skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A generative graph model for electrical infrastructure networks

Abstract

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 apparentmore » 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.« less

Authors:
 [1];  [2];  [3];  [1];
  1. Pacific Northwest National Laboratory, Richland, WA, USA
  2. Pacific Northwest National Laboratory, Seattle, WA, USA
  3. School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1494985
Report Number(s):
PNNL-SA-136381
Journal ID: ISSN 2051-1310
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Complex Networks
Additional Journal Information:
Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2051-1310
Publisher:
Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
power-grid model, Chung-Lu model, network-of-networks

Citation Formats

Aksoy, Sinan G., Purvine, Emilie, Cotilla-Sanchez, Eduardo, Halappanavar, Mahantesh, and Lambiotte, Renaud. A generative graph model for electrical infrastructure networks. United States: N. p., 2018. Web. doi:10.1093/comnet/cny016.
Aksoy, Sinan G., Purvine, Emilie, Cotilla-Sanchez, Eduardo, Halappanavar, Mahantesh, & Lambiotte, Renaud. A generative graph model for electrical infrastructure networks. United States. doi:10.1093/comnet/cny016.
Aksoy, Sinan G., Purvine, Emilie, Cotilla-Sanchez, Eduardo, Halappanavar, Mahantesh, and Lambiotte, Renaud. Mon . "A generative graph model for electrical infrastructure networks". United States. doi:10.1093/comnet/cny016.
@article{osti_1494985,
title = {A generative graph model for electrical infrastructure networks},
author = {Aksoy, Sinan G. and Purvine, Emilie and Cotilla-Sanchez, Eduardo and Halappanavar, Mahantesh and Lambiotte, Renaud},
abstractNote = {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.},
doi = {10.1093/comnet/cny016},
journal = {Journal of Complex Networks},
issn = {2051-1310},
number = 1,
volume = 7,
place = {United States},
year = {2018},
month = {8}
}