IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i15p3619-d1441288.html
   My bibliography  Save this article

Optimal Resilience and Risk-Driven Strategies for Pre-Disaster Protection of Electric Power Systems against Uncertain Disaster Scenarios

Author

Listed:
  • Chen Wang

    (State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China)

  • Chao Zhang

    (Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Ling Luo

    (State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China)

  • Xiaoman Qi

    (State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China)

  • Jingjing Kong

    (School of Civil Engineering, Shanghai Normal University, Shanghai 201418, China)

Abstract

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters.

Suggested Citation

  • Chen Wang & Chao Zhang & Ling Luo & Xiaoman Qi & Jingjing Kong, 2024. "Optimal Resilience and Risk-Driven Strategies for Pre-Disaster Protection of Electric Power Systems against Uncertain Disaster Scenarios," Energies, MDPI, vol. 17(15), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3619-:d:1441288
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/15/3619/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/15/3619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan, Wei & Zhao, Long & Zeng, Bo, 2014. "Optimal power grid protection through a defender–attacker–defender model," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 83-89.
    2. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    3. Gary D. Eppen & R. Kipp Martin & Linus Schrage, 1989. "OR Practice—A Scenario Approach to Capacity Planning," Operations Research, INFORMS, vol. 37(4), pages 517-527, August.
    4. Liu, Xing & Fang, Yi-Ping & Zio, Enrico, 2021. "A Hierarchical Resilience Enhancement Framework for Interdependent Critical Infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Ouyang, Min & Xu, Min & Zhang, Chi & Huang, Shitong, 2017. "Mitigating electric power system vulnerability to worst-case spatially localized attacks," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 144-154.
    6. Hussain, Akhtar & Bui, Van-Hai & Kim, Hak-Man, 2019. "Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience," Applied Energy, Elsevier, vol. 240(C), pages 56-72.
    7. Fang, Yi-Ping & Zio, Enrico, 2019. "An adaptive robust framework for the optimization of the resilience of interdependent infrastructures under natural hazards," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1119-1136.
    8. Jingjing Kong & Slobodan P. Simonovic & Chao Zhang, 2019. "Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1141-1168, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Jingjing Kong & Slobodan P. Simonovic & Chao Zhang, 2019. "Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies," Sustainability, MDPI, vol. 11(23), pages 1-31, November.
    3. Bellè, Andrea & Abdin, Adam F. & Fang, Yi-Ping & Zeng, Zhiguo & Barros, Anne, 2023. "A resilience-based framework for the optimal coupling of interdependent critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Umunnakwe, A. & Huang, H. & Oikonomou, K. & Davis, K.R., 2021. "Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Bellè, Andrea & Abdin, Adam F. & Fang, Yi-Ping & Zeng, Zhiguo & Barros, Anne, 2023. "A data-driven distributionally robust approach for the optimal coupling of interdependent critical infrastructures under random failures," European Journal of Operational Research, Elsevier, vol. 309(2), pages 872-889.
    6. Hasanzad, Fardin & Rastegar, Hasan, 2022. "Application of optimal hardening for improving resilience of integrated power and natural gas system in case of earthquake," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & Small, Michael & Li, Man, 2023. "Improving resilience of high-speed train by optimizing repair strategies," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    11. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    12. Ouyang, Min & Liu, Chuang & Xu, Min, 2019. "Value of resilience-based solutions on critical infrastructure protection: Comparing with robustness-based solutions," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    13. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    14. Ilalokhoin, Ohis & Pant, Raghav & Hall, Jim W., 2023. "A model and methodology for resilience assessment of interdependent rail networks – Case study of Great Britain's rail network," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    15. Dui, Hongyan & Liu, Meng & Song, Jiaying & Wu, Shaomin, 2023. "Importance measure-based resilience management: Review, methodology and perspectives on maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    16. Wang, Feng & Tian, Jin & Shi, Chenli & Ling, Jiamu & Chen, Zian & Xu, Zhengguo, 2024. "A multi-stage quantitative resilience analysis and optimization framework considering dynamic decisions for urban infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    17. Su, Wenjing & Blumsack, Seth & Webster, Mort, 2024. "A stochastic optimization framework to planing for geographically correlated failures in coupled natural gas and electric power systems," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    18. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    19. Youba Nait Belaid & Patrick Coudray & José Sanchez-Torres & Yi-Ping Fang & Zhiguo Zeng & Anne Barros, 2021. "Resilience Quantification of Smart Distribution Networks—A Bird’s Eye View Perspective," Energies, MDPI, vol. 14(10), pages 1-29, May.
    20. Xu, Min & Li, Guoyuan & Chen, Anthony, 2024. "Resilience-driven post-disaster restoration of interdependent infrastructure systems under different decision-making environments," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3619-:d:1441288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.