Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Predicting Electric Vehicle impacts on residential distribution networks with Distributed Generation

Papadopoulos, P., Skarvelis-Kazakos, S., Grau, I., Cipcigan, Liana Mirela ORCID: https://orcid.org/0000-0002-5015-3334 and Jenkins, N. 2010. Predicting Electric Vehicle impacts on residential distribution networks with Distributed Generation. Presented at: Vehicle Power and Propulsion Conference (VPPC), 2010, Lille, France, 1-3 September 2010. Vehicle Power and Propulsion Conference (VPPC), 2010. IEEE, pp. 1-5. 10.1109/VPPC.2010.5729009

Full text not available from this repository.

Abstract

Battery charging of Electric Vehicles (EVs) will increase the power demand in distribution networks. It is anticipated that this will cause voltage drops, thermal overloads and an increase in losses. These impacts will be affected by the behaviour of the owners of EVs. A typical 3-phase LV residential distribution network model is used to evaluate the effects of EV battery charging on distribution networks with Distributed Generation (DG). The uncertainties associated with the ownership of EVs, the rating of charging equipment, the occurrence and the duration of charging, together with the spatial distribution uncertainties of DG installation, were addressed with a probabilistic approach. A case study was performed for the year 2030, considering three EV and two DG penetration levels. A control function which reschedules EV battery charging was defined based on customer preferences and distribution network constraints. Thermal overloads, voltage drops, and losses associated with each case were reported. The effects of the coordinated EV battery charging on these impacts were analysed.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: IEEE
Last Modified: 27 Oct 2022 09:48
URI: https://orca.cardiff.ac.uk/id/eprint/67799

Citation Data

Cited 42 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item