User behaviour and electric vehicle charging infrastructure: An agent-based model assessment

M. Pagani, W. Korosec, N. Chokani and R.S. Abhari

Applied Energy, 2019, vol. 254, issue C

Abstract: The transition to electric mobility is accelerating, and, thus it is increasingly important to be able to anticipate and adapt future development of the electric vehicle charging infrastructure. A novel agent-based simulation framework coupled with a detailed geo-referenced digital model of the built infrastructure is developed and applied. The charging behaviour of individual electric vehicle users as well as the spatial distributions of electric vehicles are accounted for in the simulation framework. More than 2500 scenarios of the transition to electric mobility in a mid-size city in Switzerland are assessed. The time to break-even of the electric vehicle charging infrastructure is up to 50% shorter when users are charged on the basis of parking fees rather than power sales. However, the revenues from parking fees are shown to be more sensitive to the behaviours and preferences of the users. At today’s low penetrations of electric vehicles, the profitability of the charging infrastructure is very uncertain, and thus entrants into the marketplace will have substantial financial exposure until the penetrations are of order 10%. Additionally, it is shown that, at specific transformers, public charging considerably increases grid loads by up to 78% during peak hours; these local increases, rather than the average city-wide increase in load, are the critical determinant of the required upgrades to the distribution grid. Overall, this novel simulation framework facilitates the planning of electric vehicle charging infrastructure that will support a successful transition to electric mobility.

Keywords: Agent-based simulations; Charging infrastructure; Distribution grid; Electric vehicles; User behaviour (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (29)

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DOI: 10.1016/j.apenergy.2019.113680

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