Enhanced Charge Scheduling of Electric Vehicle Using Optimal Andean Condor Algorithm

The rapid expansion of Electric Vehicles (EVs) has led to developments in various research areas in this field, including the establishment of a charging cost approach, charging management, charging station the spot, and charging station organization.The article presents the most effective design of an EV charging station structure utilizing an efficient Andean Condor Algorithm (ACA) to improve the charge scheduling of EV.By simulating each charger incoming EVs with stochastic charging requirements, this suggested solution tackles the problem of dealing with dimension-varying state and action space associated with each charger. Considering the significance of charging station facilities this work examines the deployment of EV charging stations. Its purpose involves taking into consideration a number of limitations, including recharging station power, the average time period required for every charge, and regular travel schedules. The ACA method is used for scheduling electric vehicle Distributed Generators (DGs) and Network Reconfiguration (NR) are used to minimize loss of electricity. This strategy reduces real and standard power losses in grid systems by fulfilling the imposing scheduling specifications. The suggested approach improves the dependability of the charging system scheduling with entirely reducing overall power losses.Based on the MATLAB simulation findings, ACA significantly minimizes total distribution network losses by appropriately installing charging elements.