Planning for Allocating Renewable Supported Charging Station with Intelligent Charging Scheduling in Distribution Network

Recently, the Electrification of Vehicle is grabbing attention in the automobile industry because of its zero-tail pipe emission. But the penetration of EVs causes enormous stress on the Distribution System (DS) in the form of power losses, generating a higher energy cost. Moreover, anxiety among electric vehicle (EV) drivers regarding selecting a proper charging station (CS) to avoid the breakdown of EVs in the middle of the road is resisting the growth of the EV market. Furthermore, the uncoordinated charging process can be costly and as well as stressful for both the DS operator and as well as EV users. Thus, in this paper, an intelligent tri-layer algorithm has been proposed. In the first layer, the suitable location for the charging station has been identified by keeping the power losses minimum. The integration of renewable energy sources (RES) can be incorporated along with the CS to reduce power losses and carbon emissions. After that, the appropriate CS has been identified in the second layer by keeping the battery energy consumption minimal. In the third layer, an optimal charging scheduling has been performed by battering soft computing technique to minimalize the daily expense of charging experienced by Charging Station Aggregator (CSA). The scheduling includes Grid to Vehicles (G2V) and Vehicles to Grid (V2G) modes of operations. Later, the techno-economical benefit of this proposed technique has been assessed with and without incorporating RES.