Solar Resource Variability

Author(s):  
Richard Perez ◽  
Thomas E. Hoff
Author(s):  
David K. Click ◽  
Houtan Moaveni ◽  
Kristopher O. Davis ◽  
Richard H. Meeker ◽  
Robert M. Reedy ◽  
...  

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Aron Habte

<p>Understanding long-term solar resource variability is essential for planning and deployment of solar energy systems. These variabilities occur due to deterministic effects such as sun cycle and nondeterministic such as complex weather patterns. The NREL’s National Solar Radiation Database (NSRDB) provides long term solar resource data covering 1998- 2019 containing more than 2 million pixels over the Americas and gets updated on an annual basis. This dataset is satellite-based and uses a two-step physical model for it’s development. In the first step we retrieve cloud properties such as cloud mask, cloud type, cloud optical depth and effective radius. The second step uses a fast radiative transfer model to compute solar radiation.  This dataset is ideal for studying solar resource variability. For this study, NSRDB version 3 which contains data from 1998-2017 on a half hourly and 4x4 km temporal and spatial resolution was used. The study analyzed the spatial and temporal trend of solar resource of global horizontal irradiance (GHI) and direct normal irradiance (DNI) using long-term 20-years NSRDB data. The coefficient of variation (COV) was used to analyze the spatio-temporal interannual and seasonal variabilities. The spatial variability was analyzed by comparing the center pixel to neighboring pixels. The spatial variability result showed higher COV as the number of neighboring pixels increased. Similarly, the temporal variability for the NSRDB domain ranges on average from ±10% for GHI and ±20% for DNI. Furthermore, the long-term variabilities were also analyzed using the Köppen-Geiger climate classification. This assisted in the interpretation of the result by reducing the originally large number of pixels into a smaller number of groups. This presentation will provided a unique look at long-term spatial and temporal variability of solar radiation using high-resolution satellite-based datasets.</p>


Author(s):  
Muhammad M. Rafique ◽  
Graham Nathan ◽  
Woei Saw

Abstract In this paper, the effect of solar resource variability has been assessed on the start-up time and different heat transfer phenomena associated with a high temperature particle receiver. The receiver analyzed in this study has a cylindrical cavity made of three different layers in order to have good absorption, higher durability and lower thermal heat losses. A detailed transient mathematical model is developed, considering the input solar energy to the receiver aperture and all heat losses from the receiver cavity. The developed transient model is employed to study the time required to achieve a receiver start-up temperature from room temperature to 1000°C, under steady-state and transient operation, for the climatic conditions of Pinjarra, Australia. Furthermore, the total energy gain by the receiver and associated heat losses including re-radiation, convection, and conduction have been accounted for, with and without considering the solar resource variability. The results revealed that an uncertainty of about 40% exists in the prediction of the receiver start-up time and associated heat losses during the start-up period under steady state operation, with a constant input heat flux. This uncertainty in the prediction of the receiver start-up time and losses will directly affect the overall performance and design of the receiver, which will result in unscheduled disruption of the industrial process. This indicates a need to analyse the performance of high temperature particle receivers under transient conditions, considering the solar resource variability for practical implementation of this technology to different processes. This will help to investigate better control strategies for the inflow of particles, based on the real-time climatic conditions, to achieve better thermal performance.


2020 ◽  
Vol 134 ◽  
pp. 110285
Author(s):  
Aron Habte ◽  
Manajit Sengupta ◽  
Christian Gueymard ◽  
Anastasios Golnas ◽  
Yu Xie

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2264 ◽  
Author(s):  
Stéphanie Monjoly ◽  
Maina André ◽  
Rudy Calif ◽  
Ted Soubdhan

The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.


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