Geo-referenced synthetic low-voltage distribution networks: A data-driven approach

To effectively analyze the impacts of rapidly evolving new and advanced distributed power generation, demand integration in distribution grids requires grid topologies that replicate the existing network topologies. Estimating these can provide datasets with quasi-network topologies maintaining functional electrical characteristics at any location of interest. Herein, these topologies will be used to identify vulnerabilities that affect grid stability, grid reinforcement, and will form the basis of economic analysis. In this study, a new process is developed for generating georeferenced, synthetic, low-voltage network topologies using openly available data procured from diverse sources for Germany as a case study. The topologies are first synthesized by collecting the geo-referenced building's data from OpenStreetMap (OSM). Then, a clustering technique is performed to group collected buildings by known information about the transformer count. Next, detailed algorithms are developed to collect street lines, generate graphs combining streets and buildings, transformer placements, and to assign real electrical parameters. Finally, the methodology is applied to Germany's building data (extracted from OSM), which yielded 500,000 low-voltage network topologies. The estimated topology data covers approximately 29 million buildings as electrical nodes and approximately 1.7 million km or 0.8 million km of electrical lines length with or without service drops.