IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v348y2023ics0306261923008711.html
   My bibliography  Save this article

Multi-stage resilient operation strategy of urban electric–gas system against rainstorms

Author

Listed:
  • Wang, Jingyao
  • Li, Yao
  • Bian, Jiayu
  • Yu, Zhiyong
  • Zhang, Min
  • Wang, Cheng
  • Bi, Tianshu

Abstract

This paper proposes a multi-stage resilient operation strategy for an urban electric–gas system (UEGS) considering the impact of traffic under rainstorms. It guarantees that the defense budget in pre-disaster hardening can be assigned to cope with the worst scenario and that the load supply can be restored as soon as possible. For pre-disaster hardening, a more realistic multi-stage robust model (MSRM) of UEGS under rainstorms is proposed to accommodate the randomness and continuity of rainstorms. After transforming MSRM into the two-stage robust (TSR) form, the nested column-and-constraint generation (C&CG) method is applied for model solving, which is a mixed integer linear program with the min–max–min form. For the unacceptable computing time of the nested C&CG method in solving the equivalent TSR, a simplification is proposed using the sliding time window to provide defense priority for allocating the defense budget in pre-disaster hardening with a reduced calculation burden. In the recovery of UEGS, the traveling salesman problem model of repair crews routing with real-time traversal path information is proposed to meet the modeling demand of multi-period attacks and waterlogging on paths caused by rainstorms. Case studies on the two test systems validate the effectiveness of the proposed method and demonstrate the support of the gas distribution network to the power distribution network in rainstorm attacks.

Suggested Citation

  • Wang, Jingyao & Li, Yao & Bian, Jiayu & Yu, Zhiyong & Zhang, Min & Wang, Cheng & Bi, Tianshu, 2023. "Multi-stage resilient operation strategy of urban electric–gas system against rainstorms," Applied Energy, Elsevier, vol. 348(C).
  • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923008711
    DOI: 10.1016/j.apenergy.2023.121507
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923008711
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121507?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huo, Zhihong & Wang, Bing, 2023. "Distributed resilient multi-event cooperative triggered mechanism based discrete sliding-mode control for wind-integrated power systems under denial of service attacks," Applied Energy, Elsevier, vol. 333(C).
    2. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
    3. Xu, Junjun & Wu, Zaijun & Zhang, Tengfei & Hu, Qinran & Wu, Qiuwei, 2022. "A secure forecasting-aided state estimation framework for power distribution systems against false data injection attacks," Applied Energy, Elsevier, vol. 328(C).
    4. Lin, Wen-Ting & Chen, Guo & Huang, Yuhan, 2022. "Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach," Applied Energy, Elsevier, vol. 314(C).
    5. Artis, Reza & Assili, Mohsen & Shivaie, Mojtaba, 2022. "A seismic-resilient multi-level framework for distribution network reinforcement planning considering renewable-based multi-microgrids," Applied Energy, Elsevier, vol. 325(C).
    6. Qiaoyun SONG & Yan ZHENG & Chenzhen LIN, 2021. "Improving Urban Resilience to Rainstorm Disasters: A Comparative Case Study of Beijing," Chinese Journal of Urban and Environmental Studies (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 1-14, June.
    7. Lin, Yanling & Bie, Zhaohong, 2018. "Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding," Applied Energy, Elsevier, vol. 210(C), pages 1266-1279.
    8. Ge, Pudong & Teng, Fei & Konstantinou, Charalambos & Hu, Shiyan, 2022. "A resilience-oriented centralised-to-decentralised framework for networked microgrids management," Applied Energy, Elsevier, vol. 308(C).
    9. 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.
    10. Dimitris Bertsimas & Dan A. Iancu & Pablo A. Parrilo, 2010. "Optimality of Affine Policies in Multistage Robust Optimization," Mathematics of Operations Research, INFORMS, vol. 35(2), pages 363-394, May.
    11. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan, 2024. "A new shared energy storage business model for data center clusters considering energy storage degradation," Renewable Energy, Elsevier, vol. 225(C).

    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. 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).
    2. Younesi, Abdollah & Shayeghi, Hossein & Wang, Zongjie & Siano, Pierluigi & Mehrizi-Sani, Ali & Safari, Amin, 2022. "Trends in modern power systems resilience: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    3. Wang, Yi & Rousis, Anastasios Oulis & Strbac, Goran, 2020. "On microgrids and resilience: A comprehensive review on modeling and operational strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Tang, Liangyu & Han, Yang & Zalhaf, Amr S. & Zhou, Siyu & Yang, Ping & Wang, Congling & Huang, Tao, 2024. "Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    5. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
    6. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(C).
    7. Xu, Luo & Guo, Qinglai & Sheng, Yujie & Muyeen, S.M. & Sun, Hongbin, 2021. "On the resilience of modern power systems: A comprehensive review from the cyber-physical perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    8. Lei, Shunbo & Pozo, David & Wang, Ming-Hao & Li, Qifeng & Li, Yupeng & Peng, Chaoyi, 2022. "Power economic dispatch against extreme weather conditions: The price of resilience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    9. Zhang, Qianzhi & Wang, Zhaoyu & Ma, Shanshan & Arif, Anmar, 2021. "Stochastic pre-event preparation for enhancing resilience of distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    10. Wang, Zekai & Ding, Tao & Jia, Wenhao & Huang, Can & Mu, Chenggang & Qu, Ming & Shahidehpour, Mohammad & Yang, Yongheng & Blaabjerg, Frede & Li, Li & Wang, Kang & Chi, Fangde, 2022. "Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    11. Kapil Deshpande & Philipp Möhl & Alexander Hämmerle & Georg Weichhart & Helmut Zörrer & Andreas Pichler, 2022. "Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience," Energies, MDPI, vol. 15(19), pages 1-35, October.
    12. Li, Xue & Du, Xiaoxue & Jiang, Tao & Zhang, Rufeng & Chen, Houhe, 2022. "Coordinating multi-energy to improve urban integrated energy system resilience against extreme weather events," Applied Energy, Elsevier, vol. 309(C).
    13. Qu, Zhaoyang & Dong, Yunchang & Li, Yang & Song, Siqi & Jiang, Tao & Li, Min & Wang, Qiming & Wang, Lei & Bo, Xiaoyong & Zang, Jiye & Xu, Qi, 2024. "Localization of dummy data injection attacks in power systems considering incomplete topological information: A spatio-temporal graph wavelet convolutional neural network approach," Applied Energy, Elsevier, vol. 360(C).
    14. Mishra, Dillip Kumar & Ghadi, Mojtaba Jabbari & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2021. "A review on resilience studies in active distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    15. Wang, Zhaoqi & Zhang, Lu & Tang, Wei & Chen, Ying & Shen, Chen, 2022. "Equilibrium allocation strategy of multiple ESSs considering the economics and restoration capability in DNs," Applied Energy, Elsevier, vol. 306(PA).
    16. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
    17. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    18. Hussain Abdalla Sajwani & Bassel Soudan & Abdul Ghani Olabi, 2024. "Empowering Sustainability: Understanding Determinants of Consumer Investment in Microgrid Technology in the UAE," Energies, MDPI, vol. 17(9), pages 1-28, May.
    19. Deng Xu & Yong Long, 2019. "The Impact of Government Subsidy on Renewable Microgrid Investment Considering Double Externalities," Sustainability, MDPI, vol. 11(11), pages 1-15, June.
    20. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski & Ibrahim Alhamrouni, 2024. "AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review," Sustainability, MDPI, vol. 16(12), pages 1-35, June.

    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:eee:appene:v:348:y:2023:i:c:s0306261923008711. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.