Determination of cloud transmittance for all sky imager based solar nowcasting

The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can provide irradiance nowcasts that can help to optimize CSP plant operation, solar power plant dispatch and grid operation. The radiative effect of clouds is highly variable and depends on micro- and macrophysical cloud properties. Frequently, nowcasting systems have to measure/estimate the radiative effect during complex multi-layer conditions with strong variations of the optical properties between individual clouds. We present a novel approach determining cloud transmittance from measurements or from correlations of transmittance with cloud height information. The cloud transmittance is measured by a pyrheliometer when shaded, as the ratio of shaded direct normal irradiance (DNI) and clear sky DNI. However, for most clouds, direct transmittance measurements are not available, as these clouds are not shading the used pyrheliometers. These clouds receive an estimated transmittance value based on (1) their height, (2) results of a probability analysis with historical cloud height and transmittance measurements as well as (3) recent transmittance measurements and their corresponding cloud height. Cloud heights are measured by a stereoscopic approach utilizing two ASIs. We discuss site dependencies of the presented transmittance estimation method and the potential integration of automatic cloud classification approaches. We validated the cloud transmittance estimation over two years (2016 and 2017) and compare the probabilistic cloud transmittance estimation approach with four simple approaches. The overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 min. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.