Urban Power Grid Topology and Hierarchy Identification from Open Data

2026-06-19

AI Summary

This study addresses the critical limitation imposed by the high confidentiality of urban power grid topology data, which hinders distribution network research and innovation. The authors propose a novel methodology that leverages entirely open data—comprising publicly available electrical infrastructure records and OpenStreetMap—to reconstruct full-scale grid topologies from high-voltage transmission networks down to individual buildings. By integrating graph-theoretic algorithms to construct the medium- and high-voltage backbone and applying geospatial machine learning to cluster building-level electricity demand and infer low-voltage connections, the framework enables comprehensive network reconstruction without proprietary data. Validated in the Alna district of Oslo, Norway, the approach successfully replicates a complete grid encompassing 7,330 buildings and all key electrical assets, thereby facilitating power flow optimization, cascading failure simulations, and resilience analysis under high penetration of distributed renewable energy resources.

Abstract

Understanding the complex topology and hierarchy of urban power grid is crucial for energy prognosis, power flow management, and system resilience analysis. However, detailed grid information remains largely proprietary. This creates significant barriers for research and innovation, especially when analyzing the last-mile distribution networks connecting individual buildings. This paper addresses this challenge by developing an open-data-driven framework for the complete identification of urban power grid topology, from high-voltage transmission down to individual building connections. Particularly, we fuse public infrastructure data (power-lines, substations, transformers, poles) to map the high and medium-voltage skeleton using graph-based algorithms. We then leverage geospatial machine learning on OpenStreetMap building data to group power demand clusters, and infer the physical topology of the final distribution lines linking the clustered buildings. We apply the developed framework to the district of Alna in Oslo, Norway, and we reconstruct the complete grid topology that connects 7,330 buildings and all major electricity infrastructure assets. With the research in this work, we provide a critical tool that facilitates power system analysis, e.g., power flow optimization, cascading failure simulation, and grid resilience against the penetration of distributed renewable generation.

Problem

• urban power grid
• topology identification
• hierarchy
• last-mile distribution
• proprietary data

Innovation

• open-data-driven
• graph-based algorithms
• geospatial machine learning
• distribution network topology
• urban power grid