Strategic bid response under automated market power mitigation in electricity markets

2025-11-25

AI Summary

This study investigates the deterrent effect of Automatic Mitigation Procedures (AMPs) on generator bidding behavior. Using empirical data from the New York and New England electricity markets, we employ a regression discontinuity design to identify the causal impact of AMP regulatory screening activation, complemented by bid-rank simulation and welfare analysis to assess the bindingness of price caps and penalty mechanisms. Results indicate that approximately 30–40% of generators proactively lower their maximum bids by $4–$10/MWh to avoid mitigation, confirming a modest deterrent effect; however, the current AMP thresholds are overly lenient, yielding statistically insignificant impacts on overall market prices. We propose a novel, welfare-maximizing threshold calibration framework, demonstrating that modestly tightening AMP thresholds could increase annual buyer surplus by $350,000–$980,000. This work contributes both empirical evidence on AMP efficacy and a normative methodology for optimizing market power mitigation policy.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In auction markets that are prone to market power abuse, preventive mitigation of bid prices can be applied through automated mitigation procedures (AMP). Despite the widespread application of AMP in US electricity markets, there exists scarce evidence on how firms strategically react to such price-cap-and-penalty regulation: when the price cap rarely leads to penalty mitigation, it is difficult to distinguish whether AMP are an effective deterrent or simply too lax. We investigate their impact on the bids of generation firms, using 2019 data from the New York and New England electricity markets (NYISO, ISO-NE). We employ a regression discontinuity design, which exploits the fact that the price cap with penalty is only activated when a structural index (e.g., congestion, pivotality) exceeds a certain cutoff. By estimating the Local Average Treatment Effect (LATE) of screening activation, we can causally identify successful deterrence of anti-competitive behavior. Around 30-40% of the analyzed bidders per market exhibit a significant strategic response - corresponding to a decrease in maximum bid prices of 4-10 $/MWh to avoid the penalty. However, there is significant heterogeneity between firms, and the regulatory impact on the overall market is not statistically detectable, suggesting lax mitigation thresholds. Using a merit-order simulation, we estimate the welfare impact of more stringent thresholds to lie between 350 and 980 thousand dollars of increased buyer surplus per mitigated hour, with the associated number of mitigated hours being below 33 hours/year. Our results motivate the empirical calibration of mitigation thresholds to improve the efficiency of AMP regulation.

Problem

• Investigates strategic bid responses to automated price mitigation in electricity markets
• Evaluates causal deterrence effects on anti-competitive bidding behaviors
• Assesses welfare impacts and efficiency of market power mitigation thresholds

Innovation

• Regression discontinuity design for causal identification
• Local Average Treatment Effect to measure deterrence
• Merit-order simulation for welfare impact estimation