Comparison of Empirical and Data Driven Digital Twins for a PV+Battery Fleet

In recent years, as the total photovoltaic (PV) capacity in the power grid continues to increase, PV installations have become more diverse than the simpler fixed-tilt, utility-scale systems of the past. Systems are installed on rooftops, trackers, deployed with batteries, and in many other configurations. Virtual models of a system, called “digital twins,” are often constructed to predict future performance, quantify losses, schedule O&M, etc. However, due to increasing system diversity and complexity, constructing these digital twins from traditional physical models (such as PVSyst or the Sandia Array Performance Model) can be quite time consuming, prone to modeler error, and tend to require detailed, expensive site-specific metadata. Therefore, models that can learn a representation of the system from data (called “data-driven digital twins”) can be used alongside or in place of traditional, end-to-end physics-based models. In this work, we present a comparison of a empirical digital twin (using pvlib-python physics-based models) and a data-driven digital twin (using spatiotemporal graph neural networks) and quantify error metrics for the models. It is found that data-driven models best represent the average case of a PV system, whereas the empirical model better corresponds to oscillation due to weather variation. Data used in this work is open access on OSF, and reference implementations in Python are provided on GitHub.