Rapid design of a heliostat field by analytic geometry methods and evaluation of maximum optical efficiency map

Abstract Many procedures have been developed to design and optimize a heliostat field, however it is a rather challenging work partly because many influencing factors of a heliostat optical efficiency, especially the shading and blocking efficiency, are computationally intensive. And it’s difficult to judge whether a heliostat field layout is an ideal design intuitively and reliable. In this paper, two concise and accurate analytic geometry methods (AGM-I and AGM-II) are developed to identify the heliostats with the possibilities of shadowing or blocking a given heliostat firstly. The results compared with the Sassi method and bounding sphere & Sassi method show that the same accurate results are obtained while the computational time is significantly reduced by 32% and 23%, respectively. Then an intuitive approach is proposed to optimize and evaluate the rationality of a heliostat field layout by applying the maximum optical efficiency map (MOEM). Finally, a heliostat field based on the Gemasolar plant is studied, whose optical efficiency is optimized and evaluated by the proposed methods. Compared with the un-optimized result and the result optimized by using the reference method, the MOEM optimization result of the heliostat field with higher instantaneous optical efficiency and annual optical efficiency is more reasonable.

[1]  Detlef P. van Vuuren,et al.  Assessing current and future techno-economic potential of concentrated solar power and photovoltaic electricity generation , 2015 .

[2]  Peter Schwarzbözl,et al.  Progress in heliostat development , 2017 .

[3]  Weidong Huang,et al.  Development of a new flux density function for a focusing heliostat , 2018 .

[4]  José Domingo Álvarez,et al.  Review of software for optical analyzing and optimizing heliostat fields , 2017 .

[5]  G. Sassi.  Some notes on shadow and blockage effects , 1983 .

[6]  Giulio Allesina,et al.  Simulation of GEMASOLAR-based solar tower plants for the Chinese energy market: Influence of plant downsizing and location change , 2013 .

[7]  Manuel Torrilhon,et al.  Heliostat field optimization: A new computationally efficient model and biomimetic layout , 2012 .

[8]  José Domingo Álvarez,et al.  Hector, a new methodology for continuous and pattern-free heliostat field optimization , 2018, Applied Energy.

[9]  Olivier Farges,et al.  Global optimization of solar power tower systems using a Monte Carlo algorithm: Application to a redesign of the PS10 solar thermal power plant , 2018 .

[10]  Francisco J. Collado,et al.  Two-stages optimised design of the collector field of solar power tower plants , 2016 .

[11]  Xiudong Wei,et al.  A new code for the design and analysis of the heliostat field layout for power tower system , 2010 .

[12]  Emilio Carrizosa,et al.  Optimization of multiple receivers solar power tower systems , 2015 .

[13]  D. Yogi Goswami,et al.  A computationally efficient method for the design of the heliostat field for solar power tower plant , 2014 .

[14]  Francisco J. Collado,et al.  A review of optimized design layouts for solar power tower plants with campo code , 2013 .

[15]  Weidong Huang,et al.  Performance analysis and optimization of an integrated azimuth tracking solar tower , 2018, Energy.

[16]  Mehrdad Boroushaki,et al.  A computational method for optimal design of the multi-tower heliostat field considering heliostats interactions , 2016 .

[17]  José Domingo Álvarez,et al.  High performance computing for the heliostat field layout evaluation , 2016, The Journal of Supercomputing.

[18]  Jean-François Cornet,et al.  Monte Carlo advances and concentrated solar applications , 2014 .

[19]  Fredrik Haglind,et al.  Thermoeconomic optimization of a Kalina cycle for a central receiver concentrating solar power plant , 2016 .

[20]  Fermín Mallor,et al.  Comparison of Heliostat Field Layout Design Methodologies and Impact on Power Plant Efficiency , 2015 .

[21]  Pierre Garcia,et al.  Codes for solar flux calculation dedicated to central receiver system applications : A comparative review , 2008 .

[22]  Emilio Carrizosa,et al.  Optimisation of aiming strategies in Solar Power Tower plants , 2017 .

[23]  Fahad A. Al-Sulaiman,et al.  Development of a mathematical model for optimizing a heliostat field layout using differential evolution method , 2015 .

[24]  Manuel Romero,et al.  An Update on Solar Central Receiver Systems, Projects, and Technologies , 2002 .

[25]  F. Collado.  One-point fitting of the flux density produced by a heliostat , 2010 .

[26]  Abdallah Khellaf,et al.  A review of studies on central receiver solar thermal power plants , 2013 .

[27]  Marcelino Sánchez,et al.  State-of-the-art of Heliostat Field Layout Algorithms and their Comparison , 2016 .

[28]  F. W. Lipps,et al.  A cellwise method for the optimization of large central receiver systems , 1978 .

[29]  D. Yogi Goswami,et al.  Principles of Solar Engineering , 1978 .

[30]  L. L. vant-Hull,et al.  Performance of optimized solar central receiver systems as a function of receiver thermal loss per unit area , 1986 .

[31]  L. L. Vant-Hull.  Central tower concentrating solar power (CSP) systems , 2012 .

[32]  Jean-Jacques Bezian,et al.  Life-time integration using Monte Carlo Methods when optimizing the design of concentrated solar power plants , 2015 .

[33]  Gregory J. Kolb,et al.  Heliostat Cost Reduction Study , 2007 .

[34]  Yongping Yang,et al.  An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field , 2018, Energy.

[35]  Francisco J. Collado,et al.  Campo: Generation of regular heliostat fields , 2012 .

[36]  Robert Pitz-Paal,et al.  A New Fast Ray Tracing Tool for High-Precision Simulation of Heliostat Fields , 2009 .

[37]  V. Badescu.  Modeling Solar Radiation at the Earth’s Surface , 2008 .