Optimal Parameter Allocation in Renewable Integrated Fast Charging EV Station considering hGPS Algorithm

Optimal Parameter Allocation in Renewable Integrated Fast Charging EV Station

Abstract

A maiden attempt has been made to propose the detailed modelling of fast charging electrical vehicle (EV)stations connected to hybrid grid-renewable energy source (RES like solar, hydro and wind) system considering EV demand characteristics, arrival time, departure time, state of charge and battery capacity. This helps in achieving the maximum profit and reduction in energy demand from the grid. Simulations are performed with a novel meta-heuristic algorithm named by hybrid genetic with pattern search (hGPS) algorithm for the first time and is used for optimizing the system parameters of charging station and maximizing the net present value (NPV).  The investigations are performed by probabilistic distribution of the EV demand based on EV behaviors and is simulated with sequential Monte-Carlo method by considering hourly intervals. The obtained economic considerations with hGPS algorithm are compared with GA, PS and is observed that hGPS maximizes the profit over others. Simulations with EVs fed by grid, EVs fed by RES and EVs fed by hybrid grid-RES explores that the maximum NPV is obtained with EV charging station fed by hybrid grid-RES. Moreover, it is also observed that, simulations with detailed modeling of EV provide better values over hybrid grid-RES system. Further, the investigations with a power transferred capacity limit among system network and grid reduces the impact of grid on system network.