The document discusses wind energy integration into power systems. It covers dynamic modeling of wind turbines, aggregated wind power time series analysis for Belgium, and indicators for quantifying the value of wind energy. Key findings include wind power capacity factors of 20-31%, capacity credit reductions of 10-30% of installed capacity, and potential 4% reduction in CO2 emissions from wind power equal to 5% of peak demand. The document concludes wind power brings technical and value assessment challenges but can provide 700+ MW of clean energy for Belgium's power system.

1. Impact of Wind Energy on Power System Operation Joris Soens web-event Leonardo ENERGY 16 February 2006 Katholieke Universiteit Leuven Faculteit Ingenieurswetenschappen Departement Elektrotechniek (ESAT) Afdeling ELECTA

4. Levels of installed wind power in Europe Introduction Dynamic Modelling Aggregated Wind Power Conclusions Installed [MW] end 2003 New [MW] 2004 Installed [MW] end 2004 Germany 14.609 2.037 16.629 Spain 6.203 2.065 8.263 Denmark 3.115 9 3.117 ... Netherlands 910 197 1.078 ... Belgium 68 28 95 (> 160 in 2005) Europe (EU25) 28.568 5.703 34.205

14. Detailed turbine model with doubly fed induction generator v wind u turb q ref p ref i turb Introduction Dynamic Modelling Aggregated Wind Power Conclusions

16. Detailed turbine model: simulation example I (1) simulation input: step-wise increasing wind speed wind speed at hub height 400 600 800 1000 1200 1600 1800 2000 10 20 [m/s] time [s] Introduction Dynamic Modelling Aggregated Wind Power Conclusions

17. Detailed turbine model: simulation example I (2) 400 600 800 1000 1200 1600 1800 2000 time [s] 0,5 1 power [p.u.] variable speed & pitch control fixed speed & pitch control fixed speed & no pitch control turbine power for increasing wind speed Introduction Dynamic Modelling Aggregated Wind Power Conclusions

18. Detailed turbine model: simulation example I (3) 400 600 800 1000 1200 1600 1800 2000 time [s] 0,5 1 speed [p.u.] turbine speed for increasing wind speed variable speed turbine constant speed turbine Introduction Dynamic Modelling Aggregated Wind Power Conclusions

19. Detailed turbine model: simulation example I (4) zoom on turbine speed variable speed: propeller speed variable speed: generator speed fixed speed: propeller speed fixed speed: generator speed 995 1000 1005 1010 1015 1020 1025 0.95 1 1,05 time [s] speed [p.u.] Introduction Dynamic Modelling Aggregated Wind Power Conclusions

20. Detailed turbine model: simulation example II (1) 1000 1001 1002 voltage at turbine generator 0.4 0.6 1 [p.u.] 0.8 0.2 time [s] simulation input: voltage dip at turbine generator Introduction Dynamic Modelling Aggregated Wind Power Conclusions

21. Detailed turbine model: simulation example II (2) 1000 1005 1010 1015 time [s] 0.9 1 1.1 1.2 speed [p.u.] propeller speed generator speed propeller and generator speed during voltage dip, for fixed-speed turbine with induction generator Introduction Dynamic Modelling Aggregated Wind Power Conclusions

22. Detailed turbine model: simulation example II (3) propeller and generator speed during voltage dip, for variable-speed turbine with doubly fed induction generator 1000 1005 1010 1015 time [s] 0.9 1 1.1 1.2 speed [p.u.] propeller speed generator speed Introduction Dynamic Modelling Aggregated Wind Power Conclusions

24. III. Aggregated wind power in the Belgian control area Introduction Dynamic Modelling Aggregated Wind Power Conclusions

25. Wind power in Belgium 95 MW wind power in total installed by end of 2004 (onshore) One offshore wind farm (216 - 300 MW) permitted and near construction phase (start construction soon) Legal supporting framework for offshore wind farms ‘established’ in January 2005 Best wind resources are offshore or in the west part (near shore) Introduction Dynamic Modelling Aggregated Wind Power Conclusions

26. High voltage grid in Belgium Introduction Dynamic Modelling Aggregated Wind Power Conclusions 150 kV 220 kV 400 kV

29. Available wind speed data Wind speed data from meteo-stations Ostend, Brussels, Elsenborn Three-year period (2001 – 2003), hourly resolution Anemometer height: 10 m Complementary to data from European Wind Atlas (turbulence, landscape roughness…) Introduction Dynamic Modelling Aggregated Wind Power Conclusions

30. Available wind speed data Ostend 140 km Brussels 110 km Elsenborn 60 km 140 km prevailing wind direction Introduction Dynamic Modelling Aggregated Wind Power Conclusions

32. Scenario I Evenly distributed Introduction Dynamic Modelling Aggregated Wind Power Conclusions

33. Scenario II Concentrated Introduction Dynamic Modelling Aggregated Wind Power Conclusions

34. Scenario III One offshore farm Introduction Dynamic Modelling Aggregated Wind Power Conclusions

35. Scenario IV Scen. II + Scen. III Introduction Dynamic Modelling Aggregated Wind Power Conclusions

36. Algorithm output: aggregated wind power time series Introduction Dynamic Modelling Aggregated Wind Power Conclusions

38. H vs. H-1 matrices for all scenarios Scenario I Scenario II Scenario III Scenario IV Introduction Dynamic Modelling Aggregated Wind Power Conclusions

42. Capacity credit: calculation H( 0 ) = LOLP = 4 h/year Assumption: probability that Total power demand > (reliable capacity + D MW ) Impact of additional power generator (park), with production probability p( P plant ) Introduction Dynamic Modelling Aggregated Wind Power Conclusions

43. LOLP graphical Introduction Dynamic Modelling Aggregated Wind Power Conclusions 0 500 4 3 2 1 0 D (Demand not served) [MW] [hour/year]  = 30 Q peak = 13.5 GW H(0) = 4 h/year LOLP H (D )

44. Capacity credit graphical 0 500 4 3 2 1 0 D (Demand not served) [MW] H (D ) & H 2 (D) Introduction Dynamic Modelling Aggregated Wind Power Conclusions [hour/year]

45. Absolute capacity credit for wind power in Belgium 1000 2000 3000 4000 0 100 200 300 400 5000 Installed wind power [MW] Capacity credit [MW] Introduction Dynamic Modelling Aggregated Wind Power Conclusions

49. Relative annual abatement of CO 2 -emission Scenario I 5 10 15 20 0 2 4 6 8 Installed wind power [% of peak demand] CO 2 emission abatement [% of reference case] Introduction Dynamic Modelling Aggregated Wind Power Conclusions

50. Relative annual abatement of CO 2 -emission 5 10 15 20 0 2 4 6 8 Installed wind power [% of peak demand] Introduction Dynamic Modelling Aggregated Wind Power Conclusions Scenario III CO 2 emission abatement [% of reference case]