مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Mohammad Rahim Soltani; Mohammad Ali Afsharkazemi; Reza Radfar
Abstract
Considering climate change, which has generated many studies today, including reducing fossil fuel consumption and using renewable energies to produce clean energy, in this research, with this aim, the design of a three-level biomass supply chain network model with two minimization functions in economic ...
Read More
Considering climate change, which has generated many studies today, including reducing fossil fuel consumption and using renewable energies to produce clean energy, in this research, with this aim, the design of a three-level biomass supply chain network model with two minimization functions in economic and environmental costs has been considered. The main research gap solved in this study is the resilience of the model, which examines the disruption in the supply of raw materials with a scenario approach. The mathematical model of the research is mixed integer linear programming. To single-target the function, under uncertainty, the fuzzy TH mathematical model has been used and the validation of the model has been investigated in a real case study in Tehran province. According to the findings from the output of GAMS software, which shows the optimal economic cost equal to 791354423200 Tomans and the emission of 1420469 grams of carbon dioxide per year, the optimal mode of construction of 4 power plants in the cities of Pakdasht, Qarchak, Parand and Mallard have been proposed. The sensitivity analysis on the parameters of the TH method and on the change of biomass supply values met the expectations. As a result, the proposed model has the necessary efficiency and has been able to be optimal in terms of cost and reduce greenhouse gas emissions by combining economic and environmental approaches. Therefore, the model has the necessary resilience.IntroductionCorrect biomass management is becoming one of the most important factors to achieve a sustainable future for human society. Although biomass is highly dependent on regional climatic conditions, it is currently the only practical renewable source for direct supply of sustainable fuels for all countries in the world. Urban waste management is one of the most important tasks of urban management, which has many costs and implementation problems. Mismanagement of these biomass causes various environmental risks. Over the past half century, the world's electricity consumption has increased continuously. Between 1980 and 2023, electricity consumption has more than tripled. The growth of industrialization and access to electricity worldwide has further increased the demand for electricity. Worldwide electricity generation is projected to triple over the next three decades. The growth and expansion of a sustainable bio economy, is proposed as an important strategy that can help the world to meet many of these challenges. In support of this strategy, more than 50 countries worldwide are currently pursuing bio economy strategies. The production of renewable fuels requires long-term planning, which requires the design of a flexible supply chain network. Optimum biofuel supply chain network must deal with the time difference of fuel supply and demand. Seasonal variation is very important due to the availability of biomass and it is challenging not to consider the seasons. Therefore, the modeling of the biofuel supply chain network should consider both long-term planning and decisions such as seasons should also be considered in the modeling.Methods and MaterialThe method of this article is two-objective mixed integer linear programming. The two-objective model designed in this research has been converted into a non-fuzzy single-objective using the fuzzy TH method and it has been solved with the exact solution method and with the help of Games software. In the designed model, strategic and tactical decisions are made to achieve the set goals. Strategic decision variables include location and allocation. For location, it is meant to choose a place from among the proposed places for the construction of power plants so that the cost of transportation and as a result the cost of electricity production is kept to the minimum possible and reduces carbon emissions. In the discussion of allocation, the optimal capacities for each of the power plants are determined from among the proposed capacities. Tactical decision variables include determining the amount of biomass to be transferred from each supplier to each power plant, as well as the amount of electricity produced and transferred from each power plant to each applicant. Biofuel supply chains are subject to uncertainty due to their dynamic and complex nature. Here, according to the opinion of the experts, the uncertainties of the fuzzy type of the research model; the costs of ordering to the supplier are the costs of purchasing raw materials (biomass) and the costs of setting up the power plant. Also, according to the opinion of experts, the cost of repair and maintenance has uncertainty of a possible type.Results and DiscussionHere, the real data to determine the values of the first and second functions have been entered into the Gems software to obtain the exact solution for the desired problem. Solving the problem by TH method with beta (satisfaction coefficient) of 0.5 for W1=0.7 and W2=0.3 is considered for it. The findings from the software outputs suggest that the best situation or in other words the optimal situation is to build four power plants among the proposed points out of the seven points. These four power plants should be built in three different capacities. A power plant with a capacity of forty megawatt hours per day in Pakdasht city, with an annual production of 14,600 megawatt hours, a power plant with a capacity of twenty megawatt hours per day in the city of Mallard, equal to 7,300 megawatt hours of annual production power, a power plant with a capacity of forty megawatt hours per day in Qarchak city, With an annual production of 14,600 megawatt hours, and a power plant with a capacity of ten megawatt hours per day in Parand city, with an annual production of 3,650 megawatt hours, it produces and supplies electricity to all four residential towns in Tehran. Electricity has been supplied to three residential towns in Parand city, and electricity to a residential town in Rabat Karim will also be produced and its need will be met. The electricity of a residential town in Islamshahr and a residential town in Pardis, which were applicants, has not been supplied and both types of biomasses are consumed in different proportions in four power plants. Biomass is purchased from all ten suppliers in ten different cities.ConclusionIn this article, the presented model has two objective functions, one for reducing total costs and the other for reducing carbon emissions, both of which aim to achieve sustainable development in the waste supply chain network. Any model that can control uncertainties and turn them into certainty, that is, that can predict uncertainties so that the supply chain network does not suffer from disruption and disorder, is a resilient model. A model can be made resilient in various ways. The current model has turned uncertainties into certainty by creating scenarios, therefore the current model is also a resilient model to use the energy known as Biomass-to-X to increase the efficiency of the network. One of the biggest challenges in the biomass supply chain is logistics management, because biomass with high moisture and low density requires more expensive transportation. Therefore, to develop the model, it is suggested to manage logistics in the waste supply chain network, in sync with today's technologies, to reduce Economic costs and reducing carbon emissions should be investigated.AcknowledgmentsThe authors of the article are grateful to all those who contributed to the preparation and improvement of the quality of the article with their valuable comments.
سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
jalal Dehnavi; Mir Hossein Mousavi; Musa Khoshkalam Khosroshahi; Lana Eivazy
Abstract
The growth and survival of a company are based on making appropriate and principled investment decisions. This is while a company always continues to operate in an unpredictable environment and under the influence of various shocks. In this regard, this issue has created a two-way relationship between ...
Read More
The growth and survival of a company are based on making appropriate and principled investment decisions. This is while a company always continues to operate in an unpredictable environment and under the influence of various shocks. In this regard, this issue has created a two-way relationship between investment and uncertainty. Therefore, this study examines the relationship between investment and uncertainty in the Iranian oil industry during the period 2010 to 2019 for 32 listed companies active in the oil industry. In this regard, using the vector auto-regression approach with generalized auto-regression conditional variance heterogeneity moment, first, the structural shocks of the oil market are extracted, and then using the generalized moments approach of the Tobin q investment model is estimated. Findings show that the shock caused by global demand (εpw), and the shock caused by the global stock market (εsp) have a negative and significant effect on the ratio of gross investment to corporate capital stock. The ratio of gross investment to the company's capital stock has a negative effect on its amount with a one-year delay, which is also statistically significant. Oil supply shock (εopw) and oil price shock (εrp) have a positive and significant effect on the ratio of gross investment to the company's capital stock. The ratio of market value to the replacement value of company assets has a positive and significant effect on the ratio of gross investment to capital stock. In this regard, due to the effectiveness of oil companies’ investments in global variables such as global oil price fluctuations and supply and demand shocks, investors' stock insurance against sudden fluctuations and shocks is recommended.
Mansour Khalili Iraqi Khalili Iraqi; Akbar Komijani; zainab Kasraei
Volume 3, Issue 10 , April 2014, , Pages 67-91
Abstract
In this paper we provide an introduction to real options in valuing investment projects. Since one of the real options applications is valuing oil and gas development projects with high uncertainty and considerable investment costs, in this research we have performed valuation of selected phases of “South ...
Read More
In this paper we provide an introduction to real options in valuing investment projects. Since one of the real options applications is valuing oil and gas development projects with high uncertainty and considerable investment costs, in this research we have performed valuation of selected phases of “South Pars” gas field development project. “South Pars” gas field as the greatest independent gas field in the world is shared between Iran and Qatar and contains approximately half of the gas reserves of Iran. Based on the results of the model, using real options approach to valuing this project as compared to traditional valuation methods such as discounted cash flow, increases the project value meanwhile there is the possibility to identify optimal time of development.
Seyyed Komeil Tayyebi; Rahaman Khoshakhlagh; Maryam Farahani
Volume 3, Issue 9 , January 2014, , Pages 175-197
Abstract
Uncertainty is different from risk. When a variable is having uncertainty, as oil prices where unique characteristics are expected, risk analysis can not explain the behavior of that variable. Stochastic differential equations are able to model the behavior of such variables. Mean reverting stochastic ...
Read More
Uncertainty is different from risk. When a variable is having uncertainty, as oil prices where unique characteristics are expected, risk analysis can not explain the behavior of that variable. Stochastic differential equations are able to model the behavior of such variables. Mean reverting stochastic process is a kind of stochastic differential equation which is assumed to have the variable fluctuating in the proximity of its long run average. In this paper, we measure a proxy of uncertainty for Iran's heavy oil prices by mean reverting stochastic process in the period of 1985-2009. The results indicate that the most uncertainties were in 2005, 2006 and 2007 and the least were in 1985, 1986 and 1998.
