Optimal deployment of electric vehicle charging stations, renewable distributed generation with battery energy storage and distribution static compensator in radial distribution network considering uncertainties of load and generation

This research article proposes a novel approach for assimilating the electric vehicle (EV) charging stations (EVCSs)/EV battery swapping stations (EVBSSs) in radial distribution system (RDS) while minimizing the unfavorable impact on various performance parameters. The deployment of EVCSs/EVBSS(s) in the RDS consumes additional active power from the power system. To enhance the system sustainability, it is essential to incorporate the renewable distributed generations (DGs) (like solar photovoltaic (SPV), wind turbine (WT) and biomass) and distribution static compensator (DSTATCOM) of best size at suitable site in the RDS to reduce the unfavorable effects of EVCSs/EVBSS(s) demand. In addition, a novel multi-objective student psychology based optimization (MOSPBO) technique is proposed to obtain the best site and size of renewable DGs and DSTATCOM in medium RDS (such as IEEE 33-node) and large-scale RDS (like practical Brazil 136-node) for four different load models contemplating the time-sequence curves of load demand and generation of SPV and WT over 96-h. The proposed MOSPBO method is initially scrutinized on multi-objective benchmark test functions to verify its optimization proficiency. The obtained outcomes reveal that the proposed algorithm yields exceptional performance in searching for the complete pareto sets and pareto front on the considered benchmark test functions. The simulation results reveal that the attained positions and size of renewable DGs and DSTATCOM along with EVCSs/EVBSS(s) in the RDS employing MOSPBO method may be viable ones. The attained outcomes will undoubtedly benefit the power distribution and EV companies in enhancing the proficiency and dependability of the system.