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<p>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&#xa0;s, compared with 30&#xa0;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 <b>≈ 0</b>, whereas PI exhibits deviations up to 26.5 × 10⁻³. In double-area systems, the AMPC reduces settling time from 20 to 40&#xa0;s (PID) to 1–2&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Mohamed Ayman,
  • Mahmoud A. Attia,
  • Ahmed M. Asim

摘要

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.