A Reproducible Method for Mapping Electricity Transmission Infrastructure for Space Weather Risk Assessment

TL;DR

This paper introduces a reproducible, open-source method for mapping electricity transmission infrastructure at the component level to improve space weather risk assessment, specifically for modeling Geomagnetically Induced Currents.

Contribution

It presents an innovative web-based annotation platform and a satellite imagery analysis pipeline to generate detailed, high-resolution transmission asset maps from open-source data.

Findings

Generated detailed substation component maps from OpenStreetMap data.

Produced GIC network models that closely match measured GICs and synthetic networks.

Demonstrated the method's effectiveness in regional GIC risk screening and nowcasting.

Abstract

Space weather risk assessment is constrained by the lack of available asset information needed to model Geomagnetically Induced Currents (GICs) in electricity transmission infrastructure. We propose a reproducible method that enables risk analysts to collect their own open-source substation data. Utilizing an innovative web-browser platform for annotation, we convert OpenStreetMap substation locations to high-resolution, component-level mappings of electricity transmission assets. For example, we convert an initial 1,313 high-voltage (>115 kV) substations to 52,273 substation components via Google Earth APIs utilizing low-altitude, satellite, and streetview imagery. Approximately 41,642 substation components (79.6%) connect to the highest substation voltage levels (>345 kV) and are potentially susceptible to GICs, with 7,949 identified transformers. Compared to the OpenStreetMap baseline, this approach provides detailed insights on voltage levels, line capacities, and substation configurations. We then construct a geospatial GIC network for the Tennessee Valley Authority region, comparing May 2024 results with the UIUC150 synthetic network and with measured ground GICs at 13 monitoring devices. Importantly, the two open-source networks produce 95th-percentile peak ground GIC values within 4% of each other, and the modeled time series broadly capture the temporal morphology of the storm at the monitoring sites. This method shows promise for spatially explicit GIC screening and regional nowcasting without requiring access to operator data.