# PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions
###### Abstract
In [20], the authors have studied a bi-level planning model of charging stations, by establishing a travel pattern model based on a Monte Carlo simulation and driving data of EVs. They aimed at satisfying the needs of EV users and minimizing the total social cost. The authors of [21] have designed the aspects and presented the practical implementation of a solar-assisted EV charging station. A smart charging strategy has been presented in [22] for a plug-in EV network that provides different charging options; battery swapping facilities at the charging station, AC level 2 charging, and DC fast charging. The strategy aimed at finding the optimal charging station considering the minimum driving time, charging cost, and charging time. In [23], the authors have evaluated the factors affecting the EV charging demand and predicted the charging demand of various EVs under different circumstances; such factors are driver behavior, electricity pricing, location of charging stations, social characteristics of the EV user, and economic elements. Their results contributed to identifying optimal locations for charging stations to maximize their utilization. The authors of [24] have analyzed competitive interactions for different EV charging stations with renewable energy sources using a game-theoretical analysis. The objective is to maximize the revenue of each EV charging station, subject to physical constraints. Their results have shown that EV charging stations equipped with renewable energy sources decrease the electricity price and increase the revenue of the EV charging station. An EV charging station based on PV sources, stationary storage, diesel generator, and a public grid connection has been implemented in [25]; so it can operate in three modes: grid-connected, islanded operation, and diesel generator set connected. Their test results have proved the capability of the EV charging station under different conditions. In [26], a real-time rule-based algorithm has been proposed for the operation of a DC microgrid-based EV charging station with imposing charging power limit depending on power availability. They have focused on the management strategy for the EV charging station, highlighting the interaction with EV users. Their results have proved the feasibility of the intelligent management proposed, including EV shedding and EV restoration priority, and its efficiency in considering user choices. In [27], the authors have proposed an optimization problem to reduce the stress on the grid and to reduce the cost of consumed energy. They have proposed a model predictive to forecast EV's power demand. They have proposed to charge the EVs by PV, storage, and grid instead of directly feeding the EVs from the grid. In [28], the authors have investigated peak load reduction using PV, storage, and a vehicle-to-grid strategy for EVs. They have focused on increasing the capacity of the storage to decrease the grid dependency. The authors of [29] have investigated the charging of EVs using PV energy in the workplace. They have studied the optimal sizing of storage to make the charging station grid-independent. However, these articles did not propose different charging modes for the EVs, they have focused on reducing peak load demand or reducing the cost of energy consumed by the grid rather than increasing the PV benefits for the EV users. Moreover, the energy distribution system and energy distribution for each EV are not depicted in these papers. However, to the extent of our knowledge, the previously cited references have not discussed the preliminary requirements and feasibility conditions for an EV charging station, while satisfying EV user needs and the factors that can influence their choice to increase PV benefits and lower their charging cost from the public grid. In previous studies, home charging represents 75% of EV charging time, the longest duration of vehicle dwelling time, and workplace charging represents 14% of EV charging time. These two locations have the largest opportunity for charging [9].