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Tri-objective optimization of two recuperative gas turbine-based CCHP systems and 4E analyses at optimal conditions

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  • Nondy, J.
  • Gogoi, T.K.

Abstract

In this study, optimal performances of two combined cooling, heating and power systems are presented using energy, exergy, exergoeconomic, and environmental analyses. The topping cycle is a recuperative gas turbine cycle, which is common in both. The bottoming cycle of the first system comprises a steam turbine, a recuperative-regenerative organic Rankine cycle, two absorption cooling systems and a water heater while in the second, the recuperative-regenerative organic Rankine cycle is replaced entirely with a condensing steam turbine cycle. A parametric analysis is performed first to determine the impact of key operating conditions on the performance of the proposed systems. Then those operating conditions are used for performing a tri-objective optimization applying Pareto Envelope-based Selection Algorithm-II considering energy efficiency, exergy efficiency and total cost rate as the objective functions. Thereafter, a multi-criteria decision analysis is performed to determine the best optimal solution from the Pareto front. Further, to show the benefit of optimization, the values of the objective functions are compared at the optimal and the base case conditions. After optimization, it was observed that the energy and exergy efficiencies improve modestly in both systems, while the total cost rate decreased by 9% and 5.3%, respectively. Further, the first system has a payback period of 10.83 years, while the second system has 13.27 years. It was also found that at their optimum condition, the overall energy output and efficiencies of the two systems are nearly the same. However, the total cost rate of the first system is substantially lower compared to the second.

Suggested Citation

  • Nondy, J. & Gogoi, T.K., 2022. "Tri-objective optimization of two recuperative gas turbine-based CCHP systems and 4E analyses at optimal conditions," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922008911
    DOI: 10.1016/j.apenergy.2022.119582
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    References listed on IDEAS

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    3. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).

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