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A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

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dc.contributor.authorSousa, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorCarvalho, João
dc.contributor.authorPinto, Tiago
dc.contributor.authorMorais, Hugo
dc.date.accessioned2014-12-09T16:13:10Z
dc.date.available2014-12-09T16:13:10Z
dc.date.issued2014
dc.description.abstractThe massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.por
dc.identifier.doi10.1016/j.energy.2014.02.025
dc.identifier.issn0360-5442
dc.identifier.urihttp://hdl.handle.net/10400.22/5252
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevierpor
dc.relation.ispartofseriesEnergy;Vol. 67
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0360544214001595por
dc.subjectAnt colony optimizationpor
dc.subjectEnergy resource managementpor
dc.subjectElectric vehiclepor
dc.subjectHybridizationpor
dc.subjectSimulated annealingpor
dc.subjectVirtual power playerpor
dc.titleA Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehiclespor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage96por
oaire.citation.startPage81por
oaire.citation.titleEnergypor
person.familyNameVale
person.familyNamePinto
person.familyNameMorais
person.givenNameZita
person.givenNameTiago
person.givenNameHugo
person.identifier632184
person.identifierR-000-T7J
person.identifier80878
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id2010-D878-271B
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0001-5906-4744
person.identifier.ridA-5824-2012
person.identifier.ridT-2245-2018
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id21834170800
rcaap.rightsclosedAccesspor
rcaap.typearticlepor
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relation.isAuthorOfPublicationb159f8c9-5ee1-444e-b890-81242ee0738e
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b

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