Power Flow Control of the Grid-Integrated DG System Using Hybrid Aquila Optimizer‑Tangent Search Algorithm

This work presents a systematic study on the regulation of power flow of a grid-integrated Distributed Generation (DG) system, with photovoltaic (PV) panels, wind turbine, fuel cell, and energy storage like batteries. The objective is to effectively control the flow of power in the system for stable and reliable operation. To accomplish this objective, Proportional-Integral (PI) controllers are used to control the generated power of the energy sources. The focus of this research is to optimize the PI controller gains to enhance the control system's performance and adaptability. In this pursuit, a novel Hybrid Aquila Optimizer-Tangent Search Algorithm (HAO-TSA) is utilized. The Hybrid Aquila Optimizer combines the strengths of Aquila Optimizer, known for its robust optimization capabilities, and the Tangent Search Algorithm, which excels in fine-tuning control parameters. This combination offers a powerful tool for achieving optimal PI controller gains. The proposed methodology is employed and verified on a grid-integrated DG system, and its efficacy is assessed using simulation using MATLAB/SIMULINK. The results demonstrate that the optimized PI controller gains significantly enhance the system's performance, leading to improved power flow control, reduced energy losses, and increased overall grid integration efficiency.