Dispatch model for analysing the impacts of electric vehicles charging patterns on power system scheduling, grid emissions intensity, and emissions abatement costs

Oliyide, Rilwan O. and Cipcigan, Liana M. ORCID: https://orcid.org/0000-0002-5015-3334 2022. Dispatch model for analysing the impacts of electric vehicles charging patterns on power system scheduling, grid emissions intensity, and emissions abatement costs. International Multidisciplinary Research Journal 12 , pp. 4-13. 10.25081/imrj.2022.v12.7544

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Abstract

Dispatching of generating resources at Power Stations is a complex task based on the balance of economics, contractual agreement, regulations, and environmental consciousness in terms of emissions produced in the course of electricity generation. The complexity of the task could be exacerbated with the integration of a large percentage of Electric Vehicles (EVs) in the quest to reduce CO2 emissions in the transportation sector. In this paper, a dispatch model, which is suitable for analysing the impacts of charging patterns of EVs on grid emissions intensity and emissions abatement costs, is described and developed for dispatching generating resources/technologies. The dispatch model is based on the correlation between historical system load and capacity factors of generating units. The dispatch model is tested on data from the UK power system on a typical winter day in December 2015 with an assumed 50% integration of EVs on the system. Results show amongst others that charging of EVs in the off-peak period may affect the optimal deployment of generating technologies/resources with storage capacity and could produce a higher average grid emissions intensity.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	This article is open access and licensed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)
ISSN:	2231-6302
Date of First Compliant Deposit:	10 August 2022
Date of Acceptance:	4 June 2022
Last Modified:	04 May 2023 10:20
URI:	https://orca.cardiff.ac.uk/id/eprint/151743

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