Thermo-economic analysis and optimization of cogeneration systems by considering economic parameters fluctuations

Document Type: Research Paper

Authors

1 School of Mechanical Engineering, College of Engineering, University of Tehran, Iran

2 Department of Mechanical Engineering, University of California, Riverside, CA, USA

10.22059/ees.2020.39013

Abstract

A successful cogeneration system design project needs an estimation of the economical parameters of the project, including capital investment, costs of fuel, expenses in maintenance and operating, and the proper cost for the products. This study describes the economic consideration of the benchmark cogeneration systems, called CGAM system located in the United States. To evaluate the profitability of alternative investments, cost estimation of the capital investment, calculation of the main product cost under the realistic assumption of fuel inflation, electricity inflation, and discount rate are required. Probabilistic analysis of lifetime discounted costs, including fuel and electricity cost changes, are defined by using the Monte-Carlo method for the next 20 years. Also, the total Revenue Requirement (TRR) method is selected as the main evaluation method for the economic model. As the result of calculations, the range of optimized value for inlet and outlet temperature of the combustion chamber, the efficiency of the gas turbine, efficiency and pressure ratio of air compressor in which the plant is economically and functionally in the best operation for the minimum cost of products of the cycle are achieved.

Keywords


[1] Çakir, U., Çomakli, K., & Yüksel, F. (2012). The role of cogeneration systems in the sustainability of energy. Energy Conversion and Management, 63, 196–202.

[2] Sayyaadi, H. (2009). The multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system. Applied Energy, 86(6), 867–879.

[3] A. Valero, M.A. Lozano, L. Serra, G. Tsatsaronis, J. Pisa, Ch. Frangopoulos, M.R. von Spakovsky, CGAM problem: definition and conventional solution, Energy 19 (1994) 279–286.

[4] Tsatsaronis, G., & Winhold, M. (1985). Exergoeconomic analysis and evaluation of energy-conversion plants — I. A new general methodology. Energy, 10(I), 69–80.

[5] Locatelli, G., & Mancini, M. (2010). Small-medium sized nuclear coal and gas power plant: A probabilistic analysis of their financial performances and influence of CO2 cost. Energy Policy, 38(10), 6360–6374.

[6] Zhou, X., Yang, J., Wang, F., & Xiao, B. (2009). Economic analysis of power generation from a floating solar chimney power plant. Renewable and Sustainable Energy Reviews, 13(4), 736–749.

[7] Frangopoulos, C. (1987). Thermo-economic functional analysis and optimization. Energy, 12(7), 563–571.

[8] Samadi, Forooza & Rastegardoost, Mohammad. (2019). Thermo-fluid simulation of the gas turbine performance based on the first law of thermodynamics. 10.22059/EES.2019.34705.

[9] Hanafizadeh, P., Eshraghi, J., Ahmadi, P., & Sattari, A. (2016). Evaluation and sizing of a CCHP system for commercial and office buildings. Journal of Building Engineering.

[10] Onovwiona, H. I., & Ugursal, V. I. (2006). Residential cogeneration systems: Review of the current technology. Renewable and Sustainable Energy Reviews, 10(5), 389–431.

[11]Papadopoulos, D. P., & Katsigiannis, P. A. (2002). Biomass energy surveying and techno-economic assessment of suitable CHP system installations. Biomass and Bioenergy, 22(2), 105–124.

[12]Bakos, G. C., Tsioliaridou, E., & Potolias, C. (2008). Techno-economic assessment and strategic analysis of heat and power co-generation (CHP) from biomass in Greece. Biomass and Bioenergy, 32(6), 558–567.

[13]Walla, C., & Schneeberger, W. (2008). The optimal size for biogas plants. Biomass and Bioenergy, 32(6), 551–557.

[14]Kosari, E., Rahnama, A., Momen, M., Hanafizadeh, P., & Rastegardoost, M. M. (2018). Drag coefficient and the Strouhal number analysis of a rectangular probe in a two-phase crossflow. Energy Equipment and Systems, 6(1), 7-15.

[15]Von Spakovsky, MR. Application of engineering functional analysis and optimization of the CGAM problem. Energy 1994;19(3):343–64.

[16]Sayyaadi, H., & Aminian, H. R. (2011). Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 225(8), 1041–1051.

[17]Soltani, R., Mohammadzadeh Keleshtery, P., Vahdati, M., Khoshgoftarmanesh, M. H., Rosen, M. A., & Amidpour, M. (2014). Multi-objective optimization of a solar-hybrid cogeneration cycle: Application to CGAM problem. Energy Conversion and Management, 81, 60–71.

[18]Engineering, C. (1996). Programming a multiobjective programming approach to waste minimization in the utility systems of chemical processes, 2509(16).

[19]Roosen, P., Uhlenbruck, S., & Lucas, K. (2003). Pareto optimization of a combined cycle power system as a decision support tool for trading off investment vs. operating costs. International Journal of Thermal Sciences, 42(6), 553–560.

[20]Momen, M., Shirinbakhsh, M., Baniassadi, A., & Behbahani-Nia, A. (2016). Application of Monte Carlo method in economic optimization of cogeneration systems - a Case study of the CGAM system. Applied Thermal Engineering, 104, 34–41.

[21]Biezma, M. V., & San Cristóbal, J. R. (2006). Investment criteria for the selection of cogeneration plants - A state of the art review. Applied Thermal Engineering, 26(5–6), 583–588.

[22]P. Hanafizadeh, J. Eshraghi, E. Kosari & W. H. Ahmed (2015) The Effect of Gas Properties on Bubble Formation, Growth, and Detachment, Particulate Science and Technology, 33:6, 645-651.

[23]Eshraghi, Javad & Kosari, Erfan & Hadikhani, P & Amini, Ali & Ashjaee, Mehdi & Hanafizadeh, Pedram. (2015). Numerical study of surface tension effects on bubble detachment in a submerged needle.

[24]Ghaebi, H., Saidi, M. H., & Ahmadi, P. (2012). Exergoeconomic optimization of a trigeneration system for heating, cooling, and power production purposes based on the TRR method and using an evolutionary algorithm. Applied Thermal Engineering, 36(1), 113–125.

[25]Bejan, A., Tsatsaronis, G., & Moran, M. J. (1996). Thermal Design and Optimization.