Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/7/1708 |
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| author | Hao Dai Ziyu Liu Guowei Liu Hao Deng Lisheng Xin Liang He Longlong Shang Dafu Liu Jiaju Shi Ziwen Xu Chen Chen |
| author_facet | Hao Dai Ziyu Liu Guowei Liu Hao Deng Lisheng Xin Liang He Longlong Shang Dafu Liu Jiaju Shi Ziwen Xu Chen Chen |
| author_sort | Hao Dai |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-bfc410cdf67c458399c6a97197d733bb |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-bfc410cdf67c458399c6a97197d733bb2025-08-20T03:08:52ZengMDPI AGEnergies1996-10732025-03-01187170810.3390/en18071708Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging ScenariosHao Dai0Ziyu Liu1Guowei Liu2Hao Deng3Lisheng Xin4Liang He5Longlong Shang6Dafu Liu7Jiaju Shi8Ziwen Xu9Chen Chen10Shenzhen Power Supply Co., Ltd., Shenzhen 518000, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, ChinaShenzhen Power Supply Co., Ltd., Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., Shenzhen 518000, ChinaShenzhen Power Supply Co., Ltd., Shenzhen 518000, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710000, ChinaFrequent 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%.https://www.mdpi.com/1996-1073/18/7/1708waterlogging disasterdistribution network restorationelectric vehicletransportation networkpower system resilience |
| spellingShingle | Hao Dai Ziyu Liu Guowei Liu Hao Deng Lisheng Xin Liang He Longlong Shang Dafu Liu Jiaju Shi Ziwen Xu Chen Chen Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios Energies waterlogging disaster distribution network restoration electric vehicle transportation network power system resilience |
| title | Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios |
| title_full | Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios |
| title_fullStr | Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios |
| title_full_unstemmed | Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios |
| title_short | Collaborative Scheduling Framework for Post-Disaster Restoration: Integrating Electric Vehicles and Traffic Dynamics in Waterlogging Scenarios |
| title_sort | collaborative scheduling framework for post disaster restoration integrating electric vehicles and traffic dynamics in waterlogging scenarios |
| topic | waterlogging disaster distribution network restoration electric vehicle transportation network power system resilience |
| url | https://www.mdpi.com/1996-1073/18/7/1708 |
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