Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach
This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on industrial informatics 2020-05, Vol.16 (5), p.3493-3503 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3503 |
|---|---|
| container_issue | 5 |
| container_start_page | 3493 |
| container_title | IEEE transactions on industrial informatics |
| container_volume | 16 |
| creator | Shin, MyungJae Choi, Dae-Hyun Kim, Joongheon |
| description | This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this article proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed method achieves a desirable performance in terms of reducing the operation costs of electric vehicle charging stations. |
| doi_str_mv | 10.1109/TII.2019.2944183 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_webofscience_primary_000519588700058</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8851270</ieee_id><sourcerecordid>2358912906</sourcerecordid><originalsourceid>FETCH-LOGICAL-c338t-741070aba51f96ed7ab982a266a3d6b29209e1a2c4ca78dfa0f811a9f31e33703</originalsourceid><addsrcrecordid>eNqNkE1v00AQhi0EEqVwR-KyEkfkdGbXH7vcIjdApFQgUnq1xptxspW7NmsHxIm_zlqu4Mpp5vA-72ieJHmNsEIEc3W73a4koFlJk2Wo1ZPkAk2GKUAOT-Oe55gqCep58mIc7wFUCcpcJL-rvh840OR-sLghT0d-YD-Jtg_iy93VZr9PN56ajg9i07GdgrPijk_OdiyqE4Wj80exnyLf-_G9WIubcze52BI7rpkH8ZWdj2V2qd0xBT8j62EIPdnTy-RZS93Irx7nZfLtw-a2-pTuPn_cVutdapXSU1pmCCVQQzm2puBDSY3RkmRRkDoUjTQSDCNJm1kq9aElaDUimVYhq_nVy-Tt0hvPfj_zONX3_Tn4eLKWKtcGpYEipmBJ2dCPY-C2HoJ7oPCrRqhnzXXUXM-a60fNEXm3ID-56dvROvaW_2IQ9aPJtS7nTce0_v905RavVX_2U0TfLKhj_odonaOM__0BINSZ7A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2358912906</pqid></control><display><type>article</type><title>Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach</title><source>IEEE Electronic Library (IEL)</source><creator>Shin, MyungJae ; Choi, Dae-Hyun ; Kim, Joongheon</creator><creatorcontrib>Shin, MyungJae ; Choi, Dae-Hyun ; Kim, Joongheon</creatorcontrib><description>This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this article proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed method achieves a desirable performance in terms of reducing the operation costs of electric vehicle charging stations.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2019.2944183</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Automation & Control Systems ; Companies ; Computer Science ; Computer Science, Interdisciplinary Applications ; Electric vehicle charging ; Electric vehicles ; Energy management ; Energy storage ; Engineering ; Engineering, Industrial ; Machine learning ; Multi-agent systems ; Multiagent systems ; neural networks ; Optimization ; Performance evaluation ; Photovoltaic cells ; Planning ; Reinforcement learning ; Schedules ; scheduling algorithms ; Science & Technology ; Stations ; Technology</subject><ispartof>IEEE transactions on industrial informatics, 2020-05, Vol.16 (5), p.3493-3503</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>114</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000519588700058</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c338t-741070aba51f96ed7ab982a266a3d6b29209e1a2c4ca78dfa0f811a9f31e33703</citedby><cites>FETCH-LOGICAL-c338t-741070aba51f96ed7ab982a266a3d6b29209e1a2c4ca78dfa0f811a9f31e33703</cites><orcidid>0000-0001-6505-1448 ; 0000-0002-1794-6076 ; 0000-0002-9248-9522</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8851270$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27931,27932,28255,54765</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8851270$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shin, MyungJae</creatorcontrib><creatorcontrib>Choi, Dae-Hyun</creatorcontrib><creatorcontrib>Kim, Joongheon</creatorcontrib><title>Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><addtitle>IEEE T IND INFORM</addtitle><description>This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this article proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed method achieves a desirable performance in terms of reducing the operation costs of electric vehicle charging stations.</description><subject>Automation & Control Systems</subject><subject>Companies</subject><subject>Computer Science</subject><subject>Computer Science, Interdisciplinary Applications</subject><subject>Electric vehicle charging</subject><subject>Electric vehicles</subject><subject>Energy management</subject><subject>Energy storage</subject><subject>Engineering</subject><subject>Engineering, Industrial</subject><subject>Machine learning</subject><subject>Multi-agent systems</subject><subject>Multiagent systems</subject><subject>neural networks</subject><subject>Optimization</subject><subject>Performance evaluation</subject><subject>Photovoltaic cells</subject><subject>Planning</subject><subject>Reinforcement learning</subject><subject>Schedules</subject><subject>scheduling algorithms</subject><subject>Science & Technology</subject><subject>Stations</subject><subject>Technology</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><recordid>eNqNkE1v00AQhi0EEqVwR-KyEkfkdGbXH7vcIjdApFQgUnq1xptxspW7NmsHxIm_zlqu4Mpp5vA-72ieJHmNsEIEc3W73a4koFlJk2Wo1ZPkAk2GKUAOT-Oe55gqCep58mIc7wFUCcpcJL-rvh840OR-sLghT0d-YD-Jtg_iy93VZr9PN56ajg9i07GdgrPijk_OdiyqE4Wj80exnyLf-_G9WIubcze52BI7rpkH8ZWdj2V2qd0xBT8j62EIPdnTy-RZS93Irx7nZfLtw-a2-pTuPn_cVutdapXSU1pmCCVQQzm2puBDSY3RkmRRkDoUjTQSDCNJm1kq9aElaDUimVYhq_nVy-Tt0hvPfj_zONX3_Tn4eLKWKtcGpYEipmBJ2dCPY-C2HoJ7oPCrRqhnzXXUXM-a60fNEXm3ID-56dvROvaW_2IQ9aPJtS7nTce0_v905RavVX_2U0TfLKhj_odonaOM__0BINSZ7A</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>Shin, MyungJae</creator><creator>Choi, Dae-Hyun</creator><creator>Kim, Joongheon</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-6505-1448</orcidid><orcidid>https://orcid.org/0000-0002-1794-6076</orcidid><orcidid>https://orcid.org/0000-0002-9248-9522</orcidid></search><sort><creationdate>20200501</creationdate><title>Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach</title><author>Shin, MyungJae ; Choi, Dae-Hyun ; Kim, Joongheon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-741070aba51f96ed7ab982a266a3d6b29209e1a2c4ca78dfa0f811a9f31e33703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Automation & Control Systems</topic><topic>Companies</topic><topic>Computer Science</topic><topic>Computer Science, Interdisciplinary Applications</topic><topic>Electric vehicle charging</topic><topic>Electric vehicles</topic><topic>Energy management</topic><topic>Energy storage</topic><topic>Engineering</topic><topic>Engineering, Industrial</topic><topic>Machine learning</topic><topic>Multi-agent systems</topic><topic>Multiagent systems</topic><topic>neural networks</topic><topic>Optimization</topic><topic>Performance evaluation</topic><topic>Photovoltaic cells</topic><topic>Planning</topic><topic>Reinforcement learning</topic><topic>Schedules</topic><topic>scheduling algorithms</topic><topic>Science & Technology</topic><topic>Stations</topic><topic>Technology</topic><toplevel>online_resources</toplevel><creatorcontrib>Shin, MyungJae</creatorcontrib><creatorcontrib>Choi, Dae-Hyun</creatorcontrib><creatorcontrib>Kim, Joongheon</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on industrial informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shin, MyungJae</au><au>Choi, Dae-Hyun</au><au>Kim, Joongheon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach</atitle><jtitle>IEEE transactions on industrial informatics</jtitle><stitle>TII</stitle><stitle>IEEE T IND INFORM</stitle><date>2020-05-01</date><risdate>2020</risdate><volume>16</volume><issue>5</issue><spage>3493</spage><epage>3503</epage><pages>3493-3503</pages><issn>1551-3203</issn><eissn>1941-0050</eissn><coden>ITIICH</coden><abstract>This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this article proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed method achieves a desirable performance in terms of reducing the operation costs of electric vehicle charging stations.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/TII.2019.2944183</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6505-1448</orcidid><orcidid>https://orcid.org/0000-0002-1794-6076</orcidid><orcidid>https://orcid.org/0000-0002-9248-9522</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1551-3203 |
| ispartof | IEEE transactions on industrial informatics, 2020-05, Vol.16 (5), p.3493-3503 |
| issn | 1551-3203 1941-0050 |
| language | eng |
| recordid | cdi_webofscience_primary_000519588700058 |
| source | IEEE Electronic Library (IEL) |
| subjects | Automation & Control Systems Companies Computer Science Computer Science, Interdisciplinary Applications Electric vehicle charging Electric vehicles Energy management Energy storage Engineering Engineering, Industrial Machine learning Multi-agent systems Multiagent systems neural networks Optimization Performance evaluation Photovoltaic cells Planning Reinforcement learning Schedules scheduling algorithms Science & Technology Stations Technology |
| title | Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-06T06%3A18%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Cooperative%20Management%20for%20PV/ESS-Enabled%20Electric%20Vehicle%20Charging%20Stations:%20A%20Multiagent%20Deep%20Reinforcement%20Learning%20Approach&rft.jtitle=IEEE%20transactions%20on%20industrial%20informatics&rft.au=Shin,%20MyungJae&rft.date=2020-05-01&rft.volume=16&rft.issue=5&rft.spage=3493&rft.epage=3503&rft.pages=3493-3503&rft.issn=1551-3203&rft.eissn=1941-0050&rft.coden=ITIICH&rft_id=info:doi/10.1109/TII.2019.2944183&rft_dat=%3Cproquest_RIE%3E2358912906%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2358912906&rft_id=info:pmid/&rft_ieee_id=8851270&rfr_iscdi=true |