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The Benefit of Regional Diversification of Cogeneration Investments in Europe: A Mean-Variance Portfolio Analysis

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  • Westner, Günther

    (E.ON Energy Projects GmbH)

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract

The EU Directive 2004/8/EC, concerning the promotion of cogeneration, established principles on how EU member states can support combined heat and power generation (CHP). Up to now, the implementation of these principles into national law has not been uniform, and has led to the adoption of different promotion schemes for CHP across the EU member states. In this paper, we first give an overview of the promotion schemes for CHP in various European countries. In a next step, we take two standard CHP technologies, combined-cycle gas turbines (CCGT-CHP) and engine-CHP, and apply exemplarily four selected support mechanisms used in the four largest European energy markets: feed-in tariffs in Germany; energy efficiency certificates in Italy; benefits through tax reduction in the UK; and purchase obligations for power from CHP generation in France. For contracting companies, it could be of interest to diversify their investment in new CHP facilities regionally over several countries in order to reduce country and regulatory risk. By applying Mean-Variance Portfolio (MVP) theory, we derive characteristic return-risk profiles of the selected CHP technologies in different countries. The results show that the returns on CHP investments differ significantly depending on the country, the support scheme, and the selected technology studied. While a regional diversification of investments in CCGT-CHP does not contribute to reducing portfolio risks, a diversification of investments in engine-CHP can decrease the risk exposure.

Suggested Citation

  • Westner, Günther & Madlener, Reinhard, 2009. "The Benefit of Regional Diversification of Cogeneration Investments in Europe: A Mean-Variance Portfolio Analysis," FCN Working Papers 5/2009, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Mar 2010.
    • Handle: RePEc:ris:fcnwpa:2009_005
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    References listed on IDEAS

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    1. Westner, Günther & Madlener, Reinhard, 2010. "The benefit of regional diversification of cogeneration investments in Europe: A mean-variance portfolio analysis," Energy Policy, Elsevier, vol. 38(12), pages 7911-7920, December.
    2. Bertoldi, Paolo & Huld, Thomas, 2006. "Tradable certificates for renewable electricity and energy savings," Energy Policy, Elsevier, vol. 34(2), pages 212-222, January.
    3. Roques, Fabien A. & Newbery, David M. & Nuttall, William J., 2008. "Fuel mix diversification incentives in liberalized electricity markets: A Mean-Variance Portfolio theory approach," Energy Economics, Elsevier, vol. 30(4), pages 1831-1849, July.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. Bonilla, David & Akisawa, Atsushi & Kashiwagi, Takao, 2003. "Modelling the adoption of industrial cogeneration in Japan using manufacturing plant survey data," Energy Policy, Elsevier, vol. 31(9), pages 895-910, July.
    6. Madlener, Reinhard & Wenk, Christioph, 2008. "Efficient Investment Portfolios for the Swiss Electricity Supply Sector," FCN Working Papers 2/2008, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    7. Uran, Vedran & Krajcar, Slavko, 2009. "Feed-in tariff and market electricity price comparison: The case of cogeneration units in Croatia," Energy Policy, Elsevier, vol. 37(3), pages 844-849, March.
    8. Helle, Christoph, 1997. "On energy efficiency-related product strategies--illustrated and analysed using contracting approaches in Germany as an example," Utilities Policy, Elsevier, vol. 6(1), pages 75-85, March.
    9. Regina Betz & Wolfgang Eichhammer & Joachim Schleich, 2004. "Designing National Allocation Plans for Eu-Emissions Trading — A First Analysis of the Outcomes," Energy & Environment, , vol. 15(3), pages 375-425, July.
    10. Westner, Günther & Madlener, Reinhard, 2009. "Development of Cogeneration in Germany: A Dynamic Portfolio Analysis Based on the New Regulatory Framework," FCN Working Papers 4/2009, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Mar 2010.
    11. Boris Krey & Peter Zweifel, 2006. "Efficient Electricity Portfolios for Switzerland and the United States," SOI - Working Papers 0602, Socioeconomic Institute - University of Zurich.
    12. Lesser, Jonathan A. & Su, Xuejuan, 2008. "Design of an economically efficient feed-in tariff structure for renewable energy development," Energy Policy, Elsevier, vol. 36(3), pages 981-990, March.
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    More about this item

    Keywords

    Combined heat and power; CHP promotion scheme; Portfolio optimization;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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