Optimization of Heliostat Field Layout in Solar System Based on Computer Double Population Genetic Algorithm to Improve Signal Processing and Computing Efficiency

This study explores the optimization of heliostat field layouts in tower-based Concentrated Solar Power (CSP) plants using a computer-based dual-population genetic algorithm, with a focus on enhancing signal processing and computing efficiency. The optical efficiency of heliostats is initially assessed by evaluating shadowing, cosine, and collector cutoff efficiencies, enabling the calculation of the annual average optical efficiency and thermal power output. The dual-population genetic algorithm is then applied to optimize heliostat positioning and parameters, targeting maximum thermal power output per unit mirror area while meeting energy requirements. To further enhance computational efficiency, the optimization framework incorporates varying heliostat sizes and installation heights, improving adaptability to diverse configurations. Applied to regions with abundant solar resources in western China, the findings not only improve CSP system efficiency but also demonstrate the potential of advanced computational algorithms in addressing complex engineering problems.