OpenDss to ReNCAT: A Graph Theory Approach for Automated Power Network Data Extraction

The growing demand for energy and the need for a resilient electricity supply have driven the development of microgrids. ReNCAT is a support tool for the design and optimization of MGs, which considers both costs and social impact in emergency situations or service interruptions. To work properly, ReNCAT requires accurate data from the power systems to be analyzed. OpenDSS, a widely used simulator, is a useful source for obtaining this data; however, its node-based organization differs from the structure used by ReNCAT, which groups nodes according to the location of the switches. This difference means that data preprocessing must be performed manually, which is laborious and error-prone. To address this limitation, this paper proposes an automated method to transform OpenDSS data into a ReNCAT-compatible format using graph theory and the Python’s NetworkX library. The effectiveness of the proposed model is evaluated in two case studies, one in a rural area and the other in an urban area. The results show that the model based on graph theory is effective in reorganizing data in systems of different sizes and configurations. Moreover, the processed data are fully functional in ReNCAT, as evidenced in a practical example.