A Study on Topological Analysis and Necessary Processing of Distribution Networks Simulation Data

Distribution systems have become pivotal to the integration of distributed energy resources (DERs) and inverter-based resources (IBRs), creating a pressing need for robust topological analysis to support simulation, optimization, and control. This need is further amplified by studies for vehicle-to-grid (V2G) applications and railway traction feeder networks. Yet OpenDSS [1] — despite its widespread use—offers no explicit nodal definitions and uses inconsistent bus–phase conventions, which complicates automated topology extraction. To address these gaps, we develop a phase-aware topological processing pipeline using the OpenDSS Python [2] API. By leveraging API calls that reliably return integer node lists per bus, we reconstruct accurate, phase-resolved graphs directly from OpenDSS input data. The graphs are then prepared for METIS-based partitioning by assigning node weights (e.g., phase count) and edge weights (e.g., line length or impedance) to guide subsystem decomposition. The resulting partitions are connected, balanced in computational load, and minimize interpartition cut sets, enabling scalable, distributed simulation aligned with real-time requirements. This methodology closes the gap between OpenDSS’s implicit topology and modern computational needs, supporting real-time analytics, hosting-capacity assessment, resilient microgrid formation in high-DER environments, and analogous use cases such as railway feeder networks.

 

 

KEYWORDS: Computational load assignment, Distribution network, METIS, OpenDSS