Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/120726
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.contributor | Research Centre for Electric Vehicles | en_US |
| dc.creator | Tang, Y | en_US |
| dc.creator | Liu, W | en_US |
| dc.creator | Hou, Y | en_US |
| dc.creator | Chau, KT | en_US |
| dc.date.accessioned | 2026-08-26T01:56:47Z | - |
| dc.date.available | 2026-08-26T01:56:47Z | - |
| dc.identifier.uri | http://hdl.handle.net/10397/120726 | - |
| dc.description | ICEE 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 | en_US |
| dc.language.iso | en | en_US |
| dc.rights | Posted with permission of the author. | en_US |
| dc.subject | (Dis)charging scheduling | en_US |
| dc.subject | Electric vehicles | en_US |
| dc.subject | Multistorey car parks | en_US |
| dc.subject | Reinforcement learning | en_US |
| dc.title | Intelligent charging and discharging scheduling in multistorey car park with wireless electric vehicle network | en_US |
| dc.type | Conference Paper | en_US |
| dc.identifier.spage | 1 | en_US |
| dc.identifier.epage | 6 | en_US |
| dcterms.abstract | Multistorey 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.accessRights | open access | en_US |
| dcterms.bibliographicCitation | ICEE 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.issued | 2024 | - |
| dc.relation.conference | International Council on Electrical Engineering [ICEE] | en_US |
| dc.identifier.artn | 5752768 | en_US |
| dc.description.validate | 202608 bcch | en_US |
| dc.description.oa | Accepted Manuscript | en_US |
| dc.identifier.FolderNumber | a4790 | - |
| dc.identifier.SubFormID | 53910 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | The Hong Kong Polytechnic University | en_US |
| dc.description.pubStatus | Unpublish | en_US |
| dc.description.oaCategory | Copyright retained by author | en_US |
| Appears in Collections: | Conference Paper | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Tang_Intelligent_Charging_Discharging.pdf | Pre-Published version | 393.71 kB | Adobe PDF | View/Open |
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