Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids
Author:
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Reference40 articles.
1. Optimal energy management for multi-microgrid under a transactive energy framework with distributionally robust optimization;Cao;IEEE Trans Smart Grid,2021
2. Optimal energy management of multi-microgrids connected to distribution system based on deep reinforcement learning;Guo;Int J Electr Power Energy Syst,2021
3. Multi-stage hybrid energy management strategy for reducing energy abandonment and load losses among multiple microgrids;Hou;Int J Electr Power Energy Syst,2023
4. Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method;Yang;Renew Energy,2021
5. Assessing hybrid supercapacitor-battery energy storage for active power management in a wind-diesel system;Shayeghi;Int J Electr Power Energy Syst,2021
Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Integrated energy cluster hierarchical regulation technology considering demand response;Electric Power Systems Research;2024-12
2. A coordinated scheduling optimization method for integrated energy systems with data centres based on deep reinforcement learning;IET Generation, Transmission & Distribution;2024-09-05
3. Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach;International Journal of Electrical Power & Energy Systems;2024-09
4. Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids;Energy Conversion and Economics;2024-08
5. Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning;Frontiers in Energy Research;2024-07-19
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3