Large Language Models for Power System Applications: A Comprehensive Literature Survey

TL;DR

This survey reviews the integration of Large Language Models into power system engineering, highlighting their potential, challenges, and future research directions for improving operations, planning, and management.

Contribution

It provides a comprehensive overview of recent LLM applications in power systems, identifying key trends, challenges, and future research pathways.

Findings

LLMs enhance fault diagnosis and load forecasting.

Challenges include limited domain data and safety concerns.

Emerging trends involve power-specific LLMs and hybrid methods.

Abstract

This comprehensive literature review examines the emerging applications of Large Language Models (LLMs) in power system engineering. Through a systematic analysis of recent research published between 2020 and 2025, we explore how LLMs are being integrated into various aspects of power system operations, planning, and management. The review covers key application areas including fault diagnosis, load forecasting, cybersecurity, control and optimization, system planning, simulation, and knowledge management. Our findings indicate that while LLMs show promising potential in enhancing power system operations through their advanced natural language processing and reasoning capabilities, significant challenges remain in their practical implementation. These challenges include limited domain-specific training data, concerns about reliability and safety in critical infrastructure, and the need for enhanced explainability. The review also highlights emerging trends such as the development of power system-specific LLMs and hybrid approaches combining LLMs with traditional power engineering methods. We identify crucial research directions for advancing the field, including the development of specialized architectures, improved security frameworks, and enhanced integration with existing power system tools. This survey provides power system researchers and practitioners with a comprehensive overview of the current state of LLM applications in the field and outlines future pathways for research and development.