A Data-Enabled Predictive Control Method for Frequency Regulation of Power Systems

In this paper, we propose a modified data-enabled predictive control (DeePC) algorithm to solve load frequency control (LFC) problem of power systems. Compared with the existing DeePC algorithm, which is based on behavioral system theory, the following three aspects of modifications are made based on the characteristics of LFC problem of power systems with high penetration of renewable energy sources. First, the external input signal, i.e., the net load demand, to the system is considered also as the control input signal so that predictive control can be achieved for LFC only using input/output data. Second, the l(2) regularization term and slack variables are added on the DeePC algorithm to address the uncertainty of net load demand. Third, the mechanical power input of the generator is regarded as an output of the power system model so that generation rate constraints (GRC) can be dealt with by making some constraints on the output. By applying the modified DeePC algorithm, effective control for LFC can be achieved in a model-free and receding horizon control framework. Simulation results on a power system with two control areas demonstrate the effectiveness of the DeePC based method.