Optimal Placement and Sizing of EV Charging Stations in Smart Distribution Network Using Hybrid PSO-GWO Algorithm with Solar PV Integration: A Multi-Objective Approach

Abstract

The rapid proliferation of Electric Vehicles (EVs) in India and globally imposes severe technical challenges on existing distribution networks, including elevated power losses, voltage profile degradation, and increased investment costs. This paper proposes a novel Hybrid Particle Swarm Optimization – Grey Wolf Optimizer (PSO-GWO) algorithm for the simultaneous optimal placement and sizing of Electric Vehicle Charging Stations (EVCSs) integrated with Solar Photovoltaic (PV) Distributed Generation (DG) in IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs). A four-component Multi-Objective Function (MOF) minimizes: (i) total real power losses, (ii) voltage deviation index, (iii) annual economic cost, and (iv) carbon emission cost. Monte Carlo Simulation (MCS) with 10,000 iterations captures stochastic EV charging demand and solar irradiance uncertainty. The Adaptive Inertia Weight (AIW) mechanism blends PSO global search with GWO leadership hierarchy via a cosine-based transition function. Simulation results on the IEEE 33-bus system achieve 77.8% power loss reduction, 92.3% voltage profile improvement, and 18.7% cost reduction compared to the unoptimized base case, outperforming GA, PSO, and standalone GWO algorithms with statistical significance (p 0.001, 30 independent runs). Index Terms—EVCS Optimal Placement, Hybrid PSO-GWO, Smart Distribution Network, Solar PV, V2G, Multi-Objective Optimization, Monte Carlo Simulation, IEEE 33-bus, IEEE 69-bus, Power Loss Minimization.

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Identifiers

DOI

10.55041/ijsrem56898

Journal

International Journal of Scientific Research in Engineering and Management

Year

2026