Stability Enhancement of Microgrid Frequency via Adaptive Model Predictive Control for VSG

With the increasing penetration rate of distributed energy, the low inertia level of microgrid becomes increasingly prominent. The low inertia of the system leads to the deterioration of the frequency and voltage fluctuations, which makes the system difficult to deal with the instantaneous power imbalance. Therefore, researchers developed a virtual synchronous generator (VSG) and connected it to the microgrid, which significantly alleviated the problem. However, previous VSG control algorithms have limitations due to their focus on parameter adjustment and cannot cope with large power imbalance scenarios. To overcome these challenges, this paper proposes a virtual synchronous generator control strategy based on adaptive model predictive control. This control strategy first establishes an adaptive prediction model of the system, optimizes the control parameters of the VSG and updates the prediction model through an adaptive algorithm. The required power change is calculated from the prediction model and the quadratic programming (QP) objective function, and finally the power reference value of the virtual synchronous machine is adjusted. Simulation results show that the proposed control strategy provides inertial support under microgrid load switching conditions and effectively suppresses frequency fluctuations in power imbalance scenarios, verifying the superiority and great autonomous performance of the proposed control strategy.