Cho, J. and Papavasiliou, A. (2023). Pricing Under Uncertainty in Multi-Interval Real-Time Markets Operations Research, 71(6):1928--1942.

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. History: This paper has been accepted for the Operations Research Special Issue on Computational Advances in Short-Term Power System Operations. Open Access Statement: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, distribute, transmit, and adapt this work, but you must attribute this work as “Operations Research.