Spatial information reconstruction framework for power grid datasets without geographic coordinates

Area of Science:

• Electrical Engineering
• Geographic Information Systems
• Computational Science

Background:

• Spatial information is crucial for power grid analysis, but security concerns often omit geographic coordinates from datasets.
• This data gap limits the application of spatially explicit analyses in power systems.

Purpose of the Study:

• To develop a framework for reconstructing missing bus-level geographic coordinates in power grid datasets.
• To enable spatially explicit analyses of power grids, including renewable energy integration and electric vehicle charging.

Main Methods:

• Integrating external facility location retrieval, network-based inference, and refinement techniques.
• Utilizing network connectivity, line attributes, and spatial placement regularities for coordinate reconstruction.
• Validating the framework on benchmark datasets and real-world French and Danish transmission data.

Main Results:

• The framework successfully reconstructs spatially consistent bus-level coordinates.
• Preserves original network topology and line-related attributes during reconstruction.
• Demonstrates effectiveness on multiple power grid datasets.

Conclusions:

• The reconstructed coordinates enhance the utility of existing transmission datasets for spatial studies.
• Facilitates advanced analyses in areas like renewable integration and electric vehicle charging.
• Overcomes limitations posed by missing spatial data in power grid operations.