A Statistical Method for Synthetic Power Grid Generation Based on the U.s. Western Interconnection

In order to develop algorithms that identify power grid vulnerabilities, there is a need to evaluate their performance with real grid topologies. However, due to security reasons, such topologies (and particularly, the locations of the nodes and edges) may not be available. Therefore, we focus on a method for generating test networks with similar characteristics to the real grid. Small-world and scale-free networks [1, 7] are two of the models that were suggested for representing power grids. However, [4] showed that none of these models can properly represent the U.S. Western Interconnection (WI) power grid transmission network (see Fig. 1). An alternative model was proposed in [6] but does not consider the nodes’ spatial distribution. While there are models for generating spatial networks [2], most of them were not designed to generate networks with properties similar to the power grid. Hence, we present a procedure to generate synthetic networks with similar structural properties and spatial distribution to the WI. It is based on a Gaussian Mixture Model (GMM) that generates the positions of the nodes and a Quadratic Discriminant Analysis (QDA) that is used to connect them. We show that obtained networks have properties similar to the ones of the real grid.