Spatial information reconstruction framework for power grid datasets without geographic coordinates

Spatial information on power grid components is essential for analyzing modern power grid operations, as many key phenomena in power systems depend explicitly on the geographic distribution of generation, demand, and transmission infrastructure. However, power grid datasets often exclude the geographic coordinates of grid elements for security reasons, which limits the applicability of spatially explicit analyses. To address this limitation, we propose a spatial information reconstruction framework that recovers missing bus-level geographic coordinates by integrating external facility location retrieval, network-based inference, and refinement. The framework retrieves candidate coordinates from legally accessible external sources, infers unresolved bus locations from network connectivity and distance-related line attributes and corrects spatially inconsistent retrieved locations. It further stabilizes the reconstructed layout through spatial placement regularities observed in transmission networks and ensemble aggregation. Results from benchmark validation on nine power grid datasets with known geographic coordinates and from application to the French and Danish transmission datasets show that the framework can reconstruct spatially consistent bus-level coordinates while preserving the original network topology and line-related attributes. The reconstructed coordinates enable existing transmission datasets to be used in spatially explicit studies of modern grids, including renewable integration and electric vehicle charging.