Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Future wind power forecast errors, and associated costs in the Swedish power system Fredrik Carlsson, Vattenfall
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