A Hybrid African Vulture Crayfish Optimization Algorithm for Optimal Allocation of Electric Vehicle Infrastructure and Distributed Power Generation

Abstract

The rapid development of electric vehicles (EVs) has necessitated efficient charging infrastructure, raising concerns about increased power losses, voltage fluctuations, and higher operational costs in distribution networks. These problems can be overcome through the coordinated operation of Distributed Generation (DG) and Battery Energy Storage Systems (BESS); however, the location and size of these systems remain complex multi-objective problems. In this paper, a novel hybrid optimization algorithm, African Vulture Optimization Algorithm–Crayfish Optimization Algorithm (AVOACOA), is proposed to determine the optimal placement and sizing of EV charging stations (EVCS), DG, and BESS within the distribution network. The suggested solution will reduce power waste, voltage variability, Total Harmonic Distortion (THD), and operational costs while enhancing voltage stability. This algorithm is tested on the IEEE 33-bus test system in MATLAB and compared with other conventional algorithms. The simulation results show that the AVOACOA method can significantly enhance system performance. The final configuration of EVCS, DG, and BESS results in reduced total power losses and voltage deviations, an optimal bus voltage, and a decrease in THD 0.4549%. The operational cost is reduced to about $1.54 × 10³, which is better than that of the benchmark optimization methods. These results demonstrate the technical and economic efficiency of the configurations. In summary, the proposed approach shows improved voltage stability, reduced energy losses, and enhanced cost performance.