# Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia
## 2 Literature Review
[13] projected a combined control policy for the EVCS. The energy storage unit at the charging station allows for the economical use of solar systems while guaranteeing uninterrupted EV charging. Droop, master-slave, and snubber circuits combined with EVCS management can result in faster battery charging as well as discharging and improved direct-current (DC) bus voltage regulation. Using MATLAB, [PERSON] et al. [14] also created and assessed a stand-alone PV-powered charging station (PVCS). The robust agreement between the simulation and experimental results confirmed the effectiveness of the suggested system. According to the findings, the EV battery charges steadily and constantly under various degrees of solar exposure. [PERSON] et al. [15] showed how to build and operate a contemporary solar-assisted EVCS in India's Aligarh Muslim University parking lot. The PV was tested using standard parameters. Compared to empirical approaches that rely on assumed data or the direct and applied approach with defined data, the MATLAB model can efficiently size and operate an entire PV-EV charging system faster. [PERSON] and [PERSON] [16] used MATLAB to assess innovative PV EVCS options for residential buildings in Sweden. Aa maximum charging power rate of 3.7 kW and a charging efficiency of 90% were established. The authors believed a significant drop in residential peak loads may result from intelligent charging networks. The study aimed to determine how many of these stations are best distributed over large route networks. [PERSON] et al. [17] used MATLAB to investigate the performance dynamics of a solar-integrated charging system. The authors stated that the output power of the solar PV system increased significantly by 47% when the solar irradiation was increased from 400 W/m\({}^{2}\) to 1000 W/m\({}^{2}\). A multi-agent deep reinforcement learning approach was presented by [PERSON] et al. [18] for energy management in distributed solar EVCSs and energy storage systems (ESSs). A thorough performance evaluation showed that the suggested method lowered operational costs for EVCSs. It was asserted that the proposed method was designed explicitly for PV/ESS-enabled EVCSs, successfully managing fluctuations in charging-related data in real time and producing the desired performance results. Using an ESS, [PERSON] et al. [19] presented a hybrid algorithm to reduce the cost of solar EVCSs in Singapore using three test cases of EV charging stations. Due to the high price of EVCSs, the study looked into the subsidies required to build nearby PV systems profitably. Therefore, using the suggested technique, ESSs on the distribution side can make deploying PV systems close to EVCSs easier at a reasonable cost. An optimization model was presented by [PERSON] et al. [20] to help determine the locations and sizes of solar-assisted EVCSs within a city. The tests used historical data from 297 customers of an EV rental business in Beijing, China. The outcomes demonstrated that the suggested method can generate superior selections in a computationally feasible time. Future research should prioritize developing efficient charging strategies in light of the growing number of EVs. PVCS roofs are attractive because of their integration of RE and EV charging potential. Consequently, [PERSON] et al. [21] suggested a method of energy management for PVCSs that accommodates several EVs. The process takes into account the objectives of EV owners as well as those of the stations. They asserted that the suggested strategy uses a learning algorithm with a two-level mechanism, producing effective energy distribution. Compared to stations without the proposed management system, the results demonstrated that the suggested method functions effectively for many charging stations. [PERSON] et al. [22] proposed a system for calculating the PV generation dispatch limitations in distribution networks with EVCS connectivity to account for related PV generation and EV uncertainties.