  Knowing the geospatial locations of power system model elements and linking
load models with end users and their communities are the foundation for
analyzing system resilience and vulnerability to natural hazards. However,
power system models and geospatial data for power grid assets are often
developed asynchronously without close coordination. Creating a direct mapping
between the two is a challenging task, mainly due to heterogeneous data
structures, target uses, historical legacies, and human errors. This work aims
to build an automatic data mapping workflow to connect the two, and to support
energy grid resilience studies for Puerto Rico. The primary steps in this
workflow include constructing graphs using geospatial data, and aligning them
to the transmission networks defined in the power system data. The results have
been evaluated against existing manual mapping practices for part of the Puerto
Rico Power Grid model to illustrate the performance of such auto-mapping
solutions.

Systems and Control;Systems and Control