As renewable energy integration increases supply variability, battery energy storage systems (BESS) present a viable solution for balancing supply and demand. This paper proposes a novel approach for optimizing battery BESS participation in multiple electricity markets. We develop a joint bidding strategy that combines participation in the primary frequency reserve market with continuous trading in the intraday market, addressing a gap in the extant literature which typically considers these markets in isolation or simplifies the continuous nature of intraday trading. Our approach utilizes a mixed integer linear programming implementation of the rolling intrinsic algorithm for intraday decisions and state of charge recovery, alongside a learned classifier strategy (LCS) that determines optimal capacity allocation between markets. A comprehensive out-of-sample backtest over more than one year of historical German market data validates our approach: The LCS increases overall profits by over 4% compared to the best-performing static strategy and by more than 3% over a naive dynamic benchmark. Crucially, our method closes the gap to a theoretical perfect foresight strategy to just 4%, demonstrating the effectiveness of dynamic, learning-based allocation in a complex, multi-market environment.

翻译：随着可再生能源并网增加电力供应的波动性，电池储能系统（BESS）为平衡供需提供了一种可行的解决方案。本文提出了一种优化电池储能系统参与多类电力市场的新方法。我们开发了一种联合竞价策略，将参与一次频率备用市场与在日内市场中的连续交易相结合，弥补了现有文献通常孤立考虑这些市场或简化日内交易连续性的研究空白。我们的方法采用混合整数线性规划实现滚动内在算法，用于日内决策与荷电状态恢复，同时结合一种学习分类器策略（LCS）来确定市场间的最优容量分配。基于超过一年的德国市场历史数据进行全面的样本外回测验证了我们的方法：与表现最佳的静态策略相比，LCS将总利润提高了4%以上；与一种简单的动态基准策略相比，利润提升超过3%。至关重要的是，我们的方法将利润与理论完美预见策略的差距缩小至仅4%，证明了在复杂的多市场环境中动态、基于学习的容量分配方法的有效性。