An Optimization-based Approach for Automated Generation of Residential Low-Voltage Grid Models Using Open Data and Open Source Software

The contribution of the present paper is a new, more refined method for the automated creation of large-scale detailed distribution grid models based solely on publicly available GIS and statistical data. Utilizing the street layouts in Open Street Maps as potential cable routes, a graph representation is created and complemented by residential units that are extracted from the same data source. This graph structure is adjusted to match the electrical low-voltage grid topology by solving a variation of the minimum cost flow linear optimization problem with provided data on secondary substations. In a final step, the generated grid representation is transferred to a DIgSILENT PowerFactory model including photovoltaic systems. The presented workflow uses open source software and is fully automated and scalable. It allows the generation of ready-to-use distribution grid simulation models for given 20kV substation locations and additional data on residential unit properties for improved results. The performance of the developed method with respect to grid utilization is presented for a selected suburban residential area with power flow simulations for eight scenarios including current residential photovoltaic installation and a future scenario with full photovoltaic expansion. For the latter, the simulation results indicate heavy congestion in the low-voltage grid for full photovoltaic capacity deployment without batteries. Furthermore, the suitability of the generated models for quasi-dynamic simulations is shown.