ABSTRACT In this era, electric vehicles (EVs) have become widely popular in the transportation sector because of their smaller carbon footprint and less noise. The charging stations for EVs are rapidly increasing to meet their charging demands in a shorter time. The hybrid charging stations, combined with renewable sources like solar and wind energy, offer an environmentally friendly solution for the massive adoption of EVs. However, the additional load of EV charging stresses the utility grid, and the intermittency of these renewable sources adds uncertainty to the performance of charging stations. The load management of the EVCS faces challenges during the unavailability of renewable energy and peak demand hours. This research focuses on the demand side management of the EV load through a coordinated demand response strategy that effectively schedules the EVs and employs a multi‐objective optimization technique to balance operational cost and Loss of Power Supply Probability (LPSP) of the charging station. Three commonly used optimization algorithms, namely Multi‐Objective Particle Swarm Optimization (MOPSO), Multi‐Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Non‐dominated Sorting Genetic Algorithm (NSGA‐II), are analyzed for a hybrid fast EVCS to determine an optimal trade‐off solution that can improve economic feasibility and reliability. Sensitivity analysis of these techniques is performed to analyse the solution of each algorithm under perturbations.
Sultan et al. (Thu,) studied this question.
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