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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 777-786
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-05-2026
The rapid global adoption of electric vehicles (EVs) necessitates strategic planning of charging infrastructure that balances investment costs, power grid constraints, and user accessibility requirements. This paper proposes an Enhanced Multi-Objective Particle Swarm Optimization (EMOPSO) algorithm for optimal placement and capacity planning of EV charging stations (EVCSs) in urban distribution networks. The proposed algorithm incorporates four novel enhancement mechanisms: (1) adaptive inertia weight based on population diversity and convergence rate, (2) dynamic cognitive-social coefficient adjustment using sinusoidal functions, (3) mutation-based diversity preservation with archive stagnation detection, and (4) adaptive grid-based external archive management for well-distributed Pareto fronts. The optimization framework simultaneously minimizes total lifecycle costs, network power losses, and voltage deviation while maximizing user accessibility through a gravity-based spatial interaction model. Comprehensive modeling includes DistFlow based power flow analysis, M/M/c queuing for charging station operations, and traffic-integrated demand estimation. A constraint handling mechanism based on feasibility rules addresses complex grid constraints including voltage limits (0.95–1.05 p.u.), line thermal capacities, and transformer loading. The proposed EMOPSO is validated on modified IEEE 33-bus and IEEE 69-bus test systems integrated with realistic urban transportation networks. Comparative analysis against standard MOPSO, NSGA-II, MOEA/D, MOGWO, and SPEA2 demonstrates superior performance with 8.7–15.2% improvement in hypervolume indicator and 23.5–41.8% improvement in inverted generational distance. Case study results show that optimized EVCS placement achieves 26.4% reduction in power losses, 34.7% improvement in accessibility index, and 21.3% total cost savings compared to conventional planning approaches. Sensitivity analyses confirm solution robustness under EV penetration levels ranging from 10% to 50% and various demand uncertainty scenarios.
Electric vehicle charging stations, multiobjective optimization, particle swarm optimization, distribution network planning, Pareto optimization, infrastructure planning, smart grid, user accessibility.
Shailendra Kumar Choubey, Naresh Sapate, Gurucharan Mashram, & Suresh Kumar Tandekar. (2026). Enhanced Multi-Objective Particle Swarm Optimization Algorithm for Electric Vehicle Charging Infrastructure Planning Considering Grid Constraints and User Accessibility. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 777-786.
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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