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dc.rights.licenseopenen_US
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorYAMASHITA, Daniela Yassuda
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorVECHIU, Ionel
ORCID: 0000-0003-4108-3546
IDREF: 102417741
dc.contributor.authorGAUBERT, Jean-Paul
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorJUPIN, Samuel
ORCID: 0000-0002-1441-5499
dc.date.accessioned2023-04-03T13:39:47Z
dc.date.available2023-04-03T13:39:47Z
dc.date.issued2022-10-22
dc.identifier.issn0093-9994en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172691
dc.description.abstractEnAiming to take full advantage of Electric Vehicles' (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles.
dc.language.isoENen_US
dc.subject.enModel Predictive Control
dc.subject.enElectric Vehicles
dc.subject.enBuilding microgrid
dc.subject.enself-consumption
dc.subject.eneconomic analysis
dc.title.enHierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids
dc.typeArticle de revueen_US
dc.identifier.doi10.1109/TIA.2022.3215978en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.journalIEEE Transactions on Industry Applicationsen_US
bordeaux.page1-10en_US
bordeaux.volume59en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-03916925
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IEEE%20Transactions%20on%20Industry%20Applications&rft.date=2022-10-22&rft.volume=59&rft.issue=1&rft.spage=1-10&rft.epage=1-10&rft.eissn=0093-9994&rft.issn=0093-9994&rft.au=YAMASHITA,%20Daniela%20Yassuda&VECHIU,%20Ionel&GAUBERT,%20Jean-Paul&JUPIN,%20Samuel&rft.genre=article


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