Exergoenvironmental analysis and optimization of a cogeneration plant system using Multimodal Genetic Algorithm (MGA)

• Published in: Energy (Oxford) Vol. 35; no. 12; pp. 5161 - 5172
• Main Authors: Ahmadi, Pouria, Dincer, Ibrahim
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2010; Elsevier
• Subjects: Applied sciences; CHP; Combined power plants; Compressors; Design parameters; Economic data; Efficiency; Electric energy; Electric power generation; Energy; Energy economics; Energy. Thermal use of fuels; Exact sciences and technology; Exergoenvironmental optimization; Exergy; Fuels; General, economic and professional studies; Genetic algorithm; Genetic algorithms; Inlet temperature; Installations for energy generation and conversion: thermal and electrical energy; Methodology. Modelling; Optimization; Power plants; Turbines; CHP; Exergy; Energy; Efficiency; Genetic algorithm; Exergoenvironmental optimization; Economic analysis; Energy cost; Methodology; Cogeneration; Evolutionary algorithm; external cost; Operating cost; Optimization; Optimal design; Exergy analysis; Energetic efficiency; Steam production; Electric power production; Cost analysis; Production unit
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2010.07.050

In the present work, a combined heat and power plant for cogeneration purposes that produces 50MW of electricity and 33.3kg/s of saturated steam at 13bar is optimized using genetic algorithm. The design parameters of the plant considered are compressor pressure ratio (rAC), compressor isentropic efficiency (ηcomp), gas turbine isentropic efficiency (ηGT), combustion chamber inlet temperature (T3), and turbine inlet temperature (TIT). In addition, to optimally find the optimum design parameters, an exergoeconomic approach is employed. A new objective function, representing total cost rate of the system product including cost rate of each equipment (sum of the operating cost, related to the fuel consumption) and cost rate of environmental impact (NOx and CO) is considered. Finally, the optimal values of decision variables are obtained by minimizing the objective function using evolutionary genetic algorithm. Moreover, the influence of changes in the demanded power on various design parameters are parametrically studied for 50, 60, 70MW of net power output. The results show that for a specific unit cost of fuel, the values of design parameters increase, as the required, with net power output increases. Also, the variations of the optimal decision variables versus unit cost of fuel reveal that by increasing the fuel cost, the pressure ratio, rAC, compressor isentropic efficiency, ηAC, turbine isentropic efficiency, ηGT, and turbine inlet temperature (TIT) increase.