An adaptive model predictive control approach for robust load frequency control under renewable energy disturbances

This paper presents an Adaptive Model Predictive Control (AMPC) strategy for robust load-frequency control (LFC) in single-area and double-area power systems under load variations, parameter uncertainty, and renewable energy disturbances. The controller integrates online system identification using Recursive Least Squares (RLS) with a receding-horizon optimization framework to ensure real-time model adaptation and constraint-aware predictive regulation. Simulation results demonstrate that the proposed AMPC significantly improves transient and steady-state performance compared with conventional PI/PID controllers. In single-area systems, the AMPC achieves settling times of 0.5-1 s, compared with 30 s for PI, and eliminates overshoot while reducing undershoot from 4.5 × 10⁻³ to 1 × 10⁻³. Under dynamic and wind disturbances, peak-to-peak deviations are reduced to ≈ 0, whereas PI exhibits deviations up to 26.5 × 10⁻³. In double-area systems, the AMPC reduces settling time from 20 to 40 s (PID) to 1-2 s and minimizes undershoot by up to an order of magnitude. Comparative studies further confirm the proposed AMPC's superiority over Harmony Search (HS), Sine-Cosine Algorithm (SCA), Teaching-Learning-Based Optimization (TLBO)-optimized PID/PIDA controllers and the Marine Predator Algorithm (MPA)-based cascaded PIDA, establishing AMPC as an effective and scalable solution for low-inertia grids with high renewable penetration.