An analytic function for the flux density due to sunlight reflected from a heliostat

Collado, Francisco Javier Conde; Gómez, A.; Turegano, José Antonio

doi:10.1016/0038-092x(86)90078-2

Cited by 96 publications

(39 citation statements)

References 8 publications

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“…In a recent comparison [39] between the HFLCAL [19][20] and UNIZAR [40] models of the energy image sent by a single heliostat, which tests both models against actual spots measured for first generation heliostats, it is concluded that the HFLCAL model is much simpler and slightly more accurate than the UNIZAR model.…”

Section: Intercept Factormentioning

confidence: 98%

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A review of optimized design layouts for solar power tower plants with campo code

Collado

Guallar

2013

Renewable and Sustainable Energy Reviews

176

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a b s t r a c t Solar power tower (SPT) systems are viewed as one of the most promising technologies for producing solar electricity, in which direct solar radiation is reflected and concentrated by a field of giant mirrors (heliostats) onto a receiver placed at the top of a tower. However, the optimized design of a heliostat field is a rather complex problem because the annual performance of a heliostat is a function of not only the instants of time considered and its own position, but also the relative location of neighbouring heliostats, which cause shadows and blockings. A variety of procedures may be found in the open literature, although there is great lack of information on the details of an optimized layout. This review shows that these complex problems have partially led to the expansion of parabolic trough technologies in USA and Spain in spite of their lower thermodynamic efficiencies compared with solar tower power. As a modest support of SPT systems, the authors have presented elsewhere the abilities of a new code called campo for fast and accurate calculations of the shadowing and blocking factor for each and every heliostat. This work explores a review of the optimized heliostat field layouts yielded by campo. Campo commences the optimization search based on the densest layout, with the worst shadowing and blocking factor, but with good values for the other optical factors, and then progresses towards gradually expanded distributions. The search for maximum annual energy through campo results in a clear, steady and reproducible procedure. Finally, as an example of this new procedure, some options of optimized heliostat field layouts are reviewed using as input parameters the scarce open literature data on Gemasolar, the first solar power tower commercial plant with molten salt storage in the world.

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Section: Intercept Factormentioning

confidence: 98%

“…where s tot is the total standard deviation result measured on the image plane [40], i.e., a plane normal to the unit vector t, and with coordinates (x 0 , y 0 ).…”

Section: Intercept Factormentioning

confidence: 99%

A review of optimized design layouts for solar power tower plants with campo code

Collado

Guallar

2013

Renewable and Sustainable Energy Reviews

176

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“…where t denotes the time instant, Π t is the outlet thermal power collected at time t, calculated as in [5,12,16,17,18], and γ 1 is a constant that mesures the fixed energy losses related with the whole system.…”

Section: Functionsmentioning

confidence: 99%

Optimization of multiple receivers solar power tower systems

Carrizosa

Domínguez-Bravo

Fernández-Cara

et al. 2015

Energy

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In this article a new procedure to optimize the design of a Multiple Receivers Solar Power Tower system is presented. The proposed procedure allows to optimize the different receivers (height, aperture tilt angle, azimuth angle and aperture size) as well as the heliostat field layout, seeking to minimize the levelized cost of thermal energy.The optimization problem is high dimensional, with a black-box nonconvex objective function that is hard to compute.Our method is based on an alternating greedy-based heuristic method, already used by the authors to design a system with a single receiver, which simultaneously optimizes the receivers and the heliostat field. The proposed procedure allows one to determine the overall number of heliostats, their locations and the aiming region of each field.

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“…For instance, if we consider 5, 9 or 13 different hours to compute the daily thermal energy, for different fixed days we obtain the results shown in The annual thermal energy collected is computed using an algorithm similar to NSPOC procedure, that is described in Crespo & Ramos (2009). We refer the reader to Biggs & Vittitoe (1976); Collado & Turégano (1986, 1989; Stine & Harrigan (2001), for further details.…”

Section: Functionsmentioning

confidence: 99%

“…In particular, f cos = f cos (t, x, y, Θ) is the cosine efficiency, see Collado & Turégano (1989); f sb = f sb (t, x, y, S, Θ) is the shading and blocking efficiency, see Sassi (1983); Collado & Guallar (2012); Stine & Harrigan (2001); f sp = f sp (t, x, y, Θ) is the interception efficiency or spillage factor, see Collado & Turégano (1986) and finally, f at = f at (x, y) is the atmospheric efficiency, which takes into account atmospheric losses between the heliostat and the receiver, see Biggs & Vittitoe (1976); Collado & Turégano (1989).…”

Section: Functionsmentioning

confidence: 99%

A heuristic method for simultaneous tower and pattern-free field optimization on solar power systems

Carrizosa

Domínguez-Bravo

Fernández-Cara

et al. 2015

Computers & Operations Research

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A heuristic method for optimizing a solar power tower system is proposed, in which both heliostat field (heliostat locations and number) and the tower (tower height and receiver size) are simultaneously considered.Maximizing the thermal energy collected per unit cost leads to a difficult optimization problem due to its characteristics: it has a nonconvex black-box objective function with computationally expensive evaluation and nonconvex constraints.The proposed method sequentially optimizes the field layout for a given tower configuration and then, the tower design is optimized for the previously obtained field layout. A greedy-based heuristic algorithm is presented to address the heliostat location problem. This algorithm follows a pattern-free method. The only constraints to be considered are: the field region and the nonconvex constraints * Corresponding author. Instituto de Matemáticas de la Universidad de Sevilla (IMUS) Edificio Celestino Mutis-1 Planta-D10.5 Avda. Reina Mercedes, s/n, 41012 Sevilla, Spain. Tel: +34 955420870.Email address: carmenanadb@us.es (C. Domínguez-Bravo)Preprint submitted to Computers & Operations Research November 25, 2014 (which allow heliostats to not collide).The absence of a geometrical pattern to design the field and the simultaneous optimization of the field and the tower designs make this approach different from the existing ones. Our method is compared against other proposals in the literature of heliostat field optimization.

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An analytic function for the flux density due to sunlight reflected from a heliostat

Cited by 96 publications

References 8 publications

A review of optimized design layouts for solar power tower plants with campo code

A review of optimized design layouts for solar power tower plants with campo code

Optimization of multiple receivers solar power tower systems

A heuristic method for simultaneous tower and pattern-free field optimization on solar power systems

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