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Article: Dynamic resource allocation for parking lot electric vehicle recharging using heuristic fuzzy particle swarm optimization algorithm

TitleDynamic resource allocation for parking lot electric vehicle recharging using heuristic fuzzy particle swarm optimization algorithm
Authors
KeywordsElectric vehicleHeuristics
Parking lot
Dynamic resource allocation
Particle swarm optimization
Fuzzy system
Issue Date2018
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc
Citation
Applied Soft Computing, 2018, v. 71, p. 538-552 How to Cite?
AbstractA parking lot (PL) dynamic resource allocation system for recharging electric vehicles (EVs) is introduced in this paper. For scheduling purposes, a day is divided into sequential timeslots. At the beginning of each timeslot, the dynamic system can determine an optimal charging schedule for that timeslot, as well as plan for subsequent timeslots. An EV may arrive at a PL with or without an appointment. Considering the variation in electricity prices during the day, the objective is to minimize the cost of electricity used to charge EVs by scheduling optimal electric quantities at the parking timeslots of each EV. The optimal solution satisfies the EV’s charging rate limit and the PL’s transformer limit. Based on particle swarm optimization (PSO), fuzzy systems and heuristics, this paper describes a heuristic fuzzy particle swarm optimization (PHFPSO) algorithm to solve the optimization problem. From the case studies, the results show the proposed dynamic resource allocation system has a significant improvement in satisfying charging requests and in reducing the electricity cost of the PL when compared with other scheduling mechanisms.
Persistent Identifierhttp://hdl.handle.net/10722/259322
ISSN
2019 Impact Factor: 5.472
2015 SCImago Journal Rankings: 1.763
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, H-
dc.contributor.authorPang, GKH-
dc.contributor.authorChoy, KL-
dc.contributor.authorLam, HY-
dc.date.accessioned2018-09-03T04:05:12Z-
dc.date.available2018-09-03T04:05:12Z-
dc.date.issued2018-
dc.identifier.citationApplied Soft Computing, 2018, v. 71, p. 538-552-
dc.identifier.issn1568-4946-
dc.identifier.urihttp://hdl.handle.net/10722/259322-
dc.description.abstractA parking lot (PL) dynamic resource allocation system for recharging electric vehicles (EVs) is introduced in this paper. For scheduling purposes, a day is divided into sequential timeslots. At the beginning of each timeslot, the dynamic system can determine an optimal charging schedule for that timeslot, as well as plan for subsequent timeslots. An EV may arrive at a PL with or without an appointment. Considering the variation in electricity prices during the day, the objective is to minimize the cost of electricity used to charge EVs by scheduling optimal electric quantities at the parking timeslots of each EV. The optimal solution satisfies the EV’s charging rate limit and the PL’s transformer limit. Based on particle swarm optimization (PSO), fuzzy systems and heuristics, this paper describes a heuristic fuzzy particle swarm optimization (PHFPSO) algorithm to solve the optimization problem. From the case studies, the results show the proposed dynamic resource allocation system has a significant improvement in satisfying charging requests and in reducing the electricity cost of the PL when compared with other scheduling mechanisms.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc-
dc.relation.ispartofApplied Soft Computing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectElectric vehicleHeuristics-
dc.subjectParking lot-
dc.subjectDynamic resource allocation-
dc.subjectParticle swarm optimization-
dc.subjectFuzzy system-
dc.titleDynamic resource allocation for parking lot electric vehicle recharging using heuristic fuzzy particle swarm optimization algorithm-
dc.typeArticle-
dc.identifier.emailPang, GKH: gpang@eee.hku.hk-
dc.identifier.authorityPang, GKH=rp00162-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.asoc.2018.07.008-
dc.identifier.scopuseid_2-s2.0-85050299702-
dc.identifier.hkuros289919-
dc.identifier.volume71-
dc.identifier.spage538-
dc.identifier.epage552-
dc.identifier.isiWOS:000445126100036-
dc.publisher.placeNetherlands-
dc.identifier.issnl1568-4946-

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