A Data-Driven Framework for Evaluating the Impacts of Advanced PV Inverter Control Functions

Joseph A. Azzolini& Matthew J. Reno Sandia National Laboratories, Albuquerque, NM, United States

All new photovoltaic (PV) systems that interconnect with the power grid must now be capable of various advanced inverter functions that can be leveraged to improve grid conditions and increase PV hosting capacity. However, conventional methods to evaluate the impacts of advanced inverter functions require detailed grid models and time-consuming simulations. To address these drawbacks, a data-driven evaluation framework is proposed that uses historical smart meter measurements to determine how different PV inverter control objectives would impact local grid conditions. The proposed framework was tested on real utility data for the application of hosting capacity analysis, and the results were compared to conventional model-based analyses. Overall, the proposed framework was found to have significant computational advantages without sacrificing accuracy.