As developments and investments in solar energy systems continue to grow, the need for accurate and reliable data becomes critical for any kind of solar energy application. The International Energy Agency Photovoltaic Power Systems Program (IEA PVPS) is at the forefront of advancing research, disseminating valuable information and ensuring the smooth integration of solar energy into modern energy systems. For years, a key resource in this mission was the “Handbook of best practices for collecting and using data on solar energy resources”, now in his fourth edition.
The manual was 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 energy resource assessment and forecasting, reflecting the significant progress made since 2021 in solar radiation measurement and modeling and related topics.
This article reviews the latest developments presented in the handbook, summarizes key updates, and explores why accurate solar resource data is essential for effective solar investment and deployment.
The importance of solar resource data
Solar energy technologies have experienced tremendous growth in recent decades, driven by both technological advances and the ever-growing need for carbon-free energy. In 2022, its deployment reached more than 1 TW of installed capacity worldwide, and it has become one of the most cost-competitive energy technologies overall.
As such, having accurate data on solar energy resources is fundamental to any form of solar energy application, impacting project feasibility, performance modeling and operational efficiency. As outlined in the handbook, accurate solar irradiance data allows developers to predict energy output, optimize system designs, and calculate return on investment for large-scale solar projects.
The handbook emphasizes that solar resource data not only influences the technical aspects of energy generation, but also plays a crucial role in conducting affordable financial analysis for different types of projects. This issue is even more important when it comes to long-term irradiance data for seasonal and diurnal variations, helping developers reduce financial risks.
Understanding the components of solar radiation
The textbook repeatedly emphasizes that to fully understand solar radiation, it is crucial to understand how it interacts with Earth’s atmosphere. Solar radiation consists of several components relevant 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 that rely heavily on DNI and PV systems that use both DNI and DHI.
The manual provides detailed definitions of the alien radiation spectrum, the solar constant and the effect that atmospheric conditions have on radiation levels. Understanding these factors is critical for both designing solar energy systems and predicting their performance.
Measurement techniques and data collection
The manual describes in detail several best practices for ensuring accuracy in solar radiation measurement, an essential part of the design, implementation and operation of solar power plant projects.
Each project uses different types of solar technology, and some of the interesting radiometers for recording 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 appropriate sensor technologies, it is also crucial that they are used and maintained correctly 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 of 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 highlights best practices to avoid inaccurate measurements, which can occur due to instrument malfunctions, environmental factors, or human supervision.
Because 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 testing and daily visual inspections to flag erroneous or suspicious data points, along with analysis of long-term data sets to identify potential measurement issues that may have gone undetected.
Although solar radiation is the main focus of the publication, it is also emphasized 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, solar collector contamination, wind conditions, humidity, precipitation and atmospheric pressure.
These considerations need to be extended to take into account unavoidable natural variations caused by the Sun-Earth geometry, as well as atmospheric processes and weather or climate patterns on different time scales. Understanding this is the key to correctly designing and financing large solar energy projects.
Forecasting and modeling
Data collection is just one aspect of solar data management. The other is short-term forecasting, which is essential for the operation of electricity grids and energy management systems, as well as for the marketing of solar energy.
Different forecast horizons are better suited to different input data and models, and depending on spatiotemporal resolution, can be derived from all-sky imagers (ASIs), satellite images, and numerical weather prediction (NWP) models.
The handbook also highlights the increasing role that statistical and machine learning (ML) methods play in predicting solar irradiance and power, and how they can complement empirical and physical models.
By integrating prediction models and inputs, PV services can provide major optimization. Prominent examples are intraday forecasting systems up to several hours in advance that integrate online measurements, satellite-based forecasts and NWP model forecasts or day-ahead 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 to developers, engineers and financiers, providing an example of a hypothetical project and the different types of data required at all stages of a project (prefeasibility and planning, feasibility, due diligence and financing). and Operation and maintenance)
This chapter also discusses the type of solar radiation data needed 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 in the handbook is how the bankability of solar radiation data sets is a critical part of securing financing and insurance for solar projects. Having an accurate assessment of a project’s risks and potential returns will lead to greater confidence in the project, which in turn will lead to increased investment and the wider adoption of renewable energy sources.
Future work
As evolving methods of solar energy generation require further improvements in our understanding, determination and prediction of solar radiation sources, the handbook elaborates on the work plan of IEA PVPS Task 16 on “Solar sources for high penetration and large-scale applications.”
There are also several suggestions for areas for 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 full Handbook of best practices for collecting and using data on solar energy resourcesyou can download the fourth edition here.
About IEA PVPS task 16
IEA PVPS Task 16 focuses on the study and advancement of solar energy resource data and forecasts for high-penetration and large-scale solar applications. Her work supports solar energy deployment by developing best practices, improving the accuracy of solar resource data, and improving methods for predicting 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 to investors, developers and energy planners.
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