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

Transcription

1 Energes 2013, 6, ; do: /en Artcle OPEN ACCESS energes ISSN A Combned Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selecton n Turkey Alev Taskn Gumus 1, *, A. Yesm Yayla 2, Erkan Çelk 1 and Aytac Yldz Department of Industral Engneerng, Mechancal Faculty, Yldz Techncal Unversty, İstanbul 34349, Turkey; E-Mal: erkcelk@yldz.edut.tr Department of Mechancal Engneerng, Faculty of Technology, Marmara Unversty, İstanbul 34722, Turkey; E-Mal: yayla@marmara.edu.tr Automotve Technologes Program, Amasya Vocatonal School, Amasya 5100, Turkey; E-Mal: aytac.yldz@amasya.edu.tr * Author to whom correspondence should be addressed; E-Mal: ataskn@yldz.edu.tr; Tel.: ; Fax: Receved: 24 Aprl 2013; n revsed form: 30 May 2013 / Accepted: 13 June 2013 / Publshed: 20 June 2013 Abstract: In ths paper, we am to select the most approprate Hydrogen Energy Storage (HES) method for Turkey from among the alternatves of tank, metal hydrde and chemcal storage, whch are determned based on expert opnons and lterature revew. Thus, we propose a Buckley extenson based fuzzy Analytcal Herarchcal Process (Fuzzy-AHP) and lnear normalzaton based fuzzy Grey Relatonal Analyss (Fuzzy-GRA) combned Mult Crtera Decson Makng (MCDM) methodology. Ths combned approach can be appled to a complex decson process, whch often makes sense wth subjectve data or vague nformaton; and used to solve to solve HES selecton problem wth dfferent defuzzfcaton methods. The proposed approach s unque both n the HES lterature and the MCDM lterature. Keywords: hydrogen energy storage; fuzzy-ahp; fuzzy-gra 1. Introducton Turkey s n a poston of havng a wde range of energy sources, but t s also a country wth a wde gap n ts energy demands [1]. It mports more than 60% of ts energy demand and ths rato has been

2 Energes 2013, ncreasng contnuously. Ths stuaton causes the country to seek new and renewable alternatve energy resources. Among the alternatve sources, solar, wnd, bomass and hydrogen are unlmted, and among these alternatves, due to ts numerous features, the best one s hydrogen energy (HE) [2]. Alternatve sources to produce hydrogen fuel n Turkey are hydraulc, solar, wnd, sea-wave, geothermal and nuclear energes. From the perspectve of developng countres and countres at technologcally transtonal stage, t seems that, n the long range, a photo-voltac solar-hydrogen system seems to be the rght choce. One of the advantages of Turkey s that t has a rather long Black Sea coastlne for preservng stored HE n chemcal form n the seabed [3]. It s rather mportant to note that the Unted Natons Industral Development Organzatons Internatonal Centre for Hydrogen Energy Technologes (UNIDO-ICHET) was establshed n Istanbul and ths provdes the country wth consderable opportuntes. Turkey has a chance to follow all the technologcal progress and t could also provde the county an opportunty to export ts know-how and transfer technology on the subject. Wth the help of the UNIDO-ICHET, Turkey could be one of the centre n the world to produce the hydrogen related technologes and products [4]. It s mportant not to mss the fact that as the natonal petroleum producton does not meet the needs of the country s consumpton, a remarkable amount of the naton s petroleum consumpton s mported, causng an ncreasng detrmental gap n the naton s economc development. At that stage, among the alternatve fuels, hydrogen has an mportant potental role to play. Currently, the soluton of the hgh storage costs, whch s the most dffcult hurdle n HE, wll be a bg advantage for a county. Ths advantage s manly due to hybrd hydrogen technology together wth the exstence of remarkable amount of the world s boron mnng reservors n Turkey [5]. For both fxed and portable applcatons, storage of hydrogen n an effcent and relable manner s requred. Hydrogen can be stored n tanks as gas or lqud n the pure form, physcally n the form of nanotubes or chemcally as a hydrde. In ths paper, based on expert opnons and lterature revew we am to determned the most approprate HES method for Turkey among the alternatves of tank, metal hydrde and chemcal storage. Thus, we propose a Buckley extenson based Fuzzy-AHP and lnear normalzaton based Fuzzy-GRA combned MCDM methodology. Ths combned approach can be appled to complex decson processes, whch often make sense wth subjectve data or vague nformaton; and used to solve Fuzzy-MCDM problems wth dfferent defuzzfcaton methods. In ths study, we prefer Buckley s Fuzzy-AHP method. The man advantage of the Fuzzy-AHP method s that t handles multple crtera wth relatve ease [6]. Due to the dffculty of provdng determnstc preferences for decson makers, percepton-based judgment ntervals can be used nstead. Furthermore It s easer to understand and can effectvely handle both qualtatve and quanttatve data. The use of Fuzzy-AHP reflects human thnkng style and does not nvolve cumbersome mathematcs. The proposed approach s unque both n the HES lterature and the MCDM lterature. We organze our paper as follows: frst, we present a lterature revew on the use of Fuzzy-AHP and Fuzzy-GRA technques n the energy feld, and especally HE. Then, we defne the computatonal detals of Buckley extenson based Fuzzy-AHP and lnear normalzaton based Fuzzy-GRA methods. Fnally, we realze a numercal applcaton for Turkey, and show the applcablty of our methodology wth the dscussons and conclusons.

3 Energes 2013, Lterature Revew In ths secton, we revew separately the Fuzzy-MCDM and Fuzzy-GRA lterature n the energy feld. The man am here s to menton the gaps n the energy lterature based on Fuzzy-MCDM usage and pont out the necessty and contrbutons of the proposed methodology Fuzzy-MCDM Lterature There s a wde range usage of Fuzzy-MCDM methods n the lterature. Here, the usage of Fuzzy-MCDM methods n energy sector s revewed. Tzeng et al. [7] apply AHP to determne the relatve weghts of evaluaton crtera. TOPSIS and VIKOR are compared and appled to determne the best compromse alternatve fuel buses for publc transportaton. Wang et al. [8] evaluate coal, petroleum, natural gas, nuclear and renewable energy resources as energy alternatves for Chna through use of a herarchcal decson model. Kaya and Kahraman [9] am at determnng the best renewable energy alternatve for Istanbul by usng an ntegrated VIKOR-AHP methodology. Cavallaro [10] used s Fuzzy-TOPSIS approach for assessng thermal-energy storage n concentrated solar power systems. Kahraman and Kaya [11] utlze Fuzzy-AHP to select the best energy polcy for Turkey. Shen et al. [12] present an assessment model for renewable energy sources n Tawan by usng Fuzzy-AHP. A modfed Fuzzy-TOPSIS methodology s proposed for the selecton of the best energy technology alternatve n Kaya and Kahraman s [13] paper. In ther study, Erol and Kılkış [14] use an AHP method to facltate energy resource plannng actvtes. Jn et al. [15] propose an ntegrated Fuzzy-MCDM process to assess the comprehensve benefts of combned coolng, heatng and power (CCHP) systems by consderng technology, economy, socety and envronment as decson crtera. Tsta and Plavach [16] present a methodology to evaluate alternatve fuels for the Greek road transport sector usng AHP. Scott et al. [17] present a detaled revew of mult-crtera decson-makng methods for boenergy systems. Dam et al. [18] present a method to evaluate energy storage technologes for nvestor-owned or publc utltes by ntegratng Fuzzy-Delph method, AHP and fuzzy consstency matrx. There are lmted number of works n the lterature usng Fuzzy-MCDM methods for problems related to HE and HES. McDowall and Eames [19] emphasze the HES methods ncrease n R&D fundng. McDowall and Eames [20] examne hydrogen economy usng a mult-crtera mappng approach to decde between sx potental HE systems for the UK. Lee et al. [21] analyze the potental of Korea to be compettve n development of HE technology usng a Fuzzy-AHP approach. Chang et al. [22] use Fuzzy-Delph methodology to evaluate hydrogen producton technologes for Tawan. Lee et al. [23] propose an ntegrated Fuzzy-AHP and Data Envelopment Analyss (DEA) approach for measurng the relatve effcency of HE technologes for mplementng the hydrogen economy. Lee et al. [24] suggest a methodology to prortze the relatve weghts of HE technologes and HE technology roadmap as they allocate R&D budget effectvely by usng a Fuzzy-AHP. Chang et al. [25] am to develop an assessment model to evaluate hydrogen fuel cell applcatons by usng Fuzzy-MCDM. Lee et al. [26] appled the ntegrated Fuzzy AHP and the data envelopment analyss (DEA) for measurng the relatve effcency of the R&D performance n the natonal hydrogen energy technology development performance n the natonal HE technology development.

4 Energes 2013, Fuzzy-GRA Lterature Here the energy lterature usng GRA and Fuzzy-GRA methods s revewed. GRA s a MCDM method, whch s orgnally proposed by Deng [27]. It s appled n solvng a varety of MCDM problems. Chang and Ln [28] apply GRA to analyze how energy-nduced CO2 emssons from 34 ndustres n Tawan are affected by producton, total energy consumpton, coal, ol, gas and electrcty uses. Lang [29] proposes GRA to schedule hydroelectrc generatons. Chen [30] proposes a combned GRA and AHP method for dstrbuton network reconfguraton. Chang and Chang [31] present a GRA model for the optmzaton of the wre electrc dscharge machnng process of partcle-renforced materal. Lu et al. [32] propose GRA to evaluate the relatve nfluence of the fuel prce, the gross domestc product, number of motor vehcles and the vehcle klometres of travel per energy ncrease. Wang [33] apples Fuzzy-GRA method to evaluate fnancal performance of Tawan contaner lnes. Azzeh et al. [34] propose fuzzy set theory wth GRA for software effort estmaton. Tseng [35] proposes a combned GRA methodology for suppler evaluaton of envronmental knowledge management capactes. Lee and Ln [36] evaluate and rank energy performances of offce buldngs by usng GRA. We [37,38] uses a GRA wth ntutonstc fuzzy nformaton n whch the nformaton about attrbute weghts s ncompletely known. Ln and Wu [39] propose a GRA applcaton for analysng the credt rsks of bankng ndustry. Pophal et al. [40] ntegrate AHP and GRA for optmal selecton of full scale tannery effluent treatment plants. Kuo and Lang [41] combne fuzzy VIKOR and GRA methods to evaluate servce qualty of arports. Zhang and Lu [42] propose a GRA based ntutonstc Fuzzy-MCDM method and apply t to personnel selecton problem. Manya and Bhatt [43] propose a modfed GRA and AHP method for selectng automated guded vehcles for materal handlng. Samved et al. [44] combne Fuzzy-AHP and GRA methods for machne tool selecton. Chen and Chen [45] propose and ntegrate DEMATEL, Fuzzy-AHP and GRA. Palankumar et al. [46] use GRA for optmzng the drllng parameters of composte materals. 3. The Methods Used n the Proposed Methodology In ths secton, the Buckley extenson based fuzzy-ahp algorthm and lnear normalzaton based fuzzy-gra method are presented Buckley Extenson Based Fuzzy-AHP Algorthm In order to deal wth the uncertanty and vagueness from the subjectve percepton and the experence of humans n the decson-makng process, decson-makers usually come across wth the fact that t s more secure to gve nterval judgments than fxed-value judgments. Ths s manly due to the fact that he/she s unable to explct about hs/her preferences due to the fuzzy nature of the comparson process [47 55]. We utlse Buckley s Fuzzy-AHP algorthm to determne crtera weghts snce t s easy to extend to the fuzzy case and guarantees a unque soluton to the recprocal comparson matrx and the steps of ths approach are relatvely easer than the other Fuzzy-AHP approaches. The steps used for the Buckley s Fuzzy-AHP algorthm can be summarzed as follows [51]:

5 Energes 2013, Step 1. Construct parwse comparson matrces among all the crtera n the herarchcal structure. Assgn lngustc terms shown n Equaton (1), to the parwse comparsons by askng whch s the more mportant of each two crtera, such as: 1 a a 1 a a 12 1n 12 1n a 1 a 1/ a 1 a M 21 2n 21 2n = = a a 1 1/ a a 1 n1 n2 n1 n2 (1) where: 1, 3, 5, 7, 9 crteron has relatve mportance to crteron j a j = 1. = j , 3, 5, 7, 9 crteron has less mportance tocrteron j (2) Step 2. Examne the consstency of fuzzy parwse comparson matrces. Step 3. Use geometrc mean technque to defne the fuzzy geometrc mean as follows: where a n ( ) 1/ n r = a a a 1 2 n (3) s fuzzy comparson value of crteron to crteron n, thus, s geometrc mean of fuzzy comparson value of crteron to each crteron. Step 4. Calculate the fuzzy weghts of each crteron usng Equaton (4): ( ) 1 w = r r1 r2 r n (4) where w s the fuzzy weght of the th crteron, can be ndcated by w = ( lw, mw, uw). Here lw, mw, and uw stand for the lower, mddle and upper values of the fuzzy weght of the th crteron. Step 5. Utlze Center of Area (COA) method to fnd out the Best Nonfuzzy Performance (BNP) value (crsp weghts) of each crteron by the Equaton (5): BNPw = [( uw lw ) + ( mw lw )]/ 3 + lw (5) Accordng to the value of the derved BNP for each of the alternatves, the rankng of the each alternatve can then proceed Lnear Normalzaton Based Fuzzy GRA Method The steps of the Fuzzy-GRA algorthm can be outlned as follows [37,38,41]: Step 1. In the frst step, a panel of Decson Makers (DMs) who are knowledgeable about the HES process s establshed. In a group that has K decson-makers (.e., DM 1, DM 2,..., DM k ) are responsble for rankng (y jk ) of each crteron (.e., C 1,C 2,..., C n ) n ncreasng order: K 1 1 K 1 e xj = xj... xj xj K + + = (6) K e = 1

6 Energes 2013, Step 2. Calculate the normalzed decson matrx R. Gven x = ( a, b, c ) the normalzed performance ratng can be calculated as: j j j j aj bj c j r j =,,, = 1,..., m; j = 1,..., n for J B cj cj c (7) j aj aj a j rj =,,, = 1,..., m and j = 1,..., n for J C cj bj a j (8) c j = max c and j a j = mn{ aj} = 1,..., m (9) where { } Step 3. Determne the reference seres. The reference seres can be defned as: [ ] 0 = 01, 02,..., 0n, where 0 j = max( j) = 1,..., (10) R r r r r r j n Step 4. Establsh the dstance matrx. The dstance δ j between the reference value and each comparson value s gven as: j r 0 j r j (11) δ = Step 5. Calculate the fuzzy grey relatonal coeffcent. The fuzzy grey relatonal coeffcent ξ j s defned as: δ mn + ζδ max ξj = δmax = max( δj ), δmn = mn( δj ) δ + ζδ j max and ζ resolvng coeffcent ζ [ ] Step 6. Estmate the fuzzy grey relatonal grade γ by the relaton: 0,1. (12) n γ = w ξ, = 1,..., m j j j= 1 (13) where w s the weght of the jth crteron, and j n j= 1 w = 1. Step 7. Apply defuzzfcaton wth respect to center of area and α-cut method Center-of-Area Defuzzfcaton j The center-of-area defuzzfcaton method [56] s a way of transformng fuzzy trangular numbers nto crsp numbers. Ths method can determne actual HES prortes and overall scores. For a convex fuzzy numberγ, a real number * x correspondng to ts center of area of γ can be estmated as [Equaton (14)]: x * μ ( x) xdx γ = μ γ ( x) dx (14)

7 Energes 2013, α-cut Method The α-cut method s used n our paper to valdate the methodology results and performance of the proposed soluton. The α-cut method compares two fuzzy numbers A and B n terms of ther α-cuts A = a, a and B = b, b [57,58]. α + α α α + α α The α-cuts can be appled to transform the total weghted performance matrces nto nterval performance matrces, whch provde αleft and α rght for each alternatve as follows: ( αleft 1, αrght 1) ( αleft, αrght )... ( αleft 1, αrght 1) 1 1 p α = αleft = α (m-l) + l αrght = u- α ( u m) (15) And then the nterval matrces are converted nto crsp values. It s done by applyng the λ functon and λ values are ranged between 0 and 1: Cλ1 Cλ 2 Cλ = Cλ = λ αrgh t + (1 λ) αleft... (16) Cλm Step 8. Rank the alternatves n accordance wth the value of grey relatonal grade; the bgger the value s, the better s among the alternatves. 4. Numercal Applcaton In ths secton, a numercal applcaton of the descrbed method s presented. For the numercal applcaton a decson makng group formed. Three experts, the frst one s the drector of fuel cell technology and educaton n UNIDO-ICHET n Turkey, the second one s an academcan specalsed n renewable energy sources and fnally the thrd one s also an academcan specalsed n MCDM, are nvolved n the group. The branstormng technque s used for qualtatve and quanttatve group consensus. The group s led by the drector of fuel cell technology and educaton to ensure an effectve branstormng sesson and the agreement of the group members. The numercal values for most of the alternatves based on each crteron are objectve (except relablty), but the mportance levels by comparng alternatves wth each other may be subjectve. By takng nto account ths probablty, we appled expert judgments. Wth ths method, the HES alternatves, whch are determned on the bass of the group decson and lterature revew, are evaluated and the most approprate one for Turkey s selected by consderng several decson crtera The Methodology In ths study, a novel methodology ntegratng Fuzzy-AHP and Fuzzy-GRA approaches s proposed. In ths methodology, Fuzzy-AHP s utlzed n decson crtera weghts evaluaton and then the rankng of the alternatves are determned va Fuzzy-GRA approach. Fgure 1 shows the detals of the proposed methodology.

8 Energes 2013, Fgure 1. The proposed combned Fuzzy-AHP and Fuzzy-GRA methodology. Frst, the group members (GMs) are asked to compare the crtera consderng the effects on selectng the best storage method and the effects on the other crtera n the Fuzzy-AHP phase. In ths process, we consder a lngustc scale for relatve mportance that s shown n Equatons (1,2). In the Fuzzy-GRA phase, the GMs are asked to evaluate alternatves consderng each crteron. The lngustc scale and correspondng trangular fuzzy numbers are llustrated n Table The Evaluaton Procedure Table 1. Lngustc terms and correspondng fuzzy numbers. Lngustc varable Fuzzy number Very Low (VL) (1,2,3) Low (L) (2,3,4) Farly Low (FL) (4,5,6) Medum (M) (5,6,7) Farly Hgh (FH) (7,8,9) Hgh (H) (8,9,10) Durng the evaluaton procedure, frst the crtera herarchy s structured. Then the combned Fuzzy-AHP and Fuzzy-GRA methodology computatons and results are presented. Fnally, the fnal decson s dscussed numercally by usng grey relatonal coeffcent Determnaton of Crtera and Alternatves In ths study, fve crtera are used for HES method selecton. The group decson s taken nto account whle determnng these crtera and ther herarchy. The herarchcal structure of the proposed methodology s shown n Fgure 2. The crtera consdered here are weghtlessness, capacty, storage loss and leak, relablty and total system cost.

9 Energes 2013, Fgure 2. The HES method selecton herarchy Applcaton of the Combned Fuzzy-AHP and Fuzzy-GRA Methodology The alternatves are consstng of tank (A1), metal hydrde (A2) and chemcal (A3) storage for Turkey case. And the crtera are determned as weghtlessness (C1), capacty (C2), storage loss and leak (C3), relablty (C4), total system cost (C5). The crtera are non-benefcal except C2 and C4. Then crtera nfluencng the HES are gathered through extensve lterature revew and decson of GMs. Table 2 summarzes the factors derved from the related lterature. Table 2. Summary of lterature revew to evaluaton crtera. Crtera Sources C1. Weghtlessness Amos [59], Chalk and Mller [61] C2. Capacty Lee et al. [23,24], Amos [59], İbrahm et al. [60],Chalk and Mller [61] C3. Storage loss and leak Amos [59] C4. Relablty Wang et al. [8], Kaya and Kahraman [9], Kahraman and Kaya [11], Kaya and Kahraman [13], Erol and Kılkış [14], Amos [59], İbrahm et al. [60], Wang et al. [8], Kaya and Kahraman [9], Kahraman and Kaya [11], C5. Total system cost Kaya and Kahraman [13], Jng et al. [15], McDowall and Eames [19], Chang et al. [22], Lee et al. [24], Amos [59], İbrahm et al. [60] The respectve terms for descrbng the mportance of materal wth respect to crtera assessed by GMs are shown n Table 3. Table 3. Importance of storage alternatves wth respect to crtera assessed by decson makers. Decson Makers Alternatves C1 C2 C3 C4 C5 A1 H H VL M FL DM1 A2 L M VL H VL A3 L M L FH VL A1 FH H L M L DM2 A2 L FH L FH L A3 L M VL H VL A1 H M FL FH FL DM3 A2 L H FL H VL A3 FL H L FH L

10 Energes 2013, The aggregated matrx for storage ratngs are calculated by usng Equaton (6) and t s shown n Table 4. Table 4. The Aggregated fuzzy values of alternatves. 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) Hgher values, that we call postve crtera or benefcal attrbutes, are desrable and smaller values are named negatve crtera or cost crtera. In ths normalzaton method, the cost crteron (C) dvded by the mnmum value and the beneft crteron (B) s dvded by the maxmum value of the decson matrx by usng Equatons (7 9) and the normalzed values are shown n Table 5. Table 5. The normalzed matrx. 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) The dstance of each canddate from the reference seres s calculated by usng Equatons (10 12). The results are shown n Table 6. Table 6. The references seres and the dstance matrx. C1 C2 C3 C4 C5 Reference seres (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) The Fuzzy Grey Relatonal Coeffcent (FGRC) s calculated by applyng Equaton (12) and t s shown n Table 7. Here, we use the resolvng coeffcent ζ = 0,5 to calculate the FGRC. Table 7. The fuzzy grey relatonal coeffcent. 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) The fuzzy relatonal grade s calculated [Equaton (13)] wth respect to the crtera weghts, whch are obtaned by Fuzzy-AHP [Equatons (3 5)], and s shown n Table 8. Also, the consstency rato (CR) of crtera parwse comparson matrx s computed to show the consstency of the experts. The CR value for the defuzzfed verson of the aggregated fuzzy evaluaton matrx s computed as 0.03

11 Energes 2013, and t s less than The results show that the decson matrx of the proposed herarchcal structure s consstent: λ n 5,136 5 CI. 0, 034 CI. = = = 0, 034; CR. = = = 0,030 n RI. 1,12 Table 8. The fuzzy grey relatonal grade. 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) Here we use each resolvng coeffcent value to demonstrate that each of them does not affect the results. Frst, t s assumed that resolvng coeffcent value ζ = 0.5, then the grey relatonal grade s obtaned by COA as > > Smlarly, the grey relatonal grade s obtaned by α-cuts as > > Accordng to results of COA and α-cuts, the rankng order of the three HES s A2 > A3 > A1. The most approprate HES method for Turkey s metal hydrde Numercal Example Results and Dscusson Frst, the concepts of senstvty analyss are used to verfy that the proposed method has ratonalty and stablty, when the condtons of resolvng coeffcent value and defuzzfcaton methods do not affect the results. Ths study uses each resolvng coeffcent value to demonstrate that each of them does not affect the results. These and the prevous results are the same, as shown n Fgure 3. The resolvng coeffcent values on the x axs and the defuzzfed results of the proposed methodology on the y axs are gven. x* (COA) and C ʎ (α-cut) method results are shown n the fgure, separately. The resolvng coeffcent values are used to examne the proposed approach between ζ = 0.1 and ζ = 1. The results show that the varaton of the x* and C ʎ values of each alternatve by usng varous resolvng coeffcent values, and also that the rankng orders of the three alternatves are the same, despte changng from a resolvng coeffcent value of ζ = 0.1 to ζ = 1. Therefore, ths paper can confrm that the results of the rankng orders of all alternatves by usng the proposed approach are relable. Then, ths study hghlghts that varous resolvng coeffcent values do not affect the results of rankng order of the three HESs and t s shown n Table 9 and Fgure 3. Fgure 3. Varaton analyss of ζ and defuzzfcaton method value for each alternatve (HES) ζ=0,1 ζ=0,2 ζ=0,3 ζ=0,4 ζ=0,5 ζ=0,6 ζ=0,7 ζ=0,8 ζ=0,9 ζ=1,0 ζ=0,1 ζ=0,2 ζ=0,3 ζ=0,4 ζ=0,5 ζ=0,6 ζ=0,7 ζ=0,8 ζ=0,9 ζ=1,0 A1 A2 A3 COA α-cut method

12 Energes 2013, Table 9. The values x* and C λ based on each grey relatonal coeffcent (ζ ). ζ = 0.1 ζ = 0.2 ζ = 0.3 ζ = 0.4 ζ = 0.5 x * C λ x * C λ x * C λ x * C λ x * C λ A A A ζ = 0.6 ζ = 0.7 ζ = 0.8 ζ = 0.9 ζ = 1.0 x * x * x * C λ x * C λ x * C λ x * C λ A A A Conclusons Ths work s amed to select the most approprate HES n Turkey among the alternatves of tank, metal hydrde and chemcal storage. Ths s determned based on decson-makng group judgment and lterature revew. Thus, t s proposed to utlze a Buckley extenson based Fuzzy-AHP and lnear normalzaton based Fuzzy-GRA combned MCDM methodology. The contrbutons of the paper to the lterature are as follows: (1) It presents a combned approach that can be appled to complex decson processes, whch often make sense wth subjectve data or vague nformaton; (2) Dfferent defuzzfcaton methods such as COA and α-cut are used to solve the HES selecton problem at the tactcal level; (3) The proposed approach s unque both n the HES lterature and the MCDM lterature. As a future suggeston, the hydrogen transportaton method selecton problem can be analyzed by takng nto account the storage method selecton problem. Furthermore, dfferent herarchcal and detaled crtera consstng socal, poltcal, envronmental and market crtera can be ncorporated nto the study. The proposed model has potental to help wth much more complex problems whch may or may not have many more herarchcal levels. Fnally, a smlar problem can be modeled by consderng the condtons n dfferent countres outsde Turkey. Acknowledgement The authors would lke to thank the drector of fuel cell technology & educaton n UNIDO-ICHET n Turkey for hs valuable contrbuton and dscusson. Conflct of Interest The authors declare no conflct of nterest. References 1. Hamzaçeb, Ç. Forecastng of Turkey s net electrcty energy consumpton on sectoral bases. Energy Polcy 2007, 35,

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16 Energes 2013, Chalk, S.G.; Mller, J.F. Key challenges and recent progress n batteres, fuel cells, and hydrogen storage for clean energy systems. J. Power Sources 2006, 159, by the authors; lcensee MDPI, Basel, Swtzerland. Ths artcle s an open access artcle dstrbuted under the terms and condtons of the Creatve Commons Attrbuton lcense (