Cluster-and-connect: an algorithmic approach to generating synthetic electric power network graphs

Understanding the graphical structure of the electric power system is important

in assessing reliability, robustness, and the risk of failure of operations of this criti-

cal infrastructure network. Statistical graph models of complex networks yield much

insight into the underlying processes that are supported by the network. Such gen-

erative graph models are also capable of generating synthetic graphs representative

of the real network. This is particularly important since the smaller number of tradi-

tionally available test systems, such as the IEEE systems, have been largely deemed

to be insucient for supporting large-scale simulation studies and commercial-grade

algorithm development. Thus, there is a need for statistical generative models of

electric power network that capture both topological and electrical properties of the

network and are scalable.

Generating synthetic network graphs that capture key topological and electrical

characteristics of real-world electric power systems is important in aiding widespread

and accurate analysis of these systems. Classical statistical models of graphs, such as

small-world networks or Erd}os-Renyi graphs, are unable to generate synthetic graphs

that accurately represent the topology of real electric power networks { networks

characterized by highly dense local connectivity and clustering and sparse long-haul

links.

This thesis presents a parametrized model that captures the above-mentioned

unique topological properties of electric power networks. Specically, a new Cluster-

and-Connect model is introduced to generate synthetic graphs using these parameters.

Using a uniform set of metrics proposed in the literature, the accuracy of the proposed

model is evaluated by comparing the synthetic models generated for specic real

electric network graphs. In addition to topological properties, the electrical properties

are captured via line impedances that have been shown to be modeled reliably by well-studied heavy tailed distributions. The details of the research, results obtained and

conclusions drawn are presented in this document.