电表前电池储能系统的数据驱动序贯市场优化
Data-Driven Sequential Market Optimization for Front-of-the-Meter Battery Energy Storage Systems
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中文总结 AI 辅助
针对电池储能系统融入电力市场时现有模型的不足，提出数据驱动序贯优化框架，明确建模市场机制与关闸时间，各阶段优化收益并保持可行性，基于机会成本的投标策略提高了系统运营的现实性和盈利能力。
中文摘要 AI 辅助
电池储能系统日益融入电力市场，凸显了跨能源和平衡服务协调参与以充分利用其运营灵活性的重要性。然而，现有收益叠加模型常简化市场序列并忽视滚动预测影响，导致调度不现实且收益高估。本文引入一个针对电表前电池储能系统的序贯、数据驱动优化框架，该框架反映实际市场运作。它明确建模市场机制及其在不同市场的关闸时间，各市场阶段利用更新价格预测优化剩余所有市场的预期收益，同时保持技术和监管限制内的可行性。关键贡献是基于机会成本的投标策略，从剩余容量优化中内生得出与市场一致的投标价格和数量。对一个代表性运营日的验证表明，该框架产生一致且可行的调度，有效适应更新预测，最小化计划与实际调度之间的偏差，从而提高电池储能系统运营的现实性和盈利能力。
英文摘要
The growing integration of Battery Energy Storage Systems into electricity markets has highlighted the importance of coordinated participation across energy and balancing services to fully exploit their operational flexibility. However, existing revenue-stacking models often simplify market sequences and neglect the impact of rolling forecasts, leading to unrealistic scheduling and overestimated revenues. This paper addresses this gap by introducing a sequential, data-driven optimization framework for Front-of-the-Meter Battery Energy Storage Systems that mirrors actual market operations. The framework explicitly models market mechanisms and its respective Gate Closure Times across Frequency Containment Reserve, automated Frequency Restoration Reserve, Day-Ahead Auction, and Intraday Continuous markets. Each market stage optimizes expected revenue over all remaining markets using updated price forecasts while maintaining feasibility within both technical and regulatory limits. A key contribution is the opportunity-cost-based bidding strategy, which endogenously derives market-consistent bid prices and quantities from residual capacity optimization. Validation for a representative operating day demonstrates that the framework yields consistent and feasible schedules, effectively adapts to updated forecasts, and minimizes deviations between planned and realized dispatch, thereby enhancing the realism and profitability of BESS operation.
