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Optimization of solar-coal hybridization for low solar augmentation

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  • Bame, Aaron T.
  • Furner, Joseph
  • Hoag, Ian
  • Mohammadi, Kasra
  • Powell, Kody
  • Iverson, Brian D.

Abstract

This work presents a process to determine the preliminary optimal configuration of a concentrating solar power-coal hybrid power plant with low solar augmentation, and is demonstrated on a regenerative steam Rankine cycle coal power plant in Castle Dale, UT, USA (average DNI of 542 W m−2). A representative plant model is developed and validated against published data for a coal power plant. The simplifications that lead to the representative model from a coal power plant model include combining multiple feedwater heaters, combining turbines, and using a mass-average calculation for extraction steam properties. Comparing net power generation and boiler heating estimates from the representative model to the benchmark power plant, the representative model predictions are accurate to within ±2.5% of the accepted value. Methods for quantifying solar resource based on geography and simulating a concentrating solar power field arrangement are provided and the solar contribution to electrical power output is estimated using an exergy balance. A financial model is also included to estimate the solar marginal levelized cost of electricity and payback time using a cash-flow analysis. A multi-objective optimization routine is then employed to determine the optimal configuration using the models described in this study.

Suggested Citation

  • Bame, Aaron T. & Furner, Joseph & Hoag, Ian & Mohammadi, Kasra & Powell, Kody & Iverson, Brian D., 2022. "Optimization of solar-coal hybridization for low solar augmentation," Applied Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:appene:v:319:y:2022:i:c:s030626192200589x
    DOI: 10.1016/j.apenergy.2022.119225
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    Cited by:

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    2. Li, Chao & Zhai, Rongrong, 2024. "A novel solar tower assisted pulverized coal power system considering solar energy cascade utilization: Performance analysis and multi-objective optimization," Renewable Energy, Elsevier, vol. 222(C).

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