Spatial-temporal distribution prediction of electric vehicle charging load based on user travel simulation

Han, Jing, Liang, Jun ORCID: https://orcid.org/0000-0001-7511-449X, Zhang, Xiawei and Wang, Yaoqiang 2025. Spatial-temporal distribution prediction of electric vehicle charging load based on user travel simulation. Presented at: UPEC 2024, Cardiff, Wales, 02-06 September 2024. Proceedings 59th International Universities Power Engineering Conference (UPEC). IEEE, 10.1109/upec61344.2024.10892574

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Abstract

Electric vehicles (EVs), as an environmentally friendly means of transportation, have attracted wide attention. The charging and discharging behaviors of electric vehicles are stochastic and spatiotemporal fluctuating. In this paper, a forecasting method of charging load distribution that incorporates user travel simulation is proposed to consider the spatiotemporal characteristics of charging demand. This study addresses the impact of traffic conditions on EV charging behavior by constructing a road traffic network model. Furthermore, it incorporates user travel characteristics and utilizes an improved Dijkstra algorithm to accurately simulate EV driving routes and charging behavior, a spatial-temporal distribution prediction model for EV charging load is developed. Finally, a simulation is carried out on a typical road network. The proposed method considers the interaction of road networks, electric vehicles, and users' charging behavior. The conclusions indicate that the model can accurately calculates the charging loads of EVs in various functional areas within a day, and verifies the feasibility of the approach.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	IEEE
ISBN:	979-8-3503-7973-0
Last Modified:	10 Mar 2025 13:00
URI:	https://orca.cardiff.ac.uk/id/eprint/176768

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