Optimal Integration of Charging Station in Power Grids using Hybrid Optimized Recursive Neural Approach

Abstract

The rapid increase in Electric Vehicle (EV) use has necessitated the construction of countless charging stations, which call for grid services and sophisticated controllers to charge. However, establishing a more effective charging schedule continues to pose significant challenges. To address this problem, a new technique called Crayfish–Lotus Optimized Recursive Model (CLORM) was utilized to arrange the distribution system's Electric Vehicle Charging Stations (EVCS) as efficiently as possible. The distributed generation system uses renewable energy sources to provide a reliable and sustainable power supply. It includes battery energy storage, hydroelectric power, wind turbines, and solar photovoltaic systems. The system's optimal placement for EVCS is determined based on low power losses in the distributed system. The system's effectiveness and resilience are tested using the IEEE 33-bus system, ensuring balanced and unbalanced power distribution and stability. The evaluation emphasizes parameters such as voltage, Total Harmonic Distortion (THD), power loss, and processing time. The developed model demonstrates a power loss of 206.7320 kW.