用大模型解决电网优化问题，首次实现大模型与电网图结构结合。
PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models
- 融合电网图结构与表格数据，引导大模型理解电力系统约束。
- 无需自研模型，直接使用现成大模型即实现快速准确求解。
- 适用于真实电网场景，支持复杂元件与多类约束处理。
高效求解电力系统的最优潮流（OPF）问题是运行规划与电网管理的关键。随着现代电网中波动性、约束和不确定性日益增加，亟需可扩展的算法以提供快速且精确的解决方案。近年来，机器学习技术尤其是图神经网络（GNNs）展现出巨大潜力。本文提出PowerGraph-LLM，首个专为利用大语言模型（LLMs）求解OPF问题设计的框架。该方法结合电网的图表示与表格数据，有效引导LLM建模电力系统的复杂关系与约束。研究引入针对OPF问题定制的上下文学习与微调协议。实验表明，该框架在无需自研模型的前提下，即可实现可靠性能。研究揭示了大模型架构、规模及微调策略的影响，并验证了其对真实电网组件与约束的处理能力。
原文摘要 · Abstract (English)
Efficiently solving Optimal Power Flow (OPF) problems in power systems is crucial for operational planning and grid management. There is a growing need for scalable algorithms capable of handling the increasing variability, constraints, and uncertainties in modern power networks while providing accurate and fast solutions. To address this, machine learning techniques, particularly Graph Neural Networks (GNNs) have emerged as promising approaches. This letter introduces PowerGraph-LLM, the first framework explicitly designed for solving OPF problems using Large Language Models (LLMs). The proposed approach combines graph and tabular representations of power grids to effectively query LLMs, capturing the complex relationships and constraints in power systems. A new implementation of in-context learning and fine-tuning protocols for LLMs is introduced, tailored specifically for the OPF problem. PowerGraph-LLM demonstrates reliable performances using off-the-shelf LLM. Our study reveals the impact of LLM architecture, size, and fine-tuning and demonstrates our framework's ability to handle realistic grid components and constraints.
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