Learn to Bid as a Price-Maker Wind Power Producer

2025-03-20

AI Summary

Wind energy producers face high imbalance costs in short-term electricity markets due to their non-dispatchable and highly uncertain generation, while large wind players with market power are commonly modeled as price takers—overlooking their role as price setters. This paper formulates price-making wind bidding as a contextual multi-armed bandit problem—the first such formulation in the literature. We propose a model-free online learning bidding strategy that requires neither a market model nor estimation of competitors’ behaviors, achieving provably low regret via a bi-level optimization approximation. Evaluated on realistic German day-ahead and real-time market simulations, our approach significantly reduces imbalance costs, achieves higher cumulative revenue than benchmark strategies (e.g., price-taker and rule-based bidding), and exhibits superior regret growth rate. The method establishes a theoretically rigorous yet practically viable paradigm for large-scale wind integration into competitive electricity markets.

Technology Category

• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model bidding as a bilevel optimization problem, but these methods require complex market models, estimating other participants' actions, and are computationally demanding. To address these challenges, we propose an online learning algorithm that leverages contextual information to optimize WPP bids in the price-maker setting. We formulate the strategic bidding problem as a contextual multi-armed bandit, ensuring provable regret minimization. The algorithm's performance is evaluated against various benchmark strategies using a numerical simulation of the German day-ahead and real-time markets.

Problem

• Reducing imbalance costs for wind power producers
• Optimizing bids for price-maker wind power producers
• Developing an online learning algorithm for strategic bidding

Innovation

• Online learning algorithm for WPP bidding
• Contextual multi-armed bandit formulation
• Regret minimization in price-maker setting