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Stochastic programming models for replication of electricity forward contracts for industry

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  • Roy H. Kwon
  • J. Scott Rogers
  • Sheena Yau

Abstract

Forward contracts for electricity are valuable to consumers (suppliers) that wish to obtain (sell) power at prices that are more stable than those typically seen in electricity markets. Only a limited variety of forward contracts are available on the market so the need is for a “custom” contract that meets a specific profile of electricity requirements (usually uncertain) over time. This paper develops stochastic programming models that can be used by the supplier of a custom contract to design a procurement strategy that minimizes its expected costs of supply in meeting contract obligations. The procurement strategy will consist of a mix of forwards available in the market, and, in each period, blending its own generation with spot purchases of power. The model also integrates spot selling of power. We consider that expected spot prices and forward prices may disagree since electricity is not storable, creating apparent arbitrage opportunities. We bound the transaction amounts to limit effects of apparent arbitrage and for consistency with the assumption of constant variable generation costs and market prices. For sample cases we compute the optimal procurement strategy, demonstrate the magnitude of the saving, and illustrate the sensitivity of this saving to the magnitude of the upper bounds on the allowed forward positions (a proxy for risk). © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006

Suggested Citation

  • Roy H. Kwon & J. Scott Rogers & Sheena Yau, 2006. "Stochastic programming models for replication of electricity forward contracts for industry," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(7), pages 713-726, October.
    • Handle: RePEc:wly:navres:v:53:y:2006:i:7:p:713-726
    DOI: 10.1002/nav.20185
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    References listed on IDEAS

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    1. Ron Dembo & Dan Rosen, 1999. "The practice of portfolio replication. A practical overview of forward and inverse problems," Annals of Operations Research, Springer, vol. 85(0), pages 267-284, January.
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    4. Richard Green, 1999. "The Electricity Contract Market in England and Wales," Journal of Industrial Economics, Wiley Blackwell, vol. 47(1), pages 107-124, March.
    5. Suvrajeet Sen & Lihua Yu & Talat Genc, 2006. "A Stochastic Programming Approach to Power Portfolio Optimization," Operations Research, INFORMS, vol. 54(1), pages 55-72, February.
    6. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    7. Paul R. Kleindorfer & Lide Li, 2005. "Multi-Period VaR-Constrained Portfolio Optimization with Applications to the Electric Power Sector," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-26.
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    Cited by:

    1. Dimitri J. Papageorgiou, 2025. "Data-Driven Distributionally Robust Optimization for Long-Term Contract vs. Spot Allocation Decisions: Application to Electricity Markets," Papers 2501.15340, arXiv.org.

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