Photovoltaic and energy storage control of partially observable distribution network based on deep reinforcement learning

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS
Q. Bu, P. Lv, Kexin Zhang, Xiaobo Dou, Fei Luo, Xufeng Zhou
{"title":"Photovoltaic and energy storage control of partially observable distribution network based on deep reinforcement learning","authors":"Q. Bu, P. Lv, Kexin Zhang, Xiaobo Dou, Fei Luo, Xufeng Zhou","doi":"10.1109/CYBER55403.2022.9907595","DOIUrl":null,"url":null,"abstract":"After a large number of distributed power sources are connected to the distribution network, the volatility and uncertainty brought by them may lead to the over-limit of the distribution network voltage and the increase of network losses; at the same time, the distribution network itself is also in a partially observable state. In view of these problems, photovoltaic and energy storage are selected as the control objects. In this paper, a photovoltaic energy storage linkage control technology based on deep reinforcement learning is designed, and an example is used to verify the feasibility and effectiveness of the method proposed in this paper.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cybersystems and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBER55403.2022.9907595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

After a large number of distributed power sources are connected to the distribution network, the volatility and uncertainty brought by them may lead to the over-limit of the distribution network voltage and the increase of network losses; at the same time, the distribution network itself is also in a partially observable state. In view of these problems, photovoltaic and energy storage are selected as the control objects. In this paper, a photovoltaic energy storage linkage control technology based on deep reinforcement learning is designed, and an example is used to verify the feasibility and effectiveness of the method proposed in this paper.
基于深度强化学习的部分可观测配电网光伏与储能控制
大量分布式电源接入配电网后,其带来的波动性和不确定性可能导致配电网电压超限,网损增加;同时,配电网本身也处于部分可观测状态。针对这些问题,选择光伏和储能作为控制对象。本文设计了一种基于深度强化学习的光伏储能联动控制技术,并通过实例验证了所提方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
×
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:481959085
Book学术官方微信