Exploration of power flow distribution to reveal scale-free characteristics in power grids

An investigation into the power flow distribution is conducted to better understand the vulnerability of power grids. The distribution of number of links connected to any random node of the power grid follows an exponential distribution. Hence, network theory based topological degree distribution of power grids does not provide useful information about critical elements (e.g., nodes and links). Hence, the distribution of an alternative functional parameter of power grids, node flow, is scrutinized with a view to gain understanding of the paradoxical robust but fragile characteristics explained by power-law distribution. We show that, although the node flow follows the exponential distribution, a meticulous examination of the distribution of high power flow nodes reveals the scale-free characteristics.

[1]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[2]  Faruk Kazi,et al.  Cascading Failure Analysis for Indian Power Grid , 2016, IEEE Transactions on Smart Grid.

[3]  Vito Latora,et al.  Modeling cascading failures in the North American power grid , 2005 .

[4]  Hui Ren,et al.  Long-Term Effect of the n-1 Criterion on Cascading Line Outages in an Evolving Power Transmission Grid , 2008, IEEE Transactions on Power Systems.

[5]  I. Kamwa,et al.  Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance , 2005, IEEE Transactions on Power Systems.

[6]  I. Dobson,et al.  Risk Assessment of Cascading Outages: Methodologies and Challenges , 2012, IEEE Transactions on Power Systems.

[7]  Ian Dobson,et al.  Using Transmission Line Outage Data to Estimate Cascading Failure Propagation in an Electric Power System , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[8]  Ian Dobson,et al.  Approximating a Loading-Dependent Cascading Failure Model With a Branching Process , 2010, IEEE Transactions on Reliability.

[9]  P. Hines,et al.  Do topological models provide good information about electricity infrastructure vulnerability? , 2010, Chaos.

[10]  Ian Dobson,et al.  Exploring Complex Systems Aspects of Blackout Risk and Mitigation , 2011, IEEE Transactions on Reliability.

[11]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[12]  Ian Dobson,et al.  Evidence for self-organized criticality in a time series of electric power system blackouts , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[13]  Anna Scaglione,et al.  Generating Statistically Correct Random Topologies for Testing Smart Grid Communication and Control Networks , 2010, IEEE Transactions on Smart Grid.

[14]  Seth Blumsack,et al.  Comparing the Topological and Electrical Structure of the North American Electric Power Infrastructure , 2011, IEEE Systems Journal.

[15]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[16]  Réka Albert,et al.  Structural vulnerability of the North American power grid. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  C. K. Michael Tse,et al.  Assessment of Robustness of Power Systems From a Network Perspective , 2015, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.