Statistical characterization of electric vehicle charging in different locations of the grid

The advent of electric vehicles (EVs) will bring forth large increases to the pre-existing demand in the power grid. Adverse impacts to the system will arise if the charging of these EVs is uncontrolled. In order to mitigate this challenge, as a first step the estimation of the additional power due to EV charging is crucial. The estimation is dependent upon the temporal (i.e. time) and spatial (i.e. location) characteristics of the EV charging process. A tool is developed in this work, which estimates the additional demand using Monte Carlo simulations performed on a large fleet of EVs over several days. The simulations include EV travel data within predefined residential, workplace, and commercial zones that are determined using traffic flow information. This tool can be used by system operators and other entities to determine the opportunities and challenges posed by additional EV demand. The results show the power consumptions at each hour of the day can be modelled by a normal distribution, thus simplifying the estimation procedure.