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Modeling and Mitigating Billing Attacks in Scalable Smart Grids with Distributed and Intelligent Systems

Author

Listed:
  • Abdelfattah Abassi

    (Mouly Ismail University)

  • Mostapha El Jai

    (Euromed University of Fes (UEMF)
    ENSAM-Meknes, Mouly Ismail University)

  • Ahmed Arid

    (Mouly Ismail University)

  • Hussain Benazza

    (Mouly Ismail University
    Moulay Ismail University)

Abstract

With the expansion of smart grids, maintenance and control costs have grown significantly. For this reason, some innovative strategies have been implemented to strengthen the control within the grid. Among these strategies, the implementation of distributed models coupled to intelligent billing systems is of high interest; it aims at achieving optimality in the system while maintaining billing fairness. Despite the advantages offered by smart grid’s paradigm, it still shows weaknesses in terms of theft and users’ fraudulence behavior detections. That is to say, the present paper focusses on three types of attacks, mainly existing in the literature, that malicious users can exploit to minimize their personal bills along with the development of related technique to troubleshot and solve such issues. Simulations and mathematical models are proposed to model these attacks, along with a mitigation strategy that has been developed.

Suggested Citation

  • Abdelfattah Abassi & Mostapha El Jai & Ahmed Arid & Hussain Benazza, 2025. "Modeling and Mitigating Billing Attacks in Scalable Smart Grids with Distributed and Intelligent Systems," SN Operations Research Forum, Springer, vol. 6(1), pages 1-34, March.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-025-00414-3
    DOI: 10.1007/s43069-025-00414-3
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    References listed on IDEAS

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    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Gustavo Pacheco Epifanio & José Federico Vizcaíno González & Fábio Luiz Usberti & Luís Tarrataca & Laura Silva Assis, 2023. "Switch Allocation Problem in Power Distribution Systems with Distributed Generation," SN Operations Research Forum, Springer, vol. 4(3), pages 1-24, September.
    3. Korhonen, Pekka & Moskowitz, Herbert & Wallenius, Jyrki, 1992. "Multiple criteria decision support - A review," European Journal of Operational Research, Elsevier, vol. 63(3), pages 361-375, December.
    4. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
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