Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915932656369664 |
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| author | Yang, Xu Lin, Chenhui Sha, Licheng Yang, Liping Wu, Shuzhou Tian, Xichen Liu, Haotian Wu, Wenchuan |
| author_facet | Yang, Xu Lin, Chenhui Sha, Licheng Yang, Liping Wu, Shuzhou Tian, Xichen Liu, Haotian Wu, Wenchuan |
| contents | With the advanced reasoning, contextual understanding, and information synthesis capabilities of large language models (LLMs), a novel paradigm emerges for the autonomous generation of dispatch strategies in modern power systems. In this paper, we propose an LLM-based experience-driven day-ahead Volt/Var schedule solution for distribution networks, which enables the self-evolution of LLM agent's strategies through the collaboration and interaction of multiple modules, specifically, experience storage, experience retrieval, experience generation, and experience modification. The experience storage module archives historical operational records and decisions, while the retrieval module selects relevant past cases according to current forecasting conditions. The LLM agent then leverages these retrieved experiences to generate new, context-aware decisions for current situation, which are subsequently refined by the modification module to realize self-evolution of the dispatch policy. Comprehensive experimental results validate the effectiveness of the proposed method and highlight the applicability of LLMs in power system dispatch problems facing incomplete information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14800 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control Yang, Xu Lin, Chenhui Sha, Licheng Yang, Liping Wu, Shuzhou Tian, Xichen Liu, Haotian Wu, Wenchuan Systems and Control Artificial Intelligence With the advanced reasoning, contextual understanding, and information synthesis capabilities of large language models (LLMs), a novel paradigm emerges for the autonomous generation of dispatch strategies in modern power systems. In this paper, we propose an LLM-based experience-driven day-ahead Volt/Var schedule solution for distribution networks, which enables the self-evolution of LLM agent's strategies through the collaboration and interaction of multiple modules, specifically, experience storage, experience retrieval, experience generation, and experience modification. The experience storage module archives historical operational records and decisions, while the retrieval module selects relevant past cases according to current forecasting conditions. The LLM agent then leverages these retrieved experiences to generate new, context-aware decisions for current situation, which are subsequently refined by the modification module to realize self-evolution of the dispatch policy. Comprehensive experimental results validate the effectiveness of the proposed method and highlight the applicability of LLMs in power system dispatch problems facing incomplete information. |
| title | Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2507.14800 |