Hierarchical Lyapunov-Based Model Predictive Control for Islanded AC Microgrid

This work proposes a hierarchical control approach for an islanded microgrid (MG) comprising of three parallel distributed generations (DGs), each consisting of a voltage source inverter (VSI) and an LCL filter. The conventional finite control set model predictive control (FCS-MPC) based inner control lacks a formal stability proof and suffers from a high computational burden. The proposed control approach features a Lyapunovbased model predictive control (LMPC) as inner-loop control level to guarantee system stability, alongside an inverse droopbased primary control layer and predictive secondary voltage and frequency restoration. The suggested LMPC improves classical FCS-MPC by including Lyapunov stability criteria into the multiobjective cost function, thus reducing the computation time by eliminating the prediction model step in the online optimization. Finally, the control framework is validated through detailed simulation tests via MATLAB/Simulink ® software tool. The LMPC based hierarchical controller is performed under various operating conditions, feeding linear and non-linear common loads, with equal and unequal power sharing. The obtained results from powers, RMS voltage, frequency and currents demonstrate accurate power sharing with enhanced quality, dynamic performance and robustness. The provided simulations prove that the proposed LMPC is a promising solution for stable and efficient MG operating systems.