A queuing theory decision support model and discrete event simulations for the smart charging of electric vehicles

Electric Vehicles (EVs) seem to represent a promising solution for the replacement of traditional internal combustion engines due to the growing concerns on the pollution in urban environments. However, their wide diffusion is expected to cause relevant issues for the operation of existing power grids. The proper management of EVs charging stations is considered as one of the most challenging questions, due to the high temporal and spatial stochasticity of power demands. The aim of the present work is to propose decision support models for the design of EV charging processes able to adequately satisfy EVs power demand, by assuring the quality of service level required by end-users, expressed in terms of queuing and service times. This study introduces two analytical models based on the queuing theory for the optimization of EVs charging process, by balancing the power demand at available charging stations, and by improving the quality of service perceived by the users. Two simulation models are then proposed to evaluate the service level offered to users in the current uncoordinated charging process, as well as in future smart charging processes. A real case concerning the charging process at the University of Brescia North Campus is presented.