Learn to Bid as a Price-Maker Wind Power Producer
This addresses the challenge of optimizing bids for wind power producers who can influence market prices, offering a computationally efficient alternative to complex bilevel optimization methods.
The paper tackled the problem of wind power producers facing high imbalance costs in short-term power markets by proposing an online learning algorithm for strategic bidding as price-makers, achieving provable regret minimization and evaluating performance through simulations of German markets.
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.