Efficient management of EV charging stations and DSTATCOM in distribution systems through POA-DTRSRN approach

The construction of infrastructure for electric vehicle charging stations (EVCS) is essential to the industry's sustainable growth in the electric vehicle market. However, the distribution system's performance can suffer as a result of EVCS installation. A novel hybrid method for managing EVCS with Distribution Static Synchronous Compensator (DSTATCOM) in distribution systems is proposed in this study. The hybrid method is the integration of Pufferfish Optimization Algorithm (POA) with Double Transformer Residual Super-Resolution Network (DTRSRN), termed as POA-DTRSRN approach. The main objective of this study is to minimize the power loss, reduce cost and improve the overall efficiency. The placement and size of the EVCS and DSTATCOM in distribution systems is optimized using the POA method. The charging demand of the electric vehicles is predicted by the DTRSRN method. The performance of the proposed method is analyzed using MATLAB and is compared with existing methods like African Vultures Optimization Algorithm (AVOA), Particle Swarm Optimization (PSO), and Backward Bat Algorithm (BBA). The proposed method yields a low cost of $1450. The outcomes demonstrate that the proposed POA-DTRSRN technique outperforms the existing techniques in terms of cost-effectiveness and energy efficiency. Thus, this approach presents a promising solution for optimizing EVCS and DSTATCOM management in modern distribution systems.