Offline Reinforcement Learning for Microgrid Voltage Regulation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910945005010944 |
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| author | Yang, Shan Zhu, Yongli |
| author_facet | Yang, Shan Zhu, Yongli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Offline Reinforcement Learning for Microgrid Voltage Regulation Yang, Shan Zhu, Yongli Artificial Intelligence Systems and Control 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. |
| title | Offline Reinforcement Learning for Microgrid Voltage Regulation |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2505.09920 |