A New Approach for Exergoeconomics Evaluation by Considering ‎Uncertainty with Monte Carlo Method

Document Type : Research Paper


1 Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19919-43344, Iran‎

2 Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19919-43344, Iran


The exergoeconomics analysis combines thermodynamic assessments based on exergy analysis with economic concepts. this article suggests a new method for exergoeconomics analysis and evaluation of energy systems by considering uncertainty in economic parameters. As the first step, the future values of economic parameters that influence the operating cost of the energy system are forecasted by the Monte Carlo Method. Then, as a novel approach, principles of exergoeconomics analysis method are coupled with the Monte Carlo Method for exergoeconomics evaluation of energy systems. Also, three new parameters, i.e. Risk Factor (RF), Risk Factor Sensitivity (RFS), and Product Cost Sensitivity (PCS), are proposed. Two different approaches are considered in the evaluation process to improve the system: a) decreasing the total cost of products and b) reducing the risk of the cost of products. Also, the proposed method is applied to the CGAM system as a benchmark. Eventually, the results of the first and second approaches show that the total cost of products can be reduced 4.1% (from 22.270 $/GJ to 21.358 $/GJ) and also the risk of the cost of the products can be reduced 5.8% (from 25.8% to 24.3%).


Main Subjects

[1] Worrell, E.,  Bernstein, L., Roy, J., Price, L., Harnisch, J., Industrial energy efficiency and climate change mitigation, Energy Efficiency, 2(2), 2009, 109-123.
[2] Spillias, S., Kareiva, P., Ruckelshaus M., McDonald-Madden, E., Renewable energy targets may undermine their sustainability, Nature Climate Change, 10, 2020, 974-976.
[3] Bejan, A., Tsatsaronis, G., Moran, M., Thermal design and optimization, John Wiley & Sons, 1996.
[4] Dincer, I., Rosen, M.A., Exergy: energy, environment and sustainable development, Newnes, 2012.
[5] Kamate, S.C., Gangavati, P.B., Exergy analysis of cogeneration power plants in sugar industries, Applied Thermal Engineering, 29(5), 2009, 1187-1194.
[6] Ameri, M., Ahmadi, P., Khanmohammadi, S., Exergy analysis of a 420 MW combined cycle power plant, International Journal of Energy Research, 32(2), 2008, 175-183.
[7] Gaggioli, R.A., Wepfer, W.J., Exergy economics: I. Cost accounting applications, Energy, 5(8-9), 1980, 823-837.
[8] Tribus, M., Evans, R., Thermo-economics of sea-water conversion, Industrial & Engineering Chemistry Process Design and Development, 4(2), 1963, 195-206.
[9] Tsatsaronis, G., Winhold, M., Exergoeconomic analysis and evaluation of energy-conversion plants—I. A new general methodology, Energy, 10(1), 1985, 69-80.
[10] Lozano, M.A., Valero, A., Theory of the exergetic cost, Energy, 18(9), 1993, 939-960.
[11] Kim, S.M., Oh, S.D., Kwon, Y.H., Kwak, H.Y., Exergoeconomic analysis of thermal systems, Energy, 23(5), 1998, 393-406.
[12] Tsatsaronis, G., Definitions and nomenclature in exergy analysis and exergoeconomics, Energy, 32(4), 2007, 249-253.
[13] Kwon, Y.H., Kwak, H.Y., Oh, S.D., Exergoeconomic analysis of gas turbine cogeneration systems, Exergy, An International Journal, 1(1), 2001, 31-40.
[14] Blumberg, T., Assar, M., Morosuk, T., and Tsatsaronis, G., Comparative exergoeconomic evaluation of the latest generation of combined-cycle power plants, Energy Conversion and Management, 153, 2017, 616-626.
[15] Wellmann, J., Meyer-Kahlen, B., Morosuk, T., Exergoeconomic evaluation of a CSP plant in combination with a desalination unit, Renewable Energy, 128, 2017, 586-602.
[16] Conejo, A.J., Carrión, M., Morales, J.M., Decision making under uncertainty in electricity markets, Springer, 2010.
[17] Soroudi, A., Amraee, T., Decision making under uncertainty in energy systems: State of the art, Renewable and Sustainable Energy Reviews, 28, 2013, 376-384.
[18] Posen, I.D., Jaramillo, P., Landis, A.E., Griffin, W.M., Greenhouse gas mitigation for US plastics production: energy first, feedstocks later, Environmental Research Letters, 12(3), 2017, 034024.
[19] da Silva Pereira, E.J., Pinho, J.T., Galhardo, M.A.B., Macêdo, W.N., Methodology of risk analysis by Monte Carlo Method applied to power generation with renewable energy, Renewable Energy, 69, 2014, 347–355.
[20] Momen, M., Shirinbakhsh, M., Baniassadi, A., Behbahani-nia, A., Application of Monte Carlo method in economic optimization of cogeneration systems–Case study of the CGAM system, Applied Thermal Engineering, 104, 2016, 34–41.
[21] Hofmann, M., Tsatsaronis, G., Comparative exergoeconomic assessment of coal-fired power plants – Binary Rankine cycle versus conventional steam cycle, Energy, 142, 2018, 168–179.
[22] Valero, A., Lozano, M.A., Serra, L., Tsatsaronis, G., Pisa, J., Frangopoulos, Ch., von Spakovsky, M.R., CGAM problem: Definition and conventional solution, Energy, 19(3), 1994, 279-286.