Shadow-camera based solar nowcasting system for shortest-term forecasts

Abstract

The rapid growth of solar power generation and the variable nature of the solar resource pose challenges for our electricity grids. Forecasting future changes in the irradiance might help to cost-efficiently manage this variability both for photovoltaic and concentration solar plants as well as grids with high solar penetrations. So far, for shortest-term forecasts with lead times of a few minutes, all-sky imager based nowcasting systems are used. However, due to the complexity of dynamically changing 3d cloud shapes as well as certain geometrical effects such as self-occlusion or near-horizon saturation, all-sky imager based nowcasting systems exhibit inherent weaknesses. Here, we present a novel system to generate shortest-term solar forecasts, which is located at Plataforma Solar de Almeria in southern Spain. This approach is based on downward-facing cameras (shadow cameras), taking images of the ground. From these images, spatially resolved irradiance maps are derived. By tracking cloud shadows, future irradiances are predicted. A demonstration system is achieved, which provides shortest-term forecasts for the next 2min. To the best of our knowledge, this is the first time such a system is developed. We benchmark several possible algorithmic approaches on 16 days and compare the deviations to a state-of-the-art all-sky imager based nowcasting system on 22 days. The root-mean-squared deviation (RMSD) of this shadow camera based nowcasting system for direct normal irradiance (DNI) and 1-min temporal averages is 15.6% for lead times of 2min (MAD, DNI: 9.6 %). In comparison to an all-sky imager system, this is an improvement as the all-sky imager system only reaches 22.0% RMSD and 14.8% MAD (both DNI). This demonstrates the feasibility and attractiveness in terms of accuracy of the proposed concept.