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Development of Cogeneration in Germany: A Dynamic Portfolio Analysis Based on the New Regulatory Framework

Author

Listed:
  • Westner, Günther

    (E.ON Energy Projects GmbH)

  • Madlener, Reinhard

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

Abstract

The Integrated Energy and Climate Protection Program of the German government includes the political target of doubling the share of combined heat and power generation (CHP) in Germany from currently about 12% to 25% by 2020. In order to reach this goal, a new CHP law was enacted to improve the framework conditions for CHP generation. In this paper, we tackle the political CHP target stipulated by the German government, and aim at identifying which CHP technologies are most likely to be installed in the near future. By applying Mean-Variance Portfolio (MVP) theory, we consider return- and risk-related aspects. In our model, we pay tribute to specific characteristics of CHP generation, such as promotion via feed-in tariffs, additional revenues from heat sales, specific operational features, and specifics concerning allocation of CO2 allowances. The investigation is carried out on the basis of four generic standard CHP technologies currently available on a commercial basis. These technologies are: large coal-fired CHP plants, combined-cycle gas turbines (CCGT-CHP), engine-CHP and micro-turbine CHP. The portfolio investigation includes a projection of fuel cost and prices for heat and power according to the BMU Leitstudie 2008. As selection criteria for the portfolio performance we take, independently from each other, the net present value (NPV) of investment in CHP and the expected annual portfolio return, and compare the results obtained from both approaches with each other. Irrespective of the chosen selection criteria, the analysis shows that CCGT-CHP and engine-CHP are the most attractive CHP technologies from a return perspective. A diversification of the portfolio with other kinds of CHP technologies can contribute to stabilizing portfolio returns. The application of the results obtained on the further development of CHP generation in Germany leads to the conclusion that a large portion of additional new CHP capacity will probably be built in the industrial sector. We conclude further that the ambitious political target of 25% CHP generation by 2020 is not realistic under the given framework.

Suggested Citation

  • 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.
    • Handle: RePEc:ris:fcnwpa:2009_004
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    Citations

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    Cited by:

    1. de-Llano Paz, Fernando & Antelo, Susana Iglesias & Calvo Silvosa, Anxo & Soares, Isabel, 2014. "The technological and environmental efficiency of the EU-27 power mix: An evaluation based on MPT," Energy, Elsevier, vol. 69(C), pages 67-81.
    2. 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.
    3. Sunderkötter, Malte & Weber, Christoph, 2012. "Valuing fuel diversification in power generation capacity planning," Energy Economics, Elsevier, vol. 34(5), pages 1664-1674.
    4. Lang, Joachim & Madlener, Reinhard, 2010. "Portfolio Optimization for Power Plants: The Impact of Credit Risk Mitigation and Margining," FCN Working Papers 11/2010, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    5. Barbara Glensk & Reinhard Madlener, 2013. "Multi-period portfolio optimization of power generation assets," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 23(4), pages 20-38.

    More about this item

    Keywords

    Combined heat and power; CHP technology; Portfolio optimization; Germany;
    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
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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