CRITIC-CoCoSo Model Application in Hybrid Solar-Wind Energy Plant Location Selection Problem: A Case Study in Vietnam

1  Introduction

Fossil fuels, including oil, gas, and coal have dominated global energy supply in the last century. However, the rapid consumption of these resources is leading to depletion, and their extraction and use have significant environmental impacts. These challenges are driving countries to transition towards clean, sustainable energy, making the development of renewable sources an inevitable trend. As countries globally pivot towards sustainable energy sources, there’s been a marked increase in the production capacity of renewable energy (Fig. 1). This shift goes beyond the adoption of greener energy; it reflects a broader transformation in our societies and economies.

Figure 1: World renewable power energy capacity (in MW) [4]

Vietnam is emerging as a powerhouse in the sustainable energy sector, thanks to its rich reserves of renewable resources with increasing production of renewable energy over the years (Fig. 2). The country owns a vast array of renewable energy sources, including but not limited to hydropower, wind, solar, and biomass energy [1]. Vietnam’s solar energy potential is particularly noteworthy, with market projections estimating its capacity to be between 260–280 TWh per year [2]. The country’s advantageous solar energy prospects are bolstered by an even distribution of sunlight throughout the year, making it an ideal candidate for solar energy projects [3].

Figure 2: Power generation structure of Vietnam’s power system 2010–2023 (in MWh) [8]

Beyond solar power, Vietnam’s renewable energy spectrum also has abundant of potential in wind power with a technical capacity potential estimated at 26,763 GW [5] with offshore wind power potential is over 600 GW [6]. This significant potential not only opens doors for economic growth but also strengthens Vietnam’s energy security, showcasing the nation’s vibrant and diverse renewable energy landscape.

However, integrating renewable energy sources into the current national power grid has been challenging due to the inconsistency in their output. To address this issue, hybrid energy systems are being considered to enhance the stability of renewable energy production. By combining two complementary energy sources, such as solar and wind, a hybrid renewable energy model can provide a more stable and continuous power supply. Given Vietnam’s abundant solar and wind resources, this approach offers a promising solution for optimizing energy generation [7].

Selecting the location of a renewable energy plant is identified as an important issue that can affect the performance of renewable energy projects. The most widely applied model in this regard is the MCDM model [9–12].

This research will focus on the development of a MCDM model to assist the location selection process of a potential solar-wind hybrid energy plant in Vietnam using Criteria Importance Through Intercriteria Correlation (CRITIC) and Combined Compromise Solution (CoCoSo) methods. CRITIC method is implemented to assess the importance of each criterion based on two main factors: the variability of the data (the dispersion of criterion values among choices) and the degree of correlation between criteria (measuring the level of independent information between them). Thus, CRITIC provides an objective approach to quantify the information value of each criterion, helping to improve the quality and transparency of the final decision. CoCoSo focuses on finding a balanced solution between conflicting or complementary criteria to achieve an optimal or near-optimal result. It helps decision-makers comprehensively evaluate options based on a diverse set of criteria, thereby identifying the most suitable solution for their specific goals and requirements. The main purpose of the CoCoSo method is to provide a multi-criteria decision-making technique to determine and evaluate optimal choices or solutions based on a range of evaluation criteria [13].

The study aims to apply decision-making methodologies to evaluate and prioritize possible sites for a future solar-wind energy facility, emphasizing the robustness, and practical applicability of the results within the context of Vietnam’s renewable energy industry. While MCDM methods have been extensively applied in renewable energy planning, there remains a gap in their application to hybrid solar-wind energy plant location selection in the Vietnam. Furthermore, few studies in this field employ the CRITIC and CoCoSo methods in combination, which offer a more balanced and data-driven approach to decision-making. This gap is especially pronounced in Vietnam, where limited research has been conducted on adapting MCDM models to the country’s specific geographical and environmental conditions.

The structure of the rest of the paper is as follows: Section 2 offers an extensive review of relevant literature. Section 3 details the methodologies employed, while Section 4 showcases a real-world case study to illustrate the viability of the approach proposed. Finally, conclusions are drawn in Section 6.

2  Literature Review

Applications of MCDM span various industries, including supply chain management [14], engergy managment [15], sustainability research [16–18], risk management [19,20], information technology [21,22], and healthcare [23]. MCDM methods have been employed across a broad spectrum of applications, from selecting alternative marine fuel technology [24], construction cost optimization [25], optimizing distribution center locations [26], and material assessment and selection [27]. These applications demonstrate the versatility and effectiveness of MCDM in addressing complex decision-making processes across diverse sectors.

MCDM methods are also widely used in the development of renewable energy projects, commonly applied in energy planning, sustainability assessment, and the ranking of renewable energy sources [28]. The use of MCDM techniques in renewable energy development projects also assists in decision-making processes from supplier selection for a particular project [29] to the evaluation of a suitable national sustainable energy strategy [30]. Researchers have emphasized the importance of MCDM methods in addressing energy sustainability issues and have highlighted their practical utility in solving such problems [31] and interest in the application of MCDM techniques in renewable energy have been increasing in recent years [32–35]. Within the expansive array of decision-making challenges encountered by renewable energy projects, the selection of a location stands out as a critical factor due to its direct influence on the project’s potential performance. Moreover, location selection issues often encompass a multitude of evaluation criteria, encompassing both quantitative and qualitative dimensions. In response to these complex challenges, recent years have witnessed the development of numerous MCDM models. These models have significantly contributed to a growing body of literature dedicated to addressing such multifaceted issues.

Wang et al. [36] conduct a study on the development of a Fuzzy MCDM model for solar power plant location selection in Vietnam. The research highlighted the use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchy Process (AHP) methodologies in evaluating and selecting suitable locations for solar power plants. The study provided insights into the decision-making process involved in renewable energy project location selection, emphasizing the importance of considering multiple sustainable criteria and objectives. Nuriyev [37] explores the efficiency and applicability of a unified approach based on Fuzzy logic, Z-information, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The study focuses on renewable energy resources and plant location selection, emphasizing the integration of these methodologies to streamline decision-making processes. By utilizing fuzzy logic and TOPSIS, the research aims to enhance the robustness and effectiveness of selecting optimal renewable energy resources and plant locations. Solangi et al. [38] conduct a study on the selection of wind power project locations in the southeastern corridor of Pakistan. The research utilized Factor Analysis, AHP, and Fuzzy TOPSIS to prioritize sites in the region. The research findings emphasize the importance of economic considerations and land availability as the most important factors in the decision-making process for selecting wind power project locations in the southeastern corridor of Pakistan.

In this research, an MCDM model employing CRITIC and CoCoSo methods is developed to assist with the location selection of a solar-wind hybrid energy plant in the southern region of Vietnam. CRITIC methods have been applied to several studies about renewable energy to calculate the weighting of quantitative criteria. Shao et al. [39] discuss the utilization of the Criteria Importance Through Intercriteria Correlation (CRITIC) method in conjunction with Geographic Information System (GIS) for identifying optimal sites for tidal current power plants in China. The CRITIC method is employed to evaluate the significance of criteria by analyzing their interrelationships. Through the incorporation of the CRITIC method into the GIS-MCDM approach, the study aims to improve the precision and efficiency of site selection for tidal current power plants. Saraji et al. [40] delve into the use of the CRITIC method for exploring the challenges in adopting renewable energy technologies in rural areas. This research combines the CRITIC method with the VIKOR technique to identify and evaluate the barriers to deploying renewable energy solutions in less urbanized regions. Through the application of the CRITIC-VIKOR framework, the study aims to offer an in-depth examination of the obstacles encountered in the implementation of renewable energy technologies in rural areas. Alkan [41] investigates the evaluation of sustainable development and the application-oriented use of renewable energy systems. This analysis integrates the CRITIC method with SWARA and CODAS approaches, utilizing interval-valued picture fuzzy sets to establish a comprehensive framework for the assessment and selection of renewable energy systems. In this context, the CRITIC method is used for the objective weighting of criteria, highlighting its effectiveness and ease of use. The research underscores the necessity of incorporating sustainability and practical usage considerations in the decision-making processes concerning renewable energy systems.

The Combined Compromise Solution (CoCoSo) method has been utilized in numerous studies within the renewable energy sector to evaluate the performance of various potential options. The study by Van Thanh [42] examines an optimal location for a waste-to-energy plant using a Fuzzy MCDM model. The research incorporates the CoCoSo method along with Fuzzy AHP to evaluate and prioritize location alternatives for a waste-to-energy project. The study by Wang et al. [43] presents a model focused on selecting a biomass furnace supplier by considering both qualitative and quantitative factors. The research highlights the challenges posed by the complex decision-making environment in renewable energy, particularly in scenarios where factors interact in a fuzzy decision-making setting.

CRITIC and CoCoSo methods have several advantages in comparison with similar MCDM methods. CRITIC was introduced by Diakoulaki et al. [44] and was highlighted for its ability to generate more balanced criteria weights as the method incorporates information from all objectives within the decision objective matrix into its calculation. Wang et al. [45] analyze weighting and selection methods for Pareto-optimal solutions in multiobjective optimization within chemical engineering applications. In this research, five objective weighting methods (CRITIC, mean, entropy, StDev, StatVar) were tested and compared using several chemical engineering and mathematical problems. CRITIC, along with the entropy method, was recommended because it includes correlations among criteria, which becomes crucial when dealing with three or more criteria. Nabavi et al. [46] examine the sensibility of the entropy method and CRITIC methods in combination with eight popular MCDM methods with different types of modification to the data (linear transformation of objectives, reciprocal objective reformulation, and removal of alternatives). The results show models that employ CRITIC provide robust results against the modifications. CoCoSo method also demonstrates robustness in decision-making models. The study by Wen et al. [47] examines the cold chain logistics management of medicine using an integrated multi-criteria decision-making method. The research covers topics such as refrigeration, ranking, antineoplastic agents, neoplasms, delivery of health care, pharmaceutical preparations, and economics. Through comparative analysis of different MCDM methods, the results suggest that CoCoSo method, in comparison with VIKOR, TOPSIS and other MCDM methods, provide results that are robust and less sensitive to changes such as adding or removing options, or altering the weights of the criteria.

In this study, a MCDM framework that utilizes a novel combination of CRITIC and CoCoSo techniques has been formulated to aid in selecting a site for a solar-wind hybrid energy facility in Vietnam’s southern region. While MCDM methods are widely used, the combination of CRITIC and CoCoSo specifically for renewable energy project location selection, demonstrates an innovative approach as few studies specifically use these methods together to solve decision making problems in renewable energy development. The case study also offers new insights into applying MCDM techniques within the specific context of Vietnam’s renewable energy sector, especially regrading hybrid renewable energy solutions. The results provide practical recommendations for location selection based on quantitative data from a set of unique criteria.

3  Materials & Methods

3.1 Research Process

The research process consists of three main phases, shown in Fig. 3.

–   Phase 1: In the Pre-processing phase, the problem is defined, and a list of evaluation criteria is formed based on existing literature and expert opinions. Then, the relevant data is collected.

∘   A list of potential criteria is identified by reviewing existing literature and interviewing a board of experts.

∘   A definitive list of criteria is determined by a Delphi process proposed by Frinsdorf et al. [48], where a group of academic and industry experts are interviewed and asked to rate each of the potential criteria and sub-criteria. The rating step will repeat until a consensus of the list of criteria and sub-criteria is reached.

∘   Data relevant to each alternative is collected for all criteria.

–   Phase 2: In this phase, the collected data is used to calculate the criteria weights using the CRITIC method. Subsequently, CoCoSo is applied to determine the performance score of each alternative. The detailed of calculation steps of CRITIC and CoCoSo methods are described in Sections 3.2 and 3.3 of this research, respectively.

–   Phase 3: In the final phase, a ranking of alternatives is obtained based on the calculated performance scores. Sensitivity analysis is performed to evaluate the results of the proposed model.

Figure 3: Research process

3.2 Criteria Importance through Intercriteria Correlation (CRITIC) Method

In this research, CRITIC method is employed to calculate the weights of the relevant decision-making criteria. The advantages of CRITIC is that the method accounts for both the variability and correlation of criteria, as well as being effective for cases where there are potential correlations between criteria. CRITIC is also relatively simple to calculate and interpret. As such it is suitable for the problem at hand, where multiple criteria with potential complex relationships must be considered.

The CRITIC method is introduced by Diakoulaki et al. [44] as a quantitative technique to calculate criteria weights based on their contrast intensity and correlation among the criteria. The multicriteria problem can be characterized as follows for a finite set A of n choices and a given system of m assessment criteria fj:

(1)

A membership function for each criterion is built in this multicriteria problem, which maps the values of fj to the interval (0, 1). The idea of the ideal point serves as the foundation for this transition. Accordingly, the number below indicates how much the alternative a deviate from the anti-ideal value , which represents the worst performance in criterion j, and approaches the ideal value , which represents the best performance in criterion j. At least one of the options under consideration is able to accomplish both and .

(2)

This process transforms the original evaluation matrix into a matrix of relative scores with generic element . A vector is created that represents the scores of all n possibilities that were taken into consideration by looking at the jth criterion separately.

(3)

The standard deviation, , quantifies the contrast strength of the corresponding criterion for each vector . Therefore, a measure of the importance of that criterion to the decision-making process is the standard deviation of . It is obvious that the standard deviation might be substituted with any other indicator of the dispersion in scores, such as variance or entropy.

The linear correlation coefficient between the vectors and , represented by the generic element , is then built into a symmetric matrix with dimensions of . It is evident that the value of decreases with increasing discordance between the scores of the options in criteria j and k. In this way, the total indicated in Eq. (4) serves as a gauge for the degree of conflict that criterion j creates in relation to the decision-making scenario that is determined by the remaining criteria.

(4)

It should be noted that in order to offer a broader measure of the link connecting the rank orders of the items included in the vectors and , the Spearman rank correlation coefficient might be used in place of .

The information found in MCDM problems relates to the decision criteria’s conflict and contrast intensity. Therefore, the quantity of data released by the jth criterion can be ascertained by assembling the measurements that measure the two concepts using the subsequent multiplicative aggregation formula:

(5)

The preceding analysis states that the greater the value the more information the relevant criterion transmits and the greater its relative significance for the decision-making process. By normalizing these values to unity using the following equation, objective weights are produced:

(6)

3.3 Combined Compromise Solution (CoCoSo) Method

Yazdani et al. [49] devised the CoCoSo MCDM technique, which combines the exponentially weighted product model with the simple additive weighting method. The CoCoSo approach in this study is employed to determine the ranking of the alternatives. There are five steps in a typical CoCoSo model with m choices and n criteria:

Step 1: The decision-making matrix is calculated for the ith alternative and the jth criterion as follows:

(7)

With and .

Step 2: The decision-making matrix is then normalized accordingly [30]:

For beneficial criterion:

(8)

For non-beneficial criterion:

(9)

Step 3: Calculate each of the alternative’s power weight of comparability () and the total of the power weight of comparability () sequence using Eqs. (10) and (11):

(10)

(11)

Step 4: Determine three aggregated performance scores. Calculate the arithmetic mean of sums of and scores to determine the as the relative performance scores of the ith alternative.

(12)

Calculate the sum of relative scores of and scores in comparison to the ideal performance values where is the relative performance scores of the ith alternative.

(13)

Calculate the compromise of and performance scores for is the relative performance scores of the ith alternative. In Eq. (14), the value is selected by the decision makers and has a value between 0 and 1 (usually .

(14)

Step 5: Alternative’s performance score ( is then calculated by:

(15)

Alternatives are subsequently ranked based on their performance scores; an alternative with a higher score is ranked more favorably.

4  Case Study

In this section, the proposed model is applied to the location selection problem of a potential solar-wind energy plant in Vietnam using available quantitative criteria. After reviewing relevant literature and discussing with industry experts and academics through a Delphi process, a list of 4 criteria and 13 sub-criteria is obtained (Table 1).

In this case, six potential locations were identified through expert discussions and were considered in this case (Table 2). The map of these locations is shown in Fig. 4.

Figure 4: Potential locations map

The relevant data of corresponding to these locations are collected as of June 2024 and shown in Table 3.

Next, CRITIC method is applied to calculate the weight of each criterion. The data is then normalized using Eq. (2) and used to calculate the weight of each sub-criterion using CRITIC. The normalized data is shown in Table 4 and the weight of each sub-criterion is shown in Table 5.

At this stage, the CoCoSo method is utilized to determine the ranking of potential solar-wind energy plant locations. The Weighted comparability sequence (), and the Exponentially weighted comparability sequence () are displayed in Tables 6 and 7, respectively.

Finally, the performance scores of each alternative are calculated (Table 8 and Fig. 5). In this case, Ham Thuan Nam District of Binh Thuan Province (DA4) is the optimal location for a hybrid solar-wind energy plant.

Figure 5: Alternatives’ performance scores

Ham Thuan Nam District (DA4) secured the top rank due to its high scores in solar radiation (C23), wind speed (C21), lower slope (C28) and low population density (C41). In contrast, Phan Thiet City (DA3) ranked second, benefiting from higher solar radiation (C23) and lower solar variation (C24), but its significantly higher population density negatively impacted its overall ranking. Ninh Phuoc District (DA6) ranked third, performing well in areas such as investment environment (C12), wind speed (C21), solar radiation (C23), proximity to settlements (C27), and land use (C32). However, its lower regional population (C11), higher slope (C28), and lower elevation (C26) contributed to its reduced overall score.

5  Sensitivity Analysis

5.1 Criteria Weight Alteration

In this session, a sensitivity analysis is performed to examine the behavior of results when changes in criteria are introduced. Various methods are used for robust testing and sensitivity analysis, including evaluating how changes in criteria weight affect alternative rankings. Firstly, the impact of removing the Regional Populace (C11) sub-criterion is examined, using the method proposed by Alinezhad et al. [62]. The criteria weights after the removal of C11 criterion are shown in Table 9.

As a result, the performance scores and rankings of the alternatives in this scenario are displayed in Table 10.

The final performance scores of the alternatives () have changed, but the rankings remain consistent, with DA4 as the optimal location for a solar-wind energy plant in this case. This indicates that the ranking of alternatives is stable, regardless of the removal of the Regional Populace sub-criterion (C11).

Next, the impact of removing each remaining sub-criterion on the final ranking is examined. In total, 13 scenarios are considered. The weights of each sub-criterion for each scenario are presented in Table 11, while the performance scores and rankings of the alternatives are displayed in Table 12 and Fig. 5.

To further verify the robustness of the sensitivity analysis results, a statistical test was applied to examine whether the changes in criteria weights significantly influenced the performance scores of the alternatives. An one-way Analysis of Variance (ANOVA) test is conducted with the null hypothesis as following: “The changes in criteria weights do not significantly affect the performance scores of alternatives”. The results are shown in Table 13.

With a 5% level of significant, the results suggest that there is no significant difference of performance scores between scenarios (p-value = 0.994276 > 0.05, fail to reject the null hypothesis). The results in Fig. 5 and Table 12 indicate that the proposed model’s outcome is highly robust to changes in criteria weights. In most scenarios, the final rankings of the alternatives stay the same, with the exception of scenario 11 where DA2 and DA5 exchange rankings. DA4 remains the consistently optimal location across most scenarios, except in scenarios 12 and 13, where DA3 (Phan Thiet City) becomes the optimal location. This is reasonable since DA3 had weak performance in criteria C32 (Land use) and C41 (Population density). Thus, removing these criteria led to a significant improvement in DA3’s performance. The top three locations remain unchanged, consisting of DA4, DA3 and DA6 in all cases.

The results in Fig. 6 and Table 12 indicate that the proposed model’s outcome is highly robust to changes in criteria weights. In most scenarios, the final rankings of the alternatives stay the same, with the exception of scenario 11 where DA2 and DA5 exchange rankings. DA4 remains the consistently optimal location across most scenarios, except in scenario 12 and 13, where DA3 (Phan Thiet City) becomes the optimal location. This is reasonable since DA3 had weak performance in criteria C32 (Land use) and C41 (Population density). Thus, removing these criteria led to a significant improvement in DA3’s performance. The top three locations remain unchanged, consisting of DA4, DA3 and DA6 in all cases.

Figure 6: Alternatives’ ranking in considered scenarios

5.2 Compromise Coeficient Value Alteration

Another sensitivity test is performed to show the robustness of the proposed method by changing the value of the compromise coefficient (λ) which reflects the decision maker’s preference for compromise solutions. Let the value of λ changing from 0.1 to 0.9 (with 0.5 is the default value), the performance of the alternatives is shown in Table 14.

Consequentially, the ranking of the alternatives corresponding to different λ values are shown in Fig. 7.

Figure 7: Alternatives’ ranking with different λ values

A one-way ANOVA statistical test is carried out to verify the robustness of the sensitivity test results. With the null hypothesis stated as: “The changes in the value of the compromise coefficient do not significantly affect the performance scores of alternatives”. The results are shown in Table 15 below:

With a 5% level of significant, the results suggest that there is no significant difference of performance scores between with different compromise coefficient values (p-value = 1 > 0.05, fail to reject the null hypothesis). Fig. 7 also shows that the rankings in all cases remain unchanged regardless of the value of the compromise coefficient. As such, the model’s results are robust against changing preference for compromise solutions.

6  Conclusions

As climate change becomes an ever-growing concern, there has been a notable acceleration in the development of renewable energy projects worldwide over the past decade. Vietnam is a country with substantial renewable energy resources has also saw an increase in renewable energy output in this time period. To enhance the success of renewable energy projects, the use of MCDM models in addressing complex decision-making challenges has also risen. This study developed a CRITIC-CoCoSo based MCDM model to support the location selection process for a hybrid solar-wind energy plant in Vietnam. The model identified Ham Thuan Nam District (DA4) as the most suitable site, with a performance score of 2.0919. Phan Thiet City (DA3) and Ninh Phuoc District (DA6) followed closely, with scores of 2.0655 and 1.8723, respectively. Sensitivity analyses confirmed the stability of the rankings under various scenarios, demonstrating the robustness of the model.

While the model has successfully assisted decision makers in identifying optimal location for a solar-wind hybrid energy plan based on quantitative data, it may overlook qualitative aspects like political risks or community acceptance of the problem. As such, future research should address these limitations by incorporating qualitative criteria by employing methods such as Fuzzy AHP or Fuzzy ANP. Comparative studies with other MCDM approaches, such as VIKOR or TOPSIS, could provide further validation to the results. Additionally, applying the proposed model to other similar renewable energy decision making problems would help verify its broader applicability.

Acknowledgement: The 1st author acknowledges Excellent Foreign Students (EFS) scholarship awarded by Sirindhorn International Institute of Technology, Thammasat University.

Funding Statement: The authors received no specific funding for this study.

Author Contributions: The authors confirm contribution to the paper as follows: study conception and design: Rujira Chaysiri; data collection: Viet Tinh Nguyen; analysis and interpretation of results: Viet Tinh Nguyen; draft manuscript preparation: Rujira Chaysiri. All authors reviewed the results and approved the final version of the manuscript.

Availability of Data and Materials: The data that supports the findings of this study are available from the corresponding author, Rujira Chaysiri, upon reasonable request.

Ethics Approval: Not applicable.

Conflicts of Interest: The authors declare no conflicts of interest to report regarding the present study.

References

1. P. T. Phuong, N. P. Duong, and N. T. T. Ha, “Factors affecting the performance of foreign direct investment in the renewable energy supply chain,” Uncertain. Suppl. Chain Manag., vol. 10, no. 1, pp. 271–276, 2022. doi: 10.5267/j.uscm.2021.9.002. [Google Scholar] [CrossRef]

2. P. M. Khuong, R. McKenna, and W. Fïchtner, “A cost-effective and transferable methodology for rooftop PV potential assessment in developing countries,” Energies, vol. 13, no. 10, 2020, Art. No. 2501. doi: 10.3390/en13102501. [Google Scholar] [CrossRef]

3. D. T. T. Than, T. T. Bui, D. T. Kien, and T. M. Tran, “Investigating the factors impact the decision to invest in rooftop solar power in Vietnam,” Int. J. Energy Eco. Policy, vol. 12, no. 3, pp. 116–124, 2022. doi: 10.32479/ijeep.12958. [Google Scholar] [CrossRef]

4. IRENA, “Renewable energy capacity statistics 2024,” 2024. Accessed: Apr. 01, 2024. [Online]. Available: https://www.irena.org/Publications/2024/Mar/Renewable-capacity-statistics-2024 [Google Scholar]

5. K. Das, “Renewables in Vietnam: Current opportunities and future outlook,” 2020. Accessed: Apr. 01, 2024. [Online]. Available: https://www.vietnam-briefing.com/news/vietnams-push-for-renewable-energy.html/ [Google Scholar]

6. T. T. Doan and Q. V. Tran, “Offshore wind power—The new trend for economic development and security of Island sovereignty,” J. Min. Earth Sci., vol. 63, no. 3, pp. 74–81, 2022. doi: 10.46326/JMES.2022.63(3).09. [Google Scholar] [CrossRef]

7. Ministry of Science and Technology, “Chính sách, Giải Pháp công Nghệ Phát triển điện mặt trò’i và điện Gió Tại Việt Nam,” 2023. Accessed: Apr. 02, 2024. [Online]. Available: https://www.most.gov.vn/vn/Pages/chitiettin.aspx?IDNews=23993 [Google Scholar]

8. N. A. Tuan, “Vietnam renewable energy in 2022: Events, achievements and identifying challenges,” Vietnam Energy. 2023. Accessed: Jan. 15, 2023. [Online]. Available: https://nangluongvietnam.vn/nang-luong-tai-tao-viet-nam-nam-2022-cac-su-kien-thanh-tuu-va-nhan-dien-thach-thuc-30046.html [Google Scholar]

9. T. H. T. Nhi, C. Wang, and N. V. Thanh, “Fuzzy multi-criteria decision making for solar power plant location selection,” Comput. Mater. Contin., vol. 72, no. 3, pp. 4853–4865, 2022. doi: 10.32604/cmc.2022.026374. [Google Scholar] [CrossRef]

10. J. Zhou, Y. Wu, C. Wu, F. He, B. Zhang and F. Liu, “A geographical information system based multi-criteria decision-making approach for location analysis and evaluation of urban photovoltaic charging station: A case study in Beijing,” Energy Convers. Manag., vol. 205, 2020, Art. no. 112340. doi: 10.1016/j.enconman.2019.112340. [Google Scholar] [CrossRef]

11. A. Ara, F. T. Zohora, N. Nurul, A. Tanvir, and N. Ahmed, “Optimal site selection for solar plant using analytical hierarchy process (AHPA case study in Bangladesh,” Environ. Progress Sustain. Energy, vol. 43, no. 2, 2024, Art. no. e14272. doi: 10.1002/ep.14272. [Google Scholar] [CrossRef]

12. J. M. Yanuar, P. N. Bambang, A. Saleh, and F. W. Akhmad, “The suitable location for a hybrid renewable energy wind-solar power plant: A review by bibliometric,” IOP Conf. Series: Earth Environ. Sci., vol. 1266, no. 1, 2023, Art. no. 012090. [Google Scholar]

13. E. Karakiş, “Machine selection for a textile company with CRITIC and MAUT methods,” Avrupa Bilim ve Teknoloji Dergisi, vol. 27, no. 27, pp. 842–848, 2021. [Google Scholar]

14. M. Wang, L. Ji, Y. Xie, and G. Huang, “Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis,” J. Environ. Manag., vol. 351, no. 3, 2023, Art. no. 119883. doi: 10.1016/j.jenvman.2023.119883. [Google Scholar] [PubMed] [CrossRef]

15. S. Ma, N. Yu, X. Wang, S. Mei, M. Zhao and X. Han, “Multi-criteria decision-making for power grid construction project investment ranking based on the prospect theory improved by rewarding good and punishing bad linear transformation,” Energy Eng., vol. 120, no. 10, pp. 2369–2392, 2023. doi: 10.32604/ee.2023.028727. [Google Scholar] [CrossRef]

16. R. Nitin, A. Anand, and S. P. Choudhary, “Multi-criteria decision-making (MCDM) as a powerful tool for sustainable development: Effective applications of AHP, FAHP, Topsis, Electre, and Vikor in Sustainability,” Int. Res. J. Moderniz. Eng. Technol. Sci., vol. 5, no. 4, pp. 2654–2670, 2023. doi: 10.56726/irjmets36215. [Google Scholar] [CrossRef]

17. A. Soltani and M. A. Imani, “Overcoming implementation barriers to renewable energy in developing nations: A case study of iran using MCDM techniques and Monte Carlo simulation,” Results Eng., vol. 24, no. 7, 2024, Art. no. 103213. doi: 10.1016/j.rineng.2024.103213. [Google Scholar] [CrossRef]

18. I. M. Hezam, A. M. Ali, K. Sallam, I. A. Hameed, and M. Abdel-Basset, “Assessment of wave energy location, technology, and converter toward sustainability using integrated spherical fuzzy MCDM approach,” Case Stud. Therm. Eng., vol. 59, no. 1, 2024, Art. no. 104527. doi: 10.1016/j.csite.2024.104527. [Google Scholar] [CrossRef]

19. Ristanović and G. Knežević, “Multi-criteria decision-making on operational risk in banks,” in Proc. 1st Int. Conf. Innov. Inf. Technol. Business (ICIITB 2022), 2023, pp. 5–21. doi: 10.2991/978-94-6463-110-4_2. [Google Scholar] [CrossRef]

20. G. N. Yücenur, “MCDM approach to investigate the effectiveness of SCRUM events in minimizing risk factors in project management,” J. Project Manag., vol. 8, no. 4, pp. 227–238, 2023. doi: 10.5267/j.jpm.2023.7.001. [Google Scholar] [CrossRef]

21. M. Erdoğan, A. Karaşan, İ. Kaya, A. Budak, and M. Çolak, “A fuzzy based MCDM methodology for risk evaluation of Cyber Security Technologies,” Intell. Fuzzy Tech. Big Data Anal. Decis. Mak., pp. 1042–1049, 2019. doi: 10.1007/978-3-030-23756-1_123. [Google Scholar] [CrossRef]

22. N. Q. Trung and N. V. Thanh, “Evaluation of digital marketing technologies with fuzzy linguistic MCDM methods,” Axioms, vol. 11, no. 5, 2022, Art. no. 230. doi: 10.3390/axioms11050230. [Google Scholar] [CrossRef]

23. G. Haseli, R. Sheikh, J. Wang, H. Tomaskova, and E. B. Tirkolaee, “A novel approach for group decision making based on the best-worst method (G-BWMApplication to supply chain management,” Mathematics, vol. 9, no. 16, 2021, Art. no. 1881. doi: 10.3390/math9161881. [Google Scholar] [CrossRef]

24. M. M. Alam, R. Mohammad, and F. A. Hira, “Selection of alternative marine fuel technology based on literature review by applying appropriate multi-criteria decision-making method,” J. Sci., Technol. Innov. Policy, vol. 7, no. 2, pp. 1–12, 2021. doi: 10.11113/jostip.v7n2.83. [Google Scholar] [CrossRef]

25. H. Anysz, A. Nicał, Ž. Stević, M Grzegorzewski, and K. Sikora, “Pareto optimal decisions in multi-criteria decision making explained with construction cost cases,” Symmetry, vol. 13, no. 1, 2020, Art. no. 46. doi: 10.3390/sym13010046. [Google Scholar] [CrossRef]

26. D. S. Pamucar, S. P. Tarle, and T. Parezanovic, “New hybrid multi-criteria decision-making DEMATEL-MAIRCA model: Sustainable selection of a location for the development of Multimodal Logistics Centre,” Economic Research-Ekonomska Istraživanja, vol. 31, no. 1, pp. 1641–1665, 2018. doi: 10.1080/1331677x.2018.1506706. [Google Scholar] [CrossRef]

27. S. K. Sahoo, B. B. Choudhury, and P. R. Dhal, “A bibliometric analysis of material selection using MCDM methods: Trends and insights,” Spectr. Mech. Eng. Operat. Res., vol. 1, no. 1, pp. 189–205, 2024. doi: 10.31181/smeor11202417. [Google Scholar] [CrossRef]

28. R. Mishra et al., “Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for Renewable Energy Source Selection,” Compl. Intell. Syst., vol. 8, no. 6, pp. 5223–5248, 2022. doi: 10.1007/s40747-022-00743-4. [Google Scholar] [CrossRef]

29. C. -N. Wang, C. -Y. Yang, and H. -C. Cheng, “Fuzzy multi-criteria decision-making model for supplier evaluation and selection in a wind power plant project,” Mathematics, vol. 7, no. 5, 2019, Art. no. 417. doi: 10.3390/math7050417. [Google Scholar] [CrossRef]

30. M. Kamali Saraji, D. Streimikiene, and R. Ciegis, “A novel pythagorean fuzzy-Swara-TOPSIS framework for evaluating the EU progress towards Sustainable Energy Development,” Environ. Monit. Assess., vol. 194, no. 42, 2022. doi: 10.1007/s10661-021-09685-9. [Google Scholar] [PubMed] [CrossRef]

31. I. Siksnelyte, E. Zavadskas, D. Streimikiene, and D. Sharma, “An overview of multi-criteria decision-making methods in dealing with sustainable energy development issues,” Energies, vol. 11, no. 10, 2018, Art. no. 2754. doi: 10.3390/en11102754. [Google Scholar] [CrossRef]

32. F. Sitorus and P. R. Brito-Parada, “A multiple criteria decision making method to weight the sustainability criteria of renewable energy technologies under uncertainty,” Renew. Sustain. Energ. Rev., vol. 127, 2020, Art. no. 109891. doi: 10.1016/j.rser.2020.109891. [Google Scholar] [CrossRef]

33. Y. T. Yürek, B. Özyörük, E. Özcan, and M. Bulut, “Socio-political evaluation of renewable energy resources under uncertain environment,” Eng. Appl. Artif. Intell., vol. 126, 2023, Art. no. 106881. doi: 10.1016/j.engappai.2023.106881. [Google Scholar] [CrossRef]

34. T. Li, H. Wang, and Y. Lin, “Selection of renewable energy development path for sustainable development using a fuzzy MCDM based on cumulative prospect theory: The case of Malaysia,” Sci. Rep., vol. 14, 2024, Art. no. 15082. doi: 10.1038/s41598-024-65982-6. [Google Scholar] [PubMed] [CrossRef]

35. T. Manirathinam et al., “Sustainable renewable energy system selection for self-sufficient households using integrated fermatean neutrosophic fuzzy stratified AHP-MARCOS approach,” Renew. Energy, vol. 218, 2023, Art. no. 119292. doi: 10.1016/j.renene.2023.119292. [Google Scholar] [CrossRef]

36. C. -N. Wang, V. T. Nguyen, H. T. Thai, and D. H. Duong, “Multi-criteria decision making (MCDM) approaches for solar power plant location selection in Viet Nam,” Energies, vol. 11, no. 6, 2018, Art. no. 1504. doi: 10.3390/en11061504. [Google Scholar] [CrossRef]

37. M. Nuriyev, “An integrated approach for Renewable Energy Resource and Plant Location Selection,” Int. J. Energy Econom. Policy, vol. 11, no. 3, pp. 64–72, 2021. doi: 10.32479/ijeep.10880. [Google Scholar] [CrossRef]

38. Y. Solangi, Q. Tan, M. Khan, N. Mirjat, and I. Ahmed, “The selection of wind power project location in the southeastern corridor of Pakistan: A factor analysis, AHP, and fuzzy-topsis application,” Energies, vol. 11, no. 8, 2018, Art. no. 1940. doi: 10.3390/en11081940. [Google Scholar] [CrossRef]

39. M. Shao, Y. Zhao, J. Sun, Z. Han, and Z. Shao, “A decision framework for tidal current power plant site selection based on GIS-MCDM: A case study in China,” Energy, vol. 262, no. 8, 2023, Art. no. 125476. doi: 10.1016/j.energy.2022.125476. [Google Scholar] [CrossRef]

40. M. -K. Saraji, E. Aliasgari, and D. Streimikiene, “Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach,” Technol. Forecast. Soc. Change, vol. 189, 2023, Art. no. 122399. doi: 10.1016/j.techfore.2023.122399. [Google Scholar] [CrossRef]

41. N. Alkan, “Evaluation of sustainable development and utilization-oriented renewable energy systems based on CRITIC-SWARA-CODAS method using interval valued picture fuzzy sets,” Sustain. Energy Grids Netw., vol. 38, no. 4, 2024, Art. no. 101263. doi: 10.1016/j.segan.2023.101263. [Google Scholar] [CrossRef]

42. N. Van Thanh, “Optimal waste-to-energy strategy assisted by fuzzy MCDM model for Sustainable Solid Waste Management,” Sustainability, vol. 14, no. 11, 2022, Art. no. 6565. doi: 10.3390/su14116565. [Google Scholar] [CrossRef]

43. C. -N. Wang, H. -P. Fu, H. -P. Hsu, V. Thanh Nguyen, V. Tinh Nguyen and A. Saleh Ahmar, “A model for selecting a biomass furnace supplier based on qualitative and quantitative factors,” Comput. Mater. Contin., vol. 69, no. 2, pp. 2339–2353, 2021. doi: 10.32604/cmc.2021.016284. [Google Scholar] [CrossRef]

44. D. Diakoulaki, G. Mavrotas, and L. Papayannakis, “Determining objective weights in multiple criteria problems: The critic method,” Comput. Operat. Res., vol. 22, no. 7, pp. 763–770, 1995. doi: 10.1016/0305-0548(94)00059-H. [Google Scholar] [CrossRef]

45. Z. Wang, S. S. Parhi, G. P. Rangaiah, and A. K. Jana, “Analysis of weighting and selection methods for pareto-optimal solutions of multiobjective optimization in Chemical Engineering Applications,” Ind. Eng. Chem. Res., vol. 59, no. 33, pp. 14850–14867, 2020. doi: 10.1021/acs.iecr.0c00969. [Google Scholar] [CrossRef]

46. S. R. Nabavi, Z. Wang, and G. P. Rangaiah, “Sensitivity analysis of multi-criteria decision-making methods for engineering applications,” Ind. Eng. Chem. Res., vol. 62, no. 17, pp. 6707–6722, 2023. doi: 10.1021/acs.iecr.2c04270. [Google Scholar] [CrossRef]

47. Z. Wen et al., “Cold chain logistics management of medicine with an integrated multi-criteria decision-making method,” Int. J. Environ. Res. Public Health, vol. 16, no. 23, 2019, Art. no. 4843. doi: 10.3390/ijerph16234843. [Google Scholar] [PubMed] [CrossRef]

48. O. Frinsdorf, J. Zuo, and B. Xia, “Critical factors for project efficiency in a defence environment,” Int. J. Proj. Manag., vol. 32, no. 5, pp. 803–814, 2014. doi: 10.1016/j.ijproman.2013.10.008. [Google Scholar] [CrossRef]

49. M. Yazdani, Z. Wen, H. Liao, A. Banaitis, and Z. Turskis, “A grey combined compromise solution (CoCoSo-G) method for supplier selection in Construction Management,” J. Civ. Eng. Manag., vol. 25, no. 8, pp. 858–874, 2019. doi: 10.3846/jcem.2019.11309. [Google Scholar] [CrossRef]

50. C. -N. Wang, T. -T. Dang, and N. -A. -T. Nguyen, “Location optimization of wind plants using DEA and fuzzy multi-criteria decision making: A case study in Vietnam,” IEEE Access, vol. 9, pp. 116265–116285, 2021. doi: 10.1109/ACCESS.2021.3106281. [Google Scholar] [CrossRef]

51. General Statistics Office of Vietnam, “Population statistics,” 2023. Accessed: Dec. 10, 2023. [Online]. Available: https://www.gso.gov.vn/en/population/ [Google Scholar]

52. PCI Vietnam, “The provincial competitiveness index (PCI),” 2019. Accessed: Nov. 03, 2023. [Online]. Available: https://pcivietnam.vn/en [Google Scholar]

53. C. -N. Wang, N. -A. -T. Nguyen, and T. -T. Dang, “Offshore wind power station (OWPS) site selection using a two-stage MCDM-based spherical fuzzy set approach,” Sci. Rep., vol. 12, 2022, Art. no. 4260. doi: 10.1038/s41598-022-08257-2. [Google Scholar] [PubMed] [CrossRef]

54. F. Samanlioglu and Z. Ayağ, “A fuzzy AHP-Promethee II approach for evaluation of solar power plant location alternatives in Turkey,” J. Intell. Fuzzy Syst., vol. 33, no. 2, pp. 859–871, 2017. doi: 10.3233/JIFS-162122. [Google Scholar] [CrossRef]

55. Global Wind Atlas, “Global wind atlas-vietnam,” 2023. Accessed: Oct. 05, 2023. [Online]. Available: https://globalwindatlas.info/en/area/Vietnam [Google Scholar]

56. D. Ghose, S. Pradhan, and Shabbiruddin, “Floating solar plants—Exploring a new dimension of energy generation: A case study,” Energ. Sources Part A, vol. 3, no. 4, pp. 1–31, 2021. doi: 10.1080/15567036.2021.1965266. [Google Scholar] [CrossRef]

57. H. Z. Al Garni and A. Awasthi, “Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia,” Appl. Energy, vol. 206, no. 3, pp. 1225–1240, 2017. doi: 10.1016/j.apenergy.2017.10.024. [Google Scholar] [CrossRef]

58. Global Solar Atlas, “Global solar atlas-vietnam,” 2023. Accessed: Oct. 03, 2023. [Online]. Available: https://globalsolaratlas.info/map?r=VNM&c=14.477144 [Google Scholar]

59. R. Alam, I. U. Bhuiyan, and N. Mohammad, “Variability assessment of solar irradiance for the safe grid integration of solar photovoltaic power plants,” J. Renew. Energy Environ., vol. 10, no. 4, pp. 107–118, 2023. doi: 10.30501/jree.2023.377735.1522. [Google Scholar] [CrossRef]

60. N. C. Chatterjee and G. K. Bose, “A COPRAS-F base multi-criteria group decision making approach for site selection of wind farm,” Decis. Sci. Lett., vol. 2, no. 1, pp. 1–10, 2013. doi: 10.5267/j.dsl.2012.11.001. [Google Scholar] [CrossRef]

61. C. -N. Wang, Y. -F. Huang, Y. -C. Chai, and V. Nguyen, “A multi-criteria decision making (MCDM) for renewable energy plants location selection in vietnam under a fuzzy environment,” Appl. Sci., vol. 8, no. 11, 2018, Art. no. 2069. doi: 10.3390/app8112069. [Google Scholar] [CrossRef]

62. A. Alinezhad and A. Amini, “Sensitivity analysis of TOPSIS technique: The results of change in the weight of one attribute on the final ranking of alternatives,” J. Optim. Ind. Eng., vol. 4, no. 7, pp. 23–28, 2011. [Google Scholar]