| Work Detail |
In its latest monthly column for pv magazine , IEA PVPS provides an overview of the recently published fourth edition of the “Handbook of Good Practices for the Collection and Use of Solar Resource Data.” The 530-page handbook, developed by IEA-PVPS Task 16 in collaboration with NREL, explains in detail the methods and models for accurately collecting solar data to effectively plan and operate energy projects.
As developments and investments in solar energy systems continue to grow, the need for accurate and reliable data becomes vitally important for any type of solar application. The International Energy Agency’s Photovoltaic Power Systems Programme (IEA PVPS) has been at the forefront 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 was developed under the leadership of the National Renewable Energy Laboratory (NREL) together with 51 authors from 15 countries working under 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 reflects the considerable progress that has been made since 2021 in solar radiation measurement and modelling and related topics.
This article delves into the latest developments presented in the handbook, summarizing its most significant updates and examining why accurate solar resource data is essential for effective solar energy investment and deployment.
The importance of solar resource data
Solar energy technologies have seen tremendous growth in recent decades, driven by both technological advances and the ever-increasing need for carbon-free energy. In fact, its deployment reached more than 1 TW of installed capacity worldwide in 2022, and it has become one of the most cost-competitive energy technologies.
As such, accurate solar resource data is critical for any type of solar application, as it impacts project feasibility, performance modeling, and operational efficiency. As outlined in the handbook, accurate solar irradiance data enables developers to predict energy production, optimize system design, and calculate ROI for large-scale solar projects.
The handbook stresses that solar resource data not only affects the technical aspects of power generation, but also plays a crucial role in performing bankable financial analysis for different types of projects. This issue is even more important when it comes to long-term irradiance data for seasonal and daily variations, which helps developers mitigate financial risks.
Understanding the components of solar radiation
The handbook repeatedly stresses that to fully understand solar irradiance, it is vital to understand how it interacts with the Earth’s atmosphere. Solar irradiance is composed of several components that are relevant to solar energy conversion technologies: direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and global horizontal irradiance (GHI). Each of these plays a different role in energy production depending on the solar technology used, such as ESTC systems that rely heavily on DNI and PV systems that use both DNI and DHI.
The manual provides detailed definitions of the extraterrestrial irradiance spectrum, the solar constant, and the effect of atmospheric conditions on irradiance levels. Understanding these factors is critical to both designing solar energy systems and predicting their performance.
Measurement and data collection techniques
The manual describes in detail several best practices to ensure accuracy in solar radiation measurement, a vital component of the design, implementation and operation of solar power plant projects.
Each project uses different types of solar technology, and some of the radiometers of interest for capturing measurements include pyranometers for GHI, pyrheliometers for DNI, and pyrgeometers for longwave radiation. Each of these tools has its strengths and drawbacks, and selecting the right tool depends on the specific needs of the project.
In addition to choosing the right sensor technologies, it is also vital that they function properly and are maintained to generate reliable irradiance measurement data.
Another factor to consider when planning a project is that the station must be designed to avoid the effects of shading or other external influences, and it is essential that qualified personnel carry out regular inspections.
Evaluating data quality and variables to be taken into account
A new chapter on data quality assessment highlights best practices for avoiding inaccurate measurements, which can result from instrument malfunctions, environmental factors, or human oversight.
Since the accuracy of modeled irradiance data approaches that of measured irradiance, the authors explain introductory concepts of quality assessment and control, as well as many specific procedures, such as measurements from tilted radiometers, photodiodes, spectral sensors, and reference photovoltaic cells.
The manual also suggests applying automated testing and daily visual inspections to flag erroneous or suspicious data points, along with analyzing long-term data sets to potentially detect some measurement issues that might have gone undetected.
Although solar radiation is the main topic of the publication, it also highlights that other meteorological variables must be taken into account, due to the effects they can have on solar installations. Among the most notable are ambient air temperature, dirtiness of solar collectors, wind conditions, humidity, precipitation and atmospheric pressure.
These considerations must be extended to take into account the inevitable natural variations caused by Sun-Earth geometry, as well as atmospheric processes and weather or climate patterns at various time scales. Understanding this is key to properly designing and financing large solar energy projects.
Forecasting and modelling
Data collection is only one aspect of solar data management. The other is short-term forecasting, which is vital for managing power grids and energy management systems, as well as for marketing solar energy.
Different forecast horizons are better suited to different input data and models and, depending on the spatiotemporal resolution, can be derived from all-sky imagery (ASI), satellite imagery, and numerical weather prediction (NWP) models.
The handbook also highlights the increasingly important role played by statistical and machine learning (ML) methods in forecasting solar irradiance and power, and how they can complement empirical and physical models.
By integrating forecasting models and inputs, PV services can provide significant optimization. Prominent examples include intraday forecasting systems several hours in advance that integrate online measurements, satellite forecasts, and NWP model forecasts, or daytime forecasting systems that combine different NWP models.
How solar resource data can be applied to energy projects
How can all this data be put to practical use? The practical applications section of the handbook is particularly relevant for developers, engineers and financiers, as it provides an example of a hypothetical project and the different types of data that would be needed in all phases of a project (Pre-feasibility and planning, Feasibility, Due diligence and financing, and Operations and maintenance).
This chapter also discusses the type of solar irradiance data required for different types of solar projects, such as flat plate thermal collectors, solar heating and cooling in buildings, smart grids, solar desalination, and other chemical applications.
A key theme throughout the handbook is how bankability of solar radiation datasets is a crucial component in ensuring the financing and insurance of solar energy projects. Having an accurate assessment of the risks and potential benefits of a project will increase confidence in the project, which in turn will lead to increased investment and greater 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 the work plan of IEA PVPS Task 16 on “Solar resource for high penetration and large-scale applications”.
There are also several suggestions for areas of future research, such as the impact of climate change on solar radiation levels and the potential of new machine learning techniques to improve both solar resource assessment and forecast accuracy.
Author: Ignacio Landivar
Download the manual
To access the Handbook of Good Practices for the Collection and Use of Solar Resource Data, you can download the fourth edition here .
About IEA PVPS Task 16
IEA PVPS Task 16 focuses on studying and advancing solar resource data and forecasts for high-penetration and large-scale solar applications. Its work supports solar energy deployment by developing best practices, improving the accuracy of solar resource data, and improving 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 essential tools and knowledge for investors, developers and energy planners. |
