Redesigning automated market power mitigation in electricity markets

Abstract

Electricity markets are prone to the abuse of market power. Several U.S. markets employ algorithms to monitor and mitigate market power abuse in real-time. The performance of automated mitigation procedures is contingent on precise estimates of firms' marginal production costs. Currently, marginal cost is inferred from the past offers of a plant. We present new estimation approaches and compare them to the currently applied benchmark method. We test the performance of all approaches on auction data from the Iberian power market. The results show that our novel approaches outperform the benchmark approach significantly, reducing the mean (median) absolute estimation error from 11.53 (6.08) €/MWh in the benchmark to 4.03 (2.64) €/MWh for our preferred approach. This approach also performs best in our subsequent simulation of mitigation procedures. Here we find large welfare transfers from supplier to buyer surplus as well as a robust overall welfare gain, stemming from both productive and allocative efficiency gains. Our research contributes to accurate monitoring of market power and improved automated mitigation. Although we focus on power markets, our findings are applicable to monitoring of renewable energy tenders or market power surveillance in rail and air traffic.