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Optimal wind power deployment in Europe--A portfolio approach

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  • Roques, Fabien
  • Hiroux, Céline
  • Saguan, Marcelo

Abstract

Geographic diversification of wind farms can smooth out the fluctuations in wind power generation and reduce the associated system balancing and reliability costs. The paper uses historical wind production data from five European countries (Austria, Denmark, France, Germany, and Spain) and applies the Mean-Variance Portfolio theory to identify cross-country portfolios that minimise the total variance of wind production for a given level of production. Theoretical unconstrained portfolios show that countries (Spain and Denmark) with the best wind resource or whose size contributes to smoothing out the country output variability dominate optimal portfolios. The methodology is then elaborated to derive optimal constrained portfolios taking into account national wind resource potential and transmission constraints and compare them with the projected portfolios for 2020. Such constraints limit the theoretical potential efficiency gains from geographical diversification, but there is still considerable room to improve performance from actual or projected portfolios. These results highlight the need for more cross-border interconnection capacity, for greater coordination of European renewable support policies, and for renewable support mechanisms and electricity market designs providing locational incentives. Under these conditions, a mechanism for renewables credits trading could help aligning wind power portfolios with the theoretically efficient geographic dispersion.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Policy.

Volume (Year): 38 (2010)
Issue (Month): 7 (July)
Pages: 3245-3256

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Handle: RePEc:eee:enepol:v:38:y:2010:i:7:p:3245-3256

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Web page: http://www.elsevier.com/locate/enpol

Related research

Keywords: Wind power variability Geographic diversification Portfolio theory;

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Cited by:
  1. Madlener, Reinhard & Glensk, Barbara & Weber, Veronika, 2011. "Fuzzy Portfolio Optimization of Onshore Wind Power Plants," FCN Working Papers 10/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
  2. Muireann Á. Lynch & Richard Tol & Mark J. O’Malley, 2014. "Minimising costs and variability of electricity generation by means of optimal electricity interconnection utilisation," Working Paper Series 6814, Department of Economics, University of Sussex.
  3. Richard S.J. Tol & Muireann Lynch & Aonghus Shortt & Mark O’Malley, 2012. "Risk-Return Incentives in Liberalised Electricity Markets," Working Paper Series 4012, Department of Economics, University of Sussex.
  4. Green, Richard & Vasilakos, Nicholas, 2011. "The economics of offshore wind," Energy Policy, Elsevier, vol. 39(2), pages 496-502, February.
  5. Schmidt, J. & Lehecka, G. & Gass, V. & Schmid, E., 2013. "Where the wind blows: Assessing the effect of fixed and premium based feed-in tariffs on the spatial diversification of wind turbines," Energy Economics, Elsevier, vol. 40(C), pages 269-276.
  6. Allan, Grant & Eromenko, Igor & McGregor, Peter & Swales, Kim, 2011. "The regional electricity generation mix in Scotland: A portfolio selection approach incorporating marine technologies," Energy Policy, Elsevier, vol. 39(1), pages 6-22, January.
  7. B. Andrew Chupp & Emily Hickey & David Loomis, 2011. "Optimal Wind Portfolios in Illinois," Working Paper Series 20110401, Illinois State University, Department of Economics.
  8. Malte Hübner & Christoph M. Schmidt & Benjamin Weigert, 2012. "Energiepolitik: Erfolgreiche Energiewende nur im europäischen Kontext," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 13(4), pages 286-307, November.
  9. Gustavo A. Marrero & Luis A. Puch & Francisco J. Ramos-Real, 2013. "Mean-variance portfolio methods for energy policy risk management," Documentos del Instituto Complutense de Análisis Económico 2013-41, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.

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