Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/120726
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Electrical and Electronic Engineeringen_US
dc.contributorResearch Centre for Electric Vehiclesen_US
dc.creatorTang, Yen_US
dc.creatorLiu, Wen_US
dc.creatorHou, Yen_US
dc.creatorChau, KTen_US
dc.date.accessioned2026-08-26T01:56:47Z-
dc.date.available2026-08-26T01:56:47Z-
dc.identifier.urihttp://hdl.handle.net/10397/120726-
dc.descriptionICEE Conference 2024: TRANSformative Electrical Engineering for Future Society: The International Council on Electrical Engineering, June 30 - July 4, 2024. Kitakyushu International Conference Center, Kitakyushu, Japanen_US
dc.language.isoenen_US
dc.rightsPosted with permission of the author.en_US
dc.subject(Dis)charging schedulingen_US
dc.subjectElectric vehiclesen_US
dc.subjectMultistorey car parksen_US
dc.subjectReinforcement learningen_US
dc.titleIntelligent charging and discharging scheduling in multistorey car park with wireless electric vehicle networken_US
dc.typeConference Paperen_US
dc.identifier.spage1en_US
dc.identifier.epage6en_US
dcterms.abstractMultistorey car park outfitted with wireless charging infrastructures can offer space-efficient and charging-convenient solutions for electric vehicles (EVs) in urban areas. Serving as energy storages, numerous EVs parked for long periods hold substantial potential to alleviate transformer load stresses and yield considerable profits for drivers. To effectively realize dual benefits, this paper investigates the EV (dis)charging scheduling problem, aiming at maximizing the driver’s profit while guaranteeing sufficient battery’s state of charge upon departure, and avoiding transformer overloading. A multi-agent reinforcement learning method is utilized based on an actor-critic framework. Also, a revised centralized training and decentralized execution is designed based on a simple statistic global observation, which is able to address the time-variable number of agents problem. The computational simulation is conducted to verify the feasibility of the proposed algorithm.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationICEE Conference 2024: TRANSformative Electrical Engineering for Future Society: The International Council on Electrical Engineering, June 30 - July 4, 2024. Kitakyushu International Conference Center, Kitakyushu, Japan, 5752768, p. 1-6, https://orbit-cs.net/icee2024/en_US
dcterms.issued2024-
dc.relation.conferenceInternational Council on Electrical Engineering [ICEE]en_US
dc.identifier.artn5752768en_US
dc.description.validate202608 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera4790-
dc.identifier.SubFormID53910-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe Hong Kong Polytechnic Universityen_US
dc.description.pubStatusUnpublishen_US
dc.description.oaCategoryCopyright retained by authoren_US
Appears in Collections:Conference Paper
Files in This Item:
File Description SizeFormat 
Tang_Intelligent_Charging_Discharging.pdfPre-Published version393.71 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Show simple item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.