Estimating cascading failure risk: Comparing Monte Carlo sampling and Random Chemistry

This paper presents a computationally efficient approach to estimate cascading failure risk in power systems. The method uses the previously published Random Chemistry algorithm [1] to find combinations of branch outages that lead to large blackouts, and then estimates risk by computing the expected blackout size based on the probabilities of various contingencies. We compare this method with Monte Carlo simulation, and show that the method is at least an order of magnitude faster than Monte Carlo simulation. Results from the IEEE RTS-96 and the 2383-bus Polish grid are presented in the paper.

[1]  Jay Apt,et al.  Phase Transitions in the Probability of Cascading Failures , 2004 .

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

[3]  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.

[4]  I. Dobson,et al.  Estimating the Propagation and Extent of Cascading Line Outages From Utility Data With a Branching Process , 2012, IEEE Transactions on Power Systems.

[5]  Ian Dobson,et al.  Cascading dynamics and mitigation assessment in power system disturbances via a hidden failure model , 2005 .

[6]  James A. Bucklew,et al.  Splitting Method for Speedy Simulation of Cascading Blackouts , 2013, IEEE Transactions on Power Systems.

[7]  Daniel S. Kirschen,et al.  Criticality in a cascading failure blackout model , 2006 .

[8]  Paul D. H. Hines,et al.  Changes in cascading failure risk with generator dispatch method and system load level , 2014, 2014 IEEE PES T&D Conference and Exposition.

[9]  Djalma M. Falcao,et al.  Composite reliability evaluation by sequential Monte Carlo simulation on parallel and distributed processing environments , 2001 .

[10]  Quan Chen,et al.  Composite Power System Vulnerability Evaluation to Cascading Failures Using Importance Sampling and Antithetic Variates , 2013, IEEE Transactions on Power Systems.

[11]  Ian Dobson,et al.  Initial evidence for self-organized criticality in electric power system blackouts , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[12]  V. E. Lynch,et al.  Critical points and transitions in an electric power transmission model for cascading failure blackouts. , 2002, Chaos.

[13]  Jason H. Moore,et al.  Genomic mining for complex disease traits with “random chemistry” , 2007, Genetic Programming and Evolvable Machines.

[14]  D.S. Kirschen,et al.  A probabilistic indicator of system stress , 2004, IEEE Transactions on Power Systems.

[15]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[16]  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.

[17]  Paul Hines,et al.  A “Random Chemistry” Algorithm for Identifying Collections of Multiple Contingencies That Initiate Cascading Failure , 2012, IEEE Transactions on Power Systems.