{"title":"利用机器学习优化微电网管理:进展、应用、挑战和未来方向","authors":"Gaurav Singh Negi , Harshit Mohan , Mukul K. Gupta , Rajesh Singh , Anita Gehlot , Amit Kumar Thakur , Sudhanshu Dogra , Lovi Raj Gupta","doi":"10.1016/j.rser.2025.116345","DOIUrl":null,"url":null,"abstract":"<div><div>This study presents a comprehensive review of recent advancements in integrating machine learning (ML) techniques into microgrid management systems, focusing on enhancing sustainability, reliability, and operational efficiency. The primary contribution of this work lies in categorizing and critically comparing various ML approaches including supervised, unsupervised, reinforcement, and deep learning based on their performance, scalability, data requirements, and real-time applicability. A novel aspect of this study is the detailed evaluation of how ML algorithms address key microgrid challenges such as fault detection, load forecasting, energy optimization, and cybersecurity. Unlike previous reviews, this work includes comparative performance metrics (e.g., MAE, RMSE) and real-world case study analyses (e.g., Brooklyn and Austin microgrids), demonstrating how ML integration resulted in up to 20 % reduction in operational costs and 15–30 % improvement in efficiency and fault response time. The study also identifies key limitations in current systems including data quality, model interpretability, and regulatory barriers and recommends future research directions involving federated learning, edge computing, and generative AI. These findings support the development of resilient, scalable, and intelligent energy systems aligned with the UN Sustainable Development Goals.</div></div>","PeriodicalId":418,"journal":{"name":"Renewable and Sustainable Energy Reviews","volume":"226 ","pages":"Article 116345"},"PeriodicalIF":16.3000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging machine learning for optimized microgrid management: Advances, applications, challenges, and future directions\",\"authors\":\"Gaurav Singh Negi , Harshit Mohan , Mukul K. Gupta , Rajesh Singh , Anita Gehlot , Amit Kumar Thakur , Sudhanshu Dogra , Lovi Raj Gupta\",\"doi\":\"10.1016/j.rser.2025.116345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study presents a comprehensive review of recent advancements in integrating machine learning (ML) techniques into microgrid management systems, focusing on enhancing sustainability, reliability, and operational efficiency. The primary contribution of this work lies in categorizing and critically comparing various ML approaches including supervised, unsupervised, reinforcement, and deep learning based on their performance, scalability, data requirements, and real-time applicability. A novel aspect of this study is the detailed evaluation of how ML algorithms address key microgrid challenges such as fault detection, load forecasting, energy optimization, and cybersecurity. Unlike previous reviews, this work includes comparative performance metrics (e.g., MAE, RMSE) and real-world case study analyses (e.g., Brooklyn and Austin microgrids), demonstrating how ML integration resulted in up to 20 % reduction in operational costs and 15–30 % improvement in efficiency and fault response time. The study also identifies key limitations in current systems including data quality, model interpretability, and regulatory barriers and recommends future research directions involving federated learning, edge computing, and generative AI. These findings support the development of resilient, scalable, and intelligent energy systems aligned with the UN Sustainable Development Goals.</div></div>\",\"PeriodicalId\":418,\"journal\":{\"name\":\"Renewable and Sustainable Energy Reviews\",\"volume\":\"226 \",\"pages\":\"Article 116345\"},\"PeriodicalIF\":16.3000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Renewable and Sustainable Energy Reviews\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364032125010184\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable and Sustainable Energy Reviews","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364032125010184","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Leveraging machine learning for optimized microgrid management: Advances, applications, challenges, and future directions
This study presents a comprehensive review of recent advancements in integrating machine learning (ML) techniques into microgrid management systems, focusing on enhancing sustainability, reliability, and operational efficiency. The primary contribution of this work lies in categorizing and critically comparing various ML approaches including supervised, unsupervised, reinforcement, and deep learning based on their performance, scalability, data requirements, and real-time applicability. A novel aspect of this study is the detailed evaluation of how ML algorithms address key microgrid challenges such as fault detection, load forecasting, energy optimization, and cybersecurity. Unlike previous reviews, this work includes comparative performance metrics (e.g., MAE, RMSE) and real-world case study analyses (e.g., Brooklyn and Austin microgrids), demonstrating how ML integration resulted in up to 20 % reduction in operational costs and 15–30 % improvement in efficiency and fault response time. The study also identifies key limitations in current systems including data quality, model interpretability, and regulatory barriers and recommends future research directions involving federated learning, edge computing, and generative AI. These findings support the development of resilient, scalable, and intelligent energy systems aligned with the UN Sustainable Development Goals.
期刊介绍:
The mission of Renewable and Sustainable Energy Reviews is to disseminate the most compelling and pertinent critical insights in renewable and sustainable energy, fostering collaboration among the research community, private sector, and policy and decision makers. The journal aims to exchange challenges, solutions, innovative concepts, and technologies, contributing to sustainable development, the transition to a low-carbon future, and the attainment of emissions targets outlined by the United Nations Framework Convention on Climate Change.
Renewable and Sustainable Energy Reviews publishes a diverse range of content, including review papers, original research, case studies, and analyses of new technologies, all featuring a substantial review component such as critique, comparison, or analysis. Introducing a distinctive paper type, Expert Insights, the journal presents commissioned mini-reviews authored by field leaders, addressing topics of significant interest. Case studies undergo consideration only if they showcase the work's applicability to other regions or contribute valuable insights to the broader field of renewable and sustainable energy. Notably, a bibliographic or literature review lacking critical analysis is deemed unsuitable for publication.