Multi Scenario Hosting Capacity Optimization of Electric Vehicle Charging Stations in Distribution Networks Considering Managed Charging and Charger Power Factor

Abstract

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort.