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

Area of Science:

• Electrical Engineering
• Control Systems
• Power Systems

Background:

• Load-frequency control (LFC) is critical for maintaining power system stability.
• Traditional controllers struggle with parameter uncertainty and renewable energy integration.
• Low-inertia grids with high renewable penetration pose significant LFC challenges.

Purpose of the Study:

• To develop and validate an Adaptive Model Predictive Control (AMPC) strategy for robust LFC.
• To enhance real-time model adaptation and constraint-aware predictive regulation in power systems.
• To demonstrate AMPC's superiority over existing control methods in dynamic and uncertain environments.

Main Methods:

• Integration of online system identification using Recursive Least Squares (RLS).
• Implementation of a receding-horizon optimization framework for predictive control.
• Simulation studies on single-area and double-area power systems under various disturbances.

Main Results:

• AMPC achieved significantly faster settling times (0.5-2s) compared to PI/PID (20-40s).
• Elimination of overshoot and substantial reduction in undershoot were observed with AMPC.
• AMPC demonstrated superior performance in mitigating deviations under dynamic and renewable energy disturbances.

Conclusions:

• The proposed AMPC strategy offers a robust and effective solution for LFC in modern power systems.
• AMPC proves scalable and superior to conventional and optimized controllers for low-inertia grids.
• The controller's adaptive nature ensures reliable performance amidst load variations and renewable energy integration.