Model reduction in power systems using a structure-preserving balanced truncation approach

Abstract

This paper contributes to the development of a computationally efficient technique for reducing large-scale power system models. The proposed method comprises two steps: the partitioning of the power system into a study area and a set of coherent clusters, and the model reduction of the remote clusters. The focus of this work is on the second step. Every remote cluster is handled separately during the reduction process. For each of them, a simple and effective model of the surrounding network is developed in order to preserve a proper dynamic behavior while limiting the computing load. The reduced clusters are then mutually reconnected to one another, which enables to keep the structural information through the lines linking these clusters. Numerical results on the IEEE 118-bus system model show that the new method is able to build a reduced model suitable for performing rotor angle stability studies.