Optimal wind power deployment in Europe—A portfolio approach

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 Mean-Variance Portfolio theory to identify crosscountry portfolios that minimize 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.

Optimal management of wind and solar energy resources

“…1 For example, Ref. [28] considers the lack of the network infrastructure for facilitating the transmission of energy between distant power generation units as a main obstacle for the disaggregation of wind power generation.…”

“…Power portfolios are optimized not only with respect to the delivered output (as measured by the mean generating capacity 3 ) but also with respect to the generation risk (temporal variability in energy production). Mean variance port folio selection has also been recently proposed by Roques et al [28] for coordinating the deployment of wind energy investments in the European zone. Their examined optimization problem utilizes historical data for the aggregate wind power production of five European countries to deliver an optimal cross border allocation of wind capacity.…”

“…Spatial displacement as a risk diversification strategy has become a popular topic in the literature; see e.g. the works of Holttinen [12], Archer and Jacobson [2], Cassola et al [4], Ostergaard [23], Kempton et al [15], Roques et al [28], Grothe and Schnieders [9], Santos Alamillos et al [30]. Still, the empirical evidence with respect to the true potential of this strategy is mixed.…”

Wind power in China – Dream or reality?

“…Belanger and Gagnon [17] conducted a study on the compensation of wind power fluctuations by using hydropower in Canada. Drake and Hubacek [16], Roques [18] and Kempton et al [19] examined the deployment of wind turbines at dispersed locations for mutual compensation given diverse wind conditions between wind turbines in the UK, the EU and the US, respectively. A number of attempts have been made to justify the approaches to stabilize the electricity output from wind power plants.…”

Mean-variance portfolio methods for energy policy risk management

“…Marrero et al (2012) considers CO2 externalities to analyze the projected generating mix for Europe in 2020 (EU-BAU) highlighting the importance of complementarity between traditional and renewable energies to reduce not only portfolio risk and average cost but also total CO2 emissions. Roques et al (2010) apply the MVPT to identify cross-country portfolios that minimize the total variance of wind generation for a given level of production across Austria, Denmark, France, Germany and Spain. They find that projected portfolios for 2020 are far from the efficient frontier, suggesting that there could be large benefits in a more coordinated European renewable deployment policy.…”