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In its latest monthly column for pv magazine, IEA PVPS provides a comprehensive overview of the recently released fourth edition of the “Best Practices Handbook for the Collection and Use of Solar Resource Data”. The 530-page handbook, developed by IEA PVPS Task 16 in collaboration with NREL, elaborates on methods and models for accurately collecting solar data to plan and operate energy projects effectively.
As solar energy systems developments and investments continue to grow, the need for accurate and reliable data becomes of vital importance for any kind of solar application. The International Energy Agency Photovoltaic Power Systems Programme (IEA PVPS) has been at the vanguard of advancing research, disseminating valuable information, and ensuring the smooth integration of solar energy into modern energy systems. For many years, a key resource in this mission has been the “Best Practices Handbook for the Collection and Use of Solar Resource Data,” now in its fourth edition.
The handbook has been prepared under the leadership of the National Renewable Energy Laboratory (NREL) together with 51 authors from 15 countries working within the framework of IEA PVPS Task 16 on “Solar Resource for High Penetration and Large-Scale Applications. It marks the rapid evolution in the field of solar resource assessment and forecasting, and it reflects the considerable progress that has occurred since 2021 in the measurement and modeling of solar radiation and related topics.
This article delves into the latest advancements presented in the handbook, summarizing its most significant updates and examining why precise solar resource data is essential for effective solar energy investing and deployment.
The importance of solar resource data
Solar energy technologies have seen tremendous growth over the past few decades, driven by both technological advancements and the ever-growing need for carbon-free energy. In fact, its deployment reached more than 1 TW of installed capacity worldwide in 2022, and it has emerged as one of the most cost-competitive energy technologies overall.
As such, having accurate solar resource data is fundamental to any kind of solar application, impacting project feasibility, performance modeling, and operational efficiency. As outlined in the handbook, precise data on solar irradiance enables developers to predict energy output, optimize system designs, and calculate the return on investment for large-scale solar projects.
The handbook emphasizes that solar resource data not only affects the technical aspects of energy generation but also plays a crucial role in conducting a bankable financial analysis for different kinds of projects. This issue carries even higher importance when it comes to long-term irradiance data for seasonal and daily variations, which helps developers mitigate financial risks.
Understanding solar radiation’s components
The handbook repeatedly emphasizes that to fully understand solar irradiance, it’s vital to understand how it interacts with the Earth’s atmosphere. Solar irradiance is composed of various components that are of relevance to solar energy conversion technologies: direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and global horizontal irradiance (GHI). Each of them plays a different role in energy production depending on the solar technology used, such as CSP systems relying heavily on DNI and PV systems using both DNI and DHI.
The handbook provides detailed definitions of the extraterrestrial irradiance spectrum, solar constant, and the effect that atmospheric conditions have on irradiance levels. Understanding these factors is critical for both designing solar energy systems and forecasting how they will perform.
Measurement techniques and data collection
The handbook describes in detail several optimal practices for ensuring accuracy when measuring solar radiation, a vital component of solar power plant project design, implementation, and operations.
Every project uses different types of solar technology, and some of the radiometers of interest to capture measurements include pyranometers for GHI, pyrheliometers for DNI, and pyrgeometers for longwave radiation. Each of these tools has its strengths and disadvantages, and selecting the right tool depends on the specific needs of the project.
Besides choosing suitable sensor technologies, it’s also vital that they are correctly operated, and maintained to generate reliable irradiance measurement data.
An additional factor to consider when planning a project is that a station must be designed to avoid effects from shading or other external influences, and periodic inspections by trained personnel are imperative.
Data quality assessments and variables to consider
A new chapter on data quality assessments emphasizes the best practices to avoid inaccurate measurements, which can occur due to instrument malfunction, environmental factors, or human oversight.
As the accuracy of modeled irradiance data approaches that of measured irradiance, the authors explain introductory quality assessment and control concepts, as well as many specific procedures, such as measurements from tilted radiometers, photodiodes, spectral sensors, and photovoltaic reference cells.
The handbook also suggests implementing automated tests and daily visual inspections to flag erroneous or suspicious data points, coupled with analysis of long-term datasets to potentially spot some measurement problems that might have not been detected.
While solar radiation is the main focus of the publication, it also highlights that other meteorological variables must be factored, due to the effects they can have on solar installations. Among the most notable are ambient air temperature, soiling of solar collectors, wind conditions, humidity, precipitation, and atmospheric pressure.
These considerations must be extended to account for unavoidable natural variations caused by the Sun-Earth geometry as well as atmospheric processes and weather or climatic patterns at various temporal scales. Understanding this is key to properly designing and financing major solar power projects.
Forecasting and modeling
Collecting data is only one aspect of solar data management. The other is forecasting in the short term, which is vital for the management of electric grids and energy management systems as well as the marketing of solar power.
Different forecast horizons are better suited to different input data and models, and, depending on the spatiotemporal resolution, they can be derived from all-sky imagers (ASIs), satellite imagery, and numerical weather prediction models (NWP).
The handbook also highlights the increasing role that statistical and machine learning (ML) methods play in solar irradiance and power forecasting, and how they can complement empirical and physical models
By integrating forecasting models and inputs, PV services can provide a major optimization. Prominent examples are intraday forecasting systems up to several hours ahead integrating online measurements, satellite-based forecasts, and NWP model forecasts or day-ahead forecasting systems combining different NWP models.
How solar resource data can be applied to energy projects
How can all this data be put to practical use? The handbook’s practical applications section is particularly relevant for developers, engineers, and financiers, providing an example of a hypothetical project and the different types of data that would be required in all stages of a project (Prefeasibility and planning, Feasibility, Due diligence and finance, and Operations and maintenance)
This chapter also discusses the type of solar irradiation data needed for different types of solar projects, such as flat-plate thermal collectors, solar heating and cooling in buildings, smart electric grids, solar desalination, and other chemical applications.
A key theme throughout the handbook is how the bankability of solar radiation datasets is a crucial component of securing financing and insurance for solar energy projects. Having an accurate assessment of a project’s risks and potential returns will lead to increased confidence in the project, which in turn will lead to increased investments and the broader adoption of renewable energy sources
Future work
As advancing solar energy generation methods require further improvements in our understanding, determination, and forecasting of solar radiation resources, the handbook elaborates on IEA PVPS Task 16’s work plan on “Solar resource for high penetration and large-scale applications.”
There are also several suggestions on areas for future research, such as the impact of climate change on solar radiation levels and the potential of new machine-learning techniques for improving both solar resource assessment and forecasting accuracy.
Author: Ignacio Landivar
Download the handbook
To access the fullBest Practices Handbook for the Collection and Use of Solar Resource Data, you can download the fourth editionhere.
About IEA PVPS Task 16
IEA PVPS Task 16 focuses on the study and advancement of solar resource data and forecasting for high-penetration and large-scale solar applications. Its work supports the deployment of solar energy by developing best practices, improving the accuracy of solar resource data, and enhancing methods for forecasting solar energy availability. The Task brings together experts from around the world to improve the quality and bankability of solar projects by providing tools and insights essential for investors, developers, and energy planners.
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