UC Merced

As an energy resource, solar energy provides a cleaner alternative to the conventional power generation systems and therefore solar energy has the potential to help achieve lower emissions standards as well to help provide domestic energy security. A major challenge, however, is the nondispatchability and variability of the solar resource which makes it necessary to develop forecasting methodologies in order to safely integrate with the electric grid. As dictated by current electricity markets, power generation is dispatched according to day-ahead unit commitment as well as 1-hour ahead and 15-minutes for load-following services. In order to integrate large penetration levels of solar energy into the current systems, forecasting at these time intervals are necessary. In this work, we develop and evaluate several solar irradiance forecast models for multiple-time horizons. The 1-day ahead forecasting models are based on forecasted elements from the National Weather System's (NWS) forecasting database (NDFD). The 1-hour ahead forecasting models are based on sky cover indices derived from ground measurements including solar and infrared radiometers as well as a sky imager and we also develop satellite-based models that utilize neural networks for time-series predictions. For very short-term forecasts of < 15-minutes ahead, we present a solar forecasting application using detailed image processing of a sky imager. Where possible, these models are compared to existing models in the literature. Because various authors use different benchmarks and forecast model quality measures, we also outline procedure that allows the comparisons of different forecasting models and discuss why the procedure is a robust indicator of forecasting skill. In determining forecasting skill, it is necessary to quantify variability. Quantifying variability is also important for analyzing the economic impacts which are introduced by the addition of variability from a solar power system, which is discussed here in some detail. A case study of the economic impacts due to solar high-frequency variability is presented for the University of California Merced and a 1-MW photovoltaic plant.