Optimizing electric vehicle charging stations with solar PV integration using reptile search algorithm

Abstract The increasing demand for energy and the climate change caused by CO2 emissions have made electric vehicle (EV) technology necessary in the transportation sector. As the number of electric vehicles on the road rises, EV charging has become a major issue. In this manuscript, an optimization strategy for EV charging stations (EVCS) powered by photovoltaic (PV) is proposed. The proposed optimization is a Reptile Search Algorithm (RSA) Algorithm. The key goal of the proposed strategy is to lower the system's overall operating costs. The proposed RSA is used to optimize daily operating costs based on anticipated PV production and EV use. By then, the proposed approach had been implemented into the MATLAB working platform, and its execution was calculated using the existing method. The proposed approach outperforms every existing technique in terms of results like recurrent neural network (RNN), convolutional neural network (CNN) and deep convolutional neural network (DCNN). The proposed approach has a cost of $0.20, an efficiency of 98.4%, and a computation time of 8.2 s, offering better performance than existing methods.