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The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects

The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects. „Capacity Credit“ in National Studies Comparison of Methodologies Empirical Investigation „Germany 2000“ Conclusions, outlook to future studies. Cornel Ensslin Alexander Badelin

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The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects

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

  2. 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. PSE 2003:Gdańsk division of Institute of Power Engineering :“Study of impact of wind energy development on operation of the Polish power system”, 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 Concept and New Developments

  3. PhD work on Wind Power Integration Focus on: Wind Power Capacity Credit Balance Management 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" Holttinen 2004: "The Impact of Large Scale Wind Power Production on the Nordic Electricity System. Engineering Physics and Mathematics." December 2004 Concept and New Developments

  4. 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. „Capacity Credit” issues in national studies 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.

  6. „Capacity Credit” issues in national studies: Critical issues Explanation: Dany had assumed 62(!) % capacity factor (‚Winter‘, German Offshore-Windfarms) Dany 2001:Dany, Gundolf: "Kraftwerksreserve in elektrischen Verbundsystemen mit hohem Windenergieanteil" 15%? Capacity credit 8%? 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 Dena used evaluation of 10 historic wind years leading to much lower CF values Concept and New Developments

  7. „Capacity Credit” issues in national studies Critical issues 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

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

  9. Capacity credit calculation / Comparison of methodologies Map of statistical and chronological approaches

  10. Capacity credit calculation “Model path” followed by Giebel for assessing a European wind power capacity credit [Giebel 2000]

  11. Capacity credit calculation “Model path” for capacity credit calculation applied in the ‘dena study’

  12. Case study ‘Germany 2000’ Different estimators for wind power in the moment of peak demand

  13. Capacity credit calculation “Model path” for capacity credit calculation applied in the ‘dena study’

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

  15. Capacity credit calculation / Comparison of methodologies Probabilistic combination of wind / conventional power

  16. Capacity credit calculation / Comparison of methodologies Probabilistic combination of wind / conventional power

  17. Capacity credit calculation / Comparison of methodologies Probabilistic combination of wind / conventional power

  18. Capacity credit calculation / Comparison of methodologies Effect of bias in wind power time series

  19. Capacity credit calculation / Comparison of methodologies Wind power probability density Effect of bias in wind power time series

  20. Capacity credit calculation / Comparison of methodologies Effect of bias in wind power time series

  21. Capacity credit calculation / Comparison of methodologies Effect of bias in wind power time series

  22. Capacity credit calculation Model path for capacity credit calculation applied in the ‘ILEX/UMIST study’

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

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

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

  26. Empirical Investigation: Case study ‘Germany 2000’ Parameter variation: Input wind regime (wind years) Roughness length z0 Wind turbine hub height Regional distribution of wind farms Level of supply security

  27. Empirical Investigation: Case study ‘Germany 2000’ Parameter variation: Input wind regime (wind years) Roughness length z0 Wind turbine hub height Regional distribution of wind farms Level of supply security

  28. Case study ‘Germany 2000’ Variation of mean annual wind resource in different German regions between 1993 and 2003

  29. Case study ‘Germany 2000’ Sensitivity of wind power capacity credit to different input wind years

  30. Case study ‘Germany 2000’ Sensitivity of wind power capacity credit Here: Variation of input wind regime

  31. Empirical Investigation: Case study ‘Germany 2000’ Parameter variation: Input wind regime (wind years) Roughness length z0 Wind turbine hub height Regional distribution of wind farms Level of supply security

  32. Case study ‘Germany 2000’ Here: Variation of roughness length assumption

  33. Case study ‘Germany 2000’ Here: Variation of hub height assumption

  34. Case study ‘Germany 2000’ Sensitivity of wind power capacity credit Here: Variation of hub height and roughness length

  35. Empirical Investigation: Case study ‘Germany 2000’ Parameter variation: Input wind regime (wind years) Roughness length z0 Wind turbine hub height Regional distribution of wind farms Level of supply security

  36. Geographical allocation of wind capacity (Scenarios) ISET (Germany) Balea, Kariniotakis (France)

  37. Case study ‘Germany 2000’ Variation of mean annual wind resource in different German regions between 1993 and 2003

  38. Case study ‘Germany 2000’ Influence of variation in geographical distribution of wind farm sites

  39. Empirical Investigation: Case study ‘Germany 2000’ Parameter variation: Input wind regime (wind years) Roughness length z0 Wind turbine hub height Regional distribution of wind farms Level of supply security

  40. Case study ‘Germany 2000’ Dependency of capacity credit on ‘security of supply” level applied (case study ‘Germany 2000’)

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

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