Feeder-Level Deep Learning-based Photovoltaic Penetration Estimation Scheme

The increasing penetration of renewable energy to the distribution grids, especially photovoltaic (PV), helps smooth out supply and demand, and reduces greenhouse gas emissions. However, the PV generation is behind-the-meter, and cannot be detected by the smart meter. To address this problem, a hybrid regression multi-layer perception (MLP) deep neural network (DNN) model is designed to separate the PV generation from the overall grid measurements. The model utilizes grid measurements, weather-related measurements, satellite-driven irradiance measurements, and temporal information as inputs to evaluate the PV generation in real-time. We also examine the performance of the model with different levels of PV penetration. We show the proposed model reduces the mean square error by 49% compares to single variable input models.