Optimized Design of Fixed-Sun Mirror Field Based on Genetic Algorithm and Monte Carlo Fusion

This paper investigates the optimization design problem in a heliostat mirror field based on genetic algorithm and Monte Carlo fusion. Firstly, the annual average optical efficiency, annual average output thermal power, and annual average output thermal power per unit mirror area of the heliostat mirror field are calculated, and the ray tracing model is established. Firstly, the altitude angle and azimuth angle of the sunlight are calculated, and then, the coordinate systems of different research objects are established to calculate the incident vector, reflection vector and normal vector of the mirror surface, and then the Monte-Carlo method is adopted to simulate the rays, and the various parameters are calculated. Then, the parameters of the heliostat field were designed so that the annual average thermal power output per unit mirror area was as large as possible under the condition of achieving the rated power. Assuming that all the heliostats had the same size and mounting height, the position coordinates of the absorber tower, the size of the heliostat mirrors, the mounting height, the number of heliostat mirrors, as well as the position of the heliostat mirrors were determined. And the optimal values of the parameters were obtained by using the fusion of genetic algorithms and Monte Carlo to solve for the optimal values of the parameters.