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Robustness analysis in supercritical CO2 power generation system configuration optimization

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  • Gao, Lei
  • Cao, Tao
  • Hwang, Yunho
  • Radermacher, Reinhard

Abstract

Supercritical CO2 power cycle has higher thermal efficiency with medium turbine inlet temperature, simple layout, and compact system design. Optimization from configuration selection to parameter tuning makes the system best suited under different applications. System configuration is the premise of parameter design and operation strategy. To evaluate the impacts of configurations on thermal performance, a stepwise method was developed to categorize, optimize, and analyze supercritical CO2 power cycle configurations. First, graph information was used to narrow configurations down to meta-configurations. Second, integer and nonlinear solvers were utilized in simulated annealing algorithm to evaluate system performances, optimize parameters, and select candidate configurations. Third, the robustness of these candidates was evaluated through a parametric study. The results show 1, 2, and 28 meta-configurations for no split, one split, and two-split configuration categories. The thermal efficiency of optimized system configurations in three categories reaches 44.6%, 48.1%, and 49.4% with source, sink temperature of 780 K and 295 K, and turbomachine efficiency of 90%, respectively. In terms of robustness, a two-split configuration degrades more than one-split configuration under various pinch points, compression efficiency, and split ratio. Considering the limited efficiency improvement of the two-split configuration, the one-split configuration is recommended. Configuration without a split is applicable when the system is designed under extreme requirements.

Suggested Citation

  • Gao, Lei & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2022. "Robustness analysis in supercritical CO2 power generation system configuration optimization," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032059
    DOI: 10.1016/j.energy.2021.122956
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    References listed on IDEAS

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    Cited by:

    1. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    2. Sun, Lei & Tang, Bo & Xie, Yonghui, 2022. "Performance assessment of two compressed and liquid carbon dioxide energy storage systems: Thermodynamic, exergoeconomic analysis and multi-objective optimization," Energy, Elsevier, vol. 256(C).
    3. Sun, Lei & Liu, Tianyuan & Wang, Ding & Huang, Chengming & Xie, Yonghui, 2022. "Deep learning method based on graph neural network for performance prediction of supercritical CO2 power systems," Applied Energy, Elsevier, vol. 324(C).

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