Hong-ze Li and Sen Guo. 1. Introduction

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1 Mathematial Problems in Engineering Volume 2013, Artile ID , 11 pages Researh Artile External Eonomies Evaluation of Wind Power Engineering Projet Based on Analyti Hierarhy Proess and Matter-Element Extension Model Hong-ze Li and Sen Guo Shool of Eonomis and Management, North China Eletri Power University, Changping Distrit, Beijing , China Correspondene should be addressed to Sen Guo; Reeived 20 Otober 2013; Aepted 24 November 2013 Aademi Editor: Hao-Chun Lu Copyright 2013 H.-z. Li and S. Guo. This is an open aess artile distributed under the Creative Commons Attribution Liense, whih permits unrestrited use, distribution, and reprodution in any medium, provided the original work is properly ited. The external eonomies of wind power engineering projet may affet the operational effiieny of wind power enterprises and sustainable development of wind power industry. In order to ensure that the wind power engineering projet is onstruted and developed in a sientifi manner, a reasonable external eonomies evaluation needs to be performed. Considering the interation relationship of the evaluation indies and the ambiguity and unertainty inherent, a hybrid model of external eonomies evaluation designed to be applied to wind power engineering projet was put forward based on the analyti hierarhy proess (AHP) and matter-element extension model in this paper. The AHP was used to determine the weights of indies, and the matter-element extension model was used to dedue final ranking. Taking a wind power engineering projet in Inner Mongolia ity as an example, the external eonomies evaluation is performed by employing this hybrid model. The result shows that the external eonomies of this wind power engineering projet are belonged to the strongest level, and the degree of inreasing region GDP, the degree of reduing pollution gas emissions, and the degree of energy onservation are the sensitive indies. 1. Introdution With the development of human soiety, the important role of energy in people s daily lives is beoming inreasingly prominent. Nowadays, the energy supply shortage and environmental pollution issues make exploiting and utilizing renewable energy as the fous of worldwide onerns [1. As akindofrenewableenergy,windenergyhastheadvantages of having huge reserves and wide distribution and being renewable and pollution-free [2. In reent years, the installed apaity of wind power in China has been growing rapidly, just as shown in Figure 1, of whih the umulative installed apaity has inreased from 0.3 GW in 2000 to 75.3 GW in In 2010, the umulative installed apaity of wind power in China reahed GW with the annual installed apaity of 16 GW, and China surpassed the United States and ranked the first in terms of umulative installed apaity of wind power at this year [3. However, due to the ontinued growth momentum and the negative impat of large-sale wind power aessing grid, the ratio of annual installed apaity in umulative installed apaity has shown downward trend in the reent years, and the ratio has delined to 17.21% in 2012 from 53.49% in In 2007, the ratio of annual installed apaity in umulative installed apaity reahed the top, whih is 56.45%. External eonomies are benefits that are reated when an ativity is onduted by a ompany or other types of entity, with those benefits enjoyed by others who are not onneted with that entity. The entity that is atually managing the ativity does not reeive the external eonomies, although the reation of these benefits for outsiders usually has no negative impat on that entity [4. Wind power engineering projets have external eonomies whih may affet the onstrution of wind farm, the sustainable development of wind power industry, and even the national energy seurity [5. In order to promote the reasonable onstrution of wind farm and sustainable development of wind power industry, the sientifi and effetive evaluation on external eonomies of

2 2 Mathematial Problems in Engineering Installed apaity (MW) Year The ratio (%) Annual installed apaity Cumulative installed apaity The ratio of annual installed apaity in umulative installed apaity Figure 1: Wind power installed apaity in China: Data soure: Chinese Wind Energy Assoiation (CWEA). wind power engineering projet is neessary. Therefore, the use of ertain models to evaluate the external eonomies of wind power engineering projet is partiularly important. Some studies have been onduted on the wind power projet in the past few years. Zhao et al. [6 analyzedand identified the suess fators ontributing towards the suess of Build-Operate-Transfer (BOT) wind power projets by using an extensive literature survey. Bolinger and Wiser [7 disussed the limitations of inentives in supporting farmer- or ommunity-owned wind projets, desribed four ownership strutures that potentially overome the limitations, and onduted omparative finanial analysis on the four strutures. Agterbosh et al. [8 explored the relative importane of soial and institutional onditions and their interdependenies in the operational proess of planning wind power sheme. In order to avoid the blindness of the urrent wind power integration deision-making, Liu et al. [9 used the improved fuzzy AHP method to evaluate the wind power integration projets by onstruting omplete index system onsidering the harateristis of the wind power integration. Coleman and Provol [10 explained the wind power projets involving many fators that require sophistiated finanial analysis tools for a omplete projet assessment, and it systematially analyzed the eonomi risks in wind power projets in the USA in terms of risk management and risk alloation. Valentine [11ontributed to eonomially optimize wind power projets from the fields of energy eonomis, wind power engineering, aerodynamis, geography, and limate siene, whih identified the ritial fators that influene the eonomi optimization of wind power projets. Zheng et al. [12 analyzed the main influene of wind power projets on environment inluding noise, waste water, solid waste, lighting, eletromagneti radiation, eology, and some ontrol measures were also put forward. Kongnam et al. [13 proposed a solution proedure to determine the optimum generation apaity of a wind park by deision analysis tehniques whih an overome the unertainty problem and refine the investment plan of wind power projets. To analyze the land use issues and onstraints for the development of new wind energy projets, Grassi et al. [14 estimated the average Annual Energy Prodution (AEP) with a GIS ustomized tool, based on physial fators, wind resoure distribution, and tehnial speifiations of the large-sale wind turbines. Georgiou et al. [15 presented a stepwise evaluation proedure for assessing the attrativeness of different developing ountries to host projets on lean tehnologies in the framework of the lean development mehanism (CDM) of the Kyoto Protool (KP) based on multiriteria analysis and ELECTRE III method, and it also highlighted the most ritial fators influening the eonomi return of wind energy projets. However, it is very regretful to find that the external eonomies of wind power projet have rarely been studied. Therefore, the external eonomies of wind power engineering projet urgently require to be researhed, namely, into how to establish a omprehensive and appropriate method to evaluate the external eonomies of wind power engineering projet. Analyti Hierarhy Proess (AHP), developed by Saaty (1980), is a subjetive tool for determining the relative importane of a set of ativities in a multiriteria deision-making (MCDM) problem [16, whih has been widely used for solving omplex problems, suh as projet deision-making, eonomi effetiveness analysis, test-sheet omposition [17, and so forth. Matter-element extension model, established and developed by Chinese sholars Cai et al. in 1983, an analyze qualitatively and quantitatively the ontradition problem based on the formalized logi tools [18, 19. This model has the onvenient advantage that it quantifies the qualitative indies, and it has been used in many fields, inluding the performane evaluation of ERP projet [20 and risk assessment of urban network planning [21. In this paper, a hybrid evaluation model of external eonomies of wind power engineering projet based on AHP and matterelement extension model is put forward: AHP is used to determine the weights of the evaluation indies; the matterelement extension model is used to dedue final ranking through the weights and the values of external eonomies evaluation indies. This paper omprises the following: Setion 2 introdues the basi theory regarding AHP for determining the weights of evaluation indies and the matter-element extension model, and then the hybrid evaluation model is introdued. Taking a speifi wind power engineering projet in China as an example, the evaluation index system of external eonomies of wind power engineering projet is built, and the external eonomies evaluation based on this hybrid evaluation model is performed in Setion 3; Setion 4 onludes this paper. 2. The Hybrid Evaluation Model 2.1. Basi Theory of AHP for Determining the Weights of Evaluation Indies. AHP is a pratial multiriteria deisionmaking (MCDM) method ombining qualitative and quantitative analysis, whih is also a ompat and effiient tool

3 Mathematial Problems in Engineering 3 Goal Criteria Subriteria (index) Figure 2: The hierarhial struture model of AHP for determining the index weight. for solving omplex system problems based on the use of pairwise omparisons [22. There are mainly four steps in using AHP for determining the weights of evaluation indies. Step 1 (build the hierarhial struture model). Aording to the overall goal and harateristi of multiriteria deisionmaking problem, the omplex determination of index weight is deomposed and framed as a bottom-up hierarhial struture, in whih the goal, riteria, and subriteria (index) are arranged similar to a family tree, just as shown in Figure 2. Step 2 (onstrut the judgment matrix). The (n n)evaluation matrix B in whih every element b ij (i, j = 1, 2,..., n) is the quotient of weights of the riteria is alled omparison judgment matrix, referred to as judgment matrix, as shown in (1): B= [ b 11 b 12 b 1n b 21 b 22 b 2n [ [ b n1 b n2 b nn b ij >0, b ii =1, b ij = 1 b ji. (1) The judgment matrix demonstrates the omparison of relative importane between the elements in the same level for a ertain element of the upper level. The value of b ij an be obtained by pairwise omparison using a standardized omparison sale of nine levels (see Table 1). Step 3 (alulate the loal weights and onsisteny test). In this step, the mathematial proess ommenes to normalize andfindtherelativeweightsforeahmatrix.aordingto (2), the relative weight of the index an be given by the right eigenvetor (w) orresponding to the largest eigenvalue (λ max )as Bw = λ max w. (2) By the same way, the weights of all the parent nodes above the indies, that is, the weights of riteria, an be alulated. It should be onsistent in the preferene ratings given in the pairwise omparison matrix when using AHP. Therefore, the onsisteny test must be performed. The onsisteny is defined by the relation between the entries of B:b ij b jk =b ik. That is, if b ij represents the importane of index i over index j and b jk represents the importane of index j over index k, b ij b jk must be equal to b ik,whereb ik represents the importane of index i over index k. For eah riteria, the onsisteny ratio (CR) is measured by the ratio of the onsisteny index (CI) to the random index (RI): The CI is CR = CI RI. (3) CI = (λ max n). (4) (n 1) ThevalueofRIislistedinTable 2. Thenumber0.1istheaeptedupperlimitforCR.CR 0.1 implies a satisfatory degree of onsisteny in the pairwise omparison matrix, but if CR exeeds this value, serious inonsisteny might exist and the evaluation proedure has to be repeated to improve the onsisteny [23. Step 4 (alulate the global weights). After the CR of eah of the pairwise omparison judgment matries is equal to or less than 0.1, the global weights an then be determined for the indies by multiplying loal weights of the indies with weights of all the parent nodes above it. The sum of global weights satisfies n w i =1. (5) 2.2. Basi Theory of Matter-Element Extension Model. Matterelement extension model is a formalized model whih studies extension possibility and extension law of things. Matterelement extension model is omposed of objets, harateristis, and values based on ertain harateristis. Things in thenameofp, harateristis, and value v arealledthe three elements of matter-element R. The basi element uses an ordered triple R = (P,, V) omposed of P,, V to desribe things, whih is also alled matter-element. Suppose objet P an be desribed by n harateristis 1,2,..., n and the orresponding values V 1, V 2,...,V n. Then, the matter-element R an be alled n-dimensional matter-element, denoted as R=(P, C, V) = [ R 1 R 2 [ = [ P 1 V 1 2 V 2 [, (6) [ R n [ n V n where C = [ 1, 2,..., n T is the eigenvetor, V = [V 1, V 2,...,V n T is the orresponding value of the eigenvetor C,andR i is alled the submatter-element of R,,2,...,n. The basi steps of matter-element extension model are as follows.

4 4 Mathematial Problems in Engineering Table 1: Nine-point omparison sale. Sale of importane Definition Explanation 1 Equally important Two elements ontribute equally 3 Moderately more important One element is slightly favoured over another 5 Strongly more important One element is strongly favoured over another 7 Very strongly more important An element is very strongly favoured over another 9 Extremely more important One element is most favoured over another 2, 4, 6, 8 Intermediate value Adjaent to the two odd number sales Table 2: Random index (RI). Matrix Size (n) RI Step 1 (determine the lassial field matter-element and the ontrolled field matter-element). Suppose the lassial field matter-element as R 0j =(P 0j,C i,v 0j ) P = [ 0j 1 V 01j P 0j 1 a 01j,b 01j 2 V 02j [ = 2 a 02j,b 02j, [ [ n V 0nj [ n a 0nj,b 0nj where P 0j represents the jth grade, C i is n different harateristis of P 0j, V 01j is the orresponding value range of P 0j and about C i,respetively;v 0ij = a 0ij,b 0ij (i = 1,2,...,n, j=1,2,...,m), namely, the lassial field. Suppose the ontrolled field matter-element as P R p =(P,C,V p )= [ 1 V p1 2 V p2 [ [ n V pn P 1 a p1,b p1 = 2 a p2,b p2, [ [ n a pn,b pn where P represents all the grades of objets to be evaluated and V p isthevaluerangeofp about C; V pi = a pi,b pi (i = 1,2,...,n), namely, the ontrolled field. Step 2 (determine the matter-element to be evaluated). Suppose the matter-element to be evaluated as (7) (8) R 0 =(P 0,C,V)= [ P 1 V 1 2 V 2 [, (9) [ n V n where P 0 is the matter-element to be evaluated and V i is the deteted onrete data of P 0 about i,respetively,i = 1,2,...,n. Step 3 (establish the orrelation funtion and alulate its value). The orrelation funtion is used to haraterize the extension set that is the set used to desribe the transformation from the things that do not have ertain properties tootherthingsthathaveproperties.thevaluerangeof orrelation funtion is (, + ). The orrelation funtion value of eah index of matter-element to be evaluated with eah level an be alulated aording to ρ(v i, V 0ij ) { K j (V i )= V, V i V 0ij 0ij ρ(v i, V 0ij ) { { ρ(v i, V pj ) ρ(v i, V 0ij ), V i V 0ij, (10) where K j (V i ) represents the orrelation funtion value of the ith index related to the jth level; ρ(v i, V 0ij ) represents the distane of the matter-element to be evaluated of the ith index related to the orresponding lassial field, ρ(v i, V 0ij )= V i 1 2 (a 0ij +b 0ij ) 1 2 (b 0ij a 0ij ) (11) V 0ij represents the value range of lassial field of the ith index related to the jth level; ρ(v i, V pj ) represents the distane of the matter-element to be evaluated of the ith index related to the ontrolled field, ρ (V i, V pj ) = V i 1 2 (a pi +b pi ) 1 2 (b pi a pi ) (12) V i V 0ij indiatesthatthevalueoftheith index is in the lassial field of the jth level. Step 4 (determine the index weight). Seleting the appropriate method to alulate the weight of the evaluation index is quite important for the feasibility and quality of a omprehensive evaluation. The evaluation index system of external eonomies of wind power engineering projet has

5 Mathematial Problems in Engineering 5 Build the evaluation index system Divide the evaluation index system to be evaluated into j grades Establish the lassial field and ontrolled field Establish the matter-element to be evaluated Build the hierarhial struture model Establish the orrelation funtion and alulate its value Determine the index weight by using AHP Construt the judgment matrix Calulate the loal weight and onsisteny test Calulate the orrelation degree and rating Calulate the global weight Conlude the grade level Figure 3: Evaluation proedure of the proposed hybrid evaluation model. several levels and many fators within eah level, and there exists the interation relationship between the evaluation indies, so the AHP is seleted to be used for determining the index weight in this paper. Step 5 (alulate the orrelation degree and rating). The orrelation degree of the matter-element to be evaluated with all grades is alulated by K j (P 0 )= n w i K j (V i ), (13) where K j (P 0 ) is the orrelation degree of the jth level, w i is the weight of the ith index, and K j (V i ) is the value of orrelation funtion. Suppose K j (P 0 )=max{k j (P 0 )}(j = 1,2,...,m);then the matter-element to be evaluated P 0 belonged to the j th level. Suppose K j (p 0 )= K j (p 0 ) min K j (p 0 ) max K j (p 0 ) min K j (p 0 ), (14) where K j (P 0 ) represents the orrelation degree of the jth level; min K j (p 0 ) represents the minimum of orrelation degrees in all levels; max K j (p 0 ) represents the maximum of orrelation degrees in all levels; j=1,2,...,m. Consider j = m j=1 jk j (p 0 ) m j=1 K j (p 0 ), (15) where j is the external eonomies level variable eigenvalue of p 0. The attributive degree of the matter-element to be evaluated tending to adjaent levels an be judged from j The Theory of the Hybrid Evaluation Model. The hybrid evaluation model of wind power engineering projet is established based on AHP and matter-element extension model in this paper. The evaluation proedure is shown in Figure 3.

6 6 Mathematial Problems in Engineering 3. Case Study In this paper, a wind power engineering projet in Inner Mongolia ity is taken as an example. Firstly, the evaluation index system of external eonomies of wind power engineering projet is built, and then an evaluation on the external eonomies of wind power engineering projet in InnerMongoliaityisarriedoutbyemployingthisproposed hybrid evaluation model. There exists a wind power projet being onstruted by China Datang Corporation in Inner Mongolia ity, whih is omprised of 58 wind turbines with the apaity of 850 kw and the orresponding anillary failities. At the same period, a 220 kv wind farm enter transformer substation is building, and the total investment is 538 million Yuan. In order to identify the external eonomies of this wind power engineering projet, the evaluation is performed, and the detailed evaluationproedureisasfollows Build the Evaluation Index System. Questionnaires, whih are formed based on the related literature and the reality of wind power engineering projet, were dispathed to experts in the field of wind power. The external eonomies evaluation index system was obtained by analyzing the result of questionnaires, whih are divided into eonomi benefit, soial benefit, and environmental benefit. The external eonomies evaluation index system is shown in Figure 4. Of whih, C1, C3, and C5 are qualitative indies, and the others arequantitativeindies.alloftheindiesarethegreatest-type index Divide the Index System to Be Evaluated into j Grades. In this paper, the external eonomies of wind power engineering projet are divided into five grades: strongest, stronger, general, weaker, and extremely weak Construt the Matter-Element Evaluation Model Establish the Classial Field. Qualitative indies in the evaluation index system use a 10-point sale with a soring system devised by experts, and the lassial field values are 0 2, 2 4, 4 6, 6 8, and 8 10, suessively. For the quantitative indies, the lassial field values are set to 0 100% by experts, and this range was divided into five lassial domains whih are suessively, 0 20%, 20 40%, 40 60%, 60 80%, and % Establish the Controlled Field. The ontrolled field of eah index is the sum of the lassial field value Establish the Matter-Element to Be Evaluated. The speifi value of the matter-element to be evaluated R 0 is omposedoftwoparts:onepartisthevalueofqualitative index, whih an be obtained through statistial analysis of the survey results made by wind experts, enterprise managers, wind enterprise ustomers, and loal residents; the other part is the value of quantitative index, whih an be obtained by pratial alulations. The values of lassial fields R 01, R 02, R 03, R 04 and R 05, ontrolled field R p, and the matter-element to be evaluated R 0 are as follows: P 01 1 (0, 2) 2 (0%,20%) 3 (0, 2) 4 (0%,20%) R 01 = 5 (0, 2), 6 (0%,20%) 7 (0%,20%) [ 8 (0%,20%) 9 (0%,20%) [ 10 (0%,20%) P 02 1 (2, 4) 2 (20%,40%) 3 (2, 4) 4 (20%,40%) R 02 = 5 (2, 4), 6 (20%,40%) 7 (20%,40%) [ 8 (20%,40%) 9 (20%,40%) [ 10 (20%,40%) P 03 1 (4, 6) 2 (40%,60%) 3 (4, 6) 4 (40%,60%) R 03 = 5 (4, 6), 6 (40%,60%) 7 (40%,60%) [ 8 (40%,60%) 9 (40%,60%) [ 10 (40%,60%) P 04 1 (6, 8) 2 (60%,80%) 3 (6, 8) 4 (60%,80%) R 04 = 5 (6, 8), 6 (60%,80%) 7 (60%,80%) [ 8 (60%,80%) 9 (60%,80%) [ 10 (60%,80%) P 05 1 (8, 10) 2 (80%, 100%) 3 (8, 10) 4 (80%, 100%) R 05 = 5 (8, 10), 6 (80%, 100%) 7 (80%, 100%) [ 8 (80%, 100%) 9 (80%, 100%) [ 10 (80%, 100%)

7 Mathematial Problems in Engineering 7 P 1 (0, 10) 2 (0%, 100%) 3 (0, 10) 4 (0%, 100%) R p = 5 (0, 10), 6 (0%, 100%) 7 (0%, 100%) [ 8 (0%, 100%) 9 (0%, 100%) [ 10 (0%, 100%) P % % R 0 = %, 7 88% [ 8 91% 9 83% [ 10 74% (16) where R 01, R 02, R 03, R 04,andR 05 represent the lassial field; R p represents the ontrolled field; R 0 represents the matterelement to be evaluated; P 01 represents the extremely weak external eonomies grade, P 02 represents weaker grade, P 03 represents general grade, P 04 represents stronger grade, and P 05 represents the strongest grade Calulate the Correlation Funtion Value. The orrelation funtion value an be alulated aording to (10), of whih theresultislistedintable Determine the Index Weight Build the Hierarhial Struture Model. The AHP hierarhial struture model for external eonomies evaluation of wind power engineering projet is shown in Figure 5.The goal of our problem is to evaluate the external eonomies of wind power engineering projet, whih is plaed on the first level of the hierarhy. Three fators, namely, eonomi benefit, soial benefit, and environmental benefit, are identified to ahieve this goal, whih form the seond level of the hierarhy, namely, riteria. The third level of the hierarhy onsists of 10 indies, and the eonomi benefit, soial benefit, and environmental benefit inlude 4 indies, 2 indies, and 4 indies, respetively Construt the Judgment Matrix. The pairwise omparison judgment matries obtained from wind experts in the dataolletionandmeasurementphaseareombinedusing the geometri mean approah at eah hierarhy level to obtain the orresponding onsensus pairwise omparison judgment matries through using a standardized omparison sale of nine levels. The results of pairwise omparison judgment matriesarelistedintable Calulate the Loal Weight and Consisteny Test. After the pairwise omparison judgment matries are onstruted, they are then translated into the orresponding largest eigenvalueproblemandfurthertofindthenormalizedandunique priorityweight for eah index. Aordingto (2) (4), the loal weight of eah index and the CR of pairwise omparison judgmentmatriesanbeobtained,justasshownintable 4. It an be seen that the CR of eah of the pairwise omparison judgment matries is well below the rule-of-thumb value of CR equal to 0.1. This learly implies that the wind experts are onsistent in the preferene ratings given in the pairwise omparison matrix Calulating the Global Weight. By alulation, the global weight of eah index is listed in Table Calulate the Correlation Degree and Rating. The orrelation degree value of eah grade is as follows: K 1 (P 0 )= K 2 (P 0 )= K 3 (P 0 )= K 4 (P 0 )= K 5 (P 0 )= w i K 1 (V i ) = 0.766, w i K 2 (V i ) = 0.688, w i K 3 (V i ) = 0.533, w i K 4 (V i ) = 0.146, w i K 5 (V i ) = (17) Sine K 5 (P 0 )=max{k j (P 0 )}(j = 1,2,3,4,5),itisshown that the external eonomies of this wind power engineering projet belongs to strongest grade Sensitivity Analysis. Sensitivity analysis is performed aording to the external eonomies index system of wind power engineering projet. The value j represents the external eonomies level defletion degree to its adjaent levels. We use j (0, 1), (1, 2), (2, 3), (3, 4) and (4, 5) to represent the external eonomies level extremely weak, weaker, general, stronger, and strongest, respetively. For example, if j = 3.2, it shows that the external eonomies level belongs to stronger but loser to the general level more; if j = 3.7, it shows that the external eonomies level belongs to stronger but loser to the strongest level more. In this paper, by alulation, j = 4.3 (4, 5), the external eonomies level belongs to strongest but loser to the stronger level more Sensitivity Analysis on Index Weight. The result of sensitivity analysis is shown in Figure 6 when the weights of external eonomies indies are hanged by ±0.1, ±0.2, ±0.3, ±0.4, ±0.5.

8 8 Mathematial Problems in Engineering External eonomies evaluation of wind power projet (A) Eonomi benefit (B1) Soial benefit (B2) Environmental benefit (B3) promoting the sustainable development of power industry (C1) inreasing region GDP (C2) promoting sientifi and tehnologial innovation (C3) land optimal utilization and value added in projet area (C4) improving region living standards (C5) promoting employment levels (C6) reduing pollution gas emissions (C7) reduing smoke, industrial wastewater disharge (C8) reduing the destrution of terrestrial vegetation and marine eosystems (C9) energy onservation (C10) Figure 4: External eonomies evaluation index system of wind power engineering projet. Goal External eonomies evaluation of wind power engineering projet Criteria Eonomi benefit Soial benefit Environmental benefit Sub-riteria (index) Promoting the sustainable development of power industry Inreasing region GDP Promoting sientifi and tehnologial innovation Improving region living standards Promoting employment levels Reduing pollution gas emissions Reduing smoke, industrial wastewater disharge Reduing the destrution of terrestrial vegetation and marine eosystems Land optimal utilization and value added in projet area Energy onservation Figure 5: Hierarhial struture of external eonomies evaluation of wind power engineering projet.

9 Mathematial Problems in Engineering 9 Table 3: The alulation result of orrelation funtion value. Index Extremely weak Weaker General Stronger Strongest K 1 (V i ) K 2 (V i ) K 3 (V i ) K 4 (V i ) K 5 (V i ) C C C C C C C C C C Table 4: Pairwise omparison judgment matries, loal weight, and CR. Goal Eonomi benefit Soial benefit Environmental benefit Weight Eonomi benefit Soial benefit Environmental benefit CR = Eonomi benefit C1 C2 C3 C4 Loal weight C C C C CR = Soial benefit C5 C6 Loal weight C C CR = Environmental benefit C7 C8 C9 C10 Loal weight C C C C CR = Table5:Theglobalweightofeahindex. Criteria Weight Index Loal weight Global weight C Eonomi benefit (B1) C C C Soial benefit (B2) C C C Environmental benefit (B3) C C C

10 10 Mathematial Problems in Engineering As we an see from Figure 6, whatevertheweightsofall theindiesflutuate,thevalueofj remains in the sope of (4.25, 4.35), so they have a really general effet on the evaluation result and it an be said that their sensitivity is general. In detail, with the weights of external eonomies indies C2, C6, C7, and C8 inreasing, the strongest level of external eonomies is enhaned gradually and the weight of C7 is the most sensitive. With the weights of external eonomies indies C1, C3, C5, and C10 inreasing, the external eonomies level has the trend of deviating from the strongest level to stronger level gradually and the weight of C10 is the most sensitive fator. The weights hanges of external eonomies indies C4, C9 have little effet on the external eonomies level, so their sensitivities are weak. j Sensitivity Analysis on the Index Soring. The sensitivity analysis result is shown in Figure 7 when the index soring values are hanged by ±0.1, ±0.2, ±0.3, ±0.4, ±0.5. As we an see from Figure 7, withthesoringvaluesof external eonomies indies C2, C6, and C7 dereasing, the external eonomies level deviates from the strongest level to stronger level gradually, whih indiates that these indies have a signifiant impat and the sensitivity is relatively stronger, and the C7 soring is the most sensitive. The external eonomies indies C1, C3, C4, C5, C8, C9, and C10 have very little effet on the evaluation result, whih indiates that the sensitivity is not strong. The external eonomies level in this wind power engineering projet lies between strongest and stronger, and as the index soring value dereases, the degree of external eonomies level will hange from strongest level to stronger level gradually. From the above two sensitivity analysis, it an safely drawtheonlusionthatc2,c7,andc10arethesensitive indies in the external eonomies evaluation of wind power engineering projet, namely, the degree of inreasing region GDP, the degree of reduing pollution gas emissions, and thedegreeofenergyonservation. Intheonstrutionand management proess of the wind power engineering projet, these fators should be foused and analyzed mainly in order to enhane the projet external eonomies and redue the obstales of wind power projet onstrution. 4. Conlusions Sientifi and effetive evaluation on the external eonomies of wind power engineering projet is an important part for the sientifi exploitation and sustainable development of wind power projet. Many fators whih are varied and omplex affet the external eonomies of wind power engineering projet, suh as eonomi fators, soial fators, and environmental fators. Therefore, a reasonable external eonomies evaluation that onsiders multiple attributes needs to be performed, whih an provide theoretial support for wind power engineering projet onstrution planning. In this paper, a hybrid evaluation model of external eonomies of wind power engineering projet is proposed based on AHP and matter-element extension model, whih an solve omplex system problems onstituted by multilevel fators Flutuation value C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Figure 6: Sensitivity analysis result on the index weight Flutuation value C1 C2 C3 C4 C5 j C6 C7 C8 C9 C10 Figure 7: Sensitivity analysis result on the index soring. and overome the shortomings and inadequaies resulting from the ambiguity and unertainty inherent. The external eonomies evaluation index system of wind power engineering projet is onstruted onsidering eonomi benefit, soial benefit, and environmental benefit. The external eonomies evaluation method based on the AHP and matterelement extension model is also formulated. Taking a wind

11 Mathematial Problems in Engineering 11 power engineering projet in Inner Mongolia ity as an example, the feasibility of this proposed hybrid evaluation model is proven. The analysis result shows that the external eonomies of wind power engineering projet in Inner Mongolia ity belong to the strongest level, and the degree of inreasing region GDP, the degree of reduing pollution gasemissions, and thedegreeofenergyonservation are the sensitive fators whih should be foused and analyzed mainly in the onstrution and management proess of wind power engineering projet. Aknowledgments This study is supported by the Beijing Philosophy and Soial Siene Planning Projet (Projet no.11jgb070) and Coonstrution Projet of Beijing Muniipal Supporting Central UniversityLoatedinBeijing.Theauthorsaregratefulto the editor and anonymous reviewers for their suggestions in improving the quality of the paper. Referenes [1 M. Tükenmez and E. Demireli, Renewable energy poliy in Turkey with the new legal regulations, Renewable Energy, vol. 39,no.1,pp.1 9,2012. [2 X. J. Sun, D. G. Huang, and G. Q. Wu, The urrent state of offshore wind energy tehnology development, Energy, vol. 41, no. 1, pp , [3 S. F. Zhang and X. M. Li, Large sale wind power integration in China: analysis from a poliy perspetive, Renewable and Sustainable Energy Reviews, vol. 16, no. 2, pp , [4 J. Markusen, Miro-foundation of external eonomies, Canadian Eonomis,vol.23,no.1,pp ,1990. [5M.C.Slattery,E.Lantz,andB.L.Johnson, Stateandloal eonomi impats from wind energy projets: Texas Case Study, Energy Poliy, vol. 39, no. 12,pp , [6 Z.-Y. Zhao, J. Zuo, G. Zillante, and X.-W. Wang, Critial suess fators for BOT eletri power projets in China: thermal power versus wind power, Renewable Energy,vol.35,no.6,pp , [7 M. Bolinger and R. Wiser, A omparative analysis of business strutures suitable for farmer-owned wind power projets in the United States, Energy Poliy, vol. 34, no. 14, pp , [8 S. Agterbosh, R. M. Meertens, and W. J. V. Vermeulen, The relative importane of soial and institutional onditions in the planning of wind power projets, Renewable and Sustainable Energy Reviews, vol. 13, no. 2, pp , [9 S.Y.Liu,J.H.Zhang,W.X.Liu,andY.Qian, Aomprehensive deision-making method for wind power integration projets based on improved fuzzy AHP, Energy Proedia, vol. 14, no. 1, pp , [10 M. Coleman and S. Provol, Wind power eonomis, Refous, vol. 6, no. 4, pp , [11 S. V. Valentine, Understanding the variability of wind power osts, Renewable and Sustainable Energy Reviews, vol. 15, no. 8, pp , [12 L. N. Zheng, L. Y. Zheng, and L. Wei, Environmental impat and ontrol measures of new wind power projets, Proedia Environmental Sienes, vol. 10, no. 3, pp , [13 C. Kongnam, S. Nuhprayoon, S. Premrudeepreehaharn, and S. Uatrongjit, Deision analysis on generation apaity of a wind park, Renewable and Sustainable Energy Reviews,vol.13, no. 8, pp , [14 S. Grassi, N. Chokani, and R. S. Abhari, Large sale tehnial and eonomial assessment of wind energy potential with a GIS tool: ase study Iowa, Energy Poliy,vol.45, pp.73 85,2012. [15 P. Georgiou, C. Tourkolias, and D. Diakoulaki, A roadmap for seleting host ountries of wind energy projets in the framework of the lean development mehanism, Renewable and Sustainable Energy Reviews,vol.12,no.3,pp ,2008. [16 T. L. Saaty, The Analyti Hierarhy Proess, MGraw-Hill, New York, NY, USA, [17 H. Duan, W. Zhao, G. G. Wang, and X. H. Feng, Test- Sheet omposition using analyti hierarhy proess and hybrid metaheuristi algorithm TS/BBO, Mathematial Problems in Engineering, vol. 2012, Artile ID , 22 pages, [18 W. Cai, The extension set and inompatibility problem, Sientifi Exploration,vol.1,no.1,pp.81 93,1983. [19 W. Cai, C. Y. Yang, and W. Lin, Extension Engineering Method, Siene Press, Beijing, China, [20 H.R.ZhaoandN.N.Li, Anovelhybridevaluationmodelfor the performane of ERP projet based on ANP and improved matter-element extension model, Mathematial Problems in Engineering, vol. 2013, Artile ID , 9 pages, [21Y.-X.He,A.-Y.Dai,J.Zhu,H.-Y.He,andF.R.Li, Risk assessment of urban network planning in hina based on the matter-element model and extension analysis, International JournalofEletrialPowerandEnergySystems,vol.33,no.3, pp , [22 M. P. Amiri, Projet seletion for oil-fields development by using the AHP and fuzzy TOPSIS methods, Expert Systems with Appliations,vol.37,no.9,pp ,2010. [23 J.-J. Wang and D.-L. Yang, Using a hybrid multi-riteria deision aid method for information systems outsouring, Computers and Operations Researh, vol.34,no.12,pp , 2007.

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