Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring approximate data for urgent decisions. However, traditional grid reconstruction approaches often rely on incomplete data, expert knowledge, or closed datasets, limiting their utility during emergencies. This study proposes a novel automated method for reconstructing medium-voltage (MV) power grids. The novelty of the proposed method lies in combining OpenStreetMap energy and land-use data in a unified and automated framework, thereby reducing the need for expert input. The proposed method employs a systematic aggregation of data, an estimation of energy demand, and the application of algorithmic techniques to generate synthetic MV grid models that functionally represent real networks, capturing key topological features. The resulting outputs include visual representations to support decision-makers in simulating "what-if” scenarios and ensuring rapid operational awareness. In a step toward eliminating reliance on proprietary data, our approach broadens access to critical infrastructure insights across diverse urban contexts, contributing to critical infrastructure resilience and potentially supporting both energy system research and crisis management. A case study demonstrates that a medium-sized city’s MV grid can be reconstructed in minutes without expert knowledge or geographically constrained datasets, underscoring the method’s deployment potential and practical value for emergency scenarios.

Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure. Comprehensive, up-to-date power grid representations are essential for researchers developing methods for analysing and optimising power systems and first responders requiring approximate data for urgent decisions. However, traditional grid-reconstruction approaches often rely on incomplete data, expert knowledge, or closed datasets, limiting their utility during emergencies. This study introduces a novel automated method for reconstructing medium-voltage (MV) power grids by leveraging and augmenting publicly available energy and land-use data from OpenStreetMap to reduce the need for expert input. By systematically aggregating the data, estimating energy demand, and applying algorithmic techniques, our method can generate synthetic MV grid models that functionally represent real networks, capturing key topological features. The resulting outputs include visual representations to support decision-makers in simulating "what-if" scenarios and ensuring rapid operational awareness. In a step toward eliminating reliance on proprietary data, our approach broadens access to critical infrastructure insights across diverse urban contexts, contributing to the reacting phase of critical infrastructure resilience and potentially supporting both energy system research and crisis management. A case study demonstrates that a medium-sized city’s MV grid can be reconstructed in minutes without expert knowledge or geographically constrained datasets, underscoring the method’s deployment potential and practical value for emergency scenarios.