Academic Journal of Computing & Information Science, 2024, 7(7); doi: 10.25236/AJCIS.2024.070710.
Ligen Chen1, Shuo Zhang1, Hang Yu2
1College of Physics and Energy, Fujian Normal University, Fuzhou City, 350117, China
2College of Mathematics and Statistics, Fujian Normal University, Fuzhou City, 350117, China
This study aims to optimize the design of a heliostat field to achieve maximum annual average power output through geometric analysis and coordinate transformation. The research establishes models for annual average optical efficiency and thermal output power, and employs a grid marking method to calculate shading efficiency. The optimization model is simplified by simulating the EB (Edge of Bubble) layout, and the Particle Swarm Optimization (PSO) algorithm is used to search for optimal layout parameters. The results demonstrate that the optimized heliostat field shows significant improvements in both annual average optical efficiency and thermal output power, providing an effective design optimization strategy for solar thermal power generation technology. Additionally, a grid marking method judgment algorithm based on the plane projection model is proposed to further simplify the calculation of complex shadow areas.
Heliostat Field, EB Layout, Particle Swarm Optimization, Optical Efficiency, Thermal Output Power, Grid Marking Method
Ligen Chen, Shuo Zhang, Hang Yu. Research on the Optimization Design of Heliostat Field Based on EB Layout and Particle Swarm Algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 7: 73-80. https://doi.org/10.25236/AJCIS.2024.070710.
[1] Pfahl A, Coventry J, Röger M, et al. Progress in heliostat development[J]. Solar Energy, 2017, 152: 3-37.
[2] Yerudkar A N, Kumar D, Dalvi V H, et al. Economically feasible solutions in concentrating solar power technology specifically for heliostats–A review[J]. Renewable and Sustainable Energy Reviews, 2024, 189: 113825.
[3] Ali K, Jifeng S. Research on modeling simulation and optimal layout of heliostat field optical efficiency for Solar Power Tower Plant[J]. Applied Solar Energy, 2023, 59(6): 957-977.
[4] Wang D, Tan D, Liu L. Particle swarm optimization algorithm: an overview[J]. Soft computing, 2018, 22(2): 387-408.
[5] Mahboob K, Awais Q, Khan A, et al. Selection of Sensors for Heliostat of Concentrated Solar Thermal Tower Power Plant[J]. Engineering Proceedings, 2021, 12(1): 41.
[6] Leonardi E, Pisani L. Analysis of Heliostats' Rotation Around the Normal Axis for Solar Tower Field Optimization[J]. Journal of Solar Energy Engineering, 2016, 138(3): 031007.