Optimizing heliostat positions with local search metaheuristics using a ray tracing optical model

Reinholz, Andreas; Husenbeth, Christof; Schwarzbözl, Peter; Buck, Reiner

doi:10.1063/1.4984557

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…layout procedure positioning of oversized field of individual heliostats in a parameterised regular pattern; annual performance calculation for each heliostat position; selection of best-performing heliostats to meet design point power requirement (see Fig. 57) (Schwarzbözl et al, 2009 improvement by using a local search metaheuristic (based on raytracer STRAL) (Reinholz et al, 2016) A completely different approach was tried with the Heliostat Growth Method (YNES), where the field is built up one by one by setting each heliostat on the best available position. This method follows pure physics and is free from any predefined patterns but lacks from the fact that in the region close to the tower some compromise must be made (i.e.…”

Section: Methods and Tools For Heliostat Field Layoutmentioning

confidence: 99%

Progress in heliostat development

Pfahl

Coventry

Röger³

et al. 2017

Solar Energy

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131

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Strong efforts are being made to drive heliostat cost down. These efforts are summarised to give an update on heliostat technology comprising: determination of wind loads, heliostat dimensioning, solutions for the different sub-functions of a heliostat, a review of commercially available and prototype heliostat designs, canting, manufacturing, qualification, heliostat field layout, and mirror cleaning. There is evidence that commercial heliostat costs have dropped significantly in the past few years, with commercial suppliers of heliostat technologies now claiming heliostat field costs around 100 USD/m 2. With new approaches even target cost of 75$/m² seem to be realistic.

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Section: Methods and Tools For Heliostat Field Layoutmentioning

confidence: 99%

Progress in heliostat development

Pfahl

Coventry

Röger³

et al. 2017

Solar Energy

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131

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“…Almost all research dealing with the optimization of CST, systems employ approaches to reduce the computational effort needed to estimate annual energy productions or other key annual characteristics, which are based upon numerical interpolation and integration over the sun path [2][3][4]. Such approaches can be based on a simple sampling of a few characteristic days using a fixed time step [5] or on more advanced concepts involving various coordinate transformations and splines [6].…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

et al. 2021

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Integrals that are of interest in the analysis, design, and optimization of concentrating solar thermal systems (CST), such as the annual optical efficiency of the light collection and concentration (LCC) subsystem, can be accurately computed or estimated in two distinct ways: on the time domain and on the spatial domain. This article explores these two ways, using a case study that is highly representative of the commercial CST systems being deployed worldwide. In the time domain, the computation of these integrals are explored using 1-min, 10-min, and 1-h solar DNI input data and using The Cyprus Institute (CyI)’s High-Performance Computing (HPC) system and an open-source ray tracer, Tonatiuh++, being actively developed at CyI. In the spatial domain, the computation of these integrals is explored using SunPATH, another open-source software tool being actively developed at CyI, in tandem with Tonatiuh++. The comparison between the time and spatial domain approach clearly indicate that the spatial domain approach using SunPATH is dramatically more computationally efficient than the time domain approach. According to the results obtained, at least for the case study analyzed in this article, to compute the annual energy delivered by the LCC subsystem with a relative error less than 0.1%, it is enough to provide SunPATH with 1-h DNI data as input, request from SunPATH the sun position and weights of just 30 points in the celestial sphere, and run Tonatiuh++ to simulate these 30 points using 15 million rays per run. As the test case is highly representative, it is expected that this approach will yield similar results for most CST systems of interest.

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4.17 Solar Tower Systems

Buck¹,

Schwarzbözl²

2018

Comprehensive Energy Systems

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Optimizing heliostat positions with local search metaheuristics using a ray tracing optical model

Cited by 4 publications

References 8 publications

Progress in heliostat development

Progress in heliostat development

Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

4.17 Solar Tower Systems

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