离线强化学习解决光伏接入微电网电压调控难题
Offline Reinforcement Learning for Microgrid Voltage Regulation
- 基于历史数据离线训练，无需实时环境交互
- 在IEEE 33节点系统上验证有效，低质量数据亦可适用
- 适合安全敏感场景，如真实微电网控制部署
本文研究了不同离线强化学习算法在高比例光伏接入微电网电压调节中的应用。由于技术或安全原因无法进行实时环境交互时，该方法可通过先前收集的数据集进行离线训练，获得可用模型，降低在线交互缺失带来的负面影响。在IEEE 33节点系统上的实验结果表明，该方法在多种离线数据集上均具备可行性与有效性，包括仅含低质量经验的数据集。
原文摘要 · Abstract (English)
This paper presents a study on using different offline reinforcement learning algorithms for microgrid voltage regulation with solar power penetration. When environment interaction is unviable due to technical or safety reasons, the proposed approach can still obtain an applicable model through offline-style training on a previously collected dataset, lowering the negative impact of lacking online environment interactions. Experiment results on the IEEE 33-bus system demonstrate the feasibility and effectiveness of the proposed approach on different offline datasets, including the one with merely low-quality experience.
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