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http://hdl.handle.net/10397/106001
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.creator | Wu, H | en_US |
dc.creator | Pang, GKH | en_US |
dc.creator | Choy, KL | en_US |
dc.creator | Lam, HY | en_US |
dc.date.accessioned | 2024-04-24T02:01:51Z | - |
dc.date.available | 2024-04-24T02:01:51Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106001 | - |
dc.language.iso | en | en_US |
dc.rights | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication H. Wu, G. K. H. Pang, K. L. Choy and H. Y. Lam, "A scheduling and control system for electric vehicle charging at parking lot," 2017 11th Asian Control Conference (ASCC), Gold Coast, QLD, Australia, 2017, pp. 13-18 is available at https://doi.org/10.1109/ASCC.2017.8287095. | en_US |
dc.title | A scheduling and control system for electric vehicle charging at parking lot | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 13 | en_US |
dc.identifier.epage | 18 | en_US |
dc.identifier.doi | 10.1109/ASCC.2017.8287095 | en_US |
dcterms.abstract | This paper proposes a new electric vehicle (EV) charging scheduling and control system for a parking lot (PL), which would minimize the PL's electricity cost of recharging all the EVs. This system is to determine an optimal charging schedule for each incoming EV by allocating the electric quantities to the parking time slots of each EV considering the varied electricity price during the day. The schedule would satisfy the EV's charging rate limit and the PL's transformer limit. This paper proposes a heuristics & proportion-based assignment (HPBA) method to generate the initial population, and adapts the particle swarm optimization (PSO) algorithm to solve the optimization problem. The performance of the proposed system is compared with random search (RS), first-in-first-serve (FIFS) and earliest-deadline-first (EDF) mechanisms, and the results show that the new scheduling system would achieve the goal on minimizing the electricity cost and satisfying the charging demands and constraints. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2017 11th Asian Control Conference (ASCC), 17-20 December 2017, Gold Coast, QLD, Australia, p. 13-18 | en_US |
dcterms.issued | 2018 | - |
dc.identifier.scopus | 2-s2.0-85047555290 | - |
dc.relation.conference | Asian Control Conference [ASCC] | en_US |
dc.description.validate | 202404 bcwh | en_US |
dc.description.oa | Author’s Original | en_US |
dc.identifier.FolderNumber | ISE-0692 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9615520 | - |
dc.description.oaCategory | Green (AO) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Choy_Scheduling_Control_System.pdf | Preprint version | 449.27 kB | Adobe PDF | View/Open |
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