Solvable model for distribution networks on random graphs

D. Nasiev*, Jort van Mourik, Reimer Kühn

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

We propose a simple model that captures the salient properties of distribution networks, and study the possible occurrence of blackouts, i.e., sudden failings of large portions of such networks. The model is defined on a random graph of finite connectivity. The nodes of the graph represent hubs of the network, while the edges of the graph represent the links of the distribution network. Both, the nodes and the edges carry dynamical two state variables representing the functioning or dysfunctional state of the node or link in question. We describe a dynamical process in which the breakdown of a link or node is triggered when the level of maintenance it receives falls below a given threshold. This form of dynamics can lead to situations of catastrophic breakdown, if levels of maintenance are themselves dependent on the functioning of the net, once maintenance levels locally fall below a critical threshold due to fluctuations. We formulate conditions under which such systems can be analyzed in terms of thermodynamic equilibrium techniques, and under these conditions derive a phase diagram characterizing the collective behavior of the system, given its model parameters. The phase diagram is confirmed qualitatively and quantitatively by simulations on explicit realizations of the graph, thus confirming the validity of our approach. © 2007 The American Physical Society.

Original languageEnglish
Article number041120
Pages (from-to)1-8
Number of pages8
JournalPhysical Review E
Volume76
Issue number4
DOIs
Publication statusPublished - 12 Oct 2007

Bibliographical note

©2007 American Physical Society. Solvable model for distribution networks on random graphs
D. Nasiev, J. van Mourik, and R. Kühn
Phys. Rev. E 76, 041120 – Published 12 October 2007

Keywords

  • distribution network
  • blackouts
  • finite connectivity
  • nodes
  • graph
  • network

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