1. I/En 2006-02 The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects Cornel Ensslin Alexander Badelin Yves-Marie Saint-Drenan ISET Institut für Solare Energieversorgungstechnik Kassel, Germany censslin@iset.uni-kassel.de  „Capacity Credit“ in National Studies  Comparison of Methodologies  Empirical Investigation „Germany 2000“  Conclusions, outlook to future studies

2. I/En 2006-02 Concept and New Developments National Wind Power Integration Studies ILEX 2002: ILEX Energy Consulting & UMIST: “Quantifying the System Costs of Additional Renewables in 2020”, A report of Department of Trade & Industry and Manchester Centre for Electrical Energy, UMIST, October 2002. GH 2003: P. Gardner, H. Snodin, A. Higgins, S. McGoldrick (Garrad Hassan and Partners); The Impacts Of Increased Levels Of Wind Penetration On The Electricity Systems Of Republic Of Ireland And Northern Ireland ; Scotland, February 2003. DTI 2003: The Carbon Trust, DTI: “Renewables Network Impacts Study”, 2003 NOVEM 2003: Jaap `t Hooft, Novem: “Survey of integration of 6000 MW offshore wind power in the Netherlands electricity grid in 2020“, NOVEM, 2003. DENA 2005: Konsortium DEWI / E.ON Netz / EWI / RWE Net / VE Transmission: Energiewirtschaftliche Planung für die Netzintegration von Windenergie in Deutschland an Land und Offshore bis zum Jahr 2020; Berlin 2005 PSE 2003: Gdańsk division of Institute of Power Engineering :“Study of impact of wind energy development on operation of the Polish power system”, 2003

3. I/En 2006-02 Concept and New Developments PhD work on Wind Power Integration Holttinen 2004: "The Impact of Large Scale Wind Power Production on the Nordic Electricity System. Engineering Physics and Mathematics." December 2004 Sontow 2000: Sontow, Jette: "Energiewirtschaftliche Analyse einer großtechnischen Windstromerzeugung." Dissertation an der Fakultät Energietechnik der Universität Stuttgart, Juli 2000 Giebel 2000: G. Giebel, "On the Benefits of Distributed Generation of Wind Energy in Europe", Dissertation Carl von Ossietzky Universität, Oldenburg, 2000. Dany 2001: Dany, Gundolf: "Kraftwerksreserve in elektrischen Verbundsystemen mit hohem Windenergieanteil" Focus on:  Wind Power Capacity Credit  Balance Management

4. I/En 2006-02 Questions arising from comparing different studies …  What are the methodologies applied in integration studies?  Which parameters and input data are used?  How can study results be transferred?  What is the sensitivity to parameter changes?  How to represent country-specific characteristics?

5. I/En 2006-02 Cacacity credit definition applied (here: dena): The amount of conventional power plant capacity that can be replaced with wind power, without decreasing the level of the security of supply for the power system. Referring to the moment of peak demand. Risk level: probability of the power system under investigation not to be able to cover its peak demand without electricity import into the system of 1 %, 9 % respectively. „Capacity Credit” issues in national studies

6. I/En 2006-02 Concept and New Developments „Capacity Credit” issues in national studies: Critical issues Dany 2001 : Dany, Gundolf: "Kraftwerksreserve in elektrischen Verbundsystemen mit hohem Windenergieanteil" DENA 2005: Konsortium DEWI / E.ON Netz / EWI / RWE Net / VE Transmission: Energiewirtschaftliche Planung für die Netzintegration von Windenergie in Deutschland an Land und Offshore bis zum Jahr 2020; Berlin 2005 15%? Capacity credit 8%? Explanation: Dany had assumed 62(!) % capacity factor (‚Winter‘, German Offshore-Windfarms) Dena used evaluation of 10 historic wind years leading to much lower CF values

7. I/En 2006-02 ILEX 2002: ILEX Energy Consulting & UMIST: “Quantifying the System Costs of Additional Renewables in 2020”, A report of Department of Trade & Industry and Manchester Centre for Electrical Energy, UMIST, October 2002. Historic UK wind farm data (1 year) Transfer of results „Capacity Credit” issues in national studies Critical issues

8. I/En 2006-02 Results may not be simply transferred! (here: Capacity Credit, ILEX/UMIST Study) Source: ILEX2002 Depending of „Level of Supply Security“ and Input data: wind data ! „Capacity Credit” issues in national studies

9. I/En 2006-02 Map of statistical and chronological approaches Capacity credit calculation / Comparison of methodologies

10. I/En 2006-02 “Model path” followed by Giebel for assessing a European wind power capacity credit [Giebel 2000] Capacity credit calculation

11. I/En 2006-02 “Model path” for capacity credit calculation applied in the ‘dena study’ Capacity credit calculation

12. I/En 2006-02 Different estimators for wind power in the moment of peak demand Case study ‘Germany 2000’

13. I/En 2006-02 “Model path” for capacity credit calculation applied in the ‘dena study’ Capacity credit calculation

14. I/En 2006-02 Wind power capacity credit in the dena-study Capacity credit calculation / Comparison of methodologies Source: dena-study Probability Power

15. I/En 2006-02 Probabilistic combination of wind / conventional power Capacity credit calculation / Comparison of methodologies

16. I/En 2006-02 Capacity credit calculation / Comparison of methodologies Probabilistic combination of wind / conventional power

17. I/En 2006-02 Capacity credit calculation / Comparison of methodologies Probabilistic combination of wind / conventional power

18. I/En 2006-02 Capacity credit calculation / Comparison of methodologies Effect of bias in wind power time series

19. I/En 2006-02 Effect of bias in wind power time series Capacity credit calculation / Comparison of methodologies Wind power probability density

20. I/En 2006-02 Capacity credit calculation / Comparison of methodologies Effect of bias in wind power time series

21. I/En 2006-02 Capacity credit calculation / Comparison of methodologies Effect of bias in wind power time series

22. I/En 2006-02 Capacity credit calculation Model path for capacity credit calculation applied in the ‘ILEX/UMIST study’

23. I/En 2006-02 Reference case “Germany 2000”: Geographic distribution of installed capacity Empirical Investigation: Case study ‘Germany 2000’ For Germany (year 2000), we know the true geographical distribution of wind capacity Motivation for the case study:

24. I/En 2006-02 Cumulative wind power time series Germany, by ISET / SepCaMo Empirical Investigation: Case study ‘Germany 2000’ We have a reliable approximation of wind power feed-in time series in 2000

25. I/En 2006-02 Power probability density of total wind power feed-in, Germany 2000 Empirical Investigation: Case study ‘Germany 2000’

26. I/En 2006-02 Empirical Investigation: Case study ‘Germany 2000’ Parameter variation:  Input wind regime (wind years)  Roughness length z 0  Wind turbine hub height  Regional distribution of wind farms  Level of supply security

27. I/En 2006-02 Empirical Investigation: Case study ‘Germany 2000’ Parameter variation:  Input wind regime (wind years)  Roughness length z 0  Wind turbine hub height  Regional distribution of wind farms  Level of supply security

28. I/En 2006-02 Variation of mean annual wind resource in different German regions between 1993 and 2003 Case study ‘Germany 2000’

29. I/En 2006-02 Sensitivity of wind power capacity credit to different input wind years Case study ‘Germany 2000’

30. I/En 2006-02 Sensitivity of wind power capacity credit Here: Variation of input wind regime Case study ‘Germany 2000’

31. I/En 2006-02 Empirical Investigation: Case study ‘Germany 2000’ Parameter variation:  Input wind regime (wind years)  Roughness length z 0  Wind turbine hub height  Regional distribution of wind farms  Level of supply security

32. I/En 2006-02 Here: Variation of roughness length assumption Case study ‘Germany 2000’

33. I/En 2006-02 Here: Variation of hub height assumption Case study ‘Germany 2000’

34. I/En 2006-02 Sensitivity of wind power capacity credit Here: Variation of hub height and roughness length Case study ‘Germany 2000’

35. I/En 2006-02 Empirical Investigation: Case study ‘Germany 2000’ Parameter variation:  Input wind regime (wind years)  Roughness length z 0  Wind turbine hub height  Regional distribution of wind farms  Level of supply security

36. I/En 2006-02 Geographical allocation of wind capacity (Scenarios) ISET (Germany) Balea, Kariniotakis (France)

37. I/En 2006-02 Variation of mean annual wind resource in different German regions between 1993 and 2003 Case study ‘Germany 2000’

38. I/En 2006-02 Influence of variation in geographical distribution of wind farm sites Case study ‘Germany 2000’

39. I/En 2006-02 Empirical Investigation: Case study ‘Germany 2000’ Parameter variation:  Input wind regime (wind years)  Roughness length z 0  Wind turbine hub height  Regional distribution of wind farms  Level of supply security

40. I/En 2006-02 Dependency of capacity credit on ‘security of supply” level applied (case study ‘Germany 2000’) Case study ‘Germany 2000’

41. I/En 2006-02 Summary  Results of national integration study may not be simply transferred.  Aggregated wind power time series are key factor for modelling accuracy.  Bias comes from biased samples (statistically insufficient number samples) and biased estimator (systematical deviations): e.g. from using as indicator specific months only, temperature, ….  The sensitivity analysis described in this work for the case study ‘Germany 2000’ showed capacity credit deviations for the factors of influence:  wind regime: -7.6% (2003) … +18.6 %(1994)  roughness length: ~- 6% (12cm)  hub height: ~+-3% / 10m deviation  distribution of sites: -15.8% (max. capacity shifted to inland)  Level of security of supply: +6.9% (91% instead of 99%)

42. I/En 2006-02 Summary / Outlook to future studies Results of capacity credit calculations are more accurate, if the following requirements are respected:  Best-possible sample of wind data.  The variation in probability densities of wind power in different wind years is covered by a sufficient number of data;  Offshore installation scenarios are treated with extra efforts in order to take the special boundary layer conditions into account;  Sufficient number and distribution of reference sites for spatial extrapolation;  Best possible scenario assumptions for regional distribution of wind farm sites,

43. I/En 2006-02 Thank you for your attention! Applications-oriented Research and Development  Wind Energy  Photovoltaics  Use of Biomass  Energy Conversion and Storage  Hybrid Systems  Energy Economy  Information and Training Systems Technology for the Utilisation of Renewable Energies and for the Decentral Power Supply Institut für Solare Energieversorgungstechnik e.V. Contact: censslin@iset.uni-kassel.de www.iset.uni-kassel.de http://reisi.iset.uni-kassel.de