Optimal allocation of electric vehicle charging station integrated with distributed generation using adaptive luminescence moth optimization

Abstract

With the increasing popularity of electric vehicles (EVs) worldwide, the need for efficient and accessible charging infrastructure has become increasingly critical. The rapid expansion of EV adoption and the importance of strategically locating charging stations (CS) to support this transition. Existing research reveals challenges, such as suboptimal placement resulting in uneven distribution, inadequate coverage, and increased energy loss due to inefficient network configurations. This research addresses the challenge of identifying optimal places and dimensions for EVCS and Distributed Generation (DG) to enhance accessibility and minimize power loss in electrical distribution networks. To overcome the conventional optimization method challenges, the adaptive luminescence moth optimization (ALMO) is utilized to identify optimal CS and DG locations with the best sizes of EVCS and DG concerning the network reconfiguration. The optimal place and dimension chosen for the placement of the EVCS and DG should show minimum voltage deviation and maximum voltage stability. To find the losses and voltage profile fast computing with less memory Backward Forward Sweep (BFS) Load Flow Analysis is considered. The provided approach aims to maximize coverage, minimize power loss and voltage deviation, and improve overall network efficiency. By considering various factors, such as voltage stability, voltage deviation, power loss, and cost analysis ALMO model ensures robust and effective placement and capacity decisions. The simulation results with the analysis based on IEEE33 and IEEE69 bus systems demonstrate the efficiency of the proposed model outperforming the other existing techniques.