# A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Fuzzy-MCDM Literature

#### 2.2. Fuzzy-GRA Literature

## 3. The Methods Used in the Proposed Methodology

#### 3.1. Buckley Extension Based Fuzzy-AHP Algorithm

**Step 1.**Construct pairwise comparison matrices among all the criteria in the hierarchical structure. Assign linguistic terms shown in Equation (1), to the pairwise comparisons by asking which is the more important of each two criteria, such as:

**Step 2**. Examine the consistency of fuzzy pairwise comparison matrices.

**Step 3**. Use geometric mean technique to define the fuzzy geometric mean as follows:

**Step 4.**Calculate the fuzzy weights of each criterion using Equation (4):

**Step 5.**Utilize Center of Area (COA) method to find out the Best Nonfuzzy Performance (BNP) value (crisp weights) of each criterion by the Equation (5):

#### 3.2. Linear Normalization Based Fuzzy GRA Method

**Step 1.**In the first step, a panel of Decision Makers (DMs) who are knowledgeable about the HES process is established. In a group that has K decision-makers (i.e., DM

_{1}, DM

_{2,}..., DM

_{k}) are responsible for ranking (y

_{jk}) of each criterion (i.e., C

_{1},C

_{2}, ..., C

_{n}) in increasing order:

**Step 2.**Calculate the normalized decision matrix R. Given ${\tilde{x}}_{ij}=({a}_{ij},{b}_{ij},{c}_{ij})$ the normalized performance rating can be calculated as:

**Step 3.**Determine the reference series. The reference series can be defined as:

**Step 4.**Establish the distance matrix. The distance ${\tilde{\delta}}_{ij}$ between the reference value and each comparison value is given as:

**Step 5.**Calculate the fuzzy grey relational coefficient. The fuzzy grey relational coefficient ${\tilde{\xi}}_{ij}$ is defined as:

**Step 6.**Estimate the fuzzy grey relational grade ${\tilde{\gamma}}_{i}$ by the relation:

**Step 7.**Apply defuzzification with respect to center of area and α-cut method.

#### 3.2.1. Center-of-Area Defuzzification

#### 3.2.2. α-Cut Method

**Step 8**. Rank the alternatives in accordance with the value of grey relational grade; the bigger the value is, the better is among the alternatives.

## 4. Numerical Application

#### 4.1. The Methodology

Linguistic variable | Fuzzy number |
---|---|

Very Low (VL) | (1,2,3) |

Low (L) | (2,3,4) |

Fairly Low (FL) | (4,5,6) |

Medium (M) | (5,6,7) |

Fairly High (FH) | (7,8,9) |

High (H) | (8,9,10) |

#### 4.2. The Evaluation Procedure

#### 4.2.1. Determination of Criteria and Alternatives

#### 4.2.2. Application of the Combined Fuzzy-AHP and Fuzzy-GRA Methodology

Criteria | Sources |
---|---|

C1. Weightlessness | Amos [59], Chalk and Miller [61] |

C2. Capacity | Lee et al. [23,24], Amos [59], İbrahim et al. [60],Chalk and Miller [61] |

C3. Storage loss and leak | Amos [59] |

C4. Reliability | Wang et al. [8], Kaya and Kahraman [9], Kahraman and Kaya [11], Kaya and Kahraman [13], Erol and Kılkış [14], Amos [59], İbrahim et al. [60], |

C5. Total system cost | Wang et al. [8], Kaya and Kahraman [9], Kahraman and Kaya [11], Kaya and Kahraman [13], Jing et al. [15], McDowall and Eames [19], Chang et al. [22], Lee et al. [24], Amos [59], İbrahim et al. [60] |

Decision Makers | Alternatives | C1 | C2 | C3 | C4 | C5 |
---|---|---|---|---|---|---|

DM1 | A1 | H | H | VL | M | FL |

A2 | L | M | VL | H | VL | |

A3 | L | M | L | FH | VL | |

DM2 | A1 | FH | H | L | M | L |

A2 | L | FH | L | FH | L | |

A3 | L | M | VL | H | VL | |

DM3 | A1 | H | M | FL | FH | FL |

A2 | L | H | FL | H | VL | |

A3 | FL | H | L | FH | L |

C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|

A1 | (7.67, 8.67, 9.67) | (7.00, 8.00, 9.00) | (2.33, 3.33, 4.33) | (5.67, 6.67, 7.67) | (3.33, 4.33, 5.33) |

A2 | (2.00, 3.00, 4.00) | (6.67, 7.67, 8.67) | (2.33, 3.33, 4.33) | (7.67, 8.67, 9.67) | (1.33, 2.33, 3.33) |

A3 | (2.67, 3.67, 4.67) | (6.00, 7.00, 8.00) | (1.67, 2.67, 3.67) | (7.33, 8.33, 9.33) | (1.33, 2.33, 3.33) |

C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|

A1 | (0.21, 0.23, 0.26) | (0.78, 0.89, 1.00) | (0.38, 0.50, 0.71) | (0.59, 0.69, 0.79) | (0.25, 0.31, 0.40) |

A2 | (0.50, 0.67, 1.00) | (0.74, 0.85, 0.96) | (0.38, 0.50, 0.71) | (0.79, 0.90, 1.00) | (0.40, 0.57, 1.00) |

A3 | (0.43, 0.55, 0.75) | (0.67, 0.78, 0.89) | (0.45, 0.63, 1.00) | (0.76, 0.86, 0.97) | (0.40, 0.57, 1.00) |

C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|

Reference series | (0.50, 0.67, 1.00) | (0.78, 0.89, 1.00) | (0.45, 0.63, 1.00) | (0.79, 0.90, 1.00) | (0.40, 0.57, 1.00) |

A1 | (0.29, 0.44, 0.74) | (0.00, 0.00, 0.00) | (0.07, 0.13, 0.29) | (0.21, 0.21, 0.21) | (0.15, 0.26, 0.60) |

A2 | (0.00, 0.00, 0.00) | (0.04, 0.04, 0.04) | (0.07, 0.13, 0.29) | (0.00, 0.00, 0.00) | (0.00, 0.00, 0.00) |

A3 | (0.07, 0.12, 0.25) | (0.11, 0.11, 0.11) | (0.00, 0.00, 0.00) | (0.03, 0.03, 0.03) | (0.00, 0.00, 0.00) |

C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|

A1 | (0.56, 0.46, 0.33) | (1.00, 1.00, 1.00) | (0.67, 0.53, 0.33) | (0.33, 0.33, 0.33) | (0.67, 0.53, 0.33) |

A2 | (1.00, 1.00, 1.00) | (0.60, 0.60, 0.60) | (0.67, 0.53, 0.33) | (1.00, 1.00, 1.00) | (1.00, 1.00, 1.00) |

A3 | (0.84, 0.75, 0.60) | (0.33, 0.33, 0.33) | (1.00, 1.00, 1.00) | (0.75, 0.75, 0.75) | (1.00, 1.00, 1.00) |

C1 | C2 | C3 | C4 | C5 | |
---|---|---|---|---|---|

A1 | (0.04, 0.02, 0.02) | (0.07, 0.05, 0.05) | (0.07, 0.07, 0.05) | (0.08, 0.09, 0.09) | (0.34, 0.27, 0.15) |

A2 | (0.07, 0.05, 0.05) | (0.04, 0.03, 0.03) | (0.07, 0.07, 0.05) | (0.24, 0.26, 0.28) | (0.51, 0.51, 0.46) |

A3 | (0.06, 0.04, 0.03) | (0.02, 0.02, 0.02) | (0.11, 0.12, 0.14) | (0.18, 0.19, 0.21) | (0.51, 0.51, 0.46) |

#### 4.3. Numerical Example Results and Discussion

_{ʎ}(α-cut) method results are shown in the figure, separately. The resolving coefficient values are used to examine the proposed approach between ζ = 0.1 and ζ = 1. The results show that the variation of the x* and C

_{ʎ}values of each alternative by using various resolving coefficient values, and also that the ranking orders of the three alternatives are the same, despite changing from a resolving coefficient value of ζ = 0.1 to ζ = 1. Therefore, this paper can confirm that the results of the ranking orders of all alternatives by using the proposed approach are reliable. Then, this study highlights that various resolving coefficient values do not affect the results of ranking order of the three HESs and it is shown in Table 9 and Figure 3.

**Table 9.**The values x* and ${C}_{\lambda}$ based on each grey relational coefficient $\left(\zeta \right).$

ζ = 0.1 | ζ = 0.2 | ζ = 0.3 | ζ = 0.4 | ζ = 0.5 | ||||||
---|---|---|---|---|---|---|---|---|---|---|

x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | |

A1 | 0.2084 | 0.2069 | 0.3097 | 0.3094 | 0.3848 | 0.3858 | 0.4435 | 0.4455 | 0.4911 | 0.4938 |

A2 | 0.8524 | 0.8539 | 0.8754 | 0.8771 | 0.8917 | 0.8935 | 0.9040 | 0.9058 | 0.9136 | 0.9154 |

A3 | 0.7460 | 0.7484 | 0.8047 | 0.8068 | 0.8394 | 0.8413 | 0.8627 | 0.8645 | 0.8796 | 0.8812 |

ζ = 0.6 | ζ = 0.7 | ζ = 0.8 | ζ = 0.9 | ζ = 1.0 | ||||||

x^{*} | x^{*} | x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | x^{*} | ${C}_{\lambda}$ | |

A1 | 0.5306 | 0.5339 | 0.5641 | 0.5677 | 0.5929 | 0.5968 | 0.6179 | 0.6221 | 0.6400 | 0.6443 |

A2 | 0.9213 | 0.9232 | 0.9277 | 0.9296 | 0.9331 | 0.9350 | 0.9378 | 0.9396 | 0.9418 | 0.9436 |

A3 | 0.8925 | 0.8940 | 0.9027 | 0.9041 | 0.9111 | 0.9124 | 0.9180 | 0.9192 | 0.9239 | 0.9250 |

## 5. Conclusions

## Acknowledgement

## Conflict of Interest

## References

- Hamzaçebi, Ç. Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy
**2007**, 35, 2009–2016. [Google Scholar] - Kaya, D. Renewable energy policies in Turkey. Renew. Sustain. Energy Rev.
**2006**, 10, 152–163. [Google Scholar] [CrossRef] - Ültanır, M.Ö. Hydrogen Energy and the Problems to Enter the Hydrogen Energy in Turkey. In Proceedings of Turkish 6th Energy Congress—Technical Session-1 World Energy Council—Turkish National Committee, Izmir, Turkey, 17–22 October 1994; pp. 549–563.
- Hidrener. Hydrogen Energy [in Turkish]. Available online: http://www.hidrener.com (accessed on 1 April 2012).
- ETKB [in Turkish]. Available online: http://www.enerji.gov.tr (accessed on 1 February 2012).
- Kahraman, C.; Cebeci, U.; Ruan, D. Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. Int. J. Prod. Econ.
**2004**, 87, 171–184. [Google Scholar] [CrossRef] - Tzeng, G.H.; Lin, C.W.; Opricovic, S. Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy
**2005**, 33, 1373–1383. [Google Scholar] [CrossRef] - Wang, B.; Kocaoglu, D.F.; Daim, T.U.; Yang, J. A decision model for energy resource selection in China. Energy Policy
**2010**, 38, 7130–7141. [Google Scholar] [CrossRef] - Kaya, T.; Kahraman, C. Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy
**2010**, 35, 2517–2527. [Google Scholar] [CrossRef] - Cavallaro, F. Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Appl. Energy
**2010**, 87, 496–503. [Google Scholar] [CrossRef] - Kahraman, C.; Kaya, İ. A fuzzy multicriteria methodology for selection among energy alternatives. Expert Syst. Appl.
**2010**, 37, 6270–6281. [Google Scholar] [CrossRef] - Shen, Y.C.; Lin, G.T.R.; Li, K.P.; Yuan, B.J.C. An assessment of exploiting renewable energy sources with concerns of policy and technology. Energy Policy
**2010**, 38, 4604–4616. [Google Scholar] - Kaya, T.; Kahraman, C. Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Syst. Appl.
**2011**, 38, 6577–6585. [Google Scholar] [CrossRef] - Erol, Ö.; Kılkış, B. An energy source policy assessment using analytical hierarchy process. Energy Convers. Manag.
**2012**, 63, 245–252. [Google Scholar] [CrossRef] - Jing, Y.Y.; Bai, H.; Wang, J.J. A fuzzy multi-criteria decision-making model for CCHP systems driven by different energy sources. Energy Policy
**2012**, 42, 286–296. [Google Scholar] [CrossRef] - Tsita, K.G.; Pilavachi, P.A. Evaluation of alternative fuels for the Greek road transport sector using the analytic hierarchy process. Energy Policy
**2012**, 48, 677–686. [Google Scholar] [CrossRef] - Scott, J.A.; Ho, W.; Dey, P.K. A review of multi-criteria decision-making methods for bioenergy systems. Energy
**2012**, 42, 146–156. [Google Scholar] [CrossRef] - Daim, T.U.; Li, X.; Kim, J.; Simms, S. Evaluation of energy storage technologies for integration with renewable electricity: Quantifying expert opinions. Environ. Innov. Soc. Trans.
**2012**, 3, 29–49. [Google Scholar] [CrossRef] - McDowall, W.; Eames, M. Forecasts, scenarios, visions, backcasts and roadmaps to the hydrogen economy: A review of the hydrogen futures literature. Energy Policy
**2006**, 34, 1236–1250. [Google Scholar] - McDowall, W.; Eames, M. Towards a sustainable hydrogen economy: A multicriteria sustainability appraisal of competing hydrogen futures. Int. J. Hydr. Energy
**2007**, 32, 4611–4626. [Google Scholar] [CrossRef] - Lee, S.K.; Mogi, G.; Kim, J.W. The competitiveness of Korea as a developer of hydrogen energy technology: The AHP approach. Energy Policy
**2008**, 36, 1284–1291. [Google Scholar] [CrossRef] - Chang, P.L.; Hsu, C.W.; Chang, P.C. Fuzzy Delphi method for evaluating hydrogen production technologies. Int. J. Hydr. Energy
**2011**, 36, 14172–14179. [Google Scholar] [CrossRef] - Lee, S.K.; Mogi, G.; Lee, S.K.; Kim, J.W. Prioritizing the weights of hydrogen energy technologies in the sector of the hydrogen economy by using a fuzzy AHP approach. Int. J. Hydr. Energy
**2011**, 36, 1897–1902. [Google Scholar] [CrossRef] - Lee, S.K.; Mogi, G.; Li, Z.; Hui, K.S.; Li, S.K.; Hui, K.N.; Park, S.Y.; Ha, Y.J.; Kim, J.W. Measuring the relative efficiency of hydrogen energy technologies for implementing the hydrogen economy: An integrated fuzzy AHP/DEA approach. Int. J. Hydr. Energy
**2011**, 36, 12655–12663. [Google Scholar] [CrossRef] - Chang, P.L.; Hsu, C.W.; Lin, C.Y. Assessment of hydrogen fuel cell applications using fuzzy multiple-criteria decision making method. Appl. Energy
**2012**, 100, 93–99. [Google Scholar] [CrossRef] - Lee, S.K.; Mogi, G.; Lee, S.K.; Hui, K.S.; Kim, J.W. Econometric analysis of the R&D performance in the national hydrogen energy technology development for measuring relative efficiency: The fuzzy AHP/DEA integrateted model approach. Int. J. Hydr. Energy
**2010**, 35, 2236–2246. [Google Scholar] [CrossRef] - Deng, J.L. Introduction to grey system theory. J. Grey Syst.
**1989**, 1, 1–24. [Google Scholar] - Chang, T.C.; Lin, S.J. Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan. J. Environ. Manag.
**1999**, 56, 247–257. [Google Scholar] [CrossRef] - Liang, R.H. Application of grey relation analysis to hydroelectric generation scheduling. Int. J. Electr. Power Energy Syst.
**1999**, 21, 357–364. [Google Scholar] [CrossRef] - Chen, W.H. A grey based approach for distribution network reconfiguration. J. Chin. Inst. Eng.
**2005**, 28, 795–802. [Google Scholar] [CrossRef] - Chiang, K.T.; Chang, F.P. Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis. J. Mater. Proc. Technol.
**2006**, 180, 96–101. [Google Scholar] [CrossRef] - Lu, I.J.; Lin, S.J.; Lewis, C. Grey relation analysis of motor vehicular energy consumption in Taiwan. Energy Policy
**2008**, 36, 2556–2561. [Google Scholar] [CrossRef] - Wang, Y.J. Combining grey relation analysis with FMCGDM to evaluate financial performance of Taiwan container lines. Expert Syst. Appl.
**2009**, 36, 2424–2432. [Google Scholar] [CrossRef] - Azzeh, M.; Neagu, D.; Cowling, P.I. Fuzzy grey relational analysis for software effort estimation. Empir. Softw. Eng.
**2010**, 15, 60–90. [Google Scholar] [CrossRef] - Tseng, M.L. Using linguistic preferences and grey relational analysis to evaluate the environmental knowledge management capacity. Expert Syst. Appl.
**2010**, 37, 70–81. [Google Scholar] [CrossRef] - Lee, W.S.; Lin, Y.C. Evaluating and ranking energy performance of office buildings using Grey relational analysis. Energy
**2011**, 36, 2551–2556. [Google Scholar] [CrossRef] - Wei, G.W. GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting. Knowl.-Based Syst.
**2010**, 23, 243–247. [Google Scholar] [CrossRef] - Wei, G.W. Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making. Expert Syst. Appl.
**2011**, 38, 11671–11677. [Google Scholar] [CrossRef] - Lin, S.L.; Wu, S.J. Is grey relational analysis superior to the conventional techniques in predicting financial crisis? Expert Syst. Appl.
**2011**, 38, 5119–5124. [Google Scholar] [CrossRef] - Pophali, G.R.; Chelani, A.B.; Dhodapkar, R.S. Optimal selection of full scale tannery effluent treatment alternative using integrated AHP and GRA approach. Expert Syst. Appl.
**2011**, 38, 10889–10895. [Google Scholar] [CrossRef] - Kuo, M.S.; Liang, G.S. Combining VIKOR with GRA techniques to evaluate service quality of airports under fuzzy environment. Expert Syst. Appl.
**2011**, 38, 1304–1312. [Google Scholar] [CrossRef] - Zhang, S.F.; Liu, S.Y. A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert Syst. Appl.
**2011**, 38, 11401–11405. [Google Scholar] [CrossRef] - Maniya, K.D.; Bhatt, M.G. A multi-attribute selection of automated guided vehicle using the AHP/M GRA technique. Int. J. Prod. Res.
**2011**, 49, 6107–6124. [Google Scholar] [CrossRef] - Samvedi, A.; Jain, V.; Chan, F.T.S. An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. Int. J. Prod. Res.
**2012**, 50, 3211–3221. [Google Scholar] [CrossRef] - Chen, J.K.; Chen, I.S. A network hierarchical feedback system for Taiwanese universities based on the integration of total quality management and innovation. Appl. Soft Comput.
**2012**, 12, 2394–2408. [Google Scholar] [CrossRef] - Palanikumar, K.; Latha, B.; Senthilkumar, V.S.; Davim, J.P. Analysis on drilling of glass fiber-reinforced polymer (GFRP) composites using grey relational analysis. Mater. Manuf. Proc.
**2012**, 27, 297–305. [Google Scholar] [CrossRef] - Buyukozkan, G.; Feyzioglu, O.; Nebol, E. Selection of the strategic alliance partner in logistics value chain. Int. J. Prod. Econ.
**2008**, 113, 148–158. [Google Scholar] [CrossRef] - Zimmermann, H.J. Fuzzy Set Theory and Its Applications; Kluwer Academic Publishers: London, UK, 1994. [Google Scholar]
- Van Laarhoven, P.J.M.; Pedrycz, W. A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst.
**1983**, 11, 229–241. [Google Scholar] [CrossRef] - Wang, Y.M.; Elhag, T.M.S.; Hua, Z. A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Sets Syst.
**2006**, 157, 3055–3071. [Google Scholar] [CrossRef] - Buckley, J.J. Fuzzy hierarchical analysis. Fuzzy Sets Syst.
**1985**, 17, 233–247. [Google Scholar] [CrossRef] - Chang, D.Y. Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res.
**1996**, 95, 649–655. [Google Scholar] [CrossRef] - Xu, R. Fuzzy least-squares priority method in the analytic hierarchy process. Fuzzy Sets Syst.
**2000**, 112, 359–404. [Google Scholar] [CrossRef] - Mikhailov, L. A fuzzy approach to deriving priorities from interval pair wise comparison judgments. Eur. J. Oper. Res.
**2004**, 159, 687–704. [Google Scholar] [CrossRef] - Csutora, R.; Buckley, J.J. Fuzzy hierarchical analysis: The Lamda-Max method. Fuzzy Sets Syst.
**2001**, 120, 181–195. [Google Scholar] [CrossRef] - Ross, T.J. Fuzzy Logic with Engineering Applications; McGraw-Hill, Inc.: New York, NY, USA, 1995. [Google Scholar]
- Pan, N.F. Fuzzy AHP approach for selecting the suitable bridge construction method. Autom. Constr.
**2008**, 17, 958–965. [Google Scholar] [CrossRef] - Vahidnia, M.H.; Alesheikh, A.A.; Alimohammadi, A. Hospital site selection using fuzzy AHP and its derivatives. J. Environ. Manag.
**2009**, 90, 3048–3056. [Google Scholar] [CrossRef] - Amos, W.A. Costs of Storing and Transporting Hydrogen; National Renewable Energy Laboratory: Denver, CO, USA, 1998. [Google Scholar]
- İbrahim, H.; Ilincaa, A.; Perron, J. Energy storage systems—Characteristics and Comparisons. Renew. Sustain. Energy Rev.
**2008**, 12, 1221–1250. [Google Scholar] [CrossRef] - Chalk, S.G.; Miller, J.F. Key challenges and recent progress in batteries, fuel cells, and hydrogen storage for clean energy systems. J. Power Sources
**2006**, 159, 73–80. [Google Scholar] [CrossRef]

© 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Gumus, A.T.; Yayla, A.Y.; Çelik, E.; Yildiz, A.
A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey. *Energies* **2013**, *6*, 3017-3032.
https://doi.org/10.3390/en6063017

**AMA Style**

Gumus AT, Yayla AY, Çelik E, Yildiz A.
A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey. *Energies*. 2013; 6(6):3017-3032.
https://doi.org/10.3390/en6063017

**Chicago/Turabian Style**

Gumus, Alev Taskin, A. Yesim Yayla, Erkan Çelik, and Aytac Yildiz.
2013. "A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey" *Energies* 6, no. 6: 3017-3032.
https://doi.org/10.3390/en6063017