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dc.contributor.authorPashajavid, E.
dc.contributor.authorShahnia, Farhad
dc.contributor.editorDr. Ahmed Abu-Siada
dc.date.accessioned2017-01-30T14:54:40Z
dc.date.available2017-01-30T14:54:40Z
dc.date.created2014-09-30T20:00:22Z
dc.date.issued2014
dc.identifier.citationPashajavid, E. and Shahnia, F. 2014. Sensitivity of Electric Vehicles Demand Profile to the Batteries Departure State-of-Charge, in Dr. Ahmed Abu-Siada (ed), 24th Australasian Universities Power Engineering Conference (AUPEC 2014), Sep 28-Oct 1 2014, pp. 1-6. Perth, WA: Institute of Electrical and Electronics Engineers (IEEE).
dc.identifier.urihttp://hdl.handle.net/20.500.11937/41708
dc.identifier.doi10.1109/AUPEC.2014.6966620
dc.description.abstract

This paper focuses on the impacts of considering batteries state-of-charge (SOC) at the departure time on thedemand modeling of plug-in electric vehicles (PEVs). Almost all of the previous researches assumed that PEVs batteries at the departure time are fully charged; however, this assumption is highly questionable because it is probable for a PEV to not be charged every day. The probability density function of a vehicle owners’ willingness to fulfill the daily charging is extracted according to the initial SOC of a PEV and the estimated distance of its next trip. Afterwards, with the aim of considering the uncertainties with the associated random variables as well as properly adjusting vehicles SOC at the departure time, a Monte Carlo based multi loop (MCML) algorithm is developed which is composed of two loops, namely the inner loop and the outer loop. In order to implement the proposed stochastic method, a case study has been conducted employing the gathered datasets related to the ICE vehicles in Tehran. Appropriate Student’s t copula functions have been fitted to the datasets in order to take into account the correlation structure among them as well as to generate the required random samples.

dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectMonte Carlo simulation
dc.subjectUncertainty
dc.subjectElectric vehicles
dc.subjectDemand modeling
dc.subjectSmart grid
dc.subjectDistribution system
dc.titleSensitivity of Electric Vehicles Demand Profile to the Batteries Departure State-of-Charge
dc.typeConference Paper
dcterms.source.startPage1
dcterms.source.endPage6
dcterms.source.title24th Australasian Universities Power Engineering Conference (AUPEC 2014)
dcterms.source.series24th Australasian Universities Power Engineering Conference (AUPEC 2014)
dcterms.source.isbn978-0-646-92375-8
dcterms.source.conference24th Australasian Universities Power Engineering Conference (AUPEC 2014)
dcterms.source.conference-start-dateSep 28 2014
dcterms.source.conferencelocationPerth, Australia
dcterms.source.placeUSA
curtin.note

Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access


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