Data Driven Reduced Pi-Model of Feeders for Distribution Network Representation With DERs for Fast Reconfiguration

Abstract

Deep electrification by 2050 is expected to increase distribution systems by three to five times and include innumerable distributed energy resources (DERs). Robust methods for operations will be required. Reconfigurations, well researched for 50+ years, are created given the size and importance of present distribution systems. This paper proposes a network configuration method which is significantly dense, heavily loaded, societally important, and has innumerable loads and DERs. This method reduces sections of feeders with DERs to equivalent reduced Pi-Model representations. It then uses a regression model to correlate loading scenarios of the distribution to reduced Pi-Model parameters feeder sections. A regression model yields reduced Pi-Models of feeder sections, and they are used to construct a complete distribution system representation, with this reduced model used for reconfiguration. The proposed method was tested on modified 33-, 69- and 123-Bus data networks and reduced the number of buses to around 50%. Computing time was reduced by 26.30%, 58.54% and 67.33%, respectively while providing accuracy of 97.35%, 97.30%, and 99.05%, respectively. The computation time was lowered by 46.45% when the methodology was expanded to the North Dakota 880-Bus network. As the method scales for larger distribution systems, it should increasingly perform better.