Distributed MPC Strategies With Application to Power System Automatic Generation Control

TLDR

The paper proposes a distributed model predictive control framework for large‑scale networked systems such as power grids. The framework decomposes the system into subsystems, each running its own MPC controller that iteratively cooperates to meet global control goals, exemplified by automatic generation control. When convergence is achieved within available computational time, the distributed MPC matches centralized performance and remains feasible and stable even with early termination.

Abstract

A distributed model predictive control (MPC) framework, suitable for controlling large-scale networked systems such as power systems, is presented. The overall system is decomposed into subsystems, each with its own MPC controller. These subsystem-based MPCs work iteratively and cooperatively towards satisfying systemwide control objectives. If available computational time allows convergence, the proposed distributed MPC framework achieves performance equivalent to centralized MPC. Furthermore, the distributed MPC algorithm is feasible and closed-loop stable under intermediate termination. Automatic generation control (AGC) provides a practical example for illustrating the efficacy of the proposed distributed MPC framework.