Research on Self-Decision Control Strategy in Distributed Photovoltaic Intelligent Monitoring Device

This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the dist...

Full description

Saved in:
Bibliographic Details
Published in:IEEE International Conference on Automation, Electronics and Electrical Engineering (Online) pp. 515 - 519
Main Authors: Lei, Tinghao, Cai, Xinya, Wu, Yang, Guo, Yingjie, Li, Yao
Format: Conference Proceeding
Language:English
Published: IEEE 27.12.2024
Subjects:
Collaboration
Distributed photovoltaic
intelligent monitoring
Inverters
Monitoring
multi-agent reinforcement learning algorithm
Optimization
Photovoltaic systems
Power grids
Power system stability
Real-time systems
Reinforcement learning
self-decision-making control
Stability analysis
ISSN:2831-4549
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the distributed photovoltaic power generation process, this paper designs a self-decision control algorithm to achieve real-time monitoring and optimization control of photovoltaic components, inverters and grid interfaces through multi-agent collaboration. Each agent represents a different control unit, which is independent and cooperative with each other, and continuously trains under the reinforcement learning framework to improve the overall performance. The model verification based on the simulation platform shows that the algorithm can significantly improve the response speed and power generation efficiency of the photovoltaic system under different weather and load conditions. The data analysis results show that after adopting this control strategy, the output fluctuation of photovoltaic power generation is reduced by 15%, the inverter efficiency is improved by 12%, the system stability is enhanced, and the impact on the power grid is effectively reduced. In addition, the algorithm has good generalization performance and can be applied to large-scale distributed photovoltaic networks.
AbstractList This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the distributed photovoltaic power generation process, this paper designs a self-decision control algorithm to achieve real-time monitoring and optimization control of photovoltaic components, inverters and grid interfaces through multi-agent collaboration. Each agent represents a different control unit, which is independent and cooperative with each other, and continuously trains under the reinforcement learning framework to improve the overall performance. The model verification based on the simulation platform shows that the algorithm can significantly improve the response speed and power generation efficiency of the photovoltaic system under different weather and load conditions. The data analysis results show that after adopting this control strategy, the output fluctuation of photovoltaic power generation is reduced by 15%, the inverter efficiency is improved by 12%, the system stability is enhanced, and the impact on the power grid is effectively reduced. In addition, the algorithm has good generalization performance and can be applied to large-scale distributed photovoltaic networks.
Author Guo, Yingjie
Lei, Tinghao
Wu, Yang
Li, Yao
Cai, Xinya
Author_xml – sequence: 1
  givenname: Tinghao
  surname: Lei
  fullname: Lei, Tinghao
  email: 850776032@qq.com
  organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China
– sequence: 2
  givenname: Xinya
  surname: Cai
  fullname: Cai, Xinya
  organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China
– sequence: 3
  givenname: Yang
  surname: Wu
  fullname: Wu, Yang
  organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China
– sequence: 4
  givenname: Yingjie
  surname: Guo
  fullname: Guo, Yingjie
  organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China
– sequence: 5
  givenname: Yao
  surname: Li
  fullname: Li, Yao
  organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China
BookMark eNo1kM1OAjEYAKvRRMR9Aw_1ARa_brv9ORJYlQSjETiT0n4LNWtrdisJb6-JeprMZQ5zTS5iikjIHYMJY2Dup5t10zSy0ppNKqjEhIGWRtXmjBRGGc1r4EoB5-dkVGnOSlELc0WKYXgHAF6BgBpGxL_hgLZ3B5oiXWHXlnN0YQg_Nksx96mjq9zbjPsTDZHOw5D7sPvK6OnrIeV0TF22wdFFzNh1YY8x0-cUQ059iHs6x2NweEMuW9sNWPxxTDYPzXr2VC5fHhez6bIMTMlcql3lvFBaOMkk1N6CQlm3Fn3FtAOplWCuBWOlZ9wDQlt7BMHRGrNTtuVjcvvbDYi4_ezDh-1P2_8x_BvMv1wO
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/AUTEEE62881.2024.10869759
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library (IEL) (UW System Shared)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798350377033
EISSN 2831-4549
EndPage 519
ExternalDocumentID 10869759
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i176t-7b2cd4784c61605da07e65faed218c068741cf09a6d13d0e0f5de043ea99b7af3
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001443032700089&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Feb 26 09:45:39 EST 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i176t-7b2cd4784c61605da07e65faed218c068741cf09a6d13d0e0f5de043ea99b7af3
PageCount 5
ParticipantIDs ieee_primary_10869759
PublicationCentury 2000
PublicationDate 2024-Dec.-27
PublicationDateYYYYMMDD 2024-12-27
PublicationDate_xml – month: 12
  year: 2024
  text: 2024-Dec.-27
  day: 27
PublicationDecade 2020
PublicationTitle IEEE International Conference on Automation, Electronics and Electrical Engineering (Online)
PublicationTitleAbbrev AUTEEE
PublicationYear 2024
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003204050
Score 1.8939887
Snippet This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic...
SourceID ieee
SourceType Publisher
StartPage 515
SubjectTerms Collaboration
Distributed photovoltaic
intelligent monitoring
Inverters
Monitoring
multi-agent reinforcement learning algorithm
Optimization
Photovoltaic systems
Power grids
Power system stability
Real-time systems
Reinforcement learning
self-decision-making control
Stability analysis
Title Research on Self-Decision Control Strategy in Distributed Photovoltaic Intelligent Monitoring Device
URI https://ieeexplore.ieee.org/document/10869759
WOSCitedRecordID wos001443032700089&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5WRDypWPFNBK9ps9lssjm3FgUpBR_0VvKY4ILsSt0K_nuT3W3FgwdvIRAImWRemfk-hG4o9UHvgyZGciDcSkU0SywR1FhNRVDLDXXCy4OcTvP5XM26ZvWmFwYAmuIzGMRh85fvKruKqbJhZAVSMlM91JNStM1am4RKysJ9zOguuu5wNIdBwiFginy6MRBkfLBe_4tJpTEkk_1_buEA9X9a8vBsY2wO0RaUR8it6-ZwVeJHePNk3FHm4FFbgo479NkvXJR4HEFyI78VODx7reoqqKZaFxbfb3A5a9w-8pjtw2OIaqSPnie3T6M70tEmkCKRoibSMOu4zLkVSQhWnKYSROY1uGDOLRV5cCKsp0oLl6SOAvWZA8pT0EoZqX16jLbLqoQThLXJWKadNznzPLhaWvDcKZdnhtPcK36K-vGIFu8tMsZifTpnf8yfo70oiFgOwuQF2q6XK7hEO_azLj6WV408vwFdKKNH
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA86RT2pOPHbCF6zpW3SNOdN2XCOgVN2G2nyggVpZXaC_71J2008ePAWcgghL3lfee_3Q-iWUuv0PiiSCgaEaSGJCgNNYppqRWOnlivqhJeRGI-T2UxOmmb1qhcGAKriM-j4YfWXbwq99KmyrmcFkoLLTbTFGQtp3a61TqlEobuRnO6gmwZJs-tk7EImz6jrQ8GQdVYr_OJSqUzJ_f4_N3GA2j9NeXiyNjeHaAPyI2RWlXO4yPETvFnSb0hzcK8uQscN_uwXznLc9zC5nuEKDJ68FmXhlFOpMo2Ha2TOEtfP3Of7cB-8Immj5_u7aW9AGuIEkgUiLolIQ22YSJiOAxeuGEUFxNwqMM6gaxonzo3QlkoVmyAyFKjlBiiLQEmZCmWjY9TKixxOEFYpD7kyNk1Cy5yzpWKWGGkSnjKaWMlOUdsf0fy9xsaYr07n7I_5a7Q7mD6O5qPh-OEc7Xmh-OKQUFygVrlYwiXa1p9l9rG4qmT7DQiTpo4
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE+International+Conference+on+Automation%2C+Electronics+and+Electrical+Engineering+%28Online%29&rft.atitle=Research+on+Self-Decision+Control+Strategy+in+Distributed+Photovoltaic+Intelligent+Monitoring+Device&rft.au=Lei%2C+Tinghao&rft.au=Cai%2C+Xinya&rft.au=Wu%2C+Yang&rft.au=Guo%2C+Yingjie&rft.date=2024-12-27&rft.pub=IEEE&rft.eissn=2831-4549&rft.spage=515&rft.epage=519&rft_id=info:doi/10.1109%2FAUTEEE62881.2024.10869759&rft.externalDocID=10869759