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: Part-I reviews the model predictive control (MPC) of the primary level of microgrids. Part-II reviews MPC in the secondary and tertiary control levels of microgrids. Models for MPC in secondary centralized, decentralized and distributed architectures. 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.