A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran

Ocean & Coastal Management 109 (2015) 17e28

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A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran Abdolvahhab Fetanat a, *, Ehsan Khorasaninejad b a b

Department of Electrical Engineering, Behbahan Branch, Islamic Azad University, Behbahan, Iran Department of Mechanical Engineering, Behbahan Branch, Islamic Azad University, Behbahan, Iran

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 October 2014 Received in revised form 10 February 2015 Accepted 15 February 2015 Available online

The multi criteria decision making (MCDM) has been applied in Integrated Energy Planning (IEP) and Integrated Coastal Management (ICM) frameworks. In this paper, a novel hybrid MCDM approach based on the fuzzy analytic network process (ANP), fuzzy decision making trail and evaluation laboratory (DEMATEL) and fuzzy elimination and choice expressing the reality (ELECTRE) methodologies is applied to assist in the site selection of offshore wind farm (OWF) as the renewable energy in the IEP and the ICM frameworks. The aim of this study is to find the best site selection of offshore wind farm for four sites (alternatives) in Bandar Deylam on the Persian Gulf in southwest of Iran. Six criteria (depths and heights, environmental issues, proximity to facilities, economic aspects, resource technical levels and culture) and the related sub-criteria are considered to select proper sites for power station of OWF. The fuzzy ANP method is employed for standpoints of the site selection (goal) subject to the criteria and is performed the criteria subject to the sub-criteria. In addition, due to considering the influences of the criterion to another criterion, the fuzzy DEMATEL is employed in criteria and sub-criteria sections. Moreover, the fuzzy ELECTRE is applied to calculate the decision making matrices of sub-criteria to alternatives. The results show that the alternative A3 is the best site of OWF for Bandar Deylam. Then A2, A4 are the best alternatives and finally alternative A1 is the worst site. Also, a sensitivity analysis is performed to investigate the robustness of the outcomes of decision making by changing the priorities of the criteria. The results indicate the robustness of this method when the experts’ opinions subject to the criteria change. The evaluation criteria and this methodology could be applied to other coastal cities for promoting the progress of ICM towards the goal of sustainability. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Fuzzy ANP Fuzzy DEMATEL Fuzzy ELECTRE Multi criteria decision making Offshore wind farm

1. Introduction Off the coast of many countries lies a significant wind resource. A number of wind turbines as Offshore Wind Farm (OWF) have already been installed in offshore locations in order to gain wind power as renewable energy. The site location decisions are used in any field of the facility establishment and management like the Integrated Coastal Management (ICM). The ICM framework has been increasingly adopted in coastal cities of Iran. The multi criteria decision making (MCDM) has been applied in Integrated Energy Planning (IEP) and ICM frameworks. The correlations between ICM governance, coastal environmental, socioeconomic sustainability and IEP are analyzed using MCDM.

* Corresponding author. E-mail address: [email protected] (A. Fetanat). http://dx.doi.org/10.1016/j.ocecoaman.2015.02.005 0964-5691/© 2015 Elsevier Ltd. All rights reserved.

Despite different definitions, IEP and ICM are often seen as two parallel, complementary and strongly interlinked processes as both of the concepts emphasize the need for integrated approaches in decision making and resource management processes. Moreover, IEP and ICM are two suitable frameworks that emphasize the importance of the ecological component and also the social, economic and managerial elements of sustainability as well (Wongthong and Harvey, 2014). In the contribution of the IEP and the ICM frameworks, the site location of OWF is very important. There are many applications of MCDM methods in environmental planning and management (Ryu and et al., 2011; Wang et al., 2010). In addition, some studies have been published on the use of MCDM techniques in coastal management (Ryu and et al., 2011). These techniques have been used for various issues in coastal areas, such as evaluating the potential impacts of climate change on coastal zones by considering different scenarios, integration of information and the development of decision support systems for

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evaluating the current state of coastal areas and predicting future trends, as well as some new methods such as Analytic Network Process (ANP), fuzzy Analytic Hierarchy Process (AHP) and VIKOR (VIsekriterijumsko KOmpromisno Rangiranje) and (Pourebrahim et al., 2010, 2014). Due to the environmental benefits, technological advance of wind power and government incentives, the offshore wind industry has exponentially grown throughout the world in recent years. Lately, the focus of wind power developers and energy planners has shifted towards the coastal zone in an increasing number of countries such as Denmark, UK, Germany and etc (Ladenburg, 2009). Many works performed in the feasibility study of OWF installation in various countries with related their criteria (Pantaleo et al., 2005; Manwell et al., 2007; Schillings et al., 2012; O'Keeffe and Haggett, 2012; Wieczorek et al., 2013; SalcedoSanz et al., 2013; Reubens et al., 2014; Martín Mederos et al., 2011; Veigas and Iglesias, 2013; Da et al., 2011; Chen, 2011; Zhixin et al., 2009; Madariaga et al., 2012; Mostafaeipour, 2010; Oh et al., 2012; Lee et al., 2013; Islam et al., 2012; Mani and Dhingra, 2013). The feasibility of wind power development depends on multi criteria like; wind resources and, especially, constructability offshore. Therefore, wind resources are the most important criteria (factors) as the standard for site selection. The sea environment criteria such as sea depth and soil condition and the length of the transmission line are also important factors (Kim et al., 2013). Jacob Ladenburg €ller (Ladenburg and Mo €ller, 2011) investigated the and Bernd Mo effect of travel distance to the nearest offshore wind farm and the wind farms attributes on attitude towards offshore wind farms. Trinh Hoang Nguyen et al. (Nguyen et al., 2013) performed the development of a framework for data integration to optimize the remote operations of offshore wind farms. Moreover, they presented a proposal for solving the data integration problem in the form of a novel data integration framework. The framework consists of the semantic model, the data source handling, and the inlez et al., formation provisioning. J. Serrano Gonz alez et al. (Gonza 2013) presented a new methodology for designing the transmission system of large offshore wind farms under the presence of risk. The technology High Voltage Ac Current (HVAC) or High Voltage Dc Current (HVDC) e Voltage Source Converter (VSC), the voltage level of the system, the number and the size of transformers (or converter stations), the number and the size of the cables, amongst other factors are selected to achieve this aim. Barberis Negra et al. (Negra et al., 2006) investigated the total transmission losses of three transmission solutions including HVAC, HVDC Line Commutated Converter (LCC) and HVDC- VSC for large offshore wind farms. Vicente Negro et al. (Negro et al., 2014) analyzed some current uncertainties consist of the design of the transition piece and the difficulties for the soil properties characterization in the offshore wind market, with the aim of going one step further in the development of this sector. These identified uncertainties are related to the lifetime and return period, loads combination, scour phenomenon and its protection. The influence of three processes consists of dissipation of wave energy due to drag resistance on the offshore wind turbine foundation, the reflection/diffraction of waves, and the effect of reduced wind shear on the wave field in and around an offshore wind farm investigated by Erik Damgaard n et al. Christensen et al. (Christensen et al., 2013). S. Ponce de Leo  n et al., 2011) performed a numerical study of irregular waves (Leo in the HAVSUL-II (a wind farm located in the Norwegian continental shelf) using 3rd generation spectral wave models. They also investigated the effect of a single windmill monopile in the local incoming wave field using an empirical JONSWAP spectrum, and a wave hindcast study in the wind farm area using realistic incoming wave spectra obtained from large scale simulations for the 1991e1992 winter periods. Moreover, the effect of a single monopile on incident waves with realistic spectra was studied.

Merete Bruun Christiansen and Charlotte B. Hasager (Christiansen and Hasager, 2005) demonstrated the effect of large offshore wind farms on the local wind climate using satellite Synthetic Aperture Radar (SAR). The wake effect considering single, partial and multiple wakes inside a wind farm evaluated by Mikel de Prada Gil et al. (Gil et al., 2012) in different wind farm scenarios, depending on the incoming wind speed and the wind direction. Madariaga et al. (Madariaga et al., 2013) demonstrated the deferent topologies in electrical issues of offshore wind power plants. Jacob Ladenburg (Ladenburg, 2009) estimated the perception of visual impact of offshore wind farms across three different samples. The three samples were a national sample (Na-sample) representing the Danish population and two samples, each representing the population living close to two existing commercial offshore wind farms at Horns Rev (Hr-sample) and Nysted (Ny-sample). Moreover, the objective of this study was to test whether the perception of visual impacts vary systematically with regard to differences in prior experience. Jeremy Firestone and Willett Kempton (Firestone and Kempton, 2007) addressed public opinion regarding offshore wind power based on a survey of residents near a proposed development off Cape Cod, MA, USA. K.C. Tong (Tong, 1998) presented the technical and economic aspect of installing an offshore wind farm based on the FLOAT (an offshore floating wind turbine) concept. Ji-Young Kim et al. (Kim et al., 2013) performed a feasibility study to select the optimal site for an offshore wind farm around the Korean Peninsula. The expected B/C (benefit to cost) ratio, the possible installation capacity of the wind farm, the convenience of grid connection, and so on, for each candidate site were considered as set rating indices in order to select an optimal site of the candidate coasts. Rehana Perveen et al. (Perveen et al., 2014) highlighted the present scenario and challenges in development of offshore wind power. The challenges and opportunities that exist in the development stages of an offshore wind farm project, from exploration to erection and installation of wind turbines, construction of platforms, up to maintenance and de-commissioning, involving important technical aspects are addressed. A number of investigations conducted by some researchers in the design and economic assessment of a wind farm addressed and help improve decision making for planners and investors (Rehman et al., 2011; € ller, 2012; Ladenburg and Lutzeyer, 2012). Hong and Mo The aim of this research is to find the best site selection of offshore wind farm in Bandar Deylam on the Persian Gulf in southwest of Iran. To achieve this goal, this study applies a novel hybrid MCDM approach based on the fuzzy ANP, fuzzy decision DEMATEL and fuzzy ELECTRE methodologies. In order to select best sites for power station of OWF, six criteria and thirty-one related sub-criteria are considered. This paper is organized as follows. In Section 2, the proposed methodology is explained. The OWF, the criteria, the sub-criteria and case study are described in Section 3. In Section 4, results and discussion are demonstrated. The conclusions are summarized in Section 5. Finally, the details of the proposed model are shown in Appendixes A, B and C. 2. Proposed model This study presents a robust Decision Support System (DSS) as a hybrid analytic approach based on the fuzzy ANP, fuzzy DEMATEL and fuzzy ELECTRE methodologies for the site selection of OWF in the IEP and the ICM frameworks. A schematic of the novel decision making algorithm is shown in Fig. 1. The fuzzy ANP method is employed for standpoints of the site selection (goal) subject to the criteria and the criteria subject to the sub-criteria. And due to considering the influences of the criterion each others, the fuzzy DEMATEL is employed in criteria and sub-

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used to achieve the principal eigenvector of criteria and sub-criteria in the super matrix. The steps of the fuzzy ANP calculations are described in Appendix A (Lee et al., 2012). 2.3. Fuzzy DEMATEL method

Fig. 1. Structure of novel decision making algorithm.

criteria sections. Moreover, the fuzzy ELECTRE is applied to calculate of decision making matrices of sub-criteria to alternatives. The above approaches have been applied in a big matrix (so called the super matrix). The super matrix can be described as follows:

Goal Criteria Sub
criteia 3 2 1 0 0 Goal 4 w21 W22 0 5 Criteria W33 0 W32 Sub
criteria where w21 is the eigenvector of the aggregated comparison matrix for the goal into criteria, W32 is the sparse matrix include of eigenvectors of the aggregated comparison matrices of criteria into sub-criteria, W22 and W33 are the aggregated comparison matrix for effectiveness of criteria into each other and effectiveness of subcriteria into each other, respectively. Then a new matrix is generated by exponentiation of the final achieved super matrix. The vector of elements of goal column according to sub-criteria in the super matrix is weighed vector. This vector is applied in the fuzzy ELECTRE method. 2.1. Fuzzy set theory Due to incomplete, unavailable and non-measurable information, much of the decision making in the real world could not perform clearly. Fuzzy set theory as a mathematical method was first developed to solve these problems in 1965 by Zadeh (Zadeh, 1965). Fuzzy theory is more advantageous than traditional set theory for decision making when dealing with the vagueness expressions. Decision makers tend to decide according to their past experiences, knowledge. Therefore, their estimations are often a function of vagueness and linguistic terms. To integrate the experiences, opinions and ideas of decision makers, it is better to convert the linguistic estimation to fuzzy number. Hence, the need for fuzzy logic in decision making problems in the real world is introduced. Some essential definitions of fuzzy logic and linguistic terms are briefly described in Ref. (Lin and Wu, 2008). The linguistic-variable approach is usually employed by the decision makers to express their assessments, which is very helpful in dealing with uncertain, incomplete and unspecific situations in traditional quantitative expressions. Linguistic values can be represented with fuzzy numbers. In particular, the triangular fuzzy numbers are commonly used (Lin and Wu, 2008). 2.2. Fuzzy analytic network process (fuzzy ANP) method Analytic network process (ANP) is the general form of analytic hierarchy process (AHP) and first introduced by Saaty in 1996 which has been used in MCDM when there is an interrelationship between the decision levels. In this study, the fuzzy ANP method is

This technique was mainly created to evaluate the complex world problems at the end of 1971 (Asgharpour, 2011). This method is very helpful to solve the problems involving causal relationships between complex factors. In this research, the fuzzy DEMATEL method is used to achieve the principal the aggregated comparison matrix for influences of criteria into each other and influences of sub-criteria into each other. The steps of the fuzzy DEMATEL for€ zkanG, 2012). mulations are explained in Appendix B (Büyüko 2.4. Super matrix Formation In this step, the inner dependence matrix of criteria and subcriteria is substituted in the unweighted super matrix. To weight the super matrix, each weight in the column is divided by the sum of that column. Then, the normalized super matrix is raised to limiting powers to obtain a priority ranking for each of the alternatives. The weights of the sub-criteria in the “Goal” column are employed to be used in the fuzzy ELECTRE method. The sub-criteria weights denote the importance of each sub-criterion when synthesizing the scoring of the four sites against each of them. 2.5. Fuzzy ELECTRE method The ELECTRE was first proposed by Bernard Roy and his colleagues at SEMA consultancy company in 1965 (Benayoun et al., 1966). The ELECTRE method is based on the study of outranking relations and uses concordance and discordance indices to analyze the outranking relations between the alternatives. Concordance and discordance indices can be viewed as measurements of satisfaction and dissatisfaction that a decision maker chooses one alternative over the other (Kabak et al., 2012). The steps of the fuzzy ELECTRE calculations are illustrated in Appendix C (Arianejad and Safakish, 2009). 3. Offshore wind farms 3.1. Concepts Offshore winds are stronger and steadier than the onshore wind; hence, the OWFs employ to produce safe and clear energy. For instance, the velocity of wind at 10e15 m from shore is higher by 20e25 per cent and is a significant advantage. Moreover, as a result of being the lesser turbulence in offshore wind in comparison with the onshore wind, the fatigue loads on the turbine are reduced and their service life will be increased. Some of the other advantages of using OWFs are briefly presented as follows (Mathew, 2006):  Due to the lesser resistance to the wind flow on the sea, taller towers are not required for offshore farms.  Since OWFs are more environmentally acceptable, the land use, noise effect and visual impact may not be major concerns for planning approvals.  As opposed to the onshore turbines, which are made to run at tip speed ratios lower than the optimum to reduce the noise pollution, the offshore systems can be designed to operate at higher speed sometimes 10 percent higher resulting in better aerodynamic efficiency.

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3.2. Criteria and sub-criteria A clear understanding of the offshore conditions is essential for the design. Apart from the wind characteristics, the important criteria and their sub criteria for selecting the best sitting of OWF are considered as follows (Mostashari, 2011): ➢ Depth and height (C1): The condition or quality of being deep and the distance from the base of water to the top.  Shallow water (SC11): Wind turbines installed in shallow water offshore. As wave loadings in shallow waters are significantly lower at frequencies below 0.04 Hz and beyond 0.4 Hz, structures with their lowest natural frequency greater than 0.45e0.5 Hz are generally adopted for offshore applications. For deep-water applications, structures with natural frequencies in excess of 1 Hz are normally used (Vepa, 2013).  Wave heights (SC12): The effects of waves (wave heights of 5e10 m) need to be considered in the OWF design (Sulaiman et al., 2013). ➢ Environmental issues (C2):  Environmental impact (SC21): The criterion evaluates the renewable energy's damage on the quality of the environment.  Noise impact (Construction and Operation) (SC22): Noise is not a direct factor to destroy environment, but it can influence the people's work or life.  Proximity to migratory paths for birds (SC23): It is defined as the distance between OWF and the paths of the migration for births.  Proximity to migratory paths for marine life (SC24): It is defined as the distance between OWF and the paths of the migration for marine life. ➢ Proximity to facilities (C3):  Proximity to area of electric demand (SC31): It is defined as the distance between OWF and the consumption energy in the system.  Proximity to power transmission grid (SC32): It is defined as the distance between OWF and the power transmission grids (lines).  Proximity to facilities for construction (SC33): It is defined as the distance between OWF and the facilities for the construction.  Impact of navigation, aviation (SC34): This criterion evaluates adjacent OWF to boat, helicopter paths. ➢ Economic aspects (C4): economic are important in every aspect of our life. When trying to make a decision for the best site selection OWF, several costs should be taken into account.  Commercial feasibility (SC41): This criterion measures the secure of the finance of the possibility for implementation of the renewable energy.  Economic externalities (SC42): This means quality or condition of being external or directed toward outside or exterior, incidental situation that may affect a course of economic process and activity.  Local economic benefits (SC43): This criterion evaluates economic benefits for the people of the region.  Ratio of local benefits to impacts (SC44): It is defined as the ratio of the benefit energy to the impacts of energy in the OWF.  Ratio of power generation to impacts (SC45): It is defined as the ratio of the generated energy to the impacts of energy in the system.  Cost-benefit analysis (SC46): This criterion judges the proposed renewable energy alternative as economically by using one of the engineering economics techniques which is present

benefit/cost analysis (B/C). This criterion analyzes the total cost of the energy investment in order to be fully operational. ➢ Resource technical (C5): a resource, human or otherwise, that is used to facilitate or which enables technical solutions.  Wind resource availability (SC51): The criterion measures the availability of the wind resource alternative to decrease financial assets and reach the high performance.  Physical feasibility (SC52): This criterion measures the secure of the possibility for implementation of the renewable energy.  Multiple resource use conflict (SC53): This criterion indicates disagreements between the various resources of OWF.  Technical feasibility and adequate wind regime (SC54): This criterion includes an evaluation which is based on a qualitative comparison between the complexity of the considered technology, and the capacity of local actors to ensure an appropriate operating support for maintenance and installation of technology for OWF alternative.  Alternative sites review (SC55): To complete assessment, the various sites are analyzed.  Sufficient study times (SC56): The number of times tested successfully can be taken into account as a decision parameter. ➢ Culture (C6): culture refers to the cumulative deposit of knowledge, experience, beliefs, values, attitudes, meanings, hierarchies, religion, notions of time, roles, spatial relations, concepts of the universe, and material objects and possessions acquired by a group of people in the course of generations through individual and group striving.  Community acceptance (SC61): The criterion enhances consensus among social partners.  Nearby shoreline sparsely inhabited (SC62): It is defined as the distance OWF to the shoreline with high population.  Adequate consideration of alternative sites (SC63): It takes into account avoiding the reactions from special interest social groups for OWF alternatives.  Aesthetic considerations (SC64): It is defined as the view aspects of OWF for the people in the shoreline.  Collaborative process (SC65): This criterion evaluates the combined work for implementation of an OWF.  Mitigation of adverse local impacts (SC66): It is defined as the lessening opposed local impacts and the intensities of OWF.  Criteria defined by federal, state, local agencies and public (SC67): The criterion searches whether or not a consensus among leaders' opinions for proposed renewable energy alternative exists. It takes into account avoiding the reactions of the politicians and satisfying political leaders.  Collaborative sitting (SC68): A binary factor that shows meeting of experts for the implementation of the project.  Federal and/or state offshore development regulatory program in place (SC69): The criterion analyzes the integration of the national energy policy and the suggested renewable energy alternative. It measures the degree of objectives' convergence between the government policy and the suggested policy. The criterion also takes into account the government's support, the tendency of institutional actors, and the policy of public information.

3.3. Case study There are many installed wind turbines in suitable regions like Manjil and Binalood, but there has not been any offshore wind installation yet in Iran (Mostafaeipour, 2010). Four sites in Bandar Deylam on the Persian Gulf in the southwest of Iran are considered as alternatives for the site selection of OWF. These alternatives are Sajafi region (site 1 or Alternative 1 (A1)), Shah Abdollah region (site

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2 or Alternative 2 (A2)), Islamic Azad University region (site 3 or Alternative 3 (A3)) and Boveirat region (site 4) or Alternative 4 (A4)). Power consumption in this city is 40 MW. Site 4 is located in the south of the city center in 7 km distance and site 3 is located in the north of the city center in 3 km distance. Site 2 is located in the northwest of the site 3 in 10 km distance and Site 1 is located in the southwest of the site 2 in 40 km distance. Fig. 2 shows the geographical location of the proposed sites. Some characteristics of these sites are shown in Table 1. According to Table 1, the underwater lives and the marshland in site 1 is greater than sites 2, 3 and 4 (the underwater lives and the marshland of sites 2, 3 and 4 are same). The Sandy land in sites 2, 3 and 4 are greater than site 1. For the migration of birds and the tidal water, rank 1 is the greatest and rank 4 is the least. The wind blows from the West to the East and its characteristics for these sites are shown in Table 2. The wind resource availability (SC51) is a sub-criteria of the decision making. But according to Table 2, if the potential OWF sites are decided by just taking in to account the monthly average wind data, site 1 compared to other sites is the best. Then sites 2 and 3 are the best alternatives and finally site 4 is the worst site. The five experts in Iranian navigation and shipment organization are invited to contribute their expertise in the selection of the best offshore wind farm. The schematic structure of the network including the criteria, the sub-criteria and the alternatives is displayed in Fig. 3. The rounded arrows show the influences of criteria (or sub-criteria) subject to another criteria (or sub-criteria). 3.4. Interactions between the criteria and the sub-criteria The aim of the phase is to determine the network relationships among criteria in influence each other. A questionnaire was used to find out influential relations from each expert for ranking each criterion on the appropriate site with a five-point scale ranging from No to VH, representing from ‘No influence (No)’ to ‘Very high influence (VH) ’, respectively (According to Table B.1). With considering of complexity of this context, the interactions between the criteria and the sub-criteria of OWF or other ocean renewable energy could be studied in the future works. It's very essential to have an in-depth research about these. These assessments could be done with statistical or data mining analysis. For example interaction between culture and environment or between economic and environment aspects can be explained as follows: The relationship between society and environment is always an important topic which people pay much attention to. The structure and function of

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Table 1 Some characteristics of the proposed sites. Aspects

Site 1

Site 2

Site 3

Site 4

Traffic

Oil shipment Oily region e

e

e

Equal e 2

Equal e 1

Vehicle traffic Academic region Thorn forest Equal Yes 4

1

2

2

Equal Yes 3 (including wild geese) 2

e 1 2 1 ~20 m

e 2 1 4 ~10 m

2 2 1 2 ~10 m

1 2 1 3 ~22 m

~0.5e1 m ~9 km

~0.5 m ~4 km

~1 m ~1e3 km

~1.5e2 m ~4 km

Political culture Forest Water salinity Cove region Rank of birds migrations Rank of under water lives Rank of Fertile land Rank of Marsh land Rank of sandy land Rank of tidal water Shallow water at 1 km of onshore Wave height Distance from power network

Religious region e

e e

the ecosystem is sustained by synergistic feedbacks between human (culture) societies and their environment. The population growth (one of the culture of the people) in a certain area would be limited by the carrying capacity of the environment. The human subsystem, in turn, actively modifies its physical and biological environment; carrying capacity of an area may be decreased through the degradation of life-support systems, or increased by organizing differently or using new technology that works with the environment (Mitsch and Jogensen, 1989). Economic development and environmental protection are not the conflicting sides. Any change in the economic aspects will have impacts on the ocean and coastal environment and vice versa, whether positively or negatively, immediately or eventually. And in many cases, negative results are irreversible (Awan, 2013). An example for interrelationships between the criteria is shown in Fig. 4.

4. Results and discussion According to the appendix A, the priority vector and lmax of the criteria are calculated and shown as follows:

Fig. 2. The geographical location of the proposed site for the offshore wind facility and the proposed alternatives (www.Persiangulfstudies.co).

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Table 2 The characteristics of wind in the proposed sites. Month

Site 1 Mean speed (m/s)

1 2 3 4 5 6 7 8 9 10 11 12 Average

4.5 4 5 4.6 4.9 5.6 4.8 4.5 4.4 3.9 3.4 4.2 4.48

Site 2 Maximum speed (m/s)

Angle (degree)

12 10 12 13 10 14 10 11 10 9 13 10

210 330 130 140 150 310 310 300 300 310 340 330

Mean speed (m/s) 3.6 4.2 3.8 3.6 4.1 4.4 3.2 4.1 3.2 3.1 2.8 3.2 3.61

Site 3 Maximum speed (m/s)

Angle (degree)

9 11 12 11 12 11 9 10 11 10 9 8

130 160 280 160 320 300 190 210 300 120 150 280

Mean speed (m/s) 2.5 2.6 2.8 3.1 3.6 3.5 2.9 2.8 2.5 2.8 2.3 2.2 2.8

Site 4 Maximum speed (m/s)

Angle (degree)

8 12 12 10 11 10 8 8 10 10 8 9

120 150 290 150 330 310 180 230 280 110 170 300

Fig. 3. The criteria, sub-criteria and alternatives network for the case.

Mean speed (m/s) 2 2.7 3.2 3.1 3.2 3.9 3.1 2.7 2.7 2.4 1.8 2.6 2.78

Maximum speed (m/s)

Angle (degree)

8 8 10 15 14 9 9 7 8 9 8 7

320 130 160 180 120 310 280 250 220 280 130 110

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Fig. 4. The interrelationship between criteria.

CR ¼

CI 0:1139 ¼ ¼ 0:0918 RI 1:24

Since CR is less than 0.1, the experts’ judgments are consistent. If the consistency test fails, the experts are asked to fill out the specific part of the questionnaires again. Priority vectors for the importance of the sub-criteria with respect to the same upper-level criterion are calculated in a similar way. According to the Appendix B, the inner dependence matrix of the Criteria is:

Fig. 5. The causal diagram.

w21

3 2 C1 0:0283 7 C2 6 6 0:1629 7 7 0:2586 C3 6 7 6 ¼ 7 C4 6 6 0:4059 7 C5 4 0:0936 5 C6 0:0507

lmax ¼ 6:5693. The consistency test is performed by calculating CI and CR:

CI ¼

lmax
n 6:5693
6 ¼ ¼ 0:1139 n
1 6
1

Also, the causal diagram is shown in Fig. 5. Looking at this causal diagram, it is clear that evaluation factors are visually divided into two groups, the cause group including C1, C3and C5and the effect group including C2, C4and C6. The inner dependence matrix for the influence of the sub-criteria with respect to the same upper-level criterion is calculated in a similar way. The normalized super matrix is shown in Table 3. To calculate the weighted super matrix, the normalized super matrix is raised to the power 10000. According to this weighted super matrix, the weights of the sub-criteria in the ‘‘Goal’’ column are shown in Table 4. According to Appendix C, the overall dominance matrix (E) is determined as:

Table 3 The normalized super matrix.

Goal C1 C2 C3 C4 C5 C6 SC1 SC2 SC3 SC4 SC5 SC6

Goal

C1

C2

C3

C4

C5

C6

SC1

SC2

SC3

SC4

SC5

SC6

1

0

0

0

0

0

0

0 0 0 w32_4 0 0

0 0 0 0 w32_5 0

0 0 0 0 0 w32_6

0 0 0 0 0 0 0 W33_1 0 0 0 0 0

0 0 0 0 0 0 0 0 W33-2 0 0 0 0

0 0 0 0 0 0 0 0 0 W33-3 0 0 0

0 0 0 0 0 0 0 0 0 0 W33-4 0 0

0 0 0 0 0 0 0 0 0 0 0 W33-5 0

0 0 0 0 0 0 0 0 0 0 0 0 W33-6

w21

0 0 0 0 0 0

W22

w32_1 0 0 0 0 0

0 w32_2 0 0 0 0

0 0 w32_3 0 0 0

wsub ¼ ½ 0:0582 0:0838 0:0331 0:0412 0:0370 0:0372 0:0473 0:0581 0:0581 0:0816 0:0326 0:0397 0:0356 0:0310 0:0251 0:0333 0:0251 0:0270 0:0196 0:0310 0:0292 0:0165 0:0110 0:0112 0:0108 0:0136 0:0141 0:0145 0:0131 0:0150 0:0154

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Table 4 The weights of the sub-criteria in the ‘‘Goal’’ column.

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Fig. 6. The diagram of overall dominance matrix (E).

2


61 6 E¼4 1 1

0
1 0

0 0
0

3 0 07 7 15


The overall dominance matrix (E) can be indicated by the diagram as follows: Fig. 6 shows that the alternative A3 is the best site of OWF for Bandar Deylam. Then A2, A4 are the best alternatives and finally alternative A1 is the worst site (in other words A3 > A2, A4 > A1). In order to investigate the robustness of the outcomes of decision making, a sensitivity analysis is performed next by changing the priorities of the criteria. Table 5 indicate the sensitivity analysis when the priority of depths and heights (C1), environmental issues (C2), proximity to facilities (C3), economic aspects (C4), resource technical levels (C5) and culture (C6) changes, respectively. Depending on the changes of the priorities of the criteria, the best site of OWF may change as a result. As shown in Table 5, no matter how much the priority of C1 to C6 changes, the ranking of the four alternatives remains the same. Therefore, the current solution (A3 > A2, A4 > A1) is rather robust. 5. Conclusions The Integrated Coastal Management (ICM) framework has been increasingly adopted in coastal cities of Iran. The correlations between ICM governance, coastal environmental, socioeconomic sustainability and Integrated Energy Planning (IEP) are analyzed using MCDM. The IEP and the ICM are two suitable frameworks that emphasize the importance of the ecological component and also the social, economic and managerial elements of sustainability as well. Development of offshore wind farms (OWFs) in different countries has become widely understood as being essential to achieve the national target for the renewable energy. Decision makers look forward to a causal analytical method which can do with the group decision making problem in the fuzzy environments of renewable energy systems. In the contribution of the IEP and the ICM frameworks, Site location of OWF is very important. A case study is carried out for four sites in Bandar Deylam on the Persian Gulf in the southwest of Iran. Six criteria (Depths and Heights, Environmental Issues, Proximity to Facilities, Economic Aspects, Resource Technical levels and Culture) and the related sub-criteria are considered. Then we have combined Fuzzy ANP, fuzzy DEMATEL and fuzzy ELECTRE approaches to develop a novel robust decision support system (DSS). This combination used in this study offered a more precise and accurate analysis by integrating interdependent relationships within and among a set of criteria. Moreover, the fuzzy ELECTE method helped to choose the alternative for ideal solution of site location for OWFs efficiently. As a result, we hope that DSS will help future innovation improvements to be more practical, efficient in the site location for the renewable energy systems. The evaluation criteria and this methodology could be applied to other coastal cities for promoting the progress of ICM towards the goal of sustainability.

A. Fetanat, E. Khorasaninejad / Ocean & Coastal Management 109 (2015) 17e28

25

Table 5 The priority changes in C1, C2, C3, C4, C5, C6 and the ranking of alternatives. Criteria and Ranking

Changes of Criteria Subject to Priority Vector and Ranking

C1 Ranking C2 Ranking C3 Ranking C4 Ranking C5 Ranking C6 Ranking

0.0211 A3 > A2, 0.1358 A3 > A2, 0.2298 A3 > A2, 0.3240 A3 > A2, 0.0716 A3 > A2, 0.0361 A3 > A2,

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

0.0220 A3 > A2, 0.1550 A3 > A2, 0.2429 A3 > A2, 0.3507 A3 > A2, 0.0723 A3 > A2, 0.0404 A3 > A2,

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

0.0244 A3 > A2, 0.1604 A3 > A2, 0.2539 A3 > A2, 0.3724 A3 > A2, 0.0829 A3 > A2, 0.0417 A3 > A2,

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

Acknowledgement The authors acknowledge financial support from Islamic Azad University grant in Behbahan branch. Appendix A. Fuzzy ANP A systematic Fuzzy ANP model is proposed here and the offered steps are as follows: Step 1. Form the super matrix by determining the criteria and sub-criteria in the whole system. Step 2. Compare the criteria/and sub-criteria with each other by a committee of experts with N members. This is done through pairwise comparisons by asking ‘‘How much importance does a criterion/or sub-criterion have compared to another criterion/or sub-criterion with respect to our interests or preferences?” The relative importance value is determined using a Fuzzy number as listed in Table A.1 to represent equal importance to extreme importance. Table A1 The fuzzy linguistic scale for ANP method. Linguistic term

Abbreviation

Triangular fuzzy numbers

Equal Very little high Little high Moderately High High Very high Very big high Extremely high

E VLH LH MH H VH VBH EH

(1, 1, 1.5) (1.5, 2.5, 3.5) (2.5, 3.5, 4.5) (3.5, 4.5, 5.5) (4.5, 5.5, 6.5) (5.5, 6.5, 7.5) (6.5, 7.5, 8.5) (7.5, 8.5, 9)

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N N N N PN B ¼ P B ¼ PN ; m ; u i;j i;j ijk ijk k¼1 k¼1 k¼1 Bijk

(A.1)

whereBijk is the pairwise comparison value between criterion i and criterion j determined by expert k. Step 4. Defuzzify each fuzzy number into a crisp number using Yager ranking method as follows (Asgharpour, 2011):

Z1 Mi;j ¼ 0

 u   li;j þ 2mi;j þ ui;j 1  ~ l ~ da ¼ M i;j þ M i;j a a 2 4

Changes of Criteria Subject to Priority Vector and Ranking

0.0283 A3 > A2, 0.1629 A3 > A2, 0.2586 A3 > A2, 0.4059 A3 > A2, 0.0936 A3 > A2, 0.0507 A3 > A2,

0.0339 A3 > A2, 0.1633 A3 > A2, 0.2623 A3 > A2, 0.4167 A3 > A2, 0.1006 A3 > A2, 0.0572 A3 > A2,

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

0.0424 A3 > A2, 0.1825 A3 > A2, 0.2703 A3 > A2, 0.4358 A3 > A2, 0.1121 A3 > A2, 0.0596 A3 > A2,

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

0.0573 A3 > A2, 0.2170 A3 > A2, 0.2949 A3 > A2, 0.4455 A3 > A2, 0.1272 A3 > A2, 0.0624 A3 > A2,

A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1 A4 > A1

~ is the triangular fuzzy number. whereM Step 5. Determine the priorities of the criteria, sub-criteria. In the other words, the eigenvector (divide the geometric average in each row into sum of all elements in column of geometric average) and the largest eigenvalue of the aggregated comparison matrix for the criteria and sub-criteria are obtained as follows:

W  w ¼ lmax  w

(A.3)

where Wis the aggregated comparison matrix, w is the eigenvector and lmax is the largest eigenvalue of W. Step 6. Examine the consistency property of the aggregated comparison matrices. The consistency index (CI) and consistency ratio (CR) are defined as follows (Lee et al., 2012):

CI ¼

lmax
n CI CR ¼ n
1 RI

(A.4)

where n is the number of items being compared in the pairwise comparison matrix and RI is a random index and its values are given in Table A.2. When the calculated value of CR becomes greater than the threshold, the committee of experts must revise the part of the questionnaire and the steps 1e6 are repeated again.

Table A2 The value of random index.

Step 3. Employ geometric average approach to aggregate experts’ responses and calculate triangular fuzzy numbers. For example, the triangular fuzzy number for the relative importance between criterion i and criterion j is calculated as follows:

li;j ¼

Priority Vector and Ranking

(A.2)

Size of matrix

22

33

44

55

66

RI

0

0.58

0.9

1.12

1.24

Appendix B. Fuzzy DEMATEL A systematic Fuzzy DEMATEL model is proposed here and the offered steps are as follows: Step 1. Compare the influences of criteria/or sub-criteria with each other by a committee of experts with N members. This is done through pairwise comparisons by asking ‘‘How much influence does a criterion/or sub-criterion have compared to another criterion/or sub-criterion with respect to our interests or preferences?” The relative influence value is determined using a Fuzzy number as listed in Table B.1 to represent no influence to extreme influence. Step 2. Employ arithmetic average approach to aggregate experts’ responses and calculate triangular fuzzy numbers (is called the fuzzy initial direct relation matrix(Ze)). For example, the triangular fuzzy number for the relative influences between criterion i and criterion j is calculated as follows:

26

A. Fetanat, E. Khorasaninejad / Ocean & Coastal Management 109 (2015) 17e28

Table B1 The fuzzy linguistic scale for DEMATEL method. Linguistic term

Abbreviation

Triangular fuzzy numbers

No influence Very low influence Low influence High influence Very high influence

No VL L H VH

(0, 0, 0.25) (0, 0.25, 0.5) (0.25, 0.5, 0.75) (0.5, 0.75, 1) (0.75, 1, 1)

l0 i;j ¼

k¼1

(B.1)

k¼1

nC X  ~ ¼sZ ~ s ¼ 1 max1inC u0 ij Y

(B.2)

j¼1

~ ¼ ðl0 ; m0 ; u0 Þ are extracted to three crisp 1 The elements of Y ij ij ij ij matrices as follows:

6 l0 6 21 Y1 ¼ 6 4 «

l0 12

/

0

/

l0 1n

3

l0 2n 7 7 7; « 5

l0 n1 l0 n2 / 0 3 2 0 m0 12 / m0 1n 0 6 m0 6 u0 0 / m0 2n 7 7 6 21 6 21 Y2 ¼ 6 7; Y3 ¼ 6 4 « 4 « « 5 2

m0 n1

m0 n2

i ¼ 1; 2; :::; nC

(B.6)

nC X

tij

j ¼ 1; 2; :::; nC

(B.7)

where Di and Rj denote the sum of rows and the sum of columns, respectively. Then Di represents total direct and indirect effects that the factor i has given to other factors and Rj shows the total effects, both direct and indirect received by a factor j given by other factors. A causal diagram can be acquired by mapping the dataset of (Di þ Rj¼i, Di
Rj¼i), where the horizontal axis (Di þ Rj¼i) is made by adding Rj¼i to Di, and the vertical axis (Di- Rj¼i) is made by subtracting Rj¼i from Di. When i ¼ j, then the term (Di þ Rj) represents the degree of importance of the factor, and the term (Di e Rj) represents the net effect that the factor contributes to the system in relation to other factors. If the term (Di e Rj) is positive, the factor i is net causer, and if the previous expression is negative factor i is a net receiver (Tzeng et al., 2007). Appendix C. Fuzzy ELECTRE

where nC is the number of the criteria/or sub-criteria in the pairwise comparison matrix. Step 4. Determine the fuzzy total-relation matrix by using the following way:

0

tij

i¼1

where B0 ijk is the relative influence value between criterion i and criterion j determined by expert k. Step 3. Normalize the fuzzy initial direct relation matrix by the following equations:

2

nC X j¼1

Rj ¼

N N N 1 X 1 X 1 X B0 ijk ; m0 i;j ¼ B0 ijk ; u0 i;j ¼ B0 ijk N N N k¼1

Di ¼

/

0

u0 n1

u0 12 0

/ /

u0 n2

/

A systematic Fuzzy ELECTRE model is proposed here and the offered steps are as follows: Step 1. Compare the ranking of malt alternatives based on the characteristic of nsub sub-criteria together to assist in selecting the best alternative by a committee of experts with N members. This is done through pairwise comparisons by asking ‘‘How much is an alternative compared to other alternative more preferable than each sub-criterion?” The relative preference value is determined using a Fuzzy number as listed in Table C.1 to represent none to excellent. The fuzzy decision matrix for malt alternatives and nsub sub-criteria is shown in Fig. C. 1.

3 u0 1n u0 2n 7 7 7 « 5 0 (B.3)

2 The total-relation fuzzy matrix T~ is defined as follows

T1 ¼ Y1 ðI
Y1 Þ
1 ;

T2 ¼ Y2 ðI
Y2 Þ
1 ;

T3 ¼ Y3 ðI
Y3 Þ
1

Table C1 The fuzzy linguistic scale for ELECTRE method.

T~

¼ ðT1 ; T2 ; T3 Þ (B.4) Step 5. Defuzzify the total-relation fuzzy matrix T~ into a crisp number using Yager ranking method to obtain the inner dependence matrix. Step 6: Calculate the sum of rows and the sum of columns through the following formulas (WuWW, 2012).

 T ¼ tij ;

i; j ¼ 1; 2; :::; nC

Fig. C.1. The fuzzy decision matrix for problem.

(B.5)

Linguistic term

Abbreviation

Triangular fuzzy numbers

None Very low Low Fairly low More or less low Medium More or less good Fairly good Good Very good Excellent

N VL L FL ML M MG FG G VG E

(0, 0, 0.1) (0, 0.1, 0.2) (0.1, 0.2, 0.3) (0.2, 0.3, 0.4) (0.3, 0.4, 0.5) (0.4, 0.5, 0.6) (0.5, 0.6, 0.7) (0.6, 0.7, 0.8) (0.7, 0.8, 0.9) (0.8, 0.9, 1) (0.9, 1, 1)

~ represents the fuzzy decision matrix with alternatives (Ai, where D i¼1,2, …, malt)and sub-criteria (SCj, j¼1,2, …, nsub). Step 2. Employ arithmetic average approach to aggregate

A. Fetanat, E. Khorasaninejad / Ocean & Coastal Management 109 (2015) 17e28

experts’ responses and calculate triangular fuzzy numbers. Step 3. Defuzzify each fuzzy number into a crisp number using Yager ranking method. Step 4. Normalize the aggregate decision matrix as follows:

d¼

rij normij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pmalt 2 r i¼1 ij

DISpq  d / Gpq ¼ 1 DISpq > d / Gpq ¼ 0

i ¼ 1; 2; ::: ; malt

and

j

¼ 1; 2; ::: ; nsub :

(C.1)

malt X malt X p¼1 q¼1

, DISpq m ðm
1Þ alt alt

27

(C.9)

(C.10)

Step 9. Determine the overall dominance matrix by the following equation:

Step 5. Calculate the weighted normalized decision matrix by the following equation:

Epq ¼ Fpq  Gpq

nij ¼ normij : wsubj

The matrix E indicates the relative sequence alternatives. If Epq ¼ 1 then Ap outranks Aq. Therefore, the following values show that Ap is a the effective alternative.

i ¼ 1; 2; :::; malt ;

j ¼ 1; 2; :::; nsub

(C.2)

where nij is the weighted normalized value and wsubj is the weight of the jth sub-criterion obtained from the first column of super matrix according to Section 2.4 and (.) is matrices dot product. Step 6. Determine the concordance and discordance sets. For each pair of alternatives Ap and Aq (p, q ¼ 1, 2, …, malt and p s q) the set of sub-criteria are divided into two distinct subsets. If alternative Ap is preferred to Aq for all sub-criteria, the concordance set is composed. This can be written as:

 CONðp; qÞ ¼ jnpj > nqi

(C.3)

wherenpj is the weighted normalized rating of alternative Ap with respect to the jth sub-criterion. In other words, CON(p, q) is the collection of attributes where Ap is better than or equal to Aq. The complement of CON(p, q), the discordance set, contains all subcriteria for which Ap is worse than Aq. This can be written as:

 DISðp; qÞ ¼ jnpj < nqi

(C.4)

Step 7. Calculate the concordance and discordance indices. The concordance index of CON(p, q) is defined as:

CONpq ¼

X

wsubj

(C.5)

j2Conðp;qÞ

where j are attributes contained in the concordance set CON(p, q). The discordance index DIS(p, q) represents the degree of disagreement in Ap /Aq and can be defined as:

DISpq ¼

  Maxj* 2DISðp;qÞ npj*
nqj*    Maxj2J npj
nqj 

(C.6)

where j* are indices of attributes contained in the discordance set DIS(p, q), vij is the weighted normalized evaluation of alternative i on sub-criterion j and J is set of the indices of sub-criteria. Step 8. Calculate the concordance and discordance dominance matrices. The concordance dominance matrix F(p, q) is obtained based on the Boolean matrix and the threshold value CON. The threshold value CON can be defined as

CON ¼

malt X malt X

, CONpq

malt ðmalt
1Þ

(C.7)

p¼1 q¼1

CONpq  CON / Fpq ¼ 1 CONpq < CON / Fpq ¼ 0

(C.8)

Also, the discordance dominance matrix G(p, q) is obtained based on the Boolean matrix and the threshold value d.The threshold value d can be defined as:

Epq ¼ 1 ; Eip ¼ 0 ;

psq; dq ¼ 1; 2; :::; malt isp; isq; ci ¼ 1; 2; :::; malt

(C.11)

(C.12)

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