Distributed Model-Based Predictive Secondary Control for Hybrid AC/DC Microgrids

Abstract

This article presents a novel scheme based on distributed model-based predictive control for the secondary level control of hybrid ac/dc microgrids (MGs). Prediction models based on droop control and power-transfer equations are proposed to characterize the generators in both the ac and dc sub-MGs, whereas power balance constraints are used to predict the behavior of interlinking converters. The operational constraints (such as powers and control action limits) are included in all the formulations. Experimental results validate the proposed scheme for the following cases: 1) load changes, working within operating constraints; 2) managing frequency regulation in the ac sub-MG, voltage regulation in the dc sub-MG, and global power consensus in the whole hybrid MG; and 3) maintaining the MG performance in the presence of communication malfunction while ensuring that plug-and-play capability is preserved.