Transmission and distribution lines form the backbone of electricity systems. To effectively manage electricity system risk and enable transitions to sustainable energy, it’s important to understand systems’ topologies, and the ways that transmission lines and their supports are spatially connected. This project of the Energy Data Analytics Lab automatically maps grid topologies in remotely sensed data.
Information is limited on grid topologies for both high voltage and lower voltage transmission lines. This makes it more challenging to identify risks like exposure to hazards from weather and vegetation encroachment. The lack of topological information also makes it difficult to identify optimal pathways for sustainable electricity expansion in communities transitioning to electricity access for the first time (e.g., whether an off-grid system or grid extension would better meet community needs).
Satellite, aerial, and drone imagery can be used to map out topologies, but developing algorithms to map those topologies both efficiently and effectively remains challenging and difficult to scale. This project seeks to overcome these obstacles by developing tools that can accurately map both transmission and distribution lines to provide data for improving system resilience and enhancing sustainable development.
