Optimal Design of Heliostat Field Layout Based on Improved Sparrow Search Algorithm

The heliostat field of a tower solar thermal power tower accounts for 40% to 50% of the total cost, so optimizing the layout of the heliostat field is crucial. This paper constructs a mathematical model of the optical efficiency of the heliostat field based on the radial staggered layout, and uses the annual average optical efficiency of the heliostat field as the objective function to propose a sparrow search algorithm ESSA based on the elite reverse learning strategy: after the sparrow update, take the top 10% of the fitness rankings of the sparrows as the elite solutions, and obtain the dynamic boundaries of the elite sparrows. Use the reverse learning strategy to solve the reverse solution and obtain the optimal value. Finally, taking the Spanish Gemasolar tower power plant as an example, the SSA and ESSA algorithms were used to optimize the objective function. The experimental results showed that after optimization using ESSA, the optical efficiency increased by about 2% and the shadow occlusion efficiency increased by about 4%, which effectively improved the concentrating efficiency of the heliostat field.