Learning to Bid in Forward Electricity Markets Using a No-Regret Algorithm
Arega Getaneh Abate, Dorsa Majdi, Jalal Kazempour, Maryam Kamgarpour
TL;DR
The paper addresses bidding in day-ahead electricity markets under limited knowledge of rivals by applying no-regret online learning with full information feedback. It uses the Hedge algorithm to adapt bids over repeated auctions and benchmarks against Nash-equilibrium/EPEC-based solutions obtained via diagonalization. Key findings indicate that learning can increase social cost and market prices, implying greater supplier market power under certain information regimes, while more information generally improves efficiency. The results motivate regulators and researchers to further explore online learning in realistic market settings and multi-stage market structures, incorporating contextual data and broader simulations.
Abstract
It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perfectly or with some uncertainty. This is not necessarily a realistic assumption. This paper takes a different approach by relaxing such an assumption and exploits a no-regret learning algorithm for repeated games. In particular, by using the \emph{a posteriori} information about rivals' bids, a learner can implement a no-regret algorithm to optimize her/his decision making. Given this information, we utilize a multiplicative weight-update algorithm, adapting bidding strategies over multiple rounds of an auction to minimize her/his regret. Our numerical results show that when the proposed learning approach is used the social cost and the market-clearing prices can be higher than those corresponding to the classical game-theoretic approaches. The takeaway for market regulators is that electricity markets might be exposed to greater market power of suppliers than what classical analysis shows.