Spatiotemporal Analysis of Irradiance Data using Krigin

Solar power variability is a concern to grid operators as unanticipated changes in photovoltaic (PV) plant power output can strain the electric grid. The main cause of solar variability is clouds passing over PV modules. However, geographic diversity across a region leads to a reduction in cloud-induced variability, but the reduction depends on cloud speed. To illustrate the magnitude of solar variability, irradiance and PV power output datasets are first evaluated, validated and applied to detect the largest aggregate ramp rates in California.;Afterwards, spatiotemporal correlations of irradiance data are analyzed and cloud motion is estimated using two different methods; the cross-correlation method (CCM) applied to two or a few consecutive time steps and cross-spectral analysis (CSA) where the cloud speed and direction are estimated by cross-spectral analysis of a longer timeseries. CSA is modified to estimate the cloud motion direction as the case with least variation for all the velocities in the cloud motion direction. To ensure reliable cloud motion estimation, quality control (QC) is added to the CSA and CCM. The results show 33% (52°) and 21% (6°) improvement in the cloud motion speed (direction) estimation using the modified CSA and CCM over the original methods (without QC), respectively.;Spatial and spatiotemporal ordinary Kriging methods are applied to model irradiation at an arbitrary point. The correlations among the irradiances at observed locations are modeled by general parametric covariance functions. Besides the isotropic covariance function (which is independent of direction), a new non-separable anisotropic parametric covariance function is proposed to model the transient clouds. Also, a new approach is proposed to estimate the spatial and temporal decorrelation distances analytically using the applied parametric covariance functions, which reduced the size of the computations without loss in accuracy (parameter shrinkage). Results confirm that the non-separable anisotropic parametric covariance function is most accurate with an average normalized root mean squared error (nRMSE) of 7.92% representing a 66% relative improvement over the persistence model.;The results confirm the accuracy and reliability of the Kriging method for estimating irradiation at an arbitrary point even in more challenging real applications where cloud motion is unknown.