Automatic Heliostat Learning for In-situ Concentrating Solar Power Plant Optimization with Differentiable Ray Tracing

Automatic Heliostat Learning for In-situ Concentrating Solar Power Plant Optimization with Differentiable Ray Tracing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Automatic Heliostat Learning for In-situ Concentrating Solar Power Plant Optimization with Differentiable Ray Tracing Max Pargmann, Jan Ebert, Daniel Maldonado Quinto, Robert Pitz-Paal, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2554998/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Nature Communications → Version 2 posted You are reading this latest preprint version Show more versions Abstract Concentrating solar power plants (CSPs) 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 CSPs, 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 data-driven manner from a small number of calibration images already collected in most solar thermal power plants. By applying gradient-based optimization and a learning NURBS 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. The new optimization pipeline enables concurrent training of physical and data-driven models, representing a pioneering effort in unifying both paradigms for CSPs and can be a blueprint for other domains. Physical sciences/Energy science and technology/Renewable energy/Solar energy/Solar thermal energy Physical sciences/Energy science and technology/Renewable energy/Solar energy/Solar fuels Physical sciences/Energy science and technology/Energy infrastructure/Power stations Physical sciences/Mathematics and computing/Software Physical sciences/Mathematics and computing/Computational science Solar Tower Heliostat Field Differentiable Ray Tracing Surface Diagnosis NURBS Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Nature Communications → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2554998","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":173981706,"identity":"082acec5-336e-45c1-81ba-4de43f52d285","order_by":0,"name":"Max 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