Optimal Allocation for Electric Vehicle Charging Stations Integrated with Multi-Distributed Generations in Distribution Networks

Abstract

The rising popularity and improved environmental awareness are making the present transportation network move towards electric vehicles (EVs). The inclusion and integration of distributed generations (DGs) in the EV charging schemes will minimize their grid dependency and add flexibility to the system. The presented article develops the optimal allocation of EV charging stations with the integration of multi-DGs. The contributions of the presented strategy are to minimize the system losses, investment costs and improve the system’s voltage stability, and maximize reliability while respecting network capacity limits, voltage constraints, DGs’ availability, and EV charging demands. To accomplish this study, the presented methods are performed in MATLAB/Simulink and employed in IEEE 14-Bus, and IEEE 33-Bus radial distribution test systems using recently discovered algorithms of Dingo Optimization Algorithm (DOA), Sand Cat Optimization Algorithm (SCSO), Grey Wolf Optimization Algorithm (GWO), and Wild Horse Optimization Algorithm(WHO), and the results obtained for the optimal location and size for the electric vehicle charging station based on minimum power losses and maximum voltage stability index were achieved in IEEE 14 Bus system at bus no.10 with capacity of 0.4MW and in IEEE 33 Bus system at Bus no.25 with capacity of 0.1MW as it has been observed that these best solutions are measured accurately using SCSO, GWO, and WHO Algorithms.