Estimation of electrical substation service area using geospatial methods and open data

This paper presents a new scalable geospatial method for estimating substation service areas to allocate electrical demand and distributed generation using publicly available datasets. The approach is inspired by the classical Watershed spreading concept and applies an optimized cost-distance propagation framework implemented using Dijkstra’s algorithm, incorporating geographic features, infrastructure, land use, administrative boundaries, and established grid planning practices. The resulting service areas provide a stable spatial framework for allocating demand and generation while remaining adaptable to grid evolution and new substation deployments. Electrical demand is derived from demographic and land-use data, and generation units are mapped using public registries. Exemplary validation against German 110 kV Distribution System Operator (DSO) service area maps demonstrates that the new method reduces spatial error by 9% compared to the classical Voronoi-based approach. The proposed method enables the first high-spatial-resolution, systematic, open-data-based estimation of substation service areas in Germany, where public substation boundaries are not available.