电池系统同时参与日内与调频市场，提升收益并逼近理想水平。
Joint Bidding on Intraday and Frequency Containment Reserve Markets
- 联合出清策略融合日内连续交易与一次调频市场，动态分配储能容量。
- 实测数据表明利润比最优静态策略高4%以上，接近理想预测水平仅差4%。
- 适合关注多市场协同优化的储能运营者与电力市场研究者。
随着可再生能源渗透率上升，电力供需波动加剧，电池储能系统（BESS）成为平衡供需的有效方案。本文提出一种新型联合出清策略，将一次频率调节市场与连续的日内市场参与相结合，弥补了现有研究通常孤立分析或简化日内交易连续性的不足。方法采用滚动内在算法的混合整数线性规划实现日内决策与电量状态恢复，并引入学习型分类器策略（LCS）以优化各市场容量分配。基于超过一年德国市场历史数据的离样本回测验证显示：相较于表现最佳的静态策略，LCS使总收益提升超4%，较简单动态基准提升超3%；关键在于，该方法将与理论完美预见策略的差距缩小至仅4%，证明了在复杂多市场环境中，基于学习的动态分配策略具有高度有效性。
原文摘要 · Abstract (English)
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.
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