Spatial information reconstruction framework for power grid datasets without geographic coordinates

Area of Science:

• Electrical Engineering
• Computer Science
• Geographic Information Systems

Background:

• Spatial information is crucial for power grid analysis, but often missing from datasets due to security concerns.
• Lack of geographic coordinates limits spatially explicit studies of power systems, including renewable energy integration and electric vehicle charging impacts.

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 systems by recovering essential location data.

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:

• Successfully reconstructed spatially consistent bus-level coordinates for power grid components.
• Preserved original network topology and line-related attributes during reconstruction.
• Demonstrated the framework's effectiveness on multiple power grid datasets.

Conclusions:

• The proposed framework effectively recovers missing geographic coordinates, enhancing power grid data usability.
• Reconstructed data supports advanced spatially explicit studies, such as renewable integration and electric vehicle charging analysis.
• This approach overcomes security limitations hindering spatial analysis of power grids.