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Article: Genetic algorithm based cost-emission optimization of unit commitment integrating with gridable vehicles
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TitleGenetic algorithm based cost-emission optimization of unit commitment integrating with gridable vehicles
 
AuthorsWu, D
Chau, KT
Liu, C
Gao, S
 
KeywordsGridable vehicle
Vehicle-to-grid
Unit commitment
Cost-emission optimization
Genetic algorithm
 
Issue Date2012
 
PublisherAsian Electric Vehicle Society. The Journal's web site is located at http://www.elec.eng.osaka-cu.ac.jp/aevc/
 
CitationJournal of Asian Electric Vehicles, 2012, v. 10 n. 1, p. 1567-1573 [How to Cite?]
 
AbstractThis paper first proposes a multilayer framework of vehicle-to-grid (V2G) system based on the concept of gridable vehicles (GVs). GVs can draw and store energy from the power grid as loads, as well as feed energy back to the grid as resources. Then, unit commitment integrating with GVs is analyzed using the proposed framework. The objective is to minimize the total operating cost and emissions of the V2G system by intelligently scheduling the generating units and GVs based on the use of genetic algorithm. The results illustrate that the operating cost and emissions can be reduced and the system reserves can be enhanced by applying V2G.
 
DescriptionFulltext link: http://www.union-services.com/aevs/10_1567.pdf
 
ISSN1348-3927
 
DC FieldValue
dc.contributor.authorWu, D
 
dc.contributor.authorChau, KT
 
dc.contributor.authorLiu, C
 
dc.contributor.authorGao, S
 
dc.date.accessioned2012-09-20T07:55:06Z
 
dc.date.available2012-09-20T07:55:06Z
 
dc.date.issued2012
 
dc.description.abstractThis paper first proposes a multilayer framework of vehicle-to-grid (V2G) system based on the concept of gridable vehicles (GVs). GVs can draw and store energy from the power grid as loads, as well as feed energy back to the grid as resources. Then, unit commitment integrating with GVs is analyzed using the proposed framework. The objective is to minimize the total operating cost and emissions of the V2G system by intelligently scheduling the generating units and GVs based on the use of genetic algorithm. The results illustrate that the operating cost and emissions can be reduced and the system reserves can be enhanced by applying V2G.
 
dc.descriptionFulltext link: http://www.union-services.com/aevs/10_1567.pdf
 
dc.identifier.citationJournal of Asian Electric Vehicles, 2012, v. 10 n. 1, p. 1567-1573 [How to Cite?]
 
dc.identifier.epage1573
 
dc.identifier.hkuros208221
 
dc.identifier.issn1348-3927
 
dc.identifier.issue1
 
dc.identifier.spage1567
 
dc.identifier.urihttp://hdl.handle.net/10722/164051
 
dc.identifier.volume10
 
dc.languageeng
 
dc.publisherAsian Electric Vehicle Society. The Journal's web site is located at http://www.elec.eng.osaka-cu.ac.jp/aevc/
 
dc.publisher.placeJapan
 
dc.relation.ispartofJournal of Asian Electric Vehicles
 
dc.subjectGridable vehicle
 
dc.subjectVehicle-to-grid
 
dc.subjectUnit commitment
 
dc.subjectCost-emission optimization
 
dc.subjectGenetic algorithm
 
dc.titleGenetic algorithm based cost-emission optimization of unit commitment integrating with gridable vehicles
 
dc.typeArticle
 
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<contributor.author>Chau, KT</contributor.author>
<contributor.author>Liu, C</contributor.author>
<contributor.author>Gao, S</contributor.author>
<date.accessioned>2012-09-20T07:55:06Z</date.accessioned>
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<description.abstract>This paper first proposes a multilayer framework of vehicle-to-grid (V2G) system based on the concept of gridable vehicles (GVs). GVs can draw and store energy from the power grid as loads, as well as feed energy back to the grid as resources. Then, unit commitment integrating with GVs is analyzed using the proposed framework. The objective is to minimize the total operating cost and emissions of the V2G system by intelligently scheduling the generating units and GVs based on the use of genetic algorithm. The results illustrate that the operating cost and emissions can be reduced and the system reserves can be enhanced by applying V2G.</description.abstract>
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<subject>Gridable vehicle</subject>
<subject>Vehicle-to-grid</subject>
<subject>Unit commitment</subject>
<subject>Cost-emission optimization</subject>
<subject>Genetic algorithm</subject>
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