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.

翻译：高效求解电力系统中的最优潮流问题对于运行规划与电网管理至关重要。随着现代电网中可变性、约束条件和不确定性的日益增加，对能够提供准确快速解决方案的可扩展算法的需求日益增长。为此，机器学习技术，特别是图神经网络，已成为前景广阔的方法。本文首次提出了PowerGraph-LLM，这是一个明确为利用大型语言模型解决最优潮流问题而设计的框架。所提出的方法结合了电网的图表示和表格表示，以有效查询大型语言模型，从而捕捉电力系统中复杂的关联与约束。本文针对最优潮流问题，专门引入了一种新的上下文学习与微调协议实现。PowerGraph-LLM使用现成的大型语言模型展现了可靠的性能。我们的研究揭示了大型语言模型架构、规模及微调的影响，并证明了该框架处理实际电网组件与约束的能力。