Validation of a closed-loop aim point management system at the Jülich solar tower

Oberkirsch, Laurin; Zanger, David; Hoffschmidt, Bernhard

doi:10.1016/j.solener.2023.111951

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…To assess the effectiveness of our approach, we conducted a proof-of-concept field test at a research CSP facility in Jülich, Germany. Our method consistently enhances the annual irradiance forecast, enabling the implementation of modern optimal aim point strategies [23]. Even for smaller power plants, such as Gemasolar in Almeria, our approach has the potential to increase yearly revenues by up to 39%, equating to approximately $3 million dollar [24].…”

Section: Introductionmentioning

confidence: 97%

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

Pargmann,

Ebert,

Quinto

et al. 2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 97%

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

Pargmann,

Ebert,

Quinto

et al. 2024

Preprint

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show abstract

Heliostat field aiming strategy based on deterministic optimization: An experimental validation

Sánchez-González,

Kontopyrgos,

Milidonis

et al. 2024

Renewable Energy

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

Pargmann,

Ebert,

Götz

et al. 2024

Nat Commun

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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 data-driven 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. The new optimization pipeline enables concurrent training of physical and data-driven models, representing a pioneering effort in unifying both paradigms for concentrating solar power plants and can be a blueprint for other domains.

show abstract

Validation of a closed-loop aim point management system at the Jülich solar tower

Cited by 3 publications

References 58 publications

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

Heliostat field aiming strategy based on deterministic optimization: An experimental validation

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

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