Charging infrastructures are under the attention of researchers and companies as they are actually the crucial point for the development of the spread of electric vehicles. For this reason It is essential to have forecasting tools that analyze the behaviour of users to try to extrapolate data and useful information. In this work we start from a group of data collected during the Teinvein project and then reworked to obtain a forecasting model based on Markov chains. The method proposed in the paper makes use of information related to the distribution of vehicles in the charging stations, their average plug time, the amount of energy withdrawn, to reconstruct a distribution of occupancy model of a single station and, consequently, the consumption profile.

Forecasting of electrical vehicle impact on infrastructure: Markov chains model of charging stations occupation

Gruosso G.;Storti Gajani G.
2020-01-01

Abstract

Charging infrastructures are under the attention of researchers and companies as they are actually the crucial point for the development of the spread of electric vehicles. For this reason It is essential to have forecasting tools that analyze the behaviour of users to try to extrapolate data and useful information. In this work we start from a group of data collected during the Teinvein project and then reworked to obtain a forecasting model based on Markov chains. The method proposed in the paper makes use of information related to the distribution of vehicles in the charging stations, their average plug time, the amount of energy withdrawn, to reconstruct a distribution of occupancy model of a single station and, consequently, the consumption profile.
2020
Charging infrastructures
Data driven
Electric vehicle (EV)
EV impact
Markov chains
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1158211
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