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dc.contributor.authorSu, Jen_NZ
dc.contributor.authorLie, Ten_NZ
dc.contributor.authorZamora, Ren_NZ
dc.date.accessioned2019-03-12T02:41:24Z
dc.date.available2019-03-12T02:41:24Z
dc.date.copyright2018-11-12en_NZ
dc.identifier.citationElectric Power Systems Research, 167, 171-182.
dc.identifier.issn0378-7796en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/12353
dc.description.abstractDue to increasing electric vehicles (EVs) uptakes, power system distribution network will have to accommodate the increased load level for charging EVs. Thus, the importance of a robust power system especially in the distribution network level is indisputable. During the planning or reinforcement stage of distribution networks, it is paramount to have some estimations and analyses done on system-wide EV charging loads that will be placed in the network. Thus, this paper systematically investigates the EV fleet composition, market shares, and charging patterns within New Zealand (NZ) area. A multivariate probabilistic modelling of dependent random variables and cumulative distribution functions is adopted for the accurate estimation of aggregated EV charging demands. Several vehicle travel survey data sets are utilised to quantitatively determine charging behaviours and driving patterns of EVs. The developed methodology based on Monte-Carlo simulation (MCS) is utilised to generate results close to the real use-cases daily power demand, which can be further utilised in the analysis of EV charging strategies. In addition, non-smart and smart EV charging strategies are introduced to mitigate impacts of the large-scale EV deployment and to guarantee the charging completion for each EV.
dc.publisherElsevieren_NZ
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0378779618303535?via%3Dihub
dc.rightsCopyright © 2018 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.subjectCharging; EV electrical load model; Probabilistic modelling; Smart charging strategies
dc.titleModelling of Large-scale Electric Vehicles Charging Demand: A New Zealand Case Studyen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1016/j.epsr.2018.10.030en_NZ
aut.relation.endpage182
aut.relation.startpage171
aut.relation.volume167en_NZ
pubs.elements-id349070
aut.relation.journalElectric Power Systems Researchen_NZ


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