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

Hao Dai, Ziyu Liu, Guowei Liu, Hao Deng, Lisheng Xin, Liang He, Longlong Shang, Dafu Liu, Jiaju Shi, Ziwen Xu and Chen Chen ()
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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

Energies, 2025, vol. 18, issue 7, 1-21

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%.

Keywords: waterlogging disaster; distribution network restoration; electric vehicle; transportation network; power system resilience (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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