Optimization Study of Fixed Solar Mirror Field for Tower Solar Power Generation Based on Genetic Algorithm

This paper addresses the optimization problem of the fixed-sun mirror field scheduling scheme in a tower solar power plant. Firstly, based on the existing heliostat mirror field parameters, a mirror field and solar correlation motion model is established, and key indexes such as optical efficiency and annual average output thermal power per unit area are calculated. Then, this paper modifies the heliostat parameters and applies genetic algorithm to optimize the parameters to improve the annual average output thermal power per unit area. Finally, the effectiveness of the optimized model in improving the annual average output thermal power per unit area is demonstrated by model validation. The results show that the optimized annual average optical efficiency reaches 0.6493, the annual average cosine efficiency is 0.9108, the annual average shadow shading efficiency is 0.9532, and the annual average output thermal power per unit area of mirror is 0.6809 kw/$\mathrm{m}^{2}$. After optimization by genetic algorithm, the size of the heliostat mirror was adjusted to 5.25 m$\times$ 3.8m, the installation height was 5.25 m, and the position coordinates of the absorption tower were (17.4, 12.6), and the optimized annual average output thermal power of the mirror per unit area was enhanced to 1.7089 kw/$\mathrm{m}^{2}$.