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Grid Chaos: An uncertainty-conscious robust dynamic EV load-altering attack strategy on power grid stability

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

Abstract

Electric Vehicles (EVs) and their charging infrastructure have witnessed rapid adoption into the smart grid owing to their ability to reduce the transportation sector's emissions. However, this hasty deployment has left the grid prone to attacks initiated through the charging ecosystem. This paper develops a family of robust dynamic load-altering attacks that manipulate the EV load to destabilize the power grid. The attack strategy is formulated based on feedback control theory and Linear Matrix Inequalities (LMIs) and incorporates mathematical modeling of the power grid uncertainties. The effectiveness of our proposed robust EV attack strategy is demonstrated through extensive simulations of attack scenarios against the New England (NE) 39-bus grid. The results demonstrate that the proposed attacks were able to destabilize the grid frequency inducing a deviation of 1.5 Hz and forcing the generators to trip after compromising as little as 6.6% of the total grid load. We also present a two-tier Deep Learning (DL) attack detection mechanism. We optimize this mechanism to achieve optimal results against dynamic and switching attacks. The proposed mechanism was evaluated against several other models and proved to be superior with an accuracy of 99.9%.

Suggested Citation

  • Sayed, Mohammad Ali & Ghafouri, Mohsen & Atallah, Ribal & Debbabi, Mourad & Assi, Chadi, 2024. "Grid Chaos: An uncertainty-conscious robust dynamic EV load-altering attack strategy on power grid stability," Applied Energy, Elsevier, vol. 363(C).
    • Handle: RePEc:eee:appene:v:363:y:2024:i:c:s0306261924003556
    DOI: 10.1016/j.apenergy.2024.122972
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    References listed on IDEAS

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    1. Jenn, Alan, 2023. "Emissions of electric vehicles in California’s transition to carbon neutrality," Applied Energy, Elsevier, vol. 339(C).
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    1. Li, Yi & Chen, Guo & Dong, Zhaoyang, 2025. "Multi-view graph contrastive representative learning for intrusion detection in EV charging station," Applied Energy, Elsevier, vol. 385(C).

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