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Assessing the Impact of Cybersecurity Attacks on Power Systems

Author

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  • Athanasios Dagoumas

    (Energy & Environmental Policy Laboratory, School of Economics, Business and International Studies, University of Piraeus, PC 18532 Piraeus, Greece)

Abstract

Cybersecurity is an emerging challenge for power systems, as it strongly affects their reliability and the whole energy system cost. The paper uses several Unit Commitments (UC) models, applying different methods to tackle renewables’ uncertainty. The selected power system is IEEE RTS 96. The UC models are used to assess the impact of different cybersecurity threats. The focus is to assess their impact on the total operating cost and the power grid adequacy to handle them. The comparison between the UC models shows that more robust UC models lead to higher total operating costs. The cost, unit dispatching, and energy mix evolution have a non-linear trend, depending on the power system characteristics and the cyberattacks types. However, the paper provides evidence of considerable price signals in the case of the examined cyberattacks. Each Transmission System Operator (TSO) should examine combinations of cyberattacks and operating conditions to identify crucial cases for system stability and power system cost operation. The applied methodology would also require substantial developments or supplementary approaches to assess cyberthreats at the distribution level.

Suggested Citation

  • Athanasios Dagoumas, 2019. "Assessing the Impact of Cybersecurity Attacks on Power Systems," Energies, MDPI, vol. 12(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:725-:d:208188
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    References listed on IDEAS

    as
    1. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    2. Xiaming Ye & Junhua Zhao & Yan Zhang & Fushuan Wen, 2015. "Quantitative Vulnerability Assessment of Cyber Security for Distribution Automation Systems," Energies, MDPI, vol. 8(6), pages 1-21, June.
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    6. Dagoumas, Athanasios S. & Koltsaklis, Nikolasos E. & Panapakidis, Ioannis P., 2017. "An integrated model for risk management in electricity trade," Energy, Elsevier, vol. 124(C), pages 350-363.
    7. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Panapakidis, Ioannis P., 2017. "Impact of the penetration of renewables on flexibility needs," Energy Policy, Elsevier, vol. 109(C), pages 360-369.
    8. Sampath Kumar Venkatachary & Jagdish Prasad & Ravi Samikannu, 2017. "Economic Impacts of Cyber Security in Energy Sector: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 7(5), pages 250-262.
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    Cited by:

    1. Berghout, Tarek & Benbouzid, Mohamed & Muyeen, S.M., 2022. "Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    2. Fei Zhao & Jinsha Yuan & Ning Wang & Zhang Zhang & Helong Wen, 2019. "Secure Load Frequency Control of Smart Grids under Deception Attack: A Piecewise Delay Approach," Energies, MDPI, vol. 12(12), pages 1-15, June.
    3. Athira M. Mohan & Nader Meskin & Hasan Mehrjerdi, 2020. "A Comprehensive Review of the Cyber-Attacks and Cyber-Security on Load Frequency Control of Power Systems," Energies, MDPI, vol. 13(15), pages 1-33, July.
    4. Martin Onyeka Okoye & Junyou Yang & Zhenjiang Lei & Jingwei Yuan & Huichao Ji & Haixin Wang & Jiawei Feng & Tunmise Ayode Otitoju & Weidong Li, 2020. "Predictive Reliability Assessment of Generation System," Energies, MDPI, vol. 13(17), pages 1-13, August.

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