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Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

Author

Listed:
  • Zhaojun Hao

    (Energy Department, Politecnico di Milano, 20156 Milan, Italy)

  • Francesco Di Maio

    (Energy Department, Politecnico di Milano, 20156 Milan, Italy)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, 20156 Milan, Italy
    Centre de Recherche sur les Risques et les Crises (CRC), MINES ParisTech/PSL Université Paris, 75272 Sophia Antipolis, France
    Department of Nuclear Engineering, Kyung Hee University, Seoul 17104, Korea)

Abstract

Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply.

Suggested Citation

  • Zhaojun Hao & Francesco Di Maio & Enrico Zio, 2021. "Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components," Energies, MDPI, vol. 14(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3241-:d:567278
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    References listed on IDEAS

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    1. Pierobon, Leonardo & Casati, Emiliano & Casella, Francesco & Haglind, Fredrik & Colonna, Piero, 2014. "Design methodology for flexible energy conversion systems accounting for dynamic performance," Energy, Elsevier, vol. 68(C), pages 667-679.
    2. Wei Wang & Francesco Di Maio & Enrico Zio, 2019. "Adversarial Risk Analysis to Allocate Optimal Defense Resources for Protecting Cyber–Physical Systems from Cyber Attacks," Risk Analysis, John Wiley & Sons, vol. 39(12), pages 2766-2785, December.
    3. Koutras, Vasilis P. & Platis, Agapios N. & Gravvanis, George A., 2007. "On the optimization of free resources using non-homogeneous Markov chain software rejuvenation model," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1724-1732.
    4. Wang, Wei & Maio, Francesco Di & Zio, Enrico, 2017. "Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 276-289.
    5. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
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    Cited by:

    1. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
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