EM599_Final Report_AKÇ&MÖ_Final

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MIDDLE EAST TECHNICAL UNIVERSITY
INDUSTRIAL ENGINEERING DEPARTMENT
ENGINEERING MANAGEMENT PROGRAM
EM 599 - TERM PROJECT
FINAL REPORT
SITE SELECTION
for
WIND-SOLAR HYBRID POWER PLANT in TURKEY
Submitted by : Ahmet Köksal ÇALIŞKAN
Murat ÖZCAN
Advisor : Assoc. Prof. Dr. İsmail Serdar BAKAL
25.01.2017

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To my family…
Ahmet Köksal ÇALIŞKAN
Thanks to my wife Naz Topkara Özcan,
my mothers Kiraz Özcan and Nevin Topkara and
my fathers Salih Özcan and Mustafa Topkara for their continuous support,
This study is dedicated to Hilmi Topkara who never surrendered and who fought until the end…
Murat ÖZCAN
Thanks to our advisor Assoc. Prof. Dr. İsmail Serdar Bakal
Ahmet Köksal ÇALIŞKAN and Murat ÖZCAN

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Table of Contents
Table of Contents .................................................................................................................................... 5
List of Tables........................................................................................................................................... 7
List of Figures ......................................................................................................................................... 9
1. Introduction ................................................................................................................................... 11
2. Methods Used in Multi Criteria Decision Making (MCDM) and Literature Review ................... 15
2.1. Multi Criteria Decision Making (MCDM) Methods Used in Site Selection......................... 15
2.1.1. Analytic Hierarchy Process (AHP) ............................................................................... 15
2.1.2. Analytic Network Process (ANP) ................................................................................. 18
2.1.3. Elimination et Choice Translating Reality (ELECTRE) ............................................... 19
2.1.4. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).................. 20
2.1.5. PROMETHEE (Preference Ranking Organization METHod for Enrichment of
Evaluations)................................................................................................................................... 21
2.1.6. Matter Element Method (ME)....................................................................................... 21
3. A Comparison of Methods for Wind-Solar Hybrid Power Plant Site Selection ........................... 23
3.1. AHP with BOCR in Site Selection for Hybrid Power Plant.................................................. 24
3.2. Ideal Matter Element Method in Site Selection for Hybrid Power Plant .............................. 29
3.2.1. Definition of the Model................................................................................................. 29
3.2.2. Classical Matter Element Definition ............................................................................. 30
3.2.3. Criteria Weight Determination...................................................................................... 31
3.2.4. Correlation Calculation ................................................................................................. 33
3.2.5. Closeness Degree Calculation ....................................................................................... 34
3.3. Site Selection Criteria and Alternative Locations for Wind-Solar Hybrid Power Plant in
Turkey35
3.3.1. Determining the Criteria................................................................................................ 35
3.3.2. Selection and Determining the Alternative Locations................................................... 39
4. Application of the Methods for Wind-Solar Hybrid Plant Site Selection in Turkey..................... 41
4.1. Application of AHP with BOCR........................................................................................... 41
4.1.1. Data Preparation............................................................................................................ 41
4.1.2. Weight Determination ................................................................................................... 43
4.1.3. Evaluation of Alternatives............................................................................................. 46
4.2. Application of Ideal Matter Element (IME) Method............................................................. 48
4.2.1. Data Preparation............................................................................................................ 48
4.2.2. Classical Domain/Matter Element Determination......................................................... 50
4.2.3. Weight Determination ................................................................................................... 51
4.2.4. Evaluation of Alternatives............................................................................................. 53
5. Evaluation of Results and Conclusion........................................................................................... 55
6. Future Works................................................................................................................................. 56
7. References ..................................................................................................................................... 57
8. Appendices.................................................................................................................................... 60
8.1. Short Biographies of Experts................................................................................................. 60
8.2. Solar and Wind Potentials of Alternative Locations ............................................................. 61
8.3. Answers of Experts to the Questionnaires............................................................................. 67
8.3.1. Answers of Expert 1.............................................................................................................. 67

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List of Tables
Table 1 – Criteria and Sub-criteria in Chen et al. (2009) ...................................................................... 17
Table 2 - Advantages and Disadvantages of MCDM Methods Yunna et al. (2014)............................. 23
Table 3 - Saaty’s Nine-Point Scale........................................................................................................ 24
Table 4 – Pairwise Comparison of BOCR ............................................................................................ 25
Table 5 - Random Index corresponding to the Number of Criteria....................................................... 25
Table 6 – Pairwise Comparison of Criteria ........................................................................................... 26
Table 7 – Calculation of Scores ............................................................................................................ 27
Table 8 – Relative Performance Results vs. Normalized Performance Results of Alternatives ........... 27
Table 9 – Example Matter Elements Definition.................................................................................... 30
Table 10 – Example of Standardization Process of Matter Elements ................................................... 30
Table 11 – Example of Classical Domain-Great................................................................................... 31
Table 12 – Example of Classical Domain-Normal................................................................................ 31
Table 13 – Example of Classical Domain-Bad ..................................................................................... 31
Table 18 – Example of Grading Alternative 1 ...................................................................................... 33
Table 21 – Example of Closeness Degrees Calculation........................................................................ 35
Table 22 – Example of Evaluation Results ........................................................................................... 35
Table 23– Candidate Criteria and General Classes ............................................................................... 36
Table 24– Candidate Criteria vs. Criteria Set with Descriptions .......................................................... 37
Table 25 – Criteria Quantification......................................................................................................... 41
Table 26 – Performance Values of Alternatives w.r.t each Criterion for AHP with BOCR................. 42
Table 27 – Normalized Performance Values of Alternatives w.r.t each Criterion for AHP with BOCR
............................................................................................................................................................... 43
Table 28 – Consistency Check for Comparison of Criteria................................................................... 44
Table 29 – Consistency Check for Comparison of Merits .................................................................... 44
Table 30 - Pairwise Comparison and Weights of Merits....................................................................... 44
Table 31 – Pairwise Comparison and Weights of Criteria .................................................................... 45
Table 32 - Importance of the Criteria in AHP with BOCR................................................................... 46
Table 33 – Performance Scores of Alternatives w.r.t the Merits .......................................................... 47
Table 34 – Priorities and Ranking of Alternatives ................................................................................ 47
Table 35 – Criteria Quantification......................................................................................................... 48
Table 36 – Performance Values of Alternatives w.r.t each Criterion for IME Method ........................ 48
Table 37 – Standardized Performance Values of Alternatives w.r.t each Criterion for IME................ 49
Table 38 – Classical Matter Element Determination............................................................................. 50
Table 39 - Section Domain.................................................................................................................... 50
Table 40 – Standardized Classical Matter Elements ............................................................................. 51
Table 41 – Relative Ranks and their Linguistic Definitions ................................................................. 52
Table 42 – Ranks and Weights of the Criteria for Each Expert ............................................................ 52
Table 43 - Importance of the Criteria in IME........................................................................................ 53
Table 44 – Matter Element Evaluation.................................................................................................. 53
Table 45 – Ideal Matter Element Evaluation......................................................................................... 54
Table 46 - Qualitative Data Evaluation for Alternatives – E1 .............................................................. 67
Table 47 – Merit Comparison – E1....................................................................................................... 67
Table 48 - Pairwise Comparison of Criteria – E1 ................................................................................. 68
Table 49 - Importance Ranking of Criteria – E1................................................................................... 69
Table 50 – Level Boundaries for Criteria – E1 ..................................................................................... 70
Table 52 – Merit Comparison – E2....................................................................................................... 71

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Table 55 - Level Boundaries for Criteria – E2...................................................................................... 74
Table 57 – Merit Comparison – E3...................................................................................................... 75
Table 62 – Merit Comparison – E4...................................................................................................... 79
Table 63- Pairwise Comparison of Criteria – E4 .................................................................................. 80

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List of Figures
Figure 1 - Total Renewable Energy Capacity – World vs. Turkey ....................................................... 11
Figure 2 - Total Renewable Energy Production – World vs. Turkey.................................................... 11
Figure 3 - Total Wind Energy Capacity– World vs. Turkey................................................................. 12
Figure 4 - Total Wind Energy Production– World vs. Turkey.............................................................. 12
Figure 5 - Total Solar Energy Capacity– World vs. Turkey ................................................................. 12
Figure 6 - Total Solar Energy Production– World vs. Turkey .............................................................. 13
Figure 7 - Turkey’s Renewable Energy Strategic Plan ......................................................................... 13
Figure 8 – A simple AHP Hierarchy..................................................................................................... 15
Figure 9 – A simple ANP Network Diagram........................................................................................ 18
Figure 10 – Candidate Areas for Alternatives....................................................................................... 39
Figure 11 – Solar Potential of İzmir...................................................................................................... 61
Figure 12 – Wind Potential of İzmir ..................................................................................................... 61
Figure 13 – Solar Potential of Muğla .................................................................................................... 62
Figure 14 – Wind Potential of Muğla.................................................................................................... 62
Figure 15 – Solar Potential of Antalya.................................................................................................. 63
Figure 16 – Wind Potential of Antalya.................................................................................................. 63
Figure 17 – Solar Potential of Konya.................................................................................................... 64
Figure 18 – Wind Potential of Konya.................................................................................................... 64
Figure 19 – Solar Potential of Karaman................................................................................................ 65
Figure 20 – Wind Potential of Karaman................................................................................................ 65
Figure 21 – Solar Potential of Mersin ................................................................................................... 66
Figure 22 – Wind Potential of Mersin.................................................................................................. 66

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1. Introduction
The World’s energy demand is increasing sharply with the fast development of the economies and
technology. The fossil fuel resources (e.g. coal, petroleum, natural gas etc.) run out day by day due to
their nature that their formation takes millions of years. As a result, renewable energy sources such as
hydropower, marine energy, wind energy, solar energy, bioenergy, liquid biofuels and geothermal
energy have been a hot debate for the last decades in the energy sector. They have become popular as
they are free but require huge investment, broadly accessible and environment friendly as stated in
Evans et al. (2009).
According to Renewable Energy Statistics 2016 of International Renewable Energy Agency (IRENA),
world’s total renewable energy capacity and production have been almost doubled from 2006 to 2015.
Turkey’s renewable energy capacity and production have also increased in parallel to the world’s trend
as seen in Figure 1 and Figure 2.
Figure 1 - Total Renewable Energy Capacity – World vs. Turkey
Figure 2 - Total Renewable Energy Production – World vs. Turkey
As shown in Figure 3 and Figure 4, Wind energy capacity and production in the world has sharply
increased to its six-fold whereas total renewable energy capacity and production have doubled as per
Renewable Energy Statistics 2016 of IRENA. That is to say wind energy is attention-grabbing to
produce renewable energy among the other sources. In addition, solar energy has the same
characteristics with wind power as seen in Figure 5 and Figure 6.
Also solar and wind power stand out among the other renewables by being free sources for producing
electricity.

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Figure 3 - Total Wind Energy Capacity– World vs. Turkey
Figure 4 - Total Wind Energy Production– World vs. Turkey
Figure 5 - Total Solar Energy Capacity– World vs. Turkey

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Figure 6 - Total Solar Energy Production– World vs. Turkey
When we have a closer look to Turkey’s case, the ratios are more remarkable. The wind energy
capacity and production have increased approximately 75 times for the last decade. Similarly, the solar
energy capacity and production have dramatically increased.
As an emerging economy, Turkey is planning to utilize renewable energy resources such as hydro,
wind, solar, geo-thermal, bio-mass, wave and tide under the MENR’s (Ministry of Energy National
Resources) Strategic Plan for national energy policy of the next five years (2015-2019). Utilizing
renewable energy resources have also benefits of being domestic and increasing the diversity.
Figure 7 - Turkey’s Renewable Energy Strategic Plan
With reference to the same plan, wind and solar energy are expected to grow more rapidly than the
others as illustrated in Figure 7. It is also stated in the plan that necessary measures shall be taken for
developing finance opportunities and incentives so that renewable energy investments will be
implemented.

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In this study, we aim to select the best location for a hybrid renewable energy system which combines
wind power and solar power since they are mutually complementary. Wind power has lower power
output at daytime and larger power output at nighttime, whereas the case for Solar Power is vice versa.
Standalone wind and solar power plants have problems in continuous electricity production. However
due to their mutually complementary feature, the hybrid power plants are more stable and efficient for
the electricity production. Utilizing the complementary of solar and wind power production is a
solution to the bottlenecks for intermittent energy generation of these two resources because of their
nature of states.
There are several studies in the literature to select the best location for a plant by using Multi Criteria
Decision Methods as highlighted in Section 2. In this project, we compare two multi criteria decision
methods (MCDM), i.e. Analytic Hierarchy Process with Benefit, Opportunity, Cost and Risk (AHP
with BOCR) and Ideal Matter Element Method (IME) for site selection of a wind-solar hybrid power
plant in Turkey. The reasons behind selecting this specific topic are as follows:
1. There are both wind and solar power plants are established and being operated separately but
there is no hybrid plant being operated in Turkey.
2. Multi Criteria Decision Making is a popular method for site selection.
3. There are several methods for MCDM, and there are no clear comparisons made yet among
them for wind-solar hybrid power plants.
AHP with BOCR and Ideal Matter Element method will be implemented on alternatives İzmir, Muğla,
Antalya, Konya, Karaman and Mersin.
Wind Speed, Wind Capacity Factor, Gross Solar Radiation, Sunshine Hours, Probability of Winning a
Bid, Electricity Consumption, Construction Cost, Operation and Maintenance (O&M) Cost, Traffic
Convenience Degree, Pollution and Natural Concerns, Local Residents Attitude, Interest Conflict,
Geological/topographic condition and Land Usage Condition are selected as the criteria for the
assessment of the alternative locations. Both methods provide similar results on the importance of the
criteria and final rankings of the alternatives, which indicate that the corresponding solutions are
robust. Based on the results of both methods; Wind Capacity Factor, Wind Speed, Gross Solar
Radiation and Sunshine Hours are included in top five criteria according to their importance.
Similarly, Karaman is found as the best suitable location among the other cities as a comparison of
both methods.
The rest of the report is organized as follows. In Section 2, we present the methods used in MCDM
and Literature Review. The comparison of the methods for wind-solar power plant site location is
studied in Section 3. In Section 4, a case study is carried out for selection of site location using two
MCDM methods. The evaluation of the results and conclusion are included in Section 5. Future works
are mentioned in Section 6. References are listed in Section 7 and Appendices are provided at the end
of the report in the last section.

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2. Methods Used in Multi Criteria Decision Making (MCDM) and Literature Review
Decision making has been an old standing and serious concern for the governmental issues,
professional companies and in our personal lives. The earliest known reference can be linked to the
American statesman Benjamin Franklin (1706–1790) who lived 200 years ago as discussed in detail in
Koksalan M. (2011). Decision making is hard since there are multiple criteria that affects the
evaluation of the alternatives.
Multi Criteria Decision Making methods are generally setting a ranking system for different
alternatives by using a set of independent criteria. Each method uses different mathematical operations
which considers same components i.e. objectives, criteria and alternatives.
In Multi Criteria Decision Making problem solving; first the problem, objectives and decision makers
are to be well defined. Then, the alternatives and related criteria are identified respectively. The
weights of each criterion are determined. After all, the alternatives are evaluated based on the
weighted criteria and finally the best alternative is found among the others.
2.1.Multi Criteria Decision Making (MCDM) Methods Used in Site Selection
There are numerous methods established using various mathematical and empirical methods for
MCDM problems. During the literature survey, Multi Criteria Decision Making (MCDM) methods
such as AHP (Analytical Hierarchy Process), ANP (Analytic Network Process), ELECTRE
(ELimination Et Choix Traduisant la REalité [ELimination and Choice Expressing Reality]), TOPSIS
(Technique for Order of Preference by Similarity to Ideal Solution), PROMETHEE (Preference
Ranking Organization METHod for Enrichment of Evaluations) and Matter Element are observed as
the most common methods.
2.1.1. Analytic Hierarchy Process (AHP)
Analytic Hierarchy Process is a technique for complex decisions developed by Prof. Dr. Thomas L.
Saaty in 1980. This method has been broadly studied and one of the most popular technique used in
MCDM. AHP structures problem with a hierarchy of objectives, criteria and alternatives as shown in
Figure 8. In AHP, final priority of the alternatives is found by pairwise comparison of criteria based on
expert opinions whose consistency is checked by a Consistency Index (CI).
Figure 8 – A simple AHP Hierarchy

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Advantages of this method are listed below:
1. Valid for both quantitative and qualitative criteria
2. Hierarchical structure can be used to demonstrate complex decision problems
3. It is easy to calculate since Spreadsheets (e.g. MS Excel) can be used for solving
4. Consistency is measurable and consistency check can be done
Disadvantages of this method are:
1. Large number of pairwise comparisons are required as number of criteria increase.
# of criteria 1 2 3 4 5 6 7 n
# of comparisons 0 1 3 6 10 15 21 n*(n-1)*0.5
2. Normalization is necessary to solve multi-dimensional problems.
Some applications of the AHP method in renewable energy issues are as follows: Eunnyeong et al.
(2010) used fuzzy AHP method for weighting the criteria in order to support Korean government’s
decision to increase the usage of renewable energy sources. There are total five main criteria such as
technological (T), market (M), economic (EC), environmental (EN) and policy (P) selected. These
criteria comprised of total seventeen subcriteria i.e. T is comprised of (T1) superiority of technology,
(T2) completeness of technology, (T3) reliability of technology and operation, (T4) possibility of
acquiring original technology; M is comprised of (M5) domestic market size and competitiveness,
(M6) global market size and competitiveness, (M7) competitive power of domestic technology; EC is
comprised of(EC8) supply capability, (EC9) economic feasibility, (EC10) supply durability; EN is
comprised of (EN11) reduction of greenhouse gas and pollutants, (EN12) requirement of resources,
(EN13) acceptability of local residents; and P is comprised of (P14) contribution to achieve
dissemination goal, (P15) spillover effect, (P16) linkage with R&D program and (P17) influence of
existing social system. In conclusion, economic feasibility (EC9) is estimated to have the highest
weight among all the subcriteria. The subcriteria that had the second highest weight is the competitive
power of domestic technology (M7). Global market size and competitiveness (M6) ranked third most
important criteria.
Wu et al. (2014) utilized AHP method for site selection of wind–solar hybrid power stations among
five alternative locations. There are eight major criteria set for the site selection i.e. grid-connection,
wind energy resource, solar energy resource, cost, benefit, social risk, environmental protection and
the adverse impact on the environment and society. Each criterion comprised of several subcriteria
and the most significant ones are the total cost, annual mean wind speed (m/s), mean wind power
density (W/m2), annual sunshine hours (h), annual sunshine radiation (MJ/m2) and pollution. As a
result, the order of the alternatives is found as A, B, D, E and C from the most important to less. Since
A has the greatest overall ranking for the performance values and performance scores with respect to
both all criteria and subcriteria; A is ranked as first choice.
Locating wind observation station is considered by using AHP method in Osmangazi University
campus of Meselik in Aras et al. (2004). In this study, the most suitable location is tried to be found
among total five alternative locations. These locations are in Meselik Campus Osmangazi University,
Turkey except one of them which is off campus. The cost, topography, infrastructure, security, and
convenience of transportation are selected as evaluation criteria. These criteria are comprised of
subcriteria. Cost is comprised of costs of establishment of the facility, maintenance & repair,
observation, ground studies. Topography is comprised of settlement, natural barrier, direction and
structure of the region. Infrastructure consists proximity to electric power source and others (WC,

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accommodation etc.). Theft and damage comprises security. The ranking of the alternatives is
determined as per their performance values as per the criteria.
Chen et al (2009) use traditional AHP method by enhancement of BOCR (Benefits, Opportunities,
Costs and Risks) which categorizes the criteria as positive (i.e. Benefit and Opportunity) or negative
(i.e. Cost and Risk) for establishing a further comprehensive way to cope with complex decisions for
strategic selection of suitable projects for hybrid wind-solar power plants.
The criteria and subcriteria selected in this study and their category with respect to B, O, C and R are
shown in below Table 1.
Table 1 – Criteria and Sub-criteria in Chen et al. (2009)
Merits Criteria Sub-criteria
Benefits
(a) Solar-wind
availability
(a1) Solar irradiation of the area
(a2) Wind atlas of the area
(a3) Degree of time and space complementarities
(b) Generation
function
(b1) Real and technical availability
(b2) Efficiency and reliability
(b3) Power factor and capacity factor
(c) Location
advantage
(c1) Influence of selected height of installation
(c2) Geomorphologic/hydrographical features
(c3) Latitude-tilted surface
Opportunities
(d) Policy support
(d1) Subsidy policies
(d2) Economic incentive policies
(d3) Other policy supports
(e) Financial feature
(e1) Tariff favorable polices
(e2) Tax remission policies
(e3) Other investment and production incentives
(f) Advanced
technology
(f1) Variable speed wind power generation
(f2) Swept area of a turbine rotor
(f3) Computerized supervisory system
(f4) New technologies to increase efficiency of PTC
Costs
(g) Construction
(g1) Preliminary construction
(g2) Peripheral construction
(h) Power
generation system
(h1) Design and development
(h2) Production fee
(h3) Installation and maintenance fee
(i) Connection
(i1) Electric connection
(i2) Grid connection

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Merits Criteria Sub-criteria
Risks
(j) Land use
difficulty
Difficulty in land purchase or lease agreement due to geology
suitability, environmental protection, etc.
(k) Technical
uncertainty
Technical complexity, uncertainties, and difficulties in the stages
of R&D, manufacturing and installation
(l) Interest conflict
Conflicts among private associations, political groups, electric
power companies, and local residents
The most important criteria are found as (1) solar-wind availability, both (2) policy support, the cost of
power generation system and (4) interest conflict under the benefits, opportunities, costs and risks
merit respectively. As a result of this study, Project C which is located in the southern coast of China
is found as the best location since it has the best values under three (B, C and R) out of the four merits
of B, O, C and R. In other words, Project C gives the best result among other projects because it has
the highest benefit and lowest costs and risks.
2.1.2. Analytic Network Process (ANP)
Analytic Network Process method was first presented by Prof. Dr. Thomas Saaty in 1980. ANP is a
general form of AHP which structures a problem as a network as shown in Figure 9 below which
shows feedbacks, inner and outer dependences among the elements i.e. goal (objective), criteria and
alternatives instead of hierarchy used in AHP. ANP also uses pairwise comparison to determine the
weights and finally orders the alternatives based on these weights. ANP does not need independence
among criteria and alternatives as it is the case in AHP. ANP can be useful for unstructured and
complicated decision making problems.
Figure 9 – A simple ANP Network Diagram
The main advantage of ANP is interdependencies and feedbacks are taken into account in the
evaluation which brings disadvantages of large numbers of pairwise comparisons than AHP. In
addition, to solve decision making problems with ANP, special software is needed.

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The most suitable fuel among coal, natural gas, LPG and Fuel Oil alternatives for residential heating is
studied using ANP with group decision-making in Erdogmus et al. (2006). The criteria of input for
national economy, input or environment, productive heating and evaluation of domestic resources are
selected to assess the problem by using the Super Decisions software v.1.4.1. As per the result of this
software, the productive heating is revealed as the most important criteria and the natural gas is the
most suitable alternative.
Waste energy recovery from engine is determined using the ANP in Xingyu et al. (2013). Weighting,
Cost, Efficiency and Effect are set as criteria for the ranking of six alternatives. Project 3 is found as
the best alternative which has the greatest performance value.
2.1.3. Elimination et Choice Translating Reality (ELECTRE)
ELECTRE (ELimination Et Choix Traduisant la REalité in French which means ELimination and
Choice Translating REality) method is first introduced by Bernard Roy in mid 1960s as explained in
Koksalan et al. (2011).
ELECTRE method is trailed by group of ELECTRE methods (ELECTRE II, ELECTRE III, and
ELECTRE IV), all of which have the same ideas with slight differences in calculations.
The idea of ELECTRE is to explore an order relation called outranking based on thresholds and
weights. The experts must set weights of each criterion. They also should set the threshold for every
criterion as per the indifference and preference of the alternatives.
Advantages of this method can be count as it is applicable for both quantitative and qualitative criteria
and also applicable even when there are incomparable alternatives. The disadvantages of this method
are as follows;
 there is no hierarchical structure to figure the problem
 needs more computational process to evaluate the alternatives
Beccali et al. (1998) use ELECTRE for the evaluation of renewable energy resources for the green
energy generation. Solar energy (1) Domestic solar water heaters, (2) Solar water heating for large
demands at low levels of temperature, (3) PV roofs: grid connected system generating electric energy
(without storage), (4) Wind energy (Wind turbines (grid connected)), Hydraulic energy as (5) Hydro
plants in derivation schemes and (6) Hydro plants in existing water distribution networks, Biomass as
(7) High efficiency wood boilers, (8) Combined Heat and Power (CHP) plants fed by agricultural
wastes or energy crops, (9) Animal manure CHP plants fed by biogas, Energy saving in residential and
industry sectors comprised of (10) Building Insulation, (11) High efficiency lighting, (12) High
efficiency electric householders appliances, (13) High efficiency boilers and (14) CHP Plants coupled
with refrigerating adsorption machines are the total fourteen alternatives to be assessed. The criteria
for the assessment are (a) Targets of primary energy saving in regional scale, (b) Technical maturity,
reliability, (c) Consistence of installation and maintenance requirements with local technical know-
how, (d) Continuity and predictability of performances, (e) Cost of primary energy saved, (f)
Sustainability according to Greenhouse pollutant emissions, (g) Sustainability according to other
pollutant emissions, (h) Land requirement, (i) Sustainability according to other environmental
impacts, (l) Labor impact, (m) Market maturity and (n) Compatibility with political, legislative and

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administrative situation. As a result, alternatives 6, 10, 11, 12, 13 are selected as the best which have
the highest fuzzy values.
Wu et al. (2016) examined site selection for wind power plants in China which is a complex multi-
criteria decision making (MCDM) problem by using ELECTRE-III. (1) Wind resources, (2)
construction and maintenance conditions, (3) supporting conditions onshore, (4) environmental
impacts, (5) economic and (6) social benefits are the criteria for the site selection problem among the
alternatives of coasts of (A1) Bingzhou, (A2) Hekou, (A3) Laizhou Dongying, (A4) Laizhou Yantai
and (A5) Long Island, China. In conclusion, alternative A3 is the most prior selection since its
performance is better than the others in terms of the resources, supporting conditions onshore and
society criteria. Because of the fact that the weight of these criteria holds the majority, alternative A3
obtains the highest priority.
Fetanat et al. (2015) proposed a novel approach and combined the fuzzy analytic network process
(ANP), fuzzy decision making trail and evaluation laboratory (DEMATEL) and fuzzy elimination and
choice expressing the reality (ELECTRE) methodologies to find out the best location for wind power
plant in Iran. Depth and height (C1), Environmental issues (C2), Proximity to facilities (C3), Economic
aspects (C4), Resource technical (C5) and Culture (C6) are the criteria set for the assessment of total
four alternatives namely, Sajafi region (A1), Shah Abdollah region (A2), Islamic Azad University
region (A3) and Boveirat region (A4). Consequently, the priority is set for alternatives as A3 > A2,
A4 > A1.
Moreover, Jun et al. (2014) used ELECTRE-II to evaluate seven regions to decide on macro-site
selection of wind-solar hybrid power station. The criteria are assessed as per their frequency in the
literature and the framework is established for the indicator system. There are total thirteen criteria i.e.
wind power density, wind direction condition, turbulence intensity, gross solar radiation, sunshine
stabilization rate, electricity demand, construction cost, operation and maintenance cost, traffic
convenience degree, transmission line length, pollution, energy saving / pollutant reduction, local
residents attitude. Their relative weights are calculated as 0.163, 0.136, 0.058, 0.163, 0.136, 0.097,
0.081, 0.048, 0.029, 0.040, 0.015, 0.012 and 0.024 respectively. The alternatives are Shantou,
Zhangjiakou, Youyu, Erenhot, Yumen, Haixi Prefecture in Qinghai province, Naqu in Tibet. The final
ranking of the alternatives from the best to worst is Erenhot, Zhangjiakou, Yumen, Shantou, Haixi,
Youyu and Naqu. Since it has high wind power density, gross solar radiation and it is located plain
area with low construction costs Erenhot is selected as the most suitable alternative.
2.1.4. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a simple MCDM method
published in first quarter of 1980s in Hwang et al. (1981). The essential of the method is finding the
best alternative by taking into account the closeness to ideal solution. In addition, all the criteria do not
necessarily be independent from each other.
Bulgurcu (2012) stated that in TOPSIS method, Euclidean distance approach is used to calculate how
far the alternative from the positive ideal and negative ideal solution is. Each alternative’s relative
closeness value to the ideal solution is calculated. The alternatives are ranked according to the
closeness values and the best alternative is chosen based on this ranking.

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The method is called as simple and used in several vast areas such as supply chain management,
vendor selection, logistics, business marketing applications, human resource management, financial
applications and energy management in Behzadian et al. (2012).
The main advantage of the method is that it is practical and comprehensible. However, necessity of
normalization and unavailability of consistency check are the bottlenecks of this method.
2.1.5. PROMETHEE (Preference Ranking Organization METHod for Enrichment of
Evaluations)
PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) method is
established by Brans, Mareschal and Vincke in 1980s as mentioned in Koksalan et al. (2011).
The basis of the method is pairwise comparison of alternatives by predefined preference functions
such as Gaussian function. The advantages of this method are its easiness to use and simplicity to
apply to real life problems.
The decision process is related to the pairwise comparison which results with the difference of the
alternatives based on each criterion. The comparison is made regarding to the preference functions
which are chosen from a predefined set (i.e. Regular, U-shape, V-Shape, Level, Linear and Gauss
criterion) for each criterion and get the values between 0 and 1.
Behzadian et al. (2010) studied a literature review for PROMETHEE method and stated that the
application of this method include wide coverage of study areas such as environment management,
hydrology, logistics, chemistry, medicine, manufacturing and assembly, education etc.
Kabir et al. (2014) determined location of a power substation in Bangladesh using fuzzy AHP and
PROMETHEE. Social factors, technological factors, economic factors, environmental factors and site
characteristics are the criteria set by specialist using Delphi Method. There are five alternatives coded
as MR, RI, SR, CS and DS and shown in a map. As per the results of PROMETHEE method, RI is
found as the most suitable location since it has the highest performance value.
On the other hand, Goumas et al. (2000) extended method with fuzzy input data for the ranking of
alternative energy usage in Nea Kessani, Greece. The alternative energies are greenhouse heating
(flowers), subsoil heating (asparagus), drying of agricultural products and water heating for fish
farming. The net present value of the investment, the creation of new jobs, the energy consumed and
the risk are the four criteria set for the ranking the alternatives. Water heating for fish farming is
ranked as first alternative since its performance values is the greatest one.
2.1.6. Matter Element Method (ME)
Matter-Element method is proposed by Cai Wen in 1994 in order to bring solution to MCDM
problems and widely used in evaluation in different areas as stated in Wu et al. (2014).
Matter-Element method is used to deal with the complex problems. Matter-Element contains criteria
and their values in a matrix form for an alternative. This method uses correlation degree which shows
the likeliness of each alternative to some rough levels. These rough levels are named as classical
matter elements.

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In some cases, Matter Element Method evaluation process may encounter some defects, some
alternatives may not be distinguished because of the closeness of the alternatives. This problem is
fixed in Ideal Matter Element Model. The distances between alternatives are recalculated for positive
and negative directions as positive, negative and closeness degrees. After that all alternatives are
evaluated and ranked based on these degrees.
Advantages of the Ideal Matter Element Method can be written as follows:
 Use of positive and negative closeness degrees in addition to correlation degrees to distinguish
closer alternatives (Applicable even when there are incomparable alternatives.)
 It can be solved using spreadsheet.
Wu et al. (2014) introduced the Matter-Element method and its improved version Ideal Matter-
Element method to evaluate the suitable locations for wind-solar hybrid plants in China. Natural
resources, economic factors, traffic conditions, environmental factors, social factors and geographical
conditions are criteria selected for the evaluation. These criteria contain its own subcriteria i.e. natural
resources is comprised of wind speed, effective wind speed (mean yearly), wind power density,
turbulence intensity, total solar radiation, sunshine hours; economic factors criterion is comprised of
gross domestic product, average construction cost, average operation cost and electric load demand;
traffic condition is comprised of means of transportation and traffic convenience degree and
transmission line length; environmental factors criterion is comprised of pollution and pollutant
reduction and energy saving; social factors criterion is comprised of local residents’ attitude and the
distance to load center; and geographical conditions criterion is comprised of geological/topographic
condition and land use condition subcriteria. There are total three alternatives, namely Site 1 in Inner
Mongolia, Site 2 in Qinghai and Site 3 in Tibet. As per the results of the analysis, Site 1 is revealed as
the most suitable location for wind-solar hybrid plant since it has the best values.

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3. A Comparison of Methods for Wind-Solar Hybrid Power Plant Site Selection
As mentioned above in Section 2, there are several methods such as AHP, ANP, ELECTRE, TOPSIS,
PROMETHEE and Matter Element used in solution for MCDM problems. All methods have their own
advantages and disadvantages which are summarized in Section 2.1 and shown in Table 2 below in
Yunna et. al (2014). As a summary, AHP method distinguishes from others because of its workload,
popularity and most importantly consistency check of expert opinions. In addition to these, AHP with
BOCR defines further evaluation steps that provide merits of positive criteria of Benefit and
Opportunity and negative criteria of Cost and Risk.
On the other hand, Tang et al. (2009) stated that the evaluation of the MCDM methods except Matter
Element method relies on strict bounds which may cause information loss and erroneous choices.
Matter Element method relies on the extension theory in mathematics which withdraws the binary
restriction of “either this or that” in classical mathematic theory. This idea points to the grey areas in
the real world instead of strict black and white areas. Tang et al. (2009) also mentioned that Matter
Element Method guarantee the information integrity with establishing correlation degree. Beside the
advantages of Matter Element method, Ideal Matter Element method defines further evaluation steps
that provides more detailed analyses.
In the light of above explanations, both AHP with BOCR and Ideal Matter-Element methods are
chosen to determine the location of a proposed wind-solar hybrid power plant in Turkey in this study.
Table 2 - Advantages and Disadvantages of MCDM Methods Yunna et al. (2014)
AHP
Advantages
① The consistency of the evaluation procedure can be
measured; ② it is applicable for quantitative and qualitative
criteria; ③ it can handle the complex decision problem in
practice and theory; ④ it is easy to be calculated for most
managers
Disadvantages
Consistency is difficult to achieve when the criteria and
alternatives are too many
TOPSIS
Advantages
① It can measure the distance of the alternatives form the
ideal solution; ②it can obtain the result which is closest to
the ideal solution; ③ it is easy to use and understandable
Disadvantages
① Normalization is required to solve multi-dimensional
problem; ② it cannot check the consistency
ANP
Advantages
① It can be capable of handling feedbacks and
interdependencies; ② it depicts the dependence and
influences of the factors involved to the goal or higher-level
performance objective
Disadvantages Specific software is required to solve it
ELECTRE Advantages
① It use thresholds of indifference and preference, and
outranking method to make decision; ② it is applicable for
quantitative and qualitative criteria; ③ it is applicable even
when there are incomparable alternatives

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Disadvantages
① It is difficult to conceptualize the problem in absence of
hierarchical structure; ② it is comparatively difficult to
solve than AHP due to complex computational procedure
Multi-objective
programming
Advantages
① Model involves linear or nonlinear objective function and
constraints; ② it may have continuous or integer decision
variables that can usually; ③ it is used when there are large
numbers of alternative choices
Disadvantages
① It is difficult to solve due to complex computational
procedure; ② specific software or meta-heuristic approach
is required to solve it; ③ it is applicable only for
quantitative criteria
3.1.AHP with BOCR in Site Selection for Hybrid Power Plant
For the details of AHP with BOCR method, the reader may refer to Wu and Geng (2014). Below we
briefly explain the method.
1. Form an expert committee whose backgrounds in the renewable energy sector, engineering
and academy
2. The criteria of site selection are set (Section 3.3.1)
3. The alternative sites are selected (Section 3.3.2)
4. Conduct a questionnaire regarding pairwise comparison of the criteria and merits (B, O, C, R)
for site selection to the experts with Saaty’s nine-point scale which is shown in Table 3 below.
Table 3 - Saaty’s Nine-Point Scale
Intensity of Relative Importance
with Fuzzy Number
Definition
1 equally important
3 moderately important
5 important
7 very important
9 extremely important
2,4,6,8
intermediate values between the
two neighboring scales
Weights of B, O, C, R
5. Ask experts to assess relative importance of benefit (B), opportunity (O), cost (C) and risk (R)
with Satty’s nine-point scale by filling the following
6. Table 4.

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Table 4 – Pairwise Comparison of BOCR
Pairwise
Comparison
Benefit Opportunity Cost Risk
Benefit 1
Opportunity 1
Cost 1
Risk 1
7. Make a Consistency Check for B, O, C, R comparison matrix, to find out acceptability of each
expert’s evaluation;
a. Calculate CI (Consistency Index)
1
max



n
n
CI

,
where n=number of criteria and max is the maximum eigenvalue
b. Find the corresponding RI (Random Index) from Table 5 which is established in Saaty
(1987).
Table 5 - Random Index corresponding to the Number of Criteria
Number of
Criteria
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59
c. Calculate Consistency Ratio (CR=CI/RI) and decide pairwise comparison matrix is
consistent (CR <0.1) or not (CR>0.1).
d. If there is no inconsistency continue with next step, otherwise ask experts to refill the
questionnaire again.
8. Create a combined pairwise comparison matrix for B, O, C, R by calculating geometric mean
of each expert’s evaluation in the format shown in step 5.
Forman et al. (1998) examined whether arithmetic mean or geometric mean is to be used to
evaluate the aggregating the individual judgments, and concluded that geometric mean should
be used with the assumption of each expert has the same experience since the comparison
matrices are having the reciprocity requirement, in other words, if the relative importance of
criteria a with respect to b is x then the relative importance of criteria b with respect to a is 1/x.
Besides, it is mentioned that the geometric mean is more consistent than arithmetic mean.
Forman et al. (1998) also examined whether arithmetic mean or geometric mean is to be used
to evaluate the aggregating the final priorities with the assumption of each expert has the same
experience, and stated that either an arithmetic or geometric mean can be applied despite most
people think and are grown up with the idea of only arithmetic mean e.g. averaging or taking
mean.

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Moreover, Saaty et al. (2006) states as a theorem that the general separable (S) synthesizing
functions satisfying the unanimity (U) and homogeneity (H) conditions are the geometric
mean and the root mean-power. If moreover the reciprocal property (R) is assumed even for a
single n-tuple (x1, x2,…,xn) of the judgments of n individuals, where not all xk are equal, then
only the geometric mean satisfies all the above conditions.
9. Find the weights of 4 merits (i.e. B, O, C and R) -namely b, o, c and r- by calculating the
combined pairwise comparison matrix’s (in step 9) eigenvector corresponding to maximum
eigenvalue ( max ).
Criteria Weights
10. Ask experts to assess the relative importance of all criteria with Saaty’s nine-point scale by
filling the following Table 6.
Table 6 – Pairwise Comparison of Criteria
Pairwise
Comparison
Criterion 1 Criterion 2 Criterion 3 ... ... ... Criterion n
Criterion 1 1
Criterion 2 1
Criterion 3 1
. .
. .
. .
Criterion n 1
11. Follow step 6 above to calculate CI, RI and CR and decide pairwise comparison matrix of
criteria evaluated by each expert is consistent or not.
12. Create a combined pairwise comparison matrix for criteria by calculating geometric mean of
each expert’s evaluation in the format shown in step 9.
13. Find the weights of criteria by calculating the combined pairwise comparison matrix’s (in step
11) eigenvector corresponding to maximum eigenvalue.
Local Criteria Weights
14. Find the local weights (wi) of each criterion within the merit (B, O, C, R) which they are
involved by dividing the weight of the criterion by the total of the weights of the criteria in the
same merit.
For example, divide one of the criterion’s weight which belongs to benefit by the total of the
weights of criteria which are all comprise benefit merit.

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Performance of Alternatives
15. Calculate the performance scores of alternatives with respect to each criterion as shown in
Table 7 where smn is the performance score of alternative m with respect to criterion n.
Table 7 – Calculation of Scores
Criteria
Alternative
1
Alternative
2
Alternative
3
. .
Alternative
m
Criterion 1 s11 s21 s31 . . sm1
. . . . . . .
. . . . . . .
Criterion n s1n s2n s3n . . smn
a. If the criterion is quantitative, get the score from its resource directly.
b. If the criterion is qualitative, get the score by combining all experts’ scoring by using
geometric mean.
c. Normalize the scores to prevent any scaling effect and find normalized performance
scores.
16. Calculate the relative performance results (rps) of all alternatives by summation of
multiplication of the local weights (wi) find in step 13 and normalized performance scores
(smn) found in step 14 by grouping them as per their merits (B, O, C, R)
mns
n
1i
im w=rps
17. Calculate the normalized performance results (Bi , Oi, , Ci , Ri) of all alternatives in order to
prevent any scaling effect in the format of Table 8. The higher the score for the criteria under
benefits and opportunities merits, the better the performance of the alternative is. Oppositely,
the higher the value for the criteria under costs and risks merits, the worse the performance of
the alternative is.
Table 8 – Relative Performance Results vs. Normalized Performance Results of Alternatives
Alternative 1 Alternative 2 Alternative 3 . . Alternative m
Benefits
Relative rps1B rps2B rps3B . . rpsmB
Normalized B1 B2 B3 . . Bm
Opportunities
Relative rps1O rps2O rps3O . . rpsmO
Normalized O1 O2 O3 . . Om
Costs
Relative rps1C rps2C rps3C . . rpsmC
Normalized C1 C2 C3 . . Cm
Risks
Relative rps1R rps2R rps3R . . rpsmR
Normalized R1 R2 R3 . . Rm

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where; rpsmB is the relative performance score of alternative m with respect to merit B
rpsmO is the relative performance score of alternative m with respect to merit O
rpsmC is the relative performance score of alternative m with respect to merit C
rpsmR is the relative performance score of alternative m with respect to merit R
where; Bi is the normalized performance score of alternative m with respect to merit B
Oi is the normalized performance score of alternative m with respect to merit O
Ci is the normalized performance score of alternative m with respect to merit C
Ri is the normalized performance score of alternative m with respect to merit R
18. Find final synthesis of priorities of alternatives (Pi) which is for the decision of how to
combine B, O, C, R values and the weights of the merits (b, o, c, r) found in step 8 as per
Additive, Probabilistic Additive, Subtractive, Multiplicative Priority Powers and
Multiplicative formulas shown below.
a. Additive
Pi = bBi + oOi + c(1/Ci)Normalized + r(1/Ri)Normalized
b. Probabilistic additive
Pi = bBi + oOi + c(1−Ci) + r(1−Ri)
c. Subtractive
Pi=bBi+oOi−cCi−rRi
d. Multiplicative
Pi= (Bi Oi ) / (Ci Ri)
e. Multiplicative priority powers
Pi=Bi
b
Oi
o
[(1/Ci) Normalized]c
[(1/Ri) Normalized]r
The first additive synthesis in a. above is summation of advantages (B and O) and normalized
reciprocals of disadvantages (1/C and 1/R) by considering these two disadvantage as positive.
The second (probabilistic additive) is found as more optimistic since it takes the difference of
disadvantages from 1 which results in lower values of disadvantages (C and R). The third
synthesis (subtractive) basically subtracts the weighted disadvantages from the weighted
advantages. The fourth one (multiplicative) stated in d. above is an exchange between
advantages (B and O) and disadvantages (C and R). The last one (multiplicative priority

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powers) considers the weights of b, o, c, r as the power of B, O, C, R respectively in addition
the trade-off stated in the fourth one and normalizes C and R to prevent any scaling effect.
In Satty & Ozdemir (2003), several ways of combining the B, O, C and R are examined. It is
stated that the synthesized values (Pi) for each of B, O, C, R may not have an ideal alternative
among a to e mentioned above and they can be considered as different approaches to different
problems to suit the necessities of problems.
19. Select the optimal site for the wind-solar hybrid power plant by ranking as per their priority
values (Pi)
3.2.Ideal Matter Element Method in Site Selection for Hybrid Power Plant
As it is detailed in both Tang et al. (2009) and Wu et al. (2014), Matter Element and its improved
version Ideal Matter Element methods can be implemented as explained below. An example is also
defined to make more sense with the method explanation together.
3.2.1. Definition of the Model
As mentioned in Section 2, Wen et al. (1994) offered Matter Element Theory to evaluate complex
problems. Matter Element Theory deals with objects (alternatives - M), features (criteria - c) and their
values (v).
The alternatives are defined by the decision maker or investor. Here, the criteria can be either
qualitative or quantitative. The values of each criterion can be obtained from some source, literature
survey or experts. The arithmetic mean is applied to get the synthesized value of each criterion that is
evaluated by the experts due to the assumption that the experts have the same experience.
A matter element “Rm” can be described as shown in equation below for each alternative m.

















nn
m
v
v
v
c
c
c
R
.
.
.
.
2
1
2
1
,
where m=1,2,...,M and M is the number of alternatives,
n is the number of criteria that characterize the alternative and
vi is the value of each criterion ci (i=1,2,…..,n).
Each criterion will have different value ranges for a real world problem. Standardization is applied to
the values of Matter Element to make an appropriate evaluation and eliminate scale effect (non-
dimensionalization).

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We defined a problem with three alternatives which is characterized with 3 criteria as an example in
Table 9. The values of the criteria is also given below. The standardization is applied to the values of
the matter elements of alternatives in
Table 10.
Table 9 – Example Matter Elements Definition
Type of
Criterion
Alternative/
Criteria
Alternative
1
Alternative
2
Alternative
3
Min. value that
the criteria can
take
Max. value that
the criteria can
take
Quantitative Criterion 1 8 5.5 6 3 14
Quantitative Criterion 2 4000 7200 6400 3000 9000
Qualitative Criterion 3 4 3 4 0 5
Table 10 – Example of Standardization Process of Matter Elements
Criteria Alternative 1 Alternative 2 Alternative 3
Criterion 1 0.45 0.23 0.27
Criterion 2 0.17 0.70 0.57
Criterion 3 0.80 0.60 0.80
3.2.2. Classical Matter Element Definition
Classical Matter elements ( jR ) are defined by the experts to grade or evaluate the alternatives roughly
such as great, good or bad. Here, the levels are determined for each criterion of an alternative (m).
jiX =  jiji ba , describes a range for an alternative that means the alternative can be graded as Level j
if its ith
criterion is in the range of jiX (where i=1,2,…..,n).
It can be generally represented as 



















jn
j
j
n
j
X
X
X
c
c
c
R
.
.
.
.
2
1
2
1
 Level j ,
where j=1,2,...k and k is the number of Classical Matter Elements/Classical Domains/Levels
For example, if there are two levels for the evaluation, the classical matter elements can be defined as
below,



















nn
X
X
X
c
c
c
R
1
12
11
2
1
1
.
.
.
.  Level 1



















n
X
X
Xc
c
c
R
n 2
.
.
.
.
22
21
2
1
2
 Level 2

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Section Domain is a Matter Element which covers all the Levels R1, R2,….,Rk as RS. The Section
Domain RS can be represented as in equation below.



















Sn
S
X
X
X
c
c
c
R
S
S
n
.
.
.
.
2
1
2
1
In Equation x, SiX =  SiSi ba , describes a range for an alternative that means the criterion (ci) of an
alternative (M) can take values between Sia (as minimum) and Sib (as maximum) which span the
possible values of criterion i (ci). When required, the Classical Matter elements should be also
standardized based on the section domain to eliminate the scaling effect.
In our explanatory example, we assume that experts defined 3 classical domains (Great, Normal and
Bad) to grade the matter elements of the alternatives. The standardized classical matter elements are
tabulated in Table 11, Table 12 and Table 13.
Table 11 – Example of Classical Domain-Great
Great min (a) max (b)
Criterion 1 0.6 1
Criterion 2 0.6 1
Criterion 3 0.5 1
Table 12 – Example of Classical Domain-Normal
Normal min (a) max (b)
Criterion 1 0.3 0.6
Criterion 2 0.4 0.6
Criterion 3 0.2 0.5
Table 13 – Example of Classical Domain-Bad
Bad min (a) max (b)
Criterion 1 0 0.3
Criterion 2 0 0.4
Criterion 3 0 0.2
3.2.3. Criteria Weight Determination
Weights of each criterion are determined based on expert opinions and Rank Correlation Analysis with
the steps listed below.
 Each expert sorts the criteria from bottom to up where the first criterion is marked as
the most important criteria.
 Each criterion except the most important one is marked with a number rki from a set
{1: same importance as below,
1.2: a little more important than below,

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1.4: obviously more important than below,
1.6: more strongly important than below,
1.8: very much important than below}
Where rki is a grade that is given from the kth
expert to the ith
criterion.
 Weights of each criterion are determined based on these grades (rki) by each expert.
1
2 2
1

 








  
n
k
k
i
ii rw , wi−1= ri × wi , i = n−1,…,2
 Final weights (wi) are determined by taking each expert opinion into account. Each
expert is supposed to be at the same experience and therefore final weights are
determined based on arithmetic mean.
In our example, we assume that experts (E1, E2, E3) sorted the criteria according to their importance
and ranked the criteria as shown in Table 14, Table 15 and Table 16. The weights of the criteria, which
are calculated based on the ranks of each expert, is also represented below. The final weights of the
criteria are calculated based on the average of the weights (w1,w2,w3) as shown in Table 17.
Importance Ranking (E1) r1 w1
Criterion 1 0.44
Criterion 2 1 0.44
Criterion 3 1.6 0.28
Importance Ranking (E2) r2 w2
Criterion 1 0.43
Importance Ranking (E2) r w3
Criterion 1 0.40
Final Weights
Criterion 1 0.36
Criterion 2 0.40
Criterion 3 0.31

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3.2.4. Correlation Calculation
The Correlation Degree  mK j represents the "fit" between level j and alternative m and is calculated
between the values (vi) for each criterion and each level j of the same criterion of an alternative based
on the equation below.
     jijijijiimimj abbavvD  5.05.0 ,
where  imj vD defines the distance of the alternative m’s ith criterion to the level j’s ith criterion
   


n
i
imjij vDwmK
1
1 ,
where iw is the weight of each criterion
The alternative is said to have similar characteristic as the level which has greatest correlation degree
in between. The correlation degrees between each alternative and each level are calculated based on
classical matter elements. Maximum correlation degree of each alternative corresponds a level among
all levels. Thus, the level of the alternative will be decided with this approach.
If there are alternatives that cannot be discriminated, which are both classified in the same level, the
Ideal Matter Element idea can be utilized. Ideal Matter Element approach states Positive Closeness
Degree and Negative Closeness Degrees in order to discriminate these alternatives.
In our example, the correlation degrees between each alternative (Alternative 1, Alternative 2 and
Alternative 3) and each level (Great Normal and Bad) are tabulated in Table 32, Table 33 and Table
34. The green cells in the tables below represents the greatest correlation degrees and levels of each
alternative based on Matter Element method. Here alternative 2 and alternative 3 cannot be
distinguished and we should go further and apply Ideal Matter Element idea for this example.
Table 18 – Example of Grading Alternative 1
Calculation Process for
Alternative 1 Values
Great Normal Bad
Dj(vim)
Weighted
Distance Dj(vim)
Weighted
Distance Dj(vim)
Weighted
Distance
Criterion 1 0.45 0.15 0.05 - 0.15 - 0.05 0.15 0.06
Criterion 2 0.17 0.43 0.17 0.23 0.09 - 0.17 -0.07
Criterion 3 0.80 - 0.20 - 0.06 0.30 0.09 0.60 0.19
Total Correlation 0.84 0.87 0.83
Great Normal Bad
Dj(vim)
Weighted
Distance Dj(vim)
Weighted
Distance Dj(vim)
Weighted
Distance
Wind Speed 0.23 0.37 0.13 0.07 0.03 - 0.07 - 0.03
Total Solar Radiaton 0.70 - 0.10 - 0.04 0.10 0.04 0.30 0.12
Pollution 0.60 - 0.10 - 0.03 0.10 0.03 0.40 0.12

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3.2.5. Closeness Degree Calculation
Here two new Matter Element definition such as Positive Matter Element (RP) and Negative Matter
Element (RN) are defined as;
Positive Ideal Matter Element consists of the best values of criteria of alternatives ( PiV , where
i=1,2,...n).



















Pn
V
V
P
Vc
c
c
R
P
P
n
.
.
.
.
2
1
2
1
Negative Ideal Matter Element consists of the worst values of criteria of alternatives ( NiV , where
i=1,2,...n).



















Nn
V
V
N
Vc
c
c
R
N
N
n
.
.
.
.
2
1
2
1
The Closeness Degree shows the distance between each alternative and the Ideal Matter Elements
(Negative and Positive). Therefore, the ranks of alternatives can be found by comparing the Closeness
Degrees. The Closeness degrees of the alternatives are calculated as below in equation,
 Positive Closeness Degree ( H j

)




n
i
Piiij VvwH
1
1
Great Normal Bad
Dj(vim)
Weighted
Distance Dj(vim)
Weighted
Distance Dj(vim)
Weighted
Distance
Wind Speed 0.27 0.33 0.12 0.03 0.01 -0.03 - 0.01
Total Solar Radiaton 0.57 0.03 0.01 - 0.03 - 0.01 0.17 0.07
Pollution 0.80 - 0.20 - 0.06 0.30 0.09 0.60 0.19

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 Negative closeness degree ( H j

)
 
Niiij VvwH 1
 Comprehensive Closeness Degree ( H j ) synthesizes Positive and Negative Closeness
Degrees
HH
H
H
jj
j
j 



Finally, further evaluation can be made regarding to the Comprehensive Closeness Degrees such as if
one alternative has a greater comprehensive closeness degree, it is said to be a better alternative.
As we mentioned above, alternative 2 and alternative 3 fitted the same level in our example. To
distinguish the alternatives we calculated closeness degrees of each alternative in Table 21.The
alternatives can now be distinguished based on the comprehensive closeness degrees. Final
evaluations are represented in Table 22.
Table 21 – Example of Closeness Degrees Calculation
Alternative 1 Alternative 2 Alternative 3
Positive Closeness 0.79 0.86 0.88
Negative Closeness 0.79 0.79 0.76
Comprehensive Closeness 0.50 0.52 0.54
Table 22 – Example of Evaluation Results
ME
Results
IME
Results
Alternative 1 Normal 3
Alternative 2 Great 2
Alternative 3 Great 1
3.3.Site Selection Criteria and Alternative Locations for Wind-Solar Hybrid Power Plant in
Turkey
Upon reviewing the literature for Wind-Solar Hybrid Power Plants in Section 2 above, possible
criteria and alternatives are selected among the articles reviewed. After completion of literature
review, there are separate meetings held with total four experts whose short biographies are provided
in Section 8.1 to evaluate and set the both site selection criteria and alternative locations for the
application of AHP with BOCR and Ideal Matter Element methods.
3.3.1. Determining the Criteria
Determining the selection criteria is the second phase of a MCDM following the first phase of
identifying the goal. The goal is named as selection of the best location for Solar-Hybrid Wind Power
Plant in this study.

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Literature statistics are taken into consideration during the selection of candidate criteria. Wu et al.
(2013) studied literature statistics in detail and stated the frequency of the criteria for site selection for
the wind-solar hybrid power station.
During the selection of the criteria, the experts were asked to evaluate the properness of the candidate
criteria shown in the Table 23 in the meetings held.
Table 23– Candidate Criteria and General Classes
General Class of Criteria Criteria
Natural resources
Wind Availability
Solar Availability
Economic Factors
Policy Support
Electricity Demand
Cost
Traffic Conditions Traffic Convenience Degree
Environmental Factors Pollution
Social Factors Stakeholders Behaviour
Geographical Conditions
Geological/topographic condition
Land Use Difficulty
Experts have reviewed the abovementioned candidate criteria and provided their verbal comments on
them. As a result of these discussions, it is decided to provide criteria in more detail to better
assessment of the problem. Finally, the criteria for this study are set and their definitions are provided
as tabulated below in Table 24.
The criteria are mainly grouped into two: quantitative criteria and qualitative criteria. Quantitative
criteria simply can be measurable values which are retrieved from its source like literature,
governmental organizations, academic resources etc. The qualitative criteria are the relative measures
depending on expert opinions which is transformed to numerical values by the quantification of
subjective assessments.
Moreover, grouping of these criteria are made for the application of AHP with BOCR method. Table
24 also shows the four merits i.e. Benefit (B), Opportunity (O), Cost (C) and Risk (R) of each
criterion.

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Table 24– Candidate Criteria vs. Criteria Set with Descriptions
Candidate Criteria Selected Criteria
Type of
Criteria
(Qualitative or
Quantitative)
Merit
(B,O,C,R)
Data Resource Description
Wind Availability
Wind Speed Quantitative Benefit REPA Speed of the wind (m/s)
Wind Capacity Factor Quantitative Benefit REPA Ratio of actual output to potential output
Solar Availability
Gross Solar Radiation Quantitative Benefit GEPA Accumulative value of solar radiation intensity (MJ/m2)
Sunshine Hours Quantitative Benefit GEPA Average Sunshine Hours in one year (hours)
Policy Support Probability of Winning a Bid Qualitative Opportunities Expert Opinion
The government sets maximum available installation capacity of
renewable energy plants and the investors apply for the bid. The chance
of winning the bid can be measured by the ratio of the total applied
capacity by the bidders and total available installation capacity set by
government.
Electricity Demand Electricity Consumption Quantitative Benefit
The map of Energy Status
of Cities (İllerin Enerji
Görünümü)
Electricity consumption (demand) of the alternative can be a benefit for
the investor. Demand and supply ratio is an important measure for every
investment. Where demand is high, it is more beneficial for the
investors.
Cost
Construction Cost Qualitative Cost Expert Opinion
Construction cost of the wind-solar hybrid plant consists of;
-availability of man power,
-availability of equipment,
-availability of other sources,
-distance to grids (cabling costs to convey the electricity to the grid is
parallel with the distance to grid)
-land purchase costs
Operation and Maintenance
(O&M) Cost
Qualitative Cost Expert Opinion
Operation & maintenance cost of the hybrid plant consists of;
-cost of manhour,
-cost of equipment-hour,
-training costs,
-repair and replacement costs
-inspection costs
-distance to grids (closer plants have less energy loss than far ones
from the grid)
-period of the maintenance need

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Candidate Criteria Selected Criteria
Type of
Criteria
(Qualitative or
Quantitative)
Merit
(B,O,C,R)
Data Resource Description
Traffic Convenience
Degree
Traffic Convenience Degree Qualitative Cost Expert Opinion
Convenience degree using traffic mean to the plant during construction,
operation and maintenance (closeness to port, availability of highways,
seaports, airports in use)
Pollution
Pollution and Natural
Concerns
Pollution degree ( (Pneumatic, Noise, Light) and natural concerns
(distortion in the environment, such as water resources, forestry, natural
life [birds path of migration] ) during the construction&operation period
Stakeholders
Behaviour
Local Residents Attitude Qualitative Risk Expert Opinion
The attitude/resistance of the local residents and NGOs (non-
governmental organizations) for the plant
Interest Conflict Qualitative Risk Expert Opinion
Conflicts among private associations, political groups and rival electric
power producer companies/investors
Geological/topographic
condition
condition
Geological and topographic conditions of plant construction consist of;
-difficulties due to extreme weather conditions (humid, rainy, snowy,
dusty
e.g.1. When the area is dusty, then the solar panels performance will
decrease since they are covered with dust
e.g.2 When the area is snowy, the performance of the wind turbines
may be adversely affected
e.g.3 Humid and rainy conditions may increase the maintenance
needs ),
-slope (when the area has steep slopes, then it will be more difficult to
construct a plant),
-terrain features (when the area is rough, then it will be more difficult to
construct a plant)
-soil type (when soil is soft or rocky, the properness of the soil changes)
Land Use Difficulty Land Usage Condition Qualitative Risk Expert Opinion
The possibility of hindrance of land usage or purchase / lease due to
environmental protection, laws, regulations etc.
e.g. There are forbidden areas because of the existing natural resource
mines and protected areas and closeness to these areas may bring some
risks together

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3.3.2. Selection and Determining the Alternative Locations
In the previous sections, first and second stages of a MCDM are explained. The third and the last stage
of a MCDM is selection and determination of the alternatives. In this study, suitable areas are being
searched for both solar and wind power production. Aydin (2009) studied finding the suitable
locations for wind and solar energy systems and carried out a case study regarding the western Turkey
using Geographic Information Systems (GIS) by overlapping the spatial data for each criterion like
solar potential, wind potential, roads, water bodies, slope, bird migration paths, natural resources,
airports, transmission lines, limitations of Ministry of Environment and Forestry, limitations of
Ministry of Agriculture and Rural Affairs and limitations set by Energy Market Regulatory Authority
(EPDK).
Jahangiri et al. (2016) studied finding the best locations for establishment of wind-solar power stations
in Middle-East using GIS. As a result of their study there are areas found suitable in Turkey as shown
in Figure 10.
Figure 10 – Candidate Areas for Alternatives
The candidates are initially considered as only cities i.e. İzmir, Muğla, Antalya, Konya, Karaman,
Mersin, Denizli and Hakkari. Afterwards, REPA (Rüzgar Enerjisi Potansiyel Atlası – Wind Power
Potential Atlas) and GEPA (Güneş Enerjisi Potansiyel Atlası – Solar Power Potential Atlas) are used
to evaluate the appropriateness of this cities. In these maps, the minimum wind speed is limited to be
as 7 m/s, minimum 1600 KWh/m2
is sufficient for Gross Solar Radiation and there might be prohibited
areas by the Government.
As a result, Denizli is eliminated since the wind speed is less than 7 m/s and Hakkari can not be
selected since it is prohibited by the Government.

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Upon the elimination of Denizli and Hakkari which are unsuitable for a wind-solar power plant; İzmir,
Muğla, Antalya, Konya, Karaman and Mersin cities are examined in detail to decide where to locate
the power plant. Karaburun is selected as a candidate location alternative since it has both high solar
and wind potential. (See Figure 11 and Figure 12 in Section 8.2. for the corresponding solar and wind
potential maps)
For similar reasons, we selected Muğla Merkez, Antalya Akseki, Konya Merkez, Karaman Merkez
and Mersin Gülnar due to their high potential in terms of wind and solar energy. Please refer to
Section 8.2. for the corresponding solar and wind potential maps.

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4. Application of the Methods for Wind-Solar Hybrid Plant Site Selection in Turkey
The AHP with BOCR and Ideal Matter Element methods are applied to determine the best location for
Wind-Solar Hybrid Plant in Turkey. The criteria and the alternatives are selected as explained in
Section 3.3 in detail.
4.1.Application of AHP with BOCR
Upon defining the problem and determining the alternatives and the criteria as stated in Sections 1,
3.3.1 and 3.3.2 respectively, the method can be applied. The application is comprised of three phases
such as data preparation, weight determination and evaluation of alternatives.
4.1.1. Data Preparation
The quantitative data is obtained from Turkish Ministry of Energy and Natural Resources General
Directorate of Renewable Energy. The data of Wind Speed and Wind Capacity Factor are taken from
the REPA (Rüzgar Enerjisi Potansiyel Atlası – Wind Power Potential Atlas) map for each alternative.
The data of Gross Solar Radiation and Sunshine Hours are retrieved from the GEPA (Güneş Enerjisi
Potansiyel Atlası – Solar Power Potential Atlas) map for each alternative similarly. In addition to
these, Electricity Consumption values of all alternatives are taken from the map of Energy Status of
Cities (İllerin Enerji Görünümü).
The qualitative data are gathered from the results of the questionnaires based on Table 25. The
questionnaires are answered by total 4 experts who has been involved in renewable energy sector and
academics relevant to renewable energy. The ranks are stated in detail for each qualitative criterion
from bad to good or vice versa.
Table 25 – Criteria Quantification
Criteria Criteria Quantification
Probability of Winning
a Bid
1 – Very low; 2 – low; 3 – normal; 4 – high; 5 – very high
Construction Cost 1 – Very low; 2 – low; 3 – normal; 4 – high; 5 – very high
Operation and
Maintenance Cost
Traffic Convenience
Degree
5 – Not convenient; 4 – less convenient; 3 – convenient; 2 – more
convenient; 1 – very convenient
Concerns
5 – Very serious; 4 – serious; 3 – normal; 2 – not serious; 1 – no
pollution
Local Residents
Attitude
5 – Very negative; 4 – negative; 3 – neutral; 2 – positive; 1 – very
positive
Interest Conflict
positive
condition
5 – Very tough; 4 – tough; 3 – normal; 2 – suitable; 1 – very suitable
Land Usage Condition 5 – Very difficult; 4 – difficult; 3 – normal; 2 – easy; 1 – very easy

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The final values of each alternative with respect to each criterion are shown in Table 26 below. The
qualitative criteria values are calculated from the experts’ evaluations using geometric mean as
explained in Section 3.1, step 14.
Table 26 – Performance Values of Alternatives w.r.t each Criterion for AHP with BOCR
Criteria
Alternative
1
İzmir
Karaburun
Alternative
2
Muğla
Merkez
Alternative
3
Antalya
Akseki
Alternative
4
Konya
Merkez
Alternative
5
Karaman
Merkez
Alternative
6
Mersin
Gülnar
Wind Speed (m/s)* 8.25 7.00 8.25 7.00 8.25 7.50
Wind Capacity Factor
(%)*
42.50 32.50 35.00 32.50 40.00 35.00
Gross Solar Radiation
(KWh/m^2-year)**
1.625.00 1.725.00 1.775.00 1.675.00 1.725.00 1.725.00
Sunshine Hours
(hours)**
8.35 8.25 8.16 7.95 8.25 8.36
a Bid
2.11 2.91 3.66 2.63 2.45 2.38
Electricity
Consumption
(MWh)***
17.89 2.60 6.54 5.74 0.64 4.00
Construction Cost 2.59 2.45 3.31 1.68 1.78 2.71
Operation and
Maintenance Cost
1.73 2.45 4.16 3.13 3.56 3.46
Traffic Convenience
Degree
1.57 2.45 3.56 2.21 2.63 2.21
Concerns
4.23 3.72 3.94 2.21 2.21 3.22
Local Residents
Attitude
4.23 3.46 3.87 1.57 1.32 2.21
Interest Conflict 4.47 3.22 3.31 1.19 1.19 2.00
condition
1.86 2.63 3.87 2.21 2.63 2.00
Land Usage Condition 3.66 3.08 4.47 1.57 1.57 3.00
*Retrieved from http://www.eie.gov.tr/YEKrepa/REPA-duyuru_01.html
** Retrieved from http://www.eie.gov.tr/MyCalculator/Default.aspx
*** Retrieved from http://www.eie.gov.tr/il_enerji.aspx
Normalization is applied to eliminate scale effect as per the following equation.
minmax
min
'
xx
xx
x



where x’ is the normalized value of variable x
xmin is the minimum value that x can take
xmax is the maximum value that x can take

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For example to calculate İzmir’s normalized wind speed, we need maximum and minimum wind
speed values that x can take for a city which is 10m/s and 3 m/s respectively in this project (see Table
39) . Then normalized wind speed of İzmir is calculated as 0.75.
75.0
710
325.8
' 


x
The final normalized values for all criteria of each alternative are shown in Table 27.
Table 27 – Normalized Performance Values of Alternatives w.r.t each Criterion for AHP with
BOCR
Criteria
Alternative 1
İzmir
Karaburun
Alternative 2
Muğla
Merkez
Alternative 3
Antalya
Akseki
Alternative 4
Konya
Merkez
Alternative 5
Karaman
Merkez
Alternative 6
Mersin
Gülnar
Wind Speed (m/s) 0.75 0.57 0.75 0.57 0.75 0.64
Wind Capacity Factor (%) 0.71 0.54 0.58 0.54 0.67 0.58
(KWh/m^2-year)
0.38 0.54 0.63 0.46 0.54 0.54
Sunshine Hours (hours) 0.64 0.63 0.63 0.61 0.63 0.64
Probability of Winning a
Bid
0.42 0.58 0.73 0.53 0.49 0.48
Electricity Consumption
(MWh)
0.51 0.07 0.19 0.16 0.02 0.11
Operation and
Maintenance Cost
0.35 0.49 0.83 0.63 0.71 0.69
Traffic Convenience Degree 0.31 0.49 0.71 0.44 0.53 0.44
Concerns
0.85 0.74 0.79 0.44 0.44 0.64
Local Residents Attitude 0.85 0.69 0.77 0.31 0.26 0.44
condition
0.37 0.53 0.77 0.44 0.53 0.40
All four experts are asked to fill the questionnaires for pairwise comparison of fourteen criteria and
four merits (B, O, C, R) separately. The answers of the experts are enclosed in Section 8.3.
4.1.2. Weight Determination
The relative comparison is assessed from each expert separately for total fourteen criteria (both
qualitative and quantitative). Upon receiving the questionnaire from the experts for pairwise
comparison of criteria, the consistency check is implemented to verify the consistency of experts’
opinions as explained in Section 3.1 step 10 above.
Since the Consistency Indices (CIs) of Experts 3 and 4 are found as greater than 0.1, the experts are
asked to reassess their questionnaire and resubmit upon providing more information how to fill the
questionnaire. In their second evaluation, the consistency is satisfied for all experts as seen in Table
28.

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Table 28 – Consistency Check for Comparison of Criteria
Consistency Ratio for
Comparison of Criteria
Expert 1 Expert 2 Expert 3 Expert 4
Evaluation 1 0.07704 0.06716 0.17125 0.20018
Evaluation 2 N/A N/A 0.02664 0.02401
Similarly, the consistency check is applied to the answers gathered from each expert for pairwise
comparison of four merits matrix as mentioned in Section 3.1 step 6. All experts except the fourth one
is found as consistent. The fourth expert is asked to evaluate her/his results again since the CI value is
greater than acceptable limit of 0,1. Revised evaluation satisfied the consistency check as shown in
Table 29.
Table 29 – Consistency Check for Comparison of Merits
Consistency Ratio for
Comparison of B,O,C,R
Expert 1 Expert 2 Expert 3 Expert 4
Evaluation 1 0.07371 0.09204 0.01611 0.10678
Evaluation 2 N/A N/A N/A 0.00293
In the same way, after satisfying the consistency check, a combined pairwise comparison matrix for
the merits (B, O, C, R) is aggregated from all experts’ evaluation by calculating geometric mean as
specified in Section 3.1. Step 7 as shown in Table 30 - Pairwise Comparison and Weights of Merits.
The weights b, o, c, r of the merits B, O, C, R are also determined respectively based on the
eigenvector which corresponds to largest eigenvalue of the matrix shown below in Table 30 - Pairwise
Comparison and Weights of Merits as stated in Section 3.1. under Step 8.
After satisfying the consistency check, a combined pairwise comparison matrix for the criteria is
aggregated from all experts’ evaluation by calculating geometric mean as specified in Section 3.1, step
11 as illustrated in Table 31 - Pairwise Comparison and Weights of Criteria. It is assumed that each
expert has equal experience.
The weights of the criteria are determined based on the eigenvector, which corresponds to largest
eigenvalue of the pairwise comparison matrix as stated in Section 3.1. For this case, the weights are
listed in Table 31 – Pairwise Comparison and Weights of Criteria under “Weights Column” for each
criterion. Furthermore, the local weights as per their merits are calculated as explained in Section 3.1
under step 13 and shown in the same table under “Weights based on merits” column.
Table 30 - Pairwise Comparison and Weights of Merits
Pairwise Comparison
of B,O,C,R
Benefit Opportunity Cost Risk Weights Description
Benefit 1 0.22 0.76 0.45 0.42 b
Opportunity 4.58 1 2.55 1.50 0.11 o
Cost 1.32 0.39 1 0.50 0.31 c
Risk 2.20 0.67 2.01 1 0.17 r
The consistency ratio of Table 31 is calculated as 0.0088759, which is less than 0.1 and this means the
consistency is still satisfied after aggregating the experts’ answers using geometric mean.

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Table 31 – Pairwise Comparison and Weights of Criteria
Pairwise Comparison
of Criteria
Wind
Speed
Wind
Capacity
Factor
Gross
Solar
Radiation
Sunshine
Hours
Prob. of
Winning a
Bid
Electricity
Consumption
Construction
Cost
O&M Cost
Traffic
Convenience
Degree
Pollution
and
Natural
Concerns
Local
Residents
Attitude
Interest
Conflict
Geological/
topographic
condition
Land
Usage
Condition
Weights
Weights
based
on
merits
Merit
(Benefit,
Opportunities,
Cost and
Risk)
Wind Speed 1 2.45 0.76 0.72 0.80 0.41 0.76 0.44 0.39 0.47 0.20 0.22 0.52 0.70 0.104 0.201 Benefit
Wind Capacity Factor 0.41 1 0.44 0.35 0.34 0.20 0.39 0.26 0.22 0.29 0.16 0.17 0.28 0.43 0.199 0.387 Benefit
Gross Solar Radiation 1.32 3.22 1 0.72 0.90 0.47 0.76 0.44 0.39 0.47 0.20 0.22 0.52 0.70 0.099 0.192 Benefit
Sunshine Hours 1.39 2.82 1.39 1 0.71 0.47 0.71 0.58 0.54 0.54 0.27 0.45 0.71 0.75 0.082 0.159 Benefit
a Bid
1.26 2.94 1.11 1.40 1 0.39 1.32 0.88 0.58 0.56 0.21 0.26 0.60 0.88 0.077 1.000 Opportunities
Electricity
Consumption
2.43 5.10 2.14 2.14 2.59 1 3.87 2.24 1.61 1.97 0.58 0.95 1.57 2.43 0.031 0.061 Benefit
Construction Cost 1.32 2.59 1.32 1.40 0.76 0.26 1 0.67 0.44 0.54 0.19 0.24 0.60 0.88 0.087 0.296 Cost
Operation and
Maintenance Cost
2.28 3.87 2.28 1.73 1.14 0.45 1.50 1 0.76 0.80 0.27 0.41 1.05 1.09 0.056 0.191 Cost
Traffic Convenience
Degree
2.55 4.53 2.55 1.86 1.73 0.62 2.28 1.32 1 1.00 0.26 0.31 1.19 1.43 0.048 0.162 Cost
Concerns
2.14 3.44 2.14 1.86 1.78 0.51 1.86 1.24 1.00 1 0.31 0.39 0.90 1.57 0.050 0.170 Cost
Local Residents
Attitude
5.01 6.42 5.01 3.71 4.70 1.72 5.14 3.66 3.87 3.20 1 2.28 4.05 4.41 0.017 0.153 Risk
Interest Conflict 4.53 5.92 4.53 2.24 3.87 1.06 4.16 2.45 3.22 2.59 0.44 1 2.63 3.20 0.024 0.216 Risk
condition
1.93 3.60 1.93 1.41 1.65 0.64 1.65 0.96 0.84 1.11 0.25 0.38 1 1.35 0.054 0.182 Cost
Land Usage Condition 1.43 2.30 1.43 1.33 1.14 0.41 1.14 0.92 0.70 0.64 0.25 0.31 0.74 1 0.07 0.630 Risk
The consistency ratio of Table 31 is calculated as 0.014176792, which is less than 0.1 and this means the consistency is still satisfied after aggregating the
experts’ answers using geometric mean.

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4.1.3. Evaluation of Alternatives
As a result of the analysis in Section 4.1.2, the importance of the criteria is listed in Table 32. As
expected, criteria such as wind capacity factor, wind speed, gross solar radiation and sunshine hours
are ranked as the most important. Due to the fact that majority of the experts work at the renewable
energy sector, the construction cost is involved in the upper ranks.
Table 32 - Importance of the Criteria in AHP with BOCR
Criteria Weight
Importance
Rank
Wind Capacity Factor 0.1973 1
Wind Speed 0.1032 2
Gross Solar Radiation 0.0984 3
Construction Cost 0.0876 4
Sunshine Hours 0.0815 5
Probability of Winning a Bid 0.0775 6
Land Usage Condition 0.0715 7
Operation and Maintenance Cost 0.0567 8
Geological/topographic condition 0.054 9
Pollution and Natural Concerns 0.0504 10
Traffic Convenience Degree 0.0481 11
Electricity Consumption 0.0315 12
Interest Conflict 0.0247 13
Local Residents Attitude 0.0174 14
Similarly, the importance ranking of the four merits are found as Benefit, Cost, Risk and Opportunity
from the most important to least according to Table 30 - Pairwise Comparison and Weights of Merits.
This result is expected since the criteria belong to Benefit merit fall in the top level of importance
ranking. The same expectations for the remaining merits are also deduced in the same way.
The performance scores of all six alternatives with respect to the four merits of AHP with BOCR are
calculated in Table 33. The calculation of performance scores of alternatives with respect to the merits
is explained in Section 3.1, step 15.

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Table 33 – Performance Scores of Alternatives w.r.t the Merits
All synthesizing methods stated in Section 3.1 Step 17 are used to learn more about differences in the
rankings. By taking average of all individual results, the final ranking is found as illustrated in the
Table 34. According to these results, Karaman is found as the best location. All alternatives are
ordered from the best to the worst as Karaman, Konya, Mersin, İzmir, Muğla and Antalya.
Table 34 – Priorities and Ranking of Alternatives
Synthesizing Methods
Additive
Probabilistic
Additive
Subtractive
Multiplicative
Priority Powers
Multiplicative Final Decision
Alternatives Priority Rank Priority Rank Priority Rank Priority Rank Priority Rank
Average
Rank
Final
Rank
Alt. 1
İzmir
Karaburun
0.16284 3 0.47970 4 0.00560 4 0.15894 3 0.68840 6 4 4
Alt. 2
Muğla
Merkez
0.15439 5 0.47536 5 0.00126 5 0.15 5 0.88645 4 4.8 5
Alt. 3
Antalya
Akseki
0.14973 6 0.46043 6 -0.01367 6 0.14401 6 0.70664 5 5.8 6
Alt. 4
Konya
Merkez
0.18477 2 0.49736 2 0.02326 2 0.18071 2 2.01512 2 2 2
Alt. 5
Karaman
Merkez
0.18998 1 0.50318 1 0.02909 1 0.18693 1 2.04403 1 1 1
Alt. 6
Mersin
Gülnar
0.15830 4 0.48035 3 0.00626 3 0.15814 4 0.90589 3 3.4 3
Merit
Performance
Score Type
Alternative
1
İzmir
Karaburun
Alternative
2
Muğla
Merkez
Alternative
3
Antalya
Akseki
Alternative
4
Konya
Merkez
Alternative
5
Karaman
Merkez
Alternative
6
Mersin
Gülnar
Benefits
Relative 0.63016 0.53375 0.60771 0.51968 0.61468 0.56809
Normalized 0.18139 0.15364 0.17493 0.14959 0.17694 0.16352
Opportunities
Relative 0.42295 0.58259 0.73257 0.52643 0.48990 0.47568
Normalized 0.13094 0.18036 0.22679 0.16298 0.15167 0.14726
Costs
Relative 0.48113 0.53972 0.74424 0.44627 0.49707 0.54635
Normalized 0.14782 0.16582 0.22866 0.13711 0.15272 0.16786
Reciprocal 6.76491 6.03047 4.37331 7.29322 6.54795 5.95732
Reciprocal
Normalized
0.18300 0.16313 0.11830 0.19729 0.17713 0.16115
Risks
Relative 0.78497 0.63400 0.82575 0.29675 0.28911 0.53260
Normalized 0.23340 0.18851 0.24553 0.08823 0.08596 0.15836
Reciprocal 4.28448 5.30468 4.07289 11.33354 11.63270 6.31461
Reciprocal
Normalized
0.09977 0.12353 0.09484 0.26392 0.27089 0.14705

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4.2.Application of Ideal Matter Element (IME) Method
4.2.1. Data Preparation
The data required for the application of Ideal Matter Element method is gathered in the same way
explained in Section 4.1.1 above. The Criteria Quantification is implemented as shown in Table 35.
Table 35 – Criteria Quantification
Criteria Criteria Quantification
a Bid
Construction Cost 1 – Very high; 2 – high; 3 – normal; 4 – low; 5 – very low
Operation and
Maintenance Cost
1 – Very high; 2 – high; 3 – normal; 4 – low; 5 – very low
Traffic Convenience
Degree
1 – Not convenient; 2 – less convenient; 3 – convenient; 4 – more
convenient; 5 – very convenient
Concerns
1 – Very serious; 2 – serious; 3 – normal; 4 – not serious; 5 – no
pollution
Local Residents
Attitude
positive
Interest Conflict
positive
condition
1 – Very tough; 2 – tough; 3 – normal; 4 – suitable; 5 – very suitable
Land Usage Condition 1 – Very difficult; 2 – difficult; 3 – normal; 4 – easy; 5 – very easy
The final values of each alternative with respect to each criterion are shown in Table 36 – Performance
Values of Alternatives w.r.t each Criterion for IME Method. This matrix forms the Matter Element of
the alternatives as stated in Section 3.2.
Table 36 – Performance Values of Alternatives w.r.t each Criterion for IME Method
Criteria
Alternative
1
İzmir
Karaburun
Alternative
2
Muğla
Merkez
Alternative
3
Antalya
Akseki
Alternative
4
Konya
Merkez
Alternative
5
Karaman
Merkez
Alternative
6
Mersin
Gülnar
Wind Speed (m/s)* 8.25 7.00 8.25 7.00 8.25 7.50
Wind Capacity Factor (%)* 42.50 32.50 35.00 32.50 40.00 35.00
(KWh/m^2-year)**
1.625.00 1.725.00 1.775.00 1.675.00 1.725.00 1.725.00
Sunshine Hours (hours)** 8.35 8.25 8.16 7.95 8.25 8.36
Bid
2.50 3.00 3.75 3.00 2.50 2.75
(MWh)***
17.89 2.60 6.54 5.74 0.64 4.00
Operation and
Maintenance Cost
3.20 2.80 1.40 2.20 1.80 2.00

of 82
Criteria
Alternative
1
İzmir
Karaburun
Alternative
2
Muğla
Merkez
Alternative
3
Antalya
Akseki
Alternative
4
Konya
Merkez
Alternative
5
Karaman
Merkez
Alternative
6
Mersin
Gülnar
Concerns
1.40 1.80 1.60 3.00 3.00 2.20
condition
3.00 2.60 1.60 2.80 2.60 3.00
*Retrieved from http://www.eie.gov.tr/YEKrepa/REPA-duyuru_01.html
** Retrieved from http://www.eie.gov.tr/MyCalculator/Default.aspx
*** Retrieved from http://www.eie.gov.tr/il_enerji.aspx
The Matter Elements of the all alternatives that will be evaluated are formed from the standardized
Table 37.
Table 37 – Standardized Performance Values of Alternatives w.r.t each Criterion for IME
Criteria
Alternative
1
İzmir
Karaburun
Alternative
2
Muğla
Merkez
Alternative
3
Antalya
Akseki
Alternative
4
Konya
Merkez
Alternative
5
Karaman
Merkez
Alternative
6
Mersin
Gülnar
Wind Speed (m/s) 0.75 0.57 0.75 0.57 0.75 0.64
Wind Capacity Factor (%) 0.71 0.54 0.58 0.54 0.67 0.58
(KWh/m^2-year)
0.38 0.54 0.63 0.46 0.54 0.54
Sunshine Hours (hours) 0.64 0.63 0.63 0.61 0.63 0.64
Bid
0.50 0.60 0.75 0.60 0.50 0.55
(MWh)
0.51 0.07 0.19 0.16 0.02 0.11
Operation and
Maintenance Cost
0.64 0.56 0.28 0.44 0.36 0.40
Concerns
0.28 0.36 0.32 0.60 0.60 0.44
condition
0.60 0.52 0.32 0.56 0.52 0.60

EM599_Final Report_AKÇ&MÖ_Final

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