Novel GBest–Lévy Adaptive Differential Ant Bee Colony Optimization for Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Smart Distribution Systems

The transition to electric vehicles (EVs) is pivotal for decarbonizing transport, yet the siting of EV charging stations (EVCSs) can load radial distribution networks with higher losses and more pronounced voltage drops. This study formulates the joint siting and sizing of EVCSs and distributed generators (DGs) as a constrained optimization that minimizes real and reactive losses and voltage deviation with integer bus location decisions. A novel version of the Artificial Bee Colony (ABC) algorithm known as GBest–Lévy Adaptive Differential ABC (GLAD-ABC) is introduced, combining global best guidance, differential perturbations, adaptive step sizes, Lévy-flight scouting, and periodic local refinement for finding the global optimum solution and avoiding local optima. The optimizer is coupled with a backward–forward sweep load flow and a EVCS power demand model. Validation on the IEEE-33 and IEEE-69 feeders across multiple scenarios shows that EVCS-only deployment degrades network performance, whereas optimizing EVCS and DG allocation via GLAD-ABC markedly improves voltage profiles and reduces both real and reactive losses. The proposed optimizer shows superior performance compared with other optimization algorithms reported in the literature, delivering consistently lower active losses alongside fast, stable convergence, indicating strong suitability for utility planning in EV-rich grids.