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Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios

Author

Listed:
  • Hao Dai

    (Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China)

  • Ziyu Liu

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Guowei Liu

    (Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China)

  • Hao Deng

    (Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China)

  • Lisheng Xin

    (Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China)

  • Liang He

    (Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China)

  • Longlong Shang

    (Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China)

  • Dafu Liu

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Jiaju Shi

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Ziwen Xu

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Chen Chen

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

Abstract

Frequent and severe waterlogging caused by climate change poses significant challenges to urban infrastructure systems, particularly transportation networks (TNs) and distribution networks (DNs), necessitating efficient restoration strategies. This study proposes a collaborative scheduling framework for post-disaster restoration in waterlogging scenarios, addressing the impact of waterlogging on both transportation and distribution systems. The method integrates electric vehicles (EVs), mobile power sources (MPSs), and repair crews (RCs) into a unified optimization model, leveraging an improved semi-dynamic traffic assignment (SDTA) model that accounts for temporal variations in road accessibility due to water depth. Simulation results based on the modified IEEE 33-node distribution network and SiouxFalls 35-node transportation network demonstrate the framework’s ability to optimize resource allocation under real-world conditions. Compared to conventional methods, the proposed approach reduces system load loss by more than 30%.

Suggested Citation

  • Hao Dai & Ziyu Liu & Guowei Liu & Hao Deng & Lisheng Xin & Liang He & Longlong Shang & Dafu Liu & Jiaju Shi & Ziwen Xu & Chen Chen, 2025. "Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios," Energies, MDPI, vol. 18(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1708-:d:1623221
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