Nature-inspired algorithms for reliability and cost optimization of hybrid wind-solar power charging station for electric vehicles

Abstract

Electricity is becoming the most important resource for environmental development. Hence, Global warming has led to the acceptance of electric vehicles as the most efficient alternative to internal combustion engines. The growing number of electric vehicles on the road cannot be charged by conventional fossil fuel power grids because they are neither economical nor efficient. Hybrid power systems for charging stations are the main source of charge for electric vehicles. Here hybrid involves two energy sources, namely wind and solar. Therefore, this paper aims to optimize the cost and reliability of the Hybrid Wind-Solar Power Charging Station for Electric Vehicles (HWSCSEV). Solar energy is converted into electric power by solar panels, and wind energy is converted into electric power by wind turbines with optimum reliability and minimum cost. A few recent metaheuristics techniques, such as the Osprey Optimization Algorithm (OOA), Pelican Optimization Algorithm (POA), Skill Optimization Algorithm (SOA), Velocity Pausing Particle Swarm Optimization (VPPSO) are successfully used for the cost and reliability calculation of the proposed model. Furthermore, the obtained results of these techniques are contrasted with each other outcomes for the best reliability and cost of the HWSCSEV. Finally, algorithms are effectively analyzed based on convergence rate, computational time, Friedman ranking test, and statistical results.