Model predictive control and optimization of networked microgrids

The power coordination of a group of electrically interconnected microgrids (MGs) demands a more efficient power optimization due to its complexity compared to individual MGs. Moreover, MGs are equipped with variable loads and renewable energy resources which are stochastic in nature. As a result, their interaction with the network, as well as power management are more complicated, and their voltage/frequency stability is a challenge. Recently, predictive control has presented huge potentials in MG applications due to its fast transient response and ability to account for multiple constraints. This paper presents an all-inclusive review of model predictive control (MPC) in networked MGs. The state-of-the-art application of three types of MPC (centralized, decentralized, and distributed) in the grid-level control of the networked MGs is highlighted in this paper. Starting from regulating voltage and controlling frequency, to power flow management and economic optimization, the MPC has surfaced as a promising alternative to traditional methods.