# Urban Power Grid Topology and Hierarchy Identification from Open Data

arXiv: https://arxiv.org/abs/2606.21352 · HTML: https://mlsanity.com/paper/2606.21352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [PowerGraph: A power grid benchmark dataset for graph neural networks](https://mlsanity.com/paper/2402.02827/) (2024)
- [Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumption](https://mlsanity.com/paper/2203.08630/) (2022)
- [Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.08681/) (2025)
- [Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications](https://mlsanity.com/paper/2503.03539/) (2025)
- [Privacy-preserving transactive energy systems: Key topics and open research challenges](https://mlsanity.com/paper/2312.11564/) (2023)
- [An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System](https://mlsanity.com/paper/2110.09030/) (2021)
- [Evaluation of Open-source Tools for Differential Privacy](https://mlsanity.com/paper/2202.09587/) (2022)
- [Ultra-short-term solar power forecasting by deep learning and data reconstruction](https://mlsanity.com/paper/2509.17095/) (2025)
- [Open Data-Driven Automation of Residential Distribution Grid Modeling with Minimal Data Requirements](https://mlsanity.com/paper/2312.06552/) (2023)
- [Exploring the Interpretability of Forecasting Models for Energy Balancing Market](https://mlsanity.com/paper/2602.00049/) (2026)
- [Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections](https://mlsanity.com/paper/2603.18914/) (2026)
- [Cenergy3: An Open Software Package for City Energy 3D Modeling](https://mlsanity.com/paper/2603.20361/) (2026)
- [AI and Open-data Driven Scalable Solar Power Profiling](https://mlsanity.com/paper/2605.02738/) (2026)

## Abstract (arXiv)

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 information, nevertheless, is crucial especially for analyzing the last-mile distribution networks connecting individual buildings. This paper addresses this challenge by developing an open-data-driven framework to identify urban power grid topology, from high-voltage transmission down to individual building connections. We integrate public infrastructure data (power-lines, substations, transformers, poles) to map the high and medium-voltage grid skeleton. We then leverage geospatial machine learning on OpenStreetMap building data to cluster loads of building energy demand. Upon the clustering, we develop the physical topology of the final distribution lines linking the clustered buildings and low-voltage distribution grids. Our derived grid topology incorporates physical and geographical constraints like voltage level and road networks, thus leading to reasonable topology and hierarchy of grid networks. We apply the developed framework to the districts of Alna in Oslo, Norway, and Waiheke Island in New Zealand, and we reconstruct the complete grid topology that connects all the buildings, power lines, and electricity infrastructure assets. With the developed framework, 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. This work also opens the opportunity to build energy digital twins that fuse power assets and grid topology, geographic constraints, and fine-grained power demand profiles.