Felix Göhring

Integrating renewable energy is critical for maintaining grid stability while striving towards global climate goals. Concentrating solar power tower plants offer a promising solution but their competitiveness is currently hindered by high operational costs, limited data availability and slow adoption of emerging technologies. To address these barriers, here we introduce PAINT, a FAIR (findable, accessible, interoperable and reusable) open-access database for operational solar tower plant data. PAINT provides 849 GB of high-resolution data collected over multiple years from the Jülich solar tower plant, including heliostat properties, calibration and deflectometry measurements and fine-grained weather data. The database is organized using the SpatioTemporal Asset Catalog metadata specification and supports the development of digital twins, artificial intelligence-based calibration methods, predictive maintenance and improved solar flux prediction. We also introduce standardized benchmarks to promote reproducibility and fair comparisons. Providing access to high-quality data, PAINT enables broader participation in solar research, accelerates innovation and facilitates data-driven solutions in solar tower power plant research.

Concentrated solar thermal (CST) plants can provide dispatchable renewable energy but remain complex and costly to operate due to tightly coupled subsystems, dynamic environmental conditions, and high safety requirements. This work presents the Solar Energy Autopilot (SOLEA), a holistic four-layer architecture that transforms a traditional solar power tower plant into an autonomous cyber-physical system (CPS). The architecture integrates plant hardware and control systems, advanced optical and meteorological measurement systems, a CPS middleware platform with a standardized digital twin, and a multi-agent automation layer for operation planning and control. SOLEA is implemented and evaluated at an operational research facility, where intelligent agents autonomously plan operating days, start and shut down the plant, and regulate the receiver flux and outlet temperature under varying environmental conditions. A dual-sided evaluation quantifies benefits in terms of efficiency, robustness, and cost-related indicators and assesses the system’s autonomy using a multi-dimensional maturity model. The results show substantial reductions in operational and investment costs, highly robust operation despite challenging environmental conditions, and efficiency gains in plant operation enabled by an increased autonomy level of the facility. Overall, the study demonstrates that the layered modular SOLEA architecture can effectively bridge the gap between isolated automation solutions and plant-wide autonomous CST operation and provides a reusable blueprint for advancing autonomy in future CST plants and comparable industrial environments.

Concentrating solar power tower plants hold significant potential for sustainable energy generation but face challenges related to high operational costs, limited data availability, and slow adoption of novel technologies. To address these barriers, we introduce PAINT, the first FAIR open-access database for operational solar tower plant data. PAINT provides 849 GB of high-resolution operational data collected over multiple years from the Jülich solar tower plant, including heliostat properties, calibration and deflectometry measurements, and fine-grained weather data. Organized using the SpatioTemporal Asset Catalog metadata specification, PAINT supports the development of digital twins, AI-based calibration methods, predictive maintenance and fault detection, and improved solar flux prediction. It also introduces standardized benchmarks to promote reproducibility and fair comparisons. By democratizing access to high-quality concentrating solar power data, PAINT enables broader participation in solar research, accelerates innovation, and lays a foundation for data-driven improvements in the performance and reliability of solar tower power plants.