Aiming at the problem of low efficiency caused by the insufficient layout planning of heliostats in the new type of tower-type solar thermal power generation system, this study proposes a two-stage genetic optimization algorithm combining Monte Carlo ray tracing and collision detection based on the radial staggered distribution theory. On the basis of the traditional genetic algorithm, the algorithm updates the receiver position by feeding back the heliostat layout and further optimizes the heliostat parameters through genetic iteration. The algorithm takes into account energy loss factors such as the light cone effect and shading obstruction. Based on the two-dimensional normal distribution model, the energy of the rays inside the light cone is discretized to construct a Monte Carlo optical simulation model, and the optical efficiency of the system is evaluated using a collision detection algorithm. A simulation was carried out for a circular heliostat field with a radius of 350 meters in Jiuquan, Gansu, and a heliostat field layout with 2977 heliostats of different installation heights and sizes was designed. The shading obstruction efficiency can reach 96.623%, the optical efficiency is 71.358%, the average annual heat power output per unit area of the heliostat is 0.690271 kW/m², and the average annual total heat power output of the heliostat field is 42.977625 MW. Compared with the improved grey wolf algorithm, the two-stage genetic optimization algorithm improves the four indicators of shading obstruction efficiency, optical efficiency, average annual heat power output per unit area of the heliostat, and average annual total heat power output of the heliostat field by 13.94%, 27.59%, 6.92%, and 29.04%, respectively.
Key words
heliostats /
ray tracing /
genetic algorithm /
optical efficiency /
concentrated solar power
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 杜尔顺, 张宁, 康重庆, 等. 太阳能光热发电并网运行及优化规划研究综述与展望[J]. 中国电机工程学报, 2016, 36(21): 5765-5775, 6019.
DU E S, ZHANG N, KANG C Q, et al.Reviews and prospects of the operation and planning optimization for grid integrated concentrating solar power[J]. Proceedings of the CSEE, 2016, 36(21): 5765-5775, 6019.
[2] NOONE C J, TORRILHON M, MITSOS A.Heliostat field optimization: a new computationally efficient model and biomimetic layout[J]. Solar energy, 2012, 86(2): 792-803.
[3] 姜香菊, 李思凡, 王瑞彤. 基于差分进化的塔式太阳能定日镜场布局优化[J]. 新能源进展, 2024, 12(3): 336-342.
JIANG X J, LI S F, WANG R T.Optimization of tower solar heliostat field layout based on differential evolution[J]. Advances in new and renewable energy, 2024, 12(3): 336-342.
[4] 高博, 刘建兴, 孙浩, 等. 基于自适应引力搜索算法的定日镜场优化布置[J]. 太阳能学报, 2022, 43(10): 119-125.
GAO B, LIU J X, SUN H, et al.Optimization of a heliostat field layout using adaptive gravity search algorithm[J]. Acta energiae solaris sinica, 2022, 43(10): 119-125.
[5] ALI K, SONG J F.Research on modeling simulation and optimal layout of heliostat field optical efficiency for solar power tower plant[J]. Applied solar energy, 2023, 59(6): 957-977.
[6] MOONEY C Z.Monte Carlo simulation[M]. Thousand Oaks: Sage Publ., 1997.
[7] 李学阔, 温佩贤, 杨林, 等. 基于光线追踪的全局光照及降噪处理研究[J]. 计算机技术与发展, 2022, 32(9): 23-27.
LI X K, WEN P X, YANG L, et al.Research on global illumination and denoising based on ray tracing[J]. Computer technology and development, 2022, 32(9): 23-27.
[8] KIATGAMOLCHAI S, CHAMNI E.Theory and experiment of a two-dimensional cone concentrator for sunlight[J]. Solar energy, 2008, 82(2): 111-117.
[9] 冯婧恒, 赵斌, 董晓冬, 等. 太阳入射角对高原槽式太阳能集热器的影响[J]. 西藏科技, 2018, 40(8): 73-77.
FENG J H, ZHAO B, DONG X D, et al.The impact of solar incidence angle on high-altitude parabolic trough solar collectors[J]. Xizang science and technology, 2018, 40(8): 73-77.
[10] 刘博文, 蒋旭桐, 窦万春. 工业互联网业务过程行为中的协同聚合与智能化提升方法[J]. 计算机集成制造系统, 2024, 30(8): 2688-2696.
LIU B W, JIANG X T, DOU W C.Collaborative aggregation and intelligent improving method in business process of industrial Internet[J]. Computer integrated manufacturing systems, 2024, 30(8): 2688-2696.
[11] 齐佳伟. 塔式太阳能电站输出功率预测[D]. 北京: 华北电力大学, 2018.
QI J W.The output forecasting of lower solar power plant[D]. Beijing: North China Electric Power University, 2018.
[12] 房淼森, 逯静, 姜奕雯. 定日镜能量传输效率建模及镜场排布设计[J]. 太阳能学报, 2021, 42(1): 112-116.
FANG M S, LU J, JIANG Y W.Heliostat energy efficincy modeling and field layout design[J]. Acta energiae solaris sinica, 2021, 42(1): 112-116.
[13] 罗嘉成, 温嘉豪, 蒲建颖, 等. 定日镜场设计中不同优化算法的比较[J]. 太阳能学报, 2025, 46(4): 342-349.
LUO J C, WEN J H, PU J Y, et al.Comparison of diffierent optimization algorithm for heliostat field design[J]. Acta energiae solaris sinica, 2025, 46(4): 342-349.
[14] 刘建兴. 塔式光热电站光学效率建模仿真及定日镜场优化布置[D]. 兰州: 兰州交通大学, 2022.
LIU J X.Research on modeling simulation and optimal layout of heliostat field optical efficiency for solar power tower plant[D]. Lanzhou: Lanzhou Jiatong University, 2022.
[15] MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[16] 谢七月, 刘广帅, 刘瑶, 等. 基于改进灰狼算法的定日镜场布局优化方法[J]. 太阳能学报, 2024, 45(11): 394-400.
XIE Q Y, LIU G S, LIU Y, et al.Improved grey wolf optimization algorithm for heliostats field layout[J]. Acta energiae solaris sinica, 2024, 45(11): 394-400.
PDF(3638 KB)
