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A novel numerical methodology of solar power tower system for dynamic characteristics analysis and performance prediction

Author

Listed:
  • Jiang, Rui
  • Li, Ming-Jia
  • Wang, Wen-Qi
  • Li, Meng-Jie
  • Ma, Teng

Abstract

Solar power tower (SPT) system is a promising candidate to improve the flexibility of renewable energy power systems. Accurately predicting the dynamic performance of the SPT system is an important prerequisite for stabilizing or dispatching the system. To this end, an integrated transient model of an SPT system has been established, taking into account the dynamic behavior of all its subsystems, including concentration, thermal energy storage, and power cycle. System dynamic response characteristics are then explored under varying environmental and operational parameters. Then, a dual-time-scale hybrid method combining steady and transient models is proposed to predict system performances. Finally, based on predicted annual performances, the capacities of subsystems are optimized. The results indicate that the direct normal irradiance (DNI) and mass flow rate of molten salts and working fluids have substantial impact on the system. When the DNI and mass flow rate of molten salt vary by more than 30 %, the required adjustment time exceeds 280 s, potentially resulting in a significant reduction in output power. The proposed hybrid method allows for rapid and accurate forecasting of overall performance, reducing calculation time by 32 % while maintaining a low relative error compared with the complete transient model. Additionally, based on the annual performances predicted by the proposed calculation method, the solar multiple and thermal storage time are optimized to be 3.0 and 12.5 h, respectively, which have considered the system efficiency, stability, and cost of energy storage.

Suggested Citation

  • Jiang, Rui & Li, Ming-Jia & Wang, Wen-Qi & Li, Meng-Jie & Ma, Teng, 2024. "A novel numerical methodology of solar power tower system for dynamic characteristics analysis and performance prediction," Energy, Elsevier, vol. 292(C).
  • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002408
    DOI: 10.1016/j.energy.2024.130469
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