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Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing

Pargmann, Max ; Ebert, Jan; Götz, Markus ORCID iD icon 1; Maldonado Quinto, Daniel; Pitz-Paal, Robert; Kesselheim, Stefan
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

Concentrating solar power plants are a clean energy source capable of competitive electricity generation even during night time, as well as the production of carbon-neutral fuels, offering a complementary role alongside photovoltaic
plants. In these power plants, thousands of mirrors (heliostats) redirect sunlight onto a receiver, potentially generating temperatures exceeding 1000°C. Practically, such efficient temperatures are never attained. Several unknown,
yet operationally crucial parameters, e.g., misalignment in sun-tracking and surface deformations can cause dangerous temperature spikes, necessitating high safety margins. For competitive levelized cost of energy and large-scale deployment, in-situ error measurements are an essential, yet unattained factor. To tackle this, we introduce a differentiable ray tracing machine learning approach that can derive the irradiance distribution of heliostats in a datadriven manner from a small number of calibration images already collected in most solar towers. By applying gradient-based optimization and a learning non-uniform rational B-spline heliostat model, our approach is able to determine sub-millimeter imperfections in a real-world setting and predict heliostat-specific irradiance profiles, exceeding the precision of the state-of-the-art and establishing full automatization. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000173960
Veröffentlicht am 05.09.2024
Originalveröffentlichung
DOI: 10.1038/s41467-024-51019-z
Scopus
Zitationen: 1
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Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 2041-1723
KITopen-ID: 1000173960
HGF-Programm 46.21.04 (POF IV, LK 01) HAICU
Weitere HGF-Programme 46.21.01 (POF IV, LK 01) Domain-Specific Simulation & SDLs and Research Groups
Erschienen in Nature Communications
Verlag Nature Research
Band 15
Heft 1
Seiten Art.-Nr.: 6997
Projektinformation ARTIST (HGF, HGF-IVF-2021 IID, ZT-I-PF-5-159)
Vorab online veröffentlicht am 14.08.2024
Nachgewiesen in Dimensions
Scopus
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