Using fuzzy systems for optimal network reconfiguration of a distribution system with electric vehicle charging stations and renewable generation

Electric vehicles (EVs) are expected to surpass conventional vehicles as a preferred means of transportation due to their improved energy efficiency and diminished ecological footprint. The performance of electric vehicles (EVs) can be negatively impacted if electric vehicle charging stations (EVCS) are not properly positioned and connected to the distribution network. The current research introduces a reliability, availability, and operability (RAO-3) methodology that employs a fuzzy classification technique to identify the most efficient dimensions and locations for electric vehicle charging stations (EVCS), distributed generators, and super capacitors in 69 radial bus distribution systems with network reconfiguration. Load flow analysis is a numerical technique employed to construct mathematical representations for the charging loads of electric vehicle (EV) batteries. This is achieved by leveraging the characteristic curves of Li-ion battery charging. The proposed approach of fuzzified RAO-3 aims to enhance the power factor of the substation by incorporating all relevant factors into the distribution network. The fuzzy multiobjective function is utilised to simultaneously optimise the positioning of electric vehicle charging stations (EVCS), distributed generators (DGs), and storage capacities (SCs), with or without network reconfiguration. Based on the outcomes of the simulation, the concurrent placement technique enhances performance through the reduction of power loss, enhancement of the voltage profile, and optimisation of electric vehicle (EV) utilisation.