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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis article investigates the impact of the fuel mix structure in power generation portfolios on expected stock returns for major European power companies. The 22 biggest publicly listed European power producers are examined between January 2005 and December 2010. Based on the capital asset pricing model (CAPM) and multi-factor market models, the systematic risk of the power companies relative to the overall market performance and other typical energy and macroeconomic risk factors is analyzed. The full-information approach is used to determine technology-specific betas and risk factor sensitivities from the sample. Although most companies are not exclusively in the power producing business, it is shown that the generation fuel mix has a significant impact on the historical stock returns of the investigated companies. In particular, the sample companies exhibit significant differences in the systematic risk of gas and nuclear generation technologies compared with renewable technologies measured by technology-specific, delevered beta factors.This study extends existing literature and contributes new insights in two ways: Firstly, this is to our knowledge the first empirical analysis comparing the financial risk of different electricity generation technologies. Secondly, the results provide practical benefit to determine adequate riskadjusted capital costs for typical generation technologies. Therewith, this study is relevant for evaluating all kinds of power plant investments.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn this article we discuss welfare-optimal capacity allocation of different electricity generation technologies available for serving system demand. While the classical peak load pricing theory derives the efficient portfolio structure from a deterministic marginal production cost curve ("merit order"), we investigate in particular the implications of possible reversals in the merit ordersometimes also referred to as fuel switch risksinduced by uncertain operating costs.We propose a static, non-convex optimization model combining the classic peak load pricing model with elements of mean-variance portfolio (MVP) theory and analytically discuss possible solution cases and important optimality properties. We examine the approach in a case study on the efficient structure of generation portfolios consisting of CCGT and hard coal technologies in Germany.With special emphasis, we study the emergence of overcapacities (exceeding maximal demand) in efficient portfolios and show that diversification is not beneficial per-se. The results show that the efficient technology mix may be significantly impacted by a risk for reversals in the merit order. Therefore, our findings support the importance of considering this risk factor especially with long-term investment horizons.The model is applicable to various investment problems related to production of nonstorable goods under price uncertainty of input factors. Similar problems can e.g. be found in transportation systems or in the process industry.