Solar power towers can play an important role in 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 now 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. (DOI: 10.1038/s41560-026-02070-1 )
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 grids. Although commercial solar tower power plants do exist, they are not widely used yet compared to photovoltaic systems. "Operating solar power tower plants safely and efficiently is a complex and expensive task," said Dr. Kaleb Phipps from 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 is 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 that are virtual replicates 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."
