Golden Cut-Oriented Q-Rung Orthopair Fuzzy Decision-Making Approach to Evaluation of Renewable Energy Alternatives for Microgeneration System Investments

Dinçer, Hasan; Aksoy, Tamer; Yüksel, Serhat; Hacioglu, Umit

doi:https://doi.org/10.1155/2022/2261166

Mathematical Problems in Engineering

On this page

Abstract Introduction Literature Review Conclusions Abbreviations Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 2261166 | https://doi.org/10.1155/2022/2261166

Golden Cut-Oriented Q-Rung Orthopair Fuzzy Decision-Making Approach to Evaluation of Renewable Energy Alternatives for Microgeneration System Investments

Hasan Dinçer,^1,2Tamer Aksoy,²Serhat Yüksel,¹and Umit Hacioglu²

Academic Editor: Stefan Cristian Gherghina

Received20 May 2022

Revised12 Jun 2022

Accepted14 Jun 2022

Published08 Jul 2022

Abstract

This study aims to find an appropriate system for microgeneration energy investments and identify optimal renewable energy alternatives for the effectiveness of these projects. In this context, a model is constructed by multi stepwise weight assessment ratio analysis (M-SWARA) and technique for order preference by similarity to ideal solution (TOPSIS) with q-rung orthopair fuzzy sets (q-ROFSs) and golden cut. At the first stage, five different systems are weighted for the effectiveness of the microgeneration energy investments. Secondly, four different renewable energy alternatives are ranked regarding the performance of these projects. In addition, a comparative analysis is also implemented with intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). The findings are the same in all different fuzzy sets that demonstrates the reliability of the findings. It is determined that grid-connected with battery backup is the most important system choice. On the other hand, solar energy is the most appropriate alternative for microgeneration system investments. Grid-connected system should be implemented for the performance of the microgeneration projects. Hence, providing a sustainable access to the electricity can be possible. Sufficient amount of electricity may not be obtained from wind and solar energy because of the climate changes. In this process, grid-connected system can handle this problem effectively.

1. Introduction

Microgeneration refers to the small-scale production of energy or heat from renewable sources to a home and business. This system has significant advantages, such as contribution to the clean energy usage. With the help of this issue, carbon emission problems can be reduced in an important manner [1]. Furthermore, by considering microgeneration system, network costs can be decreased. This situation has a positive influence on the profitability of the energy investments. Moreover, consumers can save money by reducing the amounts of the electricity bills [2]. In spite of these advantages, some drawbacks of the microgeneration make people or businesses doubtful regarding microgeneration system investments. For example, high initial cost is an essential barrier for these projects. Moreover, unpredicted energy supply of renewable energy alternatives increases uncertainty about these investments [3]. Additionally, many locations are very limited for different types of microgeneration. For instance, wind turbines are not appropriate for the locations in which there is no powerful wind.

Therefore, microgeneration systems should be designed appropriately to increase the effectiveness of this process. Within this framework, a comprehensive evaluation should be made for the system choices for microgeneration energy investments. These projects can be either “grid-connected” or “off-grid” [4]. Being grid-connected has some advantages, such as having sustainable access to the electricity every time [5]. In other words, regardless of the weather conditions, electricity can always be provided for the parties. However, high cost is a significant disadvantage of this system [6]. On the other side, “off-grid” microgenerators use batteries instead of connecting to a grid [7]. These battery systems can be expensive and require regular maintenance that is accepted as an important drawback of the off-grid systems [8]. Because of this situation, various factors should be taken into consideration to design microgeneration systems efficiently and effectively.

In addition, appropriate renewable energy alternatives should also be selected for the effectiveness of microgeneration system investments. For instance, micro-wind generation is an important alternative of this system. While comparing with traditional wind turbines, it is quite smaller so that it becomes convenient for residential energy production [9]. Moreover, solar energy is also another important alternative for this situation. Within this framework, solar panels can be installed on the roofs [10]. Furthermore, the biomass sources can also be incorporated into microgeneration system. Additionally, micro hydropower can also be considered with respect to the microgeneration alternatives [11]. Nonetheless, each alternative has advantages and disadvantages. For example, to build a small hydropower system for electricity generation, there should be flowing water near the property. Similarly, one of the most fundamental problems of off-grid solar and wind systems is the need for power when the sun is not out, or the wind is not blowing. Hence, while constructing microgeneration system, these alternatives should be evaluated in a detailed manner.

In this study, it is aimed to find appropriate system for microgeneration energy investments and identify optimal renewable energy alternatives for the effectiveness of these projects. In this scope, a unique model is proposed with M-SWARA and TOPSIS by considering q-ROFSs and golden cut. In the first part of this model, five different systems are weighted for the effectiveness of the microgeneration energy investments. Secondly, four different renewable energy alternatives are ranked with respect to the performance of these projects. Moreover, the calculations are also made with IFSs and PFSs. Thus, the main novelty of this study is to make comprehensive examination to increase the performance of microgeneration system with a novel methodology.

The proposed model has also some superiorities by comparing previous ones in the literature. Considering hybrid methodology by using both SWARA and TOPSIS provides some advantages. With the help of this situation, any pre-acceptance regarding criterion weights has not been taken into account so that it positively affects the objectivity of the analysis. Additionally, in this study, SWARA is extended with the name of M-SWARA for the purpose of making more appropriate analysis. Therefore, this proposed model can handle uncertainty in a better way while comparing with other models [12, 13]. Another important benefit of this model is computing degrees with golden cut. This situation has also a positive influence on reaching more precise results and providing methodological originality. These two new implementations increase the originality of the proposed model. In addition, analyses are performed by using q-ROFSs, IFSs and PFSs so that the coherency of the results can be checked. This situation increases the benefits of this model over other models in the literature that considered only one type of fuzzy set [14–16].

The second part is related to the literature examination. Methodology is detailed in the third part. The fourth part includes analysis results. The final parts focus on discussions and conclusions.

2. Literature Review

The literature related to the microgeneration focuses on different topics. Some scholars evaluated the differences between grid-connected or off-grid systems. Having a sustainable access to the electricity is accepted as a significant benefit of the grid-connected system [17]. Even if a sufficient amount of electricity is not obtained from wind and solar energy, energy can always be obtained thanks to this system [18]. Nevertheless, the main drawback of this system is high cost [19, 20]. On the other hand, batteries are used instead of connecting to a grid regarding off-grid microgenerators. In this process, batteries can be expensive, and a regular maintenance can be required. This situation increases the costs of these projects. Khelil et al. [21] focused on the effectiveness of the grid-connected photovoltaic systems. They proposed a new model to minimize the faults in this system so that the effectiveness of the grid-connected microgeneration system can be increased. Akhter et al. [22] assessed the performance of three different grid-connected photovoltaic systems. They claimed that these systems have positive contribution to obtain clean energy. Moreover, Ortega-Arriaga et al. [23] evaluated the economic and environmental impacts of grid and off-grid electricity access options. They pointed out the cost problem of the batteries and regular maintenance of the off-grid systems. Zebra et al. [24] also examined off-grid electrification in developing countries. They reached a conclusion that government support and local community organization play a critical role for the success of this system.

In some studies, micro-wind generation systems were evaluated by considering different issues. In this process, small-scale wind turbines are generated to benefit from the flow of the wind in energy production. Because of its small size, it becomes convenient for residential energy production [25]. While constructing this system, some key issues should be taken into consideration. As an example, rotational wind speeds of the wind turbines should be evaluated for the effectiveness of this system [26]. For this situation, a comprehensive evaluation should be conducted. Micro-wind generation systems have also some disadvantages. The strength of the wind can change during the day [27, 28]. As a result, there may be instability in the amount of energy obtained with this system. This situation both leads to uncertainty and creates extra costs. Tailor et al. [29] focused on the ways to improve microgeneration systems. They underlined that rotational wind speeds should be examined comprehensively for the performance improvement of small-scale wind turbines. Rezaeiha et al. [30] made an evaluation with respect to the roof-mounted wind turbines. They claimed that uncertainties regarding the cost issues should be solved for the effectiveness of these systems. Gruber et al. [31] also reached a conclusion that wind capacity should be taken into consideration appropriately for the performance of the micro-wind turbines.

Researchers also identified solar energy as another important alternative for microgeneration energy investments. In this context, solar panels are installed on the roofs to generate electricity. The rays from the sun generate electricity through the panels [32]. Thanks to this electric inverter, it meets the electrical needs of the building. Micro solar panels can be designed as on-grid and off-grid. In on-grid systems, excess energy is given to the grid [33, 34]. Similarly, if the energy produced by the micro solar panels does not meet the need, the missing part is supplied from the grid [35]. On the other hand, in off-grid systems, excess electricity is stored in batteries. In the opposite case, the electricity needed is supplied from the battery. Alipour et al. [36] evaluated the key factor of residential solar PV adoption in California. They reached a conclusion that educated households tended to purchase solar panel more in comparison with others. Schulte et al. [37] aimed to examine the acceptance of the people with respect to the micro solar panels. They claimed that technical factors play a crucial role in this situation. Best and Trück [38] evaluated policy impacts on Australian small-scale solar installations. It is concluded that there is a negative relationship among average income and these projects.

In addition, micro hydropower can also be considered with respect to the microgeneration alternatives by many different studies. Within this framework, flowing water is converted into electricity. Micro hydropower system has also some drawbacks [39, 40]. For example, hydropower resources tend to be more seasonal in nature. This situation increases uncertainty in energy generation. In other words, seasonal effects lead to unstable amount of electricity production [41, 42]. Additionally, to build a small hydropower system for electricity generation, there should be flowing water near the property. Hence, it is not possible to build micro hydropower in all locations [43, 44]. Nag and Sarkar [45] made an evaluation of micro-hydropower plants. They claimed that technological development plays a key role for the success of these projects. Clements et al. [46] tried to examine micro-hydropower mini grids in Nepali. They determined that insufficient land for micro hydropower poses an important obstacle to the development of this system. Butchers et al. [47] aimed to identify key issues for the sustainability of the micro-hydropower. They also reached a conclusion that costs of these projects should be decreased with the help of technological development to achieve sustainable energy production by micro-hydropower mini grids.

Literature review shows that microgeneration system investments were examined in various studies. They mainly focused on the advantages and disadvantages of grid-connected and off-grid systems. Furthermore, different renewable energy alternatives for microgeneration were also taken into consideration. Comparing different microgeneration systems and renewable energy alternatives can make a contribution to the literature. In this study, it is aimed to find appropriate system for microgeneration energy investments and identify optimal renewable energy alternatives for the effectiveness of these projects. In this scope, a unique model is proposed by M-SWARA and TOPSIS with q-ROFSs and golden cut.

3. Methodology

In this part, q-ROFSs with golden cut, M-SWARA and TOPSIS techniques are identified.

3.1. q-ROFSs with Golden Cut

IFSs considers membership and non-membership degrees ( and ) to have better solutions as in equation (1). The circumstance of should be met [48].

PFS also identify new grades ( and ) for this purpose as in equation (2) [49].

The condition is given in equation.

q-ROFSs are generated with the extension of IFSs PFSs with the degrees ( and ) as in equation (4) [50].

Equation (5) includes the condition of these sets.

Indeterminacy degree is detailed in equation

Equations (7)–(11) represent computational details.where is positive real numbers.

Equation (12) gives information about defuzzification calculation.

However, one of the most prominent issues in the fuzzy decision-making models is to determine the membership and non-membership degrees properly. The fuzzy preferences are generally defined by only considering the essential limitations of the selected fuzzy methodology such as the sum of membership and non-membership degrees. Indeed, the optimal rate and sum of membership and non-membership degrees for the fuzzy sets could be explained by using the assumptions of golden ratio more accurately. In this process, the degrees are calculated with golden ration to reach accurate solutions. Golden cut includes specific patterns of geometry problems. This ration is also associated with Fibonacci numbers [51, 52]. Equation (13) details this ratio whereas a and b define the large and small quantities.where, and is golden cut, a defines the large quantity and b is the small quantity of the straight line.

Equation (14) explains the algebraic form.

The degrees generated by golden cut are shown in equation.

Equations (15) and (16) explains the revitalization of golden cut with q-ROFSs with new degrees ( and )

3.2. M-SWARA with q-ROFSs

SWARA weights the items by considering hierarchical priorities of the experts [53]. In the analysis process, SWARA is extended with the name of multi-SWARA (M-SWARA) for the purpose of making more appropriate analysis. Relation matrix is constructed by the evaluations as in equation.

Next, q-ROFSs and score functions are generated with equations (5) and (12). Then, the values of (comparative importance rate), (coefficient), (recalculated weight), and (weight) are identified with equations (19)–(21).

Later, the values in the matrix are transposed and limited to the power of 2t+1. Finally, by threshold values, impact-relation degrees are defined.

3.3. TOPSIS with q-ROFSs

TOPSIS is considered by ranking different alternatives. In this process, evaluations of the experts are taken and decision matrix is generated in equation (22) [54].

Later, q-ROFSs and score functions are constructed with equations (5) and (12). Also, equation (23) is used for normalization.

Weighted values are computed by equation.

Equations (25) and (26) demonstrate positive () and negative () ideal solutions.

Equations (27) and (28) indicate the distances to the best () and worst alternatives ().

Relative closeness () is defined with equation.

4. Analysis Results

In this study, renewable energy alternatives are evaluated for effective microgeneration system investments. Within this context, a unique model is constructed by M-SWARA and TOPSIS with q-ROFSs and golden cut. Figure 1 illustrates the details of the model.

Table 1 includes selected system choices for microgeneration energy investments.

Table 1 indicates that system choices are related to grid or off-grid and with battery or without battery. Evaluations are provided from three experts by considering the scales in Table 2. The expert team consists of three different decision makers. These people have minimum 19-year experience with respect to the microgeneration systems. Two of them work as top managers in the energy investment companies. On the other hand, the third decision maker is an academician regarding energy investments.

Evaluations are presented in Table 3.

Average values are computed as in Table 4.

Score function values are given in Table 5.

Table 6 demonstrates s_j, k_j, q_j, and values.

Relation matrix is constructed as in Table 7.

Stable matrix is created as in Table 8.

Figure 2 explains the results of causal relationship. In this framework, impact-relation degrees are defined based on the threshold values. Threshold value is calculated as the average value of the values of the relation matrix stated in Table 7. The values that are greater than this threshold value gives information about the influencing impact.

Figure 2 demonstrates that there is a mutual relationship between off-grid including battery (C1) and grid-connected with battery backup (C3). Additionally, off-grid excluding battery (C2) has an influence on both grid-connected with net energy metering (C5) and off-grid including battery (C1). Table 9 includes comparative results.

Ranking results are the same in all different fuzzy sets that demonstrates the reliability of the findings. It is found that grid-connected with battery backup (C3) is the most important system choice. The second stage of the model is related to the ranking alternatives. In this scope, four different renewable energy alternatives are selected for the microgeneration system choices as in Table 10.

Table 11 indicates evaluations for the alternatives.

Average values are defined as in Table 12.

Score values are computed and shown in Table 13.

This matrix is normalized as in Table 14.

Table 15 includes weighted matrix.

The values of D+, D−, RCi are shown in Table 16.

Comparative ranking results of the renewable energy alternatives for microgeneration system investments are presented in Table 17.

Table 17 demonstrates that solar energy is the most appropriate alternative (A4) for microgeneration system investments. Similarly, wind is another significant alternative for this purpose.

5. Discussions

The findings demonstrate that the grid-connected system should be implemented for the performance of the microgeneration projects. Thus, providing a sustainable access to the electricity can be possible. This situation has a positive impact on the performance of this system. Sufficient amount of electricity may not be obtained from wind and solar energy because of the climate changes. In this process, grid-connected system can handle this problem effectively. Singh et al. [18]; Xie et al. [56] and Weschenfelder et al. [19] claimed that grid-connected system should be preferred for the microgeneration energy investments due to having uninterrupted energy. Additionally, Mazzeo et al. [17] also discussed that batteries create high costs, and they also need periodical maintenance. Because of this situation, they highlighted the significance of the grid-connected system. According to the results of this study, it is also seen that micro solar panels should be mainly preferred for microgeneration projects. By technological improvements, the cost of solar energy decreases importantly by comparing with other renewable energy types. Papurello et al. [32] and Zare et al. [33] underlined the importance of micro solar panels for the microgeneration systems. However, there are also opposite views in the literature regarding this issue. For instance, Meng et al. [57]; Shang et al. [58] and Pellegrini et al. [25] focused on micro wind generation system and Tapia et al. [39] considered micro hydropower with respect to the microgeneration alternatives.

6. Conclusions

In this study, it is aimed to define optimal microgeneration energy system investments and determine appropriate renewable energy alternatives for the performance improvements of these projects. A model is made by M-SWARA and TOPSIS with q-ROFSs and golden cut. Firstly, five different systems are weighted for the effectiveness of the microgeneration energy investments. In the second part, four different renewable energy alternatives are ranked regarding the performance of these projects. Furthermore, a comparative analysis is also implemented with IFSs and PFSs. Weighting results are the same in all different fuzzy sets that demonstrates the reliability of the findings. It is defined that grid-connected with battery backup is the most important system choice. On the other hand, solar energy is the most appropriate alternative for microgeneration system investments. In addition, wind is another significant alternative in this regard.

The main novelty of this study is to make comprehensive examination to increase the performance of microgeneration system with a novel methodology. On the other hand, the main limitation of this study is to just provide recommendations for the effectiveness of the microgeneration systems. In other words, an implementation has not been made in the industry about the effectiveness of these issues. Hence, for the future research direction, a case study can be conducted to evaluate the effectiveness this system. In the next studies, different fuzzy sets can also be preferred such as, Gaussian fuzzy sets. This situation provides to make comparative evaluations so that more specific results can be conducted.

Abbreviations

:	Membership degrees of IFS
:	Non-membership degrees of IFS
:	Membership degrees of PFS
:	Non-membership degrees of PFS
:	Membership degrees of q-ROFS
:	Non-membership degrees of q-ROFS
:	Score function
:	Golden cut
a:	Large quantity
b:	Small quantity
:	Membership degrees of q-ROFS based on golden cut
:	Non-membership degrees of q-ROFS based on golden cut
:	Relation matrix
:	Comparative importance rate
:	Coefficient
:	Recalculated weight
:	Weight
:	Decision matrix
:	Positive ideal solution
:	Negative ideal solution
:	Relative closeness.

Data Availability

All information and data are given in the text completely.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

There is no funding information for this study.

References

B. D. Boruah, “Recent advances in off-grid electrochemical capacitors,” Energy Storage Materials, vol. 34, pp. 53–75, 2021.
View at: Publisher Site | Google Scholar
S. Ma, M. Lin, T. E. Lin et al., “Fuel cell-battery hybrid systems for mobility and off-grid applications: a review,” Renewable and Sustainable Energy Reviews, vol. 135, Article ID 110119, 2021.
View at: Publisher Site | Google Scholar
A. de Almeida, P. Moura, and N. Quaresma, “Energy-efficient off-grid systems,” Energy Efficiency, vol. 13, no. 2, pp. 349–376, 2020.
View at: Publisher Site | Google Scholar
H. A. Kazem, M. T. Chaichan, A. H. Al-Waeli, and K. Sopian, “Evaluation of aging and performance of grid-connected photovoltaic system northern Oman: seven years’ experimental study,” Solar Energy, vol. 207, pp. 1247–1258, 2020.
View at: Publisher Site | Google Scholar
S. R. Mantri, R. S. Kasibhatla, and V. K. B. Chennapragada, “Grid-connected vs. off-grid solar water pumping systems for agriculture in India: a comparative study,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1–15, 2020.
View at: Publisher Site | Google Scholar
R. Khezri and A. Mahmoudi, “Review on the state-of-the-art multi-objective optimisation of hybrid standalone/grid-connected energy systems,” IET Generation, Transmission & Distribution, vol. 14, no. 20, pp. 4285–4300, 2020.
View at: Publisher Site | Google Scholar
P. A. Trotter, “From silos to systems: enabling off-grid electrification of healthcare facilities, households, and businesses in sub-Saharan Africa,” One Earth, vol. 4, no. 11, pp. 1543–1545, 2021.
View at: Publisher Site | Google Scholar
M. S. Javed, T. Ma, J. Jurasz, S. Ahmed, and J. Mikulik, “Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems,” Energy, vol. 210, Article ID 118599, 2020.
View at: Publisher Site | Google Scholar
Z. Tasneem, A. Al Noman, S. K. Das et al., “An analytical review on the evaluation of wind resource and wind turbine for urban application: prospect and challenges,” Developments in the Built Environment, vol. 4, Article ID 100033, 2020.
View at: Publisher Site | Google Scholar
T. H. J. Inderberg, H. Sæle, H. Westskog, and T. Winther, “The dynamics of solar prosuming: exploring interconnections between actor groups in Norway,” Energy Research & Social Science, vol. 70, Article ID 101816, 2020.
View at: Publisher Site | Google Scholar
M. S. Asanov, M. K. Safaraliev, T. Z. Zhabudaev et al., “Algorithm for calculation and selection of micro hydropower plant taking into account hydrological parameters of small watercourses mountain rivers of Central Asia,” International Journal of Hydrogen Energy, vol. 46, no. 75, pp. 37109–37119, 2021.
View at: Publisher Site | Google Scholar
S. Perçin, “An integrated fuzzy SWARA and fuzzy AD approach for outsourcing provider selection,” Journal of Manufacturing Technology Management, vol. 30, 2019.
View at: Publisher Site | Google Scholar
H. Prajapati, R. Kant, and R. Shankar, “Prioritizing the solutions of reverse logistics implementation to mitigate its barriers: a hybrid modified SWARA and WASPAS approach,” Journal of Cleaner Production, vol. 240, Article ID 118219, 2019.
View at: Publisher Site | Google Scholar
S. Yüksel, S. Eti, S. Prosekov, and G. S. Uluer, “A study on the energy problem in the eastern mediterranean: text mining and AHP-based strategy recommendations,” Strategic Approaches to Energy Management, Springer, Cham, pp. 27–38, 2021.
View at: Publisher Site | Google Scholar
S. Wang, Q. Liu, S. Yuksel, and H. Dincer, “Hesitant linguistic term sets-based hybrid analysis for renewable energy investments,” IEEE Access, vol. 7, pp. 114223–114235, 2019.
View at: Publisher Site | Google Scholar
X. Shi, J. Li, F. Wang, H. Dinçer, and S. Yüksel, “A hybrid decision-making approach for the service and financial-based measurement of universal health coverage for the E7 economies,” International Journal of Environmental Research and Public Health, vol. 16, no. 18, p. 3295, 2019.
View at: Publisher Site | Google Scholar
D. Mazzeo, N. Matera, P. De Luca, C. Baglivo, P. M. Congedo, and G. Oliveti, “Worldwide geographical mapping and optimization of stand-alone and grid-connected hybrid renewable system techno-economic performance across Köppen-Geiger climates,” Applied Energy, vol. 276, Article ID 115507, 2020.
View at: Publisher Site | Google Scholar
S. Singh, P. Chauhan, and N. Singh, “Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm,” International Journal of Hydrogen Energy, vol. 45, no. 16, pp. 10070–10088, 2020.
View at: Publisher Site | Google Scholar
F. Weschenfelder, G. D. N. P. Leite, A. C. A. da Costa et al., “A review on the complementarity between grid-connected solar and wind power systems,” Journal of Cleaner Production, vol. 257, Article ID 120617, 2020.
View at: Publisher Site | Google Scholar
G. Kou, S. Yüksel, and H. Dinçer, “Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects,” Applied Energy, vol. 311, Article ID 118680, 2022.
View at: Publisher Site | Google Scholar
C. K. M. Khelil, B. Amrouche, A. soufiane Benyoucef, K. Kara, and A. Chouder, “New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems,” Energy, vol. 211, Article ID 118591, 2020.
View at: Publisher Site | Google Scholar
M. N. Akhter, S. Mekhilef, H. Mokhlis, L. Olatomiwa, and M. A. Muhammad, “Performance assessment of three grid-connected photovoltaic systems with combined capacity of 6.575 kWp in Malaysia,” Journal of Cleaner Production, vol. 277, Article ID 123242, 2020.
View at: Publisher Site | Google Scholar
P. Ortega-Arriaga, O. Babacan, J. Nelson, and A. Gambhir, “Grid versus off-grid electricity access options: a review on the economic and environmental impacts,” Renewable and Sustainable Energy Reviews, vol. 143, Article ID 110864, 2021.
View at: Publisher Site | Google Scholar
E. I. C. Zebra, H. J. van der Windt, G. Nhumaio, and A. P. Faaij, “A review of hybrid renewable energy systems in mini-grids for off-grid electrification in developing countries,” Renewable and Sustainable Energy Reviews, vol. 144, Article ID 111036, 2021.
View at: Publisher Site | Google Scholar
M. Pellegrini, A. Guzzini, and C. Saccani, “Experimental measurements of the performance of a micro-wind turbine located in an urban area,” Energy Reports, vol. 7, pp. 3922–3934, 2021.
View at: Publisher Site | Google Scholar
A. Latif, D. Chandra Das, A. Kumar Barik, and S. Ranjan, “Illustration of demand response supported co‐ordinated system performance evaluation of YSGA optimized dual stage PIFOD‐(1+ PI) controller employed with wind‐tidal‐biodiesel based independent two‐area interconnected microgrid system,” IET Renewable Power Generation, vol. 14, no. 6, pp. 1074–1086, 2020.
View at: Publisher Site | Google Scholar
M. Alemi-Rostami, G. Rezazadeh, F. Tahami, and M. H. Ravanji, “A low cost front-end converter with maximum power per ampere for rooftop wind turbines,” IEEE Access, vol. 9, pp. 131236–131244, 2021.
View at: Publisher Site | Google Scholar
H. Dinçer, S. Yüksel, and L. Martínez, “Collaboration enhanced hybrid fuzzy decision-making approach to analyze the renewable energy investment projects,” Energy Reports, vol. 8, pp. 377–389, 2022.
View at: Publisher Site | Google Scholar
R. Tailor, Z. Čonka, M. Kolcun, and L. U. Beňa, “Electric energy flow algorithm for household in smart street development,” in Proceedings of the 2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE), pp. 000073–000078, IEEE, Budapest, Hungary, 2020, November.
View at: Publisher Site | Google Scholar
A. Rezaeiha, H. Montazeri, and B. Blocken, “A framework for preliminary large-scale urban wind energy potential assessment: roof-mounted wind turbines,” Energy Conversion and Management, vol. 214, Article ID 11277, 2020.
View at: Publisher Site | Google Scholar
L. Gruber, U. Bachhiesl, and S. Wogrin, “The current state of research on energy communities,” E & I: Elektrotechnik und Informationstechnik, vol. 138, no. 8, pp. 515–524, 2021.
View at: Publisher Site | Google Scholar
D. Papurello, D. Bertino, and M. Santarelli, “CFD performance analysis of a dish-stirling system for microgeneration,” Processes, vol. 9, no. 7, p. 1142, 2021.
View at: Publisher Site | Google Scholar
S. G. Zare, R. Hafezi, M. Alipour, R. Parsaei Tabar, and R. A. Stewart, “Residential solar water heater adoption behaviour: a review of economic and technical predictors and their correlation with the adoption decision,” Energies, vol. 14, no. 20, p. 663, 2021.
View at: Publisher Site | Google Scholar
Y. Zhang, Y. Zhang, C. Gong, H. Dinçer, and S. Yüksel, “An integrated hesitant 2-tuple Pythagorean fuzzy analysis of QFD-based innovation cost and duration for renewable energy projects,” Energy, vol. 248, Article ID 123561, 2022.
View at: Publisher Site | Google Scholar
Q. Bao, E. Sinitskaya, K. J. Gomez, E. F. MacDonald, and M. C. Yang, “A human-centered design approach to evaluating factors in residential solar PV adoption: a survey of homeowners in California and Massachusetts,” Renewable Energy, vol. 151, pp. 503–513, 2020.
View at: Publisher Site | Google Scholar
M. Alipour, H. Salim, R. A. Stewart, and O. Sahin, “Predictors, taxonomy of predictors, and correlations of predictors with the decision behaviour of residential solar photovoltaics adoption: a review,” Renewable and Sustainable Energy Reviews, vol. 123, Article ID 109749, 2020.
View at: Publisher Site | Google Scholar
E. Schulte, F. Scheller, D. Sloot, and T. Bruckner, “A meta-analysis of residential PV adoption: the important role of perceived benefits, intentions and antecedents in solar energy acceptance,” Energy Research & Social Science, vol. 84, Article ID 102339, 2022.
View at: Publisher Site | Google Scholar
R. Best and S. Trück, “Capital and policy impacts on Australian small-scale solar installations,” Energy Policy, vol. 136, Article ID 111082, 2020.
View at: Publisher Site | Google Scholar
A. Tapia, A. R. del Nozal, D. G. Reina, and P. Millán, “Three-dimensional optimization of penstock layouts for micro-hydropower plants using genetic algorithms,” Applied Energy, vol. 301, Article ID 117499, 2021.
View at: Publisher Site | Google Scholar
J. Li, S. Yüksel, H. Dınçer, A. Mikhaylov, and S. E. Barykin, “Bipolar q-ROF hybrid decision making model with golden cut for analyzing the levelized cost of renewable energy alternatives,” IEEE Access, vol. 10, pp. 42507–42517, 2022.
View at: Publisher Site | Google Scholar
M. C. Chacón, J. A. R. Díaz, J. G. Morillo, and A. McNabola, “Evaluation of the design and performance of a micro hydropower plant in a pressurised irrigation network: real world application at farm-level in Southern Spain,” Renewable Energy, vol. 169, pp. 1106–1112, 2021.
View at: Publisher Site | Google Scholar
W. Dong, G. Zhao, S. Yüksel, H. Dinçer, and G. G. Ubay, “A novel hybrid decision making approach for the strategic selection of wind energy projects,” Renewable Energy, vol. 185, pp. 321–337, 2022.
View at: Publisher Site | Google Scholar
N. M. Eshra, A. F. Zobaa, and S. H. A. Aleem, “Assessment of mini and micro hydropower potential in Egypt: multi-criteria analysis,” Energy Reports, vol. 7, pp. 81–94, 2021.
View at: Publisher Site | Google Scholar
S. Mukhtarov, S. Yüksel, and H. Dinçer, “The impact of financial development on renewable energy consumption: evidence from turkey,” Renewable Energy, vol. 187, 2022.
View at: Publisher Site | Google Scholar
A. K. Nag and S. Sarkar, “Techno-economic analysis of a micro-hydropower plant consists of hydrokinetic turbines arranged in different array formations for rural power supply,” Renewable Energy, vol. 179, pp. 475–487, 2021.
View at: Publisher Site | Google Scholar
W. Clements, K. Silwal, S. Pandit et al., “Unlocking electric cooking on Nepali micro-hydropower mini-grids,” Energy for Sustainable Development, vol. 57, pp. 119–131, 2020.
View at: Publisher Site | Google Scholar
J. Butchers, S. Williamson, J. Booker, A. Tran, P. B. Karki, and B. Gautam, “Understanding sustainable operation of micro-hydropower: a field study in Nepal,” Energy for Sustainable Development, vol. 57, pp. 12–21, 2020.
View at: Publisher Site | Google Scholar
K. T. Atanassov, “Intuitionistic fuzzy sets,” Intuitionistic Fuzzy Sets, Physica, Heidelberg, pp. 1–137, 1999.
View at: Publisher Site | Google Scholar
R. R. Yager, “Pythagorean fuzzy subsets,” in Proceedings of the 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), pp. 57–61, IEEE, Edmonton, AB, Canada, 2013, June.
View at: Publisher Site | Google Scholar
R. R. Yager, “Generalized orthopair fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 5, pp. 1222–1230, 2016.
View at: Publisher Site | Google Scholar
R. A. Dunlap, The golden Ratio and Fibonacci Numbers, World Scientific, Singapore, 1997.
M. Livio, The golden Ratio: The story of Phi, the World's Most Astonishing Number, Crown, New York, 2008.
M. Alimardani, S. H. Zolfani, M. H. Aghdaie, and J. Tamošaitienė, “A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment,” Technological and Economic Development of Economy, vol. 19, no. 3, pp. 533–548, 2013.
View at: Publisher Site | Google Scholar
Y. J. Lai, T. Y. Liu, and C. L. Hwang, “Topsis for MODM,” European Journal of Operational Research, vol. 76, no. 3, pp. 486–500, 1994.
View at: Publisher Site | Google Scholar
L. Abdullah and L. Najib, “A new preference scale mcdm method based on interval-valued intuitionistic fuzzy sets and the analytic hierarchy process,” Soft Computing, vol. 20, no. 2, pp. 511–523, 2016.
View at: Publisher Site | Google Scholar
Y. Xie, Y. Zhou, Y. Peng, H. Dinçer, S. Yüksel, and P. an Xiang, “An extended pythagorean fuzzy approach to group decision-making with incomplete preferences for analyzing balanced scorecard-based renewable energy investments,” IEEE Access, vol. 9, pp. 43020–43035, 2021.
View at: Publisher Site | Google Scholar
Y. Meng, H. Wu, W. Zhao, W. Chen, H. Dinçer, and S. Yüksel, “A hybrid heterogeneous Pythagorean fuzzy group decision modelling for crowdfunding development process pathways of fintech-based clean energy investment projects,” Financial Innovation, vol. 7, no. 1, pp. 1–34, 2021.
View at: Publisher Site | Google Scholar
H. Shang, F. Su, S. Yüksel, and H. Dinçer, “Identifying the strategic priorities of the technical factors for the sustainable low carbon industry based on macroeconomic conditions,” Sage Open, vol. 11, no. 2, Article ID 21582440211016345, 2021.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2022 Hasan Dinçer et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

246

Downloads

316

Citations