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

Transcription

1 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: October 2014 Dr Jonathan Whale, Dr Justin Wood i Page

2 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 fossilfueled 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. ii Page

3 Table of Contents 1 Introduction Background Research context Research aim Research questions Wind energy statistics in the NEM Background Data source Energy Carbon emissions Generation displacement Price setters Summary Methodology and data sources Background Data sources Methodology Wind power variability Storm effects on variability Carbon contribution of FCAS generators Frequency regulation a brief guide Frequency control basics The FCAS generators Frequency regulation impacts FCAS Costs FCAS Regulation amounts Time error Summary Wind power variations in the NEM 2012 and Introduction Data source iii Page

4 5.3 Installed capacity Calculations Wind power summary Wind power summary Variations greater than normal regulation Variations Discussion Summary Effect of storms on wind power output Introduction Selected storms Wind turbine operation overview Storm 1 Snowtown 14 March Storm 2 Snowtown 23 August Storm 3 Snowtown & Port Augusta 30 September Time error Data matching March 2012 time error September 2013 time error Summary Wind power variation impacts on other generators Introduction Data sources Generator production data Generator data Emission curves for FCAS generators AEMO Planning assumptions Average emissions determining what is average Other assumptions Curve fitting to emission output Resulting emissions curves Applying emissions curves Data analysis iv Page

5 7.4.1 Storm 1 14 March Storm 2 23 August Storm 3 30 September Summary Conclusions and recommendations References 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 Figure 3 - Carbon emission intensity in the NEM 2012 and Figure 4 - Hydro generation 2012 and Figure 5 - Change in generation mix 2013 compared with Figure 6 - Pool price allocation and wind energy 2012 and Figure 7 - Black coal pool price and energy Figure 8 - Dispatch weighted pool price Figure 9 - Dispatch weighted pool price Figure 10 - Parameters for FCAS regulation generators Figure 11 - Regulation generators raise/lower limits (MW) Figure 12 - Regulation generators raise/lower limits %Pmax Figure 13 - Weekly regulation costs in the NEM Figure 14 - Mainland NEM distribution of time error Figure 15 5-minute wind variation October to December Figure wind power variations duration curve Figure 17 - Variation duration curve v Page

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

7 Figure 48 - NEM Generation 30 September PM to 7PM Figure September PM to 7 PM total emissions and wind generation. 62 Figure 50 - Bayswater emissions curve Figure 51 - Callide B emissions curve Figure 52 - Eraring emissions curve Figure 53 - Gladstone emissions curve Figure 54 - Liddell emissions curve Figure 55 - Loy Yang A emissions curve Figure 56 - Stanwell emissions curve Figure 57 - Tarong emissions curve Figure 58 - Torrens Island B emissions curve Figure 59 - Vales Point emissions curve List of tables Table 1 - NEM price setters by State 2012 and Table 2 - Installed wind capacity by State, December Table wind variations and capacity factors Table wind variation and capacity factors Table 5 Amount by which wind power must change to exceed regulation amounts Table 6 - Number of excursions where wind power exceeds FCAS amounts Table 7 - Details of selected storms in study period (BOM 2014b) Table 8 Regulation generator data Table 9 - List of included wind farms Table 10 - List of excluded wind farms Table 11 - List of excluded incomplete wind farms as at December Table 12 - List of recorded wind gust for storms studied vii Page

8 Glossary AEMC AEMO AWEFS BOM CO 2 FCAS GHG IEA MMS MW MWh NEM NEMDE NEMMCO NER NSW Pmax RET SA tco 2 -e UKERC Australian Energy Market Commission Australian Energy Market Operator Australian wind energy forecasting system Bureau of Meteorology Carbon dioxide emissions Frequency control ancillary services Greenhouse gas International Energy Agency Market management system Mega Watt Mega Watt hour National Electricity Market National electricity market dispatch engine Former name of AEMO National Electricity Rules New South Wales Registered maximum power of a generator (MW) Renewable Energy Target South Australia tonnes of carbon dioxide equivalent emissions United Kingdom Energy Research Centre viii Page

9 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 generators 1 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

10 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 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. 2 Page

11 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) 3 Page

12 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. 4 Page

13 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 tco 2 -e/mwh 2 (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 TWh, of which wind energy contributed 6.4 TWh (3.2%). The following year (2013) electricity generation dropped by 2.7% to TWh and wind energy increased 2 Hazelwood Power Station s emissions are published at 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

14 GWh by 25% to 7.96 TWh. generation. This meant that wind power contributed 4.1% to the total 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 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 2 - Wind energy 2010 to 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: E daily = O daily x E av Where O daily is the energy produced in MWh and E av is the average emissions per MWh in tco 2 -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 tco 2 -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 tco 2 -e/mwh. 6 Page

15 GWh tco2/mwh Emissions per MWh - NEM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3 - Carbon emission intensity in the NEM 2010 to 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 ktco 2 ) 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 tco 2 -e/mwh in the period 2010 to 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 Hydro energy - NEM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 4 - Hydro generation 2010 to Page

16 % change in energy 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 The data has been normalized with the 2.7% reduction in overall electricity production so all changes are relative to this reduction. 0.3 Generation mix change Biomass Black Coal Brown Coal Gas Hydro Wind 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 negative 4 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

17 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 % 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% Energy cost vs energy input 2012 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec % Wind energy % Pool $ 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% Energy cost vs energy input 2013 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec % Wind energy % Pool $ Figure 6 - Pool price allocation and wind energy 2012 and Page

18 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 Here black coal (the most dominant of the fuel sources in Australia) varies depending on market conditions. 58% Energy cost vs energy input % 54% 52% 50% 48% 46% % Black coal energy % Pool $ 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). 10 Page

19 $/MWh $/MWh Dispatch-weighted pool price 2012 Carbon tax introduced Black Coal Brown Coal Gas Wind Hydro 10 0 Figure 8 - Dispatch weighted pool price Dispatch-weighted pool price Black Coal Brown Coal Gas Wind Hydro 0 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 Of course the price paid to each of the generators is the price of the last generator (or part thereof 5 ) 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

20 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 Diesel Gas Hydro Wind Total intervals % 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 gasfired electricity, with black coal-fired electricity remaining largely unaffected. The carbon tax also had an impact on these changes as it was introduced mid 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. 12 Page

21 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 19 th 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 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 Methodology 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

22 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 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 ( 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

23 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 Page

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25 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 error 7 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

26 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 Lower limit Upper limit Unit max MW 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 Page

27 300 Raise/lower limits (MW) Hydro Coal-fired generators Raise Lower Figure 11 - Regulation generators raise/lower limits (MW) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Raise/lower limits (% Pmax) Coal-fired generators Gasfired Gasfired Hydro 14% 4% 13% 26% 4% 8% 8% 6% 7% 12% 95% 38% %Pmax 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. 19 Page

28 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 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 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. 20 Page

29 $k/week $500 $450 $400 $350 $300 $250 $200 $150 $100 $50 $- Regulation weekly costs 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 Page

30 The 5-minute dispatch time-frame and accurate wind forecasting system also contribute to keeping this requirement to a minimum. 4.7 Summary Figure 14 - Mainland NEM distribution of time error (AEMO 2013b, 3 36) 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 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. 22 Page

31 5 Wind power variations in the NEM 2012 and 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 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 State New South Wales South Australia Tasmania Victoria Installed (MW) wind 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 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 23 Page

32 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. Variation n (%) = (Equation 2) Capacity factor n (%) = (Equation 3) Total wind penetration n (%) = (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 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 The largest wind capacity factor was 85.64% during a September afternoon, which was 7.5% of the NEM s total generation at the time 8. Installed capacity Table wind variations and capacity factors Variation - data excursions removed Capacity factor % Wind of total NEM supplied generation 2012 Wind Max Min Max Min Average Max Min Average Jan-March % -9.34% 84.17% 0.85% 26.76% 8.48% 0.06% 2.51% Apr-Jun % -9.67% 80.71% 0.33% 30.53% 8.62% 0.03% 2.83% July-Sept % % 85.64% 0.33% 37.70% 9.62% 0.03% 3.50% Oct-Dec % -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 % 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

33 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 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 wind variation and capacity factors Variation - data Installed capacity excursions removed Capacity factor % Wind of total NEM supplied generation 2013 Wind Max Min Max Min Average Max Min Average Jan-March % -7.11% 83.00% 0.79% 30.58% 10.16% 0.11% 3.46% Apr-Jun % % 84.92% 0.67% 28.59% 10.89% 0.07% 3.34% July-Sept % % 88.21% 1.36% 42.85% 12.31% 0.14% 5.29% Oct-Dec % -6.37% 87.77% 1.29% 35.24% 13.20% 0.16% 4.51% The largest excursions were % on 4 July and % 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. 25 Page

34 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) 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 Table 6 - Number of excursions where wind power exceeds FCAS amounts 2012 Raise intervals Lower intervals Raise minutes Lower minutes Jan-March Apr-Jun July-Sept Oct-Dec Total Raise intervals Lower intervals Raise minutes Lower minutes Jan-March Apr-Jun July-Sept Oct-Dec Total 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 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 26 Page

35 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 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 minute data. 10.0% October - December 2012 Storm passing 8.0% 6.0% 4.0% 2.0% 0.0% -2.0% -4.0% -6.0% -8.0% -10.0% Negative price events -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. 27 Page

36 15.00% minute wind power variation 10.00% 5.00% +/- 1% variation 0.00% -5.00% 97% 94% 90% 87% 84% 81% 77% 74% 71% 68% 65% 61% 58% 55% 52% 48% 45% 42% 39% 35% 32% 29% 26% 23% 19% 16% 13% 10% 6% 3% 0% % % % variation Figure 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. 28 Page

37 0% 3% 5% 8% 11% 14% 16% 19% 22% 24% 27% 30% 32% 35% 38% 41% 43% 46% 49% 51% 54% 57% 59% 62% 65% 68% 70% 73% 76% 78% 81% 84% 86% 89% 92% 95% 97% 15% minute wind power variation 10% 5% +/- 1% variation 0% -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 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%. 29 Page

38 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. 30 Page