Learning to Bid in FCR Markets: A Best-of-Both-Worlds Approach

2026-05-29

AI Summary

This study addresses the challenge faced by bidders in the European Frequency Containment Reserve (FCR) market, who struggle to optimize their bids due to limited feedback—only clearing prices and awarded volumes. To tackle this, the work models multi-country FCR clearing as a repeated multi-unit uniform-price auction and introduces a “best-of-both-worlds” online learning algorithm based on combinatorial semi-bandits. The proposed algorithm achieves logarithmic pseudo-regret under stochastic environments and maintains a √T regret bound in adversarial non-stationary settings, relying solely on standard market feedback for strategy updates. Empirical validation confirms the theoretical guarantees, and backtesting on historical data demonstrates significant performance gains over baseline methods such as EXP3, particularly on stable products—a feature closely aligned with real-world market characteristics.

Abstract

Bidding in the European Frequency Containment Reserve (FCR) market is challenging for flexibility providers because competing offers are hidden and bidders observe only partial feedback form the market, such as, clearing price and awarded quantity. For a participant active in a single country, we show that the multi-country FCR clearing problem can be recast as a repeated multi-unit uniform-price auction against an endogenous vector of opposing bids. This reformulation yields an online learning problem and allows us to adapt a Best-of-Both-Worlds combinatorial semi-bandit algorithm implementable from this standard market feedback. The resulting bidder achieves logarithmic pseudo-regret in stochastic environments and $\mathcal{O}(\sqrt{T})$ regret in adversarial ones. Synthetic experiments confirm the expected scaling, and backtests on historical European FCR data show competitive performance in practice: the method performs especially well on stable products, while EXP3-type baselines can be safer under stronger non-stationarity. Overall, the results show that learning-based bidding in FCR markets is theoretically grounded and practically useful when the learning rule matches product-level market stability.

Problem

• FCR market
• bidding
• partial feedback
• online learning
• uniform-price auction

Innovation

• Best-of-Both-Worlds
• combinatorial semi-bandit
• frequency containment reserve
• online learning
• uniform-price auction