Theory of cellwise optimization for solar central receiver systems

Lipps, F.W.

doi:10.2172/5734792

Cited by 11 publications

(10 citation statements)

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“…In particular, radial‐staggered configuration, originally proposed by the University of Houston for the UHC‐RCELL code, is the most popular and commonly used field layout which has been adopted vastly in many heliostat field projects for both polar and circular fields. In radial‐staggered configurations, back to back rows of mirrors do not share the same azimuth angle resulting in a significant improvement of the blocking factor.…”

Section: Heliostat Field Assessment Methodsmentioning

confidence: 99%

“…The second category of available heliostat field software includes those developed for layout optimization by taking some simplification assumptions to speed up the optimization process such as UHC‐RCELL, DELSOL3 (winDELSOL), HFLCAL, SCT‐HGM, RADIANCE, SENSOL, HFLD, NSPOC, CSPSim, TieSOL, Biomimetic, Campo, and SoFIA . There are several heliostat field algorithms that combine the process of layout generation and optimization.…”

Section: Heliostat Field Assessment Methodsmentioning

confidence: 99%

“…These algorithms and techniques are commonly used in the abovementioned software for heliostat field optimization. Heliostat field optimization algorithms as stated by Barberena et al are as follows: cellwise method, Campo code, heliostat minimum radial spacing method, MUUEN algorithm, HGM (Yearly Normal Energy Surface) methodology, and Greedy algorithm . It should be mentioned that radial staggered is the utilized heliostat field layout in the cell wise method, Campo code, heliostat minimum radial spacing method, and MUUEN algorithm whereas HGM methodology and Greedy algorithm do not put any restrain on the position of the mirrors, ie, there is not any predefined heliostat field layout.…”

Section: Heliostat Field Assessment Methodsmentioning

confidence: 99%

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A concise overview of heliostat fields-solar thermal collectors: Current state of art and future perspective

Gadalla

Saghafifar

2018

Int J Energy Res

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Summary Heliostat field or solar tower collector is one of the most promising concentrated solar power technologies available in the market. Due to its high operating temperature, heliostat field collector can be implemented in a wide range of applications from solar power generation to industrial commodity production. There are several currently operating, under construction, and planned heliostat fields around the world. In this paper, a brief overview of the current state of the art, applications, assessment methods, future perspective, and methods of improvement are discussed.

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Section: Heliostat Field Assessment Methodsmentioning

confidence: 99%

Section: Heliostat Field Assessment Methodsmentioning

confidence: 99%

Section: Heliostat Field Assessment Methodsmentioning

confidence: 99%

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A concise overview of heliostat fields-solar thermal collectors: Current state of art and future perspective

Gadalla

Saghafifar

2018

Int J Energy Res

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“…A cell wise approach was proposed for optimizing a solar tower system by Lipps and Vant‐hull . In the approach described by them, the whole heliostat field was divided into cells, and a layout of heliostat was generated based on the optimization of these cells by determining an optimum value for the figure of merit.…”

Section: Introductionmentioning

confidence: 99%

“…This figure of merit is defined as the ratio of the cost of the thermal system to the annual thermal energy collected. Lastly, the conversion from the optimum spacing from the cells to the individual heliostats was mainly based on the shading and blocking, and the insolation data .…”

Section: Introductionmentioning

confidence: 99%

Development of a mathematical model for optimizing a heliostat field layout using differential evolution method

Atif

Al‐Sulaiman

2015

Int. J. Energy Res.

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Summary In this study, differential evolution was employed to perform optimization of a heliostat field. A complete mathematical code was developed for this purpose, which generates a heliostat field and calculates the optimum spacing between heliostats through differential evolution optimization technique. The optimization was executed for two sets of two cases and compared with an un‐optimized case. In the first case, only the optical performance was optimized, whereas in the second case, the normalized ratio of the optical performance to the land area covered by the heliostat field was maximized. In the first set of cases, the extra security distance between the heliostats was neglected, whereas in the second set of cases, the extra security distance was taken into account. To apply and examine the application of the optimization algorithm developed, 3 days of the year were selected: March 21, June 21, and December 21, considering Dhahran, Saudi Arabia as an illustrative example. For June 21, when the extra security distance between the heliostats is neglected, the optical efficiency of the un‐optimized case was 0.6026, while for the first optimized case, it was 0.6395, and for the second optimized case, it was 0.6033. However, when the extra security distance was considered, the optical efficiency of the un‐optimized case was 0.6167; while for the first optimized case, it was 0.6241, and for the second optimized case, it was 0.6167. Similar observations were realized for the other cases selected. Copyright © 2015 John Wiley & Sons, Ltd.

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Rose pattern for heliostat field optimization with a dynamic speciation‐based mutation differential evolution

Deng

Guo

et al. 2019

Int J Energy Res

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Summary Concentrated solar power technology is one of the most promising technologies in energy field. Arguably, the heliostat field layout is a crucial component in solar power tower system. Numerous studies have been developed for heliostat field optimization. However, most of the existing layouts which utilize radial staggered patterns are based on only two or four variables, leading to relatively rigid modes due to strong configuration constraints. In this article, we propose a new method called rose layout, which divides the regular radial staggered pattern into six sectors and they are optimized separately. Therefore, the radial increments between consecutive rows are not restricted to zones or rows, only relevant to which sector they belong to. This arrangement is more flexible and also efficient. Furthermore, a new differential evolution algorithm with a dynamic speciation‐based mutation strategy (DSM‐DE) is developed to solve this high‐dimensional problem. In order to validate the proposed rose pattern and DSM‐DE, three sets of comparative experiments were carried out. The first set of tests were operated with the conventional four optimization variables, the second series optimized total 43 radial increments between consecutive rows, whereas the third series employed the rose layout. All sets of cases were optimized by four competitive variants of differential evolution algorithm, ie, JADE, SHADE, EB‐LSHADE, and DSM‐DE. Experimental results verified that the rose layout can obtain higher overall optical efficiency and less land coverage than previous methods and DSM‐DE is superior to other DE variants for this high‐dimensional problem. The heliostat field studied in this article is simulated in Qinghai, China. By integrating rose layout with DSM‐DE, the field unweighted efficiency progressed from 44.386% to 53.972%, and the annual weighted efficiency reached 59.091%, which was 0.318% higher than the 43‐variable optimization.

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Theory of cellwise optimization for solar central receiver systems

Cited by 11 publications

References 0 publications

A concise overview of heliostat fields-solar thermal collectors: Current state of art and future perspective

A concise overview of heliostat fields-solar thermal collectors: Current state of art and future perspective

Development of a mathematical model for optimizing a heliostat field layout using differential evolution method

Rose pattern for heliostat field optimization with a dynamic speciation‐based mutation differential evolution

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