GridKit VS GridMapping

GridKit - https://github.com/bdw/GridKitGridKit uses spatial and topological analysis to transform map objects from OpenStreetMap into a network model of the electric power system. It has been developed in the context of the SciGRID project at the Next Energy research institute, to investigate the possibility of 'heuristic' analysis to augment the route-based analysis used in SciGRID. This has been implemented as a series of scripts for the PostgreSQL database using the PostGIS spatial extensions.

GridMapping: https://github.com/wangzhecheng/GridMappinghttps://www.nature.com/articles/s41467-023-39647-3Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.