ARTICLE IN PRESS Energy Policy 38 (2010) 4479–4488
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Comparative analysis for energy production processes (EPPs): Sustainable energy futures for Turkey Ilhan Talinli a, Emel Topuz a,n, Mehmet Uygar Akbay a a
Istanbul Technical University (ITU), Environmental Engineering Department, Maslak 34469, Istanbul, Turkey
a r t i c l e in fo
abstract
Article history: Received 2 October 2009 Accepted 31 March 2010 Available online 27 April 2010
This study presents a comparative analysis of three different energy production process (EPP) scenarios for Turkey. Main goal is to incorporate the prioritization criteria for the assessment of various energy policies for power alternatives, and evaluating these policies against these criteria. The three types of EPPs reviewed in this study are: electricity production from wind farms in the future, existing coalbased thermal power plants and planned nuclear power plants. The analytical hierarchy process (AHP) is utilized to assess the main and sub-factors of EPPs. Main factors such as economic, technical, social and environmental are assigned in first level of the AHP. The importance weights of factors are produced and priority values with realistic numbers are obtained using Fuzzy-AHP Chang’s Model. Priority value for wind energy was determined as two times higher than the others when making the ultimate decision. On aggregate, importance weights of environmental (0.68) and social (0.69) factors make wind power leader. Sub-factors such as public acceptance, waste-emission and environmental impacts cause both nuclear and thermal power to have the lowest priority numbers. Additionally, the CO2 emissions trade was determined to be a very important criterion associated with both economic and environmental factors according to Kyoto Protocol. This study concludes that Turkey’s existing thermal power stations should gradually be substituted by renewable energy options according to a schedule of Turkish energy policies in future. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Energy production process Multi-criteria analysis Environmental impact assessment
1. Overview With the recent increase in environmental awareness and macro trends such as global warming, the environmental impact of energy production processes (EPPs) has become a major concern in drafting of environmental policies, especially in developing countries. The main cause of global warming and climate change has been determined to be an increase in carbon dioxide in the biosphere due to the excess consumption of fossil fuels (Nordell, 2003; Nel and Cooper, 2009). For this reason, renewable energies like wind and solar energy have been replacing fossil fuels since the 25-year span following the Kyoto Protocol (Boyd and Ibarraran, 2002; Korhonen and Savolainen, 1999; IPCC, 2001). Nuclear energy, as it is free of carbon emissions, has found itself in the ranks of ‘‘clean energy’’ and has been considered renewable, causing a misconception. Despite all the advances in environmental awareness, we still face difficulties in assessing these EPPs due to conflicts in core concepts especially affecting the decision-making mechanisms in environmental risk and impact assessment methodologies. As it
n
Corresponding author. E-mail addresses:
[email protected] (I. Talinli),
[email protected] (E. Topuz).
0301-4215/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2010.03.081
is valid for environmental management systems, the decision making involved in energy production process (EPP) is a mechanism characterized by complexity and uncertainty (Lee et al., 2007). Additionally, energy policies are inevitably a balancing act of many diverse factors such as social, economic, political, legal, technical and scientific issues. In fact, environmental policies are also a combination of all of these factors. As a solution, multiple-criteria analyses (MCA) can reduce uncertainties by quantifying the factors for comparison of available EPP options. The analyses can help decision makers (DM), as well as scientists, stakeholders and society to systematically consider and apply their judgments to come up with a strategic choice of energy alternatives. Despite MCA’s advantages, there are still rare applications that can deal with big complexity of EPPs (Georgopoulou et al., 2003).
1.1. Definition of problem For decision-makers and societies, selecting the most appropriate EPP options is influenced by linguistic terms, causing vagueness and ambiguity. Furthermore, the basis of this selection triggers conflicts among politicians, environmentalists, nongovernmental organizations (NGOs) and societies, which is the
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major issue today (Greening and Bernow, 2004). The problem is in qualitative thinking of human brain which is influenced by undefined linguistic terms in classical logic. On the other hand, environmental decisions can be systematically made by environmental risk assessment (ERA), environmental impact assessment (EIA) and subsequently environmental management system (EMS) to establish a framework to reduce these risks that are characterized by ERA (EPA, 2000). A large number of uncertainties in risk factors should be converted to tangible numbers. For example, production of fossil fuel energy based on a report that was not prepared according to structured, number-based analysis can lead to an air pollution disaster. An EMS application is generally focused on waste management to reduce adverse effects of emissions in ecosystems. However, a larger combination of factors that predict environmental impacts should instead be used to draw the conclusions of the EMS (Cai et al., 2009). Predicting environmental factors is very difficult with linguistic and subjective descriptions. Economic, social and technical factors must also be taken into consideration together with environmental factors in a certain hierarchy. The solution to this problem can be found in using fuzzy logic and AHP methods together. 1.2. Scope and objective The main objective of this work is to identify a priority schedule within the framework of the global environment and energy policies to assist decision-makers in the selection of EPP options. To achieve this aim, an approach based on comparisons of three basic EPPs; nuclear, renewable energy (wind) and fossil fuel energy has been implemented. Specific factors for these EPPs were analyzed with multiple-criteria for the decision-making mechanism, which tries to resolve the complexity caused by linguistic terms and uncertainties. Main factors such as economic, technical, social and environmental impacts for EEP options are hierarchically assigned within AHP method. Finally, the comparison of the options is performed by converting the subjective variables to numerical values and priority values are obtained.
2. Methodology While the scientific risk assessment has ostensibly been the primary factor and driving force for most regulatory and risk management decisions, it is apparent that other factors in addition to scientific risk assessment (and economic analyses) play an important role in decision making. The scientific risk assessment and its peer review provide the sound scientific underpinnings for a decision. However, it is only one of the many factors that a decision maker considers in arriving at a final environmental decision. Decision making factors in a risk management are involved by a variety of factors such as scientific, economic, social, technological, political, legal factors and public values (EPA, 2000). The AHP enables the decision makers to structure a complex problem in the form of a simple hierarchy and to evaluate a large number of quantitative and qualitative factors in a systematic manner under multiple-criteria environment in confliction (Saaty, 1980). The method computes and aggregates their eigenvectors until the composite final vector of weight coefficients for alternatives is obtained. The entry of final weight coefficients vector reflect the relative importance of each alternative with respect to the goal stated at the top in the hierarchy (Pohekar and Ramachandran, 2004). A decision maker may use this vector according to his particular needs and interests.
In a pair wise comparison, the decision maker examines two alternatives by considering one criterion and indicates a preference. These comparisons are made using a preference scale, which assigns numerical values to different levels of preference (Taha, 2003). The standard preference scale used for AHP is 1–9 scale, which lies between ‘‘equal importances’’ and ‘‘extreme importance’’ where sometimes different evaluation scales can be used such as 1–5. In the pair wise comparison matrix, the value of 9 indicates that one factor is extremely more important than the other; the value of 1/9 indicates that one factor is extremely less important than the other, and the value of 1 indicates equal importance (Sarkis and Talluri, 2004). There is an extensive literature that addresses the situation where the comparison ratios are imprecise judgments (Leung and Chao, 2000). In most of the real-world problems, some of the decision data can be precisely assessed while others not. These applications are performed with many different perspectives and proposed methods for fuzzy AHP. In this study, Chang’s (1992) model of extent analysis, which is based on fuzzy AHP, is used for the selection of best EPP option. Chang’s extent analysis depends on the degree of possibility of each criterion. The corresponding triangular fuzzy values for the linguistic terms are placed according to the responses of the expert opinions, and the pair wise comparison matrix is constructed for each particular level on the hierarchy. Sub-totals are calculated for each row of the matrix and new (l, m, u) set is obtained. The latest Mi (li, mi, ui) set for criterion Mi is developed by calculating li/Sli, mi/Smi, ui/Sui, (i¼1,2, y, n) values which are overall triangular fuzzy values. In the next step, membership functions are constructed for the each criterion and intersections are determined by comparing each couple. In fuzzy logic approach, the intersection point and their membership values, which imply their weight in their corresponding sets, are found for each comparison. This membership value can also be defined as the degree of possibility of that value. For a particular criterion, the minimum degree of possibility of the situations, where the value is greater than the others, is also the weight of this criterion before normalization. After obtaining the weights for each criterion, they are normalized and called as the final importance degrees or weights for the hierarchy level. The method of Chang’s (1992) extent analysis is performed for each criterion, gi in the hierarchy. Therefore, values of m extent analysis for each criterion can be obtained using the following notation (Kahraman et al., 2004): 2 31 m n X m X X j j Mgi 4 Mgi 5 ð1Þ Si ¼ j¼1
i¼1j¼1
where gi is the goal set (i¼1,2,3,4,5, y, n) and all the gi M (j ¼1,2,3,4,5, y, m) are triangular fuzzy numbers (TFNs). The steps of Chang’s analysis can be explained as follows: Step 1: The fuzzy synthetic extent value (Si) with respect to the ith criterion is defined as Eq. (1). Step 2: The degree of possibility of M2 ¼(l2, m2, u2) 4M1 ¼(l1, m1, u1) is defined as Eq. (2) below VðM2 Z M1 Þ ¼ sup½minðmM1 ðxÞ, mM2 ðyÞÞ
ð2Þ
yZx
and x and y are the values on the axis of membership function of each criterion. This expression can be equivalently written as given in Eq. (3) below 8 1, if m2 Zm1 , > > > < 0, if l1 Zu2 , VðM2 Z M1 Þ ¼ ð3Þ l1 u2 > > > : ðm u Þðm l Þ otherwise, 2 2 1 1
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Excel Software program was used to run Chang’s Fuzzy-AHP (FAHP) Model in this study.
3. Case description of the EPPs Fossil fuels, renewable energy and nuclear are known as three major energy sources of the world. Forsberg (2009) emphasized about these energy sources that they are treated as competing energy resources and economics, and environmental constraints determine which energy source will be selected. In all projections, the world energy consumption is expected to increase depending on various demographic, technological and economic growth assumptions particularly in developing countries (Nakicenovic and Swart, 2000; Duffey, 2005; Fiore, 2006). In the framework of global warming, environmental strategies are required for transition from fossil fuels to renewable energy for energy futures. Vaillancourta et al. (2008) point out main advantages of nuclear power production as its capacity to produce large amount of energy from small amount of resources and producing of nongreen house gases (GHG) emitting energy. However, these advantages of nuclear are not sufficient to determine the nuclear energy policy. An appropriate EMS according to EIA should be applied for EPP options in order to determine energy policy. Leea and Kohb (2002) state that application of life cycle assessment (LCA) to nuclear energy is important to make a decision of nuclear energy policy in environmental aspect. Electricity sector in Turkey has been controlled by a state-owned monopoly like many developed countries. Turkey started to consider nuclear option because of rapid increase in electricity demand (Erdogdu, 2007). Howewer, public acceptance does not exist due to some uncertainties related to nuclear energy such as economic performance, proliferation of dangerous material, the threat of terrorism, operation safety and radioactive waste disposal. Costs have always been a very important factor in decision making, in particular for selection of alternative energy sources and electricity generation technologies. The costs of power units consist of two groups: construction cost and operating cost including fuel and operating and maintenance (O&M) costs (Matsui et al., 2008). Yildirim and Erkan (2007) determined that nuclear energy is able to compete with other energy sources when the operating cost is less than 210$/kWh year or 2.4cent/kWh. Coal is an essential energy source to generate electricity for thermal power plants. Turkey also uses the coal (lignite) in 13 large-scale coal-based thermal power plants (TP) constructed in different regions. However, Turkish lignite has low calorific value and contains relatively higher amounts of ash, moisture and sulfur. The poor quality of this lignite is responsible for a considerable amount of air pollution. Say (2006) investigated the adverse effects of these plants, which is important for energy policy of Turkey. Wind power as a practical electric power generation is now becoming more prominent among renewable and the other energy options and all researches focused on improving wind energy generation. In most countries, wind plants were initially installed and operated by independent power producers, which, in several cases (e.g. the USA, Denmark, etc.), were asked by legislation to grant access to the grid and powerpurchase contracts to renewable energy developers (Sesto and Casale, 1998). Wind energy is accepted by public, industries and politics as a clean, practical, economical and eco-friendly option. History of wind energy, meteorology, the energy–climate relations, windturbine technology, economy, wind–hybrid applications and installed wind energy capacity are accepted as critical factors for assessment of the wind energy systems (Sahin, 2004). In
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addition, Crawford (2009) concluded that the size of wind turbines is not an important factor in optimising their life cycle energy performance. In reference to strategic decision making according to energy policies, analyzing, comparing and evaluating energy alternatives with multiple criteria pose fundamental challenges. For correct characterization of these factors and criteria, identifying the correct factors and criteria and making the correct process based sector classification is essential. In this study, civil nuclear power reactors (CNPR), coal fuel thermal power stations (TPS) and wind farms (WF) for electricity generation were defined as suitable types of EPPs for comparison.
3.1. Application of the comparative analysis to EPPs A framework for comparative analysis of EPPs is proposed in Fig. 1. The framework starts with a problem for decision makers (i.e. which energy types are suitable for Turkey) or decisions through ERA–EIA–EMS series. It is followed by an application of the FAHP methodology, which is based on the installation scenario. Finally, the EPP options are evaluated and compared according to the previously determined prioritization criteria and values (i.e. priority numbers), which help to conclude the analysis. In AHP, four main factors including economic, technological, social and environmental factors are assigned to develop weight
Start
Decission
Strategic Decission Making For EPPs
Which energy types are suitable for Turkey
Makers
ERA – EIA - EMS
Built Scenario Determine the main and sub factors
AHP
Determine the weights of importance for factors
Comparison of EPPs based on priorities
Stop
Fig. 1. Framework for comparison of EPPs.
Chang FAHP
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Comparison of EPPs
Economic Factors
Capital Cost
O&M Cost
Fuel Cost
Emission Trade
Nuclear Power
Technical Properties
Capacity
Efficienc
Ever Ready
Social Factors
Technological Ability
Occupational Public Safety Acceptanc
Wind Power
Environmental Factors
E.Risk
E.Impact
WasteEmission
Thermal Power
Fig. 2. Hierarchy structure of EPP alternatives.
points in the first level. The structure of the proposed AHP is given in Fig. 2. Sub-factors are assigned in the second level and three EPPs are at the bottom of the AHP. Costs associated with capital, operations and maintenance (O&M), fuel, CO2 emission trading and economic factors are reflected in the second level of AHP. Technical properties of the EPPs are evaluated with their capacity, efficiency, technical ability and ever ready for electricity. Occupational and public safety and public acceptance have been considered as two basic sub-factors of social factors. Finally, environmental risks, implications and waste-emissions resulting in pollution are defined as sub-factors of environmental factors. All the aforementioned factors listed in the AHP are discussed below.
3.2. Technical factors Capacity per unit for EPPs such as large-scale CNPR, WF and TPS is commonly expressed in MW (1000 KW) per year electricity production capacity of the plant. Installed or total capacity is known as maximum capacity of the plant. Unit capacity can also be given as KWh per unit fuel or unit process such as one vendor or turbine in wind farms. For example, historically the capacity of a utility-scale wind turbine has varied from 50 KW to 5 MW for the last two decades (WWEA, 2009). Daim et al. (2009) chose similar types of utility wind turbines with output power ranging from 2 to 2.5 MW from each vendor in their technical assessment. On the other hand, since wind speed is not constant, the annual capacity of a wind farm is limited by wind potential, and is therefore never called with theoretical capacity of a vendor. A capacity factor is defined as the ratio of the actual energy produced in a given period to the hypothetical maximum possible capacity (i.e. running full time at rated power). Capacity factors vary depending on resource, technology and purpose. It should be noted that capacity factors are quite different from efficiency, which is defined as the ratio of the useful output to the effort input. Typical capacity factors for wind, nuclear and thermal plants are given as 20–40%, 60–100% and 70–90%, respectively. For example, a 1 MW turbine with a capacity factor of 35% (calculated by yearly output) will produce only 3066 MWh (1 0.35 24 365), averaging to 0.35 MW (i.e. operating hours of turbine per year is approximately 3000 h or 125 days) (Wind Power, 2009). Capacity of nuclear power can also be given as total capacity in any country. For example, a country’s total nuclear capacity is indicated by the total capacity of 5000 MW. Similarly, the capacity of a reactor or the project can be given as the output from electricity generation (i.e. 1000 MW a reactor or
2 1000 MW of the twin reactors). 85% capacity factor for nuclear power and the average annual operating time is given as 8000 h. It can be generally presumed that capacity or output of new generation reactors (especially pressurized water reactor, PWR, boiled water reactor, BWR, etc.) is 1000–1500 MW/year, although low capacity nuclear power reactors (LCNP) are projected at 2–10 MW capacity nowadays in Russia (Adanlovich et al., 2007; Alekseev et al., 2007). In Armenian Yerevan, nuclear power is planned, constructed with a projected capacity of 1000–1200 MW much more than twice from existing reactor of Metsamor (Asbarez, 2009). Since 1985, 11 nuclear reactors work with installed capacity about 9 million KW in China (average capacity is approximately 1000 MW), and China would require 7000 metric tons of uranium a year to operate 40 GW of nuclear capacity according to main source of fuel (Harding, 2007). Turkish authorities are planning to build first nuclear power plant of Turkey at Akkuyu on the coast of Mediterranean Sea. By 2016, three nuclear power plants (heavy water reactor, HWR) with the total capacity of about 5000 MW and worth a total of $7.8 billion are expected to be built in Turkey (Power Technology, 2009). Capacity of fossil fuel power plants is generally given according to their installed capacity (i.e. annually 500 MW per unit). Multiple generating units may be built at a single site for more efficient use of land and natural resources. Their aim is to generate electricity by simple means through coal (i.e. lignite) and transfer it to a nearby high-voltage network. However, they are highly complex systems due to the requirement that the power plant has to be operated continuously. It depends on a careful planning of their fuel supply (i.e. a large plant may require a total load of 10,000 tons of coal for every day), fuel combustion components such as coal handling plants, furnace installations, soot blowers, grit arrester, etc. and difficult characteristic of the particular coal being used. For coal-based thermal power plants of 5000 MW total capacity, 70–90% of capacity factor is given for 6000 h/year fuel load (operating hours) as average values (Kfw, 2009).
3.3. Economic factors Capital cost (initial investment cost), O&M cost, fuel cost and unit price of electricity produced are usually considered to be subfactors of economic factors for EPPs. Also, payments resulting from the CO2 emissions for a fossil fuel plant must be taken into consideration in economic factors (i.e. emission trading). All of these sub-factors for an EPP can be assessed according to either their average total capacity or unit electricity production. For example, capital costs of nuclear power plants reported in the
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2005 OECD report was 1000 $/KW in Czech Republic, 2500 $/KW in Japan and 1500 $/KW in average (WNA, 2009a). Also, capital costs are given as $1000–1500/KW for coal-based thermal power plants and h1300/KW in 2007 compared to h1100/KW in 2005 for wind power plants (GWEC, 2007, 2005). Initial investment costs, O&M, cost of fuel used and emission trade costs (20h/t CO2) of these three EPPs have been compared by Tarjarnne and Luostarinen (2003). Total costs were calculated in order to present to expert group for fuzzification as given in Table 1. It is assumed that each of the EPP has a total capacity of 1500 MW/year. Moreover, there are some other economic factors such as organizational costs including transactional cost that might be considered separately in the hierarchy. These sub-factors may have significant impact on selection process depending on the specific conditions of processes or regions. 3.4. Social factors Social factors, such as prosperity, community values, and availability of health care, may affect the susceptibility of an individual or a definable group to risks from a particular stressor. Decision making for sustainable energy future requires methods that allow for evaluating the complexities of social factors (Kowalski et al., 2009). Occupational and public health and public acceptance have become increasingly popular sub-factors of social factors in decision making analysis about energy issues. There are some conflicts among sustainable energy supply and public acceptance which considers wider environmental issues as well as socio-economic interests and public safety. The question here is: which energy supply is acceptable and safer for people in public policy contexts? Accidents such as Chernobyl and Three Mile Island caused high resistance and fear against nuclear power in the world. In general, NGOs and environmentalist have become opposed to CO2 emissions from fossil fuel power, and some groups began supporting renewable energies such as wind and solar energy, etc. In fact, there was no public acceptance for nuclear and thermal power plants even though the government kept changing their policies frequently till today. For example, a nuclear power plant in Turkey was first mooted in 1970s. However, the project fell through for financial reasons and public resistance, which is caused by health care, life style and independence of fuel sources in public opinion. 3.5. Environmental factors Environmental risk, impacts and waste-emissions are set as sub-factors in hierarchy in order to assess environmental consequences of EPPs. Potential dangers that may lead to big damages such as environmental disasters are taken into consideration within the environmental risk factors. Estimation of adverse effects on the environment or calculated environmental consequence index (Arunraj and Maiti, 2008) provides evaluation of environmental impact factors. Waste-emission factors include impacts caused by emissions and wastes from diffuse or point
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sources of EPPs. Those three sub-factors should be evaluated by considering the contents listed as follows: (1) Environmental risks of EPPs including hazard potential (e.g. explosion, fire, terrorism, radioactivity, toxicity, etc.). (2) Environmental impacts of EPPs including both short-term (e.g. air, soil pollution) and long-term (e.g. carcinogenic effects, epidemiologic problems). (3) Waste-emission sub-factor includes the evaluation of sources for waste emission such as air pollution on a large scale, global warming caused by high CO2 emissions or radiation disasters caused by high-level radiation dissemination. Coal burning power plants have potential to cause high air pollution, which is the major problem of those plants. SO2, NOx and Hg are air pollutants that can be sourced from thermal power plants depending on what type of coal is used and with which process. Although SO2 can be removed only by using lime in a fluidized bed reactor, the pile of thousands of tons of hazardous waste (fly ash, gypsum) is produced (Daim et al., 2009). For nuclear power plants, waste-emission is the most important environmental factor, because in addition to no treatment solution for radioactive waste, safe storage and their disposal is a problem unresolved yet. Besides, environmental risk factors including the adverse effects of radiation on human health and disasters occurring with accidents seems to be much more important than the waste-emission factors. Several techniques can be used to remove emissions of coal plants including coral washing, changing to a lower sulphur coal, flue gas desulphurization treatment process and wet gypsumlime stone process. However, coal combustion still contributes the most to acid rain and air pollution and has been connected with global warming. In addition, it causes serious concerns due to environmental impact of burning fossil fuels (coal in particular). A nuclear power cycle starts with mining, enrichment and manufacturing of the fuel bars, and ends in the nuclear reactor. The cycle produces four types of wastes: mining waste, transuranic waste, low-level radioactive waste (LLRW) and high-level radioactive waste (HLRW), which may be in solid or liquid phase and radiation-contaminated materials (Blackman, 1996). Even though, a nuclear reactor using manufactured fuels is only a part of the life cycle for nuclear power production, a large nuclear reactor produces 25–30 t of spent fuel each year (WNA, 2009b). Composition of the waste scale is mostly unconverted uranium as well as significant amount of transuranic actinides (plutonium, curium) and about 3% of it is made of fission products. The spent fuels in solid form have long-term highlife radioactivity from actinides and short-term highlife radioactivity from fission products (Ojovan and Lee, 2005). Therefore, spent fuels or HLRWs need to be handled with great care and fare thought or they need to become less radioactive over the course of thousands of years. Spent fuel rods are stored in water pools, usually located on site or underground storage tanks at safe geological levels. 50,000 metric tons of spent nuclear fuel had accumulated at Yucca Mountain in US as permanent storage and the spent nuclear fuel will no longer pose a threat to public health and
Table 1 Ratios of economic factors for EPPs. EPP
O&M/capital%
Fuel/capital%
O&M+ fuel/capital%
Capital/totala%
Emission trade/total%
Total cost as $ billion
Nuclear Coal Wind
50 97 25
20 172 No fuel
71 269 25
58 17 80
n/ab 36 n/a
2.25 1.875 2.73
a b
Total cost was calculated as sum of the O&M cost, capital cost (initial investment cost), fuel cost and emission trade. n/a: not available.
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safety according to US EPA standards but only after 10,000 years of radioactive decay (NEI, 2009). The environmental risks, impacts and waste-emissions of wind energy production systems can be neglected compared to others, and depend on regional characteristics as mentioned below:
Erosion, which can occur due to installation and poor land-
Turkey has still not come up with a new strategic decision under the vision of Kyoto. On the one hand, all nuclear lobbies present nuclear power as clean production. On the other hand, renewable energies such as wind and solar energy is the best solution for energy demand of Turkey according to the public opinion.
scaping techniques.
Birds occasionally collide with wind turbines. Differences in size and types of wind turbines and nonuniformity in spacing do not satisfy most aesthetic concerns.
Noise is a minor impact in case of using setbacks to separate plant from nearby residences.
Shadow flicker is occasionally raised as an issue by close neighbors of wind farm projects.
3.6. Scenario building for Turkey
These conflicts in decision making and strategic analysis of the national judgments require a scientific solution. For this reason, a scenario for Turkey has been built with key relationships of AHP factors as given in Table 2. 3.7. Assumptions A set of assumptions internally consistent with the scenario above are given as follows:
It is assumed that there will be an increase in energy demand
In this study, a scenario was constructed in order to analyze which EPP option must be selected for electricity production in Turkey. Results of this analysis can help governmental decision makers while they are orienting energy policy of Turkey. For scientific evaluation, the following cases are analyzed in this scenario:
of Turkey until 2020.
Energy policies are being closely monitored by the environmental policies.
Existing laws and regulations associated with the environment
a. A nuclear power plant for electricity production. b. A wind power plant project (that is not given priority at the moment). c. Already existing fossil fuel-based thermal power plants.
It should be noted that there are still a lot of conflicts between governmental policies and public opinion for future energy demands. Other points to be considered include:
Energy policies are restricted by global and international long
term objectives of environmental policies (Nakicenovic et al., 2004). Kyoto protocols (which Turkey signed recently—with a 17-year delay). Electricity demand of Turkey is mainly provided by hydroelectric stations and thermal power which causes high CO2 and SO2 emissions due to combustion of low-quality lignite, and
and energy production do not effectively cover the current issues/concerns to be included in a scientific assessment. International and global laws and protocols are constituents of this scenario. Capacity of the EPPs evaluated in this scenario is balanced to 1500 MW/year. As costs related with capital, O&M, fuel and emission are given in the unit of per MW electricity generation and the electricity generation capacity of the plants are equal to each other, given costs enable us to consider unit electricity price. As a result, there is no need to include unit electricity price under economic factors.
3.8. Application of the methodology to case study In this study, MCA is used to identify priority numbers which will be useful for decision making among the EPP alternatives for the proposed scenario.
Table 2 Scenario building for EPP alternatives. FAHP factors
Nuclear
Wind
Thermal
Technical properties Capacity Efficiency Ever ready Ability
1500 MW/year 85% Very high High
1500 MW/year 35% Very low Low
1500 MW/year 80% High High
$1.5 billion 0.75 0.3 nm
$2.5 billion 0.6 nm nm
$0.35 billion 0.33 0.6 0.15
Direct exposure to radiation risk Persistency and fear to radiation; dependency to fuel
No risk, no exposure Clean and renewable; source independency
Low air quality and epidemiologic Low fuel quality; reserve dependency
High explosion risk, terrorism, earthquake RA pollution by radiation Treatment impossible, difficult and expensive disposal and storage
Risk for airport, birds collide
Carcinogen, heavy metal toxicity
Shadow, visual impact Noise
Acid rain, climate change CO2, NOx, SO2 , HM, HW and fly ash
Economic factors Capital cost O&M Fuel Emission trade Social factors Occupational safety Public acceptance Environmental factors Environmental risk Environmental impact Waste-emissions
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Table 3 Triangle fuzzy number for intensity of importance.
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Table 4 Fuzzy evaluation matrix for main factors.
Intensity of importance
Triangle fuzzy number
Very low Low Moderately low Moderate low Just equal Moderate high Moderately high High Very high
(1/9, 1/9, 1/7) (1/9, 1/7, 1/5) (1/7, 1/5, 1/3) (1/5, 1/3, 1) (1, 1, 1) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 9)
In this analysis, all factors that may be associated with both regional and global EPPs are assigned to the AHP structure. A triangle fuzzy number scale (Table 3) is required to make a series of pair wise comparison among both main factors and subfactors using Chang FAHP model. All elements have been defined and scored by an expert group using proposed fuzzy scale in Table 3. Members of this expert group were mostly selected among from academicians, as this study was an academic exercise. Two electrical and one mechanical engineer, who have expertise in nuclear, renewable, especially wind energy, and coal-based thermal energy production processes, were invited in order to make contributions to the evaluation of technical properties. An environmental engineer provided the evaluation of environmental factors, and another environmental engineer, who has background in environmental economics, contributed to assessment of emission trades. Beside this, an economist, who has expertise in energy economics, shared his opinions about economic factors. For social factors, an environmental engineer whose background based on occupational health and an authorized person from two different NGOs participated in the expert group. A meeting was organized in which all of the invited experts participated. All of the experts shared their knowledge with each other by discussing on each factor, and they gave a common score for each comparison instead of giving a score themselves. As a result, a holistic approach was provided by bringing different kinds of experts together in order to have a common conclusion for comparison of factors. To reach comparison of energy types in top of the AHP: (a) make a series of pair wise comparisons. (b) Calculate synthetic numbers by Chang Eq. (1). (c) The synthetic numbers are compared and minimum ones are chosen by Eq. (2). (d) The synthetic results are normalized and priority numbers are found. (e) These priority numbers (relative weights) are aggregated and they are synthesized for the final measurement of given main goal, which is comparison of EPPs based on scenario.
4. Results and discussion Importance weights of first-level factors in AHP are calculated using the triangular fuzzy numbers in Table 3. Comparison of main factors with performed fuzzy evaluation matrix is given in Table 4. For example, in pair wise comparison between environmental factors (EnF) and economic factors (EcF), intensity of importance of EnF vs. EcF is assigned as moderate to high (1, 3, 5). The importance weights of all factors are normalized by Eqs. (2) and (3) of Chang’s model and synthetic numbers and priority numbers are calculated as summarized in Table 5.
EcF EcF TP SF EnF
1, 1, 1/5, 1/3, 1, 3, 1, 3,
TP 1 1 5 5
1, 1, 1, 1,
3, 1, 3, 3,
5 1 5 5
SF
EnF
1/5, 1/3, 1 1/5, 1/3, 1 1, 1, 1 1/5, 1/3, 1
1/5, 1/3, 1 1/5, 1/3, 1 1, 3, 5 1, 1, 1
Table 5 Calculating the priority numbers by Chang model S number Eq. (2)
Economic factors Capital cost O&M cost Fuel cost Emission trade Technical properties Capacity Efficiency Ever ready Technological ability Social factors Occupational safety Public acceptance Environmental factors Environmental risk Environmental impact Waste-emission
[0.06, [0.20, [0.13, [0.06, [0.04, [0.04, [0.04, [0.08, [0.10, [0.06, [0.10, [0.15, [0.25, [0.08,
0.19, 0.58, 0.39, 0.19, 0.07, 0.08, 0.08, 0.31, 0.42, 0.19, 0.42, 0.25, 0.75, 0.31,
minV(Si 4Sj) Eq. (3) 0.71] 1.79] 1.25] 0.65] 0.24] 0.36] 0.36] 1.07] 1.43] 0.71] 1.43] 0.63] 1.88] 1.07]
0.73 1 0.84 0.54 0.07 0.73 0.44 0.90 1.00 0.73 1 0.43 1 0.9
[0.10, 0.29, 0.82] [0.13, 0.47, 1.29] [0.06, 0.11, 0.35]
0.79 1 0.38
Priority normalization
0.24 0.33 0.28 0.17 0.02 0.14 0.14 0.29 0.33 0.24 0.33 0.30 0.70 0.29 0,37 0,47 0,18
Among the main factors, ‘‘social factors’’ have the highest priority number (0.33). That goes to show that public safety and acceptance is more important than any other risk factors especially in energy sector. Nevertheless, the priority number of environmental factors (0.29) is almost as high as social factors and is higher than both technical and economic factors (0.14 and 0.24, respectively). Comparative analysis of EPPs according to priority numbers of sub-factors are given in Table 6. The highest priority numbers are seen in public acceptance and environmental risk factors for wind power. Sub-factors such as capacity, efficiency and ever ready have the lowest priority number. Obviously, wind energy takes these higher priority numbers from negligible environmental risks and impacts such as erosion, birds kill visual impact, etc. and no waste except noise emission. Therefore, wind energy is known as clean, renewable energy that is fuel independent; these are sufficient reasons for the public acceptance and a higher priority number. On the other hand technical sub-factors of wind power have the lowest priority number because of low capacity per wind turbines, wind speed dependency of electricity production and restricted capacity of electronic equipment such as new developing technologies of controls, electrical cables, ground support equipment and interconnection equipment. In comparison, although capital cost and O&M costs of wind energy are higher than others, it has still lower priority numbers for both capital cost and O&M costs (0 and 0.24, respectively) because of fuel independency and being free of carbon emissions (no emission trade). For nuclear energy, the results are reversed; the highest numbers of priorities are for technical factors while the lowest are for social and environmental factors. Because of fuel dependence
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Table 6 Comparison of sub-factors by Chang Nuclear power S number
Thermal power minV(Si 4Sj)
Priority
S number
Wind power minV(Si 4Sj)
Priority
S number
minV(Si 4Sj)
Priority
Economic factors Capital cost O&M cost Fuel cost Emission trade
[0.10, [0.09, [0.16, [0.39,
0.29, 0.25, 0.31, 0.47,
0.82] 0.06] 0.62] 0.57]
0.65 0.43 0.45 1
0.40 0.23 0.31 1
[0.22, [0.30, [0.05, [0.05,
0.61, 0.66, 0.06, 0.05,
1.52] 1.43] 0.10] 0.07]
1 1 0 0
0.60 0.53 0 0
[0.06, [0.06, [0.35, [0.39,
0.10, 0.09, 0.63, 0.47,
0.27] 0.22] 1.04] 0.57]
0 0.45 1 1
0 0.24 0.69 1
Technical properties Capacity Efficiency Ever ready Technological ability
[0.41, [0.25, [0.39, [0.10,
0.67, 0.45, 0.47, 0.33,
1.04] 0.80] 0.57] 1.06]
1 1 1 0.82
0.83 0.50 0.50 0.36
[0.15, [0.25, [0.39, [0.14,
0.28, 0.45, 0.47, 0.54,
0.51] 0.80] 0.57] 1.67]
0.2 1 1 1
0.17 0.50 0.50 0.44
[0.05, [0.07, [0.05, [0.07,
0.06, 0.09, 0.05, 0.13,
0.09] 0.15] 0.07] 0.45]
0 0 0 0.43
0 0 0 0.19
Social factors Occupational safety Public acceptance
[0.10, 0.33, 1.06] [0.05, 0.08, 0.18]
0.82 0
0.36 0
[0.07, 0.13, 0.45] [0.08, 0.22, 0.51]
0.43 0.25
0.19 0.20
[0.14, 0.54, 1.67] [0.35, 0.70, 1.37]
1 1
0.44 0.80
Environmental factors Environmental risk [0.05, 0.08, 0.18] Environmental impact [0.16, 0.34, 0.72] Waste-emission [0.15, 0.30, 0.58]
0 0.64 0.42
0 0.39 0.30
[0.08, 0.22, 0.51] [0.05, 0.07, 0.12] [0.05, 0.07, 0.11]
0.25 0 0
0.20 0 0
[0.35, 0.70, 1.37] [0.27, 0.59, 1.20] [0.33, 0.63, 0.11]
1 1 1
0.80 0.61 0.70
Comparison of
Economic Factors 0,24
Capital Cost 0.33
O&M Cost
Fuel Cost
Emission Trade
0.28
0.17
0.02
Nuclear Power
Social Factors 0,33
Technical Properties 0,14
Capacity 0.14
Efficienc y 0.29
Ever Technological Ready Ability 0.33
Occupational Safety
0.24
Wind Power
0.30
Environmental Factors 0,29
Public Accepta nce
E.Risk
E.Impact
0.37
0.47
0.70
WasteEmission 0.18
Thermal Power
Fig. 3. Priority numbers of the factors.
and fear of radiation, it is easy to observe a resistance in the population leading to no public acceptance. Another reason for this low value of priority may be high explosion risk and risk of terrorist attack. Nuclear power has higher priority of technical factors because of its high capacity factor, efficiency and ever ready of generating electricity. Although economic sub-factors such as capital cost and O&M cost of nuclear power have lower priority number than the thermal power, fuel costs of nuclear are seen more expensive than thermal powers. Thermal power takes the lowest priority from environmental sub-factors because of huge amount of waste and high air pollution profiles related with climate change and global warming. Moreover, CO2 emission trade in the lowest priority value also shows the relationship between economic and environmental factors (i.e. emission trade as sub-factor is not only economic, but also environmental). An aggregation of priority numbers among main and sub-factors for each EPP option is applied, and aggregate priority numbers are assigned to each level of the AHP (as shown in Figs. 3 and 4). The aggregate priority numbers for main factors are synthesized for a final measurement based on comparison of the EPPs. Synthesized results show that environmental and social factors have much higher priorities than technical and economical ones (Fig. 5). However, these results may change according to the sub-
factors that are considered in the selection, for example some detailed economic factors may cause increase/decrease in the priority number of economic factors. According to the results shown in Fig. 5, wind energy must be the primary source as much as possible for electricity production in Turkey.
5. Conclusion Selection of appropriate activities is a major goal for decision makers. As in every activity, which requires ERA and EIA, decision of EPP-type selection should also be made by an approved EIA report and public acceptance. Unfortunately, in Turkey, processes such as ERA and EIA have been excluded by the new laws for the energy sector. For Turkey, comparison of EPPs with multiplecriteria analysis minimized complexity of the process and uncertainties during the evaluation. An energy policy that has been designed without taking into account any environmental policy objectives is a major problem. In this study, priority values for EPP alternatives have been calculated by Fuzzy-AHP methods to aid in decision making in Turkey’s situation.
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Environmental and social factors make wind power the leader
in the public eye as it has negligible negative impacts on the environment and human health (Figs. 4 and 5). It must be clearly stated that wind power offers both cleaner production and source independency, and therefore, the best alternative that favors the environment (Fig. 4). Value of carbon dioxide emission trade is an important economic factor for both wind and nuclear energies, which are non-GHG emitting and make their economical priorities approximately equal. Conclusions for Turkey are summarized below:
Public acceptance in social factors, which is the reason of the
Fig. 4. Aggregated priority numbers for main factors.
lowest priority number for nuclear, is the main indicator on decision making. Therefore, public acceptance is the key point that must be ensured by Turkish authorities in order to decide upon constructing nuclear power plant. Although the priority numbers of both nuclear and thermal power are overall equal, they have no priority against wind power. Therefore existing thermal power stations should gradually be substituted by renewable energies in certain schedule in Turkey. Decision makers for energy policies should work with/toward sustainable planning including continuous feedback from these priorities.
Numerical values obtained as a result of this study might be used for a further analytical analysis. Moreover, the hierarchy that was structured in this study can be applied for any country in order to select its best EPP option and orient its energy policy. This model was already constructed and applied by an academician expert group. Therefore all factors and criteria, which are assigned to hierarchy levels, should also be exactly defined according to scientist, especially public and environmentalist opinions in order to improve this application. Participation of public and environmentalist to the process of selecting best EPP is important due to results pointing out the highest priority of social and environmental factors. A detailed hierarchy can be constructed for each of the main factors included in this study (economical, social, technical, environmental) in order to provide input data for this selection process.
References
Fig. 5. Comparison of EPPs with synthesized priority numbers for main goal.
Comparisons made with priority values can be concluded in general as follows:
Renewable energies particularly wind power should be considered with twice the priority than coal-based thermal and nuclear energy by decision makers and politicians.
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