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Generation Supply Bidding in Perfectly Competitive Electricity Markets

Author

Listed:
  • George Gross

    (University of Illinois at Urbana-Champaign)

  • David Finlay

    (OOCL Transportation Co.)

Abstract

This paper reports on the development of a comprehensive framework for the analysis and formulation of bids in competitive electricity markets. Competing entities submit offers of power and energy to meet the next day's load. We use the England and Wales Power Pool as the basis for the development of a very general competitive power pool (CPP) framework. The framework provides the basis for solving the CPP dispatcher problem and for specifying the optimal bidding strategies. The CPP dispatcher selects the winning bids for the right to serve load each period of the scheduling horizon. The dispatcher must commit sufficient generation to meet the forecasted load and reserve requirements throughout the scheduling horizon. All the unique constraints under which electrical generators operate including start-up and shut-down time restrictions, reserve requirements and unit output limits must be taken into account. We develop an analytical formulation of the problem faced by a bidder in the CPP by specifying a strategy that maximizes his profits. The optimal bidding strategy is solved analytically for the case of perfect competition. The study in this work takes into account the principal sources of uncertainty—the load forecast and the actions of the other competitors. The formulation and solution methodology effectively exploit a Lagrangian relaxation based approach. We have conducted a wide range of numerical studies; a sample of numerical results are presented to illustrate the robustness and superiority of the analytically developed bidding strategies.

Suggested Citation

  • George Gross & David Finlay, 2000. "Generation Supply Bidding in Perfectly Competitive Electricity Markets," Computational and Mathematical Organization Theory, Springer, vol. 6(1), pages 83-98, May.
  • Handle: RePEc:spr:comaot:v:6:y:2000:i:1:d:10.1023_a:1009677326718
    DOI: 10.1023/A:1009677326718
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    References listed on IDEAS

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    1. Wu, Felix & Varaiya, Pravin & Spiller, Pablo & Oren, Shmuel, 1996. "Folk Theorems on Transmission Access: Proofs and Counterexamples," Journal of Regulatory Economics, Springer, vol. 10(1), pages 5-23, July.
    2. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
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    Cited by:

    1. Bin Luo & Shumin Miao & Chuntian Cheng & Yi Lei & Gang Chen & Lang Gao, 2019. "Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets," Energies, MDPI, vol. 12(12), pages 1-17, June.
    2. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    3. Steeger, Gregory & Rebennack, Steffen, 2017. "Dynamic convexification within nested Benders decomposition using Lagrangian relaxation: An application to the strategic bidding problem," European Journal of Operational Research, Elsevier, vol. 257(2), pages 669-686.
    4. Fanzeres, Bruno & Ahmed, Shabbir & Street, Alexandre, 2019. "Robust strategic bidding in auction-based markets," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1158-1172.
    5. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2016. "Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 421-432.
    6. George Kozanidis & Eftychia Kostarelou, 2023. "An Exact Solution Algorithm for Integer Bilevel Programming with Application in Energy Market Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 573-607, May.
    7. Timo Lohmann & Steffen Rebennack, 2017. "Tailored Benders Decomposition for a Long-Term Power Expansion Model with Short-Term Demand Response," Management Science, INFORMS, vol. 63(6), pages 2027-2048, June.

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