Solar towers in test operation. In Jülich, the DLR operates a large-scale research facility for solar irradiation testing that is unique in Europe.
Photo Credit: German Aerospace Center (DLR)
Scientific Frontline: Extended "At a Glance" Summary: The PAINT Database for Solar Power Tower Plants
The Core Concept: The PAINT database is a freely accessible, FAIR-compliant dataset containing comprehensive operational data from the Jülich Solar Tower test power plant. It provides researchers with real-world information to accelerate the development of more efficient and reliable solar thermal energy generation.
Key Distinction/Mechanism: While photovoltaic systems generate electricity directly, solar towers use movable mirrors (heliostats) to direct sunlight onto a central receiver to generate heat. Operating these systems is highly complex; PAINT bridges the research gap by offering open-source access to 849 gigabytes of structured operational data, allowing engineers to simulate and optimize control mechanisms through digital twins and AI without needing direct access to physical power plants.
Major Frameworks/Components:
- FAIR Principles: Guiding data formatting to ensure it is Findable, Accessible, Interoperable, and Reusable.
- Spatio-Temporal Asset Catalog (STAC): A standard used to structure spatial and temporal data for optimal human and machine readability.
- Python Integration: Dedicated software that allows researchers to download specific heliostat data and feed it directly into machine-learning models.
- Extensive Metric Repositories: Includes the precise positions, dimensions, and dynamic movements of 2,014 mirrors, alongside weather data, measurements of mirror surface warping, and over 218,000 alignment-verification images.
Branch of Science: Renewable Energy, Artificial Intelligence, Scientific Computing, Data Science, and Mechanical Engineering.
Future Application: The dataset will serve as a foundational tool for training machine-learning models and building digital twins. These virtual replicates will enable the real-time correction of mirror misalignment and the optimization of operational control values, ultimately improving the durability and output of commercial solar power towers.
Why It Matters: Safe, efficient operation of solar power towers is traditionally expensive and mechanically difficult. By providing the global research community with the real-world data necessary to test optimization algorithms, PAINT accelerates the adoption of solar thermal technology, which provides a critical advantage: the ability to store thermal energy to stabilize power grids at night or during cloudy days.
Researchers from KIT and DLR Publish a Freely Accessible Dataset to Accelerate Research on Solar Thermal Energy Generation and Its Subsequent Development
Solar power towers can play an important role in the energy transition. They convert sunlight into heat that can be stored or used to generate electricity. Until now, however, data to test new methods for more efficient and reliable systems have been lacking. In a world first, researchers from the Karlsruhe Institute of Technology (KIT) and the German Aerospace Center (DLR) are publishing freely accessible operational data from the Jülich Solar Tower test power plant. This provides a foundation for developing new AI methods and digital twins. The results were published in Nature Energy.
Solar tower power plants do not convert sunlight directly into electricity but generate heat as an intermediate step. An array of movable mirrors, known as heliostats, directs the light precisely onto a receiver located at the top of a central tower. The heat generated there can be stored, used directly for electricity generation, or utilized in industrial processes. However, if there is no immediate electricity demand, such a power plant can also supply energy at night or on cloudy days, thereby helping to stabilize the power grid. Although commercial solar tower power plants do exist, they are not yet widely used compared with photovoltaic systems. “Operating solar power tower plants safely and efficiently is a complex and expensive task,” said Dr. Kaleb Phipps of KIT’s Scientific Computing Center. “To develop and reliably test new processes, researchers need real-world operational data. Our PAINT database provides this information in an open and structured format.”
Data for AI Models and Digital Twins
PAINT adheres to the FAIR principles: data should be findable, accessible, interoperable, and reusable. The data provided by the research team are based on the Spatio-Temporal Asset Catalog (STAC) standard. It describes spatial and temporal data in a way that is readable by both humans and machines. In addition, the team provides Python software that allows researchers to download data for individual heliostats or specific time periods and integrate it directly into machine-learning models. The data can also be used to develop digital twins of solar tower power plants, which are virtual replicas of real-world facilities.
“Digital twins like these enable us to test power plant operation on a simulation model first,” said DLR scientist Dr. Daniel Maldonado Quinto. “If we combine them with machine learning, we will be able to determine in real time whether the mirrors are properly aligned and how the power plant’s control values need to be adjusted to ensure safe and efficient operation.”
A Basis for Further Research
PAINT comprises 849 gigabytes of operational data from the Jülich Solar Tower covering the years 2021 through 2024. This includes information on the exact positions of the 2,014 mirrors, their dimensions, and their possible rotation and tilting movements. Additionally, more than 218,000 images are available to verify whether the mirrors direct the light precisely to the intended point. Additional measurement data indicate any slight warping of mirror surfaces. Weather data for the entire period can also be retrieved.
The alignment of the heliostats is one of the key challenges. Even minor deviations—caused by factors such as wind, wear and tear, or imprecise control—can reduce performance or put a strain on the components. PAINT is therefore intended to help investigate such effects in the future and to enable comparable testing of control methods. “We would like to continue the development of PAINT in collaboration with other research institutions and power plant operators,” said Phipps. “As data from different facilities are added in the future, it will be possible to develop a common standard for open operational data in solar tower research. This would speed up development and promote widespread adoption of this technology.”
PAINT emerged from work on ARTIST, an AI-based, differentiable ray-tracing model for digital solar tower twins. The project involved researchers, engineers, and technicians from KIT, DLR, and the Helmholtz AI platform.
Research material: The PAINT Database
Published in journal: Nature Energy
Authors: Kaleb Phipps, Mathias Kuhl, Marie Weiel, Marlene Busch, Jan Lewen, Nicolas Blumenröhr, Daniel Maldonado Quinto, Charlotte Debus, Felix Göhring, Oliver Kaufhold, Achim Streit, Robert Pitz-Paal, Markus Götz, and Max Pargmann
Source/Credit: Karlsruhe Institute of Technology
Edited by: Scientific Frontline
Reference Number: env061626_01
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