用大模型模拟电网调度员，自动优化配电网络电压控制方案。
Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control
- 构建多模块协同的LLM调度系统，通过历史经验自适应生成策略。
- 在缺乏完整信息条件下，实现日间无功电压调度的自主演化与优化。
- 适合电力系统智能调度、大模型应用研究者参考。
得益于大语言模型（LLM）的推理、上下文理解与信息整合能力，现代电力系统中自主生成调度策略的新范式应运而生。本文提出一种基于LLM的经验驱动型配电网络日间电压/无功（Volt/Var）调度方案，通过多个模块协同实现调度策略的自我演化：经验存储模块归档历史运行记录与决策，检索模块根据当前预测条件选取相关过往案例，LLM代理利用这些检索到的经验生成符合当前情境的新决策，并由修正模块进行优化，从而实现调度策略的持续进化。大量实验验证了该方法的有效性，凸显了LLM在信息不完整场景下电力系统调度问题中的适用性。
原文摘要 · Abstract (English)
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.
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