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.

翻译：摘要：本文提出一种基于新型建筑负荷估算方法与两阶段优化（用于生成20 kV和400 V电网拓扑）的居民配电网模型自动生成新方法。通过所引入的负荷估算方法，可利用多种开放或专有数据源估算居民建筑负荷。这些数据源包括OpenStreetMap提供的建筑足迹、OSM Buildings提供的建筑三维数据，以及由相应配电系统运营商提供的每地址电表数量。为评估所提方法，我们针对特定郊区居民区，利用不同可用数据源生成的电网模型与配电系统运营商提供的实际电网拓扑进行对比。评估得出两项关键结论：首先，与真实电网相比，所提出的20 kV网络自动生成方法表现良好；其次，与基于二维数据的方案相比，利用公开三维建筑数据进行负荷估算可显著提升生成模型的精度，且能获得与基于配电系统运营商提供的电表数据模型相近的结果。这极大降低了对这类通常具有专有性质的数据的依赖。