Elliptical Heliostat Field Layout Optimization Based on MOEA/D
[1]DENG Libao,WU Yiran,GUO Su.Elliptical Heliostat Field Layout Optimization Based on MOEA/D[J].Journal of Zhengzhou University (Engineering Science),2020,41(05):37-43.[doi:10.13705/j.issn.1671-6833.2020.03.012]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41卷
Number of periods:
2020 05
Page number:
37-43
Column:
Public date:
2020-10-01
- Title:
- Elliptical Heliostat Field Layout Optimization Based on MOEA/D
- Keywords:
- multi-objective optimization; MOEA/D; solar power tower system; heliostat field layout; good-point set; opposition-based learning; normalization
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2020.03.012
- Abstract:
- To solve the multi-objective heliostat field layout optimization in solar power tower system, multi-objective evolutionary algorithm based on decomposition (MOEA/D) was introduced into the domain of heliostat field layout, and a heliostat field layout optimization algorithm based on an improved MOEA/D (MOEA/D-HFL) was proposed in this paper. In this method, firstly an elliptical heliostat field model was set up aimed at optimizing annual-averaged overall optical efficiency and the land area occupied. Secondly, initial population generation strategy based on good-point set and opposition-based learning, stable normalization of objectives and dynamic genetic crossover distribution index were applied into MOEA/D to solve this problem. Pareto front of heliostat field layout problem was obtained and optimal compromise solution was got through fuzzy set theory. To validate the performance of the proposed algorithm, MOEA/D-HFL was compared with NSGA-II and original MOEA/D algorithms, and the simulation results confirmed the effectiveness and accuracy of the proposed method.
- References:
- -
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Last Update:
2020-10-23