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

Zhi Qiao, Jin Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference
PublisherIEEE
Pages156-160
Number of pages5
ISBN (Electronic)978-1-5090-5418-3
ISBN (Print)978-1-5090-5417-6
DOIs
Publication statusPublished - 9 Dec 2016
Event2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016 - Xi'an, China
Duration: 25 Oct 201628 Oct 2016

Conference

Conference2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016
CountryChina
CityXi'an
Period25/10/1628/10/16

Fingerprint

Electric vehicles
Electric power distribution
Electric potential
Electricity
MATLAB
Communication systems
Reinforcement
Control systems
Costs

Bibliographical note

-© 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.

Keywords

  • charging management
  • electric vehicles
  • high resolution load profile
  • low-voltage distribution networks
  • multi-objective optimization

Cite this

Qiao, Z., & Yang, J. (2016). Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network. In IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference (pp. 156-160). IEEE. https://doi.org/10.1109/APPEEC.2016.7779489
Qiao, Zhi ; Yang, Jin. / Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network. IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference. IEEE, 2016. pp. 156-160
@inproceedings{a795058e185a41febf2c1e400f3cf676,
title = "Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network",
abstract = "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.",
keywords = "charging management, electric vehicles, high resolution load profile, low-voltage distribution networks, multi-objective optimization",
author = "Zhi Qiao and Jin Yang",
note = "-{\circledC} 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.",
year = "2016",
month = "12",
day = "9",
doi = "10.1109/APPEEC.2016.7779489",
language = "English",
isbn = "978-1-5090-5417-6",
pages = "156--160",
booktitle = "IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference",
publisher = "IEEE",
address = "United States",

}

Qiao, Z & Yang, J 2016, Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network. in IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference. IEEE, pp. 156-160, 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016, Xi'an, China, 25/10/16. https://doi.org/10.1109/APPEEC.2016.7779489

Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network. / Qiao, Zhi; Yang, Jin.

IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference. IEEE, 2016. p. 156-160.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network

AU - Qiao, Zhi

AU - Yang, Jin

N1 - -© 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.

PY - 2016/12/9

Y1 - 2016/12/9

N2 - 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.

AB - 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.

KW - charging management

KW - electric vehicles

KW - high resolution load profile

KW - low-voltage distribution networks

KW - multi-objective optimization

UR - http://ieeexplore.ieee.org/document/7779489/

UR - http://www.scopus.com/inward/record.url?scp=85009997052&partnerID=8YFLogxK

U2 - 10.1109/APPEEC.2016.7779489

DO - 10.1109/APPEEC.2016.7779489

M3 - Conference contribution

AN - SCOPUS:85009997052

SN - 978-1-5090-5417-6

SP - 156

EP - 160

BT - IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference

PB - IEEE

ER -

Qiao Z, Yang J. Electric Vehicle charging management algorithm for a UK low-voltage residential distribution network. In IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference. IEEE. 2016. p. 156-160 https://doi.org/10.1109/APPEEC.2016.7779489