A regression-based parametric model for radiative flux density distribution considering shadowing and blocking effects

In solar power tower system, the Radiative Flux Density Distribution (RFDD) on the receiver surface reflected by a heliostat is influenced by various factors, referred to as scene parameters. The previous analytical models simplify the complex optical modeling process, thus neglecting the comprehensive impacts of the scene parameters, resulting in simulation errors. In this paper, a regression-based parametric model, namely Neural Elliptical Gaussian (NEG), is proposed to address this issue. The NEG model comprehensively considers the impacts of various scene parameters on the RFDD, including heliostat size, slant distance of heliostat, incident angle of sunlight, sunlight distribution parameter, slope error, etc. The relationship between the scene parameters and the RFDD is established using a neural network. Additionally, the overlooked shadowing and blocking effects in the conventional analytical models and data-driven methods are addressed by introducing the flux spot centroid offset in the NEG model. Since the NEG model is established based on statistical regression using a sampled and more accurate flux spot dataset, it shows more accurate flux spot prediction ability. Experimental results show that, for most scenarios, the root mean squared error is less than 0.35%, and the total energy error and peak value error are less than 5%.