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10.3390/pr13103075
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Article

Service Restoration Strategy for Distribution Networks Considering Multi-Source Collaboration and Incomplete Fault Information

by
Xunting Wang
1,
Cheng Xie
2,
Lingzhi Xia
1,
Jianlin Li
2,
Han Wang
3,* and
Lei Sun
3
1
Electric Power Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei 230601, China
2
State Grid Anhui Electric Power Co., Ltd., Hefei 230041, China
3
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(10), 3075; https://doi.org/10.3390/pr13103075
Submission received: 26 August 2025 / Revised: 22 September 2025 / Accepted: 22 September 2025 / Published: 25 September 2025
(This article belongs to the Special Issue Modeling, Optimization, and Control of Distributed Energy Systems)
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Abstract

To address the severe damage and outage risks to distribution networks caused by extreme weather, this paper proposes a coordinated optimization strategy for distribution network repair sequencing and rapid restoration, which considers multi-source collaboration and incomplete fault information. In response to the challenge of incomplete fault information after a disaster, a two-layer robust optimization model is constructed. The upper-layer model aims to minimize the completion time of repairs for all faults under the most unfavorable fault scenario to obtain a robust repair time for potential faulty lines, providing a reliable basis for the restoration decisions of the lower-layer model. The lower-layer model’s objective is to maximize the weighted restored load quantity by comprehensively coordinating mobile diesel generators (MDGs), distributed generators (DGs), photovoltaics (PVs), wind turbines (WTs), and energy storage systems (ESSs) to achieve the optimal restoration strategy. The proposed service restoration strategy is validated through simulation on a modified IEEE 33-bus power system, and the results demonstrate that the strategy can efficiently and comprehensively utilize multi-source collaborative resources and improve the resilience of the distribution network.
Keywords: distribution network; service restoration; distributionally robust optimization; incomplete fault information; multi-source collaboration distribution network; service restoration; distributionally robust optimization; incomplete fault information; multi-source collaboration

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MDPI and ACS Style

Wang, X.; Xie, C.; Xia, L.; Li, J.; Wang, H.; Sun, L. Service Restoration Strategy for Distribution Networks Considering Multi-Source Collaboration and Incomplete Fault Information. Processes 2025, 13, 3075. https://doi.org/10.3390/pr13103075

AMA Style

Wang X, Xie C, Xia L, Li J, Wang H, Sun L. Service Restoration Strategy for Distribution Networks Considering Multi-Source Collaboration and Incomplete Fault Information. Processes. 2025; 13(10):3075. https://doi.org/10.3390/pr13103075

Chicago/Turabian Style

Wang, Xunting, Cheng Xie, Lingzhi Xia, Jianlin Li, Han Wang, and Lei Sun. 2025. "Service Restoration Strategy for Distribution Networks Considering Multi-Source Collaboration and Incomplete Fault Information" Processes 13, no. 10: 3075. https://doi.org/10.3390/pr13103075

APA Style

Wang, X., Xie, C., Xia, L., Li, J., Wang, H., & Sun, L. (2025). Service Restoration Strategy for Distribution Networks Considering Multi-Source Collaboration and Incomplete Fault Information. Processes, 13(10), 3075. https://doi.org/10.3390/pr13103075

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