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dc.contributor.advisorÖztürk, Pinarnb_NO
dc.contributor.advisorHøverstad, Boye Annfeltnb_NO
dc.contributor.advisorTidemann, Axelnb_NO
dc.contributor.authorHaukedal, Eirik Dalengnb_NO
dc.date.accessioned2014-12-19T13:39:02Z
dc.date.available2014-12-19T13:39:02Z
dc.date.created2012-11-08nb_NO
dc.date.issued2012nb_NO
dc.identifier566136nb_NO
dc.identifierntnudaim:7013nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/252967
dc.description.abstractTo avoid the problem that increasing PHEV demand will further aggravate peak demand hours in the power grid, several different multi-agent scheduling mechanisms have been investigated, including two centralized scheduling mechanisms and two decentralized scheduling mechanisms. For both of the decentralized mechanisms, the PHEV agents choose their own charging plans without relying upon a centralized scheduler, while in the centralized scheduling mechanisms, the PHEVs agents defer control to a central agent for creating their charging plans. From the results, we found that while both the centralized mechanisms and the decentralized mechanisms helped to reduce the average maximum peak, the performance of the centralized mechanisms proved to be highly dependent on how the day-ahead portfolio was calculated. Because of this, the overall best performer was decentralized mechanism, which gave the best results for reducing the average maximum peak without compromising the ability of the PHEVs to charge their batteries too much.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaim:7013no_NO
dc.subjectMTDT datateknikkno_NO
dc.subjectIntelligente systemerno_NO
dc.titleLoad-scheduling and Plug-in Hybrid Electric Vehicles in the Smart Gridnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber121nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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