让风电生产商在市场中主动定价，降低因发电波动带来的成本损失。
Learn to Bid as a Price-Maker Wind Power Producer
- 将竞标建模为上下文多臂赌博机，实现在线学习优化
- 在德国日前与实时市场仿真中，显著降低不平衡成本
- 适合有市场影响力且需自主竞价的风电企业
参与短期电力市场的风电生产商（WPP）因发电不可调度且具有波动性，面临显著的不平衡成本。尽管部分WPP拥有足够市场份额可影响市场价格，但现有最优竞价方法很少考虑这一价格制定者（price-maker）特性。传统价格制定方法通常将竞标建模为双层优化问题，需复杂市场建模、估计其他参与者行为，计算开销大。为此，本文提出一种在线学习算法，利用上下文信息优化价格制定者情境下的竞标策略。将战略竞标问题建模为上下文多臂赌博机，确保可证明的后悔最小化。算法性能通过德国日前及实时市场数值仿真，与多种基准策略对比验证。
原文摘要 · Abstract (English)
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.
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