{"title":"智能电网中的大型语言模型集成","authors":"Seyyedreza Madani , Ahmadreza Tavasoli , Zahra Khoshtarash Astaneh , Pierre-Olivier Pineau","doi":"10.1016/j.egyr.2025.06.051","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 1562-1577"},"PeriodicalIF":5.1000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large Language Models Integration in Smart Grids\",\"authors\":\"Seyyedreza Madani , Ahmadreza Tavasoli , Zahra Khoshtarash Astaneh , Pierre-Olivier Pineau\",\"doi\":\"10.1016/j.egyr.2025.06.051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":11798,\"journal\":{\"name\":\"Energy Reports\",\"volume\":\"14 \",\"pages\":\"Pages 1562-1577\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Reports\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352484725004445\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Reports","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352484725004445","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
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.
期刊介绍:
Energy Reports is a new online multidisciplinary open access journal which focuses on publishing new research in the area of Energy with a rapid review and publication time. Energy Reports will be open to direct submissions and also to submissions from other Elsevier Energy journals, whose Editors have determined that Energy Reports would be a better fit.