Physics Journal, Vol. 1, No. 2, September 2015 Publish Date: Sep. 1, 2015 Pages: 112-120

Environmental and Economic Optimization Model for Electric System Planning in Qazvin, Iran:
A LEAP Model

Abtin Ataei*, Seyed Mehdi Ebadi

Department of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran


Iran is known as one of the richest countries in fossil fuel resources. This country has also great resources of renewable energy. Qazvin province is one of the best areas in Iran for developing wind power plants thanks to its strong wind resource. In this paper, a new method for calculation of optimum combination of thermal and renewable energy power plants in year of 2030 for Qazvin province with the aim of reducing environmental pollutants was developed. To achieve the aim, the optimization tool of Long-range Energy Alternatives Planning system (LEAP) software was employed considering three different scenarios; direct cost, direct and external cost and carbon limitation. The year of 2013 was considered as the base year. The electricity demand in the base year was 11,288,216 MWh which its annual growth rate was estimated as 5.51%. Thus, the electricity demand in 2030 is estimated to be 25,586,700 MWh. Although the share of renewable power plants is almost zero at the current account, the optimization model showed that the share of wind power plants may reach to 78% due to direct cost and externalities consideration. The energy optimization model indicated not only a potential of 150 million tons of CO2 saving but also possibility of 1.3 billion U.S. dollars saving in power generation until 2030 by introducing the optimal combination of power plants for Qazvin province. According to the results of the energy optimization model, in spite of rich fossil fuel resources in Iran, the renewable energy power plants should have a key role in electricity generation to reduce the power generation cost and environmental pollutants.


Renewable Energy, LEAP Model, Optimization, Qazvin Province, Electricity Generation

1. Introduction

Rapid population growth, increasing human dependence on energy sources, excessive consumption of natural resources, limited energy recourses, and environmental pollution are primary factors which threaten the world and cause severe environmental crisis in the future (Rosen 2009, Ediger et al.2007, Zecca and Chiari 2010). Considering proper use of energy ensures sustainable development in each community, conservation of valuable source of energy and proper management of its consumption are the main issues that have been placed on the agenda by all countries. Given the importance of the energy supply issue, all policy makers and stake holders in the energy sectors are concerned with finding solutions to the aforementioned problems (Martinsen and Kerey 2008, Thollander et al. 2009, Tichi et al. 2010, Ataei et al. 2015a).

Binding emission reduction targets agreed by the parties of the Kyoto protocol are linked to the United Nations framework convention on climate change (UNFCCC) and aim to provide solution to global environmental concerns. The world is increasingly facing the dual challenges of energy shortage and environmental deterioration that mainly result from our society’s over dependence on fossil energy (Xiaoyu and Crookes2010). According to EIA (2012), thermal power plants are the biggest source of CO2 emissions which is expected to grow manifold up to 2050 due to increase in energy expenditure worldwide (Cai et al. 2013).

In Iran, fossil fuels have the major share of electricity generation. The share of electricity generated from renewable resources is about 15 percent which more than 90% of that share belongs to the big hydro power plants. According to shortage of water resources in Iran, usage of other renewable resources such as wind and solar is necessary in the regions which have a high potential (EBS 2011). The map of Iranian wind potential prepared by SUNA organization, shows that the reachable amount of wind power is estimated to be about 18000 MW and indicates the high wind energy potential countries (SUNA 2012).

Fig. 1. Map of the wind potential in Iran at height of 80 m (SUNA 2012).

Qazvin province and Kahak are two of best areas of Iran for installation of wind power plants (SUNA 2012). Table 1, shows some wind characteristics of Kahak wind tunnel (MAPNA 2012).

Table 1. Properties of the KAHAK wind tunnel (MAPNA 2012).

Max. wind velocity (m/s) Min. wind velocity (m/s) Max. wind energy density (W/m2) Min. wind energy density (W/m2)
15 7.6 4562 582

In this paper, an energy optimization model based on Long-range Energy Alternatives Planning system (LEAP) is developed to calculate the optimal combination of thermal and renewable energy power plants in year of 2030 for Qazvin province with the aim of reducing environmental pollutants.

To develop the LEAP model, three following scenarios are considered;

1.   Direct cost

2.   Direct and external cost

3.   Carbon limitation

2. Material and Methods

2.1. The LEAP Model

In this research LEAP software was employed to analyze the electrical power systems, GHG emissions and costs. LEAP is an energy-environment modeling tool developed by Stockholm environment institute (SEI) Boston, to assess the physical, economic and environmental effects of alternative energy programs technologies and other energy initiatives.

There are many studies to show how LEAP has a considerable effect in shaping energy and environmental polices worldwide. For example in California, LEAP has been used for energy forecasting and identifying alternative fuels (Ghanadan and Koomey 2005). In Mexico, it was employed to specify the feasibility of future scenarios based on moderate and high use of biofuels in the transportation and electricity generation sectors (Islas et al. 2007). In Lebanon, mitigation options were evaluated to decrease the emissions from electricity generation with emphasis on the usage of renewable energy resources (El-Fadel et al. 2003). The energy consumption and various types of emissions in consumption sectors in Iran were analyzed by using LEAP model (Avami and Farahmandpour 2008). Concerning the urban transportation, a research project was carried out in Kathmandu Valley, Nepal in 2005, to assess the emission of air pollutants, carbon dioxide (CO2) and energy used in the Kathmandu Valley (Dhakal 2006). Similarly, LEAP model has been employed to determine the total energy demand and the vehicular emissions for passenger transport in the city of Delhi (Bose 1996). In another research, Shabbir and Ahmad studied the energy demands and air pollutants resulted from transportation sector in Rawalpindi and Islamabad, Pakistan (Shabbir and Ahmad 2010). In that study, LEAP was used to predict the total energy demand and the vehicular emissions for the base year of 2000 and extrapolated to 2030 for the future predictions. So far, LEAP has been successfully used in more than 150 countries throughout the world for different goals (Ataei et al. 2015).

By using the LEAP model, it is possible to analyze the energy supply and GHG emissions at the local, national and multinational level (Ghanadan and Koomey 2005). Therefore the model can analyze the reduction potential of energy consumption and emissions by each demand sector such as industry transport and others (Caei et al. 2008). There have been studies, which analyze emissions according to the charge of electricity generation structure or diffusion of some generation technologies (Caei et al. 2007). In regard to analyzes concerning scenarios, some studies use exploratory scenarios (Ghanadan and Koomey 2005), while others use normative scenarios (Heaps 2010). Some studies analyze economic feasibility by examining the cost variations of each scenario (Heaps 2009) or analyze the change of external costs (Zhang et al. 2007).

LEAP includes the Technology and Environmental Database (TED) that provides extensive information describing the technical characteristics, costs and environmental impacts of a wide range of energy technologies including existing technologies, current best practices and next generation devices. The central concept of LEAP is an end-use driven scenario analysis. LEAP contains a full energy system accounting framework, which enables consideration of both demand and supply-side technologies and accounts for total system impacts. With its links to the environmental database, LEAP can track the pollution resulting from each stage of the fuel chain, including the reduction in greenhouse gases emissions from extraction, processing, distribution, and combustion activities that might result from more efficient use of electricity or other fuels (Lazarus et al. 1994)

In this paper, the LEAP software tool is used to analyze the current energy patterns of Qazvin province to simulate alternative energy futures along with environmental emissions under a range of user-defined assumptions. The LEAP model emphasizes the detailed evaluation of specific energy problems within the context of integrated energy and environmental planning for each ‘what if’ scenario or combinations of scenarios (SEI-B 2011) associated with future of power generation and supply in Qazvin. Four of the Energy Scenario programs address the main components of an integrated energy analysis relevant to mitigation analyses: energy demand analysis (Demand), energy conversion and resource assessment (Transformation), emissions estimation (Environment), and the comparison of scenarios in terms of costs and physical impacts (Evaluation). Furthermore, the optimization tool of LEAP software is employed to calculated the optimum shares of renewable and fossil energy in the future of power supply in Qazvin under proper scenarios with direct cost and environmental pollutants consideration.

2.2. Main Assumptions

According to the information of energy balance sheet of Iran represented in 2011, the total amount of electricity demand and combination of power plants are shown in Table 2 (EBS 2011):

Table 2. Electricity generation configuration (EBS 2011).

Plant Capacity (MW) Efficiency (%) Non-specific Production (MWh) Specific production (MWh)
Steam PP 1000 37.7 5795275 5383384
NGCC PP 1042 45.4 5999528 5909832

Based on the information provided in the balance sheet of electricity demand in base year which is equivalent to 11288216 MWh and according to recent ten year average of the growth rate of electricity demand which is calculated nearly to 5.51%, the demand in 2030 is estimated to be 25586700 MWh. Based on provided information in the balance sheet, the losses in conversion and transitions is set to be 20%.

Currently, electricity generation in Qazvin province consists of two technologies; steam cycle and natural gas combined cycle (NGCC). With respect to electricity generation plans from renewable resources, wind and solar technologies can also be added. Therefore, a part of electricity generation can come from renewable resources in 2030. To calculate the optimum share of renewable energy, an energy optimization model is developed in this study.

Table 3. Total cost of installing, operating and maintenance for each feasible power generation technology (EIA 2012).

Process Feedstock Efficiency (%) Max. Availability (%) Capacity credit (%) Capital Cost (1000 $/MW) Fixed O&M Cost ($/MW) Variable O&M Cost ($/MWh) Fuel Costs Lifetime (yrs)
Wind wind 100 35 25 1100 4.9 4 n/a 25
NGCC Natural Gas 45.7 77 88 700 3.9 6.78 $8/MMBTU 30
Steam Turbine Natural Gas 37.7 88.4 77 500 1 10 $8/MMBTU 30
Solar Solar 100 40 30 3000 3 3 n/a 25

The total cost of installing, operating and maintenance (O&M) of all feasible power generation technologies are shown in Table 3.

2.3. The Suggested Scenarios

In order to calculate the optimal combination of power plants, three following scenarios are considered;

1. Optimization with the aim of minimizing the direct cost:

In this case, the goal of optimization is only minimizing the investment and O&M costs.

2. Optimization by considering direct cost and externalities:

This includes the social cost associated with the environmental pollutions from power plants and the mentioned costs in first scenario.

3. Optimization by considering the Kyoto Protocol:

In this scenario, we optimize the combination of power plants considering the carbon emission limitations with respect to Kyoto Protocol.

3. Results and Discussion

3.1. Electrical Analysis

An energy optimization model on LEAP platform was developed under the suggested scenarios to obtain optimal combination of power plants in Qazvin province up to 2030. Fig. 2 shows the growth of electricity demand up to 2030 which begins from 11 thousand of GWh in 2013 and experience around 5.5 % growth rate every year and reach the amount of 25 thousand GWh in 2030. As discussed earlier, there are two technologies include Steam power plant (PP) and NGCC in current account which are available in the base year and the share of each one is 47% and 53%, respectively. Fig. 3 represented that in optimization scenario with considering the described parameters the share of both technologies at the end of 2030, will decrease to around 4% and 18% respectively. Therefore, it illustrates that the share of wind power plant increases instantly form 1% to 78% at the end of 2030 with respect to the optimal combination.

Fig. 2. Final Electricity Demand (Scenario: Steam only).

Table 4. Energy balance information for Qazvin province.

  2013 2014 2015 2016 2017 2018 2019 2020 2021
Production - 69.4 74.5 79.6 84.8 89.9 95.0 100.1 105.3
Imports 50.8 0.6 0.6 0.6 0.7 0.7 0.8 0.8 0.8
Exports - - - - - - - - -
Total Primary supply 50.8 70.0 75.1 80.3 85.4 90.6 95.8 100.9 106.1
Electricity Generation - -15.1 -16.2 -17.3 -18.5 -19.6 -20.7 -21.8 -22.9
Transmission and Distribution -10.2 -11.0 -11.8 -12.6 -13.4 -14.2 -15.0 -15.8 -16.6
Total Transformation -10.2 -26.1 -28.0 -29.9 -31.9 -33.8 -35.7 -37.6 -39.6
All Electricity 40.6 43.9 47.1 50.4 53.6 56.8 60.1 63.3 66.6
Electricity 40.6 43.9 47.1 50.4 53.6 56.8 60.1 63.3 66.6
Total Demand 40.6 43.9 47.1 50.4 53.6 56.8 60.1 63.3 66.6
Unmet Requirements -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0

Table 4. Continuous.

  2022 2023 2024 2025 2026 2027 2028 2029 2030
Production 110.4 115.5 120.6 125.8 130.9 136.0 141.1 146.3 151.4
Imports 0.9 0.9 1.0 1.0 1.0 1.1 1.1 1.2 1.2
Exports - - - - - - - - -
Total Primary supply 111.3 116.4 121.6 126.8 131.9 137.1 142.3 147.4 152.6
Electricity Generation -24.0 -25.1 -26.3 -27.4 -28.5 -29.6 -30.7 -31.8 -33.0
Transmission and Distribution -17.4 -18.3 -19.1 -19.9 -20.7 -21.5 -22.3 -23.1 -23.9
Total Transformation -41.5 -43.4 -45.3 -47.3 -49.2 -51.1 -53.0 -55.0 -56.9
All Electricity 69.8 73.0 76.3 79.5 82.8 86.0 89.2 92.5 95.7
Electricity 69.8 73.0 76.3 79.5 82.8 86.0 89.2 92.5 95.7
Total Demand 69.8 73.0 76.3 79.5 82.8 86.0 89.2 92.5 95.7
Unmet Requirements -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0

Fig. 3. Electricity generation after the optimization by considering the first scenario.

Fig. 4. Cumulative discounted costs in 2030 for each scenario (without externalities consideration).

3.2. Cost Analysis

The results of the optimization considering the direct cost are presented in Fig. 4. This figure shows that the optimization scenario has the lowest social cost and the configuration presented by LEAP model is the cheapest way to produce electricity. There is a potential to save up to 1.3 billion U.S. dollars in electricity generation in 2030 by introducing the optimal combination of the power plants.

It worthwhile to mention that in the optimization scenarios, LEAP model selects a combination of power plants. Since the pick load shape changes in every season and hour, in some periods, the peak load is very high. In such periods, usually some plants will be employed which have low investment cost while they have high O&M cost (like steam turbine). In timescales with base load demand, power plants with high investment cost and low O&M cost are suitable (such as wind turbine). Figs. 4 and 5 represent the total cost of two different scenarios. The first optimization is done with considering just direct cost, while the next one is to initiate the optimal combination with respect to the direct cost and externalities. Total cost of first scenario was around 7 billion U.S. dollars, while in the second scenario total cost was near to 8 billion U.S. Dollars as a result of considering externalities.

As it is clear from Figs. 6 and 7, in optimal combination of power plants, the share of wind power plant is more than others. This results reveal the fact that, although Iran has reach resources of Natural Gas and ease of access to this fuel, renewable power plants will have the main role in electricity generation in the future considering the social cost and externalities.

Fig. 5. Total cost predicted in 2030 by considering externalities.

Fig. 6. Average dispatched power estimated in 2030 by considering the optimization scenario.

Fig. 7. Total installed capacity expected by 2030.

3.3. Environmental Analysis

Total amount of GHG emissions by each scenario is estimated and presented in Table 5. The results approved that by running optimization scenarios, we could save approximately 150 million tons of CO2 equivalent at the end of 2030 compared to applying only thermal power plants. Fig. 8 illustrates that to follow the restrictions on carbon emissions, the share of thermal power plants will reduce in 2030, although the total cost of optimal combination will increase.

Fig. 8. Forecasted CO2 emissions by each scenario.

Table 5. Total cost and revenues calculated from 2013 to 2030, discounted at 18% to year 2013. Units: 2013 Billion U.S. Dollar.

  Steam only optimization Combine cycle pp only Solar only Wind only CO2 limit
Demand - - - - - -
All Electricity - - - - - -
Transformation 3.0 6.5 3.0 29.7 16.2 7.5
Transmission and Distribution - - - - - -
Electricity Generation 3.0 6.5 3.0 29.7 16.2 7.5
Resources 7.0 1.3 5.8 - - 0.7
Production 7.0 1.3 5.8 - - 0.7
Imports - - - - - -
Exports - - - - - -
Unmet Requirements - - - - - -
Environmental Externalities 1.5 0.3 1.2 - - 0.1
Net Present Value 11.5 8.1 10.0 29.7 16.2 8.3
GHG Emissions (million tons of CO2 equivalent) 217.4 40.9 179.2 - - 17.0

Fig. 9. Combination of power plant technologies after considering CO2 limitations.

Fig. 10. Expected capacity by 2030 with considering the CO2 limitations.

4. Conclusion

Nowadays, the world is increasingly facing the dual challenges of energy shortage and environmental deterioration that mainly result from dependency on fossil fuels. According to EIA (2012), thermal power plants are the highest source of CO2 emissions which are expected to increase manifold up to 2050 due to the increase in energy consumption worldwide. In Iran, a major fraction of electricity generation depends on burning fossil fuels and the share of renewable energy power generation is less than 15 percent. Thanks to KAHAK wind tunnel, Qazvin province is considered as one of the highest potential areas in Iran for installing wind power plants.

In this study, with respect to the renewable energy potential of the Qazvin province, an energy optimization model on platform of LEAP was developed under different feasible scenarios to obtain the optimal combination of power plants in Qazvin province up to 2030.

Electricity demand of Qazvin province in year of 2013 was 11 thousand GWh and the annual growth rate was almost 5.5 %. According to the energy model, that demand will reach to 25 thousand GWh in 2030. To calculate the optimal combination of thermal and renewable energy power plants in year of 2030 for Qazvin province with the aim of reducing environmental pollutants, an energy optimization model based on LEAP was developed in this study.

The energy optimization model showed that the optimum share of the wind power plant for Qazvin province in 2030 is almost 78%. Therefore, although Iran has reach resources of natural gas and an easy access to fossil fuels, renewable power plants shall have the main role in electricity generation in the future considering the social cost and externalities. This research calculated the amount of GHG emissions associated with each scenario and illustrated that in optimal combination, at the end of 2030, 150 million tons of CO2 equivalent could be saved. Moreover, there is a potential to save up to 1.3 billion U.S. dollars in electricity generation cost until 2030 by implementing the optimum combination of power plant technologies suggested in this research.


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