Smart Data Mapping for Connecting Power System Model and Geospatial Data

TL;DR

This paper presents an automated workflow to accurately map power system models to geospatial data, enhancing analysis of grid resilience and vulnerability, especially for Puerto Rico.

Contribution

It introduces a novel automatic data mapping method linking power system models with geospatial data, addressing heterogeneity and errors in existing datasets.

Findings

The auto-mapping workflow outperforms manual mapping in accuracy.

The method effectively links power grid models with geospatial data.

Application to Puerto Rico demonstrates practical utility.

Abstract

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.