HelioCon: A roadmap for advanced heliostat technologies for concentrating solar power

Zhu, Guangdong; Augustine, Chad; Mitchell, Rebecca; Muller, Matthew; Kurup, Parthiv; Zolan, Alexander; Yellapantula, Shashank; Brost, Randy; Armijo, Kenneth; Sment, Jeremy; Schaller, Rebecca; Gordon, Margaret; Collins, Mike; Coventry, Joe; Pye, John; Cholette, Michael; Picotti, Giovanni; Arjomandi, Maziar; Emes, Matthew; Potter, Daniel; Rae, Michael

doi:10.1016/j.solener.2023.111917

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Thereby, the heliostat field is one of the largest contributors to the energy yield as well as levelized cost. Heliostat technology is consequently subject to high research interest and rapid engineering improvements [10]. High-quality optical heliostat performance at minimal cost is a key design goal to achieve CSPs' commercial success.…”

Section: Introductionmentioning

confidence: 99%

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: 99%

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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“…This technology stands out for its cleanliness, high efficiency, and energy storage capabilities, rendering it a pivotal choice for future energy demands [2]. The U.S. Department of Energy's (DOE) Office of Solar Energy Technology (SETO) launched the HelioCon Consortium and presented a roadmap study to advance heliostat technology [3], demonstrating the strategic value of solar thermal power plants.…”

Section: Introductionmentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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Confronted with the challenges posed by climate change and the ongoing energy transition, solar energy is one of the important new energy sources, and the tower solar power plant has become an innovative solution to promote clean energy development. The optimization of heliostat field layout constitutes a crucial aspect in enhancing the operational efficiency of a concentrated solar power tower plant. Currently, the optimization of heliostat field layout has garnered widespread attention. In this paper, we propose the swarm optimization algorithm with niching and elite competition called NECSO to solve the large-scale heliostat field layout optimization. First, aiming to increase diversity and heterogeneity within the population, we employ a random grouping strategy to partition the population into distinct sub-swarms. Then, we design a niching and elite competition mechanism to harmonize the performance of the exploration. The niching competition is carried out within any sub-swarm to enhance the explorability of particles. The elite competition occurs between the elites which select from each sub-swarm to improve the convergence of particles. Additionally, we develop a mathematical model for the optimization of heliostat field layout. This model employs currently advanced computational methods, facilitating prompt and precise calculation of the optical efficiency in the heliostat field layout. To evaluate the performance of NECSO, we design 15 practical cases of heliostat field layout with varying scales. And then, we conduct comparative experiments with eight currently mainstream and excellent algorithms. The results indicate that NECSO exhibits competitive performance in solving the heliostat field layout optimization, particularly in large-scale cases.

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Heliostat wind loads in the atmospheric boundary layer (ABL): Reconciling field measurements with wind tunnel experiments

Emes,

Marano,

Arjomandi

2024

Solar Energy

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HelioCon: A roadmap for advanced heliostat technologies for concentrating solar power

Cited by 7 publications

References 18 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 Layout via Niching and Elite Competition Swarm Optimization

Heliostat wind loads in the atmospheric boundary layer (ABL): Reconciling field measurements with wind tunnel experiments

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