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Pricing in Day-Ahead Electricity Markets with Near-Optimal Unit Commitment

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Listed:
  • Eldridge, B.
  • O’Neill, R.
  • Hobbs, B.

Abstract

This paper revisits some peculiar pricing properties of near-optimal unit commitment solutions. Previous work has found that prices can behave erratically even as unit commitment solutions approach the optimal solution, resulting in potentially large wealth transfers due to suboptimality of the solution. Our analysis considers how recently proposed pricing models affect this behavior. Results demonstrate a previously unknown property of one of these pricing models, called approximate Convex Hull Pricing (aCHP), that eliminates erratic price behavior, and therefore limits wealth transfers with respect to the optimal unit commitment solution. The absence of wealth transfers may imply fewer strategic bidding incentives, which could enhance market efficiency.

Suggested Citation

  • Eldridge, B. & O’Neill, R. & Hobbs, B., 2018. "Pricing in Day-Ahead Electricity Markets with Near-Optimal Unit Commitment," Cambridge Working Papers in Economics 1872, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:1872
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    References listed on IDEAS

    as
    1. Vazquez, Carlos & Hallack, Michelle & Vazquez, Miguel, 2017. "Price computation in electricity auctions with complex rules: An analysis of investment signals," Energy Policy, Elsevier, vol. 105(C), pages 550-561.
    2. Herbert Scarf, 1994. "The Allocation of Resources in the Presence of Indivisibilities," Journal of Economic Perspectives, American Economic Association, vol. 8(4), pages 111-128, Fall.
    3. Johnson, Raymond B. & Oren, Shmuel S. & Svoboda, Alva J., 1997. "Equity and efficiency of unit commitment in competitive electricity markets," Utilities Policy, Elsevier, vol. 6(1), pages 9-19, March.
    4. VAN VYVE, Mathieu, 2011. "Linear prices for non-convex electricity markets: models and algorithms," LIDAM Discussion Papers CORE 2011050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Herrero, Ignacio & Rodilla, Pablo & Batlle, Carlos, 2015. "Electricity market-clearing prices and investment incentives: The role of pricing rules," Energy Economics, Elsevier, vol. 47(C), pages 42-51.
    6. Huppmann, Daniel & Siddiqui, Sauleh, 2018. "An exact solution method for binary equilibrium problems with compensation and the power market uplift problem," European Journal of Operational Research, Elsevier, vol. 266(2), pages 622-638.
    7. of England, Bank, 2016. "Markets and operations," Bank of England Quarterly Bulletin, Bank of England, vol. 56(4), pages 212-221.
    8. George Liberopoulos & Panagiotis Andrianesis, 2016. "Critical Review of Pricing Schemes in Markets with Non-Convex Costs," Operations Research, INFORMS, vol. 64(1), pages 17-31, February.
    9. C. Gentile & G. Morales-España & A. Ramos, 2017. "A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 177-201, March.
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    More about this item

    Keywords

    Unit commitment; nonconvex pricing; mixed integer programming; market design;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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