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8 - Big data processing for smart grid security

from Part II - Big data over cyber networks

Published online by Cambridge University Press:  18 December 2015

By
Lanchao Liu ,
Zhu Han ,
H. Vincent Poor  and
Shuguang Cui
Edited by
Shuguang Cui ,
Alfred O. Hero, III ,
Zhi-Quan Luo  and
José M. F. Moura
Lanchao Liu
Affiliation:
University of Houston, USA
Zhu Han
Affiliation:
University of Houston, USA
H. Vincent Poor
Affiliation:
Princeton University, USA
Shuguang Cui
Affiliation:
Texas A&M University, USA
Shuguang Cui
Affiliation:
Texas A & M University
Alfred O. Hero, III
Affiliation:
University of Michigan, Ann Arbor
Zhi-Quan Luo
Affiliation:
University of Minnesota
José M. F. Moura
Affiliation:
Carnegie Mellon University, Pennsylvania
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Summary

The development of the smart grid, impelled by the increasing demand from industrial and residential customers together with the aging power infrastructure, has become an urgent global priority due to its potential economic, environmental, and societal benefits. Smart grid refers to the next-generation electric power system that aims to provide reliable, efficient, secure, and quality energy generation, distribution, and consumption, using modern information, communications, and electronics technology. A distributed and user-centric system will be introduced in smart grid, which will incorporate end consumers into its decision processes to provide a cost-effective and reliable energy supply. In the smart grid, the modern communication infrastructure will play a vital role in managing, controlling, and optimizing different devices and systems. Information and communication technologies will provide the power grid with the capability of supporting two-way energy and information flows, rapid isolation, and restoring of power outages, facilitating the integration of renewable energy sources into the grid and empowering the consumer with tools for optimizing their energy consumption. The introduction of the cyber infrastructure needed to realize the smart grid also brings with it vulnerability to security breaches. Thus security is amajor concern in the development of the smart grid. Moreover, the widespread deployment of smart meters and sensors such as phasor measurement units results in the generation of massive amounts of data that can be exploited in optimizing and securing the grid. This chapter addresses these two issues by investigating the applications of big data processing techniques for smart grid security from two perspectives: exploiting the inherent structure of the data, and dealing with the huge size of the data sets. Two specific applications are included in this chapter: sparse optimization for false data injection detection, and a distributed parallel approach for the security constrained optimal power flow problem.

Preliminaries and motivations

The smart grid is a modernized power system, which enables bidirectional flows of energy and uses two-way communication and control capabilities to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity [1]. In the conceptual model of the smart gird, seven components are introduced as described in Table 8.1 [2], and an illustration of their interactions is given in Figure 8.1.

Type
Chapter
Information
Big Data over Networks , pp. 217 - 244
Publisher: Cambridge University Press
Print publication year: 2016

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References

[1] E., Hossain, Z., Han, and H.V., Poor, SmartGridCommunications and Networking, Cambridge, UK: Cambridge University Press, 2012.Google Scholar
[2] C., Greer, D. A., Wollman, D. E., Prochaska, et al., “NIST framework and roadmap for smart grid interoperability standards, release 3.0,” The National Institute of Standards and Technology, Tech. Rep. NIST SP - 1108r3, October 2014.
[3] S., Gorman, “Effect of stealthy bad data injection on network congestion in market based power system,” The Wall Street Journal, April 2009.Google Scholar
[4] S., Borlase, Smart Girds: Infrastructure, Technology and Solutions, Boca Raton, FL: CRC Press, 2012.Google Scholar
[5] A., Abur and A. G., Exposito, Power System State Estimation: Theory and Implementation, New York: Marcel Dekker, Inc., 2004.Google Scholar
[6] J. J., Grainger and W. D., Stevenson Jr, Power System Analysis, New York: McGraw-Hill, 1994.Google Scholar
[7] Y., Liu,M. K., Reiter, and P., Ning, “False data injection attacks against state estimation in electric power grids,” in Proceedings 16th ACM Conference on Computer and Communications Security, Chicago, IL, November 2009.Google Scholar
[8] L., Xie, Y., Mo, and B., Sinopoli, “False data injection attacks in electricity markets,” in Proceedings IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, October 2010.Google Scholar
[9] M., Esmalifalak, Z., Han, and L., Song, “Effect of stealthy bad data injection on network congestion in market based power system,” in Proceedings IEEE Wireless Communications and Networking Conference, Paris, France, April 2012.Google Scholar
[10] G., Dán and H., Sandberg, “Stealth attacks and protection schemes for state estimators in power systems,” in Proceedings IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, October 2010.Google Scholar
[11] M., Esmalifalak, G., Shi, Z., Han, and L., Song, “Bad data injection attack and defense in electricity market using game theory study,” IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 160–169, March 2013.Google Scholar
[12] O., Kousut, L., Jia, R. J., Thomas, and L., Tong, “Malicious data attacks on the smart grid,” IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 645–658, December 2011.Google Scholar
[13] T. T., Kim and H. V., Poor, “Strategic protection against data injection attacks on power grids,” IEEE Transactions on Smart Grid, vol. 2, no. 2, pp. 326–333, June 2011.Google Scholar
[14] S., Cui, Z., Han, S., Kar, et al., “Coordinated data-injection attack and detection in the smart grid: a detailed look at enriching detection solutions,” IEEE Signal Processing Magazine, vol. 29, no. 5, pp. 106–115, September 2012.Google Scholar
[15] Y., Zhao, A., Goldsmith, and H. V., Poor, “Fundamental limits of cyber-physical security in smart power grids,” in Proceedings IEEE 52nd Annual Conference on Decision and Control, Florence, Italy, December 2013.Google Scholar
[16] Z., Han, H., Li, and W., Yin, Compressive Sensing for Wireless Communication, Cambridge, UK: Cambridge University Press, 2012.Google Scholar
[17] E. J., Candès and B., Recht, “Exact matrix completion via convex optimization,” Communications of the ACM, vol. 55, no. 6, pp. 111–119, June 2009.Google Scholar
[18] E. J., Candès, X., Li, Y., Ma, and J., Wright, “Robust principal component analysis?” Journal of the ACM, vol. 58, no. 3, pp. 1–37, May 2011.Google Scholar
[19] Z., Lin, M., Chen, L., Wu, and Y., Ma, “The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices,” UIUC, Tech. Rep. UILU-ENG-09-2215, Urbana, FL, 2009.
[20] D. P., Bertsekas, Nonlinear Programming, Belmont, MA: Athena Scientific, 1999.Google Scholar
[21] J., Cai, E. J., Candès, and Z., Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956–1982, January 2010.Google Scholar
[22] Y., Shen, Z., Wen, and Y., Zhang, “Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization,” Rice CAAM, Tech. Rep. TR11-02, Houston, TX, 2011.
[23] Z., Wen, W., Yin, and Y., Zhang, “Solving a low-rank factorization model formatrix completion by a nonlinear successive over-relaxation algorithm,” Rice CAAM, Tech. Rep. TR10-07, Houston, TX, 2010.
[24] R. D., Zimmerman, C. E., Murillo-Sánchez, and R. J., Thomas, “MAT-POWER steady-state operations, planning and analysis tools for power systems research and education,” IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12–19, February 2011.Google Scholar
[25] M., Shahidehpour, W. F., Tinney, and Y., Fu, “Impact of security on power system operation,” Proceedings of the IEEE, vol. 93, no. 11, pp. 2013–2025, November 2001.Google Scholar
[26] O., Alsac and B., Scott, “Optimal load flow with steady-state security,” IEEE Transaction on Power Apparatus and System, vol. 93, no. 3, pp. 745–751, May 1974.Google Scholar
[27] M. V. F., Pereira, A., Monticelli, and L. M. V. G., Pinto, “Security-constrained dispatch with corrective rescheduling,” in Proceedings IFAC Symposium on Planning and Operation of Electric Energy System, Rio de Janeiro, Brazil, July 1985.Google Scholar
[28] A. J., Wood and B. F., Wollenberg, Power Generation Operation and Control, New York: Wiley, 1996.Google Scholar
[29] A., Monticelli, M. V. F., Pereira, and S., Granville, “Security-constrained optimal power flow with post-contingency corrective rescheduling,” IEEE Transactions on Power Systems, vol. 2, no. 1, pp. 175–180, February 1987.Google Scholar
[30] F., Capitanescu, J. L. M., Ramos, P., Panciatici, et al., “State-of-the-art, challenges, and future trends in security constrained optimal power flow,” Electric Power System Research, vol. 81, no. 8, pp. 1731–1741, August 2011.Google Scholar
[31] J., Martínez-Crespo, J., Usaola, and J. L., Fernández, “Security-constrained optimal generation scheduling in large-scale power systems,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 321–332, February 2006.Google Scholar
[32] Y., Fu, M., Shahidehpour, and Z., Li, “AC contingency dispatch based on security-constrained unit commitment,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 897–908, May 2006.Google Scholar
[33] F., Capitanescu and L., Wehenkel, “A new iterative approach to the corrective securityconstrained optimal power flow problem,” IEEE Transactions on Power Systems, vol. 23, no. 4, pp. 1533–1541, November 2008.Google Scholar
[34] Y., Li and J.D., McCalley, “Decomposed SCOPF for improving efficiency,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 494–495, February 2009.Google Scholar
[35] S., Boyd, N., Parikh, E., Chu, B., Peleato, and J., Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundation and Trends in Machine Learning, vol. 3, no. 1, pp. 1–122, November 2010.Google Scholar
[36] D., Bertsekas and J., Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, 2nd edn, Belmont, MA: Athena Scientific, 1997.Google Scholar
[37] R., Baldick, B. H., Kim, C., Chase, and Y., Luo, “A fast distributed implementation of optimal power flow,” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 858–864, August 1989.Google Scholar
[38] M., Kraning, E., Chu, J., Lavaei, and S., Boyd, “Dynamic network energy management via proximal message passing,” Foundations and Trends in Optimization, vol. 1, no. 2, pp. 1–54, January 2014.Google Scholar
[39] W., Deng and W., Yin, “On the global and linear convergence of the generalized alternating direction method of multipliers,” Rice CAAM, Tech. Rep. TR12-14, Houston, TX, 2012.
[40] J., Nocedal and S. J., Wright, Numerical Optimization, 2nd edn, New York: Springer, 2006.Google Scholar

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