WIND INTEGRATION IN ELECTRICITY GRIDS WORK PACKAGE 3: SIMULATION USING HISTORICAL WIND DATA

Transcription

1 WIND INTEGRATION IN ELECTRICITY GRIDS WORK PACKAGE 3: SIMULATION USING PREPARED BY: Strategy and Economics DATE: 18 January 2012 FINAL Australian Energy Market Operator Ltd ABN NEW SOUTH WALES QUEENSLAND SOUTH AUSTRALIA VICTORIA AUSTRALIAN CAPITAL TERRITORY TASMANIA

2 Executive Summary The analysis in this report is an extension of the work developed in the 2010 NTNDP. The 2010 NTNDP predicted large increases of wind and other renewable generation in the future. To explore potential issues before they occur, a set of work packages were developed to analyse the situation. This work package analyses the simulation of wind generation using historical data. Due to the chaotic nature of weather patterns, peak wind does not necessarily correlate with peak electricity demand. There is no evidence of a correlation between wind and demand on average across states, however individual sites may have a more significant wind and demand correlation. Advantages stem from a correlation between generation and demand, maximising energy output during peak demand while minimising the need for transmission augmentation. Plotting peak wind in South Australia against peak wind in Victoria showed a significant correlation, as with Tasmania and Victoria. Peak wind plots between New South Wales and Queensland showed almost an anti-correlation. As wind penetration increases, some benefit may derive from upgrading certain interconnectors to enable excess generation to be more broadly shared between the regions. Large changes in either demand or generation over a short period need to be managed to maintain system security. As a consequence of considerable local wind development predicted in the 2010 NTNDP, South Australia and Tasmania have the greatest hourly wind generation variations, significantly greater than the variation of demand in those regions. In other regions the predicted levels of wind penetration do not show large changes in wind generation beyond demand changes. The NEM is well-designed for integrating large amounts of wind generation; if hourly changes in wind demand can be accurately predicted, and in the absence of network limitations this variability should not add significantly to the difficulty of power system management on a NEM-wide basis. However, the high levels of penetration predicted for South Australia and Tasmania provide an idea of what is in store for the NEM should high levels of penetration become consistent in all regions. There is an importance for dependable wind forecasting, particularly for the magnitude and timing of rapid changes. This also potentially offers additional value for fast response generation or additional interconnection. Further work in this area should focus on exploring the variability and rates of change analysis with smaller step change intervals (5 minute and 30 minute intervals), an analysis of real wind data to confirm results and extending the analysis to solar generation. WP3 v1.0 18/01/2012 Page 2 of 23

3 Contents Executive Summary Introduction Data and assumptions Wind characteristics Correlation with demand Diversity Diversity between states Diversity between bubbles Wind Contribution to Peak Demand Variability and rates of change Next steps...23 WP3 v1.0 18/01/2012 Page 3 of 23

4 Version Release History VERSION DATE BY CHANGES /01/2012 Paul Ravalli Approved for Publication WP3 v1.0 18/01/2012 Page 4 of 23

5 1 Introduction The 2010 NTNDP presented a range of scenarios projecting significant future renewable generation, mostly wind, growing to between 4 GW to 6 GW by , and up to 10GW by There are potential integration issues for such large quantities of wind entry into the NEM. These issues can arise in a number of areas including: The technical performance of the power systems under various operating conditions with relatively high concentration of wind energy; The efficient operation of the market and utilisation of the wind generation within the overall generation portfolio; and The regulatory and policy settings required to support the expansion and augmentation of the network to efficiently and effectively accommodate high penetrations of wind generation. As part of the 2011 NTNDP, AEMO commenced a review of wind integration issues, providing industry with information about how a significant increase in wind generation can be efficiently accommodated in the NEM while ensuring system security is maintained. The further analysis was broken up into five studies, or work packages: WP1: Review of International Practice and Experience WP2: Review of Grid Codes WP3: Simulation Using Historical Wind Data WP4a: Review of International Technical Studies WP4b: NEM Technical Studies WP5: Network Congestion Studies This report covers WP3: Simulation of Historical Wind Data, and will look at wind correlation with demand, wind contribution to peak demand, diversity between states and between wind generation bubbles, and variability of wind generation. 2 Data and assumptions The statistical wind analysis performed in this work package utilised synthetic hourly wind power profiles generated by the CSIRO Meso-scale atmospheric wind model for 148 locations across the NEM for a period from 1 January 2002 to 1 January Each of these locations for which a wind trace was generated corresponds to either an existing or proposed wind farm site as identified within the 2010 ESOO. Each hourly data point was developed by calculating the wind at a hub height of 60m at one single geographical point for each wind farm site. This was translated to expected electrical power using wind turbine power curves for the specific model turbine for each existing or proposed site. If the proposed turbine was unknown then a suitable standard turbine was selected to obtain an acceptable generation capacity factor. Each wind farm was modelled as possessing only one turbine of the capacity of all of the combined turbines actually present or predicted to be present from the information given in the 2010 ESOO. No allowances were made for factors that may in reality reduce the potential wind energy available such as local, regional or interconnector network constraints, temperature, turbine and other station maintenance, and intra-wind farm diversity. The data used for section 3.4 is a future projection based on historical data. The CSIRO data was extracted for the period 1 July 2009 to 30 June 2010, and scaled to the wind penetration levels as WP3 v1.0 18/01/2012 Page 5 of 23

6 predicted by the 2010 NTNDP for 1 July 2019 to 30 June 2020, under the decentralised world, medium carbon price (DW-M) scenario. It should be noted that while the CSIRO modelling included predicted potential wind farm sites in Queensland, there are currently none, and the 2010 NTNDP predicted no wind farms within Queensland in for the DW-M scenario. This means that the section 3.4 analysis is based on zero wind penetration within Queensland, while the rest of section 3 assumes Queensland has the wind capacity for several wind farms. 3 Wind characteristics This section considers the characteristics of wind generation as they relate to factors identified above that facilitate integration into the power system. 3.1 Correlation with demand At higher levels of penetration of wind generation developers may seek sites with a higher correlation between wind generation and demand. Such correlation is likely to minimise the congestion faced by the wind farms as the generation will be used locally and is less likely to be exported or curtailed. It will also allow developers to maximise the revenue stream by capturing the higher spot prices that tend to occur at times of high demand. Scaled hourly wind data from 1 January 2002 to 1 January 2011 was plotted against the scaled hourly demand data for the same time period. The correlation of wind generation with demand varies significantly between regions. AEMO s analysis shows that there is no correlation between wind and demand across the states. Individual wind farm sites may have a strong correlation, depending on location, however this analysis did not go to that level of detail. Figure 1 through to Figure 5 show the relationship between demand and wind for South Australia, New South Wales, Queensland, Victoria and Tasmania, respectively. Figure 1 South Australia wind forecast demand and wind generation correlation WP3 v1.0 18/01/2012 Page 6 of 23

7 Figure 2 New South Wales wind forecast demand and wind generation correlation Figure 3 Queensland wind forecast demand and wind generation correlation WP3 v1.0 18/01/2012 Page 7 of 23

8 Figure 4 Victoria wind forecast demand and wind generation correlation Figure 5 Tasmania wind forecast demand and wind generation correlation WP3 v1.0 18/01/2012 Page 8 of 23

9 3.2 Diversity The simulated wind data was used to research the diversity between states and between the wind bubbles. For some time there has been discussion about the benefits of diversity, particularly to mitigate variability in wind farm contribution to power system stability. This study was proposed to examine the level of diversity of wind generation in the NEM Diversity between states The scaled hourly data from 1 January 2002 to 1 January 2011 was plotted for each state in the NEM against every other state in the NEM. As can be seen from Figure 6, there is, for example, some correlation between South Australia and Victoria, with a marginally weaker correlation between Tasmania and Victoria (Figure 15) and between South Australia and New South Wales (Figure 7), and a slight anti-correlation between Queensland and New South Wales (Figure 10). This may indicate that as wind penetration increases, there may be some benefits to upgrading some interconnectors and allowing the excess generation to be shared around the NEM. Figure 6 Interstate wind diversity South Australia vs Queensland WP3 v1.0 18/01/2012 Page 9 of 23

10 Figure 7 Interstate wind diversity South Australia vs New South Wales Figure 8 Interstate wind diversity South Australia vs Tasmania WP3 v1.0 18/01/2012 Page 10 of 23

11 Figure 9 Interstate wind diversity South Australia vs Victoria Figure 10 Interstate wind diversity Queensland vs New South Wales WP3 v1.0 18/01/2012 Page 11 of 23

12 Figure 11 Interstate wind diversity Queensland vs Tasmania Figure 12 Interstate wind diversity Queensland vs Victoria WP3 v1.0 18/01/2012 Page 12 of 23

13 Figure 13 Interstate wind diversity New South Wales vs Tasmania Figure 14 Interstate wind diversity New South Wales vs Victoria WP3 v1.0 18/01/2012 Page 13 of 23

14 Figure 15 Interstate wind diversity Tasmania vs Victoria The relative levels of correlation between the states has been summarised in Table 1. Table 1 wind correlation between states SA QLD NSW TAS QLD Uncorrelated NSW Slight Correlation Some Anticorrelation TAS Correlation around low wind Uncorrelated Correlation around low wind VIC Correlated Uncorrelated Slight Correlation Correlated The percentage of correlation between each state was calculated in Table 2, which supports the visual representation of the correlation. Table 2 Percentage level of correlation between states SA QLD NSW TAS QLD 0% NSW 38% -7% TAS 32% -1% 35% VIC 69% 0% 50% 64% Diversity between bubbles AEMO has established a series of wind bubbles on a broad assumption that wind in these bubbles would be broadly the same. Subsequent to that, some assessment was made about the most appropriate connection points for wind within these bubbles. The current wind bubbles are shown in Figure 16. A sample of the inter-bubble diversity results is shown in Figure 17. From these charts it can be seen that the correlation between neighbouring bubbles that share the same wind resource is strong, while those that are geographically separated or are utilising different wind regimes show little or no correlation. WP3 v1.0 18/01/2012 Page 14 of 23

15 AEMO is working with the University of Adelaide to investigate alternative bubble arrangements designed to maximise the correlation between sites within each bubble, while maximising the diversity between bubbles. Figure 16 NEM wind bubbles WP3 v1.0 18/01/2012 Page 15 of 23

16 Table 3 Bubble Locations Bubble Location Description FNQ NQ CQ SWQ NEN HUN WEN MRN MUN SEN NWV SWV SEV Far North Queensland North Queensland Central Queensland South West Queensland New England New South Wales Hunter New South Wales West New South Wales Marulan New South Wales Murray New South Wales South East New South Wales North West Victoria South West Victoria South East Victoria CS Central South 1 FLS MNS YPS EPS WCS NWT NET WCT ST Fleurieu Peninsula South Australia Mid North South Australia Yorke Peninsula South Australia Eyre Peninsula South Australia West Coast South Australia North West Tasmania North East Tasmania West Coast Tasmania South Tasmania 1 Central South is shared between Victoria and Tasmania WP3 v1.0 18/01/2012 Page 16 of 23

17 Figure 17 Wind bubble diversity Nominal percentage of forecast wind generation in each bubble as indicated. CS-FLS; WCS-SEV; CS-MNS; NWV-SWV; FLS-SWV; MRN-NEN; NET-ST; EPS-WCS; NEN-SWQ; FNQ- NEN. WP3 v1.0 18/01/2012 Page 17 of 23

18 Some wind bubbles, for example WCS and EPS, are closely correlated. This strong correlation is largely due to the geographic adjacency of the bubbles. Other bubbles have only a very small number of wind farms within them (two or less), reducing the impact of analysis by bubbles that is, increasing variation within bubbles to avoid any specific characteristics a single wind farm may have. 3.3 Wind Contribution to Peak Demand Contribution of a generation source to meeting peak demand is considered in the assessment of system reliability and the Supply Demand Balance. Assessing the contribution of all generators and particularly intermittent generators during peak times provides some signals to the market about development opportunities. Figure 18 shows the wind probability of distribution at times of peak from the simulated data. The peak demand contribution was assessed by taking a cumulative probability distribution of the forecast wind generation during peak demand (that is, the top 10% levels of demand) for each state. Figure 18 Wind probability distribution for top 10% of peak demand periods WP3 v1.0 18/01/2012 Page 18 of 23

19 The contribution factors represent the minimum level of output available at least 85% of the time during the top 10% of the seasonal demands in a region. Table 4 shows the contribution factors calculated both from historical actual data and the higher wind penetration in the simulated data. These results suggest a higher contribution from wind in the future compared with historical records. This difference may be a result of including more wind farms in each state, increasing the geographical diversity The limitations in the simulated data such as such as network constraints, temperature, turbine maintenance and intra-wind farm diversity may reduces their direct comparability with historical results and are likely to show a higher contribution than will be seen in practice. Although international experience suggests that high levels of wind penetration may reduce overall contributions from wind, the results suggest a higher contribution from wind in the future. This may be due to the inclusion of more wind farms in each region, increasing the geographical diversity. Table 4 Comparison of wind and solar contribution factors State NSW QLD SA TAS VIC Wind summer peak contribution (ESOO 9.2% - 5.0% 1.0% 7.7% historical) based on existing wind farms Wind summer peak contribution (CSIRO Simulated) 15% 13% 14% 6% 13% 3.4 Variability and rates of change Variability is a key characteristic of wind generation, which arises from the variability of the energy source. Using the simulated data, the predicted change in demand and wind from one hour to the next was examined, using the wind penetration levels predicted in the DW-M scenario from the 2010 NTNDP for the year Table 5 shows the predicted installed wind capacity for each state and Table 6 shows the maximum hourly variability of wind, demand and the difference between demand and wind. These are important as they represent the scale of the system management issues that may arise as a result of the wind penetration levels forecast in the 2010 NTNDP. Logically the South Australia and Tasmania have the greatest wind variations, significantly greater than the variation of demand, as a consequence of the considerable local wind development. Table 5 Projected installed wind capacity State NSW QLD VIC SA TAS NEM Wind generation installed capacity ,340 3,404 1,540 8,203 Table 6 Hourly variability State NSW QLD VIC SA TAS NEM Max hourly increase (wind) ,517 Max hourly increase (demand) 1,996 1,137 1, ,472 Max hourly increase (wind and demand) 2 2,025 1,137 1,365 1, ,539 Max hourly decrease (wind) ,709 Max hourly decrease (demand) 1, ,281 Max hourly decrease (wind and demand) 2 1, , ,753 Based on the 2019/20 projections, the NEM-wide hourly variability of wind in combination with demand is not significantly different from the variability of demand alone, as can be seen from Figure 19. Therefore, if hourly changes in wind demand can be accurately predicted, and in the absence of network limitations, this variability should not add significantly to the difficulty of power system management on a NEM-wide basis. 2 This number represents the net aggregate of wind generation output and demand. WP3 v1.0 18/01/2012 Page 19 of 23

20 In individual states where the penetration of wind is forecast to be significant, such as South Australia in Figure 20, the frequency of large hourly change sin wind generation can be significantly greater than that of demand. This places particular focus on the importance of dependable wind forecasting, particularly for the magnitude and timing of rapid change events. This potentially offers additional value for fast response generation or additional interconnection. Figure 21 to Figure 24 show the hourly changes in demand compared to wind in combination with demand for Queensland, Victoria, New South Wales and Tasmania. Figure 19 NEM hourly variability of wind and wind + demand WP3 v1.0 18/01/2012 Page 20 of 23

21 Figure 20 SA hourly variability of wind and wind + demand Figure 21 QLD hourly variability of wind and wind + demand WP3 v1.0 18/01/2012 Page 21 of 23

22 Figure 22 VIC hourly variability of wind and wind + demand Figure 23 NSW hourly variability of wind and wind + demand WP3 v1.0 18/01/2012 Page 22 of 23

23 Figure 24 TAS hourly variability of wind and wind + demand 4 Next steps As mentioned in Section 3.2.2, the configuration of the wind farm bubbles is not optimal. The University of Adelaide has been engaged to analyse the placement of wind farms in the NEM and create a new set of bubbles for further analysis. Moving forward, more analysis should be completed using real wind data to confirm these results. It would also be beneficial to complete further work on the variability and rates of change analysis (as in Section 3.4), focusing step change intervals of 5 and 30 minutes, to inspect the variations at an increased resolution. The assumed contribution of wind and other variable generation sources is important in estimating the future supply demand balance and identifying the opportunities for new generation. Further work could be undertaken to refine the methodology currently used to determine the peak contribution and to further investigate the impacts of the wider geographic spread of wind generation predicted. Similar research into the impacts on the electricity grid of the large scale deployment of other renewable generation such as solar PV and solar thermal would also be beneficial. WP3 v1.0 18/01/2012 Page 23 of 23