Assessment of electric vehicle charging hub based on stochastic models of user profiles

Abstract

A significant challenge in the electric mobility transition is the planning of proper charging infrastructures to incentivize the use of electric vehicles (EV) and guarantee a reliable charging service to EV users. This paper proposes to model generic EV user profiles (e.g. worktime, commuters, etc.) together with a simulation framework to appropriately assess charging hubs that become undersized due to growing EV demand. First, Gaussian Mixture Models (GMM) of different EV user profiles are developed in order to simulate multiple scenarios of EV sessions per day (N). Second, an algorithm is presented to simulate the occupancy of a charging hub based on two parameters: (1) the number of charging points (P) and (2) the connection time limit (H). Finally, the charging hub assessment is performed according to a metric designed to consider the interests of both the EV user and the charging hub operator, recommending the optimal P for expandable hubs, or the optimal H for limited hubs. Both cases are analysed in the validation section of this work employing a real-world use case. Results validate that the presented methodology can be used by EV charging hub operators to achieve a balance between the exploitation of the charging installation and the satisfaction of EV users

The author Marc Cañigueral has been awarded a PhD-scholarship (Ref. FPU18/03626) by the Spanish Ministry of Education and Culture through the Training program for Academic Staff (FPU-programme). This work has been carried out by the research group eXiT (http://exit.udg.edu), awarded with the consolidated research award (SITES group, Ref. 2021 SGR 01125) by the Generalitat de Catalunya, Spain. The research was funded by the European Union Horizon 2020 under E-LAND project grant agreement No 824388, by European Union Horizon Europe under RESCHOOL project grant agreement No 101096490, and founded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR under OptiREC project grant agreement TED2021-131365B-C41

Open Access funding was provided thanks to the CRUE-CSIC agreement with Elsevier

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