Hybrid approach based combined allocation of electric vehicle charging stations and capacitors in distribution systems

Abstract

This manuscript proposes a hybrid EOO-QNN method for the combined allocation of electric vehicle charging stations (EVCS) and capacitors in the distribution systems (DS). The proposed hybrid approach is combined with Eurasian Oystercatcher Optimiser (EOO) and Quantum Neural Network (QNN). Hence it is known as an EOO-QNN approach. The primary objective of EOO-QNN algorithm is to control the capacitors to maintain the voltage-profile, increase the net gain and minimize active power loss. The EOO approach is utilized to produce solutions for the power loss problems while improving the dependability of distribution network, and the QNN is employed to forecast the converter's ideal control signal. By then, the proposed method is executed in MATLAB and is compared with other existing methods. The EOO-QNN method shows better results in all approaches, like Particle swarm optimization (PSO), Wild horse optimizer (WHO), and Scalp Swarm Algorithm (SSA). From the simulation, it accomplishes that the voltage of EOO-QNN approach is high compared to existing techniques. From the simulation analysis, the proposed method based high voltage 500 V is high compared to other existing methods. From the result, it proven that the EOO-QNN method provides higher voltage compared to existing methods.