Model Predictive Control of Overloaded Stand-Alone Hybrid AC/DC Microgrids

In recent years, hybrid AC/DC microgrids have become popular due to their combined benefits from both AC and DC microgrids. One of the challenges in such a system stems from high AC load demands, which can jeopardize the stability of the system. The existing droop controllers, which are based on the per-unit values of DC voltage and AC frequency, have poor performances in sharing the active power between the DC and AC microgrids in the case of high AC load demands. These controllers are required to provide very high gains to mitigate the power sharing problem in overloaded cases, which make the system more sensitive to overload disturbances. Therefore, this paper proposes a novel control scheme based on model predictive controllers to compensate for the poor performance of the existing power sharing droop controllers. The proposed controller minimizes the active power difference between the DC and AC microgrids by transferring more power from the DC to the AC microgrid. The simulation results illustrate the outperformance of the proposed model predictive control method as compared to the benchmark techniques including Fuzzy-proportional-integral-derivative, adaptive-fractional-two-degree-of-freedom proportional-integral-derivative, and virtual impedance in terms of steady-state overload power sharing, transient overload damping, nonlinear load harmonic attenuation, and integral time absolute error analyses.