Electric vehicle demand response and charging scheduling method based on deep reinforcement learning
The invention relates to an electric vehicle demand response and charging scheduling method based on deep reinforcement learning, the method can be executed by one or more processors, and the method comprises the following steps: the one or more processors construct an electric vehicle demand respon...
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | ZHU JIE CHEN AODONG ZHAO WENJING ZHENG YINGYING WANG JINGLONG GUO KAILEI ZHAO YONGNING YE LIN |
| description | The invention relates to an electric vehicle demand response and charging scheduling method based on deep reinforcement learning, the method can be executed by one or more processors, and the method comprises the following steps: the one or more processors construct an electric vehicle demand response and electric vehicle aggregator operation scheduling model; the one or more processors classify the vehicle based on the charging flexibility of the vehicle; the one or more processors construct an operation process of the electric vehicle aggregator into a Markov decision process capable of ensuring that constraint conditions are satisfied, and determine a state St, an action at and a reward Rt corresponding to a moment t; the one or more processors solve the Markov decision process, and a robust agent containing mapping from any state St to action at is obtained through training; and the one or more processors deploy the robust intelligent agent into the electric vehicle charging station so as to perform optim |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116384845A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116384845A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116384845A3</originalsourceid><addsrcrecordid>eNqNjUEKwjAURLtxIeodvgdwUVqlWykVV67cl_gzbQLJT0mi5zcFD-BqZuA9ZlvpwYFztEwfGMsOpOGVaIpIS5AEWgcbFWcrMyU20G-3Vo9sgqaXStAUpHhYimVlCpHhIZkcVJTC7qvNpFzC4Ze76ngbnv39hCWM5UcxBHnsH3V9abq2a8_X5h_mC_VKPxY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Electric vehicle demand response and charging scheduling method based on deep reinforcement learning</title><source>esp@cenet</source><creator>ZHU JIE ; CHEN AODONG ; ZHAO WENJING ; ZHENG YINGYING ; WANG JINGLONG ; GUO KAILEI ; ZHAO YONGNING ; YE LIN</creator><creatorcontrib>ZHU JIE ; CHEN AODONG ; ZHAO WENJING ; ZHENG YINGYING ; WANG JINGLONG ; GUO KAILEI ; ZHAO YONGNING ; YE LIN</creatorcontrib><description>The invention relates to an electric vehicle demand response and charging scheduling method based on deep reinforcement learning, the method can be executed by one or more processors, and the method comprises the following steps: the one or more processors construct an electric vehicle demand response and electric vehicle aggregator operation scheduling model; the one or more processors classify the vehicle based on the charging flexibility of the vehicle; the one or more processors construct an operation process of the electric vehicle aggregator into a Markov decision process capable of ensuring that constraint conditions are satisfied, and determine a state St, an action at and a reward Rt corresponding to a moment t; the one or more processors solve the Markov decision process, and a robust agent containing mapping from any state St to action at is obtained through training; and the one or more processors deploy the robust intelligent agent into the electric vehicle charging station so as to perform optim</description><language>chi ; eng</language><subject>CALCULATING ; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRICITY ; GENERATION ; PHYSICS ; SYSTEMS FOR STORING ELECTRIC ENERGY ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230704&DB=EPODOC&CC=CN&NR=116384845A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25566,76549</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230704&DB=EPODOC&CC=CN&NR=116384845A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHU JIE</creatorcontrib><creatorcontrib>CHEN AODONG</creatorcontrib><creatorcontrib>ZHAO WENJING</creatorcontrib><creatorcontrib>ZHENG YINGYING</creatorcontrib><creatorcontrib>WANG JINGLONG</creatorcontrib><creatorcontrib>GUO KAILEI</creatorcontrib><creatorcontrib>ZHAO YONGNING</creatorcontrib><creatorcontrib>YE LIN</creatorcontrib><title>Electric vehicle demand response and charging scheduling method based on deep reinforcement learning</title><description>The invention relates to an electric vehicle demand response and charging scheduling method based on deep reinforcement learning, the method can be executed by one or more processors, and the method comprises the following steps: the one or more processors construct an electric vehicle demand response and electric vehicle aggregator operation scheduling model; the one or more processors classify the vehicle based on the charging flexibility of the vehicle; the one or more processors construct an operation process of the electric vehicle aggregator into a Markov decision process capable of ensuring that constraint conditions are satisfied, and determine a state St, an action at and a reward Rt corresponding to a moment t; the one or more processors solve the Markov decision process, and a robust agent containing mapping from any state St to action at is obtained through training; and the one or more processors deploy the robust intelligent agent into the electric vehicle charging station so as to perform optim</description><subject>CALCULATING</subject><subject>CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>CONVERSION OR DISTRIBUTION OF ELECTRIC POWER</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRICITY</subject><subject>GENERATION</subject><subject>PHYSICS</subject><subject>SYSTEMS FOR STORING ELECTRIC ENERGY</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjUEKwjAURLtxIeodvgdwUVqlWykVV67cl_gzbQLJT0mi5zcFD-BqZuA9ZlvpwYFztEwfGMsOpOGVaIpIS5AEWgcbFWcrMyU20G-3Vo9sgqaXStAUpHhYimVlCpHhIZkcVJTC7qvNpFzC4Ze76ngbnv39hCWM5UcxBHnsH3V9abq2a8_X5h_mC_VKPxY</recordid><startdate>20230704</startdate><enddate>20230704</enddate><creator>ZHU JIE</creator><creator>CHEN AODONG</creator><creator>ZHAO WENJING</creator><creator>ZHENG YINGYING</creator><creator>WANG JINGLONG</creator><creator>GUO KAILEI</creator><creator>ZHAO YONGNING</creator><creator>YE LIN</creator><scope>EVB</scope></search><sort><creationdate>20230704</creationdate><title>Electric vehicle demand response and charging scheduling method based on deep reinforcement learning</title><author>ZHU JIE ; CHEN AODONG ; ZHAO WENJING ; ZHENG YINGYING ; WANG JINGLONG ; GUO KAILEI ; ZHAO YONGNING ; YE LIN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116384845A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>CONVERSION OR DISTRIBUTION OF ELECTRIC POWER</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRICITY</topic><topic>GENERATION</topic><topic>PHYSICS</topic><topic>SYSTEMS FOR STORING ELECTRIC ENERGY</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHU JIE</creatorcontrib><creatorcontrib>CHEN AODONG</creatorcontrib><creatorcontrib>ZHAO WENJING</creatorcontrib><creatorcontrib>ZHENG YINGYING</creatorcontrib><creatorcontrib>WANG JINGLONG</creatorcontrib><creatorcontrib>GUO KAILEI</creatorcontrib><creatorcontrib>ZHAO YONGNING</creatorcontrib><creatorcontrib>YE LIN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHU JIE</au><au>CHEN AODONG</au><au>ZHAO WENJING</au><au>ZHENG YINGYING</au><au>WANG JINGLONG</au><au>GUO KAILEI</au><au>ZHAO YONGNING</au><au>YE LIN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Electric vehicle demand response and charging scheduling method based on deep reinforcement learning</title><date>2023-07-04</date><risdate>2023</risdate><abstract>The invention relates to an electric vehicle demand response and charging scheduling method based on deep reinforcement learning, the method can be executed by one or more processors, and the method comprises the following steps: the one or more processors construct an electric vehicle demand response and electric vehicle aggregator operation scheduling model; the one or more processors classify the vehicle based on the charging flexibility of the vehicle; the one or more processors construct an operation process of the electric vehicle aggregator into a Markov decision process capable of ensuring that constraint conditions are satisfied, and determine a state St, an action at and a reward Rt corresponding to a moment t; the one or more processors solve the Markov decision process, and a robust agent containing mapping from any state St to action at is obtained through training; and the one or more processors deploy the robust intelligent agent into the electric vehicle charging station so as to perform optim</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116384845A |
| source | esp@cenet |
| subjects | CALCULATING CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONVERSION OR DISTRIBUTION OF ELECTRIC POWER COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRICITY GENERATION PHYSICS SYSTEMS FOR STORING ELECTRIC ENERGY SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Electric vehicle demand response and charging scheduling method based on deep reinforcement learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T10%3A17%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ZHU%20JIE&rft.date=2023-07-04&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116384845A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |