Pricing Optimal Outcomes in Coupled and Non-convex Markets: Theory and Applications to Electricity Markets

Abstract

The clearing of nonconvex markets poses unique challenges. The canonical market clearing, a Walrasian equilibrium, needs not exist when participants’ preferences display nonconvexities, for example, due to technical constraints. Then market clearing prices might not exist, and side payments may be required to compensate losses. Electricity markets are a prime example. Market operators in the United States use heuristic pricing rules to compute market prices; the magnitude of the side payments, potential lost opportunity costs, and the quality of network congestion signals in prices have all become a concern. In “Pricing Optimal Outcomes in Coupled and Non-Convex Markets: Theory and Applications to Electricity Markets,” Ahunbay, Bichler, and Knörr propose a multiobjective framework for pricing such markets. The design goals are shown to be inherently conflicting but may be balanced against each other through traditional methods in multiobjective optimization. Pricing rules used in practice are identified as prices for a suitably convexified market, which motivates a novel pricing method that drastically reduces side payments while maintaining congestion signals in realistic test cases.In many real-world markets, participants have non-convex preferences, and the allocation problem needs to consider complex constraints. Electricity markets are a prime example, but similar problems appear in many markets, which has led to a growing literature on market design. Competitive equilibrium does not generally exist in such markets. Today, power markets use heuristic pricing rules based on the dual of a relaxed allocation problem. With increasing levels of renewables, these rules have come under scrutiny as they lead to high out-of-market side payments to some participants and inadequate congestion signals. We show that existing pricing heuristics optimize specific design goals that can be conflicting. The tradeoffs can be substantial, and we establish that the design of pricing rules is fundamentally a multiobjective optimization problem addressing different incentives. In addition to traditional multiobjective optimization techniques that involve weighting individual objectives, we introduce a novel parameter-free pricing rule that minimizes incentives for market participants to deviate locally. Our theoretical and experimental findings show how the new pricing rule capitalizes on the upsides of existing pricing rules under scrutiny today. It leads to prices that incur low make-whole payments while providing adequate congestion signals and low lost opportunity costs. Our suggested pricing rule does not require weighing objectives, it is computationally scalable, and balances tradeoffs in a principled manner, addressing a critical policy issue in electricity markets.Funding: The financial support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) [Grant BI 1057/9-1] is gratefully acknowledged.Supplemental Material: The computer code and data that supports the findings of this study are available within this article’s supplemental material at https://doi.org/10.1287/opre.2023.0401 .