Microgrid Frequency Control Using Electric Vehicles and Battery Storage System through Adaptive and ANN based PI Control Strategies

The integration of renewable energy sources (RES ) into traditional power systems is a key element of global efforts to shift toward more sustainable and eco-friendly energy sources. Utilizing electric vehicles and battery storage systems within an isolated microgrid can enhance the system's inertia. Electric vehicles have become increasingly cost-effective, and vehicle-to-grid (V2G) technologies have garnered significant interest. Combining electric vehicles and energy storage systems can effectively manage frequency fluctuations and ensure the efficient operation of the microgrid. This paper examines an isolated microgrid that includes, renewable sources such as wind and solar power systems, and prosumers including electric vehicles and Battery storage systems (BSS). The Adaptive Droop and Proportional-Integral (PI) controllers aim to address frequency regulation issues in the isolated microgrid by effectively managing the Battery storage system (BSS) and electric vehicles (EVs). The PI controller parameters are optimized through Artificial Neural Network (ANN). The proposed model is developed in MATLAB/Simulink, applying the aforementioned control method. Additionally, three case studies are conducted to validate the performance of the PI controller in regulating frequency in the isolated microgrid.