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Modeling and optimal design of heliostat field based on particle swarm optimization algorithm

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DOI: 10.23977/jeis.2023.080609 | Downloads: 10 | Views: 215

Author(s)

Qi Zhang 1

Affiliation(s)

1 Institution of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China

Corresponding Author

Qi Zhang

ABSTRACT

Building a new type of power system with new energy as the main body is the goal of achieving "carbon peak" and "carbon neutrality" in China. An important measure of the target. Tower solar thermal power generation is a new type of clean energy technology with low carbon and environmental protection. The annual average optical efficiency of the fixed sun station is affected by shadow occlusion, cosine efficiency, atmospheric transmittance, collector truncation efficiency, mirror reflectance, size layout of heliostat and so on. In this paper, the heliostat field is modeled based on reflection theorem, solar cone theory and solar motion law, and optimized based on particle swarm optimization algorithm to calculate the annual average optical efficiency, annual average output thermal power, and annual average output thermal power per unit mirror area of heliostat field. To solve this problem, The paper need to consider the height and Angle of the sun, the blocking of sunlight, and the cone model of sunlight and other factors, first calculate the sun's height Angle, azimuth Angle and cosine loss, and then calculate the shadow blocking efficiency through the tower shadow blocking, and then calculate the collector truncation efficiency according to the sun light cone theory, etc. The mathematical model of a single heliostat can be modeled, and the average optical efficiency and output thermal power of the heliostat field can be calculated by traversing the average. Then, this paper adopts single objective optimization model and particle swarm optimization algorithm. Firstly, an optimization model is established with the rated power reaching 60MW as the constraint condition, and the annual average output thermal power per unit mirror area is as large as possible to optimize the target. Then particle swarm optimization algorithm is used to find the maximum output thermal power and the final particle convergence, indicating the rationality of the mathematical model.

KEYWORDS

Tower Solar Power Plant, Particle Swarm Optimization, Heliostat Field

CITE THIS PAPER

Qi Zhang, Modeling and optimal design of heliostat field based on particle swarm optimization algorithm. Journal of Electronics and Information Science (2023) Vol. 8: 69-76. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080609.

REFERENCES

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