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Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods

Author

Listed:
  • Daogui Tang

    (LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay, WHUT - Wuhan University of Technology)

  • Yi-Ping Fang

    (LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay, LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay)

  • Enrico Zio

    (CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres)

Abstract

The two-way information exchange between customers and the utility in smart grids enables demand-response programs of customers and the integration of distributed renewable energy resources. However, this also makes the demand-response programs vulnerable to cyber attacks. In this paper, we study cyber attacks that target customers' demand-response programs in smart grids by injecting false consumption and generation information. Then, as a countermeasure, an online detector based on convolutional neural networks is designed to detect the cyber attacks and mitigate impacts. The vulnerability of power distribution systems with and without the proposed detector is analyzed with reference to a case study concerning the IEEE 34 bus test feeder. The results show that the power distribution systems is vulnerable to the studied cyber attack and the proposed detector can achieve high accuracy and mitigate the impact of cyber attacks with fixed change rates, whereas the attacks with variable change rates are inherently challenging to detect.

Suggested Citation

  • Daogui Tang & Yi-Ping Fang & Enrico Zio, 2023. "Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods," Post-Print hal-04103525, HAL.
    • Handle: RePEc:hal:journl:hal-04103525
    DOI: 10.1016/j.ress.2023.109212
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    Cited by:

    1. Fouzi Harrou & Benamar Bouyeddou & Abdelkader Dairi & Ying Sun, 2024. "Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids," Future Internet, MDPI, vol. 16(6), pages 1-19, May.
    2. 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).

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