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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.17 No.1 pp.146-154

Predicting the Coming Energy of IRAN Using Alternative Energy Planning Simulation

Mohsen Jalalimajidi, S. M. Seyedhosseini*, Masoud Babakhani, Ahmad Makui
Department of industrial Management, College of Management and Economies, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Corresponding Author,
September 18, 2017 September 26, 2017 September 27, 2017


The main focus of this paper involves determining Iran’s energy system features, acknowledging energy modeling approaches by making a good scenario for the developing countries and developing scenarios using these confirmed models for simulating the transfer of energy compatible with the environment. This approach which is carried out through the multi-purpose optimization proposes an alternative to design an energy system based on the economic and environmental norms. In addition this paper mentions the chances and the consequences of transferring the replaceable energies and the potential paths to come. Thus, determining and modeling of the energy system is dealt with and the required strategic program to solve the country’s energy system problems are shown through the basic structure, the mixed program framework and the practical & case studies so as to prepare the scenarios to transfer the renewable energies Iran. These scenarios are based on the present economy depending upon the fossil fuel, and the aims of government for an economic future relying more on the renewable energies. This paper also tries to predict the plans for the country’s future energy using the alternative energy planning systems (such as simulator application of LEAP). At the end of the paper, analyzing the data and the conclusions is done.



    This paper suggests the way a scenario should be developed to transfer a sustainable energy. The technologies of the sustainable energy studied here are renewable ones and are formed by sun, wind, water, biomass which could be called clean energy technologies, which emits fewer greenhouse gases than the ordinary coal and oil. At first this research briefly explains the present status of energy in Iran. The researchers will show that considering the present local energy policy, if this tendency continues to locally use the energy and products, the economy will suffer and Iran will undergo economic problems more serious than it has experienced up to now.

    Additionally, since Iran is under the effect of some international factors, such as the oil policies of many countries (mostly in OPEC), the oil international markets and more public oil international markets, the procedure to use the energy in the world in the future, the policies of the environment, etc. it is necessary to identify opportunities and threats which is possible by bringing a SWOT analysis and formulization of the strategies. Thus, the researchers will elaborate on how a SWOT analysis is able to analyze Iran’s local energy.

    There is no doubt that energy is one of the main factors for the economic growth for all countries. The increase in demand for energy requires a balanced and long term strategy which includes various sources and locationspecific solutions such as more utilization of the alternative energy resources, technology of producing the optimized energy and encouraging the private sector to use the green technology. For years using the new energy is the basis to carry out extensive studies and to gain access to the new technology from which our country has not benefited sufficiently. The large part of consuming energy in the world is provided by the fossil fuels, combustion of the fossil fuels brings a huge mass of Sulfur oxide and nitrogen, Mono carbon and Carbon Dioxide into the air. However, the complicated interaction depicted in Figure 1 makes the decision-making harder UN DESA (2010).

    Iran is one of the most affluent countries in the world for its diverse energy resources; it possesses massive resources of oil and gas, and also a great potential for the renewable energy such as wind, geothermal, solar, etc. With the statements given above, reconsideration seems necessary as when the fossil energy declines the efficient use of these energies is essential. This paper looks for a model to facilitate an alternative process for the renewable energies based on a specific timing, according to the perspective document and the plan of developing the country. So it could be concluded that Iran has a serious need to reconsider the energy consumption pattern and to replace the energy (Bina, 2013).

    Currently, the portion of renewable energies from all the available energies is rare and the portion of this resources from all the produced energy reported to be 0.08 million barrels of crude oil (0.17 percent of all the produced energy) .The potential of this resource in the country, especially solar and wind energy, is very high than countries pioneer in these fields. Non-realization of the aims to provide the one-percent of the electricity needed in the country from the renewable sources and also nonrealization of the 62%of the capacity of the renewable powerhouses make the researcher to propose the authorities to reconsider the policies, use the technical-scientific potentials appropriately in the present management and executive systems (Bina, 2013).

    In this section, after a quick review on the growth patterns of development procedure, empirical studies in Iran and other countries has been dealt with. As one of the most attractive topics in modern energy policy, the renewable energy has been studied by the researchers, academics, public and private sectors. There are several studies to identify which renewable energy resource in the world should be prioritized. Some of the studies have been carried out by considering the environmental, economic and technical aspects on assessing the renewable energy resources.

    Throughout the world, of the most valuable studies done on this could be CHAKORTI, ROMASTOTSE (Mousavi et al., 2011).

    In this study, OECD of fossil fuels and consequences of energy demand for the countries with solar energy has been reviewed by developing a model in optimization control method and using the cost of extracting the optimized path. The results of this study shows that if the historical rate of cost reduction of solar energy production is maintained, more than 90 percent of the coal used in the country will never be used, and the world will move toward solar energy rather than natural oil and gas.

    Fang et al. (2011), an expert in wall street journal, and a manager in the external council in the U.S mentions that there are three reasons why solar energy is better than the other resources. Firstly, the sun might use the improvement of material, calculation and nanotechnology where the other resources fail to be as much effective. Secondly, solar energy has a host from the first storehouse in which it could grow in that from producing the roof to the external energy from network and mini energy of network in most developing countries where they do not have good infrastructures. Thirdly, the solar energy had the best accordance with the energy that we ask than the wind. Finally, it suggests that the biggest obstacle for the solar energy is the cost of installation (Martinot et al., 2007).

    In another study, Mousavi et al. (2011) created a model to produce electricity from a mixture of renewable resources to provide electrical needs in a remote village in India which was out of network by HOOMER software. In this study, a small hydropower; photovoltaic hydro, wind, solar turbine generator; biodiesel generator are known to be renewable resource energy. As a result of this study, Mousavi et al. (2011) concluded that a mixture of hydropower, solar photovoltaic and biodiesel generator is the best combination. The growth patterns came formally into economical literature by famous model of Sato (1964). Sato (1964) created a new change in the growth patterns through introducing extrinsic technological development and presenting the problem of waste.

    The growth patterns without the natural resources include most studies regarding economic growth. Such as Dessai et al. (2013), Montazeri-Gh and Mahmoodi-K (2016), Energy Vortex-Energy Dictionary (2016). The ‘growth patterns ‘includes another set of economical growth by considering the resources and without considering the technological development. Martinot et al. (2007) by generalizing the growth patterns and considering the natural resources in these patterns, showed that by each constant discount rate, the efficient growth path leads to natural resources erosion and this causes the economy to fall and the relief to fade in long terms. But in this study, technological development was not considered. Xerox Company used AHP for R&D decisions for managing the samples, technological implementation, and choice of energy engineering program. The car designers in General Motors used AHP to assess the design options, to implement risk management, and to achieve the best and the most affordable car designs. Moreover, NASA used AHP to assess the options like photovoltaic cell of farms to the nuclear reactor based on the norm to propose a power house for the first moon base. Lee et al. (2009) made a hierarchal structure and used AHP for selecting the best wind turbine among all the turbine candidate options. In this study, the researchers regarded four criteria including technical specification, economical specification, environmental effects, and customer services to assess the six wind turbine options.

    As a result of the study, Lee et al. (2009) concluded that the technical specification and then the economical specification are the best criterion to choose the wind turbine option and based on this, VESTAS was identified as the best option among these six wind turbine option. In another study, Castronuovo et al. (2007) used AHP to determine the type of submarine which is preferred in the fleet submarine. The researchers purpose to carry out this study was to make R&D project selection method which is practical and flexible; it could be used by Turkish navy managers. In this study they considered 5 criteria including secrecy, strength, speed, censors and machinery, the weapon systems; for the assessment of the four submarines they concluded that secrecy is the most important criterion, and the AIP submarine is the most preferred submarine. In a different study, Güngör et al. (2010) used AHP to assess regions qualified for turning into a province in turkey. In this study, they used 9 different criteria to assess eight regions located in different parts of Turkey. Similarly, Cleland (1981) used AHP to prioritize water power, geothermal, biomass, wind force and photovoltaic considering the cost (different types of power houses related to investing cost), CO2 emissions and efficiency (Cleland, 1981) .

    In another study, Jobert, and Laborgne (2007) used AHP to make a framework of assessment about hazards related to health, environment and social advantages of power generation powerhouse from the renewable energy sources. In their study, solar powerhouse, PV powerhouse, biomass, biogas, solid waste were assessed as renewable energy sources. According to this study, solar powerhouse and then PV powerhouse had the least risk for human and environment health (Coates and Coates, 2016).


    ERASME pattern was developed to predict in short time for the EU zone. ERASEME stands for Energy Relations in an Aggregate Short term Model for Europe. These words recite two main features of this pattern:

    • Pattern for the short term prediction

    • Pattern in the EU, but in aggregation (not individual)

    • The ERASME was developed for these purposes:

    • Predicting the average of crude oil and coal cost (in Dollar)

    • Predicting the energy carrier cost

    • Predicting the demand and energy carriers

    • Predicting the energy production amount

    • Amount of change of tanks and the net energy imports

    Basically, the ERASME pattern is posed in the prediction of demand and cost pattern. But the equations of supply and balance are also considered in these two. 55 behavioral equations and more than thousand variables have the duty to draw the energy market and relationship among different sectors of the Europe’s market. These equations are reconsidered each two years for increasing the precision of the calculation. The initial data and the required data for the equation are provided by SIRENE statistical database of the Statistical Office of the European Community (SOEC) data base. The cost data is provided by the IEA database. The structure of the abovementioned pattern is depicted below. So, the main and primary purpose of this pattern is to predict the demand (Cleland, 1981).

    For predicting the demand in this pattern, three variables are used:

    1. Final energy price

    2. Macroeconomic price: we mean the GDP, private consumption, industrial production etc.

    3. Weather: of the other important variables to determine the energy in Europe is the weather and it change.

    Though ERASME is in the demand prediction group, the supply sector equation and communication are available there. The chief discriminant variables for the production amount (supply) regarding the regency of the pattern calculation (every three months) include: the capacity of production and tanks, cost of energy and energy demand. Using the pattern entry data in these, and balance between supply and demand in calculation, the amount of net importand change of the tank capacity will be calculated.

    The relation of the fuel cost calculation in ERASME is based on this:

    P f   =  f  ( PM ,  XR ,  T ,  SF )

    In which in this relation, Pf is the cost of fuel which is introduced as a function of PM (import fuel cost like oil and coal), XR (conversion rate), T (tax or tax of economical procedure).

    The relation of demand sector in ERASME pattern is based on this:

    D f   =  f  ( Q ,  PF ,  PS ,  DD ,  SF ,   )

    In which Df is demand for fuel which is a function of economic activities (Q), cost of fuels (Pf), cost of competing fuels (Ps), Weather conditions (DD), seasonal factors (SF), and the other variables which could be calculated through econometrics.

    The demand of energy for the base year and for the future year in each scenario could be calculated:

    ED b , s , t = TA b , s , t × EI b , s , t

    • ED = energy demand

    • TA = the total activity level

    • EI = the energy strength

    • B = technical branch

    • S = scenario

    • T = year

    For each of the technical branch, energy demand could be calculated individually. So, for each branch, ascertain fuel is determined. When all the technical branches are calculated, the demand for the final energy is calculated for each fuel. The level of the total activities for a fuel is producing all the activity levels in all the branches. The level of activity by SEI is titled ‘the extent of the social and economic activity is spent upon which energy’. This defines the share of using each technology and fuel in every section and year. For scenario-making assumptions in industrial change and change of fuel and policy development were the main simulators for the activity level changes. The strength of energy was defined as energy in activity. In this study, the strengths of energy were calculated individually for each of the scenarios and each year.

    Emissions of CO2 have about 95% of the share of the greenhouse gas emissions. The other emissions of greenhouse gases including Methane, non-methane light biological substances, nitrogen oxide, nitrous oxide and Sulfur dioxide were come from an introduction to the LEAP greenhouse national index. The energy costs were calculated as the total cost for satisfying the energy demand in a specific year. In the household sector, the cost includes: the supply cost, for example the cost per each KWH of power, the cost of the investment of tool for example a lamp or a radio where it is executable, the cost of installation and fix, for example the cost of installation and maintenance of a PV panel; the costs are calculated based on the tool lifespan, separation between the regular cost including energy cost and exclusive cost such as investing costs, calculated costs for each technology and each fuel individually.

    CH b , s , t = ( IC b , s , t / L b , s , t ) + SC b , s , t + OMC b , s , t ) × ND

    • CH = cost per family

    • IC = the capital cost of each tool

    • L = Life span

    • SC = the supply cost

    • OMC = the cost to set up, maintain and fix (where it is executable, otherwise it equals with zero)

    • B = the technology branch

    • S = scenario

    • T = year

    The total costs in each household sector equals with all the cost for the individual cost in each year and scenario. The cost of agricultural, business, construction, industrial, transportation and heating sectors is calculated as the activity costs. The activity cost is calculated as:

    C b , s , t = CA b , s , t × AL b , s , t

    • C = osts

    • CA = cost per activity

    • AL = activity level

    • b = technology branch

    • s = scenario

    • t = year

    All the costs, the sum of all the individual costs in each sector were given for every year and scenario. The costs for the externalities like the environment pollution costs were not considered. The costs are not on discount, and no inflation rate was not considered, due to lack of data.


    Long term and general planning in producing and consuming energy is of significant importance. A temporary appropriate management of energy is possible so that the energy experts have a clear understanding about the coming procedure of the energy demand. It is common that planning for energy in countries is carried out based on the energy system models. In this research, LEAP 1was used for the energy system modeling. This software has a bottom- up approach. Its importance is in its long term planning. In this study, to gain an appropriate understanding of the energy flow of Iran, investigating and modeling of the balance sheet of energy in country have been dealt with. Considering the procedure of the fossil fuels which is declining in the energy program of the country, optimized use of the available energy such as renewable energy should be tackled. So, use of this clean energy could be raised as a solution to decline the fossil fuel consumption (Montazeri- Gh and Mahmoodi-K, 2016).

    This study is an analytic-descriptive research in which the researchers used documentary and library resources, and also internet sources to collect the data. The researchers look at new energy outlook in Iran by modeling the energy optimized system modeling for the country, and we introduce and explain about the renewable energy types. This study combines qualitative and quantitative methods to respond to the purposes and questions of the research and uses kind of desk research so that the possibility of the criteria and indicators are possible.


    The energy balance sheet presents a report from different energy sectors. This report has dealt with renewable energies, energy and the environment and optimization of the energy consumption, in addition to assigning some chapters to discussion such as demand and supply of oil, natural gas, power, and solid fuels. The reference energy system is a graphic picture of types of energy through which we could achieve from one layer of balance sheet to the other. This matrix is called conversion matrix (Razavizade, 2014).

    Since the aim is to model the country energy change procedure, so by obtaining the conversion matrix and having different energy consumption, different sectors of the balance sheet could be modeled and at last a certain amount of production for each energy could be obtained. In this part, modeling in a step-by-step way, and obtaining different matrixes of Iran’s balance sheet are dealt with. This balance sheet is shown in the table No.1.


    The process to decide about choosing the renewable energy sources to provide energy is multi-dimensional, and it is made from several aspects in different economical, technical, environmental and social levels. So, achieving the clear solutions might be hard. Because of these problems, it is necessary to develop a tool to decide about the renewable energy. The tool should enable determinant (such as politician, military power, investor, electric tools) to arrange a set of alternatives (based on the types, often contradictory, opinions), and also to choose the best agreement to be acceptable. Looking for a compromised solution needs a sufficient assessment method based on the methods having multiple criteria. The matters related to network integration are challenged for explaining the energy resources. Since the Hybrid system designs are under effect of two issues: first the amount of energy which is acceptable for the renewable resources, and then the ability of the power systems to maintain the balance between producer and consumer. Using the decision- making tools based on the multi-criteria method is considered to achieve the determinant to make a set of relations among the alternative variables.

    A back-up system could be defined as an interactive system which is able to provide the data and information, and in some cases, it could even be an agreement related to application domain in the order for assisting to solve the complex and ill-defined problems. The decisionmaking processes were analyzed in different forms and the analytical implementation and support model should not consider the opinions as the internal organized forms. But it includes methods, processes and decision-making activities. The main issue is supporting the multi-criteria decision-making that makes a support tool for the decision- makers which are compatible with the subjects and priorities.

    The criteria of choice to achieve are difficult. So, it is necessary to find a solution among the assumed solutions. That is why a choice coming from the MCDA is justified and suboptimal. The other important feature is that the shape of the demand curve affects more on the dimensions of the system. Thus, designing the system in the future needs characters and systematic operation methods which are useful in the order to assess the future efficiency evaluation and its effect on the performance of the project. Developing the decision-making support methods for the investing decisions in RES in different regions, need merging the advanced prediction and simulated techniques to investigate RES operation.

    In this study, to replace the new energies instead of fossil fuels, a control model is designed using genetic algorithm method. The alternative optimized paths for the new energy are drawn instead of the fossil fuels during the time in Iran.

    Then, the designed optimized control model is presented first, and after that statistics and data are reviewed, and finally the results of estimating functions of energy demand in different economical sections, cost of extracting the fossil fuels and the energy alternative optimized path are presented in five scenarios.


    In a study, Sato (1964) developed the model used to determine the new energy alternative optimized path instead of the fossil energy in Iran’s energy section. The purpose of this model is to maximize the social relief considering the constant amount of fossil fuels.

    M a x 0 e r t [ j = 1 J 0 i = 1 I d i j ( t ) D j 1 ( θ ) d θ j = 1 J i = 1 I w i j u i j d i j ( t ) ] d t

    In the model mentioned above, the available i is resources (oil, coal, natural gas, solar energy and wind), j is economical sectors (household, business and public, transportation, agriculture and power sectors) and r shows the rate of decline. Also Dj−1 (θ) was considered the energy reversal demand function in jth sectors the control variable.

    Since converting the resource conversion process (oil, gas, stone, sun and wind) to energy is accompanied by waste of energy, uij is the fraction of the delivery energy units to the jth section to all the raw energy available in a unit of jth resource which is known as efficiency coefficient. Also, dij is the pure energy delivered by the i resource demanded by j from qij(t) unit of the resource defined as:

    d ij (t) =  u ij × q ij (t)

    qij(t) includes the estimated and proved stores of oil, gas and coal resources which were considered as situation variable. In the above-mentioned model Wij is shown as sum of the

    W ij = c i + z ij

    Conversion and extraction costs as:

    Where ci is the final cost of resource extraction (energy), and zij is the cost of converting the I energy resource than the j demand which equals total sum of the operation costs, repair and maintenance of the equipment in conversion of the resource. In this study, it is assumed the cost of extracting the solar energy and wind energy is zero but it conversion cost equals to Zb.

    The control variables include energy consumption in different economical sectors including oil, gas, coal and solar and wind energy. The status variables include stores of oil, gas and coal that will be depleted by demanding energy from these stores during specific time. In this model, three scarcity variables are considered for the stores of oil, gas and coal that they increase continuously and as much as interest rate during time. Model exogenous variable is the GDP in each year which is calculated using GDP in the base year (yo), the average for the past years’ growth rate of GDP (g) and the decline rate (r) as:

    Y=y. ( 1 + g ) t 1 / ( 1 + r ) t-1

    In this study, to assess the energy demand functions in different sectors, Cobb-Douglas function form was used:

    E=AP α Y B E t-k Y

    Where E is the energy demand in the year t , Et-k the energy demand in k years ago, Y and A constant values, Y income (GDP) in the year t, p is the weight cost of energy in the year t, α and ʙ is the elasticity and the short term income of demand, and at last (B = ʙ /1-Y) and (α =α /1-y) are respectively long term income and cost elasticity.

    Also in this form, the energy demand in this sector includes sum of demand from the oil, gas, coal energies and solar and wind energy; and the paid cost in each section includes mean of weight of paid cost in each section than the oil, gas and coal energy, and solar and wind energy. So the reversal demand function form used in the relation 1 with k = 1 is:

    p=E/e θ  E ( 1 ) Y Y B ) a

    The software used in this study, LEAP, which is bottom- up tool was used for stimulating the scenario. By the similar data and using different models and or with different modeling method the output won’t differ greatly because LEAP is a reliable tool which is calibrated and authentic. One of the chief benefits of LEAP is its bottomup approach which allows the details to be modeled technologically leading to separate explanation of the energy technology processes. One of the chief faults is that the economical processes are modeled in aggregated method because LEAP is not a balanced or economical optimized model. The scenario underlying assumptions determine the results of modeling. There is a complete awareness regarding uncertainty in modeling as it is shown in the table below. So, the presented scenarios of a variable are about the coming probable changes, but not prediction.


    Some of the scenarios were designed and MCDM was implemented for computing the total priorities of the renewable resources for these scenarios. These three scenarios were assessed in general.

    • Scenario 1: Here, the significance of each of the criteria (energy purposes and environmental purposes) are considered equal, and the priorities were considered without change in the other results 0.5.

    • Scenario 2: In this scenario the priority of the energy purpose criterion was considered more important than the environmental purpose, and its priority was set without change in the pollen 0.9.

    • Scenario 3: The environmental purpose was considered more important than the energy purpose and the priority of the environmental purpose without change was considered to be 0.900 in the rest of the poll.


    In this research, the energy demand in future and its economic, social, environmental effects and its effects about the energy to convert to the renewable energies are modeled.

    Time of replacing the new energies instead of the fossil energy in different scenarios:

    • First scenario: The result revealed that, with constancy of the cost of the conversion of the solar and wind energy, the public and private sector respectively after 25 years, transportation after 27 years, agriculture after 30 years, power after 35 years, household after 41 years, and industry after 77 years will transfer their demand for fossil energies to solar and wind.

    • The second scenario: Assuming the 10 percent reduce in the cost of conversion of solar and wind energy in every ten years, the public and private sector after 21 years, transportation after 23 years, agriculture after 26 years, power after 30 years, household after 33 years, and industry after 54 years, the transfer will be from the fossil energy to the solar and wind energy. Based on this scenario, in 54 years, the demand for the fossil energy will get zero in different economical zones and the transfer to the solar and wind energies will be completed.

    • The third scenario: By assuming 30 percent reduce, the cost of transferring the solar and wind energy in ten years, the public and private sector after 18 years, transportation after 20 years, agriculture after 21 years, power after 21 years, household after 25 years, and industry after 30 years, the transfer will go from fossil energy to the solar and wind energy.

    • The fourth scenario: The public and private sector respectively after 13 years, transportation after 16 years, agriculture after 18 years, power after 20 years, household after 20 years, and industry after 20 years, the transfer will go from fossil energy to the solar and wind energy.

    • The fifth scenario: By entering the social costs made by the emissions of Carbon Dioxide in the model, its effect on the excess of the social relief and the energy alternative time path was investigated. The results revealed that the excess amount of social relief will decline from 1.7764 e + 0.18 in the first scenario to1.71177e+0.18 in the fifth scenario. But there would be no change in the alternative path.


    LEAP was used as a model to move toward the renewable energies. It simulates the renewable energy scenario of the government purposes to establish 4 percent in 2015 and 6 percent in 2020. This survey revealed that moving toward the renewable energy is very important, as much as the energy demand will increase, it made 15.7 percent decline in demand for fossil fuels which equals to15.6 miotce. Emissions of CO2 gas between 2005 and 2020 will increase 60 percent, while using the renewable energies could reduce just 22.7 percent equaling 53.6 miotce of CO2

    So, installing and setting up of the small sector of renewable energies could have considerable effect. By replacing the fossil fuels by the renewable energies, the consumption level will not be limited. The most significant effect is related to decline of the energy demand and decline of the greenhouse during the passing process toward the renewable energies on the industry and the construction. The renewable construction will have major effect in reducing the household, agricultural, business and heating costs. For passing from the fossil energy to the renewable energies and to do it in lower costs, the appropriate method is to guide the small appliances to use renewable energies. The renewable energies could be used in different areas such as household sectors, heating, solar heaters, solar lamps, etc. the decline in energy demand could be anticipated when the renewable energies are used in a bigger scale. Plans and financial motivations of the government should be presented in places where the renewable energies are in large scale. In sectors such as industries or construction where processes and big machines should be modified, the change of fuel could be costly. Though, moving to the renewable energies from the two mentioned sectors is so effective. Using the renewable energies, especially in industry, construction and transportation various effects could be achieved, and in the household sector easy and affordable installation and set-up are advised. Using hybrid and gas vehicles is suggested in the transportation. Governmental plans, financial motivations or local awards and the tax plan for this issue, using the renewable energies, should be used for the large-scale implementation. The energy store could hardly reduce the speed of the development of the energy demand, and diffusion of its effects.



    Integration Energy Systems.


    ERASME structure pattern (Cleland, 1981).


    Iran energy balance sheet

    Note: From “Iran formal energy magazine”, (2016).


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