Multi-objective optimization of the solar absorptivity distribution inside a cavity solar receiver for solar power towers

Kun Wang, Ya Ling He, Peiwen Li, Ming Jia Li, Wen Quan Tao

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

The solar flux distribution on the receiver of a solar power tower is usually not uniform, which can cause a number of problems for the energy efficiency and system safety, particularly, the local hot spot and the thereby caused thermal stress and thermal deformation. Therefore, homogenization of the solar flux distribution is critical and important. The objective of the present study is to homogenize the solar flux distribution while keeping the optical loss (reflection loss) as low as possible through optimization of the distribution of the solar absorptive coating. An integrated approach coupling the Monte-Carlo ray tracing method and the Gebhart method is applied to simulate the process of the solar radiation transfer in the solar power system. The multi-objective optimization of the distribution of solar absorptivity is performed by using the non-dominated sorting genetic algorithm. The following conclusions are drawn from the study. (1) The improvement of the uniformity of the distribution of solar flux can lead to more reflection loss due to the fact that more solar energy is distributed on the position with greater view factor to the aperture; (2) The Pareto optimal front obtained from the multi-objective optimization provides the trade-off between the non-uniformity of the solar flux distribution and the reflection loss. (3) The optimal solar absorptivity distribution provided by the Pareto optimal front can significantly flatten the solar flux distribution at a minimum cost of optical loss. (4) The optimal distribution of the solar absorptivity is approximately opposite to the distribution of solar flux projected onto the active surfaces from the heliostat fields.

Original languageEnglish (US)
Pages (from-to)247-258
Number of pages12
JournalSolar Energy
Volume158
DOIs
StatePublished - 2017

Fingerprint

Multiobjective optimization
Solar energy
Towers
Fluxes
Optical losses
Ray tracing
Solar radiation
Sorting
Security systems
Thermal stress
Energy efficiency
Genetic algorithms
Coatings
Costs

Keywords

  • Non-dominated sorting genetic algorithm
  • Optimal solar absorptivity distribution
  • Solar flux distribution
  • Solar power tower

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

Cite this

Multi-objective optimization of the solar absorptivity distribution inside a cavity solar receiver for solar power towers. / Wang, Kun; He, Ya Ling; Li, Peiwen; Li, Ming Jia; Tao, Wen Quan.

In: Solar Energy, Vol. 158, 2017, p. 247-258.

Research output: Contribution to journalArticle

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AB - The solar flux distribution on the receiver of a solar power tower is usually not uniform, which can cause a number of problems for the energy efficiency and system safety, particularly, the local hot spot and the thereby caused thermal stress and thermal deformation. Therefore, homogenization of the solar flux distribution is critical and important. The objective of the present study is to homogenize the solar flux distribution while keeping the optical loss (reflection loss) as low as possible through optimization of the distribution of the solar absorptive coating. An integrated approach coupling the Monte-Carlo ray tracing method and the Gebhart method is applied to simulate the process of the solar radiation transfer in the solar power system. The multi-objective optimization of the distribution of solar absorptivity is performed by using the non-dominated sorting genetic algorithm. The following conclusions are drawn from the study. (1) The improvement of the uniformity of the distribution of solar flux can lead to more reflection loss due to the fact that more solar energy is distributed on the position with greater view factor to the aperture; (2) The Pareto optimal front obtained from the multi-objective optimization provides the trade-off between the non-uniformity of the solar flux distribution and the reflection loss. (3) The optimal solar absorptivity distribution provided by the Pareto optimal front can significantly flatten the solar flux distribution at a minimum cost of optical loss. (4) The optimal distribution of the solar absorptivity is approximately opposite to the distribution of solar flux projected onto the active surfaces from the heliostat fields.

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