Power flow optimization in wind energy and storage systems using a PSO and CPSO approach for PI controller

Due to their environmental advantages and renewable nature, wind turbines are widely utilized in various applications, particularly in remote and isolated areas not connected to the electrical grid. However, their inherent intermittency and variability, caused by fluctuating wind conditions, remain a significant challenge for reliable power supply. This paper focuses on the development of an advanced energy management strategy for a hybrid energy storage system (HESS) that combines batteries and supercapacitors (SCs) integrated with a wind energy conversion system. The proposed control scheme relies on proportional-integral (PI) controllers whose parameters are optimally tuned using Particle Swarm Optimization (PSO) and Chaotic Particle Swarm Optimization (CPSO) algorithms. The main objective of this study is to design an efficient control strategy capable of satisfying the load energy demand under varying operating conditions. To achieve this, an Energy Management System (EMS) is developed to intelligently manage power flows by coordinating the operation of the storage devices, thereby ensuring a continuous balance between energy generation and consumption. The standalone wind turbine and the associated storage systems are modeled and simulated in MATLAB/Simulink. The effectiveness and robustness of the proposed control approach are validated through simulation results, which take into account key factors such as the state of charge (SOC) of the storage systems, the power generated by the wind turbine, and the load energy requirements. The results further demonstrate the feasibility and robustness of the PI-CPSO controller, highlighting its ability to provide fast and accurate dynamic responses even under variable load and fluctuating wind conditions.