Model-and-Data-Driven Adaptive Frequency Control for Microgrid Systems

This paper proposes a novel model-and-data-driven adaptive frequency control (MDAFC) for microgrid (MG) systems. The proposed MDAFC includes two control loops, i.e., an adaptive model predictive control (AMPC) loop and a model free adaptive control (MFAC) loop. The AMPC loop not only utilizes the exact model information to improve the control performance but also employs an unscented Kalman filter (UKF) to estimate the unknown parameters of the internal prediction model to improve the robustness to a certain degree. The MFAC loop is designed to address the unmodeled dynamics, nonlinear uncertainty and disturbance of the MG system by virtue of its adaptation mechanism and the data-driven characteristics. Therefore, the MFAC loop can compensate the poor impact of the inaccuracy model information on the AMPC method. To validate the effectiveness of the proposed method, a low inertia MG system containing renewable energy sources (RESs) is considered in this research. Simulation results show that the proposed method can achieve a high control performance. It can effectively cope with the frequency fluctuation caused by RESs, large load consumption, and other unknown uncertain factors. Compared with the existing single AMPC loop and the single proportional integral control loop, the proposed dual-loop-based MDAFC performs better since the two control loops cooperate with each other to leverage their advantages and compensate for their shortcomings. Note to Practitioners—Frequency is a critical variable in ensuring stability and power quality of the MG systems. The frequency dynamics of the MG can be represented using a well-established mathematical model. However, the increasing integration of RESs, the multi-energy coupling between regional grids and equipment, and the diverse electric loads have significantly increased the uncertainty of the model and the nonlinearity of the MG systems, thus posing many difficulties in frequency control. Therefore, this study proposes an MDAFC for the MG systems by combining an AMPC loop with an MFAC loop. The AMPC loop utilizes mechanism model of MG to achieve a good control performance with the help of the predictive information. The MFAC loop is used to address the nonlinear unmodeled dynamics, the system uncertainties and the exogenous disturbances by virtue of the input and output data. The complementary interaction between the two control loops can improve frequency control performance. Furthermore, the proposed MDAFC method adapts an iterative computational structure, which can offer straightforward implementation for the nonlinear and uncertain MG systems with light computation burden, and the proposed MDAFC is easy to implement in practical applications.