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EV-based Load-altering Attacks and their Impacts on the Stability of Power Grids

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EV-based Load-altering Attacks and their Impacts on the Stability of Power Grids

Abazari, Ahmadreza (2024) EV-based Load-altering Attacks and their Impacts on the Stability of Power Grids. PhD thesis, Concordia University.

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Abstract

The extensive use of electric vehicles (EVs) provides energy-critical infrastructures with some advantages and drawbacks at the same time. The large-scale deployment of EVs can improve the reliability and efficiency of the power grid through, for instance, bidirectional energy transfers between grids and EVs, reduction in electricity bills, and ancillary services. The majority of these advantages are enabled by the use of communication and information technologies (ICTs) in the EV infrastructures and their associated smart power grids. Moreover, EV supplies equipment (EVSE) network, e.g., charging stations, including a variety of Internet of Things (IoT) devices and smartphone applications that facilitate the charging process for users. However, such a broad deployment of cyber devices and information technologies makes the EV ecosystem prone to cyber-attacks in the form of data manipulation, malware, and intrusions. The attacks against public and private EV charging stations, which are often designed without security concerns in mind, are threats against owners and can lead to complicated security issues for smart grids. Additionally, compromising the security of these large-scale EV infrastructures can propagate into the wide-area transmission power grid, cause resonance events, and result in instability and even blackouts. Studying potentially vulnerable points in the EV ecosystems that adversaries can exploit to impact the stability of power grids, and suggesting proper detection and mitigation strategies is of paramount importance. Finally, designing security metrics for distribution and transmission systems can assist power grid utilities in informing about the power grid security status in the presence of attacks originating from EV ecosystems.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (PhD)
Authors:Abazari, Ahmadreza
Institution:Concordia University
Degree Name:Ph. D.
Program:Information and Systems Engineering
Date:4 November 2024
Thesis Supervisor(s):Ghafouri, Mohsen and Assi, Chadi
ID Code:994974
Deposited By: Ahmadreza Abazari
Deposited On:17 Jun 2025 13:55
Last Modified:17 Jun 2025 13:55
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