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Valuing fuel diversification in optimal investment policies for electricity generation portfolios

Author

Listed:
  • Malte Sunderkoetter
  • Christoph Weber

    (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen)

Abstract

Optimal capacity allocation for investments in electricity generation assets can be deterministically derived by comparing technology specific long-term and short-term marginal costs. In an uncertain market environment, Mean-Variance Portfolio (MVP) theory provides a consistent framework to valuate financial risks in power generation portfolios that allows to derive the efficient fuel mix of a system portfolio with different generation technologies from a welfare maximization perspective. Because existing literature on MVP applications in electricity generation markets uses predominantly numerical methods to characterize portfolio risks, this article presents a novel analytical approach combining conceptual elements of peak-load pricing and MVP theory to derive optimal portfolios consisting of an arbitrary number of plant technologies given uncertain fuel prices. For this purpose, we provide a static optimization model which allows to fully capture fuel price risks in a mean variance portfolio framework. The analytically derived optimality conditions contribute to a much better understanding of the optimal investment policy and its risk characteristics compared to existing numerical methods. Furthermore, we demonstrate an application of the proposed framework and results to the German electricity market which has not yet been treated in MVP literature on electricity markets.

Suggested Citation

  • Malte Sunderkoetter & Christoph Weber, 2009. "Valuing fuel diversification in optimal investment policies for electricity generation portfolios," EWL Working Papers 0904, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Nov 2009.
    • Handle: RePEc:dui:wpaper:0904
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    References listed on IDEAS

    as
    1. Fabien A. Roques & William J. Nuttall & David M. Newbery & Richard de Neufville & Stephen Connors, 2006. "Nuclear Power: A Hedge against Uncertain Gas and Carbon Prices?," The Energy Journal, , vol. 27(4), pages 1-24, October.
    2. Stirling, Andrew, 1994. "Diversity and ignorance in electricity supply investment : Addressing the solution rather than the problem," Energy Policy, Elsevier, vol. 22(3), pages 195-216, March.
    3. Bar-Lev, Dan & Katz, Steven, 1976. "A Portfolio Approach to Fossil Fuel Procurement in the Electric Utility Industry," Journal of Finance, American Finance Association, vol. 31(3), pages 933-947, June.
    4. Christoph Weber, 2005. "Uncertainty in the Electric Power Industry," International Series in Operations Research and Management Science, Springer, number 978-0-387-23048-1, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Steffen, Bjarne & Weber, Christoph, 2013. "Efficient storage capacity in power systems with thermal and renewable generation," Energy Economics, Elsevier, vol. 36(C), pages 556-567.
    2. Malte Sunderkötter & Christoph Weber, 2011. "Mean-Variance optimization of power generation portfolios under uncertainty in the merit order," EWL Working Papers 1105, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2011.

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    More about this item

    Keywords

    power plant investments; peak load pricing; mean-variance portfolio theory; fuel mix diversification;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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