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Applying portfolio theory to the electricity sector: Energy versus power

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Author Info

  • Delarue, Erik
  • De Jonghe, Cedric
  • Belmans, Ronnie
  • D'haeseleer, William
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    Abstract

    Portfolio theory has found its way in numerous applications for optimizing the electricity generation mix of a particular region. Existing models, however, consider typically a single time period and correspondingly do not properly account for actual dispatch constraints and energy sources with a non-dispatchable, variable output. This paper presents a portfolio theory model that explicitly distinguishes between installed capacity (power), electricity generation (energy) and actual instantaneous power delivery. This way, the variability of wind power and ramp limits of conventional power plants are correctly included in the investment optimization. The model is written as a quadratically constrained programming problem and illustrated in a case study. The results show that the introduction of wind power can be motivated to lower the risk on generation cost, albeit to smaller levels than typically reported in the literature. This wind power deployment further requires the need for sufficiently rampable technologies, to deal with its fluctuating output.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 33 (2011)
    Issue (Month): 1 (January)
    Pages: 12-23

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    Handle: RePEc:eee:eneeco:v:33:y:2011:i:1:p:12-23

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Portfolio theory Electricity generation investment Wind power deployment;

    References

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    1. Boris Krey & Peter Zweifel, 2006. "Efficient Electricity Portfolios for Switzerland and the United States," SOI - Working Papers 0602, Socioeconomic Institute - University of Zurich.
    2. Huang, Yun-Hsun & Wu, Jung-Hua, 2008. "A portfolio risk analysis on electricity supply planning," Energy Policy, Elsevier, vol. 36(2), pages 627-641, February.
    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 933-47, June.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
    5. Huisman, R. & Mahieu, R.J. & Schlichter, F., 2007. "Electricity Portfolio Management: Optimal Peak / Off-Peak Allocations," ERIM Report Series Research in Management ERS-2007-089-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
    6. Shimon Awerbuch, 2006. "Portfolio-Based Electricity Generation Planning: Policy Implications For Renewables And Energy Security," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 11(3), pages 693-710, May.
    7. Gotham, Douglas & Muthuraman, Kumar & Preckel, Paul & Rardin, Ronald & Ruangpattana, Suriya, 2009. "A load factor based mean-variance analysis for fuel diversification," Energy Economics, Elsevier, vol. 31(2), pages 249-256, March.
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    Citations

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    Cited by:
    1. Ge, Fenglong & Fan, Ying, 2013. "Quantifying the risk to crude oil imports in China: An improved portfolio approach," Energy Economics, Elsevier, vol. 40(C), pages 72-80.
    2. Losekann, Luciano & Marrero, Gustavo A. & Ramos-Real, Francisco J. & de Almeida, Edmar Luiz Fagundes, 2013. "Efficient power generating portfolio in Brazil: Conciliating cost, emissions and risk," Energy Policy, Elsevier, vol. 62(C), pages 301-314.
    3. Lynch, Muireann Á. & Shortt, Aonghus & Tol, Richard S.J. & O'Malley, Mark J., 2013. "Risk–return incentives in liberalised electricity markets," Energy Economics, Elsevier, vol. 40(C), pages 598-608.
    4. De Jonghe, C. & Hobbs, B. F. & Belmans, R., 2011. "Integrating short-term demand response into long-term investment planning," Cambridge Working Papers in Economics 1132, Faculty of Economics, University of Cambridge.
    5. de Oliveira, Francisco Alexandre & de Paiva, Anderson Paulo & Lima, José Wanderley Marangon & Balestrassi, Pedro Paulo & Mendes, Ronã Rinston Amaury, 2011. "Portfolio optimization using Mixture Design of Experiments: Scheduling trades within electricity markets," Energy Economics, Elsevier, vol. 33(1), pages 24-32, January.
    6. Davis, Clay D. & Gotham, Douglas J. & Preckel, Paul V. & Liu, Andrew L., 2013. "Determining the impact of wind on system costs via the temporal patterns of load and wind generation," Energy Policy, Elsevier, vol. 60(C), pages 122-131.
    7. Böckers, Veit & Giessing, Leonie & Rösch, Jürgen, 2013. "The green game changer: An empirical assessment of the effects of wind and solar power on the merit order," DICE Discussion Papers 104, Heinrich‐Heine‐Universität Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).

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