Enhancing Electric Vehicle Charging Stations with Renewable Energy Integration through Advanced Control Algorithms

This paper presents a novel microgrid model for EV charging stations, primarily powered by renewable energy sources such as solar photovoltaics (PV) and wind. The proposed model employs an advanced control algorithm to independently manage solar PV, wind turbines, battery storage, and the EV charging process, intricately coordinated with the AC grids. A key innovation of our model is the optimization of on-grid charging through the strategic selection of component ratings for PV and wind converters. This optimization is achieved using a Fuzzy Proportional-Integral-Derivative (PID) controller enhanced by Grey Wolf Optimization, ensuring maximal energy extraction from renewable sources. Furthermore, the model innovatively incorporates Q Learning algorithms to repurpose the braking torque energy of EVs back into the battery charging circuit. Empirical results demonstrate the superiority of the proposed model over existing methods. Notably, it exhibits a 15.5 % improvement in energy efficiency and an 8.3 % reduction in overall operational costs.