Simulation-based study of MPC and GA-PID optimization for frequency regulation in a hybrid wind-diesel power system under load variation

• This paper presents a comparative study of Model Predictive Control (MPC) and Genetic Algorithm-optimized PID (GA-PID) for Load Frequency Control (LFC) in a wind-diesel hybrid system. A state-space model is proposed that accurately represents the system dynamics, including the diesel and wind turbine subsystems. Simulation results show that MPC provides superior robustness, faster settling time, and minimal overshoot compared to GA-PID and conventional PID.GA-PID tuning improves overshoot and steady-state error compared to conventional PID, with a simpler implementation compared to MPC.This paper offers a quantitative performance comparison (settling time, overshoot, IAE, ITAE, robustness) under small, medium, and transient load disturbances.The findings provide practical insights for selecting optimal control strategies in renewable energy-based microgrids. This paper presents a simulation-based comparative study of Model Predictive Control (MPC) and Genetic Algorithm-optimized PID (GA-PID) for Load Frequency Control (LFC) in a hybrid wind–diesel power system. The system is modeled in state space and simulated under different load variations to evaluate frequency stability. MPC employs predictive optimization within control constraints, while GA-PID tunes the controller gains using Integral of Absolute Error (IAE) minimization. Simulation results show that MPC achieves faster settling time, smaller overshoot, and stronger robustness against ±20 % load variations compared to GA-PID and conventional PID. Meanwhile, GA-PID provides simpler implementation with improved performance over classical PID. The novelty of this work lies in delivering a systematic quantitative comparison between predictive control and evolutionary optimization for frequency regulation. This study provides practical insights for designing reliable and sustainable microgrid controllers.