PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models

TL;DR

PowerGraph-LLM introduces a novel framework that leverages Large Language Models to solve Optimal Power Flow problems by combining graph and tabular data representations, enabling scalable and accurate power grid optimization.

Contribution

This work is the first to utilize LLMs for OPF problems, integrating graph and tabular data with new in-context learning and fine-tuning protocols tailored for power systems.

Findings

Reliable performance with off-the-shelf LLMs

Impact of LLM architecture and size on results

Effective handling of realistic grid constraints

Abstract

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.