IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics0360544223019072.html
   My bibliography  Save this article

Optimal cooperative cyber–physical attack strategy against gas–electricity interconnected system

Author

Listed:
  • Huang, Chongxin
  • Fu, Shuai
  • Hong, Minglei
  • Deng, Song

Abstract

In the context of the in-depth integration of the cyber system and the physical system, cooperative cyber–physical attacks (CCPAs) pose an increasing threat to the security and economy of the integrated energy system (IES). In this article, considering a gas–electricity interconnected IES, a new bi-level programming model is formulated to study the impact of the CCPA strategies on the economy of the IES. At the upper level, from the attacker’s perspective, an attack decision model is built to maximize the operational expenditure of the IES subject to the attack resource constraints. At the lower level, from the dispatcher’s perspective, an optimal scheduling model is established to minimize the operation cost in the case of the IES suffering from the CCPAs. Since the bi-level programming model is mixed-integer, nonlinear, and non-convex, a joint solution method (PSO+Yalmip+Cplex) is proposed to compute the optimal CCPA strategies. The impacts of the CCPA strategies on the economic dispatch (ED) of the IES are evaluated via numerical simulations.

Suggested Citation

  • Huang, Chongxin & Fu, Shuai & Hong, Minglei & Deng, Song, 2023. "Optimal cooperative cyber–physical attack strategy against gas–electricity interconnected system," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223019072
    DOI: 10.1016/j.energy.2023.128513
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223019072
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128513?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. He, Gui-Xiong & Yan, Hua-guang & Chen, Lei & Tao, Wen-Quan, 2020. "Economic dispatch analysis of regional Electricity–Gas system integrated with distributed gas injection," Energy, Elsevier, vol. 201(C).
    2. Wu, Shimeng & Jiang, Yuchen & Luo, Hao & Zhang, Jiusi & Yin, Shen & Kaynak, Okyay, 2022. "An integrated data-driven scheme for the defense of typical cyber–physical attacks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Daogui & Fang, Yi-Ping & Zio, Enrico, 2023. "Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Xie, Haipeng & Tang, Lingfeng & Zhu, Hao & Cheng, Xiaofeng & Bie, Zhaohong, 2023. "Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Wang, Chong & Ju, Ping & Wu, Feng & Lei, Shunbo & Hou, Yunhe, 2021. "Coordinated scheduling of integrated power and gas grids in consideration of gas flow dynamics," Energy, Elsevier, vol. 220(C).
    4. Berghout, Tarek & Benbouzid, Mohamed, 2022. "EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Sayed, Ahmed Rabee & Wang, Cheng & Chen, Sheng & Shang, Ce & Bi, Tianshu, 2021. "Distributionally robust day-ahead operation of power systems with two-stage gas contracting," Energy, Elsevier, vol. 231(C).
    6. Zhou, Dengji & Yan, Siyun & Huang, Dawen & Shao, Tiemin & Xiao, Wang & Hao, Jiarui & Wang, Chen & Yu, Tianqi, 2022. "Modeling and simulation of the hydrogen blended gas-electricity integrated energy system and influence analysis of hydrogen blending modes," Energy, Elsevier, vol. 239(PA).
    7. Badrsimaei, Hamed & Hooshmand, Rahmat-Allah & Nobakhtian, Soghra, 2023. "Observable placement of phasor measurement units for defense against data integrity attacks in real time power markets," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Yu, Haiquan & Zhou, Jianxin & Si, Fengqi & Nord, Lars O., 2022. "Combined heat and power dynamic economic dispatch considering field operational characteristics of natural gas combined cycle plants," Energy, Elsevier, vol. 244(PA).
    9. Zhang, Xi & Liu, Dong & Tu, Haicheng & Tse, Chi Kong, 2022. "An integrated modeling framework for cascading failure study and robustness assessment of cyber-coupled power grids," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    10. Zhuang, Wennan & Zhou, Suyang & Chen, Jinyi & Gu, Wei, 2024. "Operation optimization of electricity-steam coupled industrial energy system considering steam accumulator," Energy, Elsevier, vol. 289(C).
    11. Cavana, Marco & Mazza, Andrea & Chicco, Gianfranco & Leone, Pierluigi, 2021. "Electrical and gas networks coupling through hydrogen blending under increasing distributed photovoltaic generation," Applied Energy, Elsevier, vol. 290(C).
    12. Zhao, Baining & Qian, Tong & Tang, Wenhu & Liang, Qiheng, 2022. "A data-enhanced distributionally robust optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty," Energy, Elsevier, vol. 243(C).
    13. Ding, Xiao & Wang, Huan & Zhang, Xi & Ma, Chuang & Zhang, Hai-Feng, 2024. "Dual nature of cyber–physical power systems and the mitigation strategies," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    14. Xu, Jiuping & Liu, Liying & Wang, Fengjuan, 2022. "Equilibrium strategy-based economic-reliable approach for day-ahead scheduling towards solar-wind-gas hybrid power generation system: A case study from China," Energy, Elsevier, vol. 240(C).
    15. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    16. Yin, Zhenqin & Zhuo, Yue & Ge, Zhiqiang, 2023. "Transfer adversarial attacks across industrial intelligent systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223019072. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.