Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network


The integration of Electric Vehicles (EVs) into low-voltage (LV) residential distribution networks inevitably increases the overall demand, especially peak demand, which may cause thermal or voltage issues. In this paper, a 400V practical residential distribution network is modelled and used to quantify these impacts due to the growing penetration of EVs. Residential load profiles in 1-minute resolution and EV charging profiles with recorded State of Charge (SOC) are randomly and statistically created. Then, a simple charging management algorithm with locally made decision is suggested at EV users' charging points. Results prove that this approach can mitigate the negative impacts of EV charging on network assets. Moreover, it can reduce EV users' electricity cost for charging based on existing UK electricity price scheme 'Economy 7,' without compromising EV usage or substantial network infrastructure reinforcement or installation of extensive monitor, control and communication system. The simulation models and analysis are implemented in MATLAB/OpenDSS as an LV distribution network simulation platform.

Publication DOI: https://doi.org/10.1109/APPEEC.2016.7779489
Divisions: College of Engineering & Physical Sciences > Power Electronics, Machines and Power System (PEMPS)
Additional Information: -© 2016 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.
Event Title: 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016
Event Type: Other
Event Dates: 2016-10-25 - 2016-10-28
Uncontrolled Keywords: charging management,electric vehicles,high resolution load profile,low-voltage distribution networks,multi-objective optimization,Energy Engineering and Power Technology
ISBN: 978-1-5090-5417-6, 978-1-5090-5418-3
Last Modified: 10 May 2024 07:19
Date Deposited: 03 May 2017 08:35
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-12-09
Accepted Date: 2016-10-01
Authors: Qiao, Zhi
Yang, Jin (ORCID Profile 0000-0002-1026-8495)



Version: Accepted Version

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