IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i19p8691-d1759348.html
   My bibliography  Save this article

A Multi-Stage Resilience Enhancement Method for Distribution Networks Employing Transportation and Hydrogen Energy Systems

Author

Listed:
  • Xi Chen

    (Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384, China)

  • Jiancun Liu

    (Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384, China)

  • Pengfei Li

    (Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384, China)

  • Junzhi Ren

    (State Key Laboratory of Intelligent Power Distribution Equipment and System, Tianjin University, Tianjin 300072, China)

  • Delong Zhang

    (Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384, China)

  • Xuesong Zhou

    (Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Tianjin 300384, China)

Abstract

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events.

Suggested Citation

  • Xi Chen & Jiancun Liu & Pengfei Li & Junzhi Ren & Delong Zhang & Xuesong Zhou, 2025. "A Multi-Stage Resilience Enhancement Method for Distribution Networks Employing Transportation and Hydrogen Energy Systems," Sustainability, MDPI, vol. 17(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8691-:d:1759348
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/19/8691/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/19/8691/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. He, Xin & Zhang, BaoLai & Jia, HaoYang & Xin, Kaixuan & Wang, Youpeng & Yu, Cheng & Zhang, BenXi, 2025. "Regret-aware optimization of hydrogen-assisted congestion control in a renewable-dominated reconfigurable distribution network," Renewable Energy, Elsevier, vol. 250(C).
    2. 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.
    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. Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    2. Xiaoge Zhang & Sankaran Mahadevan & Kai Goebel, 2019. "Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 2054-2075, September.
    3. Habiba Drias & Lydia Sonia Bendimerad & Yassine Drias, 2022. "A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global Scientific Publishing, vol. 13(1), pages 1-20, January.
    4. 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).
    5. 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).
    6. Xiao, Yunpeng & Zhu, Yuerong & Qu, Ying & Xie, Haipeng & Wang, Xiuli & Wang, Xifan, 2025. "A market for power system resilience provision," Applied Energy, Elsevier, vol. 382(C).
    7. Junyu Liang & Jun Zhou & Xingyu Yuan & Wei Huang & Xinyong Gong & Guipeng Zhang, 2024. "An Active Distribution Network Voltage Optimization Method Based on Source-Network-Load-Storage Coordination and Interaction," Energies, MDPI, vol. 17(18), pages 1-18, September.
    8. Hou, Hui & Tang, Junyi & Zhang, Zhiwei & Wang, Zhuo & Wei, Ruizeng & Wang, Lei & He, Huan & Wu, Xixiu, 2023. "Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming," Applied Energy, Elsevier, vol. 338(C).
    9. Liu, Jia & Cheng, Haozhong & Zeng, Pingliang & Yao, Liangzhong & Shang, Ce & Tian, Yuan, 2018. "Decentralized stochastic optimization based planning of integrated transmission and distribution networks with distributed generation penetration," Applied Energy, Elsevier, vol. 220(C), pages 800-813.
    10. 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).
    11. Moon, Sangkeun, 2025. "Adaptive distribution topology learning on distributed source energisation and islanding," Energy, Elsevier, vol. 320(C).
    12. Gilani, Mohammad Amin & Kazemi, Ahad & Ghasemi, Mostafa, 2020. "Distribution system resilience enhancement by microgrid formation considering distributed energy resources," Energy, Elsevier, vol. 191(C).
    13. Shang, Ce & Lin, Teng & Li, Canbing & Wang, Keyou & Ai, Qian, 2021. "Joining resilience and reliability evaluation against both weather and ageing causes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    14. Chen, Jie & Zhang, Hongwei & Tang, Zao & Cao, Yijia, 2025. "An enhanced MES strategy for economic recovery and low-carbon operations: Incorporating time-lag superposition and hydrogen-electricity interdependency in chain accidents," Energy, Elsevier, vol. 330(C).
    15. Weicheng Zhou & Ping Zhao & Yifei Lu, 2023. "Collaborative Optimal Configuration of a Mobile Energy Storage System and a Stationary Energy Storage System to Cope with Regional Grid Blackouts in Extreme Scenarios," Energies, MDPI, vol. 16(23), pages 1-17, December.
    16. Wang, Han & Hou, Kai & Zhao, Junbo & Yu, Xiaodan & Jia, Hongjie & Mu, Yunfei, 2022. "Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters," Applied Energy, Elsevier, vol. 315(C).
    17. Matelli, José Alexandre & Goebel, Kai, 2018. "Conceptual design of cogeneration plants under a resilient design perspective: Resilience metrics and case study," Applied Energy, Elsevier, vol. 215(C), pages 736-750.
    18. Omid Sadeghian & Behnam Mohammadi-Ivatloo & Fazel Mohammadi & Zulkurnain Abdul-Malek, 2022. "Protecting Power Transmission Systems against Intelligent Physical Attacks: A Critical Systematic Review," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    19. Zhong, Haiwang & Zhang, Guanglun & Tan, Zhenfei & Ruan, Guangchun & Wang, Xuan, 2022. "Hierarchical collaborative expansion planning for transmission and distribution networks considering transmission cost allocation," Applied Energy, Elsevier, vol. 307(C).
    20. Peng Jiang & Shengjun Huang & Tao Zhang, 2020. "Asymmetric Information in Military Microgrid Confrontations—Evaluation Metric and Influence Analysis," Energies, MDPI, vol. 13(8), pages 1-21, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:17:y:2025:i:19:p:8691-:d:1759348. 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.