an steep-fuzzy ahp-topsis framework for evaluation and selection of thermal power plant location: a...

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Energy 42 (2012) 510e521

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An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selectionof thermal power plant location: A case study from India

Devendra Choudhary*, Ravi ShankarDepartment of Management Studies, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

a r t i c l e i n f o

Article history:Received 27 July 2011Received in revised form2 March 2012Accepted 5 March 2012Available online 10 April 2012

Keywords:Location selectionThermal power plantMulti-criteria decision makingFuzzy AHPTOPSISIndia

* Corresponding author. Tel.: þ91 9818955052.E-mail addresses: [email protected] (D. Choud

(R. Shankar).

0360-5442/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.energy.2012.03.010

a b s t r a c t

The location selection for thermal power plant (TPP) is a strategic decision, which has significant impacton economic operation of the plant and sustainable development of the region. Further, ranking of thealternative locations, and selection of the most suitable and efficient locations for TPPs is an importantmulti-criteria decision making problem. This paper proposes an STEEP-fuzzy AHP-TOPSIS based frame-work for evaluation and selection of optimal locations for TPPs. Potential feasible locations are identifiedbased on social, technical, economical, environmental, and political (STEEP) considerations. The fuzzyAHP, a multi-criteria decision making method, has been applied to determine the weights of qualitativeand quantitative criteria impacting location selection process. The fuzzy AHP is adapted to model thelinguistic vagueness, ambiguity, and incomplete knowledge. Furthermore, Technique for Order Prefer-ence by Similarity to Ideal Solution (TOPSIS), a ranking multi-criteria decision making method, has beenapplied to rank the alternative locations based on their overall performance. The applicability ofproposed method is demonstrated by a case study of TPPs location selection in India. The paper bringsout a more accurate, effective, and systematic decision support tool for decision makers to conduct theevaluation process and to select optimal locations for TPPs.

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1. Introduction

In recent years, multi-criteria decision making methods havebeen applied substantially in energy and environment modelling[1]. The evaluation of the alternative locations, and selection of themost suitable and efficient locations for thermal power plants(TPPs) is also an important multi-criteria decision making problem[2,3]. In fact, location selection problem for TPPs is a vital strategicdecision, which has significant impact on economic operation ofTPPs as well as sustainable development of the region. However, ithas not received due attention in academic literature. Literature onlocation for TPP is less reported in scholarly journals. When wesearch for ‘location selection for thermal power plant’ or ‘siteselection for thermal power plant’ on Google Scholar, it producesonly three results which closely relates to the problem addressed inthis paper.

According to Valadan Zoej et al. [4], location choice for TPPaffects the amount of generated energy, power plant’s productivity,cost of power generation and transmission, economical develop-ment and environment. Furthermore, energy resources and

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consumption affects the environmental quality and other vitalresources such as water and food [5]. Thus, selection of unsuitablelocation for TPP will lead to increased costs, waste of energy andresources, and increased environmental pollution, which hasa tremendous negative impact on society [6].

1.1. Research motive

Electricity is a critical infrastructure on which the socio-economic development of any country depends. In India, demandfor electric power is increasing continuously due to populationgrowth, development of industrial and agricultural sectors. Since itsindependence in 1947, the growth of power sector in India has beennoteworthy. Over the years (since 1950) the installed capacity ofpower plant has increased to 176,990.40 MW (as on 30/06/2011)from a meagre 1713 MW in 1950, registering a 103 fold increase in61 years [7]. The per capita consumption of electricity in thecountry also increased from 15 kWh in 1950 to about 720 kWh in2010e11, which is about 48 times. However, the demand for powerhas been outstripping the growth of availability. Substantial energyshortages prevail in the country, and 35.5 percent of population isgetting electricity less than 1 h per day. The energy deficit is about8.3% and the power shortage during the peak demand is about12.5%. Additionally, India’s electricity consumption per capita is one

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D. Choudhary, R. Shankar / Energy 42 (2012) 510e521 511

quarter of the global average. The total demand for electricity inIndia is expected to cross 950,000 MW by 2030.

In order to meet supply demand gap of electricity power, theIndian Ministry of Power has prepared a comprehensive blueprintfor power sector development with following objectives [8]:sufficient power to achieve GDP growth rate of 8 percent; reliablepower; quality power; optimum power cost; commercial viabilityof power industry; and power for all by 2012. Further, theseobjectives are to be achieved with the help of following strategies:low cost generation; optimization of capacity utilization; optimi-zation of transmission cost; minimization of distribution losses;environment protection; and political consensus.

In India, sources of power generating installed capacity can beseen in Fig. 1. Of this, 65.34% is constituted by thermal power,21.53% by hydro electricity power, 2.7% by nuclear power and10.43% by wind and solar power [7]. Indeed, in India, coal basedthermal power has been the main source of generating electricity,and would necessarily continue to remain the main generatingsource for meeting the future electricity demand. This is due to thefact that large coal reserves in the country provide a ready andeconomical resource and ensure electricity security. Further,emphasis has been laid on setting up new TPPs at optimal locationsto avoid high costs, and concerns for sustainability of growth. Themain concerns for sustainability relate to quality of air and water,productivity of land, preservation of biodiversity, ecological healthand threat of climate change [9]. In addition, locations for TPPs areto be chosen on the basis of broad social and political consensus.

1.2. Research goal

The goal of the TPP location selection process is to maximize theoverall value of the power plant, reduce power generation and itstransmission cost, minimize adverse impact on environment, andmaximize the power plant’s productivity [8]. Hence, the purpose ofthis research is to propose a novel methodology to select bestlocations for TPPs with least socio-economic, environmental andinfrastructural development costs, and high return through powergeneration.

This paper proposes an STEEP-fuzzy AHP-TOPSIS based frame-work for evaluating and selecting optimal locations for thermalpower plants. The framework involves three stages. Firstly, poten-tial feasible locations are identified based on social, technical,economical, environmental, and political (STEEP) considerations.Then, fuzzy AHP and TOPSIS methods are used to determine theweights of criteria, and to rank the alternative locations, respec-tively. Lastly, a case study is presented to clarify the proposedmethodology. Also, results are discussedwith the help of sensitivityanalysis. The paper brings out a new insight of multi-criteria

Fig. 1. Power generation sources in India (as on 30/06/2011).

decision making process to conduct evaluation and selectionprocess for deciding optimal locations for TPPs.

2. Literature review

In this section, we provide brief review of available relevantliterature on power plant location selection problem. We alsoreview some of the literature in which multi-criteria decisionmaking tools such as AHP, ANP, TOPSIS, ELECTRE, etc. are utilizedfor solving location problems as well as applied in energymanagement.

The thermal power plant location selection is an importantstrategic decision but it has not received due attention in academicliterature. The available research literature on topic raised in thispaper is very rare. Barda et al. [2] considered thermal power plantlocation problem as multi-criteria decision problem and appliedELECTRE III method to select best location. Ramos et al. [3]considered economic risks while selecting site for thermal powerplant. Valadan Zoej et al. [4] used Geographic Information System(GIS) approach for selecting suitable location for construction ofthermal power plant. Feng [6] used rough sets to obtain weight forquantitative and qualitative information and proposed a multi-objective model to balance the two goals of cost minimum andefficiency maximum for optimal site selection of thermal powerplant.

Zangeneh et al. [10] proposed a static fuzzy multi-objectivemodel to determine the optimal size, location and also the propertechnology of distributed generation station. Gamboa and Munda[11] proposed a social multi-criteria evaluation (SMCE) frameworkfor dealing with the problem of wind park location. SMCE approachis used to integrate both socio-economic and technical dimensionsinside a coherent framework. Topcu and Ulengin [12] applied anintegrated decision framework for evaluation of suitable electricitygeneration alternative for Turkey. The research showed that theproblemof selecting an energy resource requiresmulti-dimensionalanalysis. Mamlook et al. [13] used neuro-fuzzy programming toperform a comparison between the different electricity powergeneration options for Jordan.

AHP has been used for wind observation location problem [14].Quintero et al. [15] applied AHP for integration of economical andenvironmental indicators of the ethanol production process fromsugarcane and corn under Colombian conditions. Nixon et al. [16]used AHP to select the best solar thermal collection technologyfor electricity generation in north-west India. Chinese et al. [17]applied AHP approach for selection of space heating systems foran industrial building. They found that qualitative attributes alsosignificantly affect industrial heating system choices and the AHP iseffective in handling these aspects.

In addition to AHP, fuzzy multi-attribute decision making is alsoapplied for selecting location when values of criteria are impreciseor vague [18,19]. Kaboli et al. [20] applied fuzzy-AHP to select theoptimal plant location that is most preferable for both investors andmanagers. Kahraman et al. [21] compared fuzzy axiomatic andfuzzy AHP methods for selecting most appropriate renewableenergy alternative for Turkey. Kaya and Kahraman [22] used anintegrated VIKOR-AHP methodology under fuzzy environment fordetermining the best renewable energy alternative and its locationfor Istanbul.

Like fuzzy AHP, fuzzy TOPSIS method has also been used forsolving location selection problems. Ertu�grul et al. [23] evaluatedthe criteria those affect the facility location decision and appliedfuzzy-AHP and fuzzy TOPSIS to a facility location selection problemof a Textile Company.

Tuzkaya et al. [24] used the analytic network process (ANP)method to evaluate and select suitable facility locations based on

D. Choudhary, R. Shankar / Energy 42 (2012) 510e521512

four criteria, namely, benefits, cost, opportunities and risks. Far-ahani et al. [25] presented a survey on recent efforts and devel-opments in multi-criteria location problems in three categoriesincluding bi-objective, multi-objective and multi-attribute prob-lems and their solution methods.

Table 1 provides the summary of pros and cons of thecontemporary multi-criteria decision making (MCDM) approachesto power plant location decision making. This would be helpful forunderstanding the need for an integrated approach, which hasbeen proposed in this paper. When setting up a large-scale energygeneration project, its benefits and detriments on modern societyshould be kept in proper perspective [35]. Further, Lior [5]emphasized that all the large energy projects should bedesigned and implemented sustainably as energy conversion anduse are associated with major environmental, economical andsocial impacts. Hence, in TPP location selection process, usuallyintangible factors such as socio-economic concerns, biological andphysical environment must be considered in addition to tangiblefactors which may include monetary costs and benefits, and theamount of energy generated. Furthermore, a large number ofdecision making parties are involved for setting up a new TPP andthe viewpoint of all the interest group must be properly incor-porated into any type of analysis. De and Hipel [35] emphasizedthat fuzzy set theoretic approach is essentially required to evaluatelarge scale energy projects, when there is a need to comparedistinct alternatives using both quantitative and qualitativecriteria and to accommodate the viewpoints of different interestgroups.

The objective of this research is to first identify the importantdecision criteria and sub-criteria relevant to the TPP locationselection decision and then provides an effective integratedframework to evaluate and select the best locations. We proposea new integrated multi-criteria decision making framework forthermal power plant location selection problem in the presence ofmultiple factors and vague information. The decision of selectingoptimal locations for TPPs involves several conflicting tangibleand intangible criteria. Firstly, the main criteria and sub-criteriaare decided considering available literature and experience ofexperts. Simultaneously, potential feasible locations are identified

Table 1Comparison of MCDM methods applied to location selection [26e34].

MCDM methods Pros

ELECTRE � Decision making by using thresholds of indifference andpreference, and outranking method.

� Applicable for quantitative and qualitative attributes.� Applicable even when there are incomparable alternatives

AHP � Applicable for quantitative and qualitative attributes.� Use of hierarchical structure to present complex decision p� It can be solved using spreadsheet.� Consistency of the evaluation procedure can be measured

TOPSIS � Measures the distance of the alternatives from the ideal so� Used for selection of the one closest to the ideal solution� Easy to use and well understandable.� It can be solved using spreadsheet.

Rough sets theory � An appropriate mathematical tool for the analysis of a vagudescription of objects or ambiguity which follows from infgranulation.

ANP � ANP is capable of handling feedbacks and interdependenci� It depicts the dependence and influences of the factors invto the goal or higher-level performance objective.

Multi-objectiveprogramming

� Model involves linear or nonlinear objective function and c� It may have continuous or integer decision variables that ctake on an infinite number of values.

� It is used when there are large numbers of alternative choi

based on social, technical, economical, environmental, and polit-ical (STEEP) considerations. Then fuzzy AHP is applied to deter-mine the weights of qualitative and quantitative criteriaimpacting location selection decision process. By using Fuzzy-AHPmethod, the linguistic preferences of experts are mapped withtriangular fuzzy numbers to decide the preferences and impor-tance of one criterion over another. Afterwards, Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS) isapplied to rank the alternative locations based on their overallperformance. This research is also motivated from a realistic casestudy from India, in which a public sector power corporationrequired to select optimal location for setting up new TPPs. Thedecision framework proposed in this paper provides usefulinsights for the practicing managers in evaluating and selectingoptimal locations for TPPs.

The choice for an integrated STEEP-fuzzy AHP-TOPSIS basedframework proposed in this paper is justified by several reasons:

� A minimum number of feasible location choices are identifiedon the basis of STEEP factors. This will not only make the taskeasier for decision makers to visualize the situation and indi-cating their preferences, but also considers the viewpoint ofmany stakeholders. As a result, possible delays can be avoidedin setting up new TPPs.

� The linguistic preferences and incomplete knowledge ofdifferent interest groups are mapped to decide the prefer-ences for both quantitative and qualitative criteria and tocompare distinct alternatives. This is essentially required toevaluating large scale energy projects [35], and fuzzy AHPallows it.

� TOPSIS can rank the alternative locations based on their overallperformance, since it may identify the best solution that isclosest to the positive ideal solution and farthest from negativeideal solution. Thus, an optimal solution is obtained from bothlowcost and concerns for sustainability of growth perspectives,as emphasized by Lior [5] and Parikh [9].

� The proposed framework can be solved easily. The case studyillustrated in this paper shows that it can be applied usinga spreadsheet.

Cons

.

� It is difficult to conceptualize the problem in absence ofhierarchical structure.

� Comparatively difficult to solve than AHP due to complexcomputational procedure.

� May or may not figure out the preferred alternatives.

roblem.� A large number of pairwise comparisons required as numberof alternatives increase.

� Due to aggregation, compensation between good scores onsome criteria and bad scores on other criteria can occur.

lution � Normalization is required to solve multi-dimensionalproblem.

� Consistency check not possible.

eormation

� It cannot handle inconsistencies used in MCDM problems,like sorting, choice or ranking.

es.olved

� There are more pairwise comparison matrices in ANP thanAHP. Hence, difficult to solve.

� Specific software is required to solve itonstraints.an usually

ces.

� Difficult to solve due to complex computational procedure.� Specific software or meta-heuristic approach is requiredto solve it

� Applicable only for quantitative attributes.� Applicable only when exact data are available.


3. Research methodology

A three phase methodology has been applied for evaluation andselection of best locations for TPPs (Fig. 2). The three phases of theresearch methodology are described in following subsections.

3.1. Phase I e STEEP considerations

In this phase, potential feasible locations are identified based onsocial, technical, economical, environmental, and political (STEEP)considerations. Firstly, meteorological data on rainfall, humidity,temperature, windfall pattern, etc., and satellite images on geology,geomorphology, land use/cover, hydrology, settlement, etc. areintegrated using GIS to identify potential locations for TPPs. After-wards, infeasible locations are dropped from above list by consid-ering location selection guidelines of Ministry of Environment andForest (MoEF) and Central Electricity Authority (CEA), and socio-political information. This preliminary phase is carried out toavoid any possible delay in getting final approval of chosen optimallocations for setting up of new TPPs from various State and CentralGovernment Departments.

3.2. Phase II e fuzzy AHP

The analytic hierarchy process (AHP), introduced by Saaty[27,36], is a useful and practical tool that provides the ability toincorporate both qualitative and quantitative factors in the deci-sion making process. But, it is generally criticized because of theuse of a discrete scale of 1e9 which can’t handle the uncertaintyand ambiguity present in deciding the priorities of differentattributes.

The fuzzy-AHP has been adopted for this research to take intoaccount the linguistic vagueness in decision maker’s mind.Recently, fuzzy-AHP has been widely used to solve multi-criteriadecision problems in a few other fields, e. g. selecting a vendor ina supply chain [37,38], selection of maintenance policy [39],selection of R&D project [40], evaluation of knowledge portaldevelopment tools [41], selecting the suitable bridge constructionmethod [42] and personnel selection problem [43].

Fuzzy set theory, proposed by Zadeh [44], is a generalization ofclassical set theory. A fuzzy set is a class of objects witha continuum of grades of membership. A fuzzy set is characterizedby a membership function, which assigns to each object a grade ofmembership ranges between zero and one. A tilde ‘w’ is placedabove a letter if the letter represents a fuzzy set. We considertriangular fuzzy number (TFN) to describe a fuzzy event as denotedas (l, m, n), as shown in Fig. 3. The parameters l, m and n respectivelydenote the smallest possible value, the most promising value, andthe largest possible value of a fuzzy event.

Some basic definitions of the fuzzy sets and fuzzy numbers arediscussed below [38,43,44].

Definition 1. Let X be a space of points, with an element of Xdenoted by x. A fuzzy set Ã in X is characterized by membershipfunction mÃ(x) ˛ [0, 1] which is assigned to represent the grade ofmembership of x to Ã. Thus, the nearer is the value of mÃ(x) to unity,the higher is the grade of membership of x to Ã [44].

Definition 2. The membership function of a triangular fuzzynumber Ã, denoted by triplet (l, m, n), is defined as

m~AðxÞ ¼

8>>><>>>:

x� lm� l

; l � x � m;

n� xn�m

; m � x � n;

0; otherwise:

(1)

The degree of membership of a fuzzy number for left and rightside representation is given by:

~A ¼ �ALðyÞ;ARðyÞ�

~A ¼ ðlþ ðm� lÞy;nþ ðn�mÞyÞ; y˛h0;1

i (2)

Definition 3. If ~A1 ¼ ðl1;m1;n1Þ and ~A2 ¼ ðl2;m2;n2Þ are twotriangular fuzzy numbers then the operational laws of addition,subtraction, multiplication, division and reciprocal can beexpressed as follows [43,44]:

~A14~A2 ¼ ðl1;m1;n1Þ4ðl2;m2;n2Þ

¼ ðl1 þ l2;m1 þm2;n1 þ n2Þ (3)

~A1.~A2 ¼ ðl1;m1;n1Þ.ðl2;m2;n2Þ

¼ ðl1 � n2;m1 �m2;n1 � l2Þ (4)

~A15~A2 ¼ ðl1;m1;n1Þ5ðl2;m2;n2Þ ¼ ðl1l2;m1m2;n1n2Þ (5)

~A1/~A2 ¼ ðl1;m1;n1Þ/ðl2;m2;n2Þ ¼ ðl1=n2;m1=m2;n1=l2Þ

(6)

l5~A1 ¼ ðll1; lm1; ln1Þ where l>0 (7)

~A�11 ¼ ðl1;m1;n1Þ�1 ¼

�1n1

;1m1

;1l1

�(8)

In fuzzy-AHP approach triangular fuzzy numbers are used togive interval judgments for the preferences of one criterion overanother and then by using the extent analysis, the synthetic extentvalue of the pairwise comparison is calculated. Based on this wedecide the weight vectors of criteria and decision alternatives.

The general fuzzy-AHP process used in this paper is discussed asfollows:

Step 1: Fuzzy synthetic extent calculation:Let X ¼ {x1, x2,.,xn} be an object set, and U ¼ {u1, u2,.,um} be

a goal set. Using Chang’s extent analysis approach [45,46], eachobject is taken and extent analysis for each goal is performedrespectively. Therefore,m extent analysis values for each object canbe calculated, and are denoted as:

A1gi ;A

2gi ;.;Am

gi i ¼ 1;2;.;n:

where all the Ajgi ðj ¼ 1;2;.;mÞ are triangular fuzzy numbers.

With respect to the ith object, the value of fuzzy synthetic extent isdefined as:

Si ¼Xmj¼1

Ajgi5

24Xn

i¼1

Xmj¼1

Ajgi

35�1

(9)

Step 2: Comparison of fuzzy values [38,45,46]:The degree of possibility of A2 ¼ (l2,m2,n2) � A1 ¼ (l1,m1,n1) is

defined as:

VðA2 � A1Þ ¼ SUP|ffl{zffl}x�y

�min

�mA1

ðxÞ; mA2ðyÞ�� (10)

When a pair (x, y) exists such that x � y and mA1(x) ¼ mA2(y) ¼ 1,then we have V(A2 � A1) ¼ 1. Since A1 and A2 are convex fuzzynumbers so they are expressed as follows:

Satellite data for• Geology• Terrain• Geomorphology• Land use/cover• Hydrology• Settlement and

infrastructure

Identification of potentiallocations

Location selectionguidelines by CEA & MoEF

Figure out feasible locations forfurther evaluation

Identification of location selection criteria Literature surveyExpert opinion

Establish pairwisecomparison using TFN

Calculate the eigenvalue andeigenvector

Derive consistency index

Is CI acceptable ?

Compute the overall weights of criteria andalternatives using Chang’s extent analysis

Yes

No

Phase IIFuzzy AHP

Calculate the normalized and weighted normalizeddecision matrix

Calculate the positive and negative ideal solutionsand separation measures

Calculate the relative closeness to ideal solution

Final ranking of alternative locations in decreasing order

Phase IIIRanking with TOPSIS

Meteorological dataCoal MapGrid Map

Phase ISTEEP considerations

Socio-political & otherrelevant information

Fig. 2. The three phase research methodology.


l nm

x

µ

1.0

0.0

AL(y) AR(y)

Fig. 3. A triangular fuzzy number, Ã


VðA2 � A1Þ ¼ hgtðA1XA2Þ ¼ mA2ðdÞ (11)

where d is the ordinate of the highest intersection point D betweenmA1 and mA2

as shown in Fig. 4. When A1 ¼ (l1,m1,n1) andA2 ¼ (l2,m2,n2) then mA2

(d) is computed by

mA2ðdÞ ¼

8>><>>:

1; m2 � m1;0; l1 � n2;

l1 � n2ðm2 � n2Þ � ðm1 � l1Þ

; otherwise:(12)

For the comparison of A1 and A2, we need both the values ofV(A1 � A2) and V(A2 � A1).

Step 3: Priority weight calculation [45e47]:The degree possibility of convex fuzzy number to be greater

than K convex fuzzy numbers Ai (i ¼ 1, 2,., k) can be defined by:

VðA � A1;A2;.;AkÞ¼ V ½ðA � A1Þ and ðA � A2Þ and.and ðA � AkÞ� (13)

VðA � A1;A2;.;AkÞ ¼ min VðA � AiÞ i ¼ 1;2;.; k (14)

If

mðPiÞ ¼ min VðSi � SkÞ for k ¼ 1; 2;.;n; ksi: (15)

Then the weight vector is given by:

Wp ¼ ðmðP1Þ;mðP2Þ;.;mðPnÞÞT (16)

Here Pi (i ¼ 1, 2,.,n) are n elements.

Step 4: Calculation of normalized weight vector:After normalization of Wp, we get the normalized weight

vectors

Fig. 4. Intersection between Ã1 and Ã2.

W ¼ ðwðP1Þ;wðP2Þ;.;wðPnÞÞT (17)

where,W is a non fuzzy number and it gives the priority weights ofone decision alternative over another.

3.3. Phase III e TOPSIS

In this phase, Technique for Order Preference by Similarity toIdeal Solution (TOPSIS), a ranking multi-criteria decision makingmethod, has been applied to rank the alternative locations based ontheir overall performance. This method considers three types ofattributes or criteria:

- Qualitative benefit attributes/criteria- Quantitative benefit attributes- Cost attributes or criteria

In this method two artificial alternatives are hypothesized:

- Positive ideal alternative: the one which has the best level forall attributes considered.

- Negative ideal alternative: the one which has the worst attri-bute values.

TOPSIS is based on a simple and intuitive concept; it enablesconsistent and systematic criteria, which is based on choosing thebest alternative having the shortest distance from the positive idealsolution and the farthest distance from the negative ideal solution.The positive ideal solution is the one with the most benefits andlowest cost of all alternatives, the negative ideal solution is the onewith the lowest benefits and highest cost. Subsequently the alter-natives are ranked with respect to the relative closeness to the idealsolutions [28,39].

TOPSIS assumes that we have m alternatives and n attributes/criteria and we have the score of each alternative with respect toeach criterion. Let xij score of alternative iwith respect to criterion j.Further, let J be the set of benefit attributes/criteria (more is better).Let J’ be the set of negative attributes/criteria (less is better).

The general TOPSIS process has following steps [28,47,48]:

Step 1: Construct normalized decision matrix. This step trans-forms various attribute dimensions into non-dimensional attri-butes, which allows comparisons across criteria. The normalizedvalue rij is calculated as

rij ¼xijffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPmi¼1x

2ij

q ; i ¼ 1;.;m; j ¼ 1;.;n: (18)

Step 2: Construct the weighted normalized decision matrix.Assume we have a set of weights for each criteria wj for j ¼ 1,., n,and

Pnj¼1wj ¼ 1. Multiply each column of the normalized decision

matrix by its associated weight. An element of the new matrix is:

vij ¼ wjrij; i ¼ 1;.;m; j ¼ 1;.;n: (19)

Step 3: Determine the ideal and negative ideal solutions.Positive ideal solution.

Aþ ¼nvþ1 ;.; vþn

o; where (20)

vþj ¼nmax

�vij�if j˛J;min

�vij�if j˛J’

o; j ¼ 1;.; n:

(21)

Negative ideal solution.

Table 2Guidelines of MoEF, Government of India, for location selection of coal-based TPPs[49].

1. Locations of TPPs are avoided within 25 km of the outer periphery of thefollowing:- Metropolitan cities,- National park and wildlife sanctuaries, and- Ecologically sensitive areas like tropical forest, biosphere reserve,

important lake and coastal areas rich in coral formation.2. The sites should be chosen in such a way that chimneys of the power

plants do not fall within the approach funnel of the runway of the nearestairport;

3. Those sites should be chosen which are at least 500 m away from theflood plain of river system.

4. Location of the sites are avoided in the vicinity(say 10 km) of places ofarchaeological, historical, cultural/religious/tourist importance and defenceinstallations.

5. Forest or prime agriculture lands are avoided for setting up of thermalpower houses or ash disposal.

Table 3Guidelines of CEA, Government of India, for location selection of coal-based TPPs[49].

1. The choice of location is based on factors like availability of land, water,coal, construction material, etc.

2. Land requirement for TPP is 0.2 km2 per 100 MW.3. The land for housing is taken as 0.4 km2 per project.4. Land requirement for ash pond is about 0.2 km2 per 100 MW.5. Water requirement is about 40 cusecs per 1000 MW.6. Location of thermal power station is avoided in the coal-bearing area.7. Coal transportation is preferred by dedicated marry-go-round rail system.


A� ¼nv�1 ;.; v�n

o; where (22)

v�j ¼nmin

�vij�if j˛J; max

�vij�if j˛J’

o; j ¼ 1;.;n: (23)

Step 4: Calculate the separation measures for each alternative.The separation from the positive ideal alternative is:

Sþi ¼8<:

Xnj¼1

vij � vþj

�29=;

1=2

; i ¼ 1;.;m: (24)

Similarly, the separation from the negative ideal alternative is:

S�i ¼8<:

Xnj¼1

vij � v�j

�29=;

1=2

; i ¼ 1;.;m: (25)

Step 5: Calculate the relative closeness to the ideal solution

Ci ¼ S�i

�Sþi þ S�i

�; i ¼ 1;.;m: Ci ˛

n0; 1

o(26)

where Ci denotes the final performance score in TOPSIS method.

Step 6: Rank the preference order. Rank the alternatives using Ciindex value in decreasing order. An alternative with largest indexvalue (Ci) has shortest distance from positive ideal solution andfarthest distance from negative ideal solution.

4. Case study example

The case pertains to decision related to evaluation and selectionof different locations for TPPs in India by a major public sectorpower corporation. For three TPPs, comprising of 1000 MWcapacity at each location in central-western part of India, thecorporation wants to meet the energy demand of the region in thecoming years. Management has to balance their profit motives withconcerns for the environment, managerial control, socio-economicaspects and the riskiness of the venture. A location selection teamof nine members is assigned the task of choosing the best locationsfor setting up new TPPs. The committee has experts from keyMinistries like Coal, Transport, Environment and Forest, WaterResource, etc., power corporation and various State ElectricityBoards. The three phase methodology discussed in Section 3 isadopted to select the best locations for TPPs as discussed in thefollowing subsections.

4.1. Application of STEEP factors in identification of feasiblealternatives

In this phase, satellite images on geology, geomorphology, landuse/cover, hydrology, settlement, etc., and meteorological data onrainfall, humidity, temperature, windfall pattern, etc. along withcoal and power Grid map are used through GIS maps to identifypotential locations for setting up TPPs. Initially, eight potentiallocations are identified based on GIS information. The locationselection committee visits these locations to collect socio-politicaland other relevant information of each location from revenuerecords and local administration. After these visits, one locationwas dropped from the list because of socio-political reasons. Twomore locations were further dropped from the list after consideringlocation selection guidelines of Ministry of Environment and Forest(MoEF) and Central Electricity Authority (CEA), as given inTables 2 and 3 [49]. The remaining five feasible locations are as

follows. See Appendix A for detailed description of the each alter-native location.

- Bansagar (L1):Sidhi district, Madhya Pradesh- Shahpura (L2):Shahdol district, Madhya Pradesh- Sasan (L3):Jabalpur district, Madhya Pradesh- Umred (L4):Nagpur district, Maharashtra- Wani (L5):Yavatmal, Maharashtra

4.2. Application of fuzzy AHP in determining weights of criteria

In fuzzy AHP, firstly, the criteria must be determined for evalu-ation of alternative locations. For this reason, we determine manyqualitative and quantitative sub-criteria impacting TPP locationselection decision process through intensive discussion withcommittee members, and exploring available literature [2,4,21,22].These sub-criteria are further grouped into six criteria as cost,availability of resources, accessibility, biological environment,physical environment and socio-economic development, andshown in Table 4.

After determining criteria and sub-criteria, the discussion hasbeen further prolonged to decide the different priority weights ofeach criteria, sub-criteria and alternatives using linguisticcomparison terms and their equivalent triangular fuzzy numbers(TFN) defined by Gumus [47] in Table 5.

The fuzzy comparison matrices are prepared with the help ofquestionnaire. See Appendix B for the questionnaire form. Thefuzzy comparison matrices of criteria and sub-criteria along withcalculated weights are shown in Tables 6e12.

The weight calculations using Chang’s extent analysis approachfor Table 6 are given below. These calculations can be performedeasily with Excel Sheet (see Appendix C).

Table 4The criteria and sub-criteria for selecting location of TPPs [2,4,21,22].

Criteria Sub-criteria

Cost (C1) Land acquisition cost (S11)Resettlement and rehabilitation cost (S12)Infrastructure cost (S13)

Availability of resources (C2) Land availability (S21)Water availability (S22)Fuel/Coal availability (S23)Skilled manpower availability (S24)

Accessibility (C3) Transmission grid accessibility (S31)Electricity consumption point (S32)Road/Rail/Airport accessibility (S33)Urban area accessibility (S34)

Biological environment (C4) Land cover and land use (S41)Water bodies (S42)Population centre (S43)

Physical environment (C5) Topography (S51)Geology and soil type (S52)Climate (S53)

Socio-economic development (C6) Effect on agriculture, employmentand tourism (S61)Effect on economic progress ofsurrounding region (S62)Possibility of capacity expansionin future (S63)

Table 5Triangular fuzzy numbers of linguistic comparison measures [47].

Linguistic terms Triangular fuzzy numbers (TFN)

Perfect (8, 9, 10)Absolute (7, 8, 9)Very good (6, 7, 8)Fairly good (5, 6, 7)Good (4, 5, 6)Preferable (3, 4, 5)Not bad (2, 3, 4)Weak advantage (1, 2, 3)Equal (1, 1, 1)

Table 7The fuzzy comparison matrix of sub-criteria with respect to criteria C1.

S11 S12 S13 Weight

S11 (1, 1, 1) (4, 5, 6) (1, 2, 3) 0.68S12 (1/6, 1/5, 1/4) (1, 1, 1) (1/4, 1/3, 1/2) 0.00S13 (1/3, 1/2, 1) (2, 3, 4) (1, 1, 1) 0.32


S21 S22 S23 S24 Weight

S21 (1, 1, 1) (1/4, 1/3, 1/2) (1/4, 1/3, 1/2) (2, 3, 4) 0.12S22 (2, 3, 4) (1, 1, 1) (1, 2, 3) (4, 5, 6) 0.52S23 (2, 3, 4) (1/3, 1/2, 1) (1, 1, 1) (2, 3, 4) 0.36S24 (1/4, 1/3, 1/2) (1/6, 1/5, 1/4) (1/4, 1/3, 1/2) (1, 1, 1) 0.00


S31 S32 S33 S34 Weight

S31 (1, 1, 1) (4, 5, 6) (2, 3, 4) (1, 2, 3) 0.49S32 (1/6, 1/5, 1/4) (1, 1, 1) (1/5, 1/4, 1/3) (1/4, 1/3, 1/2) 0.00S33 (1/4, 1/3, 1/2) (3, 4, 5) (1, 1, 1) (1/4, 1/3, 1/2) 0.18S34 (1/3, 1/2, 1) (2, 3, 4) (2, 3, 4) (1, 1, 1) 0.33


S41 S42 S43 Weight

S41 (1, 1, 1) (1/6, 1/5, 1/4) (1/4, 1/3, 1/2) 0.00S42 (4, 5, 6) (1, 1, 1) (2, 3, 4) 0.82S43 (2, 3, 4) (1/4, 1/3, 1/2) (1, 1, 1) 0.18


The values of fuzzy synthetic extent of six criteriawith respect tothe goal are calculated as below by using Eq. (9).

S1 ¼ (3.83, 6.17, 9.00) 5 (31.25, 47.17, 66.50)�1

¼ (0.0576, 0.1307, 0.2880)S2 ¼ (9.00, 14.00, 19.00) 5 (31.25, 47.17, 66.50)�1

¼ (0.1353, 0.2968, 0.6080)S3 ¼ (3.17, 4.67, 7.00) 5 (31.25, 47.17, 66.50)�1

¼ (0.0476, 0.0989, 0.2240)S4 ¼ (7.33, 10.50, 14.00) 5 (31.25, 47.17, 66.50)�1

¼ (0.1103, 0.2226, 0.4480)S5 ¼ (5.33, 8.50, 12.00) 5 (31.25, 47.17, 66.50)�1

¼ (0.0802, 0.1802, 0.3840)S6 ¼ (2.58, 3.33, 5.50) 5 (31.25, 47.17, 66.50)�1

¼ (0.0388, 0.0707, 0.1760)The V values calculated using Eqns. (11) and (12) are shown in

Table 13.We obtain the minimum degree of possibility with the help of

Eq. (15) as

Table 6The fuzzy comparison matrix of criteria.

C1 C2 C3

C1 (1, 1, 1) (1/4, 1/3, 1/2) (1, 2, 3)C2 (2, 3, 4) (1, 1, 1) (2, 3, 4)C3 (1/3, 1/2, 1) (1/4, 1/3, 1/2) (1, 1, 1)C4 (2, 3, 4) (1/3, 1/2, 1) (2, 3, 4)C5 (1, 2, 3) (1/3, 1/2, 1) (1, 2, 3)C6 (1/3, 1/2, 1) (1/4, 1/3, 1/2) (1/3, 1/2, 1)

mðC1Þ ¼minVðSi�SkÞ ¼minð0:48; 1:00; 0:66; 0:81; 1:00Þ ¼ 0:48Similarly; mðC2Þ ¼ 1:00 mðC3Þ ¼ 0:31 mðC4Þ ¼ 0:81

mðC5Þ ¼ 068 mðC6Þ ¼ 0:15

Then the weight vector is given by:

Wp ¼ ð0:48;1:00;0:31;0:81;0:68;0:15ÞT

After normalization of Wp, we get the normalized weightvectors as

W ¼ ð0:14;0:29;0:09;0:24;0:20;0:04ÞT

The other weight calculations are not given here because theyfollow the same procedure as discussed above.

Now, five locations must be evaluated with respect to each sub-criterion. The fuzzy evaluation matrices of alternative locations andcorresponding weight vector of each location with respect to sub-criteria S11 is shown in Table 14. Other comparison matrices ofalternative locations with respect to sub-criteria are not shownhere. But, Table 15 shows all weight vectors, calculated by pairwisecomparisons as similar to S11, of alternative locations with respectto the sub-criteria.

C4 C5 C6 Weight

(1/4, 1/3, 1/2) (1/3, 1/2, 1) (1, 2, 3) 0.14(1, 2, 3) (1, 2, 3) (2, 3, 4) 0.29(1/4,1/3, 1/2) (1/3, 1/2, 1) (1, 2, 3) 0.09(1, 1, 1) (1, 1, 1) (1, 2, 3) 0.24(1, 1, 1) (1, 1, 1) (1, 2, 3) 0.20(1/3, 1/2, 1) (1/3, 1/2, 1) (1, 1, 1) 0.04


S51 S52 S53 Weight

S51 (1, 1, 1) (1, 2, 3) (1/4, 1/3, 1/2) 0.18S52 (1/3, 1/2, 1) (1, 1, 1) (1/5, 1/4, 1/3) 0.00S53 (2, 3, 4) (3, 4, 5) (1, 1, 1) 0.82


S61 S62 S63 Weight

S61 (1, 1, 1) (1/4, 1/3, 1/2) (1/3, 1/2, 1) 0.08S62 (2, 3, 4) (1, 1, 1) (1, 2, 3) 0.56S63 (1, 2, 3) (1/3, 1/2, 1) (1, 1, 1) 0.36

Table 13V values for criteria.

1 2 3 4 5 6

1 1.00 0.84 1.00 1.00 0.662 0.48 0.31 0.81 0.68 0.153 1.00 1.00 1.00 1.00 0.824 0.66 1.00 0.48 0.87 0.305 0.81 1.00 0.64 1.00 0.476 1.00 1.00 1.00 1.00 1.00

Table 14The fuzzy comparison matrix of the locations with respect to sub-criterion S11.

L1 L2 L3 L4 L5 Weight

L1 (1, 1, 1) (1, 2, 3) (4, 5, 6) (1/3, 1/2, 1) (6, 7, 8) 0.38L2 (1/3, 1/2, 1) (1, 1, 1) (2, 3, 4) (1/5, 1/4, 1/3) (3, 4, 5) 0.02L3 (1/6, 1/5, 1/4) (1/4, 1/3, 1/2) (1, 1, 1) (1/7, 1/6, 1/5) (2, 3, 4) 0.00L4 (1, 2, 3) (3, 4, 5) (5, 6, 7) (1, 1, 1) (7, 8, 9) 0.60L5 (1/8, 1/7, 1/6) (1/5, 1/4, 1/3) (1/4, 1/3, 1/2) (1/9, 1/8, 1/7) (1, 1, 1) 0.00

Table 15The weights of alternative locations with respect to sub-criteria.

Alternative locations

Sub-criteria L1 L2 L3 L4 L5

S11 0.38 0.02 0.00 0.60 0.00S12 0.00 0.33 0.00 0.08 0.59S13 0.38 0.00 0.00 0.53 0.09S21 0.00 0.36 0.00 0.64 0.00S22 0.04 0.61 0.35 0.00 0.00S23 0.00 0.00 0.66 0.00 0.34S24 0.00 0.18 0.55 0.27 0.00S31 0.00 0.12 0.32 0.00 0.56S32 0.00 0.30 0.09 0.61 0.00S33 0.56 0.00 0.10 0.36 0.00S34 0.20 0.39 0.00 0.35 0.06S41 0.00 0.11 0.35 0.00 0.54S42 0.36 0.00 0.13 0.00 0.51S43 0.22 0.04 0.32 0.00 0.42S51 0.00 0.54 0.11 0.00 0.35S52 0.12 0.00 0.33 0.55 0.00S53 0.11 0.00 0.54 0.00 0.35S61 0.22 0.00 0.00 0.48 0.30S62 0.20 0.43 0.00 0.04 0.33S63 0.10 0.55 0.00 0.35 0.00

Table 16The priority weights of alternative locations with respect to criteria.

C1 C2 C3 C4 C5 C6

L1 0.3800 0.0208 0.1668 0.3348 0.0902 0.1656L2 0.0136 0.3604 0.1875 0.0072 0.0972 0.4388L3 0.00 0.4196 0.1748 0.1642 0.4626 0.00L4 0.5776 0.0768 0.1803 0.00 0.00 0.1868L5 0.0288 0.1224 0.2942 0.4938 0.3500 0.2088


The weights of alternative locations with respect to the eachcriterion is determined by adding the weights per location multi-plied by weights of the corresponding sub-criteria and shown inTable 16.

4.3. Application of TOPSIS in ranking of alternatives

Finally, TOPSIS method is applied to rank the alternative loca-tions. The priority weights of alternative locations with respect tocriteria, calculated by fuzzy AHP and shown in Table 16, can be usedin TOPSIS. The weighted normalized decision matrix, prepared byusing Eq. (19), can be seen from Table 17.

Now, using TOPSIS method (Section 3.3), the ranking of alter-native locations are calculated. Table 18 shows the evaluationresults and final ranking of alternative locations for setting upTPPs.

Table 17The weighted normalized decision matrix.

C1 C2 C3 C4 C5 C6

L1 0.0769 0.0105 0.0326 0.1298 0.0303 0.0121L2 0.0028 0.1827 0.0366 0.0028 0.0327 0.0321L3 0.0000 0.2127 0.0341 0.0637 0.1555 0.0000L4 0.1168 0.0389 0.0352 0.0000 0.0000 0.0137L5 0.0058 0.0620 0.0574 0.1915 0.1176 0.0153

Table 18The final evaluation and ranking of alternative locations.

Locations Sþi S�i Ci Ranking

L1: Bansagar 0.2509 0.1544 0.3809 4L2: Shahpura 0.2550 0.1782 0.4114 3L3: Sasan 0.1777 0.2629 0.5967 1L4: Umred 0.3031 0.1210 0.2854 5L5: Wani 0.1917 0.2325 0.5481 2

4.4. Results and discussion

According to the Ci values the ranking of the alternative loca-tions are Sasan-Wani-Shahpura-Bansagar-Umred from the mostpreferable to the least preferable. If one best location is to beselected, then Sasan must be chosen because of having the highestCi value. Management may choose Sasan(L3)-Wani(L5)-Shahpu-ra(L2) locations for setting up three new TPPs.

Decision makers may perform sensitivity analysis to reveal theeffect on the evaluation process and ranking of alternatives bychanging the priority weights of decision attributes. For this reason,we exchange the weights for two decision attributes while theothers are constant. In other words, theweight of first criterion C1 ischanged with C2, C3, C4, C5, and C6, sequentially. Then index values(Ci) are calculated using TOPSIS method.

The results of sensitivity analysis are given in Table 19 andgraphically in Fig. 5. Indeed, the index values (Ci) and the ranking ofalternative locations changes when priority weights of criteria arechangedmutually. If C1 and C20s priorityweights are exchanged, thenindex value of L1 springs from 0.3809 to 0.5200, and ranking of L1 ischanging from4 to 1. This also leads to change in ranking of L3 from 1to 4. Except this, L3 has the largest Ci value when faced with otherchanges. Location L4 gets second and third preference ranking whenthe weights of C1 and C4, and weights of C1 and C2 are exchanged,respectively. Otherwise, L4 has the lowest ranking. According to thesensitivity analysis results, locations L3, L5, and L2 are the mostappropriate alternatives for setting upnewTPPs because they usually

Table 19The sensitivity analysis results.

Sensitivityanalysis run

Weight ofcriteria

Performance score(Ci) of locationsusing TOPSIS

Relative ranking of locations

1 C1 ¼ 0.14 L1 ¼ 0.3809 L3 / L5 / L2 / L1 / L4C2 ¼ 0.29 L2 ¼ 0.4114C3 ¼ 0.09 L3 ¼ 0.5967C4 ¼ 0.24 L4 ¼ 0.2854C5 ¼ 0.20 L5 ¼ 0.5481C6 ¼ 0.04






0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1 2 3 4 5 6

Inde

x va

lue

(Ci)

Sensitivity analysis number

L1 L2 L3 L4 L5

Fig. 5. Effect on ranking of locations due to sensitivity analysis.


have higher rankings after exchanging priority weights here. Sensi-tivity analysis can be further expanded by exchanging weights indifferent manners. The analysis makes the evaluation process easierfor decision makers.

5. Conclusion

Multi-criteria decision making is a powerful tool used widely tosolve energy planning problems containing multiple conflictingcriteria [1,50]. Several approaches have been proposed formulti-criteria decisions, such as AHP, ANP, TOPSIS, ELECTRE,

PROMETHEE, Rough Set Theory, etc. Among numerous methods ofmulti-criteria decision making, the fuzzy AHP, which is a combi-nation of AHP and fuzzy numeric logic, is very suitable for evalu-ating alternatives when qualitative and quantitative observationand preferences are expressed only with linguistic vagueness.However, a disadvantage of the fuzzy AHP approach is that inputdata, expressed in linguistic terms, rely on opinions and experienceof decision makers and thus involves subjectivity. Evaluation ofcriteria, sub-criteria and alternative locations usually requiresspecified knowledge, information as well as experience, but expertsmay display subjectivity in judgments during providing prefer-ences of one criterion over another criterion. The TOPSIS issystematic method for ranking of alternatives. In addition, bothfuzzy AHP and TOPSIS are quite simple in conception and appli-cation compared to other methods for multi-criteria analysis.

In academic literature, the thermal power plant location selec-tion problem is discussed less despite the fact that it is very impor-tant strategic decision. Generally, location selection choice for TPP isgoverned by traditional way of decision making or by politicalinterests. The traditional decision making approach considers costand resource availability, then generally selects location near to themouth of a coal mine or near to the water source without takingholistic and systematic approach. Moreover, the political interest ofthe ruling party and opinion of the pressure groups may also influ-ence the location decision, resulting into high operational andtransmission cost, low productivity, and tremendous negativeimpact on society due to increased environmental pollution. Thus,the thermal power plant location selection process should carefullyconsider not only the technical issues, but also its impact on social,economical and ecological environment. Hence, a systematic andholistic approach is needed for in-depth analysis of all relevantfactors when seeking to determine the best locations for thermalpower plants in order to safeguard the interest of all stakeholders.

In this paper, we propose STEEP-fuzzy AHP-TOPSIS frameworkfor evaluation and selection of optimal locations for TPPs. Initially,we found feasible locations by considering social, technical,economical, environmental, and political (STEEP) factors. Thesefactors are considered to minimize decision risks because of tradi-tional way of decision making, political influence, socio-economicfactors, and subjectivity in opinions and preferences of decisionmakers. We also explored literature and interviewed experts toidentify qualitative and quantitative criteria and sub-criteriaimpacting TPPs location selection decision process. Additionally,we integrate fuzzy AHP and TOPSIS methods to determine theweights of criteria, and to rank the alternative locations, respec-tively. Fuzzy set theorywithAHP is used to capture the linguistic andvague description of pairwise comparison. Then a case study ispresented to demonstrate the applicability of the proposed frame-work. Here, sensitivity analysis is also performed to discuss andexplain the results. This paper proposes an integrated approach ina systematic way with an aim to select the best alternative despiteconflicting criteria. Thus, the contribution of this paper is to proposean efficient and effective decision framework for evaluation andranking of alternative locations for setting up new TPPs.

Furthermore, in this study, evaluation criteria are consideredindependent. We observe that these criteria also have some inter-dependencies, which cannot be captured by AHP method. There-fore, in future, analytic network process (ANP) may be explored tocapture interdependencies among decision attributes [51].

Acknowledgement

The authors would like to thank the editor and two anonymousreferees for their valuable suggestions, which have been of greathelp in improving the quality of this paper.

Appendix A. Description of potential locations for TPPs.

Location Distance fromrailway station

Land availability Water source Coal source Resettlement &rehabilitation

Powertransmission

Environmental aspects Accessibility

Sasan 8 Km 1525 ha, waste landplus agricultural land(single crop)

Gobindh Vallabh pantreservoir 1.5 km away

22 Km a little required Nearby No eco-sensitive spot,but five TPPs exist insurrounding

Good

Bansagar 12 Km 860 acres, green beltplus agricultural land(single crop)

Bansagar reservoir3 Km away

190 Km No R&R Needed Surrounded by reservedforests

Poor

Shahpur 20 Km 1000 acres, wasteland with someundulations

Narmada river1 km away

380 Km No R&R Needed No eco-sensitive spotwithin a distance of 25 km

Good

Umred 50 Km 750 acres, undulatingand many streams ofriver passes

Goshikhurd dam3 Km away

Not nearby a little required Needed No environment issue Good

Wani 23 Km 600 ha barren land plus600 ha agricultural land

Wardha river1.5 km away

15 Km No R&R Needed No environment issue Good

Appendix B. Questionnaire form to facilitate the comparison of criteria with respect to goal (Similar types of questionnaire areused for comparing sub-criteria with respect to each criterion, and alternatives locations with respect to each sub-criterion.).

Questions

With respectto goal

How important is criterion Cost when it is compared with criterion Availability of resources?How important is criterion Cost when it is compared with criterion Accessibility?How important is criterion Cost when it is compared with criterion Biological env.?How important is criterion Cost when it is compared with criterion Physical env.?How important is criterion Cost when it is compared with criterion Socio-economic?And so on..

Preferencesof experts

Equal(1, 1, 1)

Weak advan(1, 2, 3)

Not bad(2, 3, 4)

Preferable(3, 4, 5)

Good(4, 5, 6)

Fairly good(5, 6, 7)

Very good(6, 7, 8)

Absolute(7, 8, 9)

Perfect(8, 9, 10)

Preferencesof experts

Cost * Avail. of resor.Cost O AccessibilityCost * Biological env.Cost * Physical env.Cost O Socio-economicAvail. of resor. O AccessibilityAvail. of resor. O Biological env.Avail. of resor. O Physical env.Avail. of resor. O Socio-economicAccessibility * Biological env.Accessibility * Physical env.Accessibility O Socio-economicBiological env. O Physical env.Biological env. O Socio-economicPhysical env. O Socio-economic

Appendix C. Excel template for weight calculation using Chang’s extent analysis approach.


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http://www.gisdevelopment.net/application/utility/power/ma05154.htm

http://www.gisdevelopment.net/application/utility/power/ma05154.htm

http://www.cea.nic.in/reports/monthly/executive_rep/jun11/8.pdf

http://www.natcomindia.org/reports_ministries/power/blueprint.pdf

http://www.natcomindia.org/reports_ministries/power/blueprint.pdf

an steep-fuzzy ahp-topsis framework for evaluation and selection of thermal power plant location: a...

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