Electric Vehicles Integration into the Power Grid Using HGAIPSO Optimization Algorithm

Abstract

The gradual depletion of conventional fossil-fuel resources, growing environmental concerns, and the increasing complexity associated with modern smart-grid deployment have accelerated the integration of renewable energy distributed generators (REDGs) and electric vehicles (EVs) into electrical power systems. Over recent decades, the global EV industry has experienced considerable growth in both vehicle production and market penetration. This study investigates the ancillary services offered by EVs and evaluates the implications of their large-scale integration into power networks. An optimization-based framework is developed to estimate EV hosting capacity, formulate an enhanced objective function, determine the optimal locations and capacities of charging stations, and evaluate the associated system operating costs. Simulation findings demonstrate that the proposed EV capacity estimation approach effectively represents vehicle charging and discharging behavior. Furthermore, a hybrid genetic algorithm-improved particle swarm optimization (HGAIPSO) technique is introduced to optimize the allocation of EV charging stations while incorporating coordinated charging strategies and renewable distributed generation. The proposed framework mitigates voltage-limit violations, minimizes energy losses and system costs, and improves the overall power-quality performance of the network. The effectiveness of the developed approach is validated through MATLAB-based simulations conducted on the IEEE 118 bus test system.