Demand estimation for electric vehicles at rapid charging hubs and peak load reduction using battery energy storage units

Sonder, H. B., Cipcigan, L. ORCID: https://orcid.org/0000-0002-5015-3334 and Ugalde Loo, C. E. ORCID: https://orcid.org/0000-0001-6361-4454 2022. Demand estimation for electric vehicles at rapid charging hubs and peak load reduction using battery energy storage units. Presented at: CIRED Porto Workshop 2022: E-mobility and power distribution systems, Hybrid Conference, Porto, Portugal, 2-3 June 2022. Proceedings of CIRED Porto Workshop 2022. IET, pp. 399-403. 10.1049/icp.2022.0735

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Abstract

Almost a third of global carbon emissions are attributed to the transportation sector – mostly resulting from a dependence on internal combustion engine cars. Electric Vehicles (EVs) have been thus identified as significant components of the carbon emissions reduction plan and, hence, developing a robust recharging infrastructure will increase electromobility's success. EVs may be recharged using slow, fast, rapid, and ultra-rapid technologies. However, rapid chargers consume significant energy from the grid within a short period of time, resulting in pulsating loads. An increased number of rapid chargers will alter the planning and management of all grid-connected operations. Using Deno runtime based on the JavaScript programming language, this paper presents an algorithm for creating stochastic charging profiles and estimating the demand of EVs from rapid and ultra-rapid devices. PSCAD/EMTDC is also used to examine the effect of chargers on the peak demand of a distribution feeder. The findings of this study increase confidence in the viability of connecting rapid chargers with battery energy storage units at strategic places of the distribution network.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IET
ISBN:	9781839537059
Last Modified:	06 Jan 2024 02:17
URI:	https://orca.cardiff.ac.uk/id/eprint/151605

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