A Novel Model Predictive Controller for Distributed Generation in Isolated Microgrids—Part I: Development and Parameterization of the Data-Driven Predictive Model

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 conventional droop control at interfacing inverters of distributed generation (DG) units. To overcome this issue, in this two-part article, a novel model predictive controller for DGs is proposed to regulate voltage and frequency at the point of common coupling in isolated microgrids. In Part 1 of the article, a data-driven predictive model is developed and parameterized by system identification approach using nonlinear least square (NLS) method. Four NLS optimization algorithms (Gauss–Newton (GN), adaptive GN, Levenberg–Marquardt, and trust region reflective)] are evaluated and GN is chosen as the best performing algorithm. To initialize the iteration for NLS, the Backcast technique is chosen after comparing with Zero and Estimate techniques. Two optimization methods, “simulation” and “prediction” focus, are considered; and the prediction focus shows higher prediction accuracy and faster convergence, and the ability to avoid the necessity of data prefiltering by introducing a built-in weighted filter in the objective function. The polynomial input–output Box–Jenkins model is chosen as the model structure. The 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. The model is further implemented in the proposed model predictive controller in Part 2 of the article.