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Grid service potential from optimal sizing and scheduling the charging hub of a commercial Electric Vehicle fleet

: Bartolucci, L.; Cordiner, S.; Mulone, V.; Santarelli, M.; Lombardi, P.; Wenge, C.; Arendarski, B.; Komarnicki, P.


Leonowicz, Z. ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020. Conference proceedings : 9-12 June, 2020, Madrid, Spain : the 2020 edition will be held on scheduled days from 09th to 12th June 2020 in web streaming
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-72817-453-2
ISBN: 978-1-72817-455-6
ISBN: 978-1-72817-456-3
6 S.
International Conference on Environment and Electrical Engineering (EEEIC) <20, 2020, Online>
Industrial and Commercial Power Systems Europe Conference (I&CPS Europe) <4, 2020, Online>
Fraunhofer IFF ()

Nowadays several companies are taking into consideration the possibility to electrify their vehicles' fleet. Users have to deal with the problem of choosing a model of Electric Vehicle and properly sizing a Charging Station in such a way that the investment costs are minimized, and the logistic constraints respected. From the grid side, the growing energy demand caused by the electrification of the transport sector is introducing grid instabilities and Distributed System Operators have to ensure the service reliability, keeping operating costs down. This work shows how the optimal allocation and control of the Electric Vehicle charging allow to both minimize the user investment costs and avoid grid congestions during peak periods at the same time. Two different charging scheduling strategies, namely a standard and an optimal control strategy, are proposed considering different Charging Station configurations (single level and multi-level type). The possible layouts are then analyzed and compared from the user and the Distribution System Operator point of view. The obtained results suggest that a multi-level Charging Station configuration integrated with an optimal control strategy leads to a reduction of around 46% of the investment costs and for the power profile a decrease of 44%, 11% and 31% of standard deviation, mean and maximum value, respectively.