An Automated Approach for Screening Residential PV Applications Using a Random Forest Model

Abstract

The rapid growth of residential solar photovoltaics (PV) applications is a challenge for distribution utilities as they work to maintain grid standards and minimize customer interconnection times. A "screening process" is typically used by utilities to approve customer interconnection request. While conventional "fast-track screening" methods (e.g., limiting PV capacity to 15% of transformer capacity) can be done quickly, they are too restrictive for new PV interconnections. On the other hand, detailed studies often require power flow modeling and would increase customer interconnection times. This work uses a random forest (RF) model to screen residential solar applications without the need for power flow analysis. The proposed RF model is based on commonly available PV application information and network data as inputs, such as application size and solar penetration. The correlation and importance of these RF inputs are investigated so that utilities have flexible implementation options. Further advantages of this data-driven approach are transparency, i.e., utilities can show how different inputs affect a pass/fail decision, and a quantified probability associated with the screening decisions can be provided. Case studies show how a utility would use the proposed approach and benchmark the proposed approach with conventional screening methods. The proposed approach was found to be more accurate than the conventional fast-track screens. It was also found to be faster than detailed power flow studies and nearly as accurate.