Iterative Mechanisms for Electricity Markets

TL;DR

This paper introduces an iterative, incentive-compatible mechanism for electricity markets that mitigates market power during contingencies, handling complex constraints and imperfect information, with applications to renewable integration.

Contribution

It proposes a novel iterative mechanism for electricity markets that improves market power mitigation without regulatory intervention, accommodating non-linear constraints and imperfect information.

Findings

Mechanism is approximately incentive compatible and efficient.

Retains properties under imperfect cost information.

Applicable to renewable integration scenarios.

Abstract

In order to deal with market power that sporadically results from contingencies (e.g., severe weather, plant outages) most electricity markets have institutions in charge of monitoring market performance and mitigating market power. The latter task is often achieved by producing estimates of marginal costs (also referred to as "reference levels") that may replace the actual bids by generators with market power. In this paper, we propose an iterative mechanism that constitutes an alternative to outright regulatory intervention in those sporadic situations in which market power is a significant concern. The iterative mechanism proposed is based upon relatively simple information exchange between the market maker and market participants and is equipped to handle general non-linear (convex) constraints related to technical and/or reliability requirements. We show the mechanism proposed has many desirable properties (approximately): incentive compatibility, efficiency, individual rationality and (weak) budget balance. In addition, we show it is robust to imperfect information regarding costs. To illustrate we consider its application to the joint clearing of day ahead dispatch and reserves, an approach that has been proposed for the large-scale integration of renewable generation. In this context, we relax the assumption that each generator has perfect information on its own expected marginal costs of adjustment and show the mechanism retains most of its properties.