A novel planning tool to manage Electric Vehicles (EV) diffusion process on an electric power systems is proposed in this paper. It consists in firstly determining the optimal power and capacity of the EV fleet in order to smooth the power plant production profile of an electrical power system, improving its efficiency and reliability. In particular, by imposing the EV rated power and capacity, it is possible to define the optimal amount of EV too. Then, a geo-referenced algorithm has been developed in order to evaluate the consequence, in time and space, of the different EV charging laws on the power grid. An application study, referred to a weakly interconnected power systems, is reported. The results highlight the effectiveness of the proposed planning tool in defining, under smart grid paradigm application, the best compromise between mobility, energy storage, energy production and energy use.

Energy planning tool for Electric Vehicle and Smart Grid Integration

DAMIANO, ALFONSO;GATTO, GIANLUCA;MARONGIU, IGNAZIO;SERPI, ALESSANDRO
2011-01-01

Abstract

A novel planning tool to manage Electric Vehicles (EV) diffusion process on an electric power systems is proposed in this paper. It consists in firstly determining the optimal power and capacity of the EV fleet in order to smooth the power plant production profile of an electrical power system, improving its efficiency and reliability. In particular, by imposing the EV rated power and capacity, it is possible to define the optimal amount of EV too. Then, a geo-referenced algorithm has been developed in order to evaluate the consequence, in time and space, of the different EV charging laws on the power grid. An application study, referred to a weakly interconnected power systems, is reported. The results highlight the effectiveness of the proposed planning tool in defining, under smart grid paradigm application, the best compromise between mobility, energy storage, energy production and energy use.
2011
Energy Planning, Distributed Storage, Vehicle to Grid, Electric Vehicle, Smart Grid
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/102150
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