Optimized Design of Heliostat Field Based on Genetic Simulated Annealing Algorithm

Multi-setting heliostat is the basic formation of tower solar thermal power station to collect solar energy. It is an important measure to build a new type of power system which is mainly based on new energy Title. In this paper, the efficiency and thermal power output of different sizes and mounting heights of heliostats are evaluated by simulation or numerical computation, and the optimal combination is found, and the number and location of heliostats are determined according to the optimal combination. First, given the location of the centers of all heliostats, the annual average optical efficiency, the annual average output thermal power, and the annual average output thermal power per unit mirror area are calculated for that heliostat field. Then, considering the case where all the heliostats have the same size and mounting height, Monte Carlo numerical simulation is used to design the parameters of the heliostat field so that the annual average output thermal power per unit mirror area is as large as possible when the heliostat field reaches the rated power. Finally, considering the situation that the size and mounting height of the heliostat mirror can be different, the genetic simulated annealing algorithm is used to optimize and redesign the parameters of the heliostat mirror field, so that the annual average output thermal power per mirror area is as large as possible under the condition of reaching the rated power. The proposed genetic simulated annealing algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, and has high flexibility and applicability.