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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Modelling of Large-scale Electric Vehicles Charging Demand: A New Zealand Case Study

Su, J; Lie, T; Zamora, R
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http://hdl.handle.net/10292/12353
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Abstract
Due 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.
Keywords
Charging; EV electrical load model; Probabilistic modelling; Smart charging strategies
Date
November 12, 2018
Source
Electric Power Systems Research, 167, 171-182.
Item Type
Journal Article
Publisher
Elsevier
DOI
10.1016/j.epsr.2018.10.030
Publisher's Version
https://www.sciencedirect.com/science/article/pii/S0378779618303535?via%3Dihub
Rights Statement
Copyright © 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).

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