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Levelized Cost of Energy Optimization Method for the Dish Solar Thermal Power Generation System

  • Genye Dang
  • Hongsheng SuEmail author
  • Biao Yue
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

In view of the high cost of power generation and the shortcomings of scale and industrialization of dish-Stirling optical thermal power station, the NSGA-II algorithm is proposed to optimize and analyze levelized cost of energy for dish solar thermal power generation system. The capacity and cost model of dish-Stirling thermal power generation system is established, and the influence of power station capacity, thermal storage medium and thermal storage time on the levelized cost of energy is analyzed by using NSGA-II genetic algorithm and particle swarm optimization. The results show that the larger the scale of power generation, the lower the levelized cost of energy, and the thermal storage medium and the thermal storage time have a greater impact on the levelized cost of energy.

Keywords

Dish-stirling optical thermal power generation system Non-dominant sorting genetic algorithm ll (NSGA-ll) Levelized cost of energy (LCOE) Optimization 

Notes

Acknowledgment

The authors would like to thank the National Natural Science Foundation of China (61867003, 61263004 and 51867012) for financial support.

References

  1. 1.
    Arora, R., Kaushik, S.C., Kumar, R.: Multi-objective optimization of solar powered ericsson cycle using genetic algorithm and fuzzy decision making. In: 2015 International Conference on Advances in Computer Engineering and Applications, Ghaziabad, pp. 553–558. IEEE (2015)Google Scholar
  2. 2.
    Li, Y., Liao, S., Liu, G.: Thermo-economic multi-objective optimization for a solar-dish Brayton system using NSGA-II and decision making. Int. J. Electr. Power Energy Syst. 64(64), 167–175 (2015)CrossRefGoogle Scholar
  3. 3.
    Nithyanandam, K., Pitchumani, R.: Cost and performance analysis of concentrating solar power systems with integrated latent thermal energy storage. Energy 64(1), 793–810 (2014)CrossRefGoogle Scholar
  4. 4.
    Su, J.: Economic analysis and development policies research on concentrating solar power industry, pp. 17–26. North China Electric Power University, Beijing (2017)Google Scholar
  5. 5.
    Liang, Y., Zhu, Y., Wang, X.: Optimal configuration of micro-grid power supply based on levelized cost of electricity analysis. South. Power Syst. Technol. 10(2), 56–61 (2016)Google Scholar
  6. 6.
    Wang, R., Xu, H., Guo, J.: Adaptive non-dominated sorting genetic algorithm. Control Decis. 33(12), 82–87 (2018)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhouChina
  2. 2.Experimental Teaching Center on Computer ScienceLanzhou Jiaotong UniversityLanzhouChina

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