Multi‐Objective Particle Swarm Optimization Algorithm for Optimal Placement of Electric Vehicle Charging Stations in Distribution System - Hsu, Chou‐Yi; Ved, Amit; Ezhilarasan, G.; Yadav, Anupam; Rameshbabu, A.; Alkhayyat, Ahmad; Aulakh, Damanjeet; Choudhury, Satish; Sunori, S. K.; Khorasaninasab, Atabak (Energy science & engineering, 2025)
ABSTRACT Renewable energy sources, such as wind, solar, biomass, hydropower, and geothermal power, have a relatively minor environmental impact compared to nonrenewable sources and are sustainable over the long term. However, the variable nature of renewable energy production and the load demands of plug‐in hybrid electric vehicles (PHEVs) can lead to significant challenges in network performance, including reduced power quality, increased power losses, and voltage fluctuations. Effective integration of these energy sources requires optimal planning that considers various output variables of renewable sources to meet the increased demand from PHEV charging. Furthermore, the development of an efficient energy management strategy for PHEVs poses an optimization challenge that can be addressed using metaheuristic algorithms. In this paper, the multi‐objective particle swarm optimization (MPSO) algorithm is implemented for the optimal placement of the EV charging points, taking into account the surrounding area and the coverage of the stations. The objective function is optimized by the MPSO algorithm with the objective of minimizing the cost of optimizing the locations of the charging points. Finally, the simulated results in standard IEEE 69‐bus distribution systems show that the proposed optimization model led to a reduction in power losses from 268.17 to 229.97 kW in the best charging scenario and to 177.32 kW in the best discharging scenario. Additionally, the minimum bus voltage improved from 0.887 to 0.908 prionits (p.u.) (in charging mode) and 0.917 p.u. (in discharging mode), confirming the effectiveness of the proposed MPSO approach in enhancing network performance