Research on Mobile Energy Storage Planning for Toughness Improvement of Highway Energy System

With the frequency of extreme weather events, improving the toughness of highway energy system is critical to ensuring road safety and responding effectively to emergencies. Existing power systems often face severe energy supply interruptions and slow restoration during disasters, which not only affects the normal operation of roads, but also may lead to delayed emergency response and increase the difficulty of rescue. Therefore, this paper proposes a pre-disaster mobile energy storage configuration method, which uses the pre-disaster layout of mobile energy storage to quickly respond and provide emergency power supply, and to improve the resilience of the highway energy system. The specific work is as follows: First, a pre-disaster deterministic mobile energy storage allocation model is constructed with the objective function of minimizing the mobile energy storage dispatch cost and load loss cost; second, a two-stage robust optimization allocation model is proposed considering the uncertainty of distributed generation. Then, the model is decoupled using the column constraint generation algorithm, and the optimal configuration scheme is obtained by iterative solving. Finally, the feasibility and effectiveness of the pre-disaster mobile energy storage configuration method are verified by taking the actual highway scenario as an example, and the results show that the method can respond quickly and provide emergency power supply in the event of a disaster, which significantly improves the recovery capability of the highway energy system.

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Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1109/icpes63746.2024.10856670
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Mobile Energy Storage Planning for Toughness Improvement of Highway Energy System

Ruibao Jia, Tongyuan Zhao, Pengbin Gao, Ming Zhang et al.
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Advanced Battery Technologies Research
article

Research on Mobile Energy Storage Planning for Toughness Improvement of Highway Energy System

Ruibao Jia, Tongyuan Zhao, Pengbin Gao, Ming Zhang, Yange Zhang
article en

Abstract

With the frequency of extreme weather events, improving the toughness of highway energy system is critical to ensuring road safety and responding effectively to emergencies. Existing power systems often face severe energy supply interruptions and slow restoration during disasters, which not only affects the normal operation of roads, but also may lead to delayed emergency response and increase the difficulty of rescue. Therefore, this paper proposes a pre-disaster mobile energy storage configuration method, which uses the pre-disaster layout of mobile energy storage to quickly respond and provide emergency power supply, and to improve the resilience of the highway energy system. The specific work is as follows: First, a pre-disaster deterministic mobile energy storage allocation model is constructed with the objective function of minimizing the mobile energy storage dispatch cost and load loss cost; second, a two-stage robust optimization allocation model is proposed considering the uncertainty of distributed generation. Then, the model is decoupled using the column constraint generation algorithm, and the optimal configuration scheme is obtained by iterative solving. Finally, the feasibility and effectiveness of the pre-disaster mobile energy storage configuration method are verified by taking the actual highway scenario as an example, and the results show that the method can respond quickly and provide emergency power supply in the event of a disaster, which significantly improves the recovery capability of the highway energy system.

Southwest Jiaotong University (CN)
National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 71%
Advanced Battery Technologies Research
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Research on Mobile Energy Storage Planning for Toughness Improvement of Highway Energy System — Ruibao Jia, Tongyuan Zhao, et al. · (2024) | TGRS Research Map | TGRS