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Open Access

Modeling and Analysis of Risk Propagation and Loss Causing Capacity for Key Nodes in Cyber-Physical Coupled Power Network

Dongqi Liu1( )Qiong Zhang1Haolan Liang2Tao Zhang3Rui Wang3
National Key Laboratory of Disaster Prevention and Reduction for Power Grid, Changsha University of Science and Technology, Changsha 410114, China
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China, and is also with the College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
School of Systems Engineering, National University of Defense Technology, Changsha 410003, China
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Abstract

The modern power system has evolved into a cyber-physical system with deep coupling of physical and information domains, which brings new security risks. Aiming at the problem that the “information-physical” cross-domain attacks with key nodes as springboards seriously threaten the safe and stable operation of power grids, a risk propagation model considering key nodes of power communication coupling networks is proposed to study the risk propagation characteristics of malicious attacks on key nodes and the impact on the system. First, combined with the complex network theory, a topological model of the power communication coupling network is established, and the key nodes of the coupling network are screened out by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method under the comprehensive evaluation index based on topological characteristics and physical characteristics. Second, a risk propagation model is established for malicious attacks on key nodes to study its propagation characteristics and analyze the state changes of each node in the coupled network. Then, two loss-causing factors: the minimum load loss ratio and transmission delay factor are constructed to quantify the impact of risk propagation on the coupled network. Finally, simulation analysis based on the IEEE 39-node system shows that the probability of node being breached (α) and the security tolerance of the system (β) are the key factors affecting the risk propagation characteristics of the coupled network, as well as the criticality of the node is positively correlated with the damage-causing factor. The proposed methodological model can provide an effective exploration of the diffusion of security risks in control systems on a macro level.

References

[1]
US-Canada Power System Outage Task Force, Final report on the August 14, 2003 blackout in the United States and Canada: Causes and recommendations, Technical report, U.S. Department of Energy, Washington, DC, USA, 2004.
[2]
F. Vandenberghe, E. Grebe, D. Klaar, K. Kleinekorte, J. M. Rodriguez, H. Erven, and L. Tassan, Final report of the investigation committee on the 28 September 2003 blackout in Italy, Technical report, UCTE, Brussels, Belgium, 2004.
[3]

Z. Li, W. Tong, and X. Jin, The construction of smart grid information security defense system and information security testing system reflection and inspiration on the network attack incidents of the state grid of Ukraine and Israel, (in Chinese), Automation of Electric Power Systems, vol. 40, no. 8, pp. 147–151, 2016.

[4]
H. He and J. Yan, Cyber-physical attacks and defenses in the smart grid: A survey, IET Cyber-Physical Systems: Theory & Applications, vol. 1, no. 1, pp. 13–27, 2016.
[5]

B. Becote and B. P. Rimal, Complexity science and cyber operations: A literature survey, Complex System Modeling and Simulation, vol. 3, no. 4, pp. 327–342, 2023.

[6]

J. Guo and D. Wang, Vulnerability analysis of power communication network based on complex network theory, (in Chinese), Telecommunications for Electric Power System, vol. 30, no. 9, pp. 6–10, 2009.

[7]

Z. Wang, S. Miao, S. Guo, and H. Yin, Construction of power communication coupling network model and node importance evaluation method based on complex system theory, (in Chinese), High Voltage Engineering, vol. 48, no. 1, pp. 84–94, 2022.

[8]
R. Li, Reliability evaluation and optimization of dependent networks in smart grid based on dynamic flow model, Master dissertation, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China, 2021.
[9]

R. Wu, W. Wu, L. Li, B. Fan, and L. Tang, Topology diagnosis of power communication network based on node influence, (in Chinese), Power System Protection and Control, vol. 47, no. 10, pp. 147–155, 2019.

[10]

X. Ji, B. Wang, C. Dong, G. Chen, D. Liu, D. Wei, and X. Wang, Vulnerability assessment and edge protection strategy of power information physical interdependent network, (in Chinese), Power System Technology, vol. 40, no. 6, pp. 1872–1878, 2016.

[11]

X. Ji, B. Wang, D. Liu, and T. Zhao, Review of dependent network theory and its application in structural vulnerability analysis of power information physical system, (in Chinese), Proceedings of the CSEE, vol. 36, no. 17, pp. 4521–4533, 2016.

[12]

X. Wang, G. Zhu, R. He, M. Tian, Z. Dong, D. Dai, J. Long, L. Zhao, and Q. Zhang, Application of complex network theory in cascading fault research of power CPS, (in Chinese), Power System Technology, vol. 41, no. 9, pp. 2947–2956, 2017.

[13]

Y. Han, C. Guo, B. Zhu, and L. Xu, Cascading fault model of power system based on information physics fusion and improved percolation theory, (in Chinese), Automation of Electric Power Systems, vol. 40, no. 17, pp. 30–37, 2016.

[14]

Z. Wu, W. Du, L. Liu, Y. Lin, and J. Liu, Risk propagation model of power coupled networks under malicious attack, (in Chinese), Power System Technology, vol. 44, no. 6, pp. 2045–2052, 2020.

[15]

S. Deng, J. Zhang, D. Wu, Y. He, X. Xie, and X. Wu, A quantitative risk assessment model for distribution cyber-physical system under cyberattack, IEEE Trans. Ind. Inform., vol. 19, no. 3, pp. 2899–2908, 2023.

[16]
S. Lin, J. Wen, X. Qu. Lu, and Q. Xiao, Optimization of distribution network structure and identification of key nodes and lines based on TOPSIS method, (in Chinese), Complex Systems and Complexity Science, vol. 21, pp. 1−9, 2024.
[17]

Y. Li and C. Liang, Research on key node identification and damage resistance of airway network based on TOPSIS fusion method, (in Chinese), Flight Dynamics, vol. 40, no. 6, pp. 83–87, 2022.

[18]

K. Yan, X. Liu, Y. Lu, and F. Qin, A cyber-physical power system risk assessment model against cyberattacks, IEEE Syst. J., vol. 17, no. 2, pp. 2018–2028, 2023.

[19]

J. Bi, F. Luo, G. Liang, X. Yang, S. He, and Z. Y. Dong, Impact assessment and defense for smart grids with FDIA against AMI, IEEE Trans. Netw. Sci. Eng., vol. 10, no. 2, pp. 578–591, 2023.

[20]

Y. Tang, L. Li, and X. Liu, State-of-the-art development of complex systems and their simulation methods, Complex System Modeling and Simulation, vol. 1, no. 4, pp. 271–290, 2021.

[21]

J. Hu, Z. Li, and X. Duan, Analysis of structural characteristics of power dispatching data network, (in Chinese), Proceedings of the CSEE, vol. 29, no. 4, pp. 53–59, 2009.

[22]

G. Li, W. Ju, X. Duan, and D. Shi, Analysis on transmission characteristics of power dispatching data network, (in Chinese), Proceedings of the CSEE, vol. 32, no. 22, pp. 141–148, 2012.

[23]
Y. Cai, Mechanism analysis and application research of multi network interaction in smart grid, PhD dissertation, College of Electrical and Information Engineering, Hunan University, Changsha, China, 2016.
[24]

T. Fu, D. Wang, X. Fan, and Q. Huang, Component importance and interdependence analysis for transmission, distribution and communication systems, CSEE J. Power Energy Syst., vol. 8, no. 2, pp. 488–498, 2022.

[25]

J. F. Donges, H. C. H. Schultz, N. Marwan, Y. Zou, and J. Kurths, Investigating the topology of interacting networks: Theory and application to coupled climate subnetworks, The European Physical Journal B, vol. 84, no. 4, pp. 635–651, 2011.

[26]

J. V. Milanović and W. Zhu, Modeling of interconnected critical infrastructure systems using complex network theory, IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 4637–4648, 2018.

[27]

B. Wu, T. Yuan, Y. Qi, and M. Dong, Public opinion dissemination with incomplete information on social network: A study based on the infectious diseases model and game theory, Complex System Modeling and Simulation, vol. 1, no. 2, pp. 109–121, 2021.

[28]

T. Wang, C. Sun, X. Gu, and X. Qin, Modeling and vulnerability analysis of electric power communication coupled network, (in Chinese), Proceedings of the CSEE, vol. 38, no. 12, pp. 3556–3567, 2018.

Complex System Modeling and Simulation
Pages 124-136
Cite this article:
Liu D, Zhang Q, Liang H, et al. Modeling and Analysis of Risk Propagation and Loss Causing Capacity for Key Nodes in Cyber-Physical Coupled Power Network. Complex System Modeling and Simulation, 2024, 4(2): 124-136. https://doi.org/10.23919/CSMS.2024.0008

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Received: 04 January 2024
Revised: 07 April 2024
Accepted: 05 May 2024
Published: 30 June 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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