*1.2. Contribution*

In this paper, we present a state of the art model for generating samples of EV session data that will generate synthetic samples of (i) arrival times, (ii) connection times and (iii) charging load, for each EV. We describe this model as synthetic data generator (SDG), as defined in our previous work [19]. This includes temporal statistical modeling of arrivals and modeling of conditional distributions for departures and the energy required for charging the EV. This differs from [3], in the sense that

we generate data on each session level, whereas they have only studied charging matrices. Herein, we also define and release trained parametric SDG models that can be used to generate session data, which were not provided in [3]. In comparison to [11], wherein load profiles were modeled using a spatial Markov chain model for five charging stations, our study includes temporal modeling of EV sessions arrivals for the joint set of multiple charging stations, derived from a large-scale real-world dataset comprising about 2000 charging stations. Along with this, we also include methods to jointly model the arrival and departure times of EVs for a large number of charging stations. Compared to [12], where the arrivals of EVs were characterized for weekends and weekdays, we propose a modeling method that can be used for any set of days that have similar properties, and adopt different statistical models. Our approach also gives us further insights into consumer behavior, by providing us the rates of EV arrivals for different hours, days and months. These generated arrivals will be used to generate the departures and required energy for each session. Our main contributions from this paper include:

