Field Optimization of Tower Solar Heliostatic Mirror Based on Ant-Field Reflector Algorithm and Particle Ray Tracing Optimization

The heliostat is the central component of a tower solar thermal power station, and its optical efficiency is a critical indicator for assessing the effective utilization of solar energy. In order to improve the output power per unit mirror and achieve maximum energy focusing efficiency under the constraint of total power, this paper proposes an Ant-Field Reflector Algorithm based on Ant Colony Algorithm and Campo Regular Arrangement method to achieve the optimal heliostatic layout specification under rated power. The difference with the ordinary optimization algorithm is that it not only reduces the calculation cost, but also makes the average annual output thermal power per unit mirror area larger under the condition of reaching the rated power. At the same time, based on Monte Carlo Ray Tracing method and Particle Swarm Optimization algorithm, Particle Ray Tracing Optimization is proposed in this paper, which can better converge the high-dimensional optimization problem to the optimal solution and reduce the number of iterations of the algorithm. We have done simulation experiments, and the results show that this method is advanced.