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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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