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. 2024 Nov 16;14:28358. doi: 10.1038/s41598-024-77677-z

RETRACTED ARTICLE: Prioritizing customer and technical requirements for microgrid battery integration via a house of quality-driven decision-making approach

Hasan Dinçer 1,2, Serhat Yüksel 1,2, Serkan Eti 3, Yaşar Gökalp 4, Alexey Mikhaylov 5,✉, Jaehyung An 6
PMCID: PMC11569139  PMID: 39550410

Abstract

The purpose of this study is to make evaluation regarding significant issues about the customer expectations and technical competencies for successfully integration of batteries in microgrid systems. In this direction, six different customer expectations and technical requirements are identified by considering literature review results. The weights of the criteria are computed with sine trigonometric Pythagorean fuzzy (STPYF) decision making trial and evaluation laboratory (DEMATEL). Moreover, technical requirements are also ranked using a newly developed technique in this study called “ranking technique by geometric mean of similarity ratio to optimal solution” (RATGOS). This new methodology is also integrated with STPYF sets. The main contribution of this study is that it can be much easier to increase the efficiency of battery integration in microgrid systems by making the priority analysis. Moreover, proposing a new ranking-based decision-making technique (RATGOS) has an increasing impact on the methodological originality. On the other side, owing to considering DEMATEL approach in criteria weighting, the causal directions between these factors can be understood. This situation can be accepted as an important superiority of this model by comparing with the previously generated ones. It is determined that efficiency of storing energy is the most critical customer expectation to increase the effectiveness of this process. Furthermore, the ranking results also demonstrate that generating smart battery control systems is the most important technical requirements to have higher performance in microgrid energy systems. It is identified that the proposed model generates similar findings with the previous ones. Based on these results, some strategies should be implemented to increase the efficiency of energy storage processes in microgrid systems. Within this framework, choosing the right battery is of vital importance. Technological infrastructure is necessary to achieve this goal. Similarly, it is important to provide cyber security to increase the efficiency of energy storage processes. In this way, it is possible for the batteries to work more safely against external interventions.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-77677-z.

Keywords: Battery integration, Microgrid systems, House of Quality, Fuzzy decision-making

Subject terms: Electrical and electronic engineering, Energy infrastructure, Mechanical engineering

Introduction

Microgrids are local systems designed to increase the efficiency and performance of energy production, consumption, and distribution processes. In this context, excess energy is kept in storage systems. Participants who cannot produce enough energy can also buy this excess energy. On the other hand, excess energy generated in this grid can be given to the main grid. Similarly, if the energy produced in the grid does not meet the need, energy can be supplied from the main grid. In this respect, it is possible to talk about many advantages of microgrid systems. Considering that renewable energy types are taken into consideration primarily in the grid, this system makes a significant contribution to increasing the use of clean energy. In addition, thanks to this system, energy production and consumption processes can be made more efficient. Moreover, the participants in the system can produce the energy they need, and thus, the dependency on foreign energy can be reduced. Additionally, owing to this system, it is possible to manage the energy demand more effectively1.

Batteries are of great importance to increase the efficiency of microgrid systems. Batteries increase the energy storage capacities in the system. This contributes to the more efficient operation of microgrid systems. Batteries can store excess energy and regulate it according to usage demands. Climate conditions affect the amount of electricity to be produced by this process2. Depending on this effect, the excess energy to be produced can be stored with batteries. In this way, this stored energy can be used easily when demand is high. Furthermore, thanks to the batteries, it is possible to reach the energy in case of a possible power outage. In other words, batteries significantly help the electricity produced from renewable energy sources to be uninterrupted3. There are some issues that need to be considered to ensure the successful integration of batteries in microgrid systems. First, the capacity of the battery is another vital aspect in this process. This capacity should be determined according to the energy storage requirement. On the other hand, this system must have the necessary technological infrastructure. In this way, it is possible to achieve energy optimization by establishing a more effective energy management system. Moreover, it is important to take necessary safety measures for batteries to store energy effectively. Furthermore, it is critical to find spare capacity of the batteries in case of unexpected demand spikes.

In summary, customer expectations must be analyzed effectively to successfully integrate batteries in microgrid systems. Similarly, it is important to have some technical competencies to manage these expectations correctly. On the other hand, both customer expectations and technical competencies required in this regard are very diverse. Therefore, it may not be very reasonable financially to try to improve all these issues. In this context, it would be a good decision to prioritize the more important of these issues. When similar studies in the literature are examined, it is understood that mainly the importance of batteries is emphasized. Nevertheless, there appears to be a restricted amount of research concentrating on which of these factors should be given importance4. Thus, to fill this gap in the literature, it is necessary to determine the most significant issues by performing a new analysis. In this way, it is possible to produce the strategies necessary for this system to work more effectively.

This paper aims to make evaluation regarding significant issues about the customer expectations and technical competencies for successfully integration of batteries in microgrid systems. The main research question is to identify which determinants should be prioritized to generate investment strategies for the appropriate integration of the batteries into these systems. The proposed methodology integrates house of quality (HoQ) approach with multi criteria decision-making (MCDM) models. In this context, six different customer expectations and technical requirements are described by considering literature review results. The criteria are weighted by using sine trigonometric STPYF DEMATEL. Additionally, technical requirements are also ranked using a newly developed technique in this study called RATGOS. This new methodology is also integrated with STPYF sets. The main motivation of this study is the necessity to make a comprehensive evaluation to integrate batteries in microgrid systems effectively. Decision-making models can be taken into consideration in this framework. However, to create a successful model, the main criticisms to the existing models should be satisfied. In this context, uncertainties should be minimized effectively in this process. However, triangular fuzzy numbers may not be considered sufficient in some cases to reduce uncertainty. Triangular fuzzy numbers have only a lower bound, an upper bound and a mode value. It is thought that this situation is not sufficient to manage uncertainty. On the other hand, VIKOR and TOPSIS techniques are used to rank alternatives in a significant majority of current models. On the other hand, some stages in the analysis processes of these techniques have been criticized significantly. Therefore, in the new model, these techniques must either be developed, or a new technique must be produced.

The main contribution of this study is that the most critical strategies can be identified with respect to the battery integration in microgrid systems by establishing a novel model. The main theoretical contribution of this study is that prior strategies can be proposed for the improvements of these investments. Hence, the findings of this study can pave the way for the investors to make their strategic investment decisions effectively. This situation has a positive influence on the cost effectiveness of these projects. With the help of this issue, it can be more possible to provide operational efficiency in this process. On the other hand, the proposed model has important superiorities as detailed below. (i) Generating a new ranking-based decision-making technique (RATGOS) is the important methodological originality of this study. Existing ranking techniques in the literature are criticized because of many ways. As an example, TOPSIS model considers Euclidean distance to identify the distances to the optimal values. However, some scholars state that this calculation may not provide effective solutions while computing the distance to the negative optimal value5,6. Moreover, while ranking the alternatives via VIKOR approach, only the distance to the positive ideal solution is taken into consideration. The fact that the distance from the negative ideal result is not used in the ranking of alternatives has been criticized in many studies7,8. Hence, to deal with these problems, RATGOS is proposed by considering the geometric mean in this process9–11. (ii) Considering sine trigonometric structure provides some advantages. Periodicity can be considered thanks to this approach. Additionally, because it is symmetrical regarding the origin, more appropriate results can be reached by using this operator. This situation can be accepted as the main superiority of the proposed model by comparing with the previously generated ones12,13. Moreover, defuzzification process can be implemented more accurately by using ST operator14. Owing to these advantages, this operator is preferred in this study to minimize uncertainty by making evaluation with fuzzy decision-making techniques. Finding the most effective investment strategies for battery integration in microgrid systems is a very complex issue. Because of this condition, there is high uncertainty in this process that has a negative impact on the effectiveness and appropriateness of the findings. Thus, using sine trigonometric structure has a strong contribution to handle this uncertainty problem. (iii) Considering the house of quality approach while determining the criteria and alternative sets contributes to the determination of more effective strategies. This technique uses both customer expectations, and the technical competencies required to meet these expectations. By taking into consideration the feedback, it is more possible to achieve customer satisfaction. Thus, it is possible to increase the efficiency of microgrid systems by determining more accurate policies15. Customer expectations and technical requirements are quite essential to generate appropriate investment strategies for these projects. Therefore, using house of quality technique in this study is quite optimal to define criteria and alternatives.

The second section gives the literature review results. The methodology is explained in the third section. Analysis results are provided in the fourth section. The last section consists of discussion and conclusion. The flow of the article is summarized in Fig. 1.

Fig. 1.

Fig. 1

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The block diagram summarizing the article.

Literature review

The establishment of a sufficiently robust technological infrastructure is of paramount importance when considering the effective and seamless integration of battery systems within microgrids, which are increasingly being recognized as vital components of modern energy systems. Microgrids represent a comprehensive framework that encompasses various facets of energy management, including but not limited to the production, storage, and consumption of energy resources, as highlighted16. Consequently, it is reasonable to assert that microgrids possess an inherently intricate and multifaceted structure17 which necessitates a high level of sophistication in their design and operation. In light of this complexity, it becomes evident that these elaborate structures are inextricably linked to the need for advanced and cutting-edge technological solutions18. When the technological infrastructure in place is deemed sufficient and capable, the likelihood of achieving superior quality in energy storage systems is significantly enhanced. Furthermore, the presence of advanced technological capabilities will facilitate the rapid identification and resolution of potential issues that may arise within the system, ultimately contributing to greater operational efficiency19. In their research20, It conducted an in-depth investigation into the storage mechanisms of hydrogen energy within microgrid networks, thereby underscoring the critical role of advanced technology in ensuring the efficient operation of these networks. The comprehensive review undertaken21 further emphasizes the significance of technological infrastructure in assessing and enhancing the reliability of microgrids, thereby reinforcing the notion that a robust technological foundation is indispensable for the overall success and sustainability of these systems. Moreover, the works22,23 strongly advocate for the necessity of a well-established technological infrastructure to drive the ongoing improvements within microgrid networks, illustrating a consensus in the academic community regarding this vital requirement. In accordance with the findings presented24, it is evident that the enhancement of microgrid efficiency is intricately linked to the integration of advanced technology, which serves as a fundamental element that underpins the operational efficacy of these sophisticated energy systems.

Another important issue affecting battery integration in microgrids is the cost of the battery. This situation directly affects the financial sustainability of microgrid projects. In addition to the cost of the batteries, the performance, life, efficiency, and integration of the batteries should also be considered25. It26,27 stated that battery costs are of great importance for the sustainability of microgrids. It28 aimed to develop a model for the evaluation of energy storage costs. It is emphasized that battery cost has an important role in energy storage costs. It29 conducted an optimal microgrid planning study to minimize carbon emissions. It is underlined that batteries are one of the important cost factors in microgrids. It30–32 also carried out the importance of battery costs to have an optimal energy planning with risk analysis for microgrids.

The efficiency of energy distribution is another important issue for microgrids. This condition directly affects the performance, sustainability, and economic efficiency of the microgrid33–35. Effective energy distribution ensures that energy losses are minimized. In addition, energy storage and distribution become more critical due to seasonal fluctuations in the renewable energy used in microgrids36,37. Accordingly, energy distribution is important for these systems. It38 carried out a study to bring microgrids into compliance with European Union laws. It is stated that microgrids can make a significant contribution to the transition to clean energy. It39 conducted a study aimed at optimizing the energy operating costs of microgrids. The efficiency of energy distribution is very important for the process. It40,41 examined how the optimal microgrid should be for rural areas. It is concluded that for a successful microgrid, the energy distribution must be efficient.

Accurate calculation of energy supply and demand in the microgrid is necessary for the success of the system. Balanced energy supply and demand prevent possible energy cuts42. Apart from this, the energy production with renewable energy sources may fluctuate depending on the seasons43. Therefore, to avoid a problem in energy consumption, supply, and demand must be balanced44. Accordingly, the amount of energy supply and demand in the process of battery integration of microgrids is an important issue45. It46 investigated the improvement of the matching degree of supply and demand in microgrids. The importance of supply and demand balance in these systems is emphasized. It47,48 carried out a review study examining microgrid networks. Balancing energy supply and demand is important for the sustainability of microgrids. It49 underlined the importance of supply and demand balance. It50 stressed that energy demand cannot be met only with traditional methods, and it is essential to balance energy supply and demand in microgrids.

The summary of the literature review is denoted in Table A1 in the appendix part. The literature review leads to the following conclusions. (i) Microgrids provide advantages in many aspects such as energy security, economy, and efficiency. (ii) Many factors affect the success of microgrids. (iii) It may not be possible to intervene in all the issues that affect the success of microgrids at the same time. Accordingly, the importance weights of these issues need to be determined. (iv) However, there is a limited number of studies in the literature addressing this situation. The aim of the study is to identify the most important strategies for improving the efficiency of microgrids. With this model, it is aimed to fill the gap in the literature.

Proposal methodology

In this part of the study, the models used for the study are introduced. DEMATEL method is used to determine the importance levels of the criteria in the study. Then, the RATGOS method is preferred for ranking the alternatives. In both methods, sine trigonometric Pythagorean fuzzy numbers (STPYFs) are used.

STPYF-DEMATEL

The DEMATEL method is a MCDM tool that takes causation between criteria into consideration while figuring out their relative relevance. In DEMATEL methods, it is assumed that there is an effect between the criteria. The importance weights of the criteria are calculated on these effects. The basis for this approach is pairwise comparisons of the criteria44. STPYF is a type of fuzzy set that incorporates linguistic uncertainty into the analysis with nonlinear structure. By integrating with DEMATEL, it includes uncertainty into the analysis and enables the weights of the criteria to be calculated more realistically. First, professional judgment is ascertained and transformed into STPYFs using the values in Table 1.

Table 1.

Linguistic variables.

Linguistic Term Score PYFs STPYFs
Inline graphic v Inline graphic v
Very low (VL) 1 0.15 0.85 0.2334 0.5136
Low (L) 2 0.25 0.75 0.3827 0.3716
Moderately low (ML) 3 0.35 0.65 0.5225 0.2651
Medium (M) 4 0.5 0.45 0.7071 0.1187
Moderately high (MH) 5 0.65 0.35 0.8526 0.0702
High (H) 6 0.75 0.25 0.9239 0.0353
Very high (VH) 7 0.85 0.15 0.9724 0.0126
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With calculating the average of the k experts’ opinions, sine trigonometric Pythagorean fuzzy initial matrix (A) is formed as in Eq. (1). In this framework, Inline graphic is the membership value while v shows the non-membership value. Equation (2) is used to calculate the arithmetic mean (STPFWA).

graphic file with name M4.gif 1
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The defuzzified values are computed using Eq. (3). Thus, the values ​​are reduced to between 0 and 1.

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Then, by Eqs. (4) and (5), a normalized direct-relation matrix (X) is created.

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In the other step, total relation matrix (T) is constructed using Eq. (6).

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Then, the row (R) and column (D) sums of this matrix are obtained with Eqs. (7) and (8). That is, these matrices are created by summing the rows and columns of the total relation matrix.

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With the help of row and column sums, weights (w) are calculated by Eq. (9).

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STPYF-RATGOS

RATGOS is a MCDM method that aims to find the ideal alternative by considering certain criteria. The basis of this method is the similarity ratio of alternatives to optimal values50–52. Since the ranking of alternatives using the distance metric is criticized in the literature, ranking methods that consider the similarity ratio are suggested. The RATGOS method calculates the similarity ratio of alternatives to the optimal value for each criterion. Since linguistic terms are used in the evaluation of alternatives, STPYF numbers are integrated into the method to include uncertainty in the analysis. The steps of the method developed with STPYF numbers are as follows. First, expert evaluations are obtained in Table 1. Equation (2) helps in calculating the average of the expert opinions. Thus, the fuzzy decision matrix (B) is shown with Eq. (10).

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Equation (3) is used to compute defuzzified all values of B matrix. Equations (11) and (12) denote optimal values for each criterion in matrix B.

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Equations (13) and (14) are used to define the similarity ratio (N). Thus, information is obtained about the degree to which it is similar to the optimal values.

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The weighted normalization matrix (Z) is constructed with the Eq. (15). Prioritization is achieved by taking into account the importance weights of the criteria. The sum of the weights should be 1. In cases where equal importance is given, 1 is divided by the number of criteria.

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Geometric mean (G) is used to calculate average similarity ratio as in Eq. (16).

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Analysis results

In this section, the results of the proposed model are provided in the following subtitles.

Defining the customer expectations and technical requirements for battery integration to microgrids

Based on the results of the literature review, six main customer expectations are selected for battery integration to microgrids as shown in Table 2.

Table 2.

Customer requirements for battery integration to microgrids.

Customer expectations Literature
Consistency of energy supply (CNGS) 51
Efficiency of storing energy (STGY) 42
Optimization in the energy distribution process (PTMS) 22
Low-cost battery solutions (LWTT) 27
Footprint reduction in energy management (FTPG) 31
Flexibility in microgrid investments (FBXT) 18
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For the performance improvements of battery integration to microgrids, necessary analysis should be made regarding the energy supply and demand balance. Continuity of energy supply is very important for battery integration. In this context, it is necessary to balance the energy supply in case of interruptions in energy production. In other words, in case of an interruption, it is possible to increase customer satisfaction by using the energy stored in the batteries. Additionally, energy storage process should be implemented efficiently to achieve this aim. If the energy storage efficiency is low, energy losses increase. This can cause negative results both economically and environmentally. Batteries can store energy with high efficiency. In this way, this stored energy can be used when needed. In this way, it is possible to make the system more sustainable. Effective energy distribution plays also a key role in this context. Energy must be distributed effectively at the right times and to the right places. In this way, it is very important to minimize energy losses. With a good distribution infrastructure, stored energy can be transmitted in sufficient quantities. The cost of battery should also be optional in this regard. Moreover, carbon emission problem should be minimized for this purpose. If battery costs are high, the applicability of energy storage systems decreases. This causes fewer investors to show interest in these projects. In this context, necessary actions should be taken to reduce the costs of battery technologies. Furthermore, microgrids should be so flexible that any technological improvements can be implemented to this system. Micro grids can operate independently of the central grid. In this way, battery integration is used to ensure energy supply-demand balance in micro grids. On the other side, five technical improvements for battery integration to microgrids are also explained in Table 3.

Table 3.

Technical requirements for battery integration to Microgrids.

Technical Requirements Literature
Storage capacity of microgrids (SCPC) 22
Long-lasting battery selection (LLBT) 39
Smart battery control systems (STBC) 31
Compatibility with safety constructions (CSCR) 24
Modular system designing (MDGG) 30
Stability in grid loads and response (SBYL) 48
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Microgrids should have sufficient storage capacity to increase the performance of battery integration to microgrids. Having sufficient storage capacity is essential to ensure continuity of energy supply and grid stability. Microgrids may experience sudden energy demand increases. Sufficient storage capacity provides a reliable energy source in these situations. This allows for uninterrupted energy supply. Moreover, the batteries should be long-lasting so that cost effectiveness can be provided. The durability and long life of batteries increases the reliability of energy storage systems. This is critical for ensuring cost-effectiveness. In addition, the use of long-life batteries creates less waste and environmental pollution. On the other side, smart battery control systems help to increase effectiveness of energy production and distribution process. Energy management can be optimized by designing smart battery control systems. Due to this issue, energy storage capacity can be used in the most efficient way. This contributes significantly to increasing energy efficiency. In addition to them, necessary safety conditions should also be provided for performance improvements in this process. Ensuring safety conditions ensure that batteries are integrated smoothly and safely, both physically and operationally. Especially in large energy storage projects, batteries may need to operate at high voltages. Furthermore, the designed system should be modular and there should be stability in grid loads and response. This is particularly critical for large-scale energy projects and microgrids. Modular systems allow batteries to be customized to meet different requirements. If energy demand increases, capacity can be increased thanks to modular structures.

Weighting customer expectations for battery integration to Microgrids

Evaluations are provided by considering 7 different scales from three decision-makers. In most of the fuzzy decision-making models, the assessments of three different experts are taken into consideration53–57. Hence, according to the methodologies in the similar studies in the literature, taking the evaluations of three people is enough to generate a decision-making model. The first expert is the general manager in an international renewable energy company. The two other experts are academicians that make significant research related to the microgrid systems, battery types, energy storage effectiveness and carbon emissions. The evaluations related to the customer expectations of battery integration to microgrids are denoted in Table 4.

Table 4.

Expert evaluations related to the customer expectations.

CNGS STGY PTMS LWTT FTPG FBXT
CNGS –; –; – L; ML; ML ML; ML; L VL; L; ML L; L; M ML; ML; M
STGY VH; VH; VH –; –; – VH; H; MH VH; H; MH VH; VH; H VH; H; VH
PTMS H; MH; M M ; M ; MH –; –; – MH; MH; H M ; MH; MH MH; M ; MH
LWTT M ; L; L L; L; ML VL; M ; L –; –; – ML; L; ML L; ML; M
FTPG ML; ML; L M ; ML; ML L; L; ML ML; L; ML –; –; – L; VL; L
FBXT L; M ; M ML; M ; L ML; ML; ML L; ML; M ML; L; ML –; –; –
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Expert opinions are and averaged by Eq. (2). Then, the matrix is defuzzified by Eq. (3). The normalized direct-relation matrix obtained with Eqs. (4) and (5). Then, T matrix is computed with Eq. (6). The weights are obtained with Eqs. (7)–(9). The results are shown in Table 5.

Table 5.

Weights.

D R R + D R-D Weights
CNGS 0.3228 0.8415 1.1643 0.5187 0.1449
STGY 1.5065 0.5108 2.0173 − 0.9957 0.2557
PTMS 1.1604 0.5262 1.6866 − 0.6342 0.2048
LWTT 0.3249 0.7211 1.0460 0.3962 0.1271
FTPG 0.2808 0.7306 1.0115 0.4498 0.1258
FBXT 0.4758 0.7411 1.2169 0.2652 0.1416
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Table 5 gives information about the significance weights of the criteria. It is defined that efficiency of storing energy is the key issue because of the highest weight (0.2557). Additionally, optimization in the energy distribution process is another important factor for this situation with the weight of 0.2048. Low-cost battery solutions and footprint reduction in energy management have the lowest significance than the other factors. To provide efficiency of storing energy for battery integration, necessary actions must be taken by businesses. In this context, first, it is important to select batteries with high efficiency. Using high efficiency battery technologies reduces losses in energy storage processes. Selecting smart battery management systems also supports this goal. This situation contributes significantly to reducing battery life. Moreover, new battery technologies and research must be followed. This situation allows more innovative solutions to be offered to increase efficiency. The priority of criteria is shown in Fig. 2.

Fig. 2.

Fig. 2

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Priority of criteria.

Ranking technical requirements for battery integration to microgrids

The evaluations for the technical requirements are also provided from the experts. The evaluations are generated based on 7 different scales. Table 6 explains the details of these evaluations.

Table 6.

Expert evaluations related to the technical requirements.

CNGS STGY PTMS LWTT FTPG FBXT
SCPC MH; MH; MH H; MH; MH MH; H; M H; M ; M H; MH; MH MH; MH; M
LLBT ML; ML; L M ; ML; ML M ; M ; ML ML; VL; L M ; L; ML L; L; ML
STBC VH; VH; H H; VH; H VH; H; VH VH; VH; VH H; VH; VH VH; VH; VH
CSCR ML; VL; L M ; VL; L ML; ML; ML L; ML; L ML; L; ML L; L; M
MDGG M ; L; M ML; M ; ML ML; ML; L M ; ML; ML M ; L; L VL; ML; ML
SBYL VL; L; M L; L; MH ML; M ; VL VL; M ; VL L; ML; MH L; ML; ML
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Expert opinions are converted into STPYFs and the average value is computed by Eq. (2). Then, the matrix is defuzzified by Eq. (3). Equation (11) is considered to determine the optimal values. Then, N matrix is obtained with Eq. (13). Using the weights in Table 5, the Z matrix is obtained with the help of Eq. (15). The result is denoted in Table 7.

Table 7.

Z matrix.

CNGS STGY PTMS LWTT FTPG FBXT G
SCPC 1.1132 1.2217 1.1605 1.0890 1.1056 1.0987 0,1305
LLBT 1.0227 1.0907 1.0908 1.0035 1.0370 1.0121 0,0422
STBC 1.1449 1.2557 1.2048 1.1271 1.1258 1.1416 0,1657
CSCR 1.0041 1.0518 1.0449 1.0108 1.0198 1.0331 0,0273
MDGG 1.0584 1.0907 1.0322 1.0427 1.0301 1.0146 0,0445
SBYL 1.0284 1.1061 1.0522 1.0183 1.0559 1.0217 0,0467
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Alternatives are ranked with Eq. (16). The biggest G gives information about the significance of the alternative. Table 8 shows that smart battery control systems (STBC) play the most crucial role for the improvements of the battery integration to microgrids because of the highest G value (0,1657). Storage capacity of microgrids (SCPC) is another key issue to achieve this objective. The G value of this alternative is 0,1305. Long-lasting battery selection and compatibility with safety constructions are on the last ranks with G values of 0,0422 and 0,0273. The results of ranking are given in Fig. 3.

Table 8.

Results of sensitivity analysis with simulation modeling.

Sim_1 Sim_2 Sim_3 Sim_4 Sim_5 Sim_6 Sim_7 Sim_8 Sim_9 Sim_10
SCPC 2 2 2 2 2 2 2 2 2 2
LLBT 5 5 5 5 5 5 5 5 5 5
STBC 1 1 1 1 1 1 1 1 1 1
CSCR 6 6 6 6 6 6 6 6 6 6
MDGG 4 4 4 4 4 4 4 4 4 4
SBYL 3 3 3 3 3 3 3 3 3 3
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Fig. 3.

Fig. 3

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The ranks of alternatives.

Sensitivity analysis

Sensitivity analysis is performed to validate the analysis results for ranking technical requirements for battery integration to microgrids. In sensitivity analysis, weights are obtained for ten different cases. Ten different scenarios are obtained by simulation. The analysis findings of the study are used as the necessary input for simulation modeling. The new weights obtained as a result of the simulation modeling are used to rank the alternatives. In this process, different weights are taken into consideration to check the coherency of the findings. Then, the alternatives are ranked again based on these weights and the results are compared. The analysis of the sensitivity is shown in Table 8. Sensitivity analysis results support ranking obtained from SFPFY RATGOS analysis.

It is concluded that the results are the same for all different scenarios. Hence, it can be understood that the proposed model provides coherent findings. This situation gives information about the quality of the strategies. Therefore, this model has a powerful leading impact for the investors to make their strategic decisions. Figure 4 is about results of ranking.

Fig. 4.

Fig. 4

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The results of ranking for cases.

Comparative analysis using STPYF TOPSIS

The result of ranking of technical requirements for battery integration to microgrids is compared with a second ranking algorithm to demonstrate the robustness of the ranking results. The results of the RATGOS method are compared with the STPYF-TOPSIS method to demonstrate the reliability of the findings. The main results are denoted in Table 9.

Table 9.

Result of two analysis.

STPYF RATGOS STPYF TOPSIS
CNGS 0.1305 0.852
STGY 0.0422 0.019
PTMS 0.1657 1.00
LWTT 0.0273 0.004
FTPG 0.0445 0.046
FBXT 0.0467 0.038
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According to the results of the two methods used to rank the alternatives, the ranking is depicted in Fig. 5.

Fig. 5.

Fig. 5

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Ranking results of alternatives.

Figure 5 identifies that the findings are similar for RATGOS and TOPSIS. It is concluded that the proposed model generates reliable solutions. RATGOS technique is a new approach to rank the alternatives. The main reason of introducing RATGOS is the criticisms to the existing ranking models. Although there are also some criticisms to the TOPSIS technique, this methodology is taken into consideration to compare the ranking results. The TOPSIS model calculates the distances to the optimal and negative optimal values ​​using the Euclidean distance. However, some studies have indicated that this approach may not effectively calculate the distance to the negative optimal value. On the other hand, TOPSIS is currently one of the most preferred techniques in the literature. Although there are criticisms in this case, it is thought that the comparative analysis to be made with TOPSIS will be the most optimal solution. The fact that the model proposed with RATGOS produces similar results to TOPSIS shows that our model provides consistency. Similarly, it is understood that this situation is compatible with existing ranking techniques.

Conclusion

In this study, it is purposed to evaluate the significance of microgrids and the critical role of battery integration in addressing challenges related to customer and technical requirements based on HoQ. Within this framework, six different customer expectations and technical requirements are selected by considering literature review results. The criteria are weighted by using STPYF DEMATEL. In addition, technical requirements are also ranked STPYF RATGOS. It is defined that efficiency of storing energy is the most important customer expectation to increase the effectiveness of this process. Moreover, the ranking results also indicate that generating smart battery control systems is the most important technical requirements to have higher performance in microgrid energy systems.

It is understood that smart battery control systems are very necessary to increase the efficiency of battery integration in microgrid systems. These systems help optimize energy-related processes in microgrids. Owing to this system, data can be followed instantly and analyzed effectively. Thus, it is possible to predict more accurately the amount of energy to be used in the grid. This also contributes to the increase in the efficiency of energy storage processes. These systems provide up-to-date information about the charging and discharging times of the batteries. This allows both energies to be used more efficiently and the lifetime of the batteries to be increased. Furthermore, smart battery control systems also enable the problems in the process to be detected at an early level. In this way, it is possible to take the necessary actions in a timely manner to solve the problems more quickly. To effectively develop smart battery control systems in microgrid environments, information security plays a critical role. This is primarily necessary to take the necessary security measures against cyber-attacks. Cyber-attacks can target these control systems and cause batteries to operate incorrectly. This can cause significant disruptions in energy production processes. Similarly, because of these attacks, system data can be manipulated. This can also cause batteries to charge at the wrong time. Therefore, necessary measures for information security must be taken at all stages from the design to the implementation of the systems.

The main contribution of this study is that it can be much easier to increase the efficiency of battery integration in microgrid systems by making the priority analysis. Moreover, proposing a new ranking-based decision-making technique (RATGOS) positively affect methodological originality. Nonetheless, the main limitation of this study is that a country-based evaluation is not conducted. The performance of the renewable energy projects can be changed according to the climatical conditions. Therefore, the effectiveness of the microgrid systems can vary in different countries. Hence, for future research direction, a specific country can be examined based on the performance of battery integration to the microgrid systems. Additionally, proposed model can also be improved in the following studies. In this context, different fuzzy sets, such as Gaussian numbers are a good alternative for managing uncertainties in decision making more effectively. In addition to this situation, another critic limitation of this proposed model is that the average of the evaluations is computed to create decision matrix. In other words, the weights of all experts are assumed as equal. However, the experts can have different qualifications, such as educational background and working experience. Therefore, in the following decision-making models, the weights of the experts can be calculated. Similarly, the facial expressions of the experts can also be taken into consideration in the following decision-making models. The experts may feel hesitancy by giving answers for some questions. Thus, with the help of considering facial expressions, this hesitancy can be managed more effectively. On the other side, the concepts of exploring emerging battery technologies or optimization techniques for microgrid energy storage systems can be evaluated as a future research direction.  

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (15.6KB, docx)

Acknowledgements

The research of Jaehyung An is funded by Hankuk University Research Fund, Seoul, South Korea.

Author contributions

A.M. wrote the main manuscript text and S.Y., H.D., S.E., Y.G., J.A. prepared figures. All authors reviewed the manuscript.

Data availability

Mikhaylov, Alexey (2024), “Linguistic terms”, Mendeley Data, V1, doi: 10.17632/g2h9zx2mxm.1.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1038/s41598-024-83370-y

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Change history

12/18/2024

This article has been retracted. Please see the Retraction Notice for more detail: 10.1038/s41598-024-83370-y

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Supplementary Material 1 (15.6KB, docx)

Data Availability Statement

Mikhaylov, Alexey (2024), “Linguistic terms”, Mendeley Data, V1, doi: 10.17632/g2h9zx2mxm.1.

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