Optimization design of heliostat field based on high-dimensional particle swarm and multiple population genetic algorithms

Yiwen Huang

doi:10.4108/ew.5653

Authors

Yiwen Huang Southeast University

DOI:

https://doi.org/10.4108/ew.5653

Keywords:

Single-objective optimization, high-dimensional particle swarm algorithm, multiple group genetic algorithm, ray tracing

Abstract

INTRODUCTION: Tower-type heliostat field is a new type of energy conversion, which has the advantages of high energy efficiency, flexibility and sustainability and environmental friendliness.

OBJECTIVES: Through the research and improvement of the tower heliostat field to promote the development of solar energy utilization technology.

METHODS: In this paper, we calculate and optimize the tower heliostat field by using single objective optimization, high-dimensional particle swarm algorithm and multiple group genetic algorithm.

RESULTS: In this case of question setting, average annual optical efficiency is 0.6696; average annual cosine efficiency is 0.7564; annual average shadow occlusion efficiency is 0.9766; average annual truncation efficiency is 0.9975; average annual output thermal power is 35539.1747W; mean annual output thermal power per unit area is 0.5657W.The optimal solution after the initial optimization of the algorithm is that the total number of mirror fields is 6,384 pieces, and the average annual output power per unit area is 530.6W.

CONCLUSION: The model of this paper can reasonably solve the problem and has strong practicability and high efficiency, but high dimensional particle swarm algorithm due to easily get local optimal solution, so can introduce the chaotic mapping to increase the randomness of the search space, improve the global search ability of the algorithm.

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References

Zhang Ping, optical efficiency of solar tower [J], Technology and Market, 2021,28 (6): 5-8.

Song Haixiang. Study on the influence of dust accumulation on heliostat reflectance and measurement method [D]. Hangzhou: China Jiliang University, 2021.

Badescu V．Theoretical derivation of heliostat tracking errors distribution[J]．Solar Energy

O. Farges, J.J. Bezian, M. El Hafi, Global optimization of solar power tower systems using a Monte Carlo algorithm: Application to a redesign of the PS10 solar thermal power plant [J], Renewable Energy, 2018, 119:345-353. DOI: https://doi.org/10.1016/j.renene.2017.12.028

Li Qiming, Zheng Jiantao, Xu Haiwei, Liu Mingyi, Pei Jie, Liu Guanjie. Discussion on solar thermal power generation in Xizang province [J]. Journal of Solar Energy, 2012 (s1), 57-62.

Guo Su, Liu Deyou, Zhang Yaoming. Solar thermal power generation series articles (5) —— tower solar thermal power generation helioscope [J]. Solar energy, 2006, (5): 34-37.

Wang Jun, Zhang Yaoming, Liu Deyou, Sun Liguo, an Cuicui. Solar thermal power generation series articles (8) Slot solar thermal power generation DSG technology [J]. Solar energy, 2007, (2): 26-27.

Yuan Jianli, Lin Rumou, Jin Hongguang, Han Wei, Hong Hui. Solar thermal power Generation System and Classification (Part A) [J]. Solar energy, 2007, (4): 29-32.

Zhang Yaoming, Zhang Wenjin, Liu Deyou, Sun Liguo, Liu Xiaohui, Wang Jun. Solar thermal power generation series article (17) Research and development of 70kW tower solar thermal power generation system (below) [J]. Solar energy, 2007, (11): 17-40.