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Thermodynamic design and power prediction of a solar power tower integrated system using neural networks

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  • Ouyang, Tiancheng
  • Pan, Mingming
  • Huang, Youbin
  • Tan, Xianlin
  • Qin, Peijia

Abstract

In spite of the fact that the solar power tower system is considered as one of the most valuable power generation facilities, it still faces challenges such as insufficient utilization of the solar salt temperature range and the dependence on high solar radiation intensity. To address this issue, an integrated recompression Brayton cycle and trans-critical regenerative organic Rankine cycle in parallel layout is proposed. The comparison with other literature shows that the specific work of the integrated system (147.8 kW/kg) outperforms the partial cooling Brayton cycle (130.1 kW/kg) under the same solar salt temperature range. In order to forecast future electricity generation and serve as a reference for plant operation, a power prediction strategy using neural networks is developed. Findings indicate that the integrated system can increase power production and maximize solar salt temperature utilization by adjusting the physical constraint strategy based on the changing temperature interval. Its equivalent work and thermal efficiency reach 12.7 MW and 38.36%, respectively, and it can recover the cost in 8 years. These results suggest that the proposed integrated system is a promising solution to enhance the performance of solar power tower systems with significant economic benefits.

Suggested Citation

  • Ouyang, Tiancheng & Pan, Mingming & Huang, Youbin & Tan, Xianlin & Qin, Peijia, 2023. "Thermodynamic design and power prediction of a solar power tower integrated system using neural networks," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012434
    DOI: 10.1016/j.energy.2023.127849
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    1. Tingting Zhu & Yiren Guo & Zhenye Li & Cong Wang, 2021. "Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory," Energies, MDPI, vol. 14(24), pages 1-16, December.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
    4. Fernández, F.J. & Prieto, M.M. & Suárez, I., 2011. "Thermodynamic analysis of high-temperature regenerative organic Rankine cycles using siloxanes as working fluids," Energy, Elsevier, vol. 36(8), pages 5239-5249.
    5. Su, Zixiang & Yang, Liu, 2022. "Peak shaving strategy for renewable hybrid system driven by solar and radiative cooling integrating carbon capture and sewage treatment," Renewable Energy, Elsevier, vol. 197(C), pages 1115-1132.
    6. Chen, Kang & Zheng, Shaoxiong & Du, Yang & Fan, Gang & Dai, Yiping & Chen, Haichao, 2021. "Thermodynamic and economic comparison of novel parallel and serial combined cooling and power systems based on sCO2 cycle," Energy, Elsevier, vol. 215(PA).
    7. Ouyang, Tiancheng & Zhao, Zhongkai & Zhang, Mingliang & Xie, Shutao & Wang, Zhiping, 2022. "A micro off-grid power solution for solid oxide fuel cell waste heat reusing enabled peak load shifting by integrating compressed-air energy storage," Applied Energy, Elsevier, vol. 323(C).
    8. Majid Hosseini & Satya Katragadda & Jessica Wojtkiewicz & Raju Gottumukkala & Anthony Maida & Terrence Lynn Chambers, 2020. "Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 13(15), pages 1-15, July.
    9. Yang, Jingze & Yang, Zhen & Duan, Yuanyuan, 2021. "Load matching and techno-economic analysis of CSP plant with S–CO2 Brayton cycle in CSP-PV-wind hybrid system," Energy, Elsevier, vol. 223(C).
    10. Behzadi, Amirmohammad & Gholamian, Ehsan & Houshfar, Ehsan & Habibollahzade, Ali, 2018. "Multi-objective optimization and exergoeconomic analysis of waste heat recovery from Tehran's waste-to-energy plant integrated with an ORC unit," Energy, Elsevier, vol. 160(C), pages 1055-1068.
    11. Segarra-Tamarit, Jorge & Pérez, Emilio & Moya, Eric & Ayuso, Pablo & Beltran, Hector, 2021. "Deep learning-based forecasting of aggregated CSP production," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 306-318.
    12. Wang, Kun & He, Ya-Ling & Zhu, Han-Hui, 2017. "Integration between supercritical CO2 Brayton cycles and molten salt solar power towers: A review and a comprehensive comparison of different cycle layouts," Applied Energy, Elsevier, vol. 195(C), pages 819-836.
    13. Gutiérrez-Alvarez, R. & Guerra, K. & Haro, P., 2023. "Market profitability of CSP-biomass hybrid power plants: Towards a firm supply of renewable energy," Applied Energy, Elsevier, vol. 335(C).
    14. Merchán, R.P. & Santos, M.J. & Medina, A. & Calvo Hernández, A., 2022. "High temperature central tower plants for concentrated solar power: 2021 overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    15. Fahad Radhi Alharbi & Denes Csala, 2021. "Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks," Energies, MDPI, vol. 14(20), pages 1-22, October.
    16. Padilla, Ricardo Vasquez & Soo Too, Yen Chean & Benito, Regano & Stein, Wes, 2015. "Exergetic analysis of supercritical CO2 Brayton cycles integrated with solar central receivers," Applied Energy, Elsevier, vol. 148(C), pages 348-365.
    17. Wang, Di & Han, Xinrui & Li, Haoyu & Li, Xiaoli, 2023. "Dynamic simulation and parameter analysis of solar-coal hybrid power plant based on the supercritical CO2 Brayton cycle," Energy, Elsevier, vol. 272(C).
    18. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    19. Islam, Md Tasbirul & Huda, Nazmul & Saidur, R., 2019. "Current energy mix and techno-economic analysis of concentrating solar power (CSP) technologies in Malaysia," Renewable Energy, Elsevier, vol. 140(C), pages 789-806.
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