Physics-Based Data-Driven Grid Modeling to Accelerate Accurate Photovoltaics Integration

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Uncertainty in distribution grid modeling can lead to inaccuracy in decisions regarding photovoltaic (PV) integration, limiting the number of PV systems that can connect to the electric grid. This project will increase the precision and accuracy of distribution system models by more efficiently processing grid measurements. The team will then develop several physics-based, data-driven, machine-learning algorithms that will enable distribution grid models to dynamically adapt to changing grid conditions. This will enable usability for all distribution feeders with monitoring. The team will validate the algorithms to improve modeling accuracy and decrease uncertainties in PV hosting capacity by at least 90 percent.