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Optimal Deception Strategies in Power System Fortification against Deliberate Attacks

Author

Listed:
  • Peng Jiang

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
    Energy Internet Research Center, China Aerospace Science and Technology Corporation, Beijing 100048, China)

  • Shengjun Huang

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Tao Zhang

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
    State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China)

Abstract

As a critical infrastructure, the modern electrical network is faced with various types of threats, such as accidental natural disaster attacks and deliberate artificial attacks, thus the power system fortification has attracted great concerns in the community of academic, industry, and military. Nevertheless, the attacker is commonly assumed to be capable of accessing all information in the literature (e.g., network configuration and defensive plan are explicitly provided to the attacker), which might always be the truth since the grid data access permission is usually restricted. In this paper, the information asymmetry between defender and attacker is investigated, leading to an optimal deception strategy problem for power system fortification. Both the proposed deception and traditional protection strategies are formulated as a tri-level mixed-integer linear programming (MILP) problem and solved via two-stage robust optimization (RO) framework and the column-and-constraint generation (CCG) algorithm. Comprehensive case studies on the 6-bus system and IEEE 57-bus system are implemented to reveal the difference between these two strategies and identify the significance of information deception. Numerical results indicate that deception strategy is superior to protection strategy. In addition, detailed discussions on the performance evaluation and convergence analysis are presented as well.

Suggested Citation

  • Peng Jiang & Shengjun Huang & Tao Zhang, 2019. "Optimal Deception Strategies in Power System Fortification against Deliberate Attacks," Energies, MDPI, vol. 12(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:342-:d:200028
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    References listed on IDEAS

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    1. Ding, Tao & Yao, Li & Li, Fangxing, 2018. "A multi-uncertainty-set based two-stage robust optimization to defender–attacker–defender model for power system protection," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 179-186.
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    5. Lai, Kexing & Illindala, Mahesh & Subramaniam, Karthikeyan, 2019. "A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment," Applied Energy, Elsevier, vol. 235(C), pages 204-218.
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    Cited by:

    1. Fakhry, Ramy & Hassini, Elkafi & Ezzeldin, Mohamed & El-Dakhakhni, Wael, 2022. "Tri-level mixed-binary linear programming: Solution approaches and application in defending critical infrastructure," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1114-1131.
    2. Omid Sadeghian & Behnam Mohammadi-Ivatloo & Fazel Mohammadi & Zulkurnain Abdul-Malek, 2022. "Protecting Power Transmission Systems against Intelligent Physical Attacks: A Critical Systematic Review," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    3. Rasa Smaliukiene & Gintaras Labutis & Ausrius Juozapavicius, 2020. "Pro-Environmental Energy Behavior in the Military: Assessing Behavior Change Factors at a Selected Military Unit," Energies, MDPI, vol. 13(1), pages 1-12, January.
    4. Peng Jiang & Shengjun Huang & Tao Zhang, 2020. "Asymmetric Information in Military Microgrid Confrontations—Evaluation Metric and Influence Analysis," Energies, MDPI, vol. 13(8), pages 1-21, April.

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