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Multi-objective linear regression based optimization of full repowering a single pressure steam power plant

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  • Mehrpanahi, A.
  • Nikbakht Naserabad, S.
  • Ahmadi, G.

Abstract

Full repowering of Be'sat steam power plant has been studied in this work. The methodology is used to simulate the new cycle according to its principal specifications and to optimize it based on the objective functions. Objective functions are electricity cost per kWh and exergy efficiency. These parameters are functions of the pinch and approach point temperature differences at high and low pressure points and at the pre-heater in the heat recovery steam generator (HRSG), steam turbine inlet flow rate, gas turbine (GT) isentropic efficiency, air compressor isentropic efficiency and compressor pressure ratio. Finally, considering the introduced objective functions, it is tried to achieve the most optimized techno-economic characteristics for Be'sat power plant repowering cycle using the genetic algorithm with two scenarios of single and multi-objective optimizations. The results show that the efficiencies of the repowered cycle are 52.59% and 51.3% for two cases of unfired and fired duct burners, respectively.

Suggested Citation

  • Mehrpanahi, A. & Nikbakht Naserabad, S. & Ahmadi, G., 2019. "Multi-objective linear regression based optimization of full repowering a single pressure steam power plant," Energy, Elsevier, vol. 179(C), pages 1017-1035.
    • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:1017-1035
    DOI: 10.1016/j.energy.2019.04.208
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    7. Ahmadi, Pouria & Dincer, Ibrahim & Rosen, Marc A., 2011. "Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants," Energy, Elsevier, vol. 36(10), pages 5886-5898.
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    2. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).

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