Forecasting is one of the enabling technologies for the integration of weather-dependent renewable resources (e.g., solar and wind) into the electric grid. Accurate forecasts can reduce operational costs associated with intra-day variability, reduce imbalance charges incurred by plant operators due to inaccurate energy bids, decrease utility costs associated with day-ahead scheduling (thereby reducing overall O&M costs), as well as assist grid operators with balancing energy demand schedules. As the market penetration of solar-based power generation continues to grow, accurate and reliable forecasting techniques become increasingly more important. In this work, two key areas of solar forecasting are advanced. First, we develop intra-day (>1-hour) and day-ahead (>24-hour) forecasting methods to directly predict the generation of operational solar power plants, without the need for intermediate solar irradiance forecasts and resource-to-power modeling. Here we take a data-driven approach, leveraging Machine Learning (ML) techniques and publicly available, spatially resolved meteorological and remote sensing datasets. The proposed methods are analyzed and validated using two grid-connected 1 MW photovoltaic (PV) power plants in California. Second, we develop a method to directly and efficiently estimate cloud optical properties from longwave remote sensing data. The output of solar-based power generation systems is strongly dependent on cloud cover and optical depth, but in most solar forecasting methodologies cloud optical properties are over-simplified due to a lack of real-time, accurate estimates. The proposed estimation method builds upon a two-stream, spectrally resolved infrared radiation model coupled with high-resolution (5-minute, 2 km) spectral satellite imagery. We show that the proposed method can provide real-time, accurate estimates of cloud optical depth (COD) and cloud top height for all-sky (clear or cloudy) conditions during both daytime and nighttime.
Cookie SettingseScholarship uses cookies to ensure you have the best experience on our website. You can manage which cookies you want us to use.Our Privacy Statement includes more details on the cookies we use and how we protect your privacy.