Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) as onsite backup sources. Yet, this auxiliary role may significantly weaken the economic potential of BESS in energy trading. Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding, but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation. We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets. We propose a novel deep reinforcement learning-based approach that decouples the system's market participation into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy, with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25\% and more wind curtailment reduction by 2.3 times. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately. Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains. The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

翻译：风能作为一种缓解气候变化的清洁能源正被广泛应用，但出力波动性导致的弃风问题给风电场所有者带来显著经济损失。利用电池储能系统（BESS）作为现场备用电源可减少弃风，然而这种辅助角色可能削弱BESS在电力交易中的经济潜力。理想的BESS调度需在降低现场弃风与参与市场竞价之间取得平衡，但由于协同复杂性及能源价格与风电出力的随机性，实际部署存在挑战。本研究针对风光储联合系统在现货市场与调频辅助服务市场中的联合竞价策略展开探讨，提出一种基于深度强化学习的新方法，将系统的市场参与行为解耦为两个关联的马尔可夫决策过程，使BESS在吸收现场弃风电量的同时，通过联合市场竞价实现运营收益最大化。基于真实风电场数据的验证表明，相较于优化基准方法，所提协同竞价策略可提升约25%的总收益，并实现弃风消纳量2.3倍的提升。研究结果证实，相较于独立参与各细分市场，联合市场竞价可显著改善风光储系统的经济表现。仿真还发现，将弃风发电作为BESS充电电源可带来额外财务增益。该算法的成功实施将激励发电资产与储能设施协同布局，从而释放更广泛的系统效益。