# PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions
###### Abstract
Delayed charging can be considered as a smart charging framework, since it changes the charging start time, charging end time, and charging power, yet most importantly delivering the requested energy to the EV. Additionally, the average charging can be considered as an uncoordinated charging framework, since it starts charging immediately when the EV is plugged-in but with limited power [11]. The delayed charging profile is similar to the uncontrolled charging profile but the peak load is shifted to overnight/dawn  
Figure 1: (**a**) Electric vehicles (EVs) charging and peak load; (**b**) Shifted EVs charging to overnight. (around 5:00 a.m. and 9:00 a.m.). Whereas, in average charging, the profile is flattened instead of having a peak [9]. Uncoordinated charging of EVs may increase the peak load, imposing a heavy burden on the public grid leading to more losses. Therefore, through smart charging or coordinated charging, EVs can be an asset for the grid by helping to increase penetration of renewable energies, balancing the energy system, and improving the efficiency of the system while satisfying EV user demands [12]. Coordinated charging is classified into two types, time coordinated charging and power coordinated charging as in [13]. In time coordinated charging, the number of EVs that can charge is controlled to ensure the total load demand is within the available power for EV charging. Whereas, in power coordinated charging, the power of EV charging is controlled to ensure the total load demand is within the available power for EV charging. The most important parameters in EV modeling are the charging/discharging rate, initial SOC, battery capacity, charge depleting distance, and user behavior, which is hard to predict in advance. In addition, the arrival time at the charging station, the departure time, and the driving distance of the EV are variables, depending on user habits. They can, however, be assumed and follow probability distribution functions [10; 14]. For this purpose, probability distribution functions are generated to determine the arrival time at the charging station, the departure time, and the driving distance of the EV. Then, the energy needed to fully charge the EV is calculated and the total charging time of the EV is the energy needed to fully charge the EV over the charging rate [10; 14; 15]. Since the EV market is growing vastly, many research studies are expanding, in this field, especially regarding the charging process for EVs. A home-scale EV charging station based on natural gas has been proposed in [16]. They have compared, environmentally and financially, their scheme with a conventional vehicle, fossil fuel-based, having the same characteristics and with an EV charged directly from the public grid. They have shown that with the EV charged with natural gas, no fossil fuel is required, it has zero carbon emissions, and the EV charged by natural gas or electricity is cheaper than oil/petrol over a year. In [17], the minimum size and cost of a charging station for EV fleets has been studied in two urban areas in Europe, as well as the impact of the charging station on the electrical grid in terms of power and energy demand. Their analysis has identified some policies and highlighted that the critical barrier for charging station deployment in urban areas could be the time required to implement charging stations. In [18], the authors have proposed an EV charging control scheme from the grid operator perspective rather than the EV user. They have proposed a method to change indirectly the route of the EV using dynamic pricing to improve the system operation, keep the voltage stable, and meet charging demands. The optimal operation of a DC microgrid-based EV charging station using mixed-integer linear programming has been studied in [19]. The operation aimed to optimize the daily operating cost, based on PV production forecast and EV needs.