Pricing Under Uncertainty in Multi-Interval Real-Time Markets

Jehum Cho () and Anthony Papavasiliou ()
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Jehum Cho: Center for Operations Research and Econometrics (CORE), Université Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium;
Anthony Papavasiliou: Center for Operations Research and Econometrics (CORE), Université Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium

Operations Research, 2023, vol. 71, issue 6, 1928-1942

Abstract: Recent research has demonstrated that real-time auctions can generate the need for side payments, even if the market clearing models are convex, because of the rolling nature of real-time market clearing. This observation has inspired proposals for modifying the real-time market-clearing model in order to account for binding past decisions. We extend this analysis in order to account for uncertainty by proposing a real-time market-clearing model with look-ahead and an endogenous representation of uncertainty. We define two different types of expected lost opportunity cost as performance metrics. Our market-clearing model provides the price signal minimizing one of these metrics using the Stochastic Gradient Descent algorithm. We present results from a case study of the ISO New England system under a scenario of significant renewable energy penetration while accounting for ramp rates, storage, and transmission constraints.

Keywords: Special Issue on Computational Advances in Short-Term Power System Operations; multi-interval real-time markets; pricing under uncertainty; stochastic gradient descent (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:71:y:2023:i:6:p:1928-1942

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