Data-driven stochastic model predictive control for regulating wind farm power generation with controlled battery storage

The uncertainty and fluctuation of the volatile wind power cause more reserve and frequency regulation capacity, so it incurs additional operational cost for the power grid. Therefore, a wind farm (WF) coordinated controller is essential to reduce the power fluctuation and trace the scheduled power generation with minimal wind curtailment and battery degradation cost. This paper proposes a data-driven stochastic model predictive control (SMPC) method for WFs to realize these objectives. Firstly, to address the non-linear dynamic model of wind turbines, the dynamic mode decomposition method accompanying Koopman operator is used to learn a lifted linear dynamic model from measurements. Secondary, this paper proposes the relationship between the curtailed wind power of wind turbines and their pitch angle, wind speed, and rotation speed, as well as the corresponding estimation formula. In addition, the prediction errors of wind speeds are characterized using the Gaussian mixture model (GMM), which may significantly affect the controller's performance. Finally, a tractable data-driven SMPC model is developed and is verified on a DIgSILENT/PowerFactory based simulation platform. Compared with conventional model predictive control algorithms, it can significantly reduce the overall operational cost of WFs.