Multi‐Objective Particle Swarm Optimization Algorithm for Optimal Placement of Electric Vehicle Charging Stations in Distribution System

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...

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Vydáno v:Energy science & engineering Ročník 13; číslo 10; s. 4991 - 5007
Hlavní autoři: Hsu, Chou‐Yi, Ved, Amit, Ezhilarasan, G., Yadav, Anupam, Rameshbabu, A., Alkhayyat, Ahmad, Aulakh, Damanjeet, Choudhury, Satish, Sunori, S. K., Khorasaninasab, Atabak
Médium: Journal Article
Jazyk:angličtina
Vydáno: London John Wiley & Sons, Inc 01.10.2025
Wiley
Témata:
Algorithms
Alternative energy sources
charging stations
Costs
Demand side management
Discharge
distribution systems
Effectiveness
Electric potential
Electric power loss
Electric vehicle charging
Electric vehicle charging stations
Electric vehicles
Electricity distribution
Energy distribution
Energy management
Environmental impact
Genetic algorithms
Geothermal energy
Geothermal power
Heuristic methods
Hybrid electric vehicles
Hydroelectric power
Infrastructure
Marginal pricing
MPSO algorithm
Objective function
Optimization
Optimization models
Particle swarm optimization
Placement
power losses
Renewable energy sources
Renewable resources
Smart grid technology
Solar energy
Voltage
Wind power
ISSN:2050-0505, 2050-0505
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Shrnutí: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
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ISSN:2050-0505
2050-0505
DOI:10.1002/ese3.70223