Topology identification and parameters estimation of LV distribution networks using open GIS data
The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. This pa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006185 |
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| author | Dong Liu Juan S. Giraldo Peter Palensky Pedro P. Vergara |
| author_facet | Dong Liu Juan S. Giraldo Peter Palensky Pedro P. Vergara |
| author_sort | Dong Liu |
| collection | DOAJ |
| description | The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. This paper develops a data-driven topology identification approach for LVDNs with a high proportion of underground cables. The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. In the second stage, based on the limited SM data, the location of breakpoints in mesh topology caused by circle roads is verified and reconstructed to guarantee the radial structure of LVDNs. Finally, given multiple incomplete SM datasets, three data-driven optimization models based on a state estimation model are constructed to mitigate the error of cable length induced by OSM data. The feasibility of the proposed topology identification approach is verified on three actual LVDNs in The Netherlands and multiple incomplete SM datasets. Furthermore, the minimal amount of SM data needed to minimize the error of cable length is analyzed. |
| format | Article |
| id | doaj-art-25776a28da524beeae2eeb9d887bb672 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-25776a28da524beeae2eeb9d887bb6722025-01-19T06:23:53ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110395Topology identification and parameters estimation of LV distribution networks using open GIS dataDong Liu0Juan S. Giraldo1Peter Palensky2Pedro P. Vergara3Intelligent Electrical Power Grids (IEPG) Group, Delft University of Technology, 2628CD, The NetherlandsEnergy Transition Studies Group, Netherlands Organisation for Applied Scientific Research, 2595 DA, The NetherlandsIntelligent Electrical Power Grids (IEPG) Group, Delft University of Technology, 2628CD, The NetherlandsIntelligent Electrical Power Grids (IEPG) Group, Delft University of Technology, 2628CD, The Netherlands; Corresponding author.The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. This paper develops a data-driven topology identification approach for LVDNs with a high proportion of underground cables. The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. In the second stage, based on the limited SM data, the location of breakpoints in mesh topology caused by circle roads is verified and reconstructed to guarantee the radial structure of LVDNs. Finally, given multiple incomplete SM datasets, three data-driven optimization models based on a state estimation model are constructed to mitigate the error of cable length induced by OSM data. The feasibility of the proposed topology identification approach is verified on three actual LVDNs in The Netherlands and multiple incomplete SM datasets. Furthermore, the minimal amount of SM data needed to minimize the error of cable length is analyzed.http://www.sciencedirect.com/science/article/pii/S0142061524006185Distribution networksTopology generationOpen source dataIncomplete dataOptimization |
| spellingShingle | Dong Liu Juan S. Giraldo Peter Palensky Pedro P. Vergara Topology identification and parameters estimation of LV distribution networks using open GIS data International Journal of Electrical Power & Energy Systems Distribution networks Topology generation Open source data Incomplete data Optimization |
| title | Topology identification and parameters estimation of LV distribution networks using open GIS data |
| title_full | Topology identification and parameters estimation of LV distribution networks using open GIS data |
| title_fullStr | Topology identification and parameters estimation of LV distribution networks using open GIS data |
| title_full_unstemmed | Topology identification and parameters estimation of LV distribution networks using open GIS data |
| title_short | Topology identification and parameters estimation of LV distribution networks using open GIS data |
| title_sort | topology identification and parameters estimation of lv distribution networks using open gis data |
| topic | Distribution networks Topology generation Open source data Incomplete data Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006185 |
| work_keys_str_mv | AT dongliu topologyidentificationandparametersestimationoflvdistributionnetworksusingopengisdata AT juansgiraldo topologyidentificationandparametersestimationoflvdistributionnetworksusingopengisdata AT peterpalensky topologyidentificationandparametersestimationoflvdistributionnetworksusingopengisdata AT pedropvergara topologyidentificationandparametersestimationoflvdistributionnetworksusingopengisdata |