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

• URL: http://arxiv.org/abs/2501.07639v1
• Date: Mon, 13 Jan 2025 19:01:58 GMT
• Title: SafePowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models
• Authors: Fabien Bernier, Jun Cao, Maxime Cordy, Salah Ghamizi,
• Abstract summary: This letter introduces SafePowerGraph-LLM, the first framework explicitly designed for solving Optimal Power Flow problems using Large Language Models (LLM) A new implementation of in-context learning and fine-tuning protocols for LLMs is introduced, tailored specifically for the OPF problem. 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.
• Score: 12.312620964361844
• License:
• 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 SafePowerGraph-LLM, the first framework explicitly designed for solving OPF problems using Large Language Models (LLM)s. 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. SafePowerGraph-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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