Large Language Models integration in Smart Grids

TL;DR

The paper argues that Large Language Models can meaningfully transform smart grids by integrating real-time grid data, markets, and consumer behavior across eight use-case clusters. It maps thirty concrete applications to operational, market, planning, security, and societal dimensions, proposing an evaluation framework and addressing critical challenges such as privacy, reliability, and interoperability. The study demonstrates that LLMs enable adaptive grid control, autonomous trading, personalized customer engagement, and cross-domain knowledge synthesis, with significant potential for resilience and efficiency gains. Ultimately, the work highlights the need for responsible deployment, governance, and human oversight to maximize benefits while mitigating risks.

Abstract

Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well - but how exactly? By integrating various data streams - including real-time grid data, market dynamics, and consumer behaviors - LLMs have the potential to make power system operations more adaptive, enhance proactive security measures, and deliver personalized energy services. This paper provides a comprehensive analysis of 30 real-world applications across eight key categories: Grid Operations and Management, Energy Markets and Trading, Personalized Energy Management and Customer Engagement, Grid Planning and Education, Grid Security and Compliance, Advanced Data Analysis and Knowledge Discovery, Emerging Applications and Societal Impact, and LLM-Enhanced Reinforcement Learning. Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions. Ultimately, this review illustrates how LLMs can significantly contribute to building more resilient, efficient, and sustainable energy infrastructures, underscoring the necessity of their responsible and equitable deployment.