Application of Scenario-based MPC in Automatic Generation Control

This paper proposes a stochastic model predictive control strategy to address power system inertia reduction and frequency control issues due to the integration of renewable energy sources. The approach considers uncertainties in load and renewable energy. Firstly, a predictive model is established to simulate the participation of various resources in automatic generation control, and the corresponding model predictive optimization problem is formulated. Then, a stochastic optimization method based on a scenario approach is used. This method transforms the uncertain optimization problem into a deterministic one by sampling multiple uncertainty variables and approximating the scenario using the Wasserstein distance. The results demonstrate that stochastic model predictive control outperforms conventional PI control and model predictive control without considering uncertainty in automatic generation control.