用强化学习优化光伏储能调度，提升电力平衡效率
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
- 基于深度强化学习动态调控光伏配储，实现近最优运行
- 平均达理论最优的61%（最高96%），优于传统控制方法
- 适合未来信号难预测场景，可缓解多站点集中充放电风险
可再生能源出力波动加剧了电力供需平衡难度。与光伏电站共址的大型储能系统有助于缓解这种失配。本文研究利用强化学习（RL）来运行光伏配套的电网级电池。结果表明，该方法在平均情况下达到约理论最优（非因果）运行水平的61%（最高可达96%），且优于现有先进控制方法。研究发现，当未来信号难以预测时，强化学习更具优势。此外，相比简单规则控制，强化学习具有两大优势：（1）更有效地将太阳能转移至高需求时段；（2）不同位置电池调度多样性增强，减少因大量相似动作叠加引发的爬坡问题。
原文摘要 · Abstract (English)
Variable renewable generation increases the challenge of balancing power supply and demand. Grid-scale batteries co-located with generation can help mitigate this misalignment. This paper explores the use of reinforcement learning (RL) for operating grid-scale batteries co-located with solar power. Our results show RL achieves an average of 61% (and up to 96%) of the approximate theoretical optimal (non-causal) operation, outperforming advanced control methods on average. Our findings suggest RL may be preferred when future signals are hard to predict. Moreover, RL has two significant advantages compared to simpler rules-based control: (1) that solar energy is more effectively shifted towards high demand periods, and (2) increased diversity of battery dispatch across different locations, reducing potential ramping issues caused by super-position of many similar actions.
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