012 and 2013 and attempts to answer some of the pressing questions around how variable the wind output is, its impact on carbon emissions, and its influence on other generators especially those balancing the power system. Starting with a static study of generation half hour data, the report then looks in more detail at the 5-minute variability experienced across the NEM, and the corresponding impact on frequency and time error for large excursions. Notably the largest variations experienced in wind power are during wind storms in the wind power zones of South Australia and Victoria. Three of these storms are analysed in detail looking at the individual performance of the wind farms and their contribution to the variability. Lastly, the effect of the wind variations on the regulation or balancing generators is studied – in particular with large increases in wind power that causes fossil- fueled generators to decrease their output and hence efficiency. Using the Australian Energy Market Operator (AEMO) planning assumptions, the carbon emissions for each of the fossil-fueled generators providing balancing are estimated to show the trends in emissions, intensity and clearly show the effects directly caused by wind power.

Acknowledgements

I would like to acknowledge the following contributions: My academic supervisors Drs Jonathan Whale and Justin Wood for their time and effort reviewing and guiding my work; AEMO for retrieving data; Cameron Lee for guiding me on generator performance issues; and David Mounter, Andrew Robbie and Geoff Henderson for reviewing content.

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Table of Contents

1 Introduction ...... 1 1.1 Background ...... 1 1.2 Research context ...... 2 1.3 Research aim ...... 3 1.4 Research questions ...... 3 2 Wind energy statistics in the NEM ...... 5 2.1 Background ...... 5 2.2 Data source ...... 5 2.3 Energy ...... 5 2.4 Carbon emissions ...... 6 2.5 Generation displacement ...... 8 2.6 Price setters ...... 8 2.7 Summary ...... 12 3 Methodology and data sources ...... 13 3.1 Background ...... 13 3.2 Data sources ...... 13 3.3 Methodology ...... 13 3.3.1 Wind power variability ...... 13 3.3.2 Storm effects on variability ...... 14 3.3.3 Carbon contribution of FCAS generators ...... 15 4 Frequency regulation – a brief guide ...... 17 4.1 Frequency control basics ...... 17 4.2 The FCAS generators ...... 18 4.3 Frequency regulation impacts ...... 20 4.4 FCAS Costs ...... 20 4.5 FCAS Regulation amounts ...... 21 4.6 Time error ...... 21 4.7 Summary ...... 22 5 Wind power variations in the NEM 2012 and 2013 ...... 23 5.1 Introduction ...... 23 5.2 Data source ...... 23

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5.3 Installed capacity ...... 23 5.4 Calculations ...... 23 5.5 2012 Wind power summary...... 24 5.6 2013 Wind power summary...... 25 5.7 Variations greater than normal regulation ...... 26 5.8 Variations ...... 27 5.9 Discussion ...... 28 5.10 Summary ...... 30 6 Effect of storms on wind power output ...... 31 6.1 Introduction ...... 31 6.2 Selected storms ...... 31 6.3 Wind turbine operation overview ...... 31 6.4 Storm 1 – Snowtown 14 March 2012 ...... 32 6.5 Storm 2 – Snowtown 23 August 2012 ...... 35 6.6 Storm 3 – Snowtown & Port Augusta 30 September 2013 ...... 39 6.7 Time error ...... 43 6.7.1 Data matching ...... 43 6.7.2 14 March 2012 time error ...... 43 6.7.3 30 September 2013 time error ...... 45 6.8 Summary ...... 46 7 Wind power variation impacts on other generators ...... 49 7.1 Introduction ...... 49 7.2 Data sources ...... 49 7.2.1 Generator production data ...... 49 7.2.2 Generator data ...... 50 7.3 Emission curves for FCAS generators ...... 51 7.3.1 AEMO Planning assumptions ...... 52 7.3.2 Average emissions – determining what is average ...... 53 7.3.3 Other assumptions ...... 53 7.3.4 Curve fitting to emission output ...... 53 7.3.5 Resulting emissions curves ...... 55 7.3.6 Applying emissions curves ...... 56 7.4 Data analysis ...... 57 iv | Page

7.4.1 Storm 1 – 14 March 2012 ...... 57 7.4.2 Storm 2 – 23 August 2012 ...... 58 7.4.3 Storm 3 – 30 September 2013 ...... 60 7.5 Summary ...... 63 8 Conclusions and recommendations ...... 64 9 References ...... 67

Appendices

Appendix A – List of included and excluded wind farms Appendix B – Certified wind storms in Australian NEM 2012 and 2013 Appendix C – Generator emission curves

List of Figures

Figure 1 - Technical issues studied in wind power integration ...... 3 Figure 2 - Wind energy 2010 to 2013 ...... 6 Figure 3 - Carbon emission intensity in the NEM 2012 and 2013 ...... 7 Figure 4 - Hydro generation 2012 and 2013 ...... 7 Figure 5 - Change in generation mix 2013 compared with 2012 ...... 8 Figure 6 - Pool price allocation and wind energy 2012 and 2013 ...... 9 Figure 7 - Black coal pool price and energy 2013 ...... 10 Figure 8 - Dispatch weighted pool price 2012 ...... 11 Figure 9 - Dispatch weighted pool price 2013 ...... 11 Figure 10 - Parameters for FCAS regulation generators ...... 18 Figure 11 - Regulation generators raise/lower limits (MW) ...... 19 Figure 12 - Regulation generators raise/lower limits %Pmax ...... 19 Figure 13 - Weekly regulation costs in the NEM ...... 21 Figure 14 - Mainland NEM distribution of time error ...... 22 Figure 15 – 5-minute wind variation October to December 2012 ...... 27 Figure 16 - 2013 wind power variations duration curve ...... 28 Figure 17 - Variation duration curve 2012 ...... 29 v | Page

Figure 18 - NEM wind generation forecasting errors from AWEFS (AEMO 2013b, 3–41) ...... 30 Figure 19 - Typical wind turbine power curve ...... 32 Figure 20 - State wind farm output during storm 14 March 2012 ...... 33 Figure 21 - NEM 5-minute wind power variation 14 March 2012...... 33 Figure 22 - Mid-North wind farm output 14 March 2012 ...... 34 Figure 23 - Mid-North wind farm output 14 March 2012; 6 to 7 PM ...... 35 Figure 24 - Wind power output 23 August 2012 ...... 36 Figure 25 – NEM 5-minute wind power variation 23 August 2012 ...... 36 Figure 26 - South Australian wind zones 23 August 2012 ...... 37 Figure 27 - South East wind farm output 23 August 2012; 1 PM to 4 PM ...... 38 Figure 28 - South Australian wind zones - 23 August 2012 12.45 pm to 4 pm ...... 39 Figure 29 - NEM wind farm output 30 September 2013 ...... 40 Figure 30 - NEM 5-minute wind power variation 30 September 2013 ...... 40 Figure 31 - Victorian wind farm output 30 September 2013 ...... 41 Figure 32 - South Australian wind zones 30 September 2013 ...... 42 Figure 33 - SA and Victoria wind farm output 30 September 2013; 5PM to 7PM ..... 42 Figure 34 - NEM time error 14 March 2012; 6:20 to 6:35 PM ...... 44 Figure 35 - System frequency 14 March 2012; 6:20 to 6:35 PM ...... 44 Figure 36 - Time error 30 September 2013; 6:45 to 7:00PM ...... 45 Figure 37 - System frequency 30 September 2013; 6.45 to 7 PM ...... 46 Figure 38 - Generic heat-rate curve for an ideal 38% efficient fossil fuel generator .. 52 Figure 39 - Adjusting generic curve for efficiency difference ...... 54 Figure 40 - Bayswater emissions curve ...... 55 Figure 41 - Emissions curves - all stations ...... 56 Figure 42 – 14 March 2012 6 to 7 PM FCAS generators emissions and output ...... 58 Figure 43 - 14 March 2012 6PM to 7PM actual emissions and wind power output ... 58 Figure 44 - 23 August 2012 2 PM to 3 PM FCAS generators emissions and output .. 59 Figure 45 - 23 August 2012 2PM to 3PM, regulation generators emissions and wind farm output ...... 60 Figure 46 - 30 September 2013 6 PM to 7 PM regulation generators output and emissions ...... 61 Figure 47 - 30 September 2013 5.45 to 6.00 PM regulation emissions and output..... 61

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Figure 48 - NEM Generation 30 September 2013 6PM to 7PM ...... 62 Figure 49 - 30 September 2013 6 PM to 7 PM total emissions and wind generation . 62 Figure 50 - Bayswater emissions curve ...... 74 Figure 51 - Callide B emissions curve ...... 74 Figure 52 - Eraring emissions curve ...... 75 Figure 53 - Gladstone emissions curve ...... 75 Figure 54 - Liddell emissions curve ...... 76 Figure 55 - Loy Yang A emissions curve ...... 76 Figure 56 - Stanwell emissions curve ...... 77 Figure 57 - Tarong emissions curve ...... 77 Figure 58 - Torrens Island B emissions curve ...... 78 Figure 59 - Vales Point emissions curve ...... 78

List of tables

Table 1 - NEM price setters by State 2012 and 2013 ...... 12 Table 2 - Installed wind capacity by State, December 2013 ...... 23 Table 3 - 2012 wind variations and capacity factors ...... 24 Table 4 - 2013 wind variation and capacity factors ...... 25 Table 5 – Amount by which wind power must change to exceed regulation amounts ...... 26 Table 6 - Number of excursions where wind power exceeds FCAS amounts ...... 26 Table 7 - Details of selected storms in study period (BOM 2014b) ...... 31 Table 8 – Regulation generator data ...... 52 Table 9 - List of included wind farms ...... 71 Table 10 - List of excluded wind farms ...... 72 Table 11 - List of excluded incomplete wind farms as at December 2013 ...... 72 Table 12 - List of recorded wind gust for storms studied ...... 73

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Glossary

AEMC Australian Energy Market Commission

AEMO Australian Energy Market Operator

AWEFS Australian wind energy forecasting system

BOM Bureau of Meteorology

CO2 Carbon dioxide emissions

FCAS Frequency control ancillary services

GHG Greenhouse gas

IEA International Energy Agency

MMS Market management system

MW Mega Watt

MWh Mega Watt hour

NEM National Electricity Market

NEMDE National electricity market dispatch engine

NEMMCO Former name of AEMO

NER National Electricity Rules

NSW New South Wales

Pmax Registered maximum power of a generator (MW)

RET Renewable Energy Target

SA South Australia tCO2-e tonnes of carbon dioxide equivalent emissions

UKERC United Kingdom Energy Research Centre

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1 Introduction

There are literally hundreds of studies of wind power across the world, each analyzing different components of the complexity of a non-firm renewable energy resource and how best to integrate it into a power system. The UK Energy Research Centre (UKERC) in their 2006 review on the impacts of intermittency studied 212 documents, and included results from 154 of them in their findings (Gross and UKERC (Organization) 2006, 31). Many of these documents studied the wind power impact on power system balancing costs and quantities, but only a few examined the efficiency losses of thermal plant offsetting wind power variability (ibid 42). More recently studies on cycling of thermal plant providing regulation services have been done in Spain and Ireland (Gutiérrez-Martín, Da Silva-Álvarez, and Montoro-Pintado 2013; Turconi et al. 2014). Each of these reports agree that whilst there are some efficiency losses by fossil-fueled thermal plant offsetting the wind power variations, overall there is still a carbon emissions saving by incorporating wind energy on a power system. They also state that the efficiency loss can be between negligible amounts and 7% of wind output, up to a wind penetration level of 20% (Gross and UKERC (Organization) 2006, 50). The carbon savings achieved overall however is highly dependent on the type of generation displaced. This report looks at the impacts of wind power generation on the Australian power system using similar metrics to the UKERC report. In particular it examines the levels of variation experienced over a two year period; how the power system adapts or balances these; the cause and effect of the larger variations; and the impact it has on the balancing generators and their greenhouse gas (GHG) emissions.

1.1 Background

The Australian Energy Market Operator (AEMO) commissioned German consultants Energynautics to review reports and experiences world-wide and determine which were relevant in the Australian NEM (Ackermann and Kuwahata 2011), before launching their own Wind Integration Studies Report (AEMO 2013b). One aspect that is agreed upon internationally is the set of criteria required of an electricity network that will best integrate wind power. Key criteria are:  A large area over which wind power is balanced in order to reduce variability and net balancing requirements (Milligan et al. 2012);  A short time frame for dispatching generators1 and balancing the power system (Vandezande et al. 2010); and  Being able to accurately forecast wind power ahead of time. (IEA and Organisation for Economic Co-operation and Development 2014)

1 Dispatch is a term used in the NER as an instruction to a generator in response to a bid or offer to be dispatched (AEMC 2014). 1 | Page

By design the Australian NEM has all of these characteristics, which has seen the successful integration of over 2500 MW of wind with little impact to the cost or operation of the network. Specifically these are:  The mainland NEM is balanced over four states with Queensland, New South Wales, Victoria and South Australia having a single regulation and dispatch market. This is an area of 3.7 million square kilometres (Australian Government 2014), which is over a third of the size of the USA.  Generators in the Australian NEM are dispatched every 5 minutes, with changes between dispatch intervals being handled by the regulation frequency control ancillary services (FCAS) market (AEMO 2010); and  The Australian Wind Energy Forecasting System (AWEFS) is used by AEMO to predict the power from all the semi-scheduled wind farms, and has a normalized mean absolute error of less than 1.5% in the 5-minute look ahead (AEMO 2013b, 3–41). Whilst the Australian NEM has the essentials of a wind power friendly system there are still many technical issues that require investigation and myths that need testing. This report looks into several of these issues using measured data from the market for the calendar years 2012 and 2013. 1.2 Research context

There are three general areas that are commonly investigated in wind studies, being system adequacy, system security and system operation (Figure 1) (Ackermann and Kuwahata 2011; Gross and UKERC (Organization) 2006; Xie et al. 2011). The segments that are evaluated in this report are around frequency regulation and balancing, which fall across two of the common areas. Findings in other studies indicate that power systems generally cope with the increased variations of wind power (Milligan et al. 2012); that the level of regulation services increases with wind penetration - some substantially (Vandezande et al. 2010; Xie et al. 2011); and balancing costs generally increase with more wind power (Gross and UKERC (Organization) 2006). The report also looks into a question often skimmed over, being the overall effect of wind power on the carbon emissions in the sector. Many reports mention that carbon emissions are generally falling with increased wind penetration (AEMO 2013b; Milligan et al. 2012). However studies on what actually happens in Australia were not found in the literary review.

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Figure 1 - Technical issues studied in wind power integration (Ackermann and Kuwahata 2011, 3) Ackermann and Kuwahata in their recommendation of further work to AEMO in the 2011 report specifically mentions that would be: Pertinent to conduct a study of the variability of the wind power based on measurement data in the NEM, particularly regarding weather-based changes in output…..and whether changes to existing regulation reserves are necessary. (Ackermann and Kuwahata 2011, 59) This report touches on this, including analyzing the cause of the largest wind power variability found in the study period (Section 1). 1.3 Research aim

The aim of this research is to examine what actually happens in the Australian NEM with regard to balancing wind power variations, including cost, quantity and carbon emissions. To date research in this field is limited, which leaves the industry exposed to questions it cannot answer, other than drawing on experiences from other countries. Each power system is unique which means any answers sought without substantiation would be generalizations at best, and again open to ambiguity. 1.4 Research questions

The fundamental questions addressed in this report are:  Does the inclusion of wind power reduce the carbon emissions from the electricity sector in the long term? (Section 2)  What compensates for wind power variations? (Section 4)  How variable is the wind power output and is it creating issues on frequency regulation? (Section 5)  How do wind turbines behave in a storm and what actually causes large variations? (Section 6)

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 It is known that when wind power decreases overall carbon emissions increase as fossil fuels take up wind’s deficit. However do large increases in wind power still have a carbon benefit even after other generators reduce output into possibly lower efficiency zones? (Section 7) The report concludes with recommendations for future work as well as possible solutions to minimize the impact of wind power variations as the wind penetration increases.

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2 Wind energy statistics in the NEM

2.1 Background

International studies agree that increasing wind penetration generally leads to lower carbon emissions and is becoming an essential element in de-carbonizing the electricity industry (IEA and Organisation for Economic Co-operation and Development 2014). This section looks to answer the first question of the study – whether the increasing wind penetration in Australia equates to lower carbon intensity in the long term. Other reports suggest that whilst the carbon intensity is generally lowered, the quantity still depends on a number of factors, including the type of energy being displaced by the wind power (Gross and UKERC (Organization) 2006) and the ability for the wind power to be forecast (Denny and O’Malley 2006). As Australia has a large number of coal-fired generators with emission intensities up to 1.6 2 tCO2-e/MWh (AEMO 2014d) for wind power to displace, and an accurate wind power forecasting system, it would be reasonable to assume that the findings should be in line with other studies. In this section the wind generation statistics in the Australian NEM are analyzed for the study period of calendar years 2012 and 2013 to see if this is in fact the case. Context is provided by also including 2010 and 2011 figures. This section examines the increases in energy penetration levels of wind, and also summarizes the trend in carbon intensity and price setting in the NEM over the same period. 2.2 Data source

The data presented in this section was extracted from the AEMO database using NEMSight®3. The dataset used is the 30-minute trading interval data of the generators, their corresponding pool price, calculated carbon emissions, and energy. The list of wind farms included in these studies is shown in Appendix A, along with their registered data. The included wind farms are all grid connected wind farms in the Australian NEM larger than 30 MW, except for Mortons Lane which is 19.5 MW. For clarity a list of the excluded wind farms and incomplete wind farms is also shown in Appendix A. Note that these studies do not include small distribution connected wind farms or turbines. 2.3 Energy

The total generated electricity from all grid connected generation in the NEM in 2012 was 200.7 TWh, of which wind energy contributed 6.4 TWh (3.2%). The following year (2013) electricity generation dropped by 2.7% to 195.3 TWh and wind energy increased

2 Hazelwood Power Station’s emissions are published at 1.558 tCO2-e/MWh (AEMO 2014d, 1) 3 NEMsight® is a front end, data analyzer software tool used to view AEMO data, produced by Creative Analytics 5 | Page

by 25% to 7.96 TWh. This meant that wind power contributed 4.1% to the total generation. Wind strength is seasonal, with highs and lows being exhibited throughout the year. In Australia the peak wind energy period is in August of each year, with the low in April. Figure 2 shows the monthly output in wind energy in GWh for the years 2010 to 2013, with 2010 and 2011 included to show a longer term trend. Wind energy - NEM 1200

1000

800 2010 600 2011 GWh 2012 400 2013

200

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 2 - Wind energy 2010 to 2013 2.4 Carbon emissions

The average carbon emissions from electricity production including wind generation, are calculated and published by AEMO on a daily basis. The calculation for carbon for each generating unit is:

Edaily = Odaily x Eav

Where Odaily is the energy produced in MWh and Eav is the average emissions per MWh in tCO2-e/MWh (Equation 1) These average emissions are published in the annual planning assumptions on the AEMO website and are updated every year (AEMO 2014d). Figure 3 shows the trend in overall carbon emissions in the NEM on a per megawatt-hour (MWh) basis from 2010 to 2013 – otherwise known as the carbon intensity. The lowest carbon intensity over the study period was August to October 2013 at 0.75 tCO2-e/MWh. This corresponds directly with the previous graph where the largest contribution from wind power was at the same time. Hydro generation is also significant at that time of the year with spring rain and snow melt, and contributes to the seasonal lower carbon intensity. The hydro trend over the study period is shown in Figure 4. The highest intensity is March 2010 at 0.92 tCO2-e/MWh.

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Emissions per MWh - NEM 0.95 0.90 0.85 0.80 2010 0.75 2011 0.70 2012

tCO2/MWh 0.65 2013 0.60 0.55 0.50 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 3 - Carbon emission intensity in the NEM 2010 to 2013 The only anomaly on the carbon intensity graph is for June where the carbon emissions per MWh were lower in June 2012 than June 2013 by 0.92%. Overall the emissions for June 2013 were less (13,310 vs 13,635 kTCO2) however the energy generated was also lower. The fuel mix for the month compared with 2012 consisted of 15% less wind energy, 3% more brown coal emissions, 1% more black coal emissions, and 4% less gas generation.

Overall the average annual carbon emissions reduced from 0.87 to 0.78 tCO2-e/MWh in the period 2010 to 2013. Whilst this is a significant reduction it cannot solely be attributed to wind penetration, with emissions intense power stations also closing during this timeframe and the implementation of a carbon tax from July 2012. Hydro energy - NEM 2500

2000

1500 2010 2011

GWh 1000 2012 2013 500

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 4 - Hydro generation 2010 to 2013

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2.5 Generation displacement

Although there was a decrease in electricity generation in 2013, there was also a 25% increase in wind energy. Overall this was less than 1% change in the generation mix, however were there any winners and losers in this arrangement? Figure 5 below shows the output of each of the fuel types in 2013 compared with 2012. The data has been normalized with the 2.7% reduction in overall electricity production so all changes are relative to this reduction. Generation mix change 2013 0.3

0.25

0.2

0.15

0.1

0.05

% change %change in energy 0

-0.05 Biomass Black Coal Brown Coal Gas Hydro Wind -0.1

Figure 5 - Change in generation mix 2013 compared with 2012 It should also be noted that a carbon tax was introduced in Australia in July 2012, which should bias a trend towards more renewable energy and reduce the largest emitting generators – i.e. brown coal. It can be seen in Figure 5 that the amount of brown coal in the fuel mix reduced; however gas generation actually reduced by 1% more. This is contrary to any planned effects of the carbon tax policy controls but in line with Forrest and MacGill’s suggestion that low cost wind power is displacing higher priced gas peaking plants (Forrest and MacGill 2013).

2.6 Price setters

Wind farms have a low short-run marginal cost as their fuel source is essentially free (MacGill 2010). This leads to participants bidding in their wind energy at a low cost to ensure that all available generation is dispatched. Sometimes these prices are actually negative4 and can lead to an overall negative pool price if there are large amounts of wind available or a low load (Cutler et al. 2011). Generally the effect of large wind power

4 Generators bid their plant in various price bands between the market floor price (-$1000/MWh) to the market price cap (indexed from $12500/MWh in 2012/13) (AEMC 2014) 8 | Page

output is to lower the market price (ibid). This section looks at which generators set the price (last generator or part of generator dispatched) and what influence wind power has in setting it and what they are generally paid for their output. AEMO publishes the monthly electricity pool sales, which are grouped into various fuel types. From this we can see if the amount being paid for wind energy is equivalent to the amount of energy being injected into the market, or otherwise. For example if wind contributes 3% of energy for the month, then if it is paid 3% of the monthly pool sales then it is being paid a fair price. If wind produces 3% of energy and is paid say 10% of pool sales, then it would be seen as setting the price and being paid more per unit of energy than the other generators. The results for pool dollars paid versus wind energy put into the pool are shown in Figure 6 for 2012 and 2013. Energy cost vs energy input 2012 4.5% 4.0% 3.5% 3.0% 2.5% % Wind energy 2.0% % Pool $ 1.5% 1.0% 0.5% 0.0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Energy cost vs energy input 2013 7.0%

6.0%

5.0%

4.0% % Wind energy 3.0% % Pool $

2.0%

1.0%

0.0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 6 - Pool price allocation and wind energy 2012 and 2013

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In all but one month during the study period (May 2013) wind energy was paid below the average pool price for energy sold. As a comparison Figure 7 below shows the same graph for black coal generators during 2013. Here black coal (the most dominant of the fuel sources in Australia) varies depending on market conditions.

Energy cost vs energy input 2013 58%

56%

54%

52%

50% % Black coal energy 48% % Pool $ 46%

44%

42% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 7 - Black coal pool price and energy 2013

Whilst it is difficult to draw a comparison between the two fuel types based on these graphs – as each has different market strategies and trading teams – it does show that wind energy on average has the effect of lowering the market price and not increasing it. Looking at dispatch-weighted pool price by fuel type also shows this (Figure 8), with wind energy consistently being the lowest priced energy in the market (67% of the time in 2012) (Figure 9).

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Dispatch-weighted pool price 2012 90

80 Carbon tax introduced 70

60 Black Coal 50 Brown Coal 40 $/MWh Gas 30 Wind

20 Hydro

Figure 8 - Dispatch weighted pool price 2012

Dispatch-weighted pool price 2013 140

120

100

Black Coal 80 Brown Coal

$/MWh 60 Gas Wind 40 Hydro 20

Figure 9 - Dispatch weighted pool price 2013 Hydro was the lowest priced energy from May to October 2013, with wind prices adopting a very similar price to brown coal for the last half of 2013. Of course the price paid to each of the generators is the price of the last generator (or part thereof5) dispatched, and not actually what they are bid in at. For example a wind

5 The capacity of a single generator can be split between price bands. For example a 700 MW plant may bid 300 MW at a low price (say $20/MWh) to ensure it is dispatched, but bid the remainder of its capacity at a higher price or a series of higher prices up to market price cap. 11 | Page

generator may bid negative to ensure it is dispatched as much as possible, but actually receives the price that is bid in by the highest priced generator for that dispatch interval. So how often does wind energy actually set the price in each of the NEM regions? The table in Table 1 below outlines how many intervals each of the fuel types sets the pool price during the study period. Table 1 - NEM price setters by State 2012 and 2013

Fuel type TAS SA VIC NSW QLD Coal 79785 150250 154329 165472 166880 Diesel 0 3 0 0 0 Gas 7829 23461 13209 10769 15678 Hydro 117564 31218 40289 31624 25316 Wind 37 2970 70 25 18 Total intervals 205215 207902 207897 207890 207892 % Intervals for wind 0.02% 1.43% 0.03% 0.01% 0.01% Not surprisingly South Australia has the largest number of intervals set by wind power as it has the largest amount of installed wind in the NEM, but this is only 1.43% of the time over the two year period. The wind farm that predominantly set the price was Infigen’s Lake Bonney Stage 2 with 2364 intervals, followed by Infigen’s Lake Bonney Stage 3 with 468 intervals across the NEM. Other than Tasmania, coal-fired generators set the price more than 72% of the time in each of the other regions. 2.7 Summary

This chapter demonstrates that the amount of carbon emissions in Australia is in fact decreasing as the wind energy penetration increases. It also shows that wind power and hydro power are generally the cheapest in the market, is a price follower more than a price setter in the pool, and has a tendency to lower the pool price and not increase it. These findings are in line with worldwide trends of increasing wind power penetration and other international studies (Gross and UKERC (Organization) 2006). The increase in wind power correlates with a decrease in both brown coal-fired and gas- fired electricity, with black coal-fired electricity remaining largely unaffected. The carbon tax also had an impact on these changes as it was introduced mid-2012. However the effect of the carbon tax should have been greater on the coal-fired plant than gas because of the difference in carbon intensities of each of these generating technologies. This indicates the inability of the carbon tax to change the merit order of generation in the NEM, and the ability of wind power to displace expensive peaking plant.

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3 Methodology and data sources

3.1 Background

The next sections involve detailed analysis of the AEMO wind power data and how it relates to the rest of the NEM. One of the fundamental issues with wind power is how the system manages with its variability in the short term (or balancing timeframe) between the 5-minute dispatch intervals. But handling variability is not a foreign concept for a power system, with systems set up to adjust for constant variations in load from their inception in the late 19th century (IEA and Organisation for Economic Co-operation and Development 2014). Wind power essentially acts as a negative load, with the power system adjusting accordingly. To give the reader an understanding of how balancing is accomplished, the fundamentals of balancing the Australian NEM are explained in Section 1 on frequency regulation. More information on this can be obtained from the 2012 AEMO publication entitled “Frequency Control Ancillary Services, SO_OP3708A”. Other useful AEMO documents about how the NEM operates are:  The NEM in a Nutshell for Wind Techos, (AEMO 2011); and  A guide to ancillary services in the NEM, (AEMO 2010). 3.2 Data sources

Unless mentioned otherwise, all the energy data used in this report is from the NEM market management system (MMS). This is publicly available information and can be found on the AEMO website. Carbon emissions data for each of the power stations, including their average efficiency was taken from the AEMO Planning Assumptions, and is detailed further in 7.3.1. For variation analysis, the 5-minute dispatch data has been used (initial MW reading). The 4-second causer pays data was used for the more complex balancing work and determining the FCAS generators output and operating levels. Data is also extracted from the BOM in relation to wind storms. This is explained further in Section 3.3.2.

3.3 Methodology

3.3.1 Wind power variability

Section 5 analyses how variable the wind is over the 2012 and 2013 calendar years. This is done by extracting the AEMO 5-minute instantaneous output data for each of the NEM wind farms (listed in Appendix A), summing them in States, and then summing the States to form the NEM total for each 5-minute interval. The 5-minute variability is then the difference between any two consecutive dispatch intervals and expressed in MW. To give perspective to this figure it is then divided by the total installed wind capacity at the time to 13 | Page

show how much of a variation (%) it is. This is consistent with similar studies in the UK (Gross and UKERC (Organization) 2006). Some editing was required to override data glitches that were found. Predominantly this was where data was not scanned for one period (presenting as zero) and then returned to around the same amount in the next period. Generally a wind farm would not reset to zero for a single 5-minute interval and then return to the same operating level (not maximum) – even though ramp rates would allow this. Hence it has been assumed that large excursions to zero such as these are errors. This presents as a “data bounce” with a negative variation followed immediately by a positive variation. The zero readings in these cases were set to the average between the two adjacent intervals. This has only been done on the large excursions that present in the dataset – generally over 5% change. The remainder of the data is presented unedited. The wind power variability shown in this section is then compared with the FCAS procurement detailed in Section 1 to show how often the variability in wind may cause additional purchase of regulation services. That is when the system would not have accommodated the wind changes without requiring more (most likely expensive) services.

3.3.2 Storm effects on variability

Section 6 analyses some of the larger wind power variations seen in section 5 that occurred during certified wind storms, and looks closer at the wind data of individual wind farms and regions to see what actually caused the large variations. A list of storms across Australia was downloaded from the BOM storm archive (http://www.bom.gov.au/australia/stormarchive). The storms were filtered to wind storms only and relevant storms are shown in Appendix B. The remainders of the storms are included on the attached disc. The data from non-NEM states was deleted, as was Queensland as there is no significant wind power located there. The resultant data was 213 instances of severe wind recorded during the 2012 to 2013 study period. There were no recordings listed for Tasmania. The majority of the storms listed recorded wind gusts of over 46 knots (23.7 m/s)6. The data with no wind speed noted were from comments received from residents where a large storm had passed but had not necessarily been recorded. All storms selected for study had gust speeds over 49 knots, which is just over the typical cut out speed of wind turbines at 25 m/s or 48.6 knots. The recorded wind gusts were at a height of 10 metres above the surface at each of the logging stations. Wind measurements for turbine operation are measured at hub height which is up to 80 metres for Type 3 wind turbines installed in the NEM (AEMO 2013a), and are always greater than those measured at lower heights.

6 Wind gusts are measured over 3 seconds, wind speed is averaged over 10 minutes (BOM 2014a) 14 | Page

3.3.3 Carbon contribution of FCAS generators

In section 7 the carbon emissions of the FCAS regulation generators are calculated for each of the three storm scenarios. The following steps were undertaken:  Obtain the 4-second data for each of the study periods;  Merge into a single file for each hour and each generating unit;  Highlight and extract the FCAS regulation generating units;  Develop an emissions curve and equation for each generating unit based on AEMO published data and assumed heat-rate curve;  Apply the emissions equation to the FCAS generating unit’s production to calculate actual emissions; and  Plot the wind power trend and the summed emissions for each period and analyze.

The data sources and extraction methods for the carbon emissions section is explained further in 7.2.

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4 Frequency regulation – a brief guide

4.1 Frequency control basics

The frequency of the power system in Australia is 50 Hertz (Hz). When the frequency goes lower than 50 Hz it means that the system is slowing down due to more load being switched on than generation being supplied. Load varies all day long with electrical devices such as industrial processes, appliances, lights or large building air-conditioning systems being turned on and off. The system detects this and sends a signal to specific generators to increase their output. Similarly when the frequency goes higher than 50 Hz the system is speeding up with generation outstripping load. Here certain generators need to slow the system down by putting in less power. This whole system is called frequency regulation and is often referred to as balancing the power system (input vs output). There are two services that are offered in the market for this – regulation raise (add more power in) and regulation lower (take power off). AEMO procures 130 MW of raise services within a 5-minute dispatch interval, and 120 MW of lower services. The system is therefore set up to manage with this amount of variability between 5-minute dispatch of the rest of the generators. If the system needs more than this, then AEMO can procure more services and does so based on the formulas below that use the system accumulated time error7 to determine how much. An accumulated time error of greater than ± 1.5 seconds may require additional support.

Dispatch raise requirement = Min (250,130 + (-1 x Min(-1.5, Time Error) – 1.5) x 60)

Dispatch lower requirement = Min (250,120 + (Max( 1.5, Time Error) – 1.5) x 60) (AEMO 2012, 10) The time error is an accumulated difference over time compared with a perfect 50 Hz system, and is an indication of how far from a nominal time the slowing down and speeding up of the system is overall. Generally the time will be behind as load increases before the morning peak, and then catches up when the load starts to drop off during the day. The actual time error used in these calculations is the average of Queensland and NSW errors for mainland Australia (ibid). Tasmania regulates its own frequency separately from mainland Australia as it has an asynchronous connection to Victoria (via a high voltage DC link) and is not synchronized with the rest of the NEM. It requires 50 MW each of raise and lower services to operate.

7 Accumulated time error is defined in the Frequency Operating Standards (Mainland) 2009 by the AEMC as meaning “…the integral over time of the difference between 20 milliseconds and the inverse of that system frequency, starting from a time published by NEMMCO” (AEMC Reliability Panel 2009, 18) 17 | Page

The generators that provide this service are called Frequency Control Ancillary Services (FCAS) regulation generators. These generators bid their capacity in on the spot market as per normal scheduled generators and also bid a component of their output as regulation. 4.2 The FCAS generators

The list of FCAS generators that provide regulation services in the NEM are listed in Appendix B. They consist of predominantly coal-fired, hydro and some gas-fired generators. At any one time an economic mix of these generators will be providing the service to the power system. Each generator can nominate how much regulation it wishes to provide (remembering there is a total band of 250 MW), and where in its operating range it cuts in and cuts out. Figure 10 shows a selection of FCAS generators and their operating ranges for regulation services (AEMO 2014a).

Frequency regulation parameters per unit

800

700

600

500

400 Lower limit 300 Upper limit 200 Unit max MW 100

Figure 10 - Parameters for FCAS regulation generators The hydro-generators (Dartmouth and Tumut 1&2) are able to provide services from zero MW due to their rapid start technology and no combustion required. The other generators shown are all coal-fired boilers except for Torrens Island B, which is a gas-fired boiler. The average cut in for regulation services for these coal-fired generators is 38% of the maximum operating range, with Eraring being the lowest at 20% and Loy Yang A being the highest at 50%. For each generator the lower limit indicates where operation generally becomes too inefficient to provide the service.

The maximum amount of regulation services that the generators bid are shown below in Figure 11 in MW and as a percentage of their maximum output (Pmax) in Figure 12.

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Raise/lower limits (MW) 300 Hydro

200 Coal-fired generators Gas- fired 100 100 15 100 75 20 50 30 25 50 25 176 250 0 -100 -15 -100 -75 -20 -50 -30 -18 -50 -40 -176 -250 Raise -100 Lower -200

-300

Figure 11 - Regulation generators raise/lower limits (MW)

Raise/lower limits (% Pmax) Hydro 100% 90% 80% Coal-fired generators Gas- 70% fired 60% 50% 40% 30% %Pmax 20% 10% 14% 4% 13% 26% 4% 8% 8% 6% 7% 12% 95% 38% 0%

Figure 12 - Regulation generators raise/lower limits %Pmax

These graphs show that the fossil-fuel regulation generators only offer a small amount of their operating capacity as regulation. Gladstone has the highest offering of up to 26%, and Callide and Liddell the lowest with 4%. The hydro-generators offer a substantial amount of their capacity as it costs them no more to run at low or high levels. It should be noted that this service is spread across the mainland States with the NEM dispatch engine calling on the most economical solution. The service is distributed amongst many generators depending on their availability, with over 20 individual units being observed at times during these studies giving an average change of around +/-6.5 MW per generator.

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4.3 Frequency regulation impacts

Noting that regulation is a voluntary but paid market service; there are only 130 MW of raise and 120 MW of lower required; and that each generator has set operating bounds, it could be concluded that each regulation generator will: a) Not provide the service till it is economic and efficient to do so; b) Not vary by a significant amount when actually providing the service (unless hydro); and c) Never be pushed into an inefficient zone. So considering the above, an increase or decrease in wind power would be absorbed by the FCAS generators in the same manner to that of load changes. This also gives us a few indicators to see the impact of wind power on the power system. That is: a) Cost of FCAS regulation services; b) Amount of FCAS regulation services; and c) Time error variations. Have any of these had a material change since the introduction of large scale wind farms in Australia? Note that we are only looking at regulation services and not contingency services, as these are provided on the same basis across the NEM, and are not exclusively impacted by wind.

4.4 FCAS Costs

The weekly costs of FCAS regulation services across the NEM for 2012 and 2013 are shown in Figure 13. The total for these two years were $4.89 million for 2012 and $4.6 million for 2013. So overall a 6% decrease in payments for this service. Noting that load has decreased over this period the cost was normalized to cost per MWh generated over the same period. This resulted in a regulation cost of 2.6 c/MWh for 2012 and 2.5 c/MWh for 2013. Over the same period wind energy in the NEM increased by 25%. This is contrary to other findings that suggest an increase in both the amount of regulation services and the associated costs with increasing levels of wind (Gross and UKERC (Organization) 2006; Xie et al. 2011). A better comparison would be to include the previous two years of data; however this was not readily available at the time of writing.

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Regulation weekly costs $500 $450 $400 $350 $300 $250

$200 2012 $k/week $150 2013 $100 $50 $-

Figure 13 - Weekly regulation costs in the NEM 4.5 FCAS Regulation amounts

The FCAS regulation amounts were 250 MW each when the NEM commenced in 1998 (AEMO 2013b, 3–21). With system refinement and optimization these have reduced over the years to the current predetermined amounts of 130/120 raise/lower or 250 MW in total. Over the past 5 years up to June 2013, the installed wind capacity has more than doubled from 1100 MW to 2500 MW, and has not required any changes to the pre-determined regulation amounts. 4.6 Time error

The time error (as detailed in Section 4.1) is an indication of how well the system is managing the variations on the power system – both wind and load. Figure 14 was taken from AEMO’s report on wind integration in Australia and shows that over the past five years the lost time error has actually slightly improved with less excursions outside the +/- 1.5 second range. This is contrary to reports that say that wind power increases the amount of balancing required in a system (De Vos et al. 2013), and other studies by IEA that attempt to calculate the amount of additional balancing required based on wind penetration levels (IEA and Organisation for Economic Co-operation and Development 2014). This may, however, be due to the wind penetration levels in Australia still being below 20% of load, which is generally when issues associated with large wind begin to appear. Up to this level, Holttinnen et al suggests that the short-term reserve requirement is 3% of installed capacity (ibid) which equates to 81 MW on an installed capacity of 2715 MW in the NEM at the end of 2013.

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The 5-minute dispatch time-frame and accurate wind forecasting system also contribute to keeping this requirement to a minimum.

Figure 14 - Mainland NEM distribution of time error (AEMO 2013b, 3–36) 4.7 Summary

The impacts of increasing levels of wind power on frequency regulation in the Australian NEM have been minimal to date. There have been no changes to the standard regulation raise/lower amounts, no increase in costs, and a slightly improved accumulated time error history over the past 5 years. Installed wind power capacity has increased from 1100 MW to 2500 MW during the same period, taking the penetration of wind power up to 13.2% of load by December 2013. The next section looks at the number of times wind power has fluctuated more than the regulation amounts – and goes on to examine the impacts this has on the regulation generators, time error and short-term carbon emissions.

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5 Wind power variations in the NEM 2012 and 2013

5.1 Introduction

This chapter analyses the wind energy variation during 2012 and 2013, using 5-minute dispatch interval data. The impact of wind power and the compensating regulation generators in the NEM can be seen by observing the 5-minute variation in wind power. As mentioned in section 4 the regulation generators adjust for load and generation differences (such as generators not meeting their targets) not just the wind power variation. This analysis looks solely at the wind without taking into account any of the other impacts. 5.2 Data source

All data was extracted from the AEMO 5-minute dispatch interval database. The data is the initial instantaneous MW output of each of the wind farms at the beginning of the dispatch period. The data manipulation and methodology in this section is explained further in Section 3.3.1.

5.3 Installed capacity

At the beginning of 2012 the installed wind power capacity in Australia was around 2110 MW and increased to 2715 MW by the end of 2013 with the addition of Macarthur (420 MW), Mortons Lane (19.5 MW) and Musselroe (168 MW) wind farms (AEMO 2013b). The final installed capacities by State are shown in Table 2. Table 2 - Installed wind capacity by State, December 2013

New South South State Tasmania Victoria Wales Australia

Installed wind 265 1203 308 939 (MW) Whilst Queensland does have one small wind farm, it has been excluded from the studies due to its size, status as a non-scheduled generator, and unavailability of its data (See Appendix A for other excluded wind farms).

5.4 Calculations

The 5-minute instantaneous megawatt (MW) data was extracted from AEMO’s database for the wind power and total generation in each state of the NEM for the calendar years 2012 and 2013. The wind farms for each state were summed to a state total for each interval, and then summed again to provide the total NEM wind for the interval. The corresponding total electricity production for the NEM was summed in the same way but

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with all generators – including wind. The wind power variation between intervals, capacity factor and total wind penetration were calculated as per equations 2, 3 and 4.

Variationn (%) = (Equation 2)

∑ Capacity factorn(%) = (Equation 3)

∑ Total wind penetrationn (%) =

(Equation 4)

Where n is the current dispatch interval. Data is summed for each quarter of a year.

Other calculated data included maximum, minimum and average capacity factors over each quarter of each year of the study period. The results of the data calculations and analysis are discussed in the next sections.

5.5 2012 Wind power summary

A summary table of the 5-minute data for 2012 is presented in Table 3. During 2012 wind power supplied up to 9.91% of the NEM’s total instantaneous generation needs. The maximum occurred in December 2012. The largest wind capacity factor was 85.64% during a September afternoon, which was 7.5% of the NEM’s total generation at the time8. Table 3 - 2012 wind variations and capacity factors

Variation - data Installed excursions % Wind of total NEM capacity removed Capacity factor supplied generation 2012 Wind Max Min Max Min Average Max Min Average Jan-March 2110 10.43% -9.34% 84.17% 0.85% 26.76% 8.48% 0.06% 2.51% Apr-Jun 2110 6.59% -9.67% 80.71% 0.33% 30.53% 8.62% 0.03% 2.83% July-Sept 2110 8.58% -10.57% 85.64% 0.33% 37.70% 9.62% 0.03% 3.50% Oct-Dec 2547 8.40% -9.31% 71.02% 0.20% 28.96% 9.91% 0.02% 3.40% The largest power swings caused by wind power were in March (10.43%) as a result of a storm (discussed further in Section 5) and -10.57% which was due to a negative pool price

8 During this interval New South Wales wind farms were operating at 95% of capacity, whilst Tasmania was at 79%. The other two states were around 85%. 24 | Page

event in South Australia on 6 September. This caused SA wind farms to ramp back their output in order to regain a positive price for energy, and saw wind generation drop from 984 to 624 MW over 10 minutes. The three wind farms involved here were Waterloo, Snowtown and Lake Bonny 2 and 3. Their combined output reduction was 326 MW in the two trading intervals. These three wind farms were back operating at maximum available capacity between 15 minutes and an hour after the event. Whilst there were several negative price events in South Australia during 2012, this one in September was out of the ordinary as it was during the middle of the day (12:20 – 12:30 PM). Normally the negative price events occur overnight whilst the wind is strong and the load is at a minimum. The 2013 AEMO document “South Australian wind study report 2013” discusses these market impacts in depth.

5.6 2013 Wind power summary

During 2013, wind power entered a different league with the addition of Macarthur Wind Farm – the first large scale wind farm over 400 MW with a single point of connection in the NEM. The 5-minute data summary is shown here in Table 4. The wind penetration level in the NEM exceeded 10% during 2013 – with each quarter recording 12 (Q1) to 1181 (Q3) intervals above this – and up to 13.2% during the final quarter. The average capacity factor also increased to 34.32% for all wind farms. Table 4 - 2013 wind variation and capacity factors

Variation - data Installed excursions % Wind of total NEM capacity removed Capacity factor supplied generation

2013 Wind Max Min Max Min Average Max Min Average Jan-March 2547 8.09% -7.11% 83.00% 0.79% 30.58% 10.16% 0.11% 3.46% Apr-Jun 2547 9.27% -10.29% 84.92% 0.67% 28.59% 10.89% 0.07% 3.34% July-Sept 2715 7.85% -17.38% 88.21% 1.36% 42.85% 12.31% 0.14% 5.29% Oct-Dec 2715 7.03% -6.37% 87.77% 1.29% 35.24% 13.20% 0.16% 4.51%

The largest excursions were -17.38% on 4 July and -16.09% on 14 August. Both of these were caused by Macarthur wind farm either tripping or shutting down from 336 MW and 382 MW respectively, within a dispatch interval. If the shutdown was planned then this would have been forecast in the bidding data. If the wind farm tripped off then this excursion would be treated like any other generator tripping off and be covered by contingency FCAS which is separate to regulation. The largest increase in wind power was 9.27% on 22 April, which appears during some data absences in South Australia and Tasmania, and may not be wind related. No storms were present at the time, however Woolnorth Wind Farm was operating near peak capacity at the time (120 MW), and could have seen some wind over-speed cut outs.

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5.7 Variations greater than normal regulation

To ensure balance in the power system is maintained, an increase in wind power activates lower regulation services, and a decrease in wind activates raise services. This is net of any load effect that may be occurring at the time which could ease or exacerbate the changes. In order for wind power to exceed the normal 130 MW raise and 120 MW lower services, then the required percentage increase or decrease of wind relative to installed capacity (using calculation i) is as shown in Table 5. These figures reduce as the installed capacity increases. Table 5 – Amount by which wind power must change to exceed regulation amounts

Installed wind

(MW) 2110 2547 2715 130 Raise 6.2% 5.1% 4.8% wind decreases by

120 Lower 5.7% 4.7% 4.4% wind increases by The number of 5-minute variations in wind power that are greater than the amounts in Table 5, are shown below in Table 6 for 2012 and 2013. Table 6 - Number of excursions where wind power exceeds FCAS amounts

Raise Lower Raise Lower 2012 intervals intervals minutes minutes Jan-March 4 7 20 35 Apr-Jun 6 4 30 20 July-Sept 5 7 25 35 Oct-Dec 7 18 35 90

Total 22 36 110 180 Raise Lower Raise Lower 2013 intervals intervals minutes minutes Jan-March 13 26 65 130 Apr-Jun 6 13 30 65 July-Sept 33 32 165 160 Oct-Dec 10 16 50 80

Total 62 87 310 435 For 2012 this resulted in 290 minutes or 4.8 hours where wind may have contributed to additional procurement of regulation services. This amount increased in 2013 to 12.41 hours or 0.14% of the year. Whilst these times are not large over the period of a year, they do show an increasing trend as installed wind capacity also increases. This effect may also be a consequence of a larger wind farm such as Macarthur operating in the NEM in a large

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cluster at a single point of connection. Certainly the amount of large variations increase in 2012 compared with 2013, especially the windiest part of the year between July and September 2013. As noted in Section 5.6, loss of Macarthur did indeed cause the largest excursions, however if this was due to a trip of the farm due to a transmission fault this is not due to wind influences, and is like any other single connected generator on the grid. The single largest wind farm prior to commissioning Macarthur was Waubra Wind Farm at 192 MW.

5.8 Variations

The NEM-wide wind variation data for October to December 2012 is shown here in chronological order in Figure 15, with horizontal brown lines indicating the limit for normal regulation services (4.7% for raise, 5.1% for lower). The equivalent graphs for each quarter in the study period are located on the data disc in the files 2012 and 2013 5 minute data.

October - December 2012 10.0% Storm passing

8.0%

6.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

-6.0%

-8.0% Negative price events -10.0%

-12.0% 5 minute wind variation Figure 15 – 5-minute wind variation October to December 2012 The two largest reductions in wind power as seen on the above graph were caused by market influences – which were negative electricity pool price events in South Australia. The largest increase was caused by a passing storm on 20 November (BOM 2014b). Whilst the graph appears quite noisy – which is expected with a variable energy source – it also demonstrates that for the majority of the time the wind variation between dispatch periods is low. Even with the large excursions included in 2013, the standard deviation about a zero mean was less than 1% (0.87%) with a 95% confidence level. Figure 16 shows the 2013 data variations on a duration curve. For 82% of the time the variation seen was less than 1% either increase or decrease in wind power compared with the 5-minute interval before.

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2013 5 minute wind power variation 15.00%

10.00%

+/- 1% 5.00% variation

0.00%

39% 84% 0% 3% 6% 10% 13% 16% 19% 23% 26% 29% 32% 35% 42% 45% 48% 52% 55% 58% 61% 65% 68% 71% 74% 77% 81% 87% 90% 94% 97%

-5.00%

-10.00%

-15.00%

-20.00%

variation

Figure 16 - 2013 wind power variations duration curve The duration curve for 2012 looks very similar and is shown in Figure 17. The standard deviation was slightly lower than 2013 with a 95% confidence level that it is below 0.84%.

5.9 Discussion

Whilst the wind power variability may be substantial from a single wind farm, once they are aggregated across a region, state and country, the variability decreases significantly. The ability to spread the wind resource across a large area is vital in successfully integrating high levels of wind energy into a system. Australia has a great geographical diversity amongst the wind farms that are connected to the NEM. The wind penetration levels approached 15% in 2013, a figure up to which integration is deemed to be routine (IEA and Organisation for Economic Co-operation and Development 2014). However for 98% of the time the variability in wind in Australia is less than ±2.5% of installed capacity. With this kind of result it is not surprising that the FCAS required for regulation has not been increased in quantity or cost over the past few years as the wind capacity has steadily increased.

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2012 5 minute wind power variation 15%

10%

+/- 1% 5% variation

0% 3% 5% 8%

46% 84% 11% 14% 16% 19% 22% 24% 27% 30% 32% 35% 38% 41% 43% 49% 51% 54% 57% 59% 62% 65% 68% 70% 73% 76% 78% 81% 86% 89% 92% 95% 97%

-5%

-10%

-15%

variation

Figure 17 - Variation duration curve 2012

AEMO in its submission to the RET review in 2014 stated that it is “technically feasible” to accommodate the additional renewables to meet the RET target as it stands (41,000 GWh plus small scale renewables) in the NEM and still maintain system security (Swift 2014). This requirement would more than double the amount of wind power that is currently on the system. AEMO also stated that whilst wind variability – including large power swings or unforeseen swings – were one of the issues of large scale wind integration, they believe that the system is well designed to manage it with much lower cost impacts than currently being speculated (ibid). Wind energy experts agree that it is not the variation that is the largest issue, it is whether the variation was predicted which is most important. In this regard, the Australian wind energy forecasting system (AWEFS) has been established to predict the amount of power coming from each of the wind farms. AWEFS combined with the semi-scheduled mechanism for intermittent generators has progressed well since originally installed in 2009. The forecasting has become fine-tuned over the past few years and now exhibits less than a 1% error in predicting the wind power in the 5-minute look ahead category (2013). Figure 18 shows the performance history from April 2012 to April 2013 for AWEFS on a NEM wind basis. One hour look ahead error is between 2 and 3%.

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Figure 18 - NEM wind generation forecasting errors from AWEFS (AEMO 2013b, 3–41) 5.10 Summary

The power system security in Australia has not been compromised by the increasing levels of wind power. The standard amount of regulation FCAS has not increased, and the predictability tools of the wind forecasting system have improved. Whilst some wind variations may cause additional services to be procured for a short time, this is not the norm. For 98% of the time the variation in wind is less than ±2.5% of installed capacity. The system and its regulation controls are sufficient to incorporate this level of variability from wind power. AEMO has said that the only time when the variability of wind power can’t be adequately predicted is large swings during storms. AEMO investigated getting a tool to predict storm wind behavior; however they have deemed them not sufficiently accurate to warrant implementation (AEMO 2013b). Instead AEMO has proposed a set of operational measures that may be implemented in the instance where turbine shut downs due to either high temperature or wind strength are credible. These include tighter controls on the interconnectors, and possibly limiting the output by imposing a cap on wind farms that are likely to be subject to turbine cut outs (ibid). The next section looks at some large variations in wind power found in the study period that were storm related and analyses the source of the power variations, the effect on the FCAS regulation generators and the short-term carbon impact of the variations.

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6 Effect of storms on wind power output

6.1 Introduction

Section 1 presents three of the larger variations in NEM wind power that occurred during wind storms during the study period. It analyses what and where the large variations occurred, how the power system adapted to the large swings, and whether the variations caused additional regulation FCAS to be procured. The first date selected was because it contained the largest 5-minute variation of wind power in the NEM. The other two dates were randomly selected from the large variations in the 5-minute data as presented in section 4, and cross-referenced with the Australian Bureau of Meteorology (BOM) storms database to ensure a wind storm occurred during that day (BOM 2014b).

6.2 Selected storms

The storms selected for analysis were those which showed significant variability in the wind power electrical output – and not necessarily the largest variations. The dates selected and maximum wind power variation during the storms is shown in Table 7. Table 7 - Details of selected storms in study period (BOM 2014b)

Date Storm location Maximum gust Time 5 minute wind power variation State Town knots metres/sec max MW time 14/03/2012 SA and Vic Snowtown 54 27.78 1800 10.43% 220 1830 23/08/2012 SA and Vic Snowtown 50 25.72 1252-1509 5.30% 109 1500 30/09/2013 SA Snowtown 58 29.84 1217-1330 3.13% 85 1330 30/09/2013 SA Port Augusta 52 26.75 1705-1911 5.93% 161 1855 (BOM 2014b) The storm on 30 September 2013 extended over 7 hours, with two distinct periods of extreme winds. Only the later one is studied in detail. Snowtown – where the majority of the maximum wind gusts were observed – is located approximately 150 km north of Adelaide, and is in the Mid-North wind farm area (AEMO 2013c, 2–11). There are 7 wind farms in the area including the Hallett wind farms, Waterloo, Clements Gap and Snowtown totaling 617 MW installed by 2013 (ibid).

6.3 Wind turbine operation overview

Wind turbines all have power curves which denote the wind speed they cut-in with or commence operation, and the speed at which they cut-out. Whilst the cut-in varies from turbine to turbine, for most large wind turbines the cut-out speed is 25 m/s. Generally the turbine will reach full operation well before this mark and will continue operating until this speed is reached, and which point it will stop completely either by braking, yawing, or both. Figure 19 shows an example of a wind turbine power curve and demonstrates the concept.

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Figure 19 - Typical wind turbine power curve (WindPower Program 2014) The turbine recommences operation when it is safe to do so, depending on its nominated cut-out wind speed. For the purposes of this study it has been assumed that if a wind farm is operating at full output and then suddenly decreases its output during a wind storm, then the wind strength has exceeded the turbine cut-out wind speed and some if not all turbines have cut-out9. Conversely if the wind farm returns rapidly to full output after an assumed cut-out due to high wind, then this is caused by the wind speed dropping below the cut-out speed – but still within the maximum output range – and the turbines returning to full load. 6.4 Storm 1 – Snowtown 14 March 2012

The wind power output for each of the States for the 14 March 2012 is shown in Figure 20 (from the 5-minute data). The storm occurred in South Australia, with a peak wind gust at 6 PM occurring at Snowtown. The graph shows that the wind output was reasonably smooth across the NEM until 1 PM, when larger irregularities began to occur in the South Australian wind farm output. Between 1 PM and 6 PM the largest variation in NEM output was -5.4% of installed capacity or a loss of 113 MW at 2 PM. The 5-minute variations for the day are shown in Figure 21. The large increase of 10.4% occurred at 6.30 PM and was 220 MW across the NEM, with an increase of 234 MW from South Australia. The wind power in South Australia continued to fluctuate until 9.30 PM and then resumed steady operation – seeing large increases in wind power in the last hour of the day.

9 Noting that due to geographical separation, not all wind turbines within a wind farm cut out at the same time, and some may continue to operate. 32 | Page

NEM wind farm output 14 March 2012 1400

1200

1000

800 NSW

MW 600 SA TAS 400 Vic 200 Total 0

Figure 20 - State wind farm output during storm 14 March 2012

14 March 2012 - wind power variation 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00%

minutevariation -2.00% - 5 -4.00% -6.00% -8.00%

Figure 21 - NEM 5-minute wind power variation 14 March 2012 The large power swing at 6.30 PM was created by wind farms in South Australia – which is understandable considering it was the location of the storm. Studying the individual wind farm outputs in SA showed that the Mid-North wind farms were catching the brunt of the storm, with their output for the afternoon shown in Figure 22. Whilst there were various

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turbine cut-outs during the day (for wind speeds greater than 25 m/s), only Waterloo Wind Farm had a complete station cut-out during this period. In fact, it had 10 full station cut- outs and 8 or more multiple turbine cut-outs during the storm. When the wind speed allowed Waterloo to fully resume production, it ramped to maximum output indicating a cut-out due to high wind. The remainder of the wind farms stayed online throughout the storm, with multiple turbine cut-outs experienced at all of them.

Mid-North wind farm output 14 March 2012 1 pm to midnight 180

160

140

120

100 Waterloo

MW Hallett 1&2 80 North Brown

60 Bluff Snowtown 40

Figure 22 - Mid-North wind farm output 14 March 2012

The Waterloo Wind Farm consists of Vestas V90 turbines (EcoGeneration 2011), whilst the other wind farms in the Mid-North are Suzlon. Even Snowtown Wind Farm – nearest to where the maximum gust was experienced at 6 PM – remained steady at maximum output through the gust. Reducing the time frame of Figure 22 to an hour shows what actually caused the large power swing at 6.30 PM (Figure 23). Waterloo and Snowtown wind farms are steady through this 5-minute period, and it is Hallet, Bluff and North Brown wind farms cutting back in at the one time (and returning to full load) that increases the output in the zone by 155 MW in 5 minutes. The remaining increases in South Australia for this time period were Canunda and Lake Bonny 2 in the South East region (80 MW increase). So in this instance, it wasn’t the wind variability that caused the large power swing. It was the turbines returning to full power after cutting out – most likely due to high wind. The ramp rates on wind turbines are extremely fast compared with most incumbent fossil-fueled generators. Hallett, North Brown and Bluff are registered to have a maximum rate of change of 30 MW per minute (AEMO 2014a), meaning they can reach full capacity in two to three minutes. Waterloo’s ramp rate is 23 MW/minute and Snowtown 20 MW/minute.

34 | Page

Of possible more concern to grid stability is Macarthur Wind Farm in Victoria that has a registered maximum rate of change of 840 MW/minute, which means it can reach full output in half a minute (more on this in 6.6). A possible solution to address the large variations when turbines come back online after a cut out, could be to limit the ramp rates on wind farms after a high wind speed cutout. AEMO has discussed placing an output cap on wind farms subject to storm activity (AEMO 2013b), however not slowing down their return to service.

Mid-North wind farm output 14 March 2012 6 pm to 7 pm 180 160 140 120 100 Waterloo

MW 80 Hallett 1&2 60 North Brown 40 Bluff 20 Snowtown 0

Figure 23 - Mid-North wind farm output 14 March 2012; 6 to 7 PM

6.5 Storm 2 – Snowtown 23 August 2012

The storm on 23 August 2012 was also located in South Australia, with peak wind of 50 knots at Snowtown. The NEM wind farm output is shown here in Figure 24. South Australia is producing the highest wind output, and the effects of the storm are seen from 2 PM to 3PM. In this case the maximum variation between 5-minute intervals is an increase of 5.3%, however there are two increases close together leading to an increase of over 400 MW in half an hour.

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23 August 2012 1800

1600

1400

1200

1000 NSW

MW SA 800 Tas 600 Vic 400 Total

200

Figure 24 - Wind power output 23 August 2012 The wind power 5-minute variability for this day is shown in Figure 25.

23 August 2012 - wind power variation 8.0%

6.0%

4.0%

2.0%

0.0%

minutewind variation -2.0%

- 5 -4.0%

-6.0%

Figure 25 – NEM 5-minute wind power variation 23 August 2012

36 | Page

Figure 26 disaggregates South Australian wind farm generation into zones for the day. The South East region shows output oscillations of during the day, whilst the Mid-North region shows a rapid drop and return to service between 2 and 3 PM. The storm was widespread in South Australia and had several high wind levels recorded. These were from Port Lincoln, Adelaide itself and further north including Snowtown and Yunta (east of Port Augusta).

South Australian wind zones 23 August 2012 700

600

500

400

MW Mid-north 300 SE 200 Coastal

100

Figure 26 - South Australian wind zones 23 August 2012

Following the dip in the Mid-North region is a spike in the South East. Whilst these appear close together they are in fact half an hour apart. However the sudden increase in South East output involves all the wind farms in the region ramping up to maximum output (except Lake Bonney 2 which topped out at 111 MW out of its maximum 159 MW) within 10 minutes, holding for half an hour, and then decreasing to around the same level before the spike. This equated to a 173 MW increase in 10 minutes, and appears to be a genuine increase in wind speed with all farms reacting together. The maximum rate of change for Lake Bonney 2 and 3 together is 32 MW/min, whilst Lake Bonney 1 and Cununda are unlisted due to their legacy status as non-scheduled generators10. They are all however Vestas turbines of varying types (Infigen 2014; Wind Prospect 2014).

10 Prior to the introduction of semi-scheduled status in 2009, all wind farms were registered as non-scheduled. The registration process for non-scheduled generators excluded data that is now required for semi-scheduled generators. (“AEMO Semi-Dispatch of Significant Intermittent Generation : Proposed Market Arrangements” 2010). 37 | Page

South East wind farm output 1pm to 4pm 23 August 2012 120

100

60 Lk Bonny 1 MW LkBon2 40 LkBon3 Canunda 20

Figure 27 - South East wind farm output 23 August 2012; 1 PM to 4 PM

The turbine response to the sudden increase in wind speed was controlled and well within the regulation FCAS bands. The increase overall across the NEM for these two periods was 4.8% and 5.3%, with all wind farms in the NEM operating at 78.3% of total capacity. The ramp rate averaged across the four wind farms is 17.3 MW/min or 4.3 MW/min per wind farm. Changing the time scale in Figure 26 to three hours shows the separation between the two regional increases (Figure 28). It also shows that the largest variations in wind power were not coincident with the largest gust of wind within a wind farm zone, which occurred at Snowtown at 12.52. There were however other peak wind gusts recorded in Port Lincoln and Adelaide between 2 PM and 3 PM which could indicate the storm front moving across South Australia from the North West; however for this storm there were no directions recorded by the BOM for any of the wind gusts in South Australia so this cannot be verified.

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Figure 28 - South Australian wind zones - 23 August 2012 12.45 pm to 4 pm

6.6 Storm 3 – Snowtown & Port Augusta 30 September 2013

The storm that occurred on the 30 September 2013 had high wind gusts registered in two main periods – midday to 1.30 PM and 5 PM to 7 PM. Whilst they were predominantly recorded in South Australia, the consequences of the storm were also seen in Victoria throughout the day. The NEM wind farm output is shown in Figure 29. The largest variability in the NEM for this storm was an increase of 5.93% of wind power (161 MW) at 6.55 PM. Considering that it was affecting both the wind zones in South Australia and Victoria this is quite low compared to other storm variations. During the day the wind farms operated up to 79.8% of the installed capacity, and supplied up to 10.2% of the NEM’s instantaneous power requirements. The 5-minute wind power variability across the NEM is shown in Figure 30. Victoria – with the addition of Macarthur and Oakland wind farms – now has a similar wind power output to South Australia in this storm, even though the installed capacity in South Australia is still considerably larger (1203 compared with 939 MW). The previous storms studied showed South Australia dominating the wind generation; however in this storm it is shared between the two States.

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NEM wind farm output 30 September 2013 2500

2000

1500 NSW

MW SA 1000 Tas Vic 500 Total

Figure 29 - NEM wind farm output 30 September 2013

30 September 2013 wind power variation 8.00%

6.00%

4.00%

2.00%

0.00%

-2.00%

minutewind variation - 5 -4.00%

-6.00%

Figure 30 - NEM 5-minute wind power variation 30 September 2013 The Victorian wind farm output for 30 September is shown in Figure 31. Large amounts of variability is seen from Macarthur wind farm, with the largest being an increase of 156 MW at 6.55 PM. The overall increase at that time from Macarthur was 305 MW in 15 minutes. The wind farm went to full output at this time which based on the previous rapid reduction, indicates that it was returning to full capacity after a cutout due to high wind speeds. The largest 5-minute reduction in output was 89 MW at 6 PM. At no time during the day did the entire wind farm cut out.

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The maximum rate of increase experienced at Macarthur wind farm during this storm was around 31 MW/min. This was the increase to 310 MW from 154 MW at 6.55 PM, which is far less than its published maximum of 840 MW/min. Macarthur reached its maximum output at 7.10 PM.

Victorian wind farm output 30 September 2013 450

400

350

300 Macarthur 250 Oakland

MW Mortons Lane 200 Challicum Hills

150 Portland Yambuck 100 Waubra

Figure 31 - Victorian wind farm output 30 September 2013 Victoria also had an increase of 120 MW at 9.45 PM when both Macarthur and Waubra – the two largest wind farms in Victoria – returned to full output at the same time. The variation in the South Australian wind power output was spread across the three regions during this storm, but it predominantly affected the Mid-North and South East as shown in Figure 32. The two large dips in output from the Mid-North were a combination of cutouts between North Brown, Waterloo and Hallett 1 & 2. Snowtown reduced its output around 9 am and stayed at low levels throughout the day until 7 PM. All the Hallett wind farms (Hallett 1&2, North Brown and Bluff) cut out coincidently between 5.40 and 6 PM. This took 243 MW off the system over the 20 minute period. Between 5 PM and 7 PM both South Australian and Victorian wind farms reduced their output by 300 MW each. Whilst the curves look similar in Figure 33, South Australia began reducing output 15 minutes before Victoria. The reduction took 20 – 25 minutes in each case which averages 75 MW per 5-minute dispatch interval. These are both well within the standard regulation FCAS capability of 130 MW raise. SA wind farms reduced output by 440 MW in the hour between 11 AM and 12 PM. The largest 5-minute variation in the NEM however was -3.9% which was a 104 MW reduction from South Australia, spread out across all wind farms.

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SA wind zones 30 September 2013 1200

1000

800

600 Mid-North MW SE 400 Coastal 200 Total

Figure 32 - South Australian wind zones 30 September 2013

SA and Victoria wind farm output 5pm to 7pm 30 September 2013 900

800

700

600

500

MW 400 Vic 300 SA 200

100

Figure 33 - SA and Victoria wind farm output 30 September 2013; 5PM to 7PM

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6.7 Time error

During the three storms studied the wind farms across the NEM behaved in a manner that did not cause any issues to the major power system, and exhibited only 2 dispatch intervals where the regulation FCAS may have needed increased services. This section looks at the time error during these two large variations in wind power output to see if additional services FCAS were required (see Section 4.5 for details on FCAS procurement). 6.7.1 Data matching

To match the 5-minute dispatch data with the 4-second time error data (FCAS causer pays) it needs to be noted that the 5-minute data is the initial MW reading of each of the wind farms for the period ending at the timestamp11. This means that an increase logged at 6.30 PM was seen at 6.25 PM and occurred in the previous 5-minute interval. Data for each of these increases was extracted for the 5-minute period of the increase and the two subsequent periods. For the purposes of dispatching the FCAS generators, frequency is monitored in two locations on the mainland – these are Frequency NEM south, and Frequency NEM north. Similarly the time error is also calculated for the same readings and has a north and a south error. Regulation FCAS is dispatched based on the average of the NSW and Queensland frequency readings. 6.7.2 14 March 2012 time error

The 220 MW increase on 14 March was seen at 6.30 PM. As discussed in the previous section, this means that the increase occurred in the period between 6.20 and 6.25 PM. The time error graph is shown in Figure 34 along with the total NEM generation which is shown as the NEM load. The NEM load remained reasonably steady during the 6.20 to 6.25 PM dispatch interval, with an overall increase of 10 MW. During this wind power increase the time error increased from 0.82 seconds to 1.1 seconds (0.28 seconds), which is the result of the system operating at over 50 hertz. It did not however, increase above 1.5 seconds which is when the system requires additional regulation FCAS to be dispatched (see Section 4.5). The frequency for the same 15 minutes is shown in Figure 35, along with the normal frequency operating bandwidth (AEMC Reliability Panel 2009). This bandwidth must be maintained by AEMO for 99% of the time and is the normal operating band whilst there are no load or contingency events on the system.

11 The dispatch data shows the initial MW and the dispatch level or target that is required to be reached at the end of that dispatch interval for scheduled generators. The time stamp is the end of the dispatch interval. Semi-scheduled generators dispatch level is their maximum available capacity. (AEMC 2014) 43 | Page

Time error 14 March 2012; 6:20 to 6:35 PM 1.2 26100

1.1 26000

1 25900

0.9 25800

MW NEM Sth seconds NEM Nth 0.8 25700 NEM load

0.7 25600

0.6 25500

Figure 34 - NEM time error 14 March 2012; 6:20 to 6:35 PM

System frequency 14 March 2012; 6:20 to 6:35 PM 50.2

50.15 frequency bandwidth with 50.1 no load or nominal 50.05 contingency frequency event 50 hertz NEM Sth 49.95 NEM Nth 49.9

49.85

49.8

Figure 35 - System frequency 14 March 2012; 6:20 to 6:35 PM The frequency is regulated to below 50 hertz for the next dispatch interval to correct the time error, which is reduced to 0.94 seconds in the next 10 minutes.

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6.7.3 30 September 2013 time error

The 161 MW increase on 30 September 2013 was at 6.55 PM in the 5-minute data files. As per the previous section this meant that the increase was recorded at 6.50 PM for the previous 5-minute period. The time error for the mainland NEM for this period and the following 2 periods is shown in Figure 36. The load is also shown in this figure, and reduces by over 50 MW at the same time as the increase in wind generation. This has a double effect on the time error as it makes the increase over the 5-minute period larger than if the load was steady. The time error was negative at the time, and decreases from -0.92 to -0.53 seconds or by 0.39 seconds during the period from 6.45 to 6.50 PM. It does not, however, exceed the band of ±1.5 seconds required to procure more regulation FCAS. The following 2 periods sees the time error continue to increase to 0.1 seconds as the wind power increases and the load further decreases.

Time error - 30 September 2013 6:45 to 7:00pm 0.2 25300

25250 0 25200 -0.2 25150

-0.4 25100

MW NEM sth seconds 25050 NEM nth -0.6 NEM load 25000 -0.8 24950

-1 24900

Figure 36 - Time error 30 September 2013; 6:45 to 7:00PM The frequency during the same 15 minute period is shown in Figure 37. The frequency stays above 50 hertz throughout which is consistent with an increasing time error12.

12 The time error is smooth compared with the frequency variations as it is accumulating the difference between 50 hertz and the actual frequency which in these cases is only varying by 0.05 to 0.1 Hz. 45 | Page

System frequency - 30 September 2013 6:45 to 7:00 PM 50.2

50.15 frequency 50.1 bandwidth with no load or 50.05 contingency event 50 hertz NEM sth 49.95 nominal frequency NEM nth 49.9

49.85

49.8

Figure 37 - System frequency 30 September 2013; 6.45 to 7 PM 6.8 Summary

In the three storm events studied, there were two large variations that may have triggered an increase in the regulation FCAS in order to compensate for the changes in wind power. By examining the accumulated time error for the actual occurrences, it has been shown that neither of the two large increases in wind – even one with a falling load scenario – triggered additional requirements for regulation FCAS. This means that the system coped admirably with even the largest wind power swings over the two year study period. The two increases in wind saw an increase in time error of 0.29 and 0.39 seconds. The smaller of the two increases caused a larger time error as it was coupled with a decreasing load scenario. The wind power variations only constituted 10% of the 3 second bandwidth (-1.5 to +1.5 second) for the time error. Of note is that the largest variations were both caused by wind turbines returning to full output after a cutout due to high wind speed. The ramp rates seen during these times were not as fast as they could have been based on the maximum ramp rates registered for each of the wind farms. This is probably because whilst the wind turbines are capable of achieving rapid ramping, they have not been configured to do so. When the semi-scheduled category for wind farms was made law in 2008, this category was also included in AEMOs causer pays procedure (AEMO 2013d) which allocates the cost of providing regulation services to each participant – based on the performance of the generating units in its fleet and how much of a disturbance to the system they were. Wind farms form part of this process and are now allocated a proportion of the regulation FCAS costs each month, including semi-scheduled and non-scheduled wind farms.

46 | Page

Whilst the wind farm ramp rates haven’t caused any issues so far, it may become so in the future if more wind farms join the NEM wind zones in the Mid-North of South Australia and Western Victoria and reduce the effect of geographical smoothing. Since this study time-frame two more wind farms have been commissioned in the Mid-North region (Snowtown 2 and 3). The variations caused by the wind fluctuations themselves during the storms did not cause any significant changes to wind power output. Nor did they cause any issues with the power system security throughout the storms.

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7 Wind power variation impacts on other generators

7.1 Introduction

The previous section studied whether or not the large wind power variations caused any additional procurement13 of regulation FCAS, and showed that this was not triggered for the storms studied. The wind farms’ output however does use the regulation FCAS generators to offset its normal variations. In this section the actual generators compensating for wind variations will be examined – in particular the greenhouse gas (GHG) emissions of short term wind power increases. The electricity system itself is set up to compensate for the constant changes in load demand (IEA and Organisation for Economic Co-operation and Development 2014). Wind power variability has added a new dimension to this. Wind effects are often studied in isolation but overall they are mixed in with the load changes, with the regulation generators unable to distinguish what they are compensating for. In this section it is assumed that wind power is the entire cause of the variation, and thus adopting a conservative assumption or a worst-case scenario. The results will therefore also hold if they were for just the wind variation alone. The load trend in each of the cases, however, is detailed and its impact during the periods studied is shown. This section takes the three one-hour storm periods analyzed in Section 1, and uses the 4- second data to track the movement in the regulation generators as a response to the variations in the wind power and load (net). Emissions curves are developed for each of the regulation generators and applied against the 4-second generator data. This produces actual GHG emissions depending on the operating point of the generators, and shows what occurs to carbon emissions in the short term, as wind power in the NEM ramps up. 7.2 Data sources

7.2.1 Generator production data

AEMO publishes data on all scheduled and semi-scheduled market generators down to 4- second intervals. This is called the Ancillary Services Market Causer Pays data, which is used in the allocation of FCAS charges across the market. The comprehensive file requires two index files, and is stored in five minute blocks on the AEMO website (AEMO 2014b). For each generator it stores several variables including the instantaneous output, the dispatch level14, and the amount the generator is contributing to regulation services. It also stores the frequency and electrical time error on specific nodes across the NEM. The causer pays files were retrieved from AEMO for one hour of data for each of the three storms analyzed in section 5 (12 files of 5 minutes each). The data was manipulated using Excel pivot tables to form a single one hour table of the 4-second data for each of the

13 Additional meaning over and above the 250 MW standard procurement range 14 The dispatch level is defined as” the estimate of the active power at the end of the dispatch interval specified in a dispatch instruction” (AEMC 2014, p1132) 49 | Page

generators, and a separate table for the generators providing regulation FCAS during the same period. 7.2.2 Generator data

Each year AEMO publishes the planning assumptions it uses in its National Transmission Network Development Plan. The generator data used in this study was released by AEMO on 23 May 2014, and is located on the AEMO website (AEMO 2014c). These assumptions are used by AEMO in publishing their next development plan.

7.2.2.1 Generator emissions data

The emissions intensity data for each generator is the average for the previous financial year’s production. This is updated annually with the most recent review done by ACIL Alen on behalf of AEMO (Acil Allen Consulting 2014). The report, released in May 2014, therefore was for the financial year 2012/2013, which is concurrent with the study period. 15 The value chosen is the Scope 1 and Scope 3 CO2-e emissions intensity for every MWh sent out for each of the power stations, and takes into account the auxiliary load of each of the stations.

7.2.2.2 Generator efficiency data

The generator efficiency for all generators in the NEM is published in the spreadsheet entitled “Existing Generator technical data”, also located at the above web address. The values are compiled from a number of sources; however, the efficiency values of the generators have remained relatively static over the past 5 years, based on a review of other publications of the same figures.

7.2.2.3 Generator gross output factor

The generator gross output factor is the capacity factor of a generating unit while it is in service and generating (IEEE 2007). In other words, it removes the times when the unit is out of service. The normal capacity factor is the energy produced for the year divided by the maximum energy the generator could produce in a year. This however does not include outages, and isn’t an appropriate figure for estimating what “average” production for each power station is. To estimate the gross output factor for 2012-13, the half-hour initial MW for each unit for the financial year was downloaded from the AEMO database summed and divided by 2 (Equation i)16. The intervals where it was producing power greater than 5 MW17 in any

15 Scope 1 emissions are those from combustion of fuel on-site, Scope 3 emissions are those associated with the “extraction, production, and transport of fuel to the power station”. (Acil Allen Consulting 2014, 4) 16 Note that this figure will be different to the result of the revenue meters, as it is sampled only every half hour and not averaged over the half hour trading interval. 17 5 MW was chosen as a clear indicator that the unit was online and generating. Most generators have a minimum generating level higher than this value. A sensitivity check was done on lowering this to 2 MW without any significant changes to the results. 50 | Page

half hour were counted and divided by 2 to give the number of service hours for the year. The gross output factor for each station was then calculated by equations 5, 6 and 7.

Output per unit Opu (MWh) = ∑ (Equation 5)

Where n is the number of trading intervals in a period

Gross output factor GOFpu = (Equation 6)

Where SH is the in service hours for the period and Pmax is the registered maximum capacity (AEMO 2014a)

∑ Gross output factor GOFstation = (Equation 7)

where U is the number of generating units at that power station

7.3 Emission curves for FCAS generators

An emissions curve for a generator is essentially a heat-rate curve, where instead of showing the energy consumed over the range of operating levels (MJ/MWh) it has kgCO2- e/MWh instead. The quantities of emissions are a direct function of the amount of fuel the generator consumes, which in turn varies with efficiency. Thus by developing an emissions curve the amount of carbon emissions can be calculated for each operating level of a generating unit and not merely be based on average. Specific heat-rate curves are not published, so in this case a generic heat-rate curve has been used and modified for each of the selected generators. The generic heat-rate curve is for fossil-fueled fired18, sub-critical boiler technology with an efficiency of 38%, To compile the emissions curve for a fossil-fueled generator the following was used:  Generator efficiency (%),  Average emissions (tCO2-e/MWh),  Gross output factor (%), and  Generic heat-rate curve for boiler technology (Figure 38).

18 Either black or brown coal, or natural gas 51 | Page

Generic heat-rate curve 10300 10200 10100 10000 9900 9800

MJ/MWh 9700 9600 9500 9400 9300 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 38 - Generic heat-rate curve for an ideal 38% efficient fossil fuel generator The generic heat-rate curve has a higher efficiency than all the generators in the study. Each of the generators is directly offset down from this curve with its individual efficiency rates (Section 7.3.4), which may bias the overall emissions results to being lower than actual (than say taking a lower efficiency generator curve and stepping it up which may bias the generators in producing more emissions than actual). 7.3.1 AEMO Planning assumptions

From the AEMO planning data and production database, and the calculations in 7.2.2.3, Table 8 was compiled for selected fossil-fuel regulation generators. Each of these generators provided FCAS regulation services at some point during the three study periods. Table 8 – Regulation generator data

Gross Carbon Installed 2012-13 output Estimated intensity Station MW Fuel type MWh factor efficiency kgCO2/MWh Bayswater 2640 Black coal 16,730,439 78% 35.9 1013 Callide B 700 Black coal 3,875,060 77% 34.1 1019 Eraring 2880 Black coal 11,480,057 58% 35.4 1011 Gladstone 1680 Black coal 6,205,753 58% 31.7 1052 Liddell 2000 Black coal 6,608,504 69% 33.8 1066 Loy Yang A 2210 Brown coal 16,281,812 92% 27.2 1280 Stanwell 1460 Black coal 8,221,732 68% 36.4 969 Tarong 1400 Black coal 5,396,235 68% 36.2 958 Torrens Island B 800 Natural gas 1,695,730 41% 30.0 712 Vales Pt 1320 Black coal 7,525,418 71% 34.1 1018

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7.3.2 Average emissions – determining what is average

The published figures for each of the stations in Table 8 were all averaged over the 2012-13 financial year. The gross output factor was calculated to help determine what the average operating level of each of the units was during the year, so that an operating level could be assigned to the average efficiency and average emissions. Whilst the gross output factor is as close to the real average as possible, it still does not take into account the ramp up and ramp down times associated with starts and stops of the individual units. For the purposes of this exercise the gross output factor rounded to the nearest 5%19 has been deemed to be the average operating level. 7.3.3 Other assumptions

For the purposes of this study, the following assumptions have also been made:  A gas-fired subcritical boiler acts in the same manner as coal-fired subcritical boiler with respect to efficiency;  Hydro-powered generators do not produce any direct carbon emissions (Acil Allen Consulting 2014); and  Tasmanian regulation generators were excluded as Tasmania regulates its own frequency separate to the mainland (AEMC Reliability Panel 2009).

The relationship between efficiency and heat rate is as per Equation 8.

Heat-rate HR = MJ/MWh (Equation 8)

Where Ef is the estimated efficiency of the generating unit from Table 8

7.3.4 Curve fitting to emission output

Given the estimated efficiency of each of the generators, the average heat-rate was calculated as per equation 8. Knowing the average emissions at this efficiency, fuel carbon intensity was calculated as per equation 9 which now gives kg CO2-e/MJ for each generator.

Fuel carbon intensity Fci = kgCO2/MJ (Equation 9)

Where Ci is the carbon intensity of the generating unit from Table 8

Therefore for each generator we know a point on its heat rate curve (average heat rate at gross output factor) and its corresponding average emissions. For example Bayswater has an average efficiency of 35.9% which equates to an average heat-rate of 10,028 MJ/MWh (using Equation 8) at an operating level of 80% (gross output factor rounded up

19 Nearest 5% so that it could be offset against the generic heat rate curve which was in 5% increments. 53 | Page

from 78%). At this operating level it emits 1013 kgCO2-e/MWh, which is a fuel carbon intensity of 0.1010 kgCO2/MJ (Equation 9). The generic heat-rate curve has an ideal efficiency of 38.1% at 80% operating level, therefore it has a lower heat-rate value (9449 MJ/MWh) than Bayswater. The curve is directly offset by 2.2% for the efficiency differences as shown in Figure 39. This assumes that the efficiency difference is static across the operating regime of the generator.

Adjusting for efficiency 11200 11000 Bayswater Efficiency: 35.9 10800 Heat-rate: 10028 10600 Operating: 80% 10400 10200 10028 Generic

MJ/MWh 10000 Bayswater 9800 curve adjusted up 9600 for lower efficiency 9400 9200 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 39 - Adjusting generic curve for efficiency difference

This results in an estimated heat-rate curve for Bayswater. To convert this to a carbon emission curve we multiply the heat-rate curve by the fuel carbon intensity value. This now gives us a curve that shows carbon emissions across the operating profile of the generating unit, and shows carbon emissions going up as operating level goes down. Once the emissions curve was constructed, the curve-fit function in Excel was used to develop the equation for the curve. The 6th order polynomial was the best fit for this curve structure, with the formula for Bayswater shown below the data curve and the polynomial trend line in Figure 40 and Equation 10. 6 5 4 3 2 EBWPS = y = -624.32x + 2158.5x - 2141.6x - 480.1x + 2246.2x - 1496.1x

+ 1351.7 kgCO2-e/MWh (Equation 10) where x is the operating level of the generator in %Pmax

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Bayswater emissions curve 1130 1110 1090 1070

1050

e/MWh - 1030 Data

1010 Poly. (Data) kg CO2 kg 990 y = -624.32x6 + 2158.5x5 - 2141.6x4 - 480.1x3 + 2246.2x2 - 970 1496.1x + 1351.7 950 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 40 - Bayswater emissions curve

The same process was used to develop emissions curves and corresponding formulas for each of the regulation generators shown in Table 8. Diagrams of each of these curves are shown in Appendix C. Calculations and graphs for all generating units are in the file Emission Curves Calculations 2013.xls on the attached disc. 7.3.5 Resulting emissions curves

Figure 41 shows each of the emissions curves for all the generating units on one graph. Whilst the emissions curves are the same shape, the emissions still vary across the operating range of the stations. Figure 41 shows the largest emitter being Loy Yang A (brown coal), the lowest emitter being Torrens Island B (natural gas), with the remainder of the black-coal generators in a cluster in the middle. This is the expected result based on the fuel type of each of the generators.

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Emissions curves - all stations 1500

1400

1300 Bayswater

1200 Callide B Eraring 1100

Gladstone

e/MWh - Liddell 1000

Loy Yang A kg CO2 kg 900 Stanwell Tarong 800 Torrens Island

700 Vales Point

600 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 41 - Emissions curves - all stations 7.3.6 Applying emissions curves

The emissions curves need to be applied to the 4-second data in order to obtain actual carbon emissions. This requires the operating level to be inserted in the emissions equation 10. This can be obtained from the instantaneous output of the generators in the 4-second file (Og) and the maximum output of the generator (Pmax) as per equation 11. (AEMO 2014a)

Operating level x = (Equation 11)

The output from the emissions curve gives us how many kilograms of CO2-e would be produced if the generating unit was at this level for an hour. To convert this to total GHG emissions during the 4-second interval requires equation 12.

GHG Emissions E = (Equation 12)

where y is the emissions curve estimate from Equation 10 in kgCO2-e/MWh The carbon emissions from equation 12 of all the regulation generators is then summed to provide a 4-second amount of total GHG emissions being produced from these generators. Noting that these generators are providing base load as well as regulation, it is the changes in this amount of carbon due to their compensation measures and not the actual amount that needs to be considered.

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For completeness the carbon intensity (kgCO2-e/MWh) of all the FCAS generators combined (Ci) was also calculated as per Equation 13 for each 4-second interval.

∑ ⁄ (Equation 13) ∑ For each generator g in the set of relevant FCAS regulation generators, G 7.4 Data analysis

A series of curves was plotted from the calculations from 7.3.6 for each of the storms. The first curve is the intensity of the carbon emissions from the FCAS generators plotted with the output of the FCAS generators in MW. The second curve is the actual carbon emissions with the wind power output. As not all wind farms were included in the AEMO 4-second data, this plot is provided as a guide to the wind variations already shown in Section 5. It does, however, still give the trend of the wind in that it is either increasing or decreasing. The two sets of curves were compiled for each of the storms to see what happens to the carbon emissions of the FCAS generators as they compensate for the changing wind. 7.4.1 Storm 1 – 14 March 2012

The first graph for Storm 1 is shown here in Figure 42. The generators providing regulation FCAS during this hour were Bayswater, Liddell and Tumut 1&2 from New South Wales; Callide B, Gladstone, Stanwell and Tarong from Queensland; and Torrens Island B in South Australia. All of these generators are black coal-fired except for Tumut 1&2 (hydro) and Torrens Island B (gas). The regulation from the hydro station (zero carbon output) was minimal and was between 6.30 PM and 6.35 PM. Figure 42 shows that generally as the FCAS amounts increase, the carbon intensity decreases; and similarly when the FCAS generation decreases the carbon intensity goes up. This is consistent with the fossil-fuel generators becoming less efficient when ramping back and shows that the emissions curves are correctly trending. The 220 MW increase in wind power output was between 6.20 and 6.25 PM. Figure 42 shows the reduction in the regulation FCAS generators (159 MW) at this time, and the corresponding increase in carbon intensity from 989 to 990.2 kgCO2-e/MWh (as the generators become less efficient). The system load during this period fluctuated by ±50 MW, however only increased by 1 MW between the two time periods. The changes in total emissions of the regulation generators (at the same time) are shown in Figure 43. Here between 6.20 and 6.25 PM the 4-second emissions reduced from 7320 to 7162 kg. Integrating this curve as the emissions reduce equates to 5.9 tonnes less emissions in this 5-minute period alone due to the sudden increase in wind.

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Changes in emission levels of FCAS gens 992.0 6900 6683 991.0 6800 6.20pm 990.0 6700 989.0 6.25pm

988.0 6600 CO2-e/MWh

e /MWh

- MW 987.0 6500 FCAS Output

kg CO2 kg 6523.9 986.0 6400 985.0 6300 984.0

983.0 6200

Figure 42 – 14 March 2012 6 to 7 PM FCAS generators emissions and output

Changes in emission levels of FCAS gens vs Wind generation 7600 1200 7320 7162 7500 1000 6.20pm 7400 6.25pm 800

7300

e - 7200 600

kg CO2-e kg CO2 CO2 kg 7100 400 Wind MW 7000 200 6900

6800 0

Figure 43 - 14 March 2012 6PM to 7PM actual emissions and wind power output

7.4.2 Storm 2 – 23 August 2012

The largest increase in wind during the storm on 23 August was across two consecutive dispatch intervals between 2.45 and 2.55 PM. Figure 44 shows the changes in regulation and the changes in emissions for the hour between 2 and 3 PM. The regulation FCAS

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during this hour was provided by Loy Yang A in Victoria; Bayswater, Liddell, Eraring, and Vales Point in New South Wales; Callide B, Gladstone, Stanwell, and Tarong in Queensland; and Torrens Island B in South Australia. The generation mix this time involved mostly black coal-fired generators, as well as brown coal-fired Loy Yang A, and gas-fired Torrens Island B.

Changes in emission levels of FCAS gens 940.0 7400

938.0 7300 936.0 7087 2.45pm 7200 934.0 7100

932.0 e /MWh

- 930.0 7000 kgCO2- 928.0

6900 e/MWh kg CO2 kg 2.55pm MW FCAS 926.0 gens 6800 924.0 6915 6700 922.0

920.0 6600

Figure 44 - 23 August 2012 2 PM to 3 PM FCAS generators emissions and output

Between 2.45 and 2.55 PM the wind power increased by 213 MW across the NEM; and the regulation generators reduced their output by 172 MW. During this time period the system load had short term fluctuations of ±67 MW on an average of 23,924 MW, and overall decreased by 18 MW. The emission intensity went up slightly from 938.1 to 938.7 kgCO2- e/MWh. Looking at the estimated GHG emissions for this time period (Figure 45) they decrease from 7387 to 7212 kgCO2-e/MWh over the 10 minute period. This equates to an 8 tonne reduction in emissions when integrating the difference from before and after the wind surge.

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Changes in emission levels of FCAS gens 7700 1600

7600 1400

7500 1200 7387

7400 1000

e - 7300 800

kg CO2 kg kg CO2-e 7200 600 Wind MW 7212 7100 400

7000 200

6900 0

Figure 45 - 23 August 2012 2PM to 3PM, regulation generators emissions and wind farm output

7.4.3 Storm 3 – 30 September 2013

The third storm studied had a wind power increase of 161 MW between 6.45 and 6.50 PM on 30 September 2013. There was also a coincident load reduction of 43 MW which increased the required compensation of the regulation generators in the same direction as the wind power increased – making it 204 MW in the 5-minute period. The analysis however still assumes all changes are due to wind fluctuations. The generators providing regulation services in this instance were Loy Yang A from Victoria; Bayswater, Liddell, Eraring, Vales Point and Dartmouth in New South Wales; and Callide B, Gladstone, Stanwell and Tarong in Queensland. Once again predominantly black coal-fired generators except for Dartmouth (hydro) and Loy Yang A (brown coal- fired). Notably no generators in South Australia were providing regulation services during this time. The emissions intensity and output of the regulation generators from 6 PM to 7 PM on this day are shown in Figure 46, and the total NEM generation (load) in Figure 48. The sharp decrease in emissions seen at 6:02 PM in Figure 46 is Dartmouth starting up and ramping to 150 MW before providing regulation services, and reduced emissions intensity by 20 kgCO2-e/MWh. The level of emissions intensity is this case is higher than the other two storms – most likely due to the brown coal-fired Loy Yang A using 3 generating units to provide the regulation services where in Storm 2 it provided one unit. Between 6.45 and 6.50 PM the regulation generators decreased their output by 197 MW, which saw a slight reduction in emissions intensity from 1040 to 1039.7 kgCO2-e/MWh.

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This is shown more clearly in Figure 47 which also displays the variation of the intensity across the 5-minute period. The sharp rise in emissions intensity at 6.56 PM is due to Dartmouth reducing its output by 20 MW over a minute.

Changes in emission levels of FCAS gens 1065.0 8900

8711 8800 1060.0 8700 1055.0 8514 8600 1050.0 8500

1045.0 8400 kgCO2-e/MWh 8300

kg CO2 CO2 kg /MWh 1040.0 MW 8200 1035.0 8100 1030.0 8000

1025.0 7900

Figure 46 - 30 September 2013 6 PM to 7 PM regulation generators output and emissions

Changes in emission levels of FCAS gens 1041.5 8900

1041.0 8800 1039.7 1040.5 8700

1040.0 8600

1039.5 8500 1040 1039.0 8400 kgCO2-e/MWh 1038.5 8300 kg CO2 CO2 kg /MWh MW 1038.0 8200

1037.5 8100

1037.0 8000

1036.5 7900

Figure 47 - 30 September 2013 5.45 to 6.00 PM regulation emissions and output Figure 48 shows the total generation or load over the hour, with the trend line added in black. The first half hour sees the load increase by 600 MW, settle then start to decline

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from 6:40 PM onwards. Figure 46 shows an increase in the regulation generators over this time which also includes any dispatch changes as well as frequency changes. This is typical of the dispatch engine (NEMDE) making the most economic selection of generation based on the bid stack.

NEM Generation 30 September 2013 - 1800 to 1900 25400 25237 25194 25200

25000

24800 MW

24600

24400

24200

Figure 48 - NEM Generation 30 September 2013 6PM to 7PM The change in overall emissions during the period of increased wind – as shown in Figure 49 – again shows a reduction. This time it is 231 kg reduction in the 4-second emissions over the 5-minute period. When integrated, this equates to 8.6 tonnes less emissions in 5 minutes. Keeping in mind this is from the reduced load as well as from the increased wind power, the wind component proportion would be 6.8 tonnes.

Changes in total emissions of FCAS gens 10400 1600

10066 10200 1400

1200 10000

9835 1000 e

- 9800 800

kg CO2 kg 9600 kg CO2-e 600 Wind MW 9400 400

9200 200

9000 0

Figure 49 - 30 September 2013 6 PM to 7 PM total emissions and wind generation

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7.5 Summary

In the three incidents of large increases in wind power studied, it was observed that the overall carbon emissions of the regulation generators decreased with the increase in wind power contribution. Each occurrence had a different mix of generators providing the regulation service; however, in all cases the zero carbon wind power injected into the power system more than offset any reduced efficiency of the fossil-fueled generators providing compensation for it. In two out of the three cases there was increased carbon intensity due to the regulation generators backing off which is expected, but overall the GHG emissions reduced with all increases in wind power. Further conclusions can be drawn:  Inclusion of a hydro-generator in the regulation generators has a large impact in reducing the emissions intensity; and  Inclusion of a brown coal-fired generator substantially increases the overall emissions intensity. Whilst it would be impossible to study every incidence of wind power fluctuation to prove that increasing wind power does not cause increased carbon emissions, these cases examined here represent a worst-case scenario in that some of the largest variations were chosen. Smaller variations would see the regulation generators reduce output only marginally, slightly increase their emissions intensity, but again be offset by the zero carbon wind taking its place. These results are consistent with a similar study in Spain using actual data and efficiency rate based emissions (Gutiérrez-Martín, Da Silva-Álvarez, and Montoro-Pintado 2013). The findings agree that even with cycling of fossil-fuel generators to balance the variability of wind power, there are still reductions in CO2 emissions with large penetrations of wind power. A simulation based study for Ireland using a life cycle assessment (LCA) concluded that whilst there was an increase in cycling-based emissions of fossil-fuel plants, these were minor and did not negate the carbon benefit of integrating large amounts of wind power (Turconi et al. 2014). This report also found that the biggest impact on the carbon output overall is the choice of fuel mix in the generation portfolio. This report has found that including a brown coal-fired generator in the FCAS generators has a significantly larger impact on overall emissions than the cycling effect caused by variations in wind power. This is also consistent with the findings in the Irish study (ibid).

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8 Conclusions and recommendations

The following conclusions have been drawn from this research: 1. Overall the GHG emission intensity of electricity production in the Australian NEM is declining. From 2010 it has reduced from 0.87 to 0.78 tCO2-e/MWh. Coincident with this was the wind energy contribution almost doubling over the same time period. Whilst there are other related factors (including the short lived carbon tax policy) that may have had an influence on this it can be reasonably assumed that wind energy is reducing the overall carbon footprint of Australian electricity. 2. The wind power variations in the NEM are less than ±1% of installed capacity between 5-minute dispatch intervals for 82% of the time. Even the largest variation in the 2-year study period (10.43% or 220 MW) did not cause the accumulated time error to exceed its ±1.5 second bandwidth and require additional procurement of regulation services. 3. Regulation services have not increased in quantity or cost with the addition of more wind power to the system. 4. The largest wind power variations seen were during certified wind storms (other than negative pool price events). They were not, however, caused by large changes in wind speed but wind turbines switching back on and returning to full load after a cut-out due to high wind speed. Often within a wind farm zone similar turbines are returning to service at the same time. 5. Wind turbines can ramp at a very fast rate; however it appears that they are mostly not configured to do so (e.g. Macarthur wind farm maximum ramp rate is 840 MW/min but the largest variation seen here was 31 MW/min). 6. There is a net carbon benefit in the power system from large increases in wind power, even though regulation generators are made marginally less efficient by offsetting the variation. The cycling effect on these generators caused a small increase in the emissions intensity due to wind power, which varied depending on the fuel mix of generators providing the service. In each case studied, however, there was always a net carbon benefit by adding more wind power. 7. The inclusion of a brown coal-fired generator in the regulation services had a much larger impact on emissions intensity than wind variability.

These studies have shown that the inclusion of 2500 MW of wind power in the NEM has not caused issues to frequency control, regulation and balancing of the power system with regard to cost or quantum. The studies have however identified some issues that may become a problem on the power system if wind power penetration increases – especially if more wind farms are installed in the current wind zones in South Australia and Victoria. Recommendations of further work and potential solutions are:

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1. Investigate the reduction of ramp rates post high wind speed cut-outs on wind turbines to reduce the speed of turbines returning to service; 2. Analyze the causer-pays allocation of regulation costs and the impact wind power variability has on it, with regard to a possible economic change in ramping speeds; 3. Investigate the carbon and accumulated time error effect for large negative variations in wind power (such as negative price events or wind speed drop out); 4. Calculate the total cycling effect of wind power variations on regulations emissions over a longer time period; 5. Study the large variations caused by storms whilst including the actual measured wind speeds from the wind farms; and 6. Simulate more wind farms being added into the Mid-North zone of South Australia and the effect it will have to wind power variability.

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9 References

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———. 2014c. “AEMO Planning Assumptions.” http://www.aemo.com.au/Electricity/Planning/Related-Information/Planning- Assumptions. ———. 2014d. “Emission Intenisty Values.” http://www.aemo.com.au/Electricity/Planning/Related-Information/Planning- Assumptions. “AEMO Semi-Dispatch of Significant Intermittent Generation : Proposed Market Arrangements.” 2010. January 12. http://www.aemo.com.au/electricityops/140- 0091.html. Australian Government. 2014. “Area of Australia - States and Territories - Geoscience Australia.” http://www.ga.gov.au/scientific-topics/geographic- information/dimensions/area-of-australia-states-and-territories. BOM. 2014a. “About Latest Weather Observations.” http://www.bom.gov.au/catalogue/observations/about-weather- observations.shtml. ———. 2014b. “Severe Storms Archive.” http://www.bom.gov.au/australia/stormarchive/. Cutler, Nicholas J., Nicholas D. Boerema, Iain F. MacGill, and Hugh R. Outhred. 2011. “High Penetration Wind Generation Impacts on Spot Prices in the Australian National Electricity Market.” Sustainability of Biofuels 39 (10): 5939–49. doi:10.1016/j.enpol.2011.06.053. Denny, E., and M. O’Malley. 2006. “Wind Generation, Power System Operation, and Emissions Reduction.” Power Systems, IEEE Transactions on 21 (1): 341–47. doi:10.1109/TPWRS.2005.857845. De Vos, Kristof, Andreas G. Petoussis, Johan Driesen, and Ronnie Belmans. 2013. “Revision of Reserve Requirements Following Wind Power Integration in Island Power Systems.” Renewable Energy 50 (0): 268–79. doi:10.1016/j.renene.2012.06.048. EcoGeneration. 2011. “Waterloo Wind Farm, a Roaring 40s Success — EcoGeneration — The Magazine for Australia’s Clean Energy Industry.” February. http://ecogeneration.com.au/news/waterloo_wind_farm_a_roaring_40s_success/0 53995/. Forrest, Sam, and Iain MacGill. 2013. “Assessing the Impact of Wind Generation on Wholesale Prices and Generator Dispatch in the Australian National Electricity Market.” Energy Policy 59 (0): 120–32. doi:10.1016/j.enpol.2013.02.026. Gross, Robert, and UKERC (Organization). 2006. The Costs and Impacts of Intermittency: An Assessment of the Evidence on the Costs and Impacts of Intermittent Generation on the British Electricity Network. London: UK Energy Research Centre.

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Gutiérrez-Martín, F., R.A. Da Silva-Álvarez, and P. Montoro-Pintado. 2013. “Effects of Wind Intermittency on Reduction of CO2 Emissions: The Case of the Spanish Power System.” Energy 61 (0): 108–17. doi:10.1016/j.energy.2013.01.057. IEA, and Organisation for Economic Co-operation and Development. 2014. The Power of Transformation Wind, Sun and the Economics of Flexible Power Systems. Paris: IEA. http://proxy.library.carleton.ca/login?url=http://dx.doi.org/10.1787/97892642080 32-en. IEEE. 2007. “IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity.” MacGill, Iain. 2010. “Electricity Market Design for Facilitating the Integration of Wind Energy: Experience and Prospects with the Australian National Electricity Market.” Large-Scale Wind Power in Electricity Markets with Regular Papers 38 (7): 3180–91. doi:10.1016/j.enpol.2009.07.047. Milligan, Michael, Kevin Porter, Edgar DeMeo, Paul Denholm, Hannele Holttinen, Brendan Kirby, Nicholas Miller, et al. 2012. “Preface: Wind Power Myths Debunked.” In Wind Power in Power Systems, 7–20. John Wiley & Sons, Ltd. http://dx.doi.org/10.1002/9781119941842.ch2. Swift, David. 2014. “AEMO Submission RET Review May 2014,” May 16. Turconi, R., C. O’Dwyer, D. Flynn, and T. Astrup. 2014. “Emissions from Cycling of Thermal Power Plants in Electricity Systems with High Penetration of Wind Power: Life Cycle Assessment for Ireland.” Applied Energy 131 (0): 1–8. doi:10.1016/j.apenergy.2014.06.006. Vandezande, Leen, Leonardo Meeus, Marcelo Saguan, and Jean-Michel J-M Glachant. 2010. “Well-Functioning Balancing Markets: A Prerequisite for Wind Power Integration.” Energy Policy 38 (7): 3146–54. WindPower Program. 2014. “Wind Turbine Power Output Variation with Steady Wind Speed.” http://www.wind-power-program.com/turbine_characteristics.htm. Xie, Le, P. M S Carvalho, L. A F M Ferreira, Juhua Liu, B.H. Krogh, N. Popli, and M.D. Ilic. 2011. “Wind Integration in Power Systems: Operational Challenges and Possible Solutions.” Proceedings of the IEEE 99 (1): 214–32. doi:10.1109/JPROC.2010.2070051.

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Appendix A – List of included and excluded wind farms

Table 9 - List of included wind farms

Included wind farms Region Category Classification Fuel Source - Physical Unit Size Aggregation DUID Reg Cap Max Primary Unit No. (MW) (MW) ROC/Min

Canunda Wind Farm SA1 Non-Market Non-Scheduled Wind 1-23 2 N - 46 Capital Wind Farm NSW1 Market Non-Scheduled Wind 1-67 2 N CAPTL_WF 140 Cathedral Rocks Wind Farm SA1 Market Non-Scheduled Wind 1-33 2 N CATHROCK 66 Challicum Hills Wind Farm VIC1 Non-Market Non-Scheduled Wind 1-35 1.5 Y - 52.5 Clements Gap Wind Farm SA1 Market Semi-Scheduled Wind 1-27 2 N CLEMGPWF 57 12 Cullerin Range Wind Farm NSW1 Market Non-Scheduled Wind 1-15 2 N CULLRGWF 30 Gunning Wind Farm NSW1 Market Semi-Scheduled Wind 1-31 2.1 N GUNNING1 47 47 Hallett 1 Wind Farm SA1 Market Semi-Scheduled Wind 1-45 2.1 Y HALLWF1 94.5 30 Hallett 2 Wind Farm SA1 Market Semi-Scheduled Wind 1-34 2.1 Y HALLWF2 71.4 30 Lake Bonney Stage 2 Windfarm SA1 Market Semi-Scheduled Wind 1-53 3 N LKBONNY2 159 32 Lake Bonney Stage 3 Wind Farm SA1 Market Semi-Scheduled Wind 1-13 39 N LKBONNY3 39 8 Lake Bonney Wind Farm SA1 Market Non-Scheduled Wind 1-46 1.75 N LKBONNY1 80.5 Macarthur Wind Farm VIC1 Market Semi-Scheduled Wind 1-140 3 N MACARTH1 420 840 Mortons Lane Wind Farm VIC1 Market Non-Scheduled Wind 1-13 1.5 N MLWF1 20 Mt Millar Wind Farm SA1 Market Non-Scheduled Wind 1-35 2 N MTMILLAR 70 Musselroe Wind Farm TAS1 Market Semi-Scheduled Wind 1-56 3 N MUSSELR1 168 17 North Brown Hill Wind Farm SA1 Market Semi-Scheduled Wind 1-63 2.1 N NBHWF1 132.3 30 Oaklands Hill Wind Farm VIC1 Market Semi-Scheduled Wind 32 2.1 N OAKLAND1 67 30 Portland Wind Farm VIC1 Non-Market Non-Scheduled Wind 1-82 2 Y - 164 Snowtown Wind Farm Units 1 And 47 SA1 Market Semi-Scheduled Wind 1-47 2.1 Y SNOWTWN1 99 20 Starfish Hill Wind Farm SA1 Market Non-Scheduled Wind 1-23 1.5 N STARHLWF 34.5 The Bluff Wind Farm SA1 Market Semi-Scheduled Wind 1-25 2.1 N BLUFF1 52.5 30 Waterloo Wind Farm SA1 Market Semi-Scheduled Wind 1-37 3 N WATERLWF 111 23 Wattle Point Wind Farm SA1 Market Non-Scheduled Wind 1-55 1.65 N WPWF 90.75 Waubra Wind Farm VIC1 Market Non-Scheduled Wind 1-128 1.5 Y WAUBRAWF 192 Woodlawn Wind Farm NSW1 Market Semi-Scheduled Wind 1-23 2.1 N WOODLWN1 48 1 Woolnorth Studland Bay / Bluff Point Wind TAS1 Market Non-Scheduled WSB 1-25 3 Y WOOLNTH1 - Wind Farm Woolnorth Studland Bay / Bluff Point Wind TAS1 Market Non-Scheduled WBP 1-37 1.75 Y WOOLNTH1 140 Wind Farm Yambuk Wind Farm VIC1 Market Non-Scheduled Wind 1-20 1.5 N YAMBUKWF 30

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Table 10 - List of excluded wind farms

Excluded wind farms - commissioned Region Category Classification Fuel Source - Physical Unit Size Aggregation DUID Reg Cap Primary Unit No. (MW) (MW) Codrington Wind Farm VIC1 Market Non-Scheduled Wind 1-14 1.3 N - 18.2 Hepburn Wind Farm VIC1 Market Non-Scheduled Wind 1-2 2.05 N HEPWIND1 4.1 Toora Wind Farm VIC1 Market Non-Scheduled Wind 1-12 1.75 Y TOORAWF 21 Windy Hill Wind Farm QLD1 Market Non-Scheduled Wind 1-20 0.6 Y WHILL1 12 Wonthaggi Wind Farm VIC1 Market Non-Scheduled Wind 1-6 2 Y WONWP 12

Table 11 - List of excluded incomplete wind farms as at December 2013

Excluded wind farms - not yet complete Region Category Classification Fuel Source - Physical Unit Size Aggregation DUID Reg Cap Primary Unit No. (MW) (MW) Mt Mercer Wind Farm VIC1 Market Semi-Scheduled Wind 1-64 2.05 N MERCER01 131 Snowtown South Wind Farm SA1 Market Semi-Scheduled Wind 1-42 3 N SNOWSTH1 126 Snowtown Wind Farm Stage 2 North SA1 Market Semi-Scheduled Wind 1-48 3 N SNOWNTH1 144

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Appendix B – Certified wind storms in Australian NEM 2012 and 2013

Table 12 - List of recorded wind gust for storms studied

Max Gust Max Gust Max Mean speed direction Wind speed Wind ID Date/Time Nearest town State (knots) (degrees) (knots) 6559 14/03/2012 6:20 ROCHESTER VIC 0 0 0 6558 14/03/2012 9:57 POINT WILSON VIC 63 0 46 6586 14/03/2012 15:10 HINDMARSH ISLAND SA 50 0 0 6587 14/03/2012 18:00 SNOWTOWN SA 54 0 0 6588 14/03/2012 18:00 PORT AUGUSTA AERODROME SA 51 0 0 6798 23/08/2012 3:58 AIREYS INLET VIC 50 290 0 6799 23/08/2012 4:30 AVALON VIC 48 330 0 6836 23/08/2012 6:03 MOUNT BOYCE NSW 49 250 24 6835 23/08/2012 7:18 NARRABRI NSW 48 290 33 6924 23/08/2012 12:52 SNOWTOWN SA 50 0 0 6797 23/08/2012 13:33 MOUNT GELLIBRAND VIC 55 320 0 6926 23/08/2012 13:35 MINLATON SA 50 0 0 6923 23/08/2012 14:07 PORT LINCOLN AIRPORT SA 54 0 0 6927 23/08/2012 14:29 OUTER HARBOR SA 49 0 0 6925 23/08/2012 15:00 MOUNT CRAWFORD SA 53 0 0 6928 23/08/2012 15:09 YUNTA SA 49 0 0 7010 30/09/2013 12:17 SNOWTOWN SA 58 350 35 7015 30/09/2013 12:30 MOUNT CRAWFORD SA 51 280 0 7016 30/09/2013 13:09 NURIOOTPA SA 49 310 0 7011 30/09/2013 13:30 ROSEWORTHY SA 50 350 35 7013 30/09/2013 16:25 TARCOOLA SA 50 280 0 7017 30/09/2013 16:51 NOARLUNGA SA 56 360 0 7012 30/09/2013 17:05 PORT AUGUSTA AERODROME SA 52 310 0 7018 30/09/2013 17:21 ADELAIDE AIRPORT SA 51 330 0 7019 30/09/2013 17:29 HINDMARSH ISLAND SA 50 330 0 7014 30/09/2013 18:30 WOOMERA SA 54 270 0 7020 30/09/2013 19:10 LOXTON SA 55 330 0 7021 30/09/2013 19:11 RENMARK SA 57 320 0

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Appendix C – Generator emission curves

Bayswater emissions curve 1130 1110 1090 1070

1050

e/MWh - 1030

1010 kg CO2 kg 990 y = -624.32x6 + 2158.5x5 - 2141.6x4 - 480.1x3 + 2246.2x2 - 1496.1x + 1351.7 970 950 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 50 - Bayswater emissions curve

Callide B emissions curve 1120

1100

1080 e/MWh

- 1060

1040 kg CO2 kg

1020 y = -627.83x6 + 2174.2x5 - 2171.3x4 - 449.93x3 + 2228.6x2 - 1490.5x + 1355.9 1000 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 51 - Callide B emissions curve

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Eraring emission curve 1100

1080

1060 e/MWh

- 1040

1020 kg CO2 kg

1000 y = -618.18x6 + 2133.6x5 - 2102.7x4 - 508.62x3 + 2254.6x2 - 1495.6x + 1342 980 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 52 - Eraring emissions curve

Gladstone emission curve 1160

1140

1120

1100

e/MWh - 1080 kg CO2 kg 1060

1040 y = -642.62x6 + 2214.1x5 - 2166.8x4 - 564.22x3 + 2376.4x2 - 1569.9x + 1398.9 1020 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 53 - Gladstone emissions curve

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Liddell emission curve 1180

1160

1140

1120

e/MWh - 1100 kg CO2 kg 1080

1060 y = -646.4x6 + 2191.5x5 - 2005.5x4 - 892.43x3 + 2689.1x2 - 1718.1x + 1446.3 1040 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 54 - Liddell emissions curve

Loy Yang A emission curve 1490

1440

1390

e/MWh -

1340 kg CO2 kg

1290

y = -626.15x6 + 1791.8x5 - 317.46x4 - 3905x3 + 5406.8x2 - 2969.8x + 1903.1 1240 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 55 - Loy Yang A emissions curve

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Stanwell emissions curve 1070

1050

1030 e/MWh

- 1010

990 kg CO2 kg

970 y = -596.78x6 + 2073.3x5 - 2096.1x4 - 367.55x3 + 2063.1x2 - 1391.1x + 1282.7 950 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 56 - Stanwell emissions curve

Tarong emissions curve 1060

1040

1020 e/MWh

- 1000

980 kg CO2 kg

960 y = -589.61x6 + 2045.2x5 - 2055.2x4 - 392.45x3 + 2065.2x2 - 1386.9x + 1270.2 940 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 57 - Tarong emissions curve

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Torrens Island B emissions curve 780

760

740 e/MWh

- 720

700 kgCO2

680 y = -399.03x6 + 1300.5x5 - 981.76x4 - 1029.6x3 + 2100.6x2 - 1265.7x + 961.51 660 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 58 - Torrens Island B emissions curve

Vales Point emission curve 1120

1100

1080 e/MWh

- 1060

1040 kg CO2 kg

1020

6 5 4 3 2 1000 y = -619.07x + 2105.8x - 1955x - 790.7x + 2516.6x - 1618.1x + 1377 0% 20% 40% 60% 80% 100% 120% Operating level

Figure 59 - Vales Point emissions curve

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