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Future wind power forecast errors, and associated costs in the Swedish power system Fredrik Carlsson, Vattenfall

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Future wind power forecast errors, and associated costs in the Swedish power system Fredrik Carlsson, Vattenfall

  1. 1. Future wind power forecast errors and associated costs in the Swedish power system Presentation at Winterwind 2012 Fredrik Carlsson/Viktoria Neimane 2012.02.07 Confidentiality class: None (C1) 1 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  2. 2. Future wind power forecast errors and associated costs in the Swedish power system • Nordic electricity market • Day ahead forecast errors • Model with different actors 10 and 30 TWh wind power scenarios • Imbalance costs • Are there any possibilities to reduce imbalance costs? • Conclusions 2 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  3. 3. Nordic Electricity market •  Bids to the day-ahead market are provided 12-36 hours in advance 3 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  4. 4. Nordic Electricity market •  Bids to the day-ahead market are provided 12-36 hours in advance •  It is possible to trade on the intra-day market in order to compensate for forecast errors 4 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  5. 5. Nordic Electricity market •  Bids to the day-ahead market are provided 12-36 hours in advance •  It is possible to trade on the intra-day market in order to compensate for forecast errors •  The forecast errors are handled by the TSO during the hour of delivery •  Those who have caused to for forecast errors then have to pay 5 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  6. 6. Future wind power forecast errors and associated costs in the Swedish power system • Nordic electricity market • Day ahead forecast errors • Model with different actors 10 and 30 TWh wind power scenarios • Imbalance costs • Are there any possibilities to reduce imbalance costs? • Conclusions 6 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  7. 7. Example of day-ahead forecast from Lillgrund wind farm 120 Real production Day-ahead (12-36h) forecast 100 80 60 MW 40 20 0 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 02-27 02-28 03-01 03-02 03-03 03-04 03-05 03-06 03-07 03-08 03-09 03-10 03-11 03-12 03-13 03-14 03-15 03-16 03-17 03-18 -20 7 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  8. 8. Example of day-ahead forecast from Lillgrund wind farm 120 Real production Day-ahead (12-36h) forecast 100 80 60 MW 40 20 0 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 02-27 02-28 03-01 03-02 03-03 03-04 03-05 03-06 03-07 03-08 03-09 03-10 03-11 03-12 03-13 03-14 03-15 03-16 03-17 03-18 -20 Weather front arrived later than forecasted 8 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  9. 9. Example of day-ahead forecast from Lillgrund wind farm 120 Real production Day-ahead (12-36h) forecast 100 80 60 MW 40 20 0 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 2009- 02-27 02-28 03-01 03-02 03-03 03-04 03-05 03-06 03-07 03-08 03-09 03-10 03-11 03-12 03-13 03-14 03-15 03-16 03-17 03-18 -20 Wind speed less than forecasted 9 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  10. 10. Analysis of Lillgrund forecast errors (day-ahead, 12-36h) •  Installed capacity 110 MW •  Yearly production: 330 GWh •  Mean power: 37 MW = 33% of installed capacity •  Forecast error: 130 GWh = 41% of yearly production •  Mean deviation: 12 MW = 10% of installed capacity •  Standard deviation: 21 MW = 19% of installed capacity Lillgrund prognosfel Prognosf el per timme 100,0 900 60,0 800 700 600 Timmar [h] 20,0 500 MW 2009-01-07 2009-02-06 2009-03-08 2009-04-07 2009-05-07 2009-06-06 400 -20,0 300 200 -60,0 100 0 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 -100,0 Prognos fe l [MWh/h] 10 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  11. 11. Future wind power forecast errors and associated costs in the Swedish power system • Nordic electricity market • Day ahead forecast errors • Model with different actors 10 and 30 TWh wind power scenarios • Imbalance costs • Are there any possibilities to reduce imbalance costs? • Conclusions 11 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  12. 12. Swedish wind power scenarios with 10 and 30 TWh Scenarios are based on: •  Projects under construction Kapacite t pe r are a •  Projects which have been 4 000 3 500 granted permission 3 000 Installerad effekt [MW] 2 500 2 000 1 500 1 000 500 + = 0 4 3 2 1 Total Are a Kapacitet per area 12000 10000 Installerad effekt [MW] 8000 6000 4000 Under With 2000 construction permission 0 4 3 2 1 Total Area 12 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  13. 13. Eight actors Different combinations of: •  Amount of installed wind power 6 •  Number of power farms 5 •  Geographical location and spread production AnnualÅrlig produktion [TWh] 4 3 2 1 0 1 2 3 4 5 6 7 8 Ak tör Actor 13 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  14. 14. Model of forecast error for 12 – 36h forecasts •  The forecast error, per farm, is assumed to have a standard deviation of 13% of installed capacity (20% in Lillgrund) •  The relative forecast error decreases when an actor owns more parks in the same area. •  The relative forecast error decreases when an actor owns farms in several areas. Correlation data are adopted from Germany 14 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  15. 15. Model of forecast error for 12 – 36h forecasts •  The forecast error, per farm, is assumed to have a standard deviation of 13% of installed capacity (20% in Lillgrund) •  The relative forecast error decreases when an actor owns more parks in the same area. •  The relative forecast error decreases when an actor owns farms in several areas. Correlation data are adopted from Germany 15 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  16. 16. Model of forecast error for 12 – 36h forecasts •  The forecast error, per farm, is assumed to have a standard deviation of 13% of installed capacity (20% in Lillgrund) •  The relative forecast error decreases when an actor owns more parks in the same area. •  The relative forecast error decreases when an actor owns farms in several areas. Correlation data are adopted from Germany 16 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  17. 17. Future wind power forecast errors and associated costs in the Swedish power system • Nordic electricity market • Day ahead forecast errors • Model with different actors 10 and 30 TWh wind power scenarios • Imbalance costs • Are there any possibilities to reduce imbalance costs? • Conclusions 17 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  18. 18. Imbalance costs: scenario with 10 TWh wind power Assumptions: 35,00 10 TWh Dagens priser •  Today s prices are Nya priser 30,00 based on statistical Nya prisområden data 25,00 •  New prices consider 20,00 kr/MWh higher price level due 15,00 to higher demand 10,00 •  New bidding zones assume that cut 4 is 5,00 congested 30% of time 0,00 1 2 3 4 5 6 7 8 Actor Aktör 18 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  19. 19. Imbalance costs: scenario with 10 TWh wind power Assumptions: 35,00 10 TWh Dagens priser •  Today s prices are Nya priser 30,00 based on statistical Nya prisområden data 25,00 •  New prices consider 20,00 kr/MWh higher price level due 15,00 to higher demand 10,00 •  New bidding zones assume that cut 4 is 5,00 congested 30% of time 0,00 1 2 3 4 5 6 7 8 Actor Aktör 19 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  20. 20. Imbalance costs: scenario with 10 TWh wind power Assumptions: 35,00 10 TWh Dagens priser •  Today s prices are Nya priser 30,00 based on statistical Nya prisområden data 25,00 •  New prices consider 20,00 kr/MWh higher price level due 15,00 to higher demand 10,00 •  New bidding zones assume that cut 4 is 5,00 congested 30% of time 0,00 1 2 3 4 5 6 7 8 Actor Aktör 20 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  21. 21. Imbalance costs: scenario with 10 TWh wind power 35,00 10 TWh Assumptions: Dagens priser Nya priser •  Today s prices are 30,00 Nya prisområden based on statistical data 25,00 •  New prices consider higher price level due 20,00 kr/MWh to higher demand 15,00 •  New bidding zones assume that cut 4 is 10,00 congested 30% of time 5,00 0,00 1 2 3 4 5 6 7 8 Actor Aktör 21 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  22. 22. Why does it become more expensive? •  Wind power dominates forecast errors – fewer hours in “correct” direction. 50% error (= independent) changes to 75 % error •  Higher regulating prices due to higher volumes •  Number of hour without activation of regulating power decreases (to 50%). •  Price areas è imbalances in different areas are not added. 50,00 Scenarier Today 5 GW = 10 - 15 TWh 45,00 12 GW = 30 TWh 40,00 35,00 30,00 kr/MWh 25,00 20,00 15,00 10,00 5,00 0,00 1 2 3 4 5 6 7 8 Aktör Actor 22 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  23. 23. Why does it become more expensive? •  Wind power dominates forecast errors – fewer hours in “correct” direction. 50% error (= independent) changes to 75 % error •  Higher regulating prices due to higher volumes •  Number of hour without activation of regulating power decreases (to 50%). •  Price areas è imbalances in different areas are not added. 50,00 Scenarier Today 5 GW = 10 - 15 TWh 45,00 12 GW = 30 TWh 40,00 35,00 30,00 kr/MWh 25,00 20,00 15,00 10,00 5,00 0,00 1 2 3 4 5 6 7 8 Aktör Actor 23 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  24. 24. Why does it become more expensive? •  Wind power dominates forecast errors – fewer hours in “correct” direction. 50% error (= independent) changes to 75 % error •  Higher regulating prices due to higher volumes •  Number of hour without activation of regulating power decreases (to 50%). •  Price areas è imbalances in different areas are not added. 50,00 Scenarier Today 5 GW = 10 - 15 TWh 45,00 12 GW = 30 TWh 40,00 35,00 30,00 kr/MWh 25,00 20,00 15,00 10,00 5,00 0,00 1 2 3 4 5 6 7 8 Aktör Actor 24 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  25. 25. Future wind power forecast errors and associated costs in the Swedish power system • Nordic electricity market • Day ahead forecast errors • Model with different actors 10 and 30 TWh wind power scenarios • Imbalance costs • Are there any possibilities to reduce imbalance costs? • Conclusions 25 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  26. 26. Possible improvement 1: Use intraday market Elbas •  Trading forecast errors on Elbas reduces the volumes with 50% •  Assumption: Forecast error (RMS) is reduced to 6% instead of 13%. 40,00 New Prices New Bidding zones 35,00 New Prices and Elbas 30,00 New Bidding zones and Elbas 25,00 kr/MWh 20,00 15,00 10,00 5,00 0,00 1 2 3 4 5 6 7 8 Actor 26 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  27. 27. Possible improvement 2: Assuming 6h spot market •  Change spot market to trade every 6 hour •  Standard deviation 10% 40 3 - 9 h new prices 35 12 - 36 h new prices 3 - 9 h new biddning zones 30 12 - 36 h new biddings zones 25 kr/MWh 20 15 10 5 0 1 2 3 4 5 6 7 8 Actor Aktör 27 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  28. 28. Future wind power forecast errors and associated costs in the Swedish power system • Nordic electricity market • Day ahead forecast errors • Model with different actors 10 and 30 TWh wind power scenarios • Imbalance costs • Are there any possibilities to reduce imbalance costs? • Conclusions 28 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  29. 29. Conclusions •  Larger volumes of wind power lead to higher forecast errors in the system which results in: -  higher regulating prices -  more hours with regulation -  more hours with regulation correlated to wind power forecast error -  thereby higher costs for balance responsible wind power owners. •  Geographical spread of the wind power leads to lower forecast error which implies lower costs. •  The introduction of price areas leads to lower possibility to counterbalance imbalances between different areas. •  Participation on intra-day market can reduce the imbalance costs with about 20%. 29 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  30. 30. Conclusions •  Larger volumes of wind power lead to higher forecast errors in the system which results in: -  higher regulating prices -  more hours with regulation -  more hours with regulation correlated to wind power forecast error -  thereby higher costs for balance responsible wind power owners. •  Geographical spread of the wind power leads to lower forecast error which implies lower costs. •  The introduction of price areas leads to lower possibility to counterbalance imbalances between different areas. •  Participation on intra-day market can reduce the imbalance costs with about 20%. 30 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  31. 31. Conclusions •  Larger volumes of wind power lead to higher forecast errors in the system which results in: -  higher regulating prices -  more hours with regulation -  more hours with regulation correlated to wind power forecast error -  thereby higher costs for balance responsible wind power owners. •  Geographical spread of the wind power leads to lower forecast error which implies lower costs. •  The introduction of price areas leads to lower possibility to counterbalance imbalances between different areas. •  Participation on intra-day market can reduce the imbalance costs with about 20%. 31 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  32. 32. Conclusions •  Larger volumes of wind power lead to higher forecast errors in the system which results in: -  higher regulating prices -  more hours with regulation -  more hours with regulation correlated to wind power forecast error -  thereby higher costs for balance responsible wind power owners. •  Geographical spread of the wind power leads to lower forecast error which implies lower costs. •  The introduction of price areas leads to lower possibility to counterbalance imbalances between different areas. •  Participation on intra-day market can reduce the imbalance costs with about 20%. 32 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 2012.02.07
  33. 33. Thank you for your attention! 33 | Future wind power forecast errors and associated costs in the Swedish power system | Fredrik Carlsson | 33 2012.02.07

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