Application of Monte Carlo method in economic optimization of cogeneration systems – Case study of the CGAM system

Abstract Similar to other energy systems, economic analysis of cogeneration systems is one of the most important steps in their design procedure. In this paper, a novel method is suggested for economic optimization of cogeneration systems. This method provides an opportunity to consider uncertainties in various economic parameters. Accordingly, by providing the probability distribution function of the net present value or payback time, this method offers further insights in economic evaluations of cogeneration systems. As a common practice for demonstrating novel methodologies in design and optimization of cogeneration systems, the proposed method of this study is applied to a well-known cogeneration case in the literature. In a coupled scheme, Monte Carlo approach is applied with net present value method to optimize the system. Accordingly, the obtained result is the probability distribution function of the net present value of the maximum profit. The results verify that compared to previously used methods which did not consider uncertainties in economic parameters, this probability distribution function provides a more general point of view on the profitability of the system. Therefore, by showing economic risks, these considerations make investments in this cogeneration system far more interesting.

[1]  Majid Amidpour,et al.  Application of R-curve analysis in evaluating the effect of integrating renewable energies in cogeneration systems , 2016 .

[2]  Yousef S.H. Najjar,et al.  Combined cycles with gas turbine engines , 1994 .

[3]  D. Papadopoulos,et al.  Biomass energy surveying and techno-economic assessment of suitable CHP system installations , 2002 .

[4]  V. I. Ugursal,et al.  Residential cogeneration systems: Review of the current technology , 2006 .

[5]  J. R. San Cristóbal,et al.  Investment criteria for the selection of cogeneration plants¿a state of the art review , 2006 .

[6]  J. A. Orlando,et al.  Cogeneration design guide , 1996 .

[7]  Fouad Al-Mansour,et al.  Risk analysis for CHP decision making within the conditions of an open electricity market , 2007 .

[8]  Majid Amidpour,et al.  Multi-objective optimization of a solar-hybrid cogeneration cycle: Application to CGAM problem , 2014 .

[9]  Antonio Valero,et al.  CGAM Problem: Definition and Conventional Solution , 1994 .

[10]  Said Farahat,et al.  A new approach for optimization of thermal power plant based on the exergoeconomic analysis and structural optimization method: Application to the CGAM problem , 2010 .

[11]  Fabricio I. Salgado,et al.  Short-term operation planning on cogeneration systems : A survey , 2008 .

[12]  Enrico Carpaneto,et al.  Cogeneration Planning under Uncertainty. Part I: Multiple Time Frame Formulation , 2011 .

[13]  Reinhard Madlener,et al.  Investment in New Power Generation Under Uncertainty: Benefits of CHP vs. Condensing Plants in a Copula-Based Analysis , 2010 .

[14]  João Tavares Pinho,et al.  Methodology of risk analysis by Monte Carlo Method applied to power generation with renewable energy , 2014 .

[15]  Hoseyn Sayyaadi,et al.  Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system , 2009 .

[16]  Özgür Yildiz,et al.  Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach , 2015 .

[17]  Yousef S.H. Najjar,et al.  Gas turbine cogeneration systems : a review of some novel cycles , 2000 .

[18]  George Mavrotas,et al.  Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters , 2010 .

[19]  M. V. Biezma,et al.  Investment criteria applied to corrosion engineering , 2004 .