Fuzzy Based Optimal Network Reconfiguration of Distribution System with Electric Vehicle Charging Stations, Distributed Generation, and Shunt Capacitors

Abstract

Electric Vehicles (EVs) are gaining popularity due to their low maintenance, better performance and zero carbon emission. To expand their adoption, Electric Vehicles Charging Stations (EVCS) must be integrated with the distribution system constructively to charge EVs. This study suggests an RAO-3 based on the fuzzy classification technique for the optimum EVCS, Distributed Generations (DGs), and Shunt Capacitors (SCs) sizing and positioning for 69 bus radial distribution systems with network reconfiguration. The proposed method has the following advantages (i) lower active power loss, (ii) enhanced voltage profiles, (iii) improved power factor at the substation, and (iv) optimum distribution of EVs at charging stations. Characteristic curves of Li-Ion battery charging are utilised for load flow analysis to build EV battery charging loads models. The proposed simultaneous fuzzy multi-objective study with a reconfigured network can handle the optimal number of EVs in EVCS and maintain the substation power factor at the required level, yielding an impressive distribution system performance. For example, the minimum active power loss of 18.0884 kW is achieved with a minimum voltage enhanced to 0.9905 p.u., maintaining the bus voltages at their permissible limit. The numerical results indicate that using the RAO-3 algorithm, the simultaneous technique with system reconfiguration is computationally efficient and scalable, outperforming the two-stage methodology and the method without system reconfiguration.