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

John Birge, Ali Hortacsu and J. Michael Pavlin ()
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J. Michael Pavlin: Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, Ontario N2L 3C5, Canada

Operations Research, 2017, vol. 65, issue 4, 837-855

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.

Keywords: inverse optimization; electricity markets; locational pricing; residual demand derivative (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (18)

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