Determining the Optimal Offshore Wind Power Station Using a Two-Stage MCDM-Based Spherical Fuzzy Set Approach

In response to challenges from the COVID-19 pandemic and climate change to achieve the goal of 10 ensuring sustainable economic growth, offshore wind power development not only provides a clean and sus11 tainable source of energy but also provides opportunities for economic growth and job creation. Offshore wind 12 energy projects have been promptly suggested in Vietnam as a result of policy advancement, with the country's 13 excellent wind resources. The success of an offshore wind energy project is decided mainly by choosing the 14 best location for offshore wind power station (OWPS) construction, which is a complex multicriteria decision15 making (MCDM) problem with the coexistence of conflicting factors. There is a problem with incomplete 16 decision information use and information loss during the decision-making process, and it is easy to overlook 17 the interaction difficulty in a fuzzy environment. To address the complex nature of the prioritization problem 18 posed, this study proposes a hybrid MCDM framework combining the spherical fuzzy analytical hierarchy 19 process (SF-AHP) and weighted aggregated sum product assessment (WASPAS). SF-AHP is used in the first 20 stage to determine the significance levels of OWPS evaluation criteria. WASPAS is then utilized to rank lo21 cations of OWPS. A comprehensive set of evaluation criteria developed based on the concept of sustainable 22 development has been recognized by reviewing the literature review and interviewing experts to practice the 23 two-stage MCDM model. A real case study for Vietnam is conducted to test the effectiveness of the proposed 24 method. The best location schemes have been determined by using the decision framework. The results of the 25 sensitivity analysis and a comparison analysis demonstrate that the decision framework is practical and robust. 26 Ultimately, the evaluation criteria and methodology presented in this work can serve as a theoretical founda27 tion for the advancement of offshore wind energy and coastal development. 28


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Among various renewable energy sources, offshore wind is key to the transition to a zero- proven to have more robust consistency and accuracy than the WPM and WSM. It also performs 109 more accurately than independent methods in alternative ranking 39 . Ever since this aggregated 110 approach appeared, a plethora of studies can be found using WASPAS in various areas. In 2013, 111 Zolfani et al. 40 applied WASPAS to business issues with a case study of selecting the best place 112 for shopping malls located from a foresight perspective; Zavadskas et al. 41  to select the best online food delivery companies; Wang et al. 47 used the method to evaluate sus-120 tainable last-mile delivery for e-commerce companies. 121 The remainder of this paper is structured as follows. Section 2 summarizes MCDM method-122 ologies applied to the site selection of OWPS and critical evaluation criteria used in the studies. In 123 Section 3, the implementation of the proposed hybrid methodology is explained in detail. In Section 124 4, the OWPS case study analysis in Vietnam is demonstrated, and then the results validation is 125 conducted in Section 5. Section 6 contains concluding remarks.  The best wind sites are determined by market design, regulatory considerations, and renewable integration targets.

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Mahdy and Bahaj (2018) AHP Egypt The established methodology is universal to produce offshore wind suitability map for appropriate offshore wind locations, with three high wind suitable areas around the Red Sea found with the minimum restrictions.
In this paper, for the first time, spherical fuzzy sets, AHP and WASPAS, are combined for the 163 site selection of OWPS. To the best of our knowledge, the proposed integrated approach is novel 164 and has not been reported elsewhere. The paper's contributions are presented as follows:    where ̃ represents a spherical fuzzy set of the universe : der the condition , 1 , 2 > 0: , the SWGM is calculated as follows: In this paper, the SF-AHP model was used to determine the criteria weights of the list of 218 criteria for building the power plant of wind offshore with a case study in Vietnam. The SF-AHP 219 model has five steps, which are described as follows.

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Step 1: A hierarchical decision tree is divided into three levels, including the research goal

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Step 2: Pairwise comparison matrices are performed regarding linguistic terms, as shown in 224   Table 3. The score indices (SI) are determined by Equations (18) and (19): for the AMI, VHI, HI, SMI, and EI.
for the EI, SLI, LI, VLI, and ALI. Step 3: A consistency check is required for pairwise comparison matrices by the consistency 229 ratio (CR), where the CR must be less than 10%.

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Step 4: Compute the criterion and alternative spherical fuzzy weights. Determine the weight 231 of each alternative using the SWAM operator using Equation (20): where = 1/ .

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Step 5: The final ranking orders for the alternatives are estimated using the defuzzification 234 global weights in Equation (21): Normalize the criteria weights using Equation (22) and apply the spherical fuzzy multiplica-236 tion shown in Equation (23): The final SF-AHP score (̃) for each alternative is obtained by carrying out spherical 238 fuzzy arithmetic addition over each global preference weight, as given in Equation (24): The second way to follow is to continue without defuzzification. In this case, spherical fuzzy 240 global preference weights are calculated using Equation (25): Sort the alternative according to their defuzzified final ratings. The highest value denotes the 242 optimal option.

Weighted Aggregated Sum Product Assessment (WASPAS) 244
The WASPAS method was proposed in 2012 39 and is the combination of the weighted prod-245 uct model (WPM) and weighted sum model (WSM); the procedure is explained as follows:

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Step 1: A decision matrix is constructed = [ ] × , where is the performance of the 247 ℎ alternative to the ℎ criterion, is the number of alternatives and is the number of criteria.

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Step 2: Equations (26) and (27) For minimizing criteria (nonbenefit): Step 3: Equation (28) is used to calculate the relative importance of the alternative using the 252 weighted sum model (WSM): where is the weight (relative importance) of the ℎ criterion.

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Step 4: The relative importance of the alternative is then calculated using the weighted product 255 model (WPM), as shown in Equation (29): where is the weight (relative importance) of the ℎ criterion. In this paper, is obtained 257 from SF-AHP model.

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Step 5: The integrated utility function of the WASPAS model is calculated using Equation

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(30): The value of (coefficient value or threshold value of the WASPAS model) is determined 261 using Equation (31):   (Table 4). In addition to reviewing the literature for this evaluation, the criteria  Table 5. The distance between OWPS and marine life migration determines the degree of coordination with sea area planning for marine life. Depending on the geographical context, the generator machine's selection and installation would disrupt the original seabed during construction. At the same time, the turbine would generate noise pollution throughout its rotation, resulting in low-frequency sound waves that would be harmful to marine species engaged in predation or migratory behaviors.

C22. Nautical environmental influence
The potential for OWPS to degrade the quality of the marine ecology and biodiversity.
C3. Construction and maintenance conditions C31. Seawater depth The suitability of OWPS building also takes into account the depth of the sea, the distance from the coast, and the width of the shore.

C32. Undersea geological conditions
This criterion assesses regional geological conditions and construction stability based on acquired data and geological prospecting.

C33. Marine conditions
Characteristics of the sea area like waves, tidal current, temperature, storm surge, sea ice, sea bed movement, and erosion must be considered when evaluating the hazard of complex hydrological conditions on project safety.

C41. Employment
The related manufacturing and service industries would grow with the project's development, and various possible job incentives would surface one after another when determining a construction location for OWPS. As a result, it is required to use employment to assess the impact, such as which station sites affect salary, relevant industries, etc. Knowing the position of the staff, the work environment, and other factors might have an impact on employment.

C42. Policy planning
The central government's and local governments' support and promote wind farm construction; this criterion also considers if necessary legislation and policies have been implemented to encourage offshore wind projects.
C5. Conditions onshore C51. Distance from the power load center The distance between the area and the electrical load center is the distance over which electricity is transmitted from the power station to the shore (submarine cable).

C52. Electrical transmission and distribution system
The electrical system's capacity to meet future power supply requirements (e.g., substation, electrical grid).
C53. Traffic condition Examines the ease with which huge equipment can be transported along the shore (e.g., highway, railway, bridge, airport, dock).
C6. Economic impact C61. Cost-to-benefit ratio Typically, the offshore wind power profit and loss balance is utilized in estimations.

C62. Construction, operation, and maintenance costs
This criterion shows the total cost of the OWPS projects, from conception to completion and delivery in its final form, and all operating and maintenance expenditures in the surrounding area after the offshore wind farm is fully operational.

C63. Provincial financial subsidies
Relates to the subsidies promoted by the local government finance

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In this stage, an example of the following calculation of the six main criteria presents the SF-  Following that, the integrated spherical fuzzy comparison matrix is calculated in Table 9.

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Then, the obtained spherical fuzzy weights of each criterion were calculated and are shown in Table   304 10. For explanation, the following calculation was presented for the spherical fuzzy weights of     Figure 5. The influence level of criteria of the SF-AHP model.

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To demonstrate the robustness and stability of the proposed MCDM model, a sensitivity anal-369 ysis is conducted for the parameters including the preference coefficient and the index weights 53 .

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First, a sensitivity analysis of the preference coefficient (i.e., the threshold value of the 371 WASPAS model, ) is conducted to validate the ranking order 54 . In a previous relevant study, the 372 value of λ was considered to be 0.5 ( = 0.5) for base case analysis. However, this setting does not 373 reflect the actual scenario in which various decision-makers have different preferences. Hence, in 374 this paper, the preference coefficient of the WASPAS model fluctuates in the range of ( = 375 0, 0.1, … , 1), as shown in Table 13. The change result is visualized in Figure 9. The ranking result 376 shows that the optimal location for building the offshore wind station is always the same when  The comparison of four kinds of ranking methods is shown in Table 14 and visualized in 411 Figure 11. The comparison shows that the ranking of the offshore wind location has given the same   Figure 11. Ranking results of compared methods.