Multi-objective optimization framework for coordinated integration of electric vehicle charging stations and distributed generators in microgrids

The rapid growth of electric vehicles(EVs) increases operational stress on radial distribution networks, raising active power losses, degrading the voltage profile, and reducing stability margins. This paper presents a multi-objective particle swarm optimization(PSO) framework for the coordinated placement and sizing of electric vehicle charging stations(EVCS) and distributed generation(DG) in radial AC microgrids. The problem is formulated as a constrained mixed-integer nonlinear program in which inverter-interfaced photovoltaic and permanent magnet synchronous generator(PMSG) wind units provide active and reactive power. A normalized weighted objective minimizes active power loss and the voltage deviation index while maximizing the voltage stability index, subject to voltage, thermal, and capacity limits, and is evaluated using a backward/forward sweep power flow. The framework is validated on the IEEE 33-bus and IEEE 69-bus systems through three scenarios of increasing coordination: EVCS placement only, partial DG integration, and full coordinated allocation. Under full coordination, active power losses are reduced by 84.0% on the 33-bus system and by 90.3% on the 69-bus system, and the minimum bus voltage is raised to 0.985 and 0.994 p.u., within the ± 5% regulatory band. Over 30 independent runs, PSO consistently outperforms Harris Hawks Optimization(HHO) in mean active loss and attains the lowest overall active loss solution on both networks, while Teaching-Learning-Based Optimization(TLBO) achieves a lower mean loss and markedly lower run to run variance than PSO under full coordination, particularly on the IEEE 69-bus system, as confirmed by the Wilcoxon signed-rank test applied to active power loss. The results indicate that coordinated EVCS-DG planning provides a computationally tractable and constraint compliant tool for the joint deployment of charging and generation assets in modern distribution networks.