# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 6 Optimal Sizing  
In recent years, the transportation sector has witnessed a rapid penetration of electric vehicles (EVs). The aim is to enable the sustainability of the system. It was driven by modern innovations in battery technology and in the electric drivetrain. However, as electric vehicles' penetration spreads, the EVs' demand increases, thus introducing additional load to the power systems. There is a need to upgrade and increase the capacities of the electricity distribution systems to contain the overloading challenge and integrate renewable energy sources (RESs) into the charging station. In addition, meeting the ever-increasing EV demands through optimum sizing and operation of the EV charging stations is the most challenging task. Several studies have been reported with regard to addressing the aforementioned challenges and are presented as follows. In [78], an EV charging station was designed with solar-wind hybrid power sources. The Hybrid Optimization Model for Electric Renewables (HOMER) software was employed for sizing the renewable energy source and for power-sharing to the loads. With one 200 kW capacity WT unit and PV panels, a total power of 250 kW, a total annual energy generation of 843,150 kWh was realized. The charging station has the capacity of charging 5 EVs in 1 h. Likewise, in [79], the MATLAB environment was used to develop a mathematical model of optimal sizing and capacity allocation using the differential evolution (DE) algorithm for a wind energy system that is integrated with an EV battery exchange station. A 200 kW wind generator and 10 kW charge and discharge machine were used to provide energy to both EVs for traveling demand and the entire system's energy balance. The analysis based on the condition of the components regarding power change at different periods reveals that the optimum solution is logical, and through the hybrid system concept, the EVs' energy demand can be achieved. In the same vein, a multi-objective optimization problem based on the DE algorithm was developed by [80] to obtain optimal sizing of EV charging stations and renewable energy sources. The performance of the proposed method is evaluated in MATLAB for different microgrids. The simulation results provided the optimal size of charging stations for the number of EVs based on the optimal load factor, power loss, and voltage profile. Similarly, a hybrid improved optimization algorithm based on Genetic Algorithm-Particle Swarm Optimization (GA-PSO) was used by [81] for the optimal sizing of renewable energy sources (RES) and EVs' charging demand. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Modeling Technique** & **Problem to Solve** & **Findings** \\ \hline
[74] & Power requirement model & The behavior of the power grid & Lacks the electrical behavior \\  & & & information of the network while \\
[74] & Power requirement model & The behavior of the power grid & charging, so these models have their \\  & & & importance if connected to an \\  & & & electrical network \\  & & & The EVs can determine the siting of \\
[75] & Queueing model & The probability distribution of & charging spots, in addition to charging \\  & & getting charged EVs & spots, the utilization of chargers \\  & & & increases, and the number of required \\  & & & chargers at each site decreases \\  & The distributional robust travel & & The model can reduce the worst-case \\
[76] & time information gain sensor & Uncertainty in the prior travel & situation with a small price of the \\  & location & time distribution & average objective value, especially \\  & (DRTTIGSL) model & & when the total budget is not high \\  & The data-driven constraints are & & \\  & reformulated into tractable & & \\  & counterparts by the sample & & \\  & average approximation & & \\  & (SAA) approach. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data-driven models.