Enhancing electric vehicle charging stations in DC microgrid using KOA–DRN approach

Because of the high current required and the fact that charging stations introduce limits and concerns into the public grid at different times and in different locations, electric vehicles are becoming increasingly popular. Short charging times for electric vehicles (EVs) due to inefficient EV charging infrastructure are the main obstacles to their expansion. This paper proposes a hybrid technique for enhancing electric vehicle charging stations in DC microgrid. The proposed hybrid approach is a combination of both dilated residual network (DRN) and Kepler optimization algorithm (KOA). Hence, it is named as KOA–DRN technique. The main objective of the proposed method is minimizing the total energy loss and charging time. The SAO algorithm is utilized to optimize the charging process, ensuring efficient and optimal use of available resources, and DRN is utilized to provide intelligent control and decision-making capabilities to the EV charging station. The proposed method is executed in the MATLAB and is compared with different existing methods like wild horse optimization (WHO), heap-based optimization (HBO), and particle swarm optimization (PSO). The peak PV power is 11 W; peak grid current is − 195 to 190 in 2 s. DC load voltage is 4.1 W. The proposed approach KOA–DRN obtains loss value of 1.2% and setting time of 0.02 s, which is less than the existing approaches.