J. Dobschinski , E. De Pascalis, A. Wessel, L. von Bremen,

1. THE POTENTIAL OF ADVANCED SHORTEST-TERMFORECASTS AND DYNAMIC PREDICTION INTERVALSFOR REDUCING THE WIND POWER INDUCEDRESERVE REQUIREMENTS J. Dobschinski, E. De Pascalis, A. Wessel, L. von Bremen, B. Lange, K. Rohrig, Y.M. Saint-Drenan * Fraunhofer IWES, Kassel, Germany European Wind Energy Conference 20. – 23. April 2010, Warsaw, Poland Session: Grid Aspects

2. Outline • Introduction - Wind power induced reserve requirements • Calculation of the wind power induced minute reserve requirements • The potential of shortest-term forecasts • Impact of the used time period of empirical forecast data • Summary, Conclusion & Outlook

3. Typical load profile of Germany Introduction – wind power induced reserve requirements … in the past: no wind energy

4. by 5 min by 15 min by 60 min secondary controlling power tertiary minute reserve stand-by reserve / RE (wind) - reserve primary controlling power generator outages load forecast errors load noise net scheduling steps responsibility of the TSO responsibility of the balancing group supervisors (BGS) Introduction – wind power induced reserve requirements … in the past: elements of uncertainty

5. Introduction – wind power induced reserve requirements today: large-scale integration of wind energy

6. Introduction – wind power induced reserve requirements today: “extreme” situation Power [MW] 25. - 27. December 2009

7. Introduction – wind power induced reserve requirements today: forecast tools are essential for the grid-integration of wind energy • BUT: • Limited predictability • 2) Wind power fluctuations Further elements of uncertainty

8. by 5 min by 15 min by 60 min secondary controlling power tertiary minute reserve stand-by reserve / RE (wind) - reserve primary controlling power generator outages load forecast errors load noise net scheduling steps wind power forecast errors wind power fluctuations responsibility of the TSO (TSO = BGS of renewable energy - BG) responsibility of the balancing group supervisors (BGS) Introduction – wind power induced reserve requirements today: elements of uncertainty wind power induced

9. by 5 min by 15 min by 60 min secondary controlling power tertiary minute reserve stand-by reserve / RE (wind) - reserve primary controlling power generator outages load forecast errors load noise net scheduling steps wind power forecast errors wind power fluctuations responsibility of the TSO (TSO = BGS of renewable energy - BG) responsibility of the balancing group supervisors (BGS) Introduction – wind power induced reserve requirements VALUE OF INTEREST = Additional reserve induced by wind power forecast errors that can be balanced in the frame of minute reserve power

10. MRP() MRP() Method for calculating the wind power induced reserve requirementsin the frame of minute reserve power (MRP) Variable parameter 0 Loss of load probability (LOLP) for total Germany = 0.0025% ≙ 13 min/y Final positive and negative MRP requirements Wind power induced reserve requirements

11. Scope of this study • Investigation of the potential of shortest-term forecasts to reduce the wind power induced minute reserve requirements. • Case scenario: • Total Germany with an installed capacity of ~25 GW • Historical forecast data from July 2008 to December 2009 • Selected loss-of-load-probabilities (LOLP) • - LOLP = 0.0025 % ≙ 13 min/y (estimated requirement for total Germany) • - LOLP = 0.1% ≙ 9 h/y (present requirements of the German TSO) • Note: Small LOLPs need many years of forecast data to get significant statistics •  Test run to investigate the impact of the used forecast data time period.

12. Balancing the difference between the day-ahead forecast and the actual value Wind power [MW] 1 2 3 hour Day-ahead forecast Actual value Pos. minute reserve Neg. minute reserve Estimation of the wind power induced reserve requirements Benchmark: Only day-ahead forecasts are in operation Used in many studies Day-ahead forecast errors have to be balanced by MRP

13. Estimation of the wind power induced reserve requirements Only day-ahead forecasts are in operation (25 GW Scenario) Estimated wind power induced minute reserve Power surplus Power deficits Loss-of-load-probability Maximum errors ~ 7 GW power deficit ~ 6.3 GW power surplus ~ ± 2 GW for 0.1% LOLP ~ ± 3 GW for 0.0025% LOLP

14. Estimation of the wind power induced reserve requirements Estimation of the wind power induced reserve requirements Correction of yesterdays day-ahead forecasts with additional shortest-term forecasts and intraday trading Status-quo in Germany Only the smaller shortest-term forecast errors have to be balanced by MRP

15. Estimation of the wind power induced reserve requirements Correction of yesterdays day-ahead forecasts with additional shortest-term forecasts and intraday trading nRMSE: Daya. ~ 5.2 % 3h ~ 3.0 % 2h ~ 2.1 % Last possible intraday-trading  lead time ~ 2-3 h Maximum positive and negative errors of the shortest term forecasts: ~ 4.2 – 4.6 GW (~ 30 % reduction compared to day-ahead)  Clear reduction of the potential remaining forecast errors

16. Loss-of-load-probabilities 0.1 % 0.0025 % Estimation of the wind power induced reserve requirements Correction of yesterdays day-ahead forecasts with additional shortest-term forecasts and intraday trading Estimated wind induced minute reserve power Reduction compared to an only operation of day-ahead forecasts: > 50 % decremental > 70 % incremental

17. BUT • What happens when changing • the time period of the used • forecast errors • ?

18. BUT • What happens when changing • the time period of the used • forecast errors • ? Test run: Including the month January 2007 (extreme wind speeds, storm event ‘Kyrill’)  Largest forecast errors has been observed in January 2007

19. Day-ahead 2h-shortest-term 3h-shortest-term Test run: Including forecast data of January 2007 • Largest errors are observable in January 2007 • Main impact on the day-ahead forecast

20. Excluding January 2007 Including January 2007 Increase of ~ 5-10 % Wind induced minute reserve [GW] incremental | decremental Increase of ~ 5-25 % 2h 3h day-ahead day-ahead 3h 2h Test run: Including forecast data of January 2007 Wind power induced minute reserve power (MRP) Increase of ~ 10-20 % Increase of ~ 40-60 % Clear impact of the used forecast data time period

21. Summary • Method to estimate the wind power induced minute reserve power (MRP) • Comparison of MRP based on day-ahead and shortest-term forecasts • Impact of the used time period of forecast data Conclusion • > 50%-Reduction of MRP using shortest-term forecast with horizons of 2-3h • Time period of forecast data has a clear impact on the estimated MRP  up to >50 % difference with respect to day-ahead forecasts Outlook • Finding global forecast error distributions • Economic comparison of minute reserve and intra day trading

22. Thank you for your attention Jan Dobschinski jdobschinski@iset.uni-kassel.de Energy Meteorology & Wind Power Management R&D Division Energy Economy and Grid Operation Fraunhofer Institute for Wind Energy and Energy System Technology IWES Königstor 59, 34119 Kassel