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Protecting the future grid: An electric vehicle robust mitigation scheme against load altering attacks on power grids

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  • Sayed, Mohammad Ali
  • Ghafouri, Mohsen
  • Atallah, Ribal
  • Debbabi, Mourad
  • Assi, Chadi

Abstract

Due to the growing threat of climate change, the world's governments have been encouraging the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially which will introduce a large EV charging load into the power grid. On this basis, we present a scheme to utilize EVs as a defense mechanism to mitigate Load-Altering (LA) attacks against the grid. The developed scheme relies on robust control theory and Linear Matrix Inequalities (LMIs). Our EV-based defense mechanism is formulated as a feedback controller synthesized using H-2 and H-∞ control techniques to eliminate the impact of unknown LA attacks. The controller synthesis considers the grid topology and the uncertainties of the EV connection to the grid. To demonstrate the effectiveness of the proposed mitigation scheme, it is tested against three types of LA attacks on the New England 39-bus grid. We test our mitigation scheme against 800 MW static, switching, and dynamic attacks in the presence of multiple sources of uncertainty that can affect the EV load during deployment. The results demonstrate how the grid remains stable under the LA attacks that would otherwise lead to serious instabilities.

Suggested Citation

  • Sayed, Mohammad Ali & Ghafouri, Mohsen & Atallah, Ribal & Debbabi, Mourad & Assi, Chadi, 2023. "Protecting the future grid: An electric vehicle robust mitigation scheme against load altering attacks on power grids," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011339
    DOI: 10.1016/j.apenergy.2023.121769
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    References listed on IDEAS

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    1. Majidi, Seyed Hossein & Hadayeghparast, Shahrzad & Karimipour, Hadis, 2022. "FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid," International Journal of Critical Infrastructure Protection, Elsevier, vol. 37(C).
    2. Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
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    Cited by:

    1. Oluwatoyin J. Gbadeyan & Joseph Muthivhi & Linda Z. Linganiso & Nirmala Deenadayalu, 2024. "Decoupling Economic Growth from Carbon Emissions: A Transition toward Low-Carbon Energy Systems—A Critical Review," Clean Technol., MDPI, vol. 6(3), pages 1-38, August.

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