Sensitivity of Electric Vehicles Demand Profile to the Batteries Departure State-of-Charge

Pashajavid, E.; Shahnia, Farhad

doi:10.1109/AUPEC.2014.6966620

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Access Status

Open access

Authors

Pashajavid, E.

Shahnia, Farhad

Date

2014

Type

Conference Paper

Metadata

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Citation

Pashajavid, 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).

Source Title

24th Australasian Universities Power Engineering Conference (AUPEC 2014)

Source Conference

24th Australasian Universities Power Engineering Conference (AUPEC 2014)

DOI

10.1109/AUPEC.2014.6966620

ISBN

978-0-646-92375-8

School

Department of Electrical and Computer Engineering

Remarks

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.

URI

http://hdl.handle.net/20.500.11937/41708

Collection

Curtin Research Publications

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.