IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v280y2020ics0306261920314586.html
   My bibliography  Save this article

Load altering attack-tolerant defense strategy for load frequency control system

Author

Listed:
  • Chen, Chunyu
  • Cui, Mingjian
  • Fang, Xin
  • Ren, Bixing
  • Chen, Yang

Abstract

Cyber attacks become emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

Suggested Citation

  • Chen, Chunyu & Cui, Mingjian & Fang, Xin & Ren, Bixing & Chen, Yang, 2020. "Load altering attack-tolerant defense strategy for load frequency control system," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314586
    DOI: 10.1016/j.apenergy.2020.116015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920314586
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.116015?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. Saad, Ahmed A. & Faddel, Samy & Mohammed, Osama, 2019. "A secured distributed control system for future interconnected smart grids," Applied Energy, Elsevier, vol. 243(C), pages 57-70.
    2. Wu, Peng & Partridge, Julius & Bucknall, Richard, 2020. "Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships," Applied Energy, Elsevier, vol. 275(C).
    3. Sayed, Ahmed R. & Wang, Cheng & Bi, Tianshu, 2019. "Resilient operational strategies for power systems considering the interactions with natural gas systems," Applied Energy, Elsevier, vol. 241(C), pages 548-566.
    4. Luo, Xiaoyuan & Wang, Xinyu & Zhang, Mingyue & Guan, Xinping, 2019. "Distributed detection and isolation of bias injection attack in smart energy grid via interval observer," Applied Energy, Elsevier, vol. 256(C).
    5. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    6. Mishra, Sakshi & Anderson, Kate & Miller, Brian & Boyer, Kyle & Warren, Adam, 2020. "Microgrid resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies," Applied Energy, Elsevier, vol. 264(C).
    7. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    8. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    9. 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.
    10. Shang-Guan, Xingchen & He, Yong & Zhang, Chuanke & Jiang, Lin & Spencer, Joseph William & Wu, Min, 2020. "Sampled-data based discrete and fast load frequency control for power systems with wind power," Applied Energy, Elsevier, vol. 259(C).
    11. Li, Yuecheng & He, Hongwen & Khajepour, Amir & Wang, Hong & Peng, Jiankun, 2019. "Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information," Applied Energy, Elsevier, vol. 255(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jianguo Ding & Attia Qammar & Zhimin Zhang & Ahmad Karim & Huansheng Ning, 2022. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions," Energies, MDPI, vol. 15(18), pages 1-37, September.
    2. Ding, Zhetong & Chen, Chunyu & Cui, Mingjian & Bi, Wenjun & Chen, Yang & Li, Fangxing, 2021. "Dynamic game-based defensive primary frequency control system considering intelligent attackers," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Lin, Wen-Ting & Chen, Guo & Huang, Yuhan, 2022. "Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach," Applied Energy, Elsevier, vol. 314(C).
    4. Yin, Hao & Ou, Zuhong & Fu, Jiajin & Cai, Yongfeng & Chen, Shun & Meng, Anbo, 2021. "A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture," Energy, Elsevier, vol. 234(C).
    5. Shangguan, Xing-Chen & He, Yong & Zhang, Chuan-Ke & Jiang, Lin & Wu, Min, 2022. "Load frequency control of time-delayed power system based on event-triggered communication scheme," Applied Energy, Elsevier, vol. 308(C).

    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. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    2. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(C).
    3. Ma, Shuyang & Li, Yan & Du, Liang & Wu, Jianzhong & Zhou, Yue & Zhang, Yichen & Xu, Tao, 2022. "Programmable intrusion detection for distributed energy resources in cyber–physical networked microgrids," Applied Energy, Elsevier, vol. 306(PB).
    4. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    5. Shangguan, Xing-Chen & He, Yong & Zhang, Chuan-Ke & Jiang, Lin & Wu, Min, 2022. "Load frequency control of time-delayed power system based on event-triggered communication scheme," Applied Energy, Elsevier, vol. 308(C).
    6. Zare Oskouei, Morteza & Mehrjerdi, Hasan & Babazadeh, Davood & Teimourzadeh Baboli, Payam & Becker, Christian & Palensky, Peter, 2022. "Resilience-oriented operation of power systems: Hierarchical partitioning-based approach," Applied Energy, Elsevier, vol. 312(C).
    7. Latif, Abdul & Hussain, S. M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2021. "Double stage controller optimization for load frequency stabilization in hybrid wind-ocean wave energy based maritime microgrid system," Applied Energy, Elsevier, vol. 282(PA).
    8. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
    9. Abulanwar, Sayed & Ghanem, Abdelhady & Rizk, Mohammad E.M. & Hu, Weihao, 2021. "Adaptive synergistic control strategy for a hybrid AC/DC microgrid during normal operation and contingencies," Applied Energy, Elsevier, vol. 304(C).
    10. Lei, Shunbo & Pozo, David & Wang, Ming-Hao & Li, Qifeng & Li, Yupeng & Peng, Chaoyi, 2022. "Power economic dispatch against extreme weather conditions: The price of resilience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    11. Wang, Yi & Qiu, Dawei & Strbac, Goran, 2022. "Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems," Applied Energy, Elsevier, vol. 310(C).
    12. Abdul Latif & S. M. Suhail Hussain & Dulal Chandra Das & Taha Selim Ustun, 2021. "Design and Implementation of Maiden Dual-Level Controller for Ameliorating Frequency Control in a Hybrid Microgrid," Energies, MDPI, vol. 14(9), pages 1-15, April.
    13. Zhang, Wei & Wang, Jixin & Xu, Zhenyu & Shen, Yuying & Gao, Guangzong, 2022. "A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning," Energy, Elsevier, vol. 260(C).
    14. Yin, Linfei & Zhao, Lulin, 2021. "Rejectable deep differential dynamic programming for real-time integrated generation dispatch and control of micro-grids," Energy, Elsevier, vol. 225(C).
    15. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    16. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    17. Navid Shirzadi & Hadise Rasoulian & Fuzhan Nasiri & Ursula Eicker, 2022. "Resilience Enhancement of an Urban Microgrid during Off-Grid Mode Operation Using Critical Load Indicators," Energies, MDPI, vol. 15(20), pages 1-15, October.
    18. Yang, Bofan & Zhang, Lin & Zhang, Bo & Xiang, Yang & An, Lei & Wang, Wenfeng, 2022. "Complex equipment system resilience: Composition, measurement and element analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    19. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    20. Omar A. Beg & Asad Ali Khan & Waqas Ur Rehman & Ali Hassan, 2023. "A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids," Energies, MDPI, vol. 16(22), pages 1-23, November.

    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:appene:v:280:y:2020:i:c:s0306261920314586. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.