Thermodynamic analysis and thermoeconomic optimization of a dual pressure combined cycle power plant with a supplementary firing unit

• Published in: Energy conversion and management 2011-05, Vol.52 (5), p.2296-2308
• Main Authors: Ahmadi, Pouria, Dincer, Ibrahim
• Format: Article
• Language: English
• Subjects: Applied sciences; Combined cycle engines; Combined cycle power plant; Combined power plants; Design engineering; Economic data; Efficiency; Electric energy; Electric power generation; Electric power plants; Energy; Energy economics; Energy. Thermal use of fuels; Exact sciences and technology; Exergy; Firing; General, economic and professional studies; Genetic algorithm; Genetic algorithms; Installations for energy generation and conversion: thermal and electrical energy; Mathematical models; Optimization; Power plants; Theoretical studies. Data and constants. Metering; Thermodynamics, mechanics etc. For energy applications; Thermoeconomics
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2010.12.023

► A comprehensive study on a dual pressure combined cycle power plant with supplementary firing unit. ► A new integrated thermodynamic modeling and thermoeconomic optimization. ► Better performance assessment. ► Utilization genetic algorithms to improve efficiency and reduce exergy destructions and cost. In this paper, a combined cycle power plant (CCPP) with a supplementary firing system is first thermodynamically analyzed through energy and exergy. The optimal design of operating parameters of the plant is then performed by defining an objective function and applying a generic algorithm (GA) type optimization method. In order to optimally find the design parameters, a thermo-economic method is employed. An objective function representing the total cost of the plant in terms of dollar per second is defined as the sum of the operating cost related to the fuel consumption and the capital investment for equipment purchase and maintenance costs. Subsequently, different parts of the objective function are expressed in terms of decision variables. Finally, the optimal values of decision variables are obtained by minimizing the objective function using a GA. Moreover, the influences of changes in the demanded power and fuel cost are studied by considering three different output powers (i.e., 160, 180 and 200 MW). To validate the present model, the results of the present simulation code are compared with the actual data. The results show that the average difference between the model results and the actual data is about 1.41%. Moreover, various cases are investigated to determine how to decrease the objective function (cost, mass flowrate, etc.) for the optimized design and operating parameters (fuel cost, power output, etc.).