利用机器学习优化微电网管理:进展、应用、挑战和未来方向

IF 16.3 1区 工程技术 Q1 ENERGY & FUELS
Gaurav Singh Negi , Harshit Mohan , Mukul K. Gupta , Rajesh Singh , Anita Gehlot , Amit Kumar Thakur , Sudhanshu Dogra , Lovi Raj Gupta
{"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 ,&nbsp;Harshit Mohan ,&nbsp;Mukul K. Gupta ,&nbsp;Rajesh Singh ,&nbsp;Anita Gehlot ,&nbsp;Amit Kumar Thakur ,&nbsp;Sudhanshu Dogra ,&nbsp;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 ,&nbsp;Harshit Mohan ,&nbsp;Mukul K. Gupta ,&nbsp;Rajesh Singh ,&nbsp;Anita Gehlot ,&nbsp;Amit Kumar Thakur ,&nbsp;Sudhanshu Dogra ,&nbsp;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}
引用次数: 0

摘要

本研究全面回顾了将机器学习(ML)技术集成到微电网管理系统中的最新进展,重点是提高可持续性、可靠性和运行效率。这项工作的主要贡献在于根据性能、可扩展性、数据需求和实时适用性对各种ML方法进行分类和批判性比较,包括监督、无监督、强化和深度学习。本研究的一个新颖方面是详细评估机器学习算法如何解决关键的微电网挑战,如故障检测、负荷预测、能源优化和网络安全。与之前的评论不同,这项工作包括比较性能指标(例如,MAE, RMSE)和实际案例研究分析(例如,布鲁克林和奥斯汀微电网),展示了机器学习集成如何使运营成本降低20%,效率和故障响应时间提高15 - 30%。该研究还确定了当前系统的主要局限性,包括数据质量、模型可解释性和监管障碍,并建议了未来的研究方向,包括联邦学习、边缘计算和生成式人工智能。这些发现支持开发符合联合国可持续发展目标的有弹性、可扩展和智能的能源系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Renewable and Sustainable Energy Reviews
Renewable and Sustainable Energy Reviews 工程技术-能源与燃料
CiteScore
31.20
自引率
5.70%
发文量
1055
审稿时长
62 days
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信