access icon openaccess EMS communication routings’ optimisation to enhance power system security considering cyber-physical interdependence

Energy management system (EMS) is one of the most essential categories of the advanced applications in current cyber-physical power systems. However, because of the tight coupling relationship between a power network and EMS's communication network, a physical break-line fault may be accompanied by a communication line outage, which may result in regional unobservability and uncontrollability. To maximally avoid and eliminate such operation risks, referring to current EMS's ‘active + standby’ communication configuration scheme in China, the authors propose a dynamic routing optimisation mechanism for its standby communication routings, assigning the most reliable communication lines to the most important information. Such optimisation considers two factors: information's significance and the reliability of communication lines. By introducing cyber-physical sensitivity index and path-branch incidence matrix, both factors can be expressed numerically. In the case study, the authors optimise the standby communication routings for a power flow corrective control application under different scenarios. The results verify the effectiveness and superiority of their approach.

Inspec keywords: power engineering computing; energy management systems; cyber-physical systems; power system security

Other keywords: power system security enhancement; energy management system; communication line outage; power network; cyber-physical interdependence; dynamic routing optimisation mechanism; cyber-physical sensitivity index; EMS communication routing optimisation; path-branch incidence matrix; physical break-line fault

Subjects: Power system management, operation and economics; Power engineering computing

References

    1. 1)
      • 5. Xin, S., Guo, Q., Sun, H., et al: ‘Cyber-physical modeling and cyber-contingency assessment of hierarchical control systems’, IEEE Trans. Smart Grid, 2015, 6, (5), pp. 23752385.
    2. 2)
      • 9. Lei, H., Singh, C., Sprintson, A.: ‘Reliability modeling and analysis of IEC 61850 based substation protection systems’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 19.
    3. 3)
      • 12. Kanemaru, K., Ooura, K., Ibuki, S., et al: ‘Application of a power line maintenance information system using OPGW to the Nishi-Gunma UHV line’, IEEE Trans. Power Deliv., 1995, 10, (1), pp. 511517.
    4. 4)
      • 26. Zhang, W., Xu, Y., Dong, Z., et al: ‘Robust security-constrained optimal power flow using multiple microgrids for corrective control under uncertainty’, IEEE Trans. Ind. Inform., 2016, 3203, (c), pp. 11.
    5. 5)
      • 6. Xin, S., Guo, Q., Sun, H., et al: ‘Information-energy flow computation and cyber-physical sensitivity analysis for power systems’, IEEE J. Emerg. Sel. Top. Circuits Syst., 2017, 7, (2), pp. 329341.
    6. 6)
      • 4. Shi, X., Li, Y., Cao, Y., et al: ‘Cyber-physical electrical energy systems: challenges and issues’, CSEE J. Power Energy Syst., 2015, 1, (2), pp. 3642.
    7. 7)
      • 14. Li, H., Treinish, L.A., Hosking, J.R.M.: ‘A statistical model for risk management of electric outage forecasts’, IBM J. Res. Dev., 2010, 54, (3), pp. 8:18:11.
    8. 8)
      • 23. Al-faiz, M.Z., Sabry, S.S.: ‘Optimal linear quadratic controller based on genetic algorithm for TCP/AQM router’. Int. Conf. Future Communication Networks, Baghdad, Iraq, April, 2012, pp. 7883.
    9. 9)
      • 16. Hassan, N.U.L., Assaad, M.: ‘Dynamic resource allocation in multi-service OFDMA systems with dynamic queue control’, IEEE Trans. Commun., 2011, 59, (6), pp. 16641674.
    10. 10)
      • 15. Ling, T.: ‘Optimal allocation of channels in an alternate route’, IEEE Trans. Commun. Syst., 1964, 12, (2), pp. 185191.
    11. 11)
      • 1. Xin, S., Guo, Q., Wang, J., et al: ‘Information masking theory for data protection in future cloud-based energy management’, IEEE Trans. Smart Grid, 2017, doi: 10.1109/TSG.2017.2693345, in press.
    12. 12)
      • 3. Wu, D., Ma, F., Javadi, M., et al: ‘Fast screening severe cyber attacks via transient energy-based impact analysis’, CSEE J. Power Energy Syst., 2016, 2, (3), pp. 2834.
    13. 13)
      • 2. He, H., Yan, J.: ‘Cyber-physical attacks and defences in the smart grid: a survey’, IET Cyber-Phys. Syst. Theory Appl., 2016, 1, (1), pp. 1327.
    14. 14)
      • 18. Wu, J., Tse, C., Lau, F.: ‘Optimizing performance of communication networks: an application of network science’, IEEE Trans. Circuits Syst. Express Briefs, 2014, 62, (1), pp. 9599.
    15. 15)
      • 21. Steinhauser, F.: ‘Propagation and interaction of ethernet packets with iec 61850 sampled values in power utility communication networks’. 12th IET Int. Conf. Developments in Power System Protection, Copenhagen, Denmark, April 2014, pp. 14.
    16. 16)
      • 10. Li, H., Eseye, A.T., Zhang, J., et al: ‘Optimal energy management for industrial microgrids with high-penetration renewables’, Prot. Control Mod. Power Syst., 2017, 2, (12), pp. 114.
    17. 17)
      • 25. Roald, L., Misra, S., Krause, T., et al: ‘Corrective control to handle forecast uncertainty: a chance constrained optimal power flow’, IEEE Trans. Power Syst., 2017, 32, (2), pp. 16261637.
    18. 18)
      • 22. Gelenbe, E., Liu, P., Lainé, J.: ‘Genetic algorithms for route discovery’, IEEE Trans. Syst. Man Cybern. B Cybern., 2006, 36, (6), pp. 12471254.
    19. 19)
      • 24. Di Fatta, G., Hoffmann, F., Re, G.L., et al: ‘A genetic algorithm for the design of a fuzzy controller for active queue management’, IEEE Trans. Syst. Man Cybern. C Appl. Rev., 2003, 33, (3), pp. 313324.
    20. 20)
      • 19. Simmhan, Y., Kumbhare, A.G., Cao, B., et al: ‘An analysis of security and privacy issues in smart grid software architectures on clouds’. Proc. 2011 IEEE 4th Int. Conf. Cloud Computing, CLOUD, Washington, DC, USA, July 2011, pp. 582589.
    21. 21)
      • 17. Chen, J., Lau, V.K.N.: ‘Delay analysis of max-weight queue algorithm for time-varying wireless ad hoc networks-control theoretical approach’, IEEE Trans. Signal Process., 2013, 61, (1), pp. 99108.
    22. 22)
      • 11. Liu, R., Vellaithurai, C., Biswas, S.: ‘Analyzing the cyber-physical impact of cyber events on the power grid’, IEEE Trans. Smart Grid, 2015, 6, (5), pp. 24442453.
    23. 23)
      • 7. Qi, J., Hahn, A., Lu, X., et al: ‘Cybersecurity for distributed energy resources and smart inverters’, IET Cyber-Phys. Syst. Theory Appl., 2016, 1, (1), pp. 2839.
    24. 24)
      • 27. Tang, Y., Li, F., Wang, Q., et al: ‘Quantitative evaluation of communication system fault effect on real-time load control of power system’, Electr. Power Autom. Equip., 2017, 37, (2), pp. 9096.
    25. 25)
      • 13. Yates, D., Luna, B.Q., Rasmussen, R., et al: ‘Stormy weather: assessing climate change hazards to electric power infrastructure: a sandy case study’, IEEE Power Energy Mag.., 2014, 12, (5), pp. 6675.
    26. 26)
      • 8. Hadjsaid, N., Tranchita, C., Rozel, B., et al: ‘Modeling cyber and physical interdependencies – application in ICT and power grids’. IEEE/PES Power Systems Conf. Exposition, PSCE, Seattle, WA, USA, March 2009, pp. 16.
    27. 27)
      • 20. Fu, Y., Bi, J., Chen, Z., et al: ‘A hybrid hierarchical control plane for flow-based large-scale software-defined networks’, IEEE Trans. Netw. Serv. Manag., 2015, 12, (2), pp. 117131.
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