A novel hybrid GA optimized PID controller for AGC in interconnected hydro-thermal power system

Wind power integration causes imbalances when actual output deviates from its forecast due to wind velocity variations, affecting the scheduling of conventional generators. Additionally, wind farms' effect on the power system's dynamic behavior may cause a different system frequency response to disturbances. Automatic Generation Control (AGC) must be robust against unknown external disturbances to keep the frequency constant, especially in the presence of renewable energy like wind power. In this paper, a Hybrid Genetic Algorithm (HGA) is proposed to reduce the frequency deviations and improve the dynamic performance of the power system in the presence of wind power. This new optimization technique based on the minimization of the Integral of Squared Errors (ISE) is applied to determine the optimal settings for the proportional-integral-derivative (PID) controller gains. HGA has been offered better and faster performance over the PID controller and Genetic Algorithm (GA). The simulation results confirm the Doubly Fed Induction Generator (DFIG) participation in the frequency system.