Optimization of biomimetic heliostat field using heuristic optimization algorithms
Central receiver systems are one of the most promising solar energy harvesting technologies. They consist of a large field of sun-tracking mirrors known as heliostats that focus sunlight onto a receiver at the top of a tower to generate high temperatures for running a heat cycle. Optimum localizatio...
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| Vydáno v: | Knowledge-based systems Ročník 258; s. 110048 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
22.12.2022
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Central receiver systems are one of the most promising solar energy harvesting technologies. They consist of a large field of sun-tracking mirrors known as heliostats that focus sunlight onto a receiver at the top of a tower to generate high temperatures for running a heat cycle. Optimum localization of heliostats in the field plays an essential role during the design phase of a central receiver system. A good design aims to obtain the highest energy yield and efficiency at the lowest cost. The primary parameter that affects the energy yield of a central receiver system is the optical efficiency of its heliostat field. Optimization is carried out to obtain an arrangement of heliostats, which maximizes optical efficiency. This study presents the optimization process of a biomimetic heliostat field design using different heuristic optimization algorithms, namely advanced particle swarm optimization, genetic algorithm, whale optimization algorithm, and gravitational search algorithm. In un-optimized biomimetic heliostat fields, the efficiency is 66.4% which is enhanced to 68.7% after optimization. The results show that the advanced particle swarm is the fastest method that converges in less than 20 iterations. The instantaneous efficiency of the field increases by approximately 3.48% after optimization by particle swarm optimization, followed by 3% with the gravitational search algorithm, 2.88% from the whale optimization algorithm, and 1.99% with the genetic algorithm. The results are compared with other similar works in optimizing the biomimetic heliostat field.
•Biomimetic Heliostat Fields are based on arrangement of seeds in sun flowers.•Design and optimization procedure for biomimetic heliostat fields is presented.•Four heuristic optimization algorithms are used for the optimization.•Objective functions are presented for biomimetic heliostat field optimization.•APSO algorithm results in highest optical efficiency by avoiding local minima. |
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| AbstractList | Central receiver systems are one of the most promising solar energy harvesting technologies. They consist of a large field of sun-tracking mirrors known as heliostats that focus sunlight onto a receiver at the top of a tower to generate high temperatures for running a heat cycle. Optimum localization of heliostats in the field plays an essential role during the design phase of a central receiver system. A good design aims to obtain the highest energy yield and efficiency at the lowest cost. The primary parameter that affects the energy yield of a central receiver system is the optical efficiency of its heliostat field. Optimization is carried out to obtain an arrangement of heliostats, which maximizes optical efficiency. This study presents the optimization process of a biomimetic heliostat field design using different heuristic optimization algorithms, namely advanced particle swarm optimization, genetic algorithm, whale optimization algorithm, and gravitational search algorithm. In un-optimized biomimetic heliostat fields, the efficiency is 66.4% which is enhanced to 68.7% after optimization. The results show that the advanced particle swarm is the fastest method that converges in less than 20 iterations. The instantaneous efficiency of the field increases by approximately 3.48% after optimization by particle swarm optimization, followed by 3% with the gravitational search algorithm, 2.88% from the whale optimization algorithm, and 1.99% with the genetic algorithm. The results are compared with other similar works in optimizing the biomimetic heliostat field.
•Biomimetic Heliostat Fields are based on arrangement of seeds in sun flowers.•Design and optimization procedure for biomimetic heliostat fields is presented.•Four heuristic optimization algorithms are used for the optimization.•Objective functions are presented for biomimetic heliostat field optimization.•APSO algorithm results in highest optical efficiency by avoiding local minima. |
| ArticleNumber | 110048 |
| Author | Khan, Talha A. Rizvi, Arslan A. Yang, Dong |
| Author_xml | – sequence: 1 givenname: Arslan A. surname: Rizvi fullname: Rizvi, Arslan A. organization: State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an 710049, Shaanxi, China – sequence: 2 givenname: Dong surname: Yang fullname: Yang, Dong email: dyang@mail.xjtu.edu.cn organization: State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an 710049, Shaanxi, China – sequence: 3 givenname: Talha A. surname: Khan fullname: Khan, Talha A. organization: Department of Data Science, University of Europe for Applied Sciences, Think Campus, Konrad-Zuse-Ring 11, 14469 Potsdam, Germany |
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| Cites_doi | 10.1016/j.solener.2013.03.001 10.1016/S0038-092X(00)00156-0 10.1016/j.cma.2020.113609 10.1016/j.solener.2018.02.042 10.1016/j.solener.2005.02.012 10.1109/SMC.2019.8914478 10.1002/er.5048 10.1016/j.egypro.2015.03.135 10.1016/j.rser.2013.02.017 10.1016/j.solener.2011.12.007 10.1016/j.ins.2009.03.004 10.1080/01430750.2018.1525581 10.1016/j.cma.2022.114570 10.1016/j.renene.2021.09.045 10.1109/ICNN.1995.488968 10.1016/j.renene.2021.05.058 10.1109/SMC.2018.00669 10.1016/j.solener.2007.10.001 10.1016/j.solener.2021.02.011 10.1016/j.applthermaleng.2017.08.164 10.1109/ACCESS.2022.3147821 10.1016/j.renene.2015.11.015 10.1016/j.eswa.2021.116158 10.1007/s10825-020-01567-6 10.1016/j.renene.2014.03.043 10.1016/j.solener.2003.12.003 10.1016/j.enconman.2015.01.089 10.1016/j.cie.2021.107250 10.1016/j.solener.2022.04.035 10.1016/j.renene.2012.03.011 10.1016/j.advengsoft.2016.01.008 10.1016/j.solener.2017.03.048 10.1016/0038-092X(83)90022-1 10.1115/1.1467921 |
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| Keywords | Heuristic optimization algorithms Central receiver system Biomimetic heliostat field Solar energy |
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| References | P.L. Leary, J.D. Hankins, User’s Guide for MIRVAL E a Computer Code for Modeling the Optical Behavior of Reflecting Solar Concentrators, Vol. 0, Sand77-8280, 1979. Blanco-Muriel, Alarcón-Padilla, López-Moratalla, Lara-Coira (b30) 2001; 70 Kiwan, Khammash (b20) 2018; 164 Noone, Torrilhon, Mitsos (b19) 2012; 86 T.A. Khan, S.H. Ling, A.S. Mohan, Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy, in: Proc. - 2018 IEEE Int. Conf. Syst. Man, Cybern. SMC 2018, 2019, pp. 3944–3949 Grena (b29) 2008; 82 Belaid, Filali, Hassani, Arrif, Guermoui, Gama, Bouakba (b24) 2022; 238 Romero, Buck, Pacheco (b4) 2002; 124 Khan, Ling (b14) 2020 Collado, Guallar (b25) 2012; 46 Oyelade, Ezugwu, Mohamed, Abualigah (b9) 2022; 10 Mirjalili, Lewis (b7) 2016; 95 Besarati, Yogi Goswami (b21) 2014; 69 Reda, Andreas (b28) 2004; 76 Behar, Khellaf, Mohammedi (b2) 2013; 23 Rizvi, Danish, El-Leathy, Al-Ansary, Yang (b5) 2021; 218 Rizvi, Yang (b36) 2022; 181 Solucar (b17) 2006 Duffie, Beckman (b27) 2013 Huang, Li, Li, Hu, Chen (b33) 2013; 92 Abualigah, Elaziz, Sumari, Geem, Gandomi (b10) 2022; 191 Deng, Wu, Guo, Zhang, Sun (b3) 2020; 44 Agushaka, Ezugwu, Abualigah (b8) 2022; 391 Abualigah, Diabat, Mirjalili, Abd Elaziz, Gandomi (b12) 2021; 376 . Sassi (b34) 1983; 31 Ho (b1) 2017; 152 Abualigah, Yousri, Abd Elaziz, Ewees, Al-qaness, Gandomi (b11) 2021; 157 Atif, Al-Sulaiman (b26) 2015; 95 Xie, Guo, Liu, Chen, Shen, Wang (b15) 2021; 176 Zhang, Yang, Xu, Du (b23) 2016; 87 T.A. Khan, S.H. Ling, A.S. Mohan, Advanced gravitational search algorithm with modified exploitation strategy, in: Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern. 2019-October, 2019, pp. 1056–1061 Arrif, Benchabane, Germoui, Bezza, Belaid (b16) 2021; 42 Schwarzbözl, Schmitz, Pitz-paal (b32) 2009 Li, Zhai, Liu, Yang, Wu (b18) 2018; 128 Rashedi, Nezamabadi-pour, Saryazdi (b13) 2009; 179 Schmitz, Schwarzbözl, Buck, Pitz-Paal (b31) 2006; 80 J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. ICNN’95 - Int. Conf. Neural Networks, Vol. 4, 1995, pp. 1942–1948 Mutuberria, Pascual, Guisado, Mallor (b22) 2015; 69 Ho (10.1016/j.knosys.2022.110048_b1) 2017; 152 Schwarzbözl (10.1016/j.knosys.2022.110048_b32) 2009 Sassi (10.1016/j.knosys.2022.110048_b34) 1983; 31 Collado (10.1016/j.knosys.2022.110048_b25) 2012; 46 Li (10.1016/j.knosys.2022.110048_b18) 2018; 128 Abualigah (10.1016/j.knosys.2022.110048_b10) 2022; 191 Schmitz (10.1016/j.knosys.2022.110048_b31) 2006; 80 Solucar (10.1016/j.knosys.2022.110048_b17) 2006 Duffie (10.1016/j.knosys.2022.110048_b27) 2013 Blanco-Muriel (10.1016/j.knosys.2022.110048_b30) 2001; 70 Huang (10.1016/j.knosys.2022.110048_b33) 2013; 92 Abualigah (10.1016/j.knosys.2022.110048_b12) 2021; 376 Atif (10.1016/j.knosys.2022.110048_b26) 2015; 95 Deng (10.1016/j.knosys.2022.110048_b3) 2020; 44 Romero (10.1016/j.knosys.2022.110048_b4) 2002; 124 Agushaka (10.1016/j.knosys.2022.110048_b8) 2022; 391 Rizvi (10.1016/j.knosys.2022.110048_b5) 2021; 218 Besarati (10.1016/j.knosys.2022.110048_b21) 2014; 69 Belaid (10.1016/j.knosys.2022.110048_b24) 2022; 238 Oyelade (10.1016/j.knosys.2022.110048_b9) 2022; 10 10.1016/j.knosys.2022.110048_b6 Rashedi (10.1016/j.knosys.2022.110048_b13) 2009; 179 Zhang (10.1016/j.knosys.2022.110048_b23) 2016; 87 Mutuberria (10.1016/j.knosys.2022.110048_b22) 2015; 69 Arrif (10.1016/j.knosys.2022.110048_b16) 2021; 42 Xie (10.1016/j.knosys.2022.110048_b15) 2021; 176 Reda (10.1016/j.knosys.2022.110048_b28) 2004; 76 Kiwan (10.1016/j.knosys.2022.110048_b20) 2018; 164 Noone (10.1016/j.knosys.2022.110048_b19) 2012; 86 Grena (10.1016/j.knosys.2022.110048_b29) 2008; 82 Mirjalili (10.1016/j.knosys.2022.110048_b7) 2016; 95 Rizvi (10.1016/j.knosys.2022.110048_b36) 2022; 181 Behar (10.1016/j.knosys.2022.110048_b2) 2013; 23 10.1016/j.knosys.2022.110048_b35 10.1016/j.knosys.2022.110048_b38 10.1016/j.knosys.2022.110048_b37 Abualigah (10.1016/j.knosys.2022.110048_b11) 2021; 157 Khan (10.1016/j.knosys.2022.110048_b14) 2020 |
| References_xml | – volume: 42 start-page: 65 year: 2021 end-page: 80 ident: b16 article-title: Optimisation of heliostat field layout for solar power tower systems using iterative artificial bee colony algorithm: a review and case study publication-title: Int. J. Ambient Energy. – volume: 44 start-page: 1951 year: 2020 end-page: 1970 ident: b3 article-title: Rose pattern for heliostat field optimization with a dynamic speciation-based mutation differential evolution publication-title: Int. J. Energy Res. – volume: 86 start-page: 792 year: 2012 end-page: 803 ident: b19 article-title: Heliostat field optimization: A new computationally efficient model and biomimetic layout publication-title: Sol. Energy. – start-page: 1 year: 2006 end-page: 10 ident: b17 article-title: 10 MW solar thermal power plant for southern Spain publication-title: Final Tech. Prog. Rep. – reference: T.A. Khan, S.H. Ling, A.S. Mohan, Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy, in: Proc. - 2018 IEEE Int. Conf. Syst. Man, Cybern. SMC 2018, 2019, pp. 3944–3949, – volume: 218 start-page: 296 year: 2021 end-page: 311 ident: b5 article-title: A review and classification of layouts and optimization techniques used in design of heliostat fields in solar central receiver systems publication-title: Sol. Energy. – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: b13 article-title: GSA: A gravitational search algorithm publication-title: Inf. Sci. (Ny). – volume: 176 start-page: 447 year: 2021 end-page: 458 ident: b15 article-title: Optimization of heliostat field distribution based on improved Gray Wolf optimization algorithm publication-title: Renew. Energy. – volume: 191 year: 2022 ident: b10 article-title: Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer publication-title: Expert Syst. Appl. – volume: 46 start-page: 49 year: 2012 end-page: 59 ident: b25 article-title: Campo: Generation of regular heliostat fields publication-title: Renew. Energy. – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b7 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. – volume: 376 year: 2021 ident: b12 article-title: The arithmetic optimization algorithm publication-title: Comput. Methods Appl. Mech. Engrg. – year: 2020 ident: b14 article-title: A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem publication-title: J. Comput. Electron. – volume: 95 start-page: 1 year: 2015 end-page: 9 ident: b26 article-title: Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm publication-title: Energy Convers. Manag. – volume: 152 start-page: 38 year: 2017 end-page: 56 ident: b1 article-title: Advances in central receivers for concentrating solar applications publication-title: Sol. Energy. – volume: 164 start-page: 25 year: 2018 end-page: 37 ident: b20 article-title: Investigations into the spiral distribution of the heliostat field in solar central tower system publication-title: Sol. Energy. – volume: 10 year: 2022 ident: b9 article-title: Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm publication-title: IEEE Access. – volume: 92 start-page: 7 year: 2013 end-page: 14 ident: b33 article-title: Gauss–Legendre integration of an analytical function to calculate the optical efficiency of a heliostat publication-title: Sol. Energy. – volume: 181 start-page: 292 year: 2022 end-page: 303 ident: b36 article-title: A detailed account of calculation of shading and blocking factor of a heliostat field publication-title: Renew. Energy. – volume: 128 start-page: 33 year: 2018 end-page: 41 ident: b18 article-title: Optimization of a heliostat field layout using hybrid PSO-GA algorithm publication-title: Appl. Therm. Eng. – reference: P.L. Leary, J.D. Hankins, User’s Guide for MIRVAL E a Computer Code for Modeling the Optical Behavior of Reflecting Solar Concentrators, Vol. 0, Sand77-8280, 1979. – volume: 124 start-page: 98 year: 2002 end-page: 108 ident: b4 article-title: An update on solar central receiver systems, projects, and technologies publication-title: J. Sol. Energy Eng. Trans. ASME. – volume: 238 start-page: 162 year: 2022 end-page: 177 ident: b24 article-title: Heliostat field optimization and comparisons between biomimetic spiral and radial-staggered layouts for different heliostat shapes publication-title: Sol. Energy. – volume: 31 start-page: 331 year: 1983 end-page: 333 ident: b34 article-title: Some notes on shadow and blockage effects publication-title: Sol. Energy. – reference: J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. ICNN’95 - Int. Conf. Neural Networks, Vol. 4, 1995, pp. 1942–1948, – volume: 391 year: 2022 ident: b8 article-title: Dwarf mongoose optimization algorithm publication-title: Comput. Methods Appl. Mech. Engrg. – volume: 80 start-page: 111 year: 2006 end-page: 120 ident: b31 article-title: Assessment of the potential improvement due to multiple apertures in central receiver systems with secondary concentrators publication-title: Sol. Energy. – reference: . – year: 2009 ident: b32 article-title: Visual hflcal – a software tool for layout and optimisation of heliostat fields publication-title: Sol. PACES 2009 – volume: 23 start-page: 12 year: 2013 end-page: 39 ident: b2 article-title: A review of studies on central receiver solar thermal power plants publication-title: Renew. Sustain. Energy Rev. – volume: 69 start-page: 226 year: 2014 end-page: 232 ident: b21 article-title: A computationally efficient method for the design of the heliostat field for solar power tower plant publication-title: Renew. Energy. – reference: T.A. Khan, S.H. Ling, A.S. Mohan, Advanced gravitational search algorithm with modified exploitation strategy, in: Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern. 2019-October, 2019, pp. 1056–1061, – volume: 69 start-page: 1360 year: 2015 end-page: 1370 ident: b22 article-title: Comparison of heliostat field layout design methodologies and impact on power plant efficiency publication-title: Energy Procedia. – volume: 76 start-page: 577 year: 2004 end-page: 589 ident: b28 article-title: Solar position algorithm for solar radiation applications publication-title: Sol. Energy. – volume: 82 start-page: 462 year: 2008 end-page: 470 ident: b29 article-title: An algorithm for the computation of the solar position publication-title: Sol. Energy. – volume: 157 year: 2021 ident: b11 article-title: Aquila Optimizer: A novel meta-heuristic optimization algorithm publication-title: Comput. Ind. Eng. – year: 2013 ident: b27 article-title: Solar engineering of thermal processes – volume: 87 start-page: 720 year: 2016 end-page: 730 ident: b23 article-title: An efficient code to optimize the heliostat field and comparisons between the biomimetic spiral and staggered layout publication-title: Renew. Energy. – volume: 70 start-page: 431 year: 2001 end-page: 441 ident: b30 article-title: Computing the solar vector publication-title: Sol. Energy. – volume: 92 start-page: 7 year: 2013 ident: 10.1016/j.knosys.2022.110048_b33 article-title: Gauss–Legendre integration of an analytical function to calculate the optical efficiency of a heliostat publication-title: Sol. Energy. doi: 10.1016/j.solener.2013.03.001 – volume: 70 start-page: 431 year: 2001 ident: 10.1016/j.knosys.2022.110048_b30 article-title: Computing the solar vector publication-title: Sol. Energy. doi: 10.1016/S0038-092X(00)00156-0 – volume: 376 year: 2021 ident: 10.1016/j.knosys.2022.110048_b12 article-title: The arithmetic optimization algorithm publication-title: Comput. Methods Appl. Mech. Engrg. doi: 10.1016/j.cma.2020.113609 – volume: 164 start-page: 25 year: 2018 ident: 10.1016/j.knosys.2022.110048_b20 article-title: Investigations into the spiral distribution of the heliostat field in solar central tower system publication-title: Sol. Energy. doi: 10.1016/j.solener.2018.02.042 – volume: 80 start-page: 111 year: 2006 ident: 10.1016/j.knosys.2022.110048_b31 article-title: Assessment of the potential improvement due to multiple apertures in central receiver systems with secondary concentrators publication-title: Sol. Energy. doi: 10.1016/j.solener.2005.02.012 – ident: 10.1016/j.knosys.2022.110048_b38 doi: 10.1109/SMC.2019.8914478 – ident: 10.1016/j.knosys.2022.110048_b35 – volume: 44 start-page: 1951 year: 2020 ident: 10.1016/j.knosys.2022.110048_b3 article-title: Rose pattern for heliostat field optimization with a dynamic speciation-based mutation differential evolution publication-title: Int. J. Energy Res. doi: 10.1002/er.5048 – start-page: 1 year: 2006 ident: 10.1016/j.knosys.2022.110048_b17 article-title: 10 MW solar thermal power plant for southern Spain publication-title: Final Tech. Prog. Rep. – volume: 69 start-page: 1360 year: 2015 ident: 10.1016/j.knosys.2022.110048_b22 article-title: Comparison of heliostat field layout design methodologies and impact on power plant efficiency publication-title: Energy Procedia. doi: 10.1016/j.egypro.2015.03.135 – volume: 23 start-page: 12 year: 2013 ident: 10.1016/j.knosys.2022.110048_b2 article-title: A review of studies on central receiver solar thermal power plants publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2013.02.017 – volume: 86 start-page: 792 year: 2012 ident: 10.1016/j.knosys.2022.110048_b19 article-title: Heliostat field optimization: A new computationally efficient model and biomimetic layout publication-title: Sol. Energy. doi: 10.1016/j.solener.2011.12.007 – volume: 179 start-page: 2232 year: 2009 ident: 10.1016/j.knosys.2022.110048_b13 article-title: GSA: A gravitational search algorithm publication-title: Inf. Sci. (Ny). doi: 10.1016/j.ins.2009.03.004 – volume: 42 start-page: 65 year: 2021 ident: 10.1016/j.knosys.2022.110048_b16 article-title: Optimisation of heliostat field layout for solar power tower systems using iterative artificial bee colony algorithm: a review and case study publication-title: Int. J. Ambient Energy. doi: 10.1080/01430750.2018.1525581 – volume: 391 year: 2022 ident: 10.1016/j.knosys.2022.110048_b8 article-title: Dwarf mongoose optimization algorithm publication-title: Comput. Methods Appl. Mech. Engrg. doi: 10.1016/j.cma.2022.114570 – volume: 181 start-page: 292 year: 2022 ident: 10.1016/j.knosys.2022.110048_b36 article-title: A detailed account of calculation of shading and blocking factor of a heliostat field publication-title: Renew. Energy. doi: 10.1016/j.renene.2021.09.045 – year: 2009 ident: 10.1016/j.knosys.2022.110048_b32 article-title: Visual hflcal – a software tool for layout and optimisation of heliostat fields – ident: 10.1016/j.knosys.2022.110048_b6 doi: 10.1109/ICNN.1995.488968 – volume: 176 start-page: 447 year: 2021 ident: 10.1016/j.knosys.2022.110048_b15 article-title: Optimization of heliostat field distribution based on improved Gray Wolf optimization algorithm publication-title: Renew. Energy. doi: 10.1016/j.renene.2021.05.058 – ident: 10.1016/j.knosys.2022.110048_b37 doi: 10.1109/SMC.2018.00669 – volume: 82 start-page: 462 year: 2008 ident: 10.1016/j.knosys.2022.110048_b29 article-title: An algorithm for the computation of the solar position publication-title: Sol. Energy. doi: 10.1016/j.solener.2007.10.001 – volume: 218 start-page: 296 year: 2021 ident: 10.1016/j.knosys.2022.110048_b5 article-title: A review and classification of layouts and optimization techniques used in design of heliostat fields in solar central receiver systems publication-title: Sol. Energy. doi: 10.1016/j.solener.2021.02.011 – volume: 128 start-page: 33 year: 2018 ident: 10.1016/j.knosys.2022.110048_b18 article-title: Optimization of a heliostat field layout using hybrid PSO-GA algorithm publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2017.08.164 – volume: 10 year: 2022 ident: 10.1016/j.knosys.2022.110048_b9 article-title: Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm publication-title: IEEE Access. doi: 10.1109/ACCESS.2022.3147821 – volume: 87 start-page: 720 year: 2016 ident: 10.1016/j.knosys.2022.110048_b23 article-title: An efficient code to optimize the heliostat field and comparisons between the biomimetic spiral and staggered layout publication-title: Renew. Energy. doi: 10.1016/j.renene.2015.11.015 – volume: 191 year: 2022 ident: 10.1016/j.knosys.2022.110048_b10 article-title: Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116158 – year: 2020 ident: 10.1016/j.knosys.2022.110048_b14 article-title: A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem publication-title: J. Comput. Electron. doi: 10.1007/s10825-020-01567-6 – volume: 69 start-page: 226 year: 2014 ident: 10.1016/j.knosys.2022.110048_b21 article-title: A computationally efficient method for the design of the heliostat field for solar power tower plant publication-title: Renew. Energy. doi: 10.1016/j.renene.2014.03.043 – volume: 76 start-page: 577 year: 2004 ident: 10.1016/j.knosys.2022.110048_b28 article-title: Solar position algorithm for solar radiation applications publication-title: Sol. Energy. doi: 10.1016/j.solener.2003.12.003 – volume: 95 start-page: 1 year: 2015 ident: 10.1016/j.knosys.2022.110048_b26 article-title: Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2015.01.089 – volume: 157 year: 2021 ident: 10.1016/j.knosys.2022.110048_b11 article-title: Aquila Optimizer: A novel meta-heuristic optimization algorithm publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2021.107250 – volume: 238 start-page: 162 year: 2022 ident: 10.1016/j.knosys.2022.110048_b24 article-title: Heliostat field optimization and comparisons between biomimetic spiral and radial-staggered layouts for different heliostat shapes publication-title: Sol. Energy. doi: 10.1016/j.solener.2022.04.035 – volume: 46 start-page: 49 year: 2012 ident: 10.1016/j.knosys.2022.110048_b25 article-title: Campo: Generation of regular heliostat fields publication-title: Renew. Energy. doi: 10.1016/j.renene.2012.03.011 – year: 2013 ident: 10.1016/j.knosys.2022.110048_b27 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.knosys.2022.110048_b7 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 152 start-page: 38 year: 2017 ident: 10.1016/j.knosys.2022.110048_b1 article-title: Advances in central receivers for concentrating solar applications publication-title: Sol. Energy. doi: 10.1016/j.solener.2017.03.048 – volume: 31 start-page: 331 year: 1983 ident: 10.1016/j.knosys.2022.110048_b34 article-title: Some notes on shadow and blockage effects publication-title: Sol. Energy. doi: 10.1016/0038-092X(83)90022-1 – volume: 124 start-page: 98 year: 2002 ident: 10.1016/j.knosys.2022.110048_b4 article-title: An update on solar central receiver systems, projects, and technologies publication-title: J. Sol. Energy Eng. Trans. ASME. doi: 10.1115/1.1467921 |
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