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Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market

Author

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  • John R. Birge

    (Booth School of Business, University of Chicago, Chicago, Illinois, 60637)

  • Ali Hortaçsu

    (Department of Economics, University of Chicago, Chicago, Illinois, 60637)

  • J. Michael Pavlin

    (Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, Ontario N2L 3C5, Canada)

Abstract

We propose an inverse optimization-based methodology to determine market structure from commodity and transportation prices. The methods are appropriate for locational marginal price -based electricity markets where prices are shadow prices in the centralized optimization used to clear the market. We apply the inverse optimization methodology to outcome data from the Midcontinent ISO electricity market (MISO) and, under noise-free assumptions, recover parameters of transmission and related constraints that are not revealed to market participants but explain the price variation. We demonstrate and evaluate analytical uses of the recovered structure including reconstruction of the pricing mechanism and investigations of locational market power through the transmission constrained residual demand derivative. Prices generated from the reconstructed mechanism are highly correlated to actual MISO prices under a wide variety of market conditions. In a case study, the residual demand derivative is shown to be correlated with coefficients of certain transmission constraints.

Suggested Citation

  • John R. Birge & Ali Hortaçsu & J. Michael Pavlin, 2017. "Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market," Operations Research, INFORMS, vol. 65(4), pages 837-855, August.
    • Handle: RePEc:inm:oropre:v:65:y:2017:i:4:p:837-855
    DOI: 10.1287/opre.2017.1606
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    8. Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(C).
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    11. Yijie Peng & Michael C. Fu & Bernd Heidergott & Henry Lam, 2020. "Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling," Operations Research, INFORMS, vol. 68(6), pages 1896-1912, November.
    12. Shi Yu & Haoran Wang & Chaosheng Dong, 2020. "Learning Risk Preferences from Investment Portfolios Using Inverse Optimization," Papers 2010.01687, arXiv.org, revised Feb 2021.
    13. Stathis Tompaidis, 2021. "Comments on “Network Structure and Its Impact on Commodity Markets”," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4577-4578, December.
    14. Timothy C. Y. Chan & Taewoo Lee & Daria Terekhov, 2019. "Inverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit," Management Science, INFORMS, vol. 65(3), pages 1115-1135, March.
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