Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

Abstract

Designing, financing, and operating successful solar heating, concentrating solar power, and photovoltaic systems requires reliable information about the solar resource available and its variability over time. In the past, seasonal and daily variability has been studied and understood; however, with new solar technologies becoming more important in energy supply grids, small time-scale effects are critical to successful deployment of these important low carbon technologies. A vital part of the bankability of solar projects is to understand the variability of the solar resource so that supply and storage technologies can be optimized. This handbook is the result of 10 years of international collaboration carried out by experts from the International Energy Agency's (IEA's) Solar Heating and Cooling (SHC), Solar PACES, and Photovoltaic Power Systems Technology Collaboration Programmes. Under IEA SHC Task 46: Solar Resource Assessment and Forecasting, experts from 11 countries produced information products and best practices on solar energy resources that will greatly benefit project developers and system operators as well as assist policymakers in advancing renewable energy programs worldwide.Meteorologists, mathematicians, solar technology specialists, and other key solar resource experts from around the world joined forces to further our understanding of the sun's temporal and spatial variability through benchmarking satellite-derived solar resource data and solar forecasts, developing best practices for measuring the solar resource, and conducting research to improve satellite-based algorithms. The results of IEA SHC Task 46 are useful to a wide range of users of solar heating and cooling, photovoltaics, and concentrating solar power systems and of building developers and owners as well as anyone else who needs to understand and predict sunlight for agricultural or other purposes.The earlier edition of the handbook, which was published in 2015, is used worldwide as a reference for each stage of a solar energy project. Since that time, there has been substantial growth in the interest in high-quality "bankable" solar resource data. This revision adds significant new methods so it will be even more useful. This publication is a summary that details the fundamentals of solar resources as well as captures the state of the art. For those wanting more depth, it also provides the references where more detailed information can be found. I would like to acknowledge the leadership of the National Renewable Energy Laboratory and express appreciation to the U.S. Department of Energy for producing the handbook and incorporating results from IEA SHC Task 46.

Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa

“…The mean bias difference (MBD), the root mean square difference (RMSD), and mean absolute difference (MAD) in absolute (Wm -2 ) and relative (%) values (rMBD, rRMSD and rMAD) were calculated according to Equations 1 to 6 [3,7,24]. In addition to these, the R 2 correlation coefficient was also calculated using Equation 7 [7,24].…”

“…Ground measurements are the first choice to describe the irradiance levels at a certain location, providing high quality data, depending on adequate maintenance of the instruments used in the measuring campaign [3]. When no on-site measurements are available, other techniques should be applied, such as empirical models based on meteorological variables like temperature or sunshine duration [3,4] or models based on reanalysis and retrospective weather prediction models [5,6]. Satellite-based models have become a very powerful tool for estimating the solar resource at high and uniform spatial resolution (typically a few kilometres) and temporal resolution (hourly or better) over large geographical areas [3,7].…”

“…When no on-site measurements are available, other techniques should be applied, such as empirical models based on meteorological variables like temperature or sunshine duration [3,4] or models based on reanalysis and retrospective weather prediction models [5,6]. Satellite-based models have become a very powerful tool for estimating the solar resource at high and uniform spatial resolution (typically a few kilometres) and temporal resolution (hourly or better) over large geographical areas [3,7]. Satellite-based estimates have been validated in the scientific literature [8][9][10][11][12].…”

Evaluation of WRF-DART (ARW v.3.9.1.1 and DART manhattan release) multi-phase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

“…Numerical weather prediction (NWP) models are appropriate to forecast clouds and solar irradiance for lead times of more than 6 hours ahead (Sengupta et al, 2017). Global circulation models (GCMs) currently provide weather information with an 15 update frequency of four times per day, a temporal resolution of typically one hour and spatial resolutions of at least 10 km.…”

Sources of uncertainty in annual global horizontal irradiance data

“…Photodiode sensors are based on the photovoltaic effect and are an attractive alternative to thermopiles for remote areas and agricultural monitoring stations because of their significantly lower cost and less maintenance. Besides, their fast time response makes them the detectors used by rotating shadowband irradiometers (RSI) with continuous rotation, which provide simultaneous records of G, B and D by shading and unshading the detector periodically (Sengupta et al, 2017). However, photodiodes generally have a lower accuracy than thermopiles, mainly due to the narrow spectral response of silicon.…”