Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/221868
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dc.contributor.authorQi Kangen_US
dc.contributor.authorShuWei Fengen_US
dc.contributor.authorMengChu Zhouen_US
dc.contributor.authorAhmed Chiheb Ammarien_US
dc.contributor.authorKhaled Sedraouien_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:19:00Z-
dc.date.available2020-04-06T08:19:00Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TITS.2016.2638898en_US
dc.identifier.urihttp://localhost/handle/Hannan/221868-
dc.description.abstractIn order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.en_US
dc.format.extent2557,en_US
dc.format.extent2568en_US
dc.publisherIEEEen_US
dc.relation.haspart7918586.pdfen_US
dc.titleOptimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithmsen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue9en_US
Appears in Collections:2017

Files in This Item:
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7918586.pdf1.9 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorQi Kangen_US
dc.contributor.authorShuWei Fengen_US
dc.contributor.authorMengChu Zhouen_US
dc.contributor.authorAhmed Chiheb Ammarien_US
dc.contributor.authorKhaled Sedraouien_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:19:00Z-
dc.date.available2020-04-06T08:19:00Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TITS.2016.2638898en_US
dc.identifier.urihttp://localhost/handle/Hannan/221868-
dc.description.abstractIn order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.en_US
dc.format.extent2557,en_US
dc.format.extent2568en_US
dc.publisherIEEEen_US
dc.relation.haspart7918586.pdfen_US
dc.titleOptimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithmsen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue9en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7918586.pdf1.9 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorQi Kangen_US
dc.contributor.authorShuWei Fengen_US
dc.contributor.authorMengChu Zhouen_US
dc.contributor.authorAhmed Chiheb Ammarien_US
dc.contributor.authorKhaled Sedraouien_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:19:00Z-
dc.date.available2020-04-06T08:19:00Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TITS.2016.2638898en_US
dc.identifier.urihttp://localhost/handle/Hannan/221868-
dc.description.abstractIn order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.en_US
dc.format.extent2557,en_US
dc.format.extent2568en_US
dc.publisherIEEEen_US
dc.relation.haspart7918586.pdfen_US
dc.titleOptimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithmsen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue9en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7918586.pdf1.9 MBAdobe PDF