Decentralized Model Predictive Load-Frequency Control (MPC) Strategy

Decentralized Model Predictive Load-Frequency Control (MPC) represents an advanced control strategy designed to maintain power system frequency stability by dynamically adjusting generator power outputs. This approach employs a distributed control architecture where computational tasks are allocated across multiple autonomous agents, typically implemented through parallel processing frameworks. In practical implementation, the MPC algorithm utilizes system state-space models to predict future frequency deviations and compute optimal control actions over a predefined horizon. Key components include: - Distributed state estimators for local area monitoring - Quadratic programming solvers for optimization calculations - Communication protocols for inter-area coordination The technique proves particularly effective in large-scale power networks with numerous interconnected generators, maintaining frequency stability during fluctuating power demands. Through model-based predictive control, the system achieves enhanced dynamic performance while ensuring operational reliability. Typical implementation involves MATLAB/Simulink models with distributed optimization algorithms that calculate control inputs while respecting generator constraints and system dynamics.