Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-04T21:13:43.095Z Has data issue: false hasContentIssue false
  • English
  • Français

A study of cascading failures in real and synthetic power grid topologies

Published online by Cambridge University Press:  23 August 2018

RUSSELL SPIEWAK Open the ORCID record for RUSSELL SPIEWAK [Opens in a new window] ,
SALEH SOLTAN Open the ORCID record for SALEH SOLTAN [Opens in a new window] ,
YAKIR FORMAN ,
SERGEY V. BULDYREV  and
GIL ZUSSMAN Open the ORCID record for GIL ZUSSMAN [Opens in a new window]
RUSSELL SPIEWAK
Affiliation:
Yeshiva University, New York, NY, USA (e-mail: [email protected])
SALEH SOLTAN
Affiliation:
Princeton University, Princeton, NJ, USA (e-mail: [email protected])
YAKIR FORMAN
Affiliation:
Yeshiva University, New York, NY, USA (e-mail: [email protected], [email protected])
SERGEY V. BULDYREV
Affiliation:
Yeshiva University, New York, NY, USA (e-mail: [email protected], [email protected])
GIL ZUSSMAN
Affiliation:
Columbia University, New York, NY, USA (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Using the direct current power flow model, we study cascading failures and their spatial and temporal properties in the U.S. Western Interconnection (USWI) power grid. We show that yield (the fraction of demand satisfied after the cascade) has a bimodal distribution typical of a first-order transition. The single line failure leads either to an insignificant power loss or to a cascade which causes a major blackout with yield less than 0.8. The former occurs with high probability if line tolerance α (the ratio of the maximal load a line can carry to its initial load) is greater than 2, while a major blackout occurs with high probability in a broad range of 1 < α < 2. We also show that major blackouts begin with a latent period (with duration proportional to α) during which few lines overload and yield remains high. The existence of the latent period suggests that intervention during early stages of a cascade can significantly reduce the risk of a major blackout. Finally, we introduce the preferential Degree And Distance Attachment model to generate random networks with similar degree, resistance, and flow distributions to the USWI. Moreover, we show that the Degree And Distance Attachment model behaves similarly to the USWI against failures.

Keywords

power gridscascading failuresline failuressynthetic power grids
Type
Research Article
Information
Network Science , Volume 6 , Issue 4 , December 2018 , pp. 448 - 468
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74 (1), 47–97.Google Scholar
Asztalos, A., Sreenivasan, S., Szymanski, B. K., & Korniss, G. (2014). Cascading failures in spatially-embedded random networks. PloS one, 9 (1), e84563.Google Scholar
Bakke, J. Ø. H., Hansen, A., & Kertész, J. (2006). Failures and avalanches in complex networks. Europhysics Letters, 76 (4), 717–723.Google Scholar
Bakshi, A. S., Velayutham, A., Srivastava, S. C., Agrawal, K. K., Nayak, R. N., Soonee, S. K., & Singh, B. (2012). Report of the enquiry committee on grid disturbance in Northern Region on 30th July 2012 and in Northern, Eastern & North-Eastern Region on 31st July 2012. New Delhi, India.Google Scholar
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286 (5439), 509–512.Google Scholar
Bernstein, A., Bienstock, D., Hay, D., Uzunoglu, M., & Zussman, G. (2014). Power grid vulnerability to geographically correlated failures–-analysis and control implications. In Proceedings of the IEEE INFOCOM'14, Toronto, Canada.Google Scholar
Bienstock, D. (2011). Optimal control of cascading power grid failures. In Proceedings of the IEEE CDC-ECC'11, Orlando, FL.Google Scholar
Bienstock, D., & Verma, A. (2010). The N-k problem in power grids: New models, formulations, and numerical experiments. SIAM Journal of Optimization, 20 (5), 2352–2380.Google Scholar
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., & Havlin, S. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464 (7291), 1025–1028.Google Scholar
Carreras, B. A., Lynch, V. E., Dobson, I., & Newman, D. E. (2002). Critical points and transitions in an electric power transmission model for cascading failure blackouts. Chaos, 12 (4), 985–994.Google Scholar
Carreras, B. A., Lynch, V. E., Dobson, I., & Newman, D. E. (2004). Complex dynamics of blackouts in power transmission systems. Chaos, 14 (3), 643–652.Google Scholar
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51 (4), 661–703.Google Scholar
Coniglio, A. (1981). Thermal phase transition of the dilute s-state potts and n-vector models at the percolation threshold. Physical Review Letters, 46 (4), 250.Google Scholar
De Arcangelis, L., Redner, S., & Herrmann, H. J. (1985). A random fuse model for breaking processes. Journal de Physique Lettres, 46 (13), 585–590.Google Scholar
Dobson, I., & Lu, L. (1992). Voltage collapse precipitated by the immediate change in stability when generator reactive power limits are encountered. IEEE Transactions on Circuits Systems I, Fundamental Theory Application, 39 (9), 762–766.Google Scholar
Glover, J. D., Sarma, M. S., & Overbye, T. (2012). Power system analysis & design, si version. Stamford, CT: Cengage Learning.Google Scholar
Hines, P., Balasubramaniam, K., & Sanchez, E. (2009). Cascading failures in power grids. IEEE Potentials, 28 (5), 24–30.Google Scholar
Kornbluth, Y., Barach, G., Tuchman, Y., Kadish, B., Cwilich, G., & Buldyrev, S. V. (2018). Network overload due to massive attacks. Physical Review E, 97 (5), 052309.Google Scholar
Manna, S. S., & Sen, P. (2002). Modulated scale-free network in euclidean space. Physical Review E, 66 (6), 066114.Google Scholar
Motter, A. E. (2004). Cascade control and defense in complex networks. Physical Review Letters, 93 (9), 098701.Google Scholar
Motter, A. E., & Lai, Y.-C. (2002). Cascade-based attacks on complex networks. Physical Review E, 66 (6), 065102.Google Scholar
Pahwa, S., Scoglio, C., & Scala, A. (2014). Abruptness of cascade failures in power grids. Scientific Reports, 4, 3694.Google Scholar
Pinar, A., Meza, J., Donde, V., & Lesieutre, B. (2010). Optimization strategies for the vulnerability analysis of the electric power grid. SIAM Journal on Optimization, 20 (4), 1786–1810.Google Scholar
Platts. (2009). Electric transmission lines GIS data. Retrieved from http://www.platts.com/Products/gisdata.Google Scholar
Soltan, S., Mazauric, D., & Zussman, G. (2014). Cascading failures in power grids: Analysis and algorithms. In Proceedings of the ACM e-Energy'14, Cambridge, UK.Google Scholar
US-Canada Power System Outage Task Force. (2004). Report on the August 14, 2003 blackout in the United States and Canada: Causes and recommendations. Retrieved from https://reports.energy.gov.Google Scholar
U.S FERC, DHS, & DOE. (2010). Detailed technical report on EMP and severe solar flare threats to the U.S. power grid.Google Scholar
Xulvi-Brunet, R., & Sokolov, I. M. (2002). Evolving networks with disadvantaged long-range connections. Physical Review E, 66 (2), 026118.Google Scholar
Zapperi, S., Ray, P., Stanley, H. E., & Vespignani, A. (1997). First-order transition in the breakdown of disordered media. Physical Review Letters, 78 (8), 1408–1411.Google Scholar