Open Data-Driven Automation of Residential Distribution Grid Modeling With Minimal Data Requirements

• Published in: IEEE transactions on smart grid Vol. 15; no. 6; pp. 5721 - 5732
• Main Authors: Weber, Moritz, Janecke, Luc, Cakmak, Huseyin K., Hagenmeyer, Veit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Buildings; Data models; Data sources; Digital mapping; distribution grid; Electric power distribution; Estimation; grid capacity analysis; Load modeling; Model generation; Open data; open-source software; optimization; Residential areas; Residential buildings; Residential energy; Soft sensors; Topology; Topology optimization; Transformers
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2024.3406765

In the present paper, we introduce a new method for the automated generation of residential distribution grid models based on novel building load estimation methods and a two-stage optimization for the generation of the 20 kV and 400 V grid topologies. Using the introduced load estimation methods, various open or proprietary data sources can be utilized to estimate the load of residential buildings. These data sources include available building footprints from OpenStreetMap, 3D building data from OSM Buildings, and the number of electricity meters per address provided by the respective distribution system operator (DSO). For the evaluation of the introduced methods, we compare the resulting grid models by utilizing different available data sources for a specific suburban residential area and the real grid topology provided by the DSO. This evaluation yields two key findings: First, the automated 20 kV network generation methodology works well when compared to the real network. Second, the utilization of public 3D building data for load estimation significantly increases the resulting model accuracy compared to 2D data and enables results similar to models based on DSO-supplied meter data. This substantially reduces the dependence on such normally proprietary data.