An Efficient Andean Condor Algorithm for Optimal Charging Stations for Electric Vehicle

In today's world, new energy science is gradually advancing, and a number of advances are taking place. Consequently, electric vehicles (EV) are being predictable by people around the world and are becoming increasingly popular in several countries. Electric vehicles are becoming increasingly popular, but their rapid growth has resulted in a number of problems, including a lack of charging stations, uneven distribution and high costs. In order to overcome such issues, this paper proposed a method for improving the charge scheduling of electric vehicles using ACA algorithm. In this way, decentralized chargers will be able to learn decentralized charger control strategies more effectively because they have access to a common replay of previous operations. The implementation of EV charging stations is examined in this paper due to the importance of charging station infrastructure. Its goal is to consider a variety of restrictions, such as, the charging station's power, typical amount of time needed for each recharge and daily travel times. Electric vehicle DG optimization is scheduled using the Andean Condor Algorithm (ACA) method as well as the Network Reconfiguration (NR) is carried out to minimize the power losses. By satisfying the grid's requirements for the charging schedule, this method minimize the active as well as reactive power losses in the distribution networks. The reliability of the charging system schedule is improved and overall power losses are entirely eliminated by the proposed technique. According to the MATLAB simulation results, ACA greatly reduces distribution system overall losses by placing charging units.