  We propose a generative graph model for electrical infrastructure networks
that accounts for heterogeneity in both node and edge type. To inform the model
design, we analyze the properties of power grid graphs derived from the U.S.
Eastern Interconnection, Texas Interconnection, and Poland transmission system
power grids. Across these datasets, we find subgraphs induced by nodes of the
same voltage level exhibit shared structural properties atypical to small-world
networks, including low local clustering, large diameter, and large average
distance. On the other hand, we find subgraphs induced by transformer edges
linking nodes of different voltage types contain a more limited structure,
consisting mainly of small, disjoint star graphs. The goal of our proposed
model is to match both these inter and intra-network properties by proceeding
in two phases: the first phase adapts the Chung-Lu random graph model, taking
desired vertex degrees and desired diameter as inputs, while the second phase
of the model is based on a simpler random star graph generation process. We
test the model's performance by comparing its output across many runs to the
aforementioned real data. In nearly all categories tested, we find our model is
more accurate in reproducing the unusual mixture of properties apparent in the
data than the Chung-Lu model. We also include graph visualization comparisons,
a brief analysis of edge-deletion resiliency, and guidelines for artificially
generating the model inputs in the absence of real data.
