A Model Predictive Control-Based Voltage and Frequency Regulation through Distributed Generation in Isolated Microgrids: Part I Development and Parameterization of the Data-Driven Predictive Model

Abstract

In a low-voltage islanded microgrid, the distribution line impedance and relatively large power angle may lead to active and reactive power coupling during voltage and frequency control actions, which cause errors for the conventional droop control at the interfacing inverter of distributed generation (DG) units. To overcome this issue, a novel model predictive control-based voltage and frequency regulation at the Point of Common Coupling (PCC) through DGs in isolated microgrids is proposed. The results of this work are presented in this two-part paper. In Part 1, a data-driven predictive model for DGs is developed and parameterized through the system identification approach using Gauss-Newton (GN)-based Nonlinear Least Square (NLS) method. The polynomial input-output Box-Jenkins model is chosen as the model structure. This model will be further used in Part 2 to implement a Model predictive controller. The proposed model incorporates distribution line parameters into the control algorithm and allows a wider variation of power angle without initiating nonlinearity. Therefore, it can substantially reduce the controller size and complexity, and widen the controller’s operational range.