*1.1. Impact of EV Charging on Power Distribution Systems*

The overall impact of EVs on the power distribution system was reviewed by [3]. This impact varies according to EV penetration, EV battery characteristics, charging demand, load characteristics, driving behavior, driving distance, demand response strategy, and electricity tariffs. The main identified

problems were the increase in peak demand, power quality problems, power loss, transformer heating, and system overloading.

Connecting EV charging stations to the grid can negatively affect the power quality and efficiency of the network by introducing losses and voltage violations [4]. The optimal location and size of charging stations and renewable energy sources help to reduce this negative effect. The charging process has a direct effect on distribution system voltage levels; therefore, EV impact assessments are conducted for worst-case scenarios while modeling for EV electrical loads. Authors of [5,6] developed a model for EV load in MATLAB SIMULINK, which represented the behavior of the battery charge and discharge loading characteristics, and concluded that integrating EV fast-charging reduces the steady-state voltage stability limit of the power system.

The battery charge demand of an EV defines the impact on the power system. For instance, uncontrolled charging will cause the charging demand to require an upgrade to the power system, while controlled charging takes into account the distribution network's constraints and does not require further upgrades to the network [7]. In other words, uncontrolled charging requires adding new equipment like transformers, cables, and protection devices to the existing distribution system in order to successfully host and deal with EV charging. Modeling the EV load can be done through a car trip destination based on the household and the person present, such as the Markov chain [8]. A probabilistic approach using the probability density function in modeling EV, grid loads, and PV outputs was reported by [9]. A stochastic modeling-implemented approach in the load flow based on the Monte Carlo method was discussed in [10]. The load demand of the EV fleet in [11] considers socio-economic, technical, and spatial factors. Deterministic EV load estimation based on actual measurements requires traffic data and surveys. The study in [12] suggested considering the degradation of EV batteries when sizing for renewable energy sources' integration for EVCSs to avoid high-capacity sizing.
