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Multi-objective robust optimization of a solar power tower plant under uncertainty

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  • Luo, Yan
  • Wang, Zhiyuan
  • Zhu, Jiamin
  • Lu, Tao
  • Xiao, Gang
  • Chu, Fengming
  • Wang, Ruixing

Abstract

The optimal design of a molten salt solar power tower (SPT) plant is sensitive to the variations of uncertainties, such as solar radiation, which result in dispersion of the model output. To mitigate the impacts of uncertainties on the thermo-economic performance of SPT plant, this study develops an uncertainty-based multi-objective robust optimization design method for the case of a SPT plant in Sevilla with the expected value (i.e. the average energy cost) and the standard deviation (i.e. the dispersion of the model output) of the levelized cost of energy (LCOE) as the objectives. The Monte Carlo (MC) simulation and simulated annealing (SA) algorithm are combined to solve the robust optimization problem. The results of Pareto frontier indicate that a trade-off is needed through decision-making. The final optimal solution is determined with expectation of LCOE of 23.09 c/kWhe and standard deviation of LCOE of 1.25 c/kWhe. Compared with the deterministic optimal design, the standard deviation of LCOE of the multi-objective robust optimum is reduced by 17.22 %, which turns out to be less sensitive to the uncertainties. Moreover, the Sobol’ global sensitivity analysis results show that the direct solar radiation, heliostat field cost and receiver cost are the most sensitive to LCOE.

Suggested Citation

  • Luo, Yan & Wang, Zhiyuan & Zhu, Jiamin & Lu, Tao & Xiao, Gang & Chu, Fengming & Wang, Ruixing, 2022. "Multi-objective robust optimization of a solar power tower plant under uncertainty," Energy, Elsevier, vol. 238(PA).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019642
    DOI: 10.1016/j.energy.2021.121716
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    References listed on IDEAS

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    Cited by:

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    2. Yan, Sizhe & Wang, Weiqing & Li, Xiaozhu & Zhao, Yi, 2022. "Research on a cross-regional robust trading strategy based on multiple market mechanisms," Energy, Elsevier, vol. 261(PB).
    3. Liu, Chunyu & Zheng, Xinrui & Yang, Haibin & Tang, Waiching & Sang, Guochen & Cui, Hongzhi, 2023. "Techno-economic evaluation of energy storage systems for concentrated solar power plants using the Monte Carlo method," Applied Energy, Elsevier, vol. 352(C).
    4. Chuan Qin & Yuqing Jin & Meng Tian & Ping Ju & Shun Zhou, 2023. "Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification," Energies, MDPI, vol. 16(16), pages 1-21, August.
    5. Arrif, Toufik & Hassani, Samir & Guermoui, Mawloud & Sánchez-González, A. & A.Taylor, Robert & Belaid, Abdelfetah, 2022. "GA-GOA hybrid algorithm and comparative study of different metaheuristic population-based algorithms for solar tower heliostat field design," Renewable Energy, Elsevier, vol. 192(C), pages 745-758.
    6. Mohamed A. M. Shaheen & Zia Ullah & Mohammed H. Qais & Hany M. Hasanien & Kian J. Chua & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.

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