Adaptive data driven multi period power supply recovery method for distribution networks

Abstract In the process of distribution network fault recovery, in order to better address the issues caused by the uncertainty of new energy power output, this paper proposes a multi period power supply recovery method for distribution networks based on adaptive data driven approach. Firstly, this...

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Main Authors: Xi Ye, Meng Yang, Zhihong Yang, Qing Xiang, Yazhuo Li
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-02853-8
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author Xi Ye
Meng Yang
Zhihong Yang
Qing Xiang
Yazhuo Li
author_facet Xi Ye
Meng Yang
Zhihong Yang
Qing Xiang
Yazhuo Li
author_sort Xi Ye
collection DOAJ
description Abstract In the process of distribution network fault recovery, in order to better address the issues caused by the uncertainty of new energy power output, this paper proposes a multi period power supply recovery method for distribution networks based on adaptive data driven approach. Firstly, this method uses the historical data of new energy power output to construct an ellipsoidal uncertainty set, and forms a data driven convex hull polyhedral set by connecting the vertices of the high-dimensional ellipsoid. Then, aiming at the problem of large conservatism during the reduction process of the convex hull polyhedral set, based on the range of the box set, cutting planes are made starting from the vertices, and a data driven hyperplane polyhedral set model is constructed. Furthermore, considering the constraints of cyber-physical integration, an adaptive data driven power supply recovery model for distribution networks is established, and the column and constraint generation (C&CG) algorithm is adopted to solve the robust scheduling model. Finally, simulations on the improved IEEE-33 bus system and actual example systems verify that the adaptive data driven power supply recovery model for distribution networks can reduce conservatism and improve the robustness of the optimization results. This power supply recovery model can reduce conservatism and enhance the robustness of the optimization results. In the actual distribution network fault recovery scenarios, it can make more efficient use of new energy and ensure the stability of power supply, which strongly demonstrates the effectiveness and application value of the proposed method.
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spelling doaj-art-ab0af499d55e46c3bf39afd537fe53fb2025-08-20T02:03:31ZengNature PortfolioScientific Reports2045-23222025-05-0115112310.1038/s41598-025-02853-8Adaptive data driven multi period power supply recovery method for distribution networksXi Ye0Meng Yang1Zhihong Yang2Qing Xiang3Yazhuo Li4Institute of Intelligent Manufacturing, Jianghan UniversityEconomics and Technology Research Institute, State Grid Hubei Electric Power CompanyInstitute of Intelligent Manufacturing, Jianghan UniversityInstitute of Intelligent Manufacturing, Jianghan UniversityInstitute of Intelligent Manufacturing, Jianghan UniversityAbstract In the process of distribution network fault recovery, in order to better address the issues caused by the uncertainty of new energy power output, this paper proposes a multi period power supply recovery method for distribution networks based on adaptive data driven approach. Firstly, this method uses the historical data of new energy power output to construct an ellipsoidal uncertainty set, and forms a data driven convex hull polyhedral set by connecting the vertices of the high-dimensional ellipsoid. Then, aiming at the problem of large conservatism during the reduction process of the convex hull polyhedral set, based on the range of the box set, cutting planes are made starting from the vertices, and a data driven hyperplane polyhedral set model is constructed. Furthermore, considering the constraints of cyber-physical integration, an adaptive data driven power supply recovery model for distribution networks is established, and the column and constraint generation (C&CG) algorithm is adopted to solve the robust scheduling model. Finally, simulations on the improved IEEE-33 bus system and actual example systems verify that the adaptive data driven power supply recovery model for distribution networks can reduce conservatism and improve the robustness of the optimization results. This power supply recovery model can reduce conservatism and enhance the robustness of the optimization results. In the actual distribution network fault recovery scenarios, it can make more efficient use of new energy and ensure the stability of power supply, which strongly demonstrates the effectiveness and application value of the proposed method.https://doi.org/10.1038/s41598-025-02853-8Two-stage robust optimizationConvex hull polyhedron setHyperplane polyhedron setPower supply recovery
spellingShingle Xi Ye
Meng Yang
Zhihong Yang
Qing Xiang
Yazhuo Li
Adaptive data driven multi period power supply recovery method for distribution networks
Scientific Reports
Two-stage robust optimization
Convex hull polyhedron set
Hyperplane polyhedron set
Power supply recovery
title Adaptive data driven multi period power supply recovery method for distribution networks
title_full Adaptive data driven multi period power supply recovery method for distribution networks
title_fullStr Adaptive data driven multi period power supply recovery method for distribution networks
title_full_unstemmed Adaptive data driven multi period power supply recovery method for distribution networks
title_short Adaptive data driven multi period power supply recovery method for distribution networks
title_sort adaptive data driven multi period power supply recovery method for distribution networks
topic Two-stage robust optimization
Convex hull polyhedron set
Hyperplane polyhedron set
Power supply recovery
url https://doi.org/10.1038/s41598-025-02853-8
work_keys_str_mv AT xiye adaptivedatadrivenmultiperiodpowersupplyrecoverymethodfordistributionnetworks
AT mengyang adaptivedatadrivenmultiperiodpowersupplyrecoverymethodfordistributionnetworks
AT zhihongyang adaptivedatadrivenmultiperiodpowersupplyrecoverymethodfordistributionnetworks
AT qingxiang adaptivedatadrivenmultiperiodpowersupplyrecoverymethodfordistributionnetworks
AT yazhuoli adaptivedatadrivenmultiperiodpowersupplyrecoverymethodfordistributionnetworks