A Computational Implementation for Creating Electric Vehicles Profiles.

The need to achieve carbon neutrality has boosted the mass adoption of electric vehicles (EVs). However, the electric mobility demand uncertainty can create several technical issues in the power system management and operation. This paper presents a computational implementation based on different probability distributions for creating EVs charging profiles. Initially, aspects related to trips, EVs characteristics, and user profiles are considered as inputs to be used to define the EVs profiles. Then, in the second step, the processing of the inputs aiming to obtain information such as trips during the day, types of EVs, user profiles, trips by users, trip duration, and energy needs are considered. The proposed case studies demonstrate the effectiveness of the computational implementation since the EV profiles created taking into account different numbers of EVs show reliable information related to the EV charging profiles and charging station usage for different days of simulation (weekdays and a weekend).