Wind energy has been rapidly gaining popularity as a means for combating climate change. However, the variable nature of wind generation can undermine system reliability and lead to wind curtailment, causing substantial economic losses to wind power producers. Battery energy storage systems (BESS) that serve as onsite backup sources are among the solutions to mitigate wind curtailment. However, such an auxiliary role of the BESS might severely weaken its economic viability. This paper addresses the issue by proposing joint wind curtailment reduction and energy arbitrage for the BESS. We decouple the market participation of the co-located wind-battery system and develop a joint-bidding framework for the wind farm and BESS. It is challenging to optimize the joint-bidding because of the stochasticity of energy prices and wind generation. Therefore, we leverage deep reinforcement learning to maximize the overall revenue from the spot market while unlocking the BESS's potential in concurrently reducing wind curtailment and conducting energy arbitrage. We validate the proposed strategy using realistic wind farm data and demonstrate that our joint-bidding strategy responds better to wind curtailment and generates higher revenues than the optimization-based benchmark. Our simulations also reveal that the extra wind generation used to be curtailed can be an effective power source to charge the BESS, resulting in additional financial returns.

翻译：风能作为应对气候变化的手段正在迅速普及。然而，风电出力的波动性可能损害系统可靠性并导致弃风现象，给风电运营商造成重大经济损失。作为现场备用电源的电池储能系统是缓解弃风问题的解决方案之一。然而，储能系统的此类辅助角色可能严重削弱其经济可行性。本文提出通过联合弃风削减与能量套利的储能调度策略来解决该问题。我们解耦了风-储联合系统的市场参与模式，并开发了风电场与储能系统的联合竞价框架。由于电价和风电出力的随机性，优化联合竞价颇具挑战性。因此，我们利用深度强化学习在最大化现货市场整体收益的同时，释放储能系统在同步实现弃风削减与能量套利方面的潜力。基于真实风电场的仿真验证表明，相比基于优化的基准方法，本文提出的联合竞价策略能更有效地应对弃风问题并产生更高收益。仿真结果还揭示，原本会被弃用的额外风电可作为对储能系统充电的有效电源，从而带来额外经济效益。