Advances and opportunities in the model predictive control of microgrids: Part I–primary layer. (January 2022) - Zhang, Zhenbin; Babayomi, Oluleke; Dragicevic, Tomislav; Heydari, Rasool; Garcia, Cristian; Rodriguez, Jose; Kennel, Ralph
Highlights: State-of-the-art of model predictive control (MPC) of microgrids is reviewed. The first paper in a two-part series on MPC of ac and dc microgrids. Advantages of MPC for power quality, inertia emulation, transportation electrification. Experimental comparison of MPC versus linear control for microgrid primary level. Emerging trends of MPC for autonomous and networked microgrids. Demonstration of new adaptive MPC of grid-forming converters. Abstract: The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC's capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters.