Optimized Electric vehicle Charging and discharging with sporadic Renewable energy source

In this paper an approach in smoothing the fluctuations of large-scale electric vehicle (EV) integrated PV farms in AC/DC microgrid with vehicle-to-grid (V2G) and grid-to-vehicle (G2V) scenarios is considered. First, an energy management EV integrated PV farms AC/DC microgrid is designed with charging stations by scheduling EV data from non-real time environment (MATLAB) to real time digital simulation (RTDS) and secondly, the genetic algorithm-based optimization technique is performed to improve the cost of charging and discharging in the V2G and G2V mode by scheduling EVs for 24 hours in non-real time environment (MATLAB). The comparison of grid-connected and islanded-connected based on output power currents and voltages is also evaluated. Furthermore, the performance of the optimization and the EV integrated microgrid shows better results over benchmark techniques for charging and discharging scenarios of EVs in real and non-real time environment.