Day-Ahead Solar Forecasting Based on Multi-Level Solar Measurements

Abstract

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies.

Battery Swapping Station as an Energy Storage for Capturing Distribution-Integrated Solar Variability

“…Equation (11) is defined to capture the aggregated prosumers net loads variability, where Δ u denotes the amount of variability being captured by the utility grid, and the rest is picked up by the BSS. The aggregated prosumers net loads variability between two successive time periods (i.e., Δt) is formulated in (12). Nevertheless, leveraging (11) and (12), the aggregated prosumers net load variability is entirely captured by the BSS through exchanged power with the utility grid.…”