Modeling Electric Grid Topology with Spatially-Aware Degree-Corrected Stochastic Block Model

Abstract

The goal of this paper is to facilitate the development of synthetic electric grid topologies that replicate the structural properties of real-world power systems. This paper demonstrates that the topology of the North American transmission grid can be modeled using a Spatially-Aware Degree-Corrected Stochastic Block Model (SA-DCSBM), which captures three key features in real grids: modularity, heterogeneous node degree distributions, and distance-constrained connectivity. Once the model is fitted to the North American transmission network data, synthetic topologies (excluding electrical phenomena) are generated to demonstrate that they accurately reproduce real grid statistics across multiple structural dimensions, including modularity, edge length distribution, degree heterogeneity, and spectral robustness. The SA-DCSBM thus offers a modeling framework for creating high-fidelity synthetic electric grid topologies that preserve spatial and structural realism.