A novel consumer-friendly electric vehicle charging scheme with vehicle to grid provision supported by genetic algorithm based optimization

Abstract

Due to escalating demand for electric vehicles (EVs) in the worldwide transportation sector, the charging facilities supported by an efficient charging scheme across the power network have a significant impact on the operation of the power system. The unsupervised and decentralized charging of the EVs have several adverse effects on various operational features of the power system. On the other hand, the centralized charging system with proper charging schemes offers less complexity in power system operation to avoid unnecessary power system network stress. This article proposes a valley-filling technique-based EV charging scheme for residential consumers facilitated by a centralized charging system. The charging scheme comprises a genetic algorithm-based optimization for EV acceptance to utilize the available energy of the supply network in the best way. The charging scheme also includes vehicle to grid (V2G) facilitation and the reallocation technique for shifting EVs to resolve any overloading of charging slots. Simulation results of Genetic Algorithm (GA) based optimization show the attainment of near-optimum EV data set with less than 50 iteration numbers and usage of more than 99.5% of available kWh for charging. Other result segments indicate the efficient use of available supply energy by incorporating EVs in the valley time slab of the demand curve. Also, the findings point out that the charging scheme successfully encounters unwanted network stress issues during EV integration by V2G provision and reallocation technique, prioritizing consumer satisfaction at the overloaded charging time slots. The test case simulation results indicate that the ‘average to peak’ demand ratio in the demand curve is increased to 90% from 68% by incorporating the proposed charging scheme for EV charging. Moreover, the outcomes indicate the novelty of the proposed methodology in allocating charging slots considering complex driving parameters like network stress and customer prioritization.