In this study, the gas turbine power plant with preheater is modeled and the simulation results are compared with one of the gas turbine power plants in Iran namely Yazd Gas Turbine. Moreover, multiobjective optimization has been performed to find the best design variables. The design parameters of the present study are selected as: air compressor pressure ratio (r AC ), compressor isentropic efficiency (Z AC ), gas turbine isentropic efficiency (Z GT ), combustion chamber inlet temperature (T 3 ) and gas turbine inlet temperature. In the optimization approach, the exergetic, economic and environmental aspects have been considered. In multiobjective optimization, the three objective functions, including the gas turbine exergy efficiency, total cost rate of the system production including cost rate of environmental impact and CO 2 emission, have been considered. The thermoenvironomic objective function is minimized while power plant exergy efficiency is maximized using a genetic algorithm. To have a good insight into this study, a sensitivity analysis of the results to the interest rate as well as fuel cost has been performed. In addition, the results showed that at the lower exergetic efficiency in which the weight of thermoenvironomic objective is higher, the sensitivity of the optimal solutions to the fuel cost is much higher than the location of Pareto Frontier with the lower weight of thermoenvironomic objective.
The thermal-economic optimization of a combined cycle power plant (CCPP) which can provide 140 MW of electrical power is discussed in this paper. The CCPP is composed of a gas turbine cycle (including, air compressor, combustion chamber, gas turbine), heat recovery steam generator (HRSG), steam turbine, condenser system, and a pump. The design parameters of such a plant are compressor pressure ratio (rAC), compressor isentropic efficiency (ηAC) gas turbine isentropic efficiency (ηGT), and turbine inlet temperature (T3), pinch difference temperature (ΔTpinch), steam turbine inlet temperature (Ta), steam turbine isentropic efficiency (ηST), and pump isentropic efficiency (ηPUMP). The objective function was the total cost of the plant in terms of dollar per second, including sum of the operating cost related to the fuel consumption, and the capital investment for equipment purchase and maintenance costs. The optimal values of decision variables were obtained by minimizing the objective function using sequential quadratic programming (SQP). The effects of change in the demanded power and fuel price on the design parameters werestudied for, 100, 120, and 140MW of net power output.
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