Optimal Placement and Sizing of PV-DG and D-STATCOM for EVCS-Integrated Distributed Network Considering Cost, Congestion and V2G Capability

In order to guarantee dependable, affordable, and sustainable power system operation, the growing number of electric vehicles (EVs) has increased the necessity for effective electric vehicle charging station (EVCS) planning. Large-scale EV integration, however, may result in greater infrastructure costs, increased power losses, network congestion, and voltage instability. To address these challenges, this paper proposes V2G Considerations in the Cost and Congestion-Aware Location and Dimensions of EV Charging Stations. First, using traffic and battery operating metrics, convolutional neural network (CNN)-based regression models are created to forecast traffic congestion levels and battery state of charge (SOC). EV charging demand and V2G power support are then estimated using the anticipated congestion and SOC values. The suggested Enhanced Black Widow Optimization Algorithm (EBWOA) is used to find the ideal location and size of DSTATCOM devices and Photovoltaic Distributed Generation (PV-DG) units based on the resulting network circumstances. To concurrently reduce power losses, the Voltage Deviation Index (VDI), the Voltage Stability Index (VSI), and the annualized investment cost, a multi-objective optimization technique is developed. The suggested methodology is contrasted with EDO, IGWO, HBA, and HO algorithms and validated on IEEE 15-bus, IEEE 33-bus, and IEEE 69-bus distribution systems. According to simulation results, EBWOA has the lowest active power losses (APL), reactive power losses (RPL), annual costs and multi-objective function values IEEE 15-bus, IEEE 33-bus and IEEE 69-bus systems, respectively. Furthermore, EBWOA exhibits good computational times and better convergence performance across all three test systems. The accurate forecasting of congestion and SOC and the effective optimization capability of EBWOA justify the proposed method as a cost-effective, dependable, and scalable alternative for EVCS planning in distribution systems with the optimal placement of PV-DG and DSTATCOM.