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Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm

Author

Listed:
  • Chao Li

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Rongrong Zhai

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Yongping Yang

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Of all the renewable power generation technologies, solar tower power system is expected to be the most promising technology that is capable of large-scale electricity production. However, the optimization of heliostat field layout is a complicated process, in which thousands of heliostats have to be considered for any heliostat field optimization process. Therefore, in this paper, in order to optimize the heliostat field to obtain the highest energy collected per unit cost (ECUC), a mathematical model of a heliostat field and a hybrid algorithm combining particle swarm optimization algorithm and genetic algorithm (PSO-GA) are coded in Matlab and the heliostat field in Lhasa is investigated as an example. The results show that, after optimization, the annual efficiency of the heliostat field increases by approximately six percentage points, and the ECUC increases from 12.50 MJ/USD to 12.97 MJ/USD, increased about 3.8%. Studies on the key parameters indicate that: for un-optimized filed, ECUC first peaks and then decline with the increase of the number of heliostats in the first row of the field (Nhel 1 ). By contrast, for optimized field, ECUC increases with Nhel 1 . What is more, for both the un-optimized and optimized field, ECUC increases with tower height and decreases with the cost of heliostat mirror collector.

Suggested Citation

  • Chao Li & Rongrong Zhai & Yongping Yang, 2017. "Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm," Energies, MDPI, vol. 10(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1924-:d:119785
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    Cited by:

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    4. Li, Chao & Yang, Zhiping & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E., 2018. "Off-design thermodynamic performances of a solar tower aided coal-fired power plant for different solar multiples with thermal energy storage," Energy, Elsevier, vol. 163(C), pages 956-968.
    5. José de Jesús Jaramillo Serna & Jesús M. López-Lezama, 2019. "Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis," Energies, MDPI, vol. 12(7), pages 1-32, April.
    6. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.

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