Two-stage fuzzy logic inference algorithm for maximizing the quality of performance under the operational constraints of power grid in electric vehicle parking lots

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dc.contributor.authorHussain, Shahid
dc.contributor.authorLee, Ki-Beom
dc.contributor.authorAhmed, Mohamed A.
dc.contributor.authorHayes, Barry P.
dc.contributor.authorKim, Young-Chon
dc.contributor.funderJeonbuk National Universityen
dc.date.accessioned2022-03-10T16:40:28Z
dc.date.available2022-03-10T16:40:28Z
dc.date.issued2020-09-06
dc.date.updated2022-03-10T16:11:19Z
dc.description.abstractThe widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. This affects the EV owner's quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid4634en
dc.identifier.citationHussain, S., Lee, K-B., Ahmed, M. A., Hayes, B. P. and Kim, Y-C. (2020) 'Two-stage fuzzy logic inference algorithm for maximizing the quality of performance under the operational constraints of power grid in electric vehicle parking lots', Energies,13(18), 4634, (31pp). doi: 10.3390/en13184634en
dc.identifier.doi10.3390/en13184634en
dc.identifier.endpage31en
dc.identifier.issn1996-1073
dc.identifier.issued18en
dc.identifier.journaltitleEnergiesen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/12891
dc.identifier.volume13en
dc.language.isoenen
dc.publisherMDPIen
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectManagement algorithmen
dc.subjectThermal managementen
dc.subjectImpacten
dc.subjectEnergyen
dc.subjectCapacityen
dc.subjectSupporten
dc.subjectSystemsen
dc.subjectModelsen
dc.subjectLevelen
dc.titleTwo-stage fuzzy logic inference algorithm for maximizing the quality of performance under the operational constraints of power grid in electric vehicle parking lotsen
dc.typeArticle (peer-reviewed)en
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