Heliostat fields with a balanced mirror density

Grigoriev, Victor; Milidonis, Kypros; Corsi, Clotilde; Blanco, Manuel J.

doi:10.1016/j.solener.2022.07.050

Cited by 12 publications

(2 citation statements)

References 51 publications

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“…This algorithm checks each grid from the edge to the center in a sequential manner, obtaining an approximate solution with lower computational cost. Grigoriev et al [36], on the other hand, approached the problem from the perspective of heliostat density. They avoided parameter optimization and, instead, found the optimal distribution of heliostat density by solving a variational problem.…”

Section: B Heliostat Field Layout Optimization Methodologymentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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Confronted with the challenges posed by climate change and the ongoing energy transition, solar energy is one of the important new energy sources, and the tower solar power plant has become an innovative solution to promote clean energy development. The optimization of heliostat field layout constitutes a crucial aspect in enhancing the operational efficiency of a concentrated solar power tower plant. Currently, the optimization of heliostat field layout has garnered widespread attention. In this paper, we propose the swarm optimization algorithm with niching and elite competition called NECSO to solve the large-scale heliostat field layout optimization. First, aiming to increase diversity and heterogeneity within the population, we employ a random grouping strategy to partition the population into distinct sub-swarms. Then, we design a niching and elite competition mechanism to harmonize the performance of the exploration. The niching competition is carried out within any sub-swarm to enhance the explorability of particles. The elite competition occurs between the elites which select from each sub-swarm to improve the convergence of particles. Additionally, we develop a mathematical model for the optimization of heliostat field layout. This model employs currently advanced computational methods, facilitating prompt and precise calculation of the optical efficiency in the heliostat field layout. To evaluate the performance of NECSO, we design 15 practical cases of heliostat field layout with varying scales. And then, we conduct comparative experiments with eight currently mainstream and excellent algorithms. The results indicate that NECSO exhibits competitive performance in solving the heliostat field layout optimization, particularly in large-scale cases.

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Section: B Heliostat Field Layout Optimization Methodologymentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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show abstract

“…Blocking and shadowing are two of the most important factors determining the field efficiency; they are also computationally expensive [ 21 ]. Analytical geometry methods are presented in the literature to find the problem heliostats, and results are compared with other methods to reduce the computational time [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%