An Improved Large-Signal Order Reduction Method of Microgrids with Stability and Accuracy Assessment

This paper proposes a novel mathematical theorem that embeds the stability and accuracy assessment into a large-signal order reduction (LSOR) method of microgrids. Using the proposed method, the dynamic stability of full microgrid models can be assessed by only leveraging their derived reduced-order models and boundary layer models. In particular, when the reduced-order system is input-to-state stable and the boundary layer system is uniformly globally asymptotically stable, the original microgrids system is stable based on several common growth conditions. In addition, we develop the conditions to guarantee the accuracy of the reduced model. We show that the error between the solutions of reduced and original models is bounded and convergent under such conditions. Further, we provide the strict mathematical proof to illustrate that the proposed order reduction method is generic and can be applied to arbitrary dynamic systems. Simulation validation is conducted on microgrid systems to show the effectiveness of proposed method.