Data-Driven Distributed MPC for Load Frequency Control of Networked Nonlinear Power Systems.

In this paper we proposed a data-driven distributed model predictive control (MPC) for the load frequency control of the multi-area interconnection nonlinear power system. The data-driven Koopman operator method is used to deal with the nonlinear wind power generation system in each area. The distributed Koopman operator algorithm is proposed to deal with the huge amount of data in the large-scale distributed system. Based on the distributed linear model obtained by the distributed Koopman operator algorithm, the distributed MPC is applied to realize the load frequency control (LFC) of the multi-area power system. The cooperation-based cost function is optimized by the MPC controller in each area, and the global optimal solution can be achieved by the proposed method. The effectiveness and practicability of the proposed algorithm are demonstrated by the simulation.