Optimization of biomimetic heliostat field using heuristic optimization algorithms
Central receiver systems are one of the most promising solar energy harvesting technologies. They consist of a large field of sun-tracking mirrors known as heliostats that focus sunlight onto a receiver at the top of a tower to generate high temperatures for running a heat cycle. Optimum localizatio...
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| Vydané v: | Knowledge-based systems Ročník 258; s. 110048 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
22.12.2022
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| Predmet: | |
| ISSN: | 0950-7051, 1872-7409 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Central receiver systems are one of the most promising solar energy harvesting technologies. They consist of a large field of sun-tracking mirrors known as heliostats that focus sunlight onto a receiver at the top of a tower to generate high temperatures for running a heat cycle. Optimum localization of heliostats in the field plays an essential role during the design phase of a central receiver system. A good design aims to obtain the highest energy yield and efficiency at the lowest cost. The primary parameter that affects the energy yield of a central receiver system is the optical efficiency of its heliostat field. Optimization is carried out to obtain an arrangement of heliostats, which maximizes optical efficiency. This study presents the optimization process of a biomimetic heliostat field design using different heuristic optimization algorithms, namely advanced particle swarm optimization, genetic algorithm, whale optimization algorithm, and gravitational search algorithm. In un-optimized biomimetic heliostat fields, the efficiency is 66.4% which is enhanced to 68.7% after optimization. The results show that the advanced particle swarm is the fastest method that converges in less than 20 iterations. The instantaneous efficiency of the field increases by approximately 3.48% after optimization by particle swarm optimization, followed by 3% with the gravitational search algorithm, 2.88% from the whale optimization algorithm, and 1.99% with the genetic algorithm. The results are compared with other similar works in optimizing the biomimetic heliostat field.
•Biomimetic Heliostat Fields are based on arrangement of seeds in sun flowers.•Design and optimization procedure for biomimetic heliostat fields is presented.•Four heuristic optimization algorithms are used for the optimization.•Objective functions are presented for biomimetic heliostat field optimization.•APSO algorithm results in highest optical efficiency by avoiding local minima. |
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| ISSN: | 0950-7051 1872-7409 |
| DOI: | 10.1016/j.knosys.2022.110048 |