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

Momen, Mahyar; Shirinbakhsh, Mehrdad; Baniassadi, Amir; Behbahaninia, Ali

doi:10.1016/j.applthermaleng.2016.04.149

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…They also reported that the cost rate of exergy destruction is greater than the capital investment of the system so that a reduction in the former should be suggested in optimizing the system performance. Momen et al [26] applied the Monte Carlo method to optimize the cogeneration systems (CGAM system as a case study) economically. They reported that compared to the results obtained from conventional economic methods, this method provides a strong decision making device for design of cogeneration systems.…”

Section: Introductionmentioning

confidence: 99%

Exergy, economic and environmental impact assessment and optimization of a novel cogeneration system including a gas turbine, a supercritical CO2 and an organic Rankine cycle (GT-HRSG/SCO2)

Nami

Mahmoudi

Nemati

2017

Applied Thermal Engineering

220

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Section: Introductionmentioning

confidence: 99%

Exergy, economic and environmental impact assessment and optimization of a novel cogeneration system including a gas turbine, a supercritical CO2 and an organic Rankine cycle (GT-HRSG/SCO2)

Nami

Mahmoudi

Nemati

2017

Applied Thermal Engineering

220

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“…Momen et al [52] Monte Carlo simulations was used to estimate variable electricity and gas prices, focusing on the commodity market. Yearly prices for individual customers (households) are currently nearly constant.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Potential for Gas Micro-Cogeneration Development in Poland Using the Monte Carlo Method

et al. 2020

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Micro-cogeneration (mCHP) is a promising solution for the generation of heat and electricity in households, it contributes to reducing carbon dioxide emissions in countries where the production of electricity is mainly based on fossil fuels. Its dissemination in Poland faces barriers in the form of high purchase prices in relation to electricity productivity. In this work 1% of the household population in Poland was analyzed using the Monte Carlo method. It was found that for mCHP to become economically profitable for a group of at least 10,000 households, its price should fall from around 18,000 euros (711.5 euros/kWth and 18,000 euros/kWe) to 4800 euros (189.7 euros/kWth and 4800 euros/kWe) and for 100,000 households to 4100 euros (162.1 euros/kWth and 4100 euros/kWe). These calculations were made for fixed gas and electricity prices. The analysis also included cases of various changes in gas and energy prices. Faster growth of electricity prices than gas prices reduce the profitability barrier. In addition, a building located in Lesser Poland region was analyzed, with an above average demand for electricity and heat. Gas micro-cogeneration becomes profitable for this household at a price of 3700 euros (146.2 euros/kWth and 3700 euros/kWe) at fixed gas and electricity prices.

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“…In the literature it is already possible to find research involving feasibility analysis of projects from renewable energy sources (RES), and some authors dedicate their studies to wind sources [7]- [12]. Moreover, in some cases, even encompassing Monte Carlo simulation (MCS) for the management of economic risk [2], [13]- [16] are motivated by the high degree of uncertainty inherent in this type of enterprise.…”

Section: Introductionmentioning

confidence: 99%

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

et al. 2020

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To reduce the risks of a new energy crisis and increase energy availability, the use of renewable energy sources (RES) is important and recommended. In Brazil, micro and small companies contribute about 25% of gross domestic product (GDP), and electric energy is employed intensively, so the importance of microgeneration is observable. This research aims to analyze the economic viability of the micro-generation wind energy project for micro and small businesses. Thus, three Brazilian states, Rio Grande do Norte, Rio Grande do Sul and Minas Gerais were considered, and different scenarios were proposed. A feasibility analysis is then performed, followed by a stochastic analysis using Monte Carlo simulation (MCS). Finally, models of artificial neural networks (ANN) are used to evaluate the relative importance (RI) of the variables. The results show that none of the states appears economically feasible under the conditions presented. In the stochastic analysis, the probability of viability is between 17% and 24% in all states, which shows the low probability of viability for microgeneration. Through ANN training, it was possible to calculate the RI, in which it is possible to identify the variables that have most impact on the net present value (NPV) in all states; it is considered the most important variable in the project's viability. In addition, the discussion explores the importance of public incentives for promoting investment in renewable energy, which can reduce investment costs and make it attractive to small and medium-sized businesses. INDEX TERMS Microgeneration, wind power, stochastic feasibility analysis, sensitivity analysis, artificial neural networks.

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

Cited by 10 publications

References 17 publications

Exergy, economic and environmental impact assessment and optimization of a novel cogeneration system including a gas turbine, a supercritical CO2 and an organic Rankine cycle (GT-HRSG/SCO2)

Exergy, economic and environmental impact assessment and optimization of a novel cogeneration system including a gas turbine, a supercritical CO2 and an organic Rankine cycle (GT-HRSG/SCO2)

Analysis of the Potential for Gas Micro-Cogeneration Development in Poland Using the Monte Carlo Method

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

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