Analysis of electricity generation options for sustainable energy decision making: The case of Turkey

Renewable Energy 146 (2020) 519e529

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

Analysis of electricity generation options for sustainable energy decision making: The case of Turkey € kçen A. Çiftçiog lu Güls¸ah Yilan*, M.A. Nes¸et Kadirgan, Go _ Department of Chemical Engineering, Faculty of Engineering, Marmara University, Istanbul, 34722, Turkey

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 October 2018 Received in revised form 25 May 2019 Accepted 28 June 2019 Available online 28 June 2019

Sustainable energy decision making requires the comparison of energy generation technologies regarding a wide range of economic, technical, environmental, and socio-economic criteria. This study aims to rank the main seven electricity generation technologies for Turkey according to their performance scores. These energy generation technologies are natural gas, coal, hydropower with dam, run-ofriver type hydropower, onshore wind, geothermal and solar PV. The sustainability scores are calculated via twelve indicators classified into abovementioned four criteria groups. Multi-criteria decision analysis methodology is employed with a weighted sum multi-attribute utility approach for five different sensitivity cases. The results reveal that the hydroelectric technology with dam is the best option for most of the sensitivity cases. We hope this study gives a scientific and objective standpoint to decisionmaker authorities in Turkey for planning sustainable electricity generation policies. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Electricity generation Energy decision making Multi-criteria decision analysis Sustainability Turkey

1. Introduction Fossil fuel related environmental impacts draw attention to emerging climate change mitigation problems for the last decades. As a consequence of debates in the United Nations Conference on Climate Change (COP 21, Paris, 2015), about 200 countries, including Turkey, have adopted the groundbreaking Paris Agreement to take measures against climate change mitigation by reducing fossil fuel consumption. Turkey announces a 21% decrease in the intended nationally determined contributions until 2030. After USA declares getting out of Paris Agreement, Turkey decides to suspend the ratification process of the agreement due to the issues on Green Climate Fund. Yet, these international initiatives like Kyoto Protocol or Paris Agreement are essential for setting goals to facilitate future strategies from sustainable point of view. The implementation of this agreement would decrease fossil fuel consumption rate and related consequences while enhancing renewable energy production technologies. Since United Nation (UN) General Assembly unanimously declares the decade 2014e2024 as the “decade of sustainable energy for all”, namely to “ensure access to affordable, reliable, sustainable and modern

* Corresponding author. E-mail addresses: [email protected] (G. Yilan), nkadirgan@marmara. lu). edu.tr (M.A.N. Kadirgan), [email protected] (G.A. Çiftçiog https://doi.org/10.1016/j.renene.2019.06.164 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

energy for all”; it is inevitable for countries to shift renewable technologies from fossil related counterparts, at all [1]. As sustainability concept emerges with increasing awareness about environment, renewable energy sources take their positions in the future energy planning throughout the world as well as in Turkey. Although electricity generation mix in 2016 is still dominated by fossil fuels with a total share of 67%, renewable energy share is gradually increasing for Turkey electricity mix as seen in Fig. 1 [2]. The sustainability assessment is defined as “a tool that can help decision-makers and policy-makers decide which actions they should or should not take in an attempt to make society more sustainable” [3,4]. There are various studies in the literature concerned with the sustainability of electricity generation technologies. In the Institute for Energy Economics and Financial Analysis (IEEFA) report, the authors propose a future scenario for Turkey with diversifying its energy mix by adding larger amounts of renewable - wind and solar - resources and keeping away from fossil fuels. Since national consensus clearly favors better energy security and greater diversification in how the country fuels its electricity grid, renewable energy has the potential to provide greater benefits and a better economic alternative for Turkey on its path to becoming a more competitive economy [5]. Incekara and Ogulata emphasize the need for energy policies for the reduction in emissions of greenhouse gases via minimization of the use of power plants that utilize fossil fuels that have significant impacts on

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100

300,000 250,000

80 70

200,000

60 50

150,000

40 100,000

30 20

50,000

Total electricity generation, GWh

Electricity generation mix share, %

90

10 0

0

Coal

Liquid fuels

Natural Gas

Hydro

Renewable Energy and wastes

Total generation

Fig. 1. Electricity generation profile for Turkey (2000e2016) [2].

ecosystem, environment and resulting in climate change [6]. Also, Balat mentions that the fossil fuel dependency problem of Turkey may be solved via diversification of the electricity generation mix from a sustainable point of view [7]. Reducing the share of fossil fuels in the electricity mix would not only reduce the environmental impacts, but also the costs, injuries and fatalities, while also improving energy security problem [8,9]. The most widely used approaches to the modelling of the energy systems have been Life Cycle Assessment (LCA), Cost Benefit Analysis (CBA) and Multi-Criteria Decision Analysis (MCDA) [10]. CBA evaluates only economic assessment while LCA is a general approach for evaluating not only environmental impacts but also economic aspects as well as social aspects. MCDA covers all of the concepts related to economic, technical, environmental and social aspects. For this reason, multi-criteria decision making (MCDM) techniques are popular in sustainable energy management. In addition, they allow the evaluation of energy systems, national or regional, with the purpose of guiding the development and formulation of energy policy. The MCDM techniques also provide solutions to the problems involving multiple objectives about energy policy and management implications. The objectives are usually conflicting and therefore, the solution is highly dependent on the preferences of the decisionmaker. This tool is becoming popular in the field of energy planning due to the flexibility it provides to the policy-makers to take decisions while considering all the criteria and objectives simultaneously [10e12]. Decision making in sustainability projects requires consideration of different aspects including socio-political, environmental, and economic impacts and is often complicated by various stakeholder views. MCDA is used as a formal methodology to handle available technical information and stakeholder values to support

decisions in many fields and can be especially valuable in environmental decision making. Various MCDA methods have been successfully used for environmental applications. Huang et al. suggest in their extensive review that even though the use of the specific methods and tools varies in different application areas and geographic regions, recommended course of action does not vary significantly with the method applied [13]. MCDA methods utilized in energy planning processes are considered as the most suitable methods of solving issues related to energy. No single MCDM model can be ranked as the best or worst. Every method has its own strength and weakness depending on its application in all the consequence and objectives of planning. Mainly, three types of MCDM models exist: Value Measurement Models; Goal, Aspiration and Reference Level Models; and Outranking Models. An application of value measurement or utility based models Multi-Attribute Utility Theory (MAUT) is among the mostly preferred methods for ranking energy technologies. MAUT is the easiest method for value normalization and also it allows performing different sensitivity case applications from an objective point of view. The weaknesses and strengths of MAUT method is discussed in the previously published study [12]. The MCDM concept is drawing attention for energy strategy planners worldwide. As Mardani et al. mention in their extensive review, out of 40 countries or nationalities employed in decision making studies from 1995 to 2015 in various fields of energy management, Turkey is the first country in the ranking with the highest number of papers [14]. Also Marttunen et al. mention that Turkey is one of the pioneer countries that publish articles concerning MCDM during 2000e2015 [15]. MCDA studies concerning national electricity grid and policy implications in Turkey are numerous; some of the examples are € zhan and Güleryüz use MCDA discussed below [8,16e28]. Büyüko

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methodology with linguistic interval fuzzy preference relations in € order to evaluate renewable energy resources [29]. Ozkale et al. employ Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) methodology in order to make suggestions regarding the energy resource that Turkey should depend on for investment, incentive, environmental, and economic policies for future energy planning with regard to the selected energy resource [30]. Balin and Baraçlı investigate renewable energy alternatives by using a fuzzy Analytic Hierarchy Process (AHP) procedure based upon type-2 fuzzy sets, and fuzzy multi-criteria decision making based upon the interval type-2 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method [31]. Uyan employs MCDA method for determining the ideal locations for solar power plant via AHP method [32]. Yalcin and Gul investigate geothermal potential of selected areas via AHP as an application of MCDA methodology [33]. Atilgan and Azapagic report sustainability analysis of future electricity generation options performed via Multi-Attribute Value Theory (MAVT) [8]. This study aims to rank the main seven electricity generation technologies; namely coal, natural gas, hydro with dam, run-ofriver hydro, onshore wind, solar photovoltaic (PV), and geothermal; for Turkey according to their MCDA scores. In this study, four criteria groups, which are economic, technical, environmental, and socio-economic, are defined in parallel with sustainable development concept. Then, a number of twelve sustainability indicators are selected from literature assisted by expert opinion. The indicator selection is based on their suitability under the main evaluation criteria. MCDA methodology is employed with a weighted sum multi-attribute utility theory approach for different sensitivity cases. Developing strategies that depend on the main topics financial, social, environmental and technical aspects may differ for various countries. And the results may differ for given preferences, thus we want to show how these preferences affect the determination process of future generation technologies. Fig. 2. MCDA process in sustainable energy decision making (adapted from Refs. [24,34]).

2. Material and methods Generally, the MCDA problem for sustainable energy decision making involves m alternatives evaluated on n criteria. The grouped decision matrix can be expressed as:

Criteria C1 C2 ::::Cn ðWeights w1 w2 ::::wn Þ Alternative 0 1 A1 B x11 x12 / x1n C A B x21 x22 / x2n C C X¼ 2B « B ┊ 1 ┊ C @ ┊ A Am xm1 xm2 / xmn

(1)

where xij is the performance of the j-th criteria of the i-th alternative, wj is the weight of criteria j, n is the number of criteria and m is the number of alternatives [34]. MCDA generally involves four successive steps: (i) identification of the electricity generation technologies to be evaluated, (ii) selection and valuation of the sustainability indicators, (iii) assigning indicator weightings regarding the different sensitivity cases, (iv) ranking of the electricity generation technologies. The aim of this paper is to rank the main seven electricity generation alternatives according to their performance scores via MCDA methodology with respect to different sensitivity cases. The flow sheet of the methodology applied is given schematically in Fig. 2.

2.1. Identification of the electricity generation technologies The electricity generation alternatives depicted in Table 1 are based on the commercially-available and/or promising technologies contributing to Turkey electricity generation mix for 2014. The total installed power, annual generation and the share of each generation technology are given in Table 2. Fossil fuels are the main components of electricity sector, but recently renewable energy shares tend to increase. Even solar PV technologies have very small shares in the total mix; their share is dramatically increasing (See Fig. 1). For example, from 2014 to 2016 geothermal and wind shares increase two fold while solar PV increases sixty fold. For this reason, promising renewable energy technologies are considered in addition to conventional fossil fuel resources. Natural gas is the biggest contributor to electricity generation although it has serious drawbacks in terms of environmental and social aspects. Natural gas power plants have two types of operation characteristics: conventional steam turbine (CST) and combined cycle gas turbine (CCGT) power plants. Majority of the natural gas power plants in Turkey are CCGT with an installed power share of 90%; and the left 10% is CST power plants [44]. The study is mainly based on the calculations for CCGT power plants since its high share in the generation mix. Due to its relatively low cost, domestic coal is preferred extensively for the electricity generation

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G. Yilan et al. / Renewable Energy 146 (2020) 519e529 Table 1 Electricity generation alternatives included in MCDA. Alternative

Resource/Fuel

Characteristics

Natural gas Coal Hydro (dam) Hydro (r-o-r) Wind Geothermal Solar PV

Natural gas Imported and domestic coal Water flow Water flow Wind Geothermal heat Solar radiation

Combined cycle gas turbine plants Steam turbine based pulverized coal plants Reservoir (dam) plants Run-of-river (r-o-r) plants Onshore wind farms Flash-steam plants Photovoltaic solar panels

Table 2 The profile of electricity generation alternatives in the Turkish energy sector.

Lignite Hard coal Natural gas Oil Hydro (dam) Hydro (r-o-r) Wind (onshore) Geothermal Solar PV Others Total

Installed capacity, MW

Annual generation, GWh

Contribution to total generation, %

8281.3 6532.6 18724.4 594.9 22085.7 1027.2 3629.7 404.9 40.2 8198.9 69519.8

36615.4 39647.3 120576.0 2145.3 28471.8 12094.2 8520.1 2364.0 17.4 1511.3 251962.8

14.53 15.74 47.85 0.85 11.30 4.80 3.38 0.94 0.01 0.60 100.00

activities. Even, 2012 is declared as the “coal year” and the domestic coal mines are operated at full capacity resulting in catastrophic Soma accident. Coal used in the electricity generation is supplied from both domestic resources and importer countries. In 2014, electricity generated from domestic coal (48%) is almost equal to the generation from imported coal (52%). So, the indicator results are calculated as the weighted sum of scores of each coal type. Hydropower technologies are amongst the oldest and cheapest electricity generation methods. In Turkey, two types of hydropower technologies are employed: reservoir (dam) and run-of-river. The two technologies are utterly different than each other in design, operation and impact assessment; for this reason they are examined in separate topics. Wind is one of the developing renewable energy technologies of which Turkey has a very high electricity generation potential. Onshore wind power plants have been utilizing in various regions of Turkey with a growing capacity, recently. Geothermal technology is also another renewable energy alternative for Turkey with an increasing generation share. Two types of geothermal power plants operate in Turkey: flash-steam and binary plants. The flash-steam power plants are considered for the calculations due to their higher installed capacity. As their share increases, binary power plants are going to be investigated in the ongoing studies. Even not having a share of greater than one in a ten thousand in the year 2014, solar technologies are essential for electricity generation since its high potential for Turkey. Solar energy can be employed for electricity generation in two ways: solar thermal and solar photovoltaic panels. Although its share is very small in electricity generation mix, solar PV technology is examined as an alternative renewable energy source. 2.2. Selection and valuation of the sustainability indicators In this study, the most widely used indicators are chosen to cover a broad range of sustainability concerns specific to electricity generation technologies [8,12,28,34,35]. Indicator selection methods are basically considered in two groups: methods based on subjectivity and rational methods [35]. In this study we choose “own opinion” method from methods based on subjectivity. The list of all available indicators, which have been mentioned extensively in the previously published study, is investigated [34]. Then, the

indicators appropriate for the study are selected with respect to the opinions of experts in energy management applications as well as the previously published papers. The selection is made via considering the following principle mentioning that an indicator should be systemic, consistent, independent, measurable, and comparable [36,37]. The selected set of twelve indicators defined in Table 3 is examined in four criteria groups: economic, technical, environmental, and socio-economic. Valuation of the sustainability indicators is one of the most challenging parts of the study since it is very hard to find countryspecific data. The regional official reports and statistics are used when accessible, but generic data is also used when countryspecific data cannot be delivered. Even the indicator values may differ according to the implementation area selected throughout the country; the generated electricity is supplied to the national interconnected system. For this reason, the location of the selected power plant has a minor effect on the country-based evaluation studies. While economic, technical, and socio-economic indicators are gathered from the literature, environmental impacts are calculated via life cycle approach following the International Standards Organization (ISO) guidelines [38,39]. Life cycle approach considers the actions of raw material extraction, power plant construction, power plant operation and maintenance, finally waste management applications including decommissioning of the power plant. Total electricity generated in 2014 is gathered from Turkish Electricity Transmission Company (TETC) statistics. Lignite and hard coal data is gathered from Turkish Statistical Institute (TurkStat) and Turkish Coal Enterprises (TCE). Natural gas data is gathered from Turkish Petroleum (TP). Hydro, wind, and geothermal processes are based on the Ecoinvent v3.01 database [40]. The environmental impacts are calculated via ReCiPe midpoint (H) methodology with SimaPro 8.2.0.0 PhD software package. The results are expressed in terms of impact per electricity generated from the related technology. 2.3. Normalization of the sustainability indicators Since quantitative and qualitative indicators have different characteristics, it is required to establish a common basis for their

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Table 3 Selected evaluation criteria and sustainability indicators. Criteria

i

Economic

1

Indicators

Definition

Benefit attribute

Levelized Cost Of Electricity The average cost of producing electricity over the entire lifetime of the unit; it takes into account all (LCOE) investment, operation and maintenance, fuel, decommissioning and even CO2 emissions cost Technical 2 Efficiency The ratio between the useful electricity output from the generating unit, in a specific time, and the energy value of the energy source supplied to the unit in the same time period 3 Flexibility The ability to respond to fluctuations in demand and to insure overall grid stability in the long term in the context of growing share of intermittent generation from some renewable energy sources 4 Electricity mix share The electricity generation share of the selected technology 5 Capacity factor The ratio of the net electricity generated, for the time considered, to the energy that could have been generated at continuous full-power operation during the same period. Environmental 6 Climate change The global warming potential calculated in CO2 equivalent 7 Ozone depletion The destruction of the stratospheric ozone layer by anthropogenic emissions of ozone depleting substances 8 Natural land transformation The amount of natural land transformed and occupied for a certain time Socio9 Job creation Job-years of full time employment created over the entire lifecycle of the unit economic 10 Social acceptability Public preference for the deployment or utilization of a certain electricity generation technology 11 Accident-related fatality Deaths from accidents involved in the entire lifecycle of the unit 12 Primary energy and The extent to which an economy relies upon imports in order to meet its energy needs technology dependence

evaluation. In order to compare the indicator values with different magnitudes, normalization process has to be carried out. There are three groups of methods for normalizing the chosen indicators, namely own analytical methods, distance-to-target methods, and linear normalization methods [12,35]. Since this is a multi-criteria evaluation study and it is not possible to find a reference “target” data for each indicator; a linear normalization method is preferred. Among the linear normalization methods, MAUT method, also known as min-max method, is used to calculate a utility value for each indicator due to its easiness to apply. First, indicators are classified according to their benefit attribution (see Table 3). Then value normalization is computed to calculate a utility value for each indicator via the following equations:

Positive attribute : uðxk Þ ¼

Negative

ðxk  xmin Þ ðxmax  xmin Þ

attribute : uðxk Þ ¼

ðxmax  xk Þ ðxmax  xmin Þ

(2)

(3)

where xk is the indicator value for the technology k; xmin the minimum value of the indicator; xmax is the maximum value of the indicator [24,34]. In order to calculate the utility values for quantitative indicators, Eq. (2) and Eq. (3) are directly used. For example, efficiency has a positive attribute and Eq. (2) applies as the highest efficiency score gets 1.0 as the utility value. Similarly, LCOE indicator has a negative attribute and Eq. (3) applies as the highest indicator score gets 0.0 as the utility value. Yet, qualitative indicators are verbal expressions and their utility scores may have three values as 1.0, 0.5, and 0.0 which represents the best case, mediocre, and the worst case, respectively. For example, social acceptability has a positive attribute and the highest acceptability score results in the highest utility score 1.0. Similarly, energy dependence has a negative attribute and the highest indicator score value gets 0.0 as the utility value. 2.4. Assigning indicator weights regarding the different sensitivity cases

Negative Positive Positive Positive Positive Negative Negative Negative Positive Positive Negative Negative

distributed weighting and rank order weights. Equally distributed weighting methods allow direct comparison of indicators in order to identify the best alternative while rank order weights indicate the superiority of an indicator amongst the others [35]. The most common weighting method in energy system decision making is the equal weights method [34]. In this study, in order to compare different preference scenarios, equal weights and additional four cases are analyzed according to the paper previously published [26] and “administrative approach” is added with strong emphasis on economic indicators over technical, environmental, and socioeconomic indicators (Table 4). 2.5. Ranking of the electricity generation technologies Final step of the methodology is to determine the preference orders of the alternative electricity generation options according to their MCDA scores. After value normalization, MCDA scores are to be calculated according to their corresponding weighting factors. There are various methods published for multi-criteria score evaluation [35]. An application of elementary aggregation methods, weighted sum method (WSM) is the most commonly used approach in sustainable energy systems decision making due to its straightforward nature [34]. The score of a technology is calculated as

S¼

Xn i¼1

wi uðxk Þ; k ¼ 1; 2; :::; m

(4)

where wi is the weighting factor of the ith criterion and u(xk) the utility value for the technology k. Then the resulting cardinal scores for each technology can be used to rank, screen, or choose an alternative. The best alternative is the one whose score is the maximum [34]. Eventually, ranking of the electricity generation technologies is performed via MAUT method for value normalization coupled with a WSM approach to calculate MCDA scores for selected generation technologies. 3. Results and discussions 3.1. Evaluation of the sustainability indicators

Indicator weights are assigned to show the relative importance of indicators amongst the others. Different weighting preferences directly influence the MCDA results. For this reason, weighting should be determined carefully. Methods to weight criteria are examined in two groups: equally

Sustainability indicators are evaluated with country-specific data from official reports, statistics, papers and online databases when reliable data is accessible. But for some cases, it is not possible to gather Turkey specific indicator values since the lack of

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Table 4 Weighting preferences for different sensitivity cases. Sensitivity Cases (%)

Economic Technical Environmental Socio-economic

Holistic

Technocratic

Mercantilist

Eco-social

Administrative

25 25 25 25

40 40 10 10

10 40 10 40

10 10 40 40

50 20 10 20

data published in the literature. Then, European countries, OECD countries or World average values have to be used. In an effort to minimize the uncertainties caused by the different data sources, the value of an indicator is based on the same reference study in order to sustain the consistency of the data in the indicator basis. Detailed explanations of evaluation of indicators are given below. 3.1.1. Levelized cost of electricity (LCOE) Levelized cost of electricity (LCOE) is used as the sole economic indicator since it is complex enough to consider capital expenditure, operational expenditure, and fuel cost as well as electrical efficiency and total electricity produced. Up to our knowledge, a full analysis of levelized cost of electricity generation technologies has not been published for Turkey, yet. IEA report indicates country specific LCOE for three of the renewable technologies namely hydropower with dam, onshore wind and geothermal [41]. For these three technologies, LCOE data is used for 7% discount rate which is considered as the average data given in the IEA report. Also the rate is compared with the interest rate of Turkish banks for foreign investors in Euro basis which is calculated very close to 7%. Turkey specific LCOE for coal is stated in the study conducted by Bloomberg New Energy Finance (BNEF) [42]. The report says LCOE differs between 80 and 105 USD/MWh at a 7% discount rate. An average of 92.5 USD/MWh is used as a reference point. LCOE for natural gas and run-of-river hydropower plant is also reported for Turkey with 10% discount rate [43]. In order to construct a constant basis, LCOE values are evaluated with respect to 7% discount rate and the values are calculated as 65 and 156 USD/MWh for run-of-river and natural gas, respectively. Turkey specific LCOE data for solar PV technology has not published in the literature yet, so European average data is used for calculations [42]. WWF report gives a range of 140e180 USD/MWh for solar PV technologies; an average of 160 USD/MWh is selected. 3.1.2. Efficiency The first technical indicator is efficiency. Efficiency scores of fossil fuel technologies are gathered from studies conducted for Turkey [44,45]. Efficiency rates of 38 and 52% are taken for coal and natural gas technologies, respectively. A standard PV module efficiency of 16% is considered for solar PV technology [46]. Hydropower efficiencies for dam and run-of-river plants are assumed to be 78 and 82%, respectively [45]. An average efficiency of 35% is used for onshore wind plants [47]. Geothermal power plant efficiency for an average flash-steam plant is calculated as 6.6% with the equation given in the previously published study [48]. 3.1.3. Flexibility The second technical indicator is flexibility defined as the ability to respond to varying demand. This indicator has a qualitative manner and can be evaluated in three options: “yes, fast” (response generated immediately), “yes, slow” (response can be generated but requires some time to reactivate the system), and “no” (unable to generate energy on demand) [24]. Natural gas power plants are able to meet the emergency demands of the power supply system in a

very short time. As a result, their flexibility is quite high. As well as natural gas power plants, hydropower plants with dam are capable of compensating sudden fluctuations in power demand. Similarly, their flexibility is as high as natural gas. Coal power plants operate continuously during the working period; but their start-up process requires some time for responding the urgent demand. For this reason, their flexibility score is not as high as natural gas or hydropower. Renewable technologies like wind, solar PV, and run-ofriver cannot reply an emergency demand due to their intermittent nature. Their flexibility score is quite lower than the other technologies, even they are considered as inflexible. Geothermal power plants are also classified as inflexible due to their low capacity. The benefit attribution of this indicator is positive; the higher the flexibility, the better is the score. For this reason, flexible technologies which are capable of responding urgent demand have the highest score, 1; while inflexible technologies have a score of zero. 3.1.4. Electricity mix share The third technical indicator is the contribution of each technology to the annual electricity production. Since 2014 is selected as the baseline year for the calculations, the electricity generation mix shares belonging this year are introduced to the calculations (See Table 2). 3.1.5. Capacity factor The last technical indicator is the capacity factor which represents the ratio of the generated electricity, for the time considered to the energy that could have been generated at continuous full power operation. Capacity factors for fossil fuel technologies are assumed 85% since they operate in base load [41]. Capacity factors for renewable energy technologies are largely site-specific, so Turkey-based factors are taken into consideration for these technologies [41,49]. The capacity factors are 54, 38, 90, and 18% for hydropower both dam and run-of-river, wind, geothermal and solar PV, respectively. 3.1.6. Climate change The first environmental indicator is the climate change impact of the selected electricity generation technologies. The calculations are based on the life cycle assessment approach with ReCiPe midpoint (H) impact assessment method. Climate change impact results are expressed in g CO2-equivalents per kWh electricity generated. Fossil fuel technologies have dramatically higher scores compared to renewable technologies. 3.1.7. Ozone depletion The second environmental indicator is the ozone depletion resulting from the anthropogenic emissions of ozone depleting substances while power generation. The calculations are based on the life cycle assessment approach with ReCiPe midpoint (H) impact assessment method. Ozone depletion impact results are expressed in g CFC 11-equivalents per GWh electricity generated. Similar to climate change results, fossil technologies have higher scores than renewable technologies.

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3.1.8. Natural land transformation The last environmental indicator is defined as the amount of natural land transformed and occupied for a certain time. The calculations are based on the LCA approach with ReCiPe midpoint (H) impact assessment method. Natural land transformation results are expressed in m2 per GWh for each technology employed. Natural gas and hydropower with dam technologies require the highest natural land transformation followed by coal. However, in general, renewable technologies like solar PV and wind require less natural land than the others. 3.1.9. Job creation The first socio-economic indicator is the job creation expressed in “job-years” (a full time employee hired over 12 months) per unit of electricity produced. Job creation indicator scores cannot be gathered from Turkey-specific statistics and subsequently the scores are based on the previously published study [50] where a comprehensive literature survey is carried out and hydropower job creation score is gathered from elsewhere [24]. As mentioned in the International Labour Office (ILO) report, employment requirements associated with generation technologies vary in a very broad range when different studies investigated. But, generally, renewable energy sources create more jobs per GWh than non-renewable energy sources [51]. The lowest job creation scores belong to fossil fuel technologies coal and natural gas while the highest score belongs to solar PV technology. The scores follow the main characteristics mentioned in the report. 3.1.10. Social acceptability The second socio-economic indicator is social acceptability. This indicator has a qualitative manner and can be evaluated in three options: “high”, “medium” and “low”. Social acceptability scores are _ based on a survey conducted in Istanbul [52]. The scores are also compared with the previously published paper which examines the social acceptability levels of different energy sources [24]. The scores are overlapping for all technologies except hydropower. Due to the public reaction of hydropower technologies including both with dam and run-of-river, social acceptability scores are considered as “low”. The benefit attribution of this indicator is positive; the higher the social acceptability, the better is the score. For this reason, technologies with high acceptability scores have the highest score, 1; while technologies with low acceptability have a score of zero.

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3.1.11. Accident-related fatality The third socio-economic indicator is accident-related fatality expressed in fatalities per GWh. Although fatality scores differ in each country, and required to be evaluated with country-based calculations; Turkey-specific statistics are not available for existing technology types. For this reason, accident-related fatality scores are taken as the nominal values per GWh [22] based on the fatality data suggested for Organization for Economic Cooperation and Development (OECD) countries. The fatality data is gathered from Intergovernmental Panel on Climate Change (IPCC) report [53] which uses the Energy-related Severe Accident Database (ENSAD) at Paul Scherrer Institut (PSI) considering severe (5 fatalities) accidents. The highest fatality rates are calculated for coal power plants, followed by natural gas. Generally, renewable technologies have lower fatality scores compared to fossil fuel technologies. 3.1.12. Primary energy and technology dependence The last socio-economic indicator is primary energy and technology dependence considering both source and technology-based imports. Primary energy dependence scores for Turkey are gathered from the online database which shows the imported resource necessities for each country [54]. In addition to resources, import dependency of power plant technologies is also scored with respect to the expert opinion. This indicator has a qualitative manner and can be evaluated in three options: “high”, “medium” and “low”. Natural gas has the highest score due to its both source and technology dependency. Renewable technologies like solar PV and wind are also quite import dependent from technological point of view even their source is available naturally. Coal and geothermal power plants have moderate scores while run-of-river and hydropower with dam plants have the minimum import dependency. The benefit attribution of this indicator is negative; the lower the dependency, the better is the score. For this reason, technologies with high dependency scores have the lowest score zero; while technologies with low dependency have the score of 1. Valuation scores of twelve indicators for seven electricity generation alternatives are depicted in Table 5. Normalized indicators are calculated considering the guidelines mentioned in Section 2.3 and are given in Table 6. 3.2. Calculation of MCDA scores for different sensitivity cases Once the set of sustainability indicators is assembled, weighting factors are to be considered for different preference scenarios

Table 5 Sustainability indicators for each electricity generation technology. Generation technology

Unit

Natural Gas Coal

Hydro (dam)

Hydro (r-o-r) Wind (onshore)

Geothermal

Solar PV

LCOE [42e44] Efficiency [45e49] Flexibilityc [24] Electricity mix share [2] Capacity factor [41,50] Climate changed Ozone depletiond Natural land transformationd Job creatione [24,51] Social acceptabilityc [24,53] Accident-related fatalityf [22,54] Primary energy and technology dependencec [55]

USD/MWh % Qualitative % % g CO2-eq/kWh g CFC 11-eq/GWh m2/GWh job-yrs./GWh Qualitative Rate x 107/GWh Qualitative

156 52 Yes, rapid 47.9 85b 482 67.7 70.3 0.11 Medium 94 High

41 78 Yes, rapid 11.3 54 50 0.5 92.3 0.55 Low 58 Low

65 82 No 4.8 54 11 0.9 1.17 0.27 Low 58 Low

116 6.6 No 0.9 90 1.9 0.1 0.22 0.25 Medium 2.1 Medium

160a 16b No 0.01 18 3.6 0.7 0.51 0.87 High 1.3 High

a b c d e f

European average. Average value of a standard technology. Expert judgement. Own calculation based on ecoinvent database. World average. OECD average.

92.5 38 Yes, slow 30.2 85b 1192 4.2 36.3 0.11 Low 170 Medium

73 35b No 3.4 38 0.1 0.03 0.06 0.17 High 5.2 High

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Table 6 Normalized scores of the sustainability indicators. Generation technology

Natural Gas

Coal

Hydro (dam)

Hydro (r-o-r)

Wind (onshore)

Geothermal

Solar PV

LCOE Efficiency Flexibility Electricity mix share Capacity factor Climate change Ozone depletion Natural land transformation Job creation Social acceptability Accident-related fatality Primary energy and technology dependence

0.0 0.6 1.0 1.0 0.9 0.6 0.0 0.2 0.0 0.5 0.5 0.0

0,6 0.4 0.5 0.6 0.9 0.0 0.9 0.6 0.0 0.0 0.0 0.5

1.0 0.9 1.0 0.2 0.5 1.0 1.0 0.0 0.6 0.0 0.7 1.0

0.8 1.0 0.0 0.1 0.5 1.0 1.0 1.0 0.2 0.0 0.7 1.0

0.7 0.4 0.0 0.1 0.3 1.0 1.0 1.0 0.1 1.0 1.0 0.0

0.4 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.2 0.5 1.0 0.5

0.0 0.1 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0

previously defined in Table 4. Since there is only one economic indicator, LCOE indicator weight is considered to be equal to the criteria group value. However, as seen in the socio-economic indicators, criteria groups with more than one indicator have evenly distributed weights on the related indicators e.g. the share of holistic approach is distributed between four socio-economic indicators as a quarter of the total 25% share. For example, sustainability scores of the coal technology according to the holistic approach and natural gas technology according to the administrative approach are calculated via Eq. (4) as follows:

decision making process rather than just one preference scenario. Even the results of the MCDA reveal that the hydropower technology with dam is the best for four out of five sensitivity cases; the evaluation considers average performance of a hydropower unit with dam anywhere across the country. The location of the dam is the most crucial decision in power plant design since it may ruin the fertile agricultural land and/or destroy the cultural and social texture. The opportunity cost of power plant implementation should be cautiously analyzed. Accordingly, site-specific analysis may change the ranking due to the geographic and socio-cultural

   0:25 0:25 0:25 0:25 0:25 *0:6 þ *0:4 þ *0:5 þ *0:6 þ *0:9 1 4 4 4 4   0:25 0:25 0:25 þ *0:0 þ *0:9 þ *0:6 3 3 3   0:25 0:25 0:25 0:25 þ *0:0 þ *0:0 þ *0:0 þ *0:5 4 4 4 4 

scoal ¼

Scoal ¼ 0:457     0:50 0:20 0:20 0:20 0:20 *0:0 þ *0:6 þ *1:0 þ *1:0 þ *0:69 1 4 4 4 4   0:10 0:10 0:10 þ *0:6 þ *0:0 þ *0:2 3 3 3   0:20 0:20 0:20 0:20 þ *0:0 þ *0:5 þ *0:5 þ *0:0 4 4 4 4 Snatural gas ¼

Snatural gas ¼ 0:269

The corresponding MCDA scores are calculated likewise and the final scores of the selected generation technologies are given in Fig. 3. According to the scores of each alternative generation technology, it is possible to make a priority ranking to assist decision making process from a sustainable point of view which associates economic, technical, environmental and socio-economic aspects with each other. A detailed discussion of the resulting scores is given in the following section. 3.3. Ranking of the electricity generation technologies Due to the nature of the WSM, the highest score indicates the best alternative among other generation technologies. Yet, as seen in Table 7, different sensitivity cases result in different rankings of the MCDA scores. This underlines the need for sensitivity analysis in

characteristics of the power plant farm. Despite the possible drawbacks, hydropower is still one of the most important electricity generation alternatives since its high natural potential as well as economic and technical advantages. The second best sustainable technology is run-of-river hydropower plant. Similar to hydropower with dam technologies, run-ofriver power plant potential is quite high for Turkey. If the policy implementations cooperate with agricultural irrigation, this technology gets quite advantageous financially and also gains support of the social community. Onshore wind power plants have reasonably high scores for each case. The potential electricity generation capacity from wind for Turkey is quite high due to its geographic location. Renewable energy alternatives are the key to fossil fuel dependency problem especially for leading energy importer countries. Therefore, wind

G. Yilan et al. / Renewable Energy 146 (2020) 519e529

527

burdens due to the fact that releasing 1 kg of CH4 into the atmosphere is about equivalent to releasing 25 kg of CO2. For a sustainable energy decision making, natural gas technology may not be a practical alternative to offer as the countries are searching for zero-emission energy generation technologies.

0.9 0.8 0.7 0.6 0.5 0.4

3.4. Comparison of the results with the previous studies

0.3 0.2 0.1 0.0 holistic

technocratic

mercantilist

eco-social

administrative

coal

natural gas

hydro (dam)

hydro (run-of-river) wind (onshore)

solar PV

geothermal Fig. 3. MCDA scores of the different electricity generation technologies.

technology has gradually increasing generation shares over the last years even the current levels cannot compete with the actual generation potential. Geothermal technology is apparently another reasonable alternative generation technology. Even geothermal technology has one of the smallest shares of the electricity generation in 2014; the potential capacity of geothermal sources of Turkey is exceedingly higher. Recently, new project investments are made especially in the southwest of Turkey aiming to diversify the main electricity generation technologies. Solar PV is a promising generation technology but it has lower scores owing to its high financial costs as well as high import dependency and low flexibility. As the sustainability notion and source diversification needs emerge, solar technology shares have started to increase recently. Also, costs of solar PV technologies tend to decrease with the rapid technological developments and solar PV technology share is expected to increase progressively as the reputation of renewable technologies grows. Coal technology has moderate scores and it has one third of the total production share in 2014. From sustainable point of view, fossil fuels are no longer an option for future generations owing to its environmental and socio-economic criteria scores. Environmental burdens resulting from fossil fuel technologies are the main contributors to global warming problem. From socio-economic view, high scores of accident-related fatality create a serious issue for occupational health and safety considerations but these technologies are preferred extensively for their low fuel costs. Natural gas has the lowest score in most sensitivity cases. Despite having the biggest share in the generation mix, natural gas technology has some serious drawbacks. Almost all of the natural gas used is imported from different countries and it results in a very high score of import dependency. The emissions related to natural gas, namely methane emissions, has considerable environmental

According to the computations of different sensitivity cases, sustainability ranking of the generation technologies may be concluded as hydropower with dam, run-of-river hydropower, onshore wind, geothermal, coal, solar PV, and natural gas. There are various studies discussed below agreeing to some extent with the results of this study. Boran et al. examine the main energy sources for electricity generation and suggest a ranking of hydro, wind, gas, fossil fuels, and geothermal power plants [27,56]. Atilgan and Azapagic investigate the current electricity generation options and suggest that the most sustainable technology is hydro followed by geothermal € and wind for different preferences [43]. Ozkale et al. consider the power plants running on renewable energy resource and conclude that hydroelectricity takes the first place according to the general results followed by solar, wind, biomass and finally geothermal [30]. S¸engül et al. analyze renewable energy options and their analysis shows that the hydro power station is the most renewable energy supply system in Turkey. Additionally, the geothermal power station, regulator and wind power station are determined to be the second, third and fourth, respectively [57]. Balin and Baraçlı examine the renewable energy alternatives and report that wind is the best alternative for Turkey's energy investments, as being followed subsequently by solar, biomass, geothermal, hydraulic and €zkan and Güleryüz develop an evaluation hydrogen [31]. Büyüko model to select the most appropriate renewable energy resource in Turkey and conclude that the best renewable energy technology is power generation from geothermal sources, followed by biogas [29]. Çelikbilek and Tüysüz present a grey based multi-criteria decision model for the evaluation of renewable energy sources and discuss that the ranking is as follows solar, wind, hydro, biomass and geothermal [58]. Kahraman et al. apply fuzzy axiomatic design to the selection of the best renewable energy alternative and mention that the ranking is as wind, solar, biomass, geothermal, and hydropower [59]. One of the main reasons for the difference in the ranking of alternative technologies is the data acquisition perspective of the studies since it is not always possible to find country-specific data. Reported indicator values show a very wide range of change even for the same technology and cause uncertainties for the results. However, this is an inevitable consequence of the studies of such broad scope. In order to avoid the uncertainties, a representative power plant of each generation technology may be selected and evaluated with site-specific data in terms of indicators. But, of course, this is a very time-consuming mission for depicting country-wide electricity generation profile.

Table 7 Ranking of the MCDA scores according to the different sensitivity cases.

1 2 3 4 5 6 7

Holistic

Technocratic

Mercantilist

Eco-social

Administrative

hydro (dam) hydro (r-o-r) wind (onshore) geothermal coal solar PV natural gas

hydro (dam) hydro (r-o-r) coal wind (onshore) natural gas geothermal solar PV

hydro (dam) hydro (r-o-r) natural gas geothermal wind (onshore) solar PV coal

hydro (r-o-r) solar PV wind (onshore) geothermal hydro (dam) coal natural gas

hydro (dam) hydro (r-o-r) wind (onshore) coal geothermal natural gas solar PV

100%

8000

90%

7000

80%

6000

70% 60%

5000

50%

4000

40%

3000

30%

2000

20%

Installed power, MW

G. Yilan et al. / Renewable Energy 146 (2020) 519e529

Investment share, %

528

1000

10% 0%

0 2010

2011

natural gas wind other

2012

2013

2014

2015

2016

coal geothermal Installed power, MW

2017

hydropower solar PV

Fig. 4. The distribution of investments in the Turkish electricity sector.

The other possible reasons for the difference in the ranking are assumptions in the generation technologies, selection principles of indicators, numeric or linguistic evaluation of the indicators, selection of the normalization method, preferences in the weighting, and also selection of the MCDA method to get the total sustainability scores. In a very broad perspective, all of the literature researches including our study show that fossil fuel technologies are not viable options from a sustainable point of view and renewable technologies are to be employed in the future generation technologies. 3.5. Energy investments in the Turkish electricity sector Despite being a country of significant potential for renewable energy sources, Turkey electricity sector mainly depends on the fossil fuels. As seen in Fig. 4, except for the year 2015, new investments are based on the fossil technologies, too. The trend of new investments does not show a linear change throughout the years not only for the total installed power but also for any type of technologies. The fluctuation in the new investments may result from the socio-eco-political situation of the country, the increase in energy efficiency scores of available technologies, and also the public opinion of related power generation alternative. Even the investment trend cannot be received from the short-term statistics, renewable energy shares can be supposed to increase from a broad perspective. In order to enhance the energy investments through renewable alternatives, multi-pillar assessments are required from sustainable point of view. In addition to the sustainability performance evaluations, decision-makers may promote these alternatives via enforcement, encouragement or even penalties. 4. Conclusions The shift of the electricity mix towards more sustainable technologies is a vital necessity not only for Turkey but also for all the countries of the world. As stated in the study, there are numerous parameters for consideration and the rankings may change due to the priorities of the decision-makers. According to the ranking results, hydropower technologies are apparently the best option. The decision-makers should establish a policy about the efficient use of potential water resources with regard to sustainability. Renewable

technologies like solar PV and wind have reasonably acceptable scores and increasing generation shares. From sustainable point of view, renewable energy sources should be fully operated. In addition to sustainability, strategical importance of renewable technologies should be taken into account, as well, in terms of supply security. In case of a natural disaster or political conflict, renewable energy technologies make it possible to generate electricity in local areas and give the opportunity to meet the demand as soon as it occurs. Fossil fuel technologies as coal and natural gas are still the driving force of electricity generation in Turkey, in 2014. Since natural gas power plants are considered as “emergency plants” due to their high flexibility; it is not possible to fully block their use but the limitations must be introduced considering the high import dependency and environmental impacts. Coal technology has serious environmental and socio-economic burdens but it is widely preferred for its low cost. Coal technology should also be regulated with respect to the sustainability aspects. In order to choose the best option for country interests, comprehensive researches and realistic models are required. We hope this study gives a scientific and objective standpoint to decision-maker authorities in Turkey for planning sustainable electricity generation policies. Acknowledgements This work was supported by Marmara University BAPKO under FEN-C-DRP-150513-0175 Project. The authors thank to Hamdi Olgunsoy for his valuable contributions to the study. References [1] The United Nations Decade of Sustainable Energy for All 2014-2024, The United Nations General Assembly, 2014. [2] Electricity Generation - Transmission Statistics of Turkey, Turkish Electricity Transmission Company (TETC), 2014. http://www.tuik.gov.tr/PreTablo.do?alt_ id¼1029 (accessed January 25, 2018). [3] B. Ness, E. Urbel-Piirsalu, S. Anderberg, L. Olsson, Categorising tools for sustainability assessment, Ecol. Econ. 60 (2007) 498e508. [4] L. Hens, W.D. Lannoy, How Green Is the City?: Sustainability Assessment and the Management of Urban Environments, Columbia University Press, 2001. [5] Turkey at a Crossroads, Invest in the Old Energy Economy or the New? Institute for Energy Economics and Financial Analysis (IEEFA), 2016. http:// ieefa.org/wp-content/uploads/2016/09/Turkey-Crossroads-Invest-in-the-OldEnergy-Economy-or-the-New_June-2016-v2.pdf (accessed January 25, 2018). [6] C.O. Incekara, S.N. Ogulata, Turkey's energy planning considering global environmental concerns, Ecol. Eng. 102 (2017) 589e595.

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