Exploring tapping potential of solar energy: Prioritization of Indian states

Renewable and Sustainable Energy Reviews 58 (2016) 397–406

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Exploring tapping potential of solar energy: Prioritization of Indian states Amritpal Singh a, Gaurav Vats b,c,d,1, Dinesh Khanduja a a

Department of Mechanical Engineering, National Institute of Technology, Kurukshetra 136119, Haryana, India IITB-Monash Research Academy, Indian Institute of Technology Bombay, Powai 400076, Mumbai, Maharashtra, India c Department of Mechanical Engineering, Indian Institute of Technology Bombay, Powai 400076, Mumbai, Maharashtra, India d Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Vic. 3800, Australia b

art ic l e i nf o

a b s t r a c t

Article history: Received 15 November 2014 Received in revised form 22 October 2015 Accepted 15 December 2015

Present study is among the first attempts to prioritize the prominent Indian states for better utilization of available solar resources. In this context, potentiality indices are performed on the basis of six prime factors that influence the effective utilization of solar energy and ranking is done accordingly. Firstly, in order to determine the weightage and hierarchy of the evaluation parameters modified digital logic (MDL) approach is employed. Availability of solar radiation is found to be the most influential parameter. Thereafter, fuzzy-analytical hierarchy process (AHP) is used for determination of the potentiality index and the corresponding ranking of different states. The ranks thus obtained are compared to current installed capacity ranks. It is found that a few states despite of high potential for the exploitation of solar energy are not getting proper attention by their respective governments. In order to promote the solar energy exploitation in such states it becomes vital to identify the vital parameters that may lead to the improvement in the current ranking. In this direction, sensitivity analysis is performed to determine the percentage change in distinct parameters which is required for the upgradation in the ranking. Such studies are capable to make a paradigm shift in technology utilization and formulation of energy policies as the proposed approach provides a concrete layout for re-evaluation of current policies as well as formulation of newer ones. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Prioritization Indian states Solar energy Fuzzy MADM

Contents 1. 2.

3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 2.1. Modified Digital Logic (MDL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 2.2. Fuzzy logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 2.3. Analytic Hierarchy Process (AHP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 2.4. Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Methodology used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Parameters for evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 4.1. Existing energy demand and availability (P1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 4.2. Availability of solar radiation (P2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.3. Government policy (P3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.4. Social acceptability (P4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.5. Land availability (P5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.6. Environment issues (P6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.6.1. High wind speeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.6.2. Snow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402

E-mail address: [email protected] (G. Vats). Tel./fax: þ 91 1744 221655.

1

http://dx.doi.org/10.1016/j.rser.2015.12.056 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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4.6.3. Floods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 4.6.4. Earth quakes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 5. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405

1. Introduction Energy crisis and green planet initiative are forcing governments, scientists, policy makers and associated experts to reassess their outlook towards sustainable green solutions under technosocio-economic constraints. This has to happen in two ways-either to go for novel solutions which are off-course pretty time consuming or to work on effective utilization of available assets. One of such assets is renewable resources of energy. Though these are available in abundance but in order to satisfy current energy demands it is a must to have a careful cognizance for their effective utilization. In India, these are primarily classified as wind, hydro and solar. Wind and hydro resources of renewable energy are undoubtedly the most explored ones besides the various serious issues associated to them [1]. Wind energy resources are clean and environment friendly but suffers issues of noise pollution, negative environment impact, effect on biosphere and fluctuating outputs [2]. Concurrently, exploitation of hydro energy have issues of time and huge investment costs of dams [3]. Moreover, their maintenance, rehabilitation and demolition are reported among the top problems in the scientific world [4]. On the other hand closeness to the equator provides much favorable conditions for efficient exploitation of solar energy in India [5]. Indian continent receives nearly 3000 h of solar radiation every year. This amount of solar radiation is equivalent to 500 trillion kWh of energy [6]. This clearly indicates that solar projects are the best solution to meet the energy demands in Indian market. It is to be noted that these projects are quite bulky and require huge amount of investments. Hence, the knowledge of technical factors, social interests, financial aspects and governments' perspective are prerequisite. Moreover, it is essential to have consistent invigilation to resolve the unforeseen issues for effective utilization of solar resources. Interestingly, the current installed capacity of solar energy grids in India has recently increased to 2208.36 MW [7] and is expected to further rise by 20,000 MW till 2022 [8]. Table 1 highlights the current state-wise installed solar capacity and the corresponding ranks based on it [9]. However, this ranking is likely to vary with change in current installed capacity. Such variation will cause huge fluctuations in financial distribution/fund allocation by the central government. This further may require an immediate re-evaluation of current policies or formulation of the Table 1 State wise installed solar capacity and their corresponding ranks (based on installed capacity). States

Capacity (MW)

Ranking

Gujarat Rajasthan Maharashtra Madhya Pradesh Andhra Pradesh Tamil Nadu Karnataka Uttar Pradesh Jharkhand Kerala Jammu and Kashmir

860.40 666.75 237.25 195.32 92.90 31.82 31.00 17.40 16.00 0.59 0.31

1 2 3 4 5 6 7 8 9 10 11

newer ones. Such domains are quite fragile and involve advanced statistical analysis. Present study comes with such an approach for prioritization of Indian states for effective utilization of available solar resources. This study aims to 1.) Determine techno-socioeconomic criteria that affects the exploitation of solar energy in India; 2.) Predict weightage and hierarchy among these parameters; 3.) Calculate potentiality index (PI) and rank Indian states as per the potentiality index; 4.) Compare current (based on installed capacity) and predicted ranking; and finally 5.) Suggest the crucial parameters that are important for improvement in the current ranking of the states with larger potential in contrast to the current installed capacity/ranking.

2. Methods Such problems can be effectively dealt with multiple attribute decision making (MADM) [10]. These methods include Modified Digital Logic (MDL), Analytic Hierarchy Process (AHP) [11], graph theory and matrix approach (GTMA) [12], TOPSIS [13], VIKOR [14] and many others. These tools have been applied to an ample spectrum of applications such as site selection [15,16], materials selection [17,18], supply chain management [19], scientific decisions [20] and engineering problems [21]. We have been dedicatedly working on these techniques [10,13,14,22–26] and attained a critical understanding for implementation of these techniques in terms of working principles and their pros and cons. Among these, GTMA is a logical and systematic approach (based on advanced graph theory) but requires complex calculations [12,28] while TOPSIS and VIKOR are quite simple and easy to implement. TOPSIS works on Euclidian distances and requires nonlinear normalization while VIKOR works on linear normalizations and is based on regret analysis. Both the methods provides nearly similar results. However, both the methods have a time consuming and complicated sensitivity analysis procedure and are therefore not suitable for the present study. Contemporary, AHP is a simpler and effective tool for dealing with complex decision making problems. The basic principle of AHP is to simplify a complex decision problem by braking down the problem into hierarchy of criteria [29]. Therefore, in limelight of the objectives of the present study and understanding gained by us in past; present study utilizes fuzzy-AHP in combination with MDL weights of the evaluation parameters to predict the potentiality index and corresponding ranking of Indian states [30]. Henceforth, the ranking is compared to the current installed capacity ranking and an attempt is made to quantitatively understand the difference between the two. The reasons behind any deviation in the current installed capacity ranking from the predicted one helps in depicting the areas to be focused on for the improvement in present ranking and so as to have effective utilization. In this context, sensitivity analysis is performed to figure out the percentage change in parameters (potentiality improvement index: PII) that may lead to improvement in the present ranking of the lacking states. Thus, the ranking/potentiality improvement index (PII) obtained can be very helpful to the states as well as central government in formulating newer policies and

A. Singh et al. / Renewable and Sustainable Energy Reviews 58 (2016) 397–406

correspondingly making fund allocations for effective utilization of available solar energy resource. 2.1. Modified Digital Logic (MDL) MDL is used to determine the weights of the evaluation criteria [31]. Different parameters (discussed later in the Section 4) will have different impact on the potentiality indices of available solar resource performance for different states and hence cannot be assigned equal weights. Thus, it becomes important to find out the priorities of each criterion. In this context, a pair-wise comparison decision matrix is formed by the decision compilers makers on the bases of discussion with various decision makers (technical, financial, environment and policy makers). The matrix is filled using 1, 2 and 3 for less, equal or more important parameters, respectively. MDL provides an opportunity to estimate the number of possible positive decisions as N ¼n(n 1)/2, where n is number of parameters. Further summation of all positive decisions (E) for a particular parameter on normalization leads to final weightage (Wj) as: Ej W j ¼ Pn

j¼1

ð1Þ

Ej

2.2. Fuzzy logic The concept was introduced by Lotfi A. Zadeh in 1965 [32] to tackle the problems where there are no clear boundaries between the two or more parameters [33]. It also deals with the problems where it is tough to distinguish between members and non-member objects of a set. Fuzzy approach was used for multiple criteria decision making where the emphasis is on possibility rather than probability [34]. Fuzzy logic is based on a set theory and is comprises of a membership function within the interval [0, 1] which describes the extent of relevance of an element for being the member of the set [35]. Linguistic variables are used for all the comparisons which are assigned numerical values without any enigma. These are the variables whose values are words or sentences in a natural or artificial language [36]. For instance, quality is a linguistic variable if its values are assumed to be the fuzzy variables labeled as “good”, “bad”, “worst” rather than the actual numbers. The main application of the linguistic approach lie in the domain of humanistic system particularly in the fields of artificial intelligence, linguistics, human decision processes, pattern recognition, psychology, brain research, economics and related areas [37]. In the present work trapezoidal fuzzy numbers (TFN) is used. Fig. 1 shows a typical TFN (b1, b2, b3, b4) for {b1 ,b2 ,b 3 ,b4 A R;

399

b1 r b2 r b3 r b4 } [38].The membership function mb(x) for TFN is defined as: 8   x – b1 > ; x ϵ b1 ; b2 > b2  b1 > >   > < 1; x ϵ b2 ; b3 ð2Þ μb ðxÞ ¼   > b4  x ; x ϵ b3 ; b4 > > b4  b3 > > : 0; Otherwise

2.3. Analytic Hierarchy Process (AHP) AHP is a MADM technique introduced by Saaty [39]. It has been one of the most widely used technique for complex decision making and is found especially suitable for planning at strategic level [40]. It allows small inconsistency in judgment because experts are not always consistent. In AHP, the decision problem is first decomposed into hierarchy of more easily comprehended sub-problems, i.e. objective, parameter and alternative for a particularly problem. The elements of the hierarchy are related to aspect of the decision problem which can be carefully measured or roughly estimated anything at all that applies to the decision making. Experts systematically evaluate the various parameters in hierarchy by comparing them to one another two at a time. It is the essence of the AHP that human judgments, and not just the underlying information, can be used in performing the evaluations. In order to compare distinct attributes, numeric priority values are assigned to the attributes on the scale of 1–9. Thereafter, principal eigen value ðλmax Þ and priority vectors are calculated for each comparison matrix. Principal eigen value is the highest eigen value of the matrix and equal to the order of the matrix. A priority vector gives the weighted factors for particular attribute and is the normalized eigenvector of the matrix. Since, the comparison is based on expert opinion, some inconsistency may occur in the system. The consistency of system can be checked by the consistency ratio (CR): CR ¼

CI RI

ð3Þ

The value of CR should be less than 0.10, if not then the procedure must be reviewed to enhance consistency [6]. Where CI is the consistency index which can be written as: CI ¼

λmax m m1

ð4Þ

The random consistency index (R.I.) is the predefined value [23]. Results are compiled on the calculations obtained for all these parameters, which rely on the expert opinions. The result is a relative value and ratio of the parameters for a given pair of alternative. In order to have better consistency Vats and Vaish [23] elucidated that despite of using expert opinion it is better to utilize the exact numerical values wherever available. Tables 2 and 3 show how a decision matrix is evolved in such cases. Table 2 illustrates an information matrix for five states and six evaluation parameters. Xij is the numerical value associated with ith Table 2 Schematic for states and evaluation parameters states. Parameters

Parameter Parameter Parameter Parameter Parameter Parameter Fig. 1. : Trapezoidal fuzzy numbers.

1 2 3 4 5 6

States State 1

State 2

State 3

State 4

State 5

X11 X21 X31 X41 X51 X61

X12 X22 X32 X42 X52 X62

X13 X23 X33 X43 X53 X63

X14 X24 X34 X44 X54 X64

X15 X25 X35 X45 X55 X65

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A. Singh et al. / Renewable and Sustainable Energy Reviews 58 (2016) 397–406

3. Methodology used

Table 3 Comparison matrix for states in contrast to parameter 1 (from Table 2).

State State State State State

1 2 3 4 5

State 1

State 2

State 3

State 4

State 5

1 X12/X11 X13/X11 X14/X11 X15/X11

X11/X12 1 X13/X12 X14/X12 X15/X12

X11/X13 X12/X13 1 X14/X13 X15/X13

X11/X14 X12/X14 X13/X14 1 X15/X14

X11/X15 X12/X15 X13/X15 X14/X15 1

parameter and jth parameter. Table 3 shows the comparison matrix for states in the contrast to parameter 1. Here, five states are compared with each other for parameter 1. The consistency obtained by this method is 100% as it is based on comparison of real data. 2.4. Sensitivity analysis Once the ranking is known one will be interested to work for improvement. But, an important question is where to improve or which parameters to be focused on. The answer to this is sensitivity analysis. It provides information about the uncertainty distribution with respect to change in inputs of a model [41]. In other words, it tells about the percentage improvement required in a particular criterion to improve the present ranking. It aims to evaluate the relative importance of each input parameter with respect to corresponding output [42]. It is important to note that a small change in the relative weights may cause huge deviation in the final ranking. For Di be theoverall priority vector of the ith alternative and N C 1 ; C 2 ; ::::; C N be the number of criteria, this minimum change in the current weight ðW k Þ of criterion C k is denoted as δk;i;j (1 rir jrM). Where, 1 rk rN such that the ranking of the parameters Ai and A j willbe interchanged [43]. The percentage change in the weights δ0k;i;j can be defined as: δ0k;i;j ¼ δk;i;j 

100 ; 1 r i r j r M and 1 r k r N WK

ð5Þ

Further, is it used to depict the ðDk' Þ degree of criticality for the criterion (Ck). It is smallest percent change by which the current value of Wk must change so that the existing ranking of parameters can be altered.   D0 k ¼ min δ0 h;i;j ; for any N Z k Z 1 ð6Þ The sensitivity coefficient of criterion (Ck) is the reciprocal of the criticality degree as: sensC k ¼

1 ; for any N Z k Z 1 D0 k

ð7Þ

δ0k;i;j can be estimated as δ0k;i;j ¼

 Dj Di 100  ; if bjk 4 bik bjk bik W k

ð8Þ

Furthermore, the following condition should also be satisfied for the value of δk;i;j' to be feasible: Dj Di 100 r bjk bik W k

ð9Þ

k

The criteria Ck is the robustness criterion if all associated δk;i;j' are not feasible, i.e.: Dj Di ZWk bjk bik

As discussed in the previous sections that the present study aims to provide a quantitative potentiality indices and ranking for evaluation of the net available (under techno-socio-economic constraints) solar resource in Indian states. Once the evaluation criteria is decided the analysis requires a methodology that is capable to convert the verbal qualitative reasoning (obtained from understanding developed discussion, brain storming sessions and available literature) to the quantitative numbers. To address this present study employs fuzzyAHP incorporation with MDL weights. The methodology is used to calculate the potentiality index and ranking the Indian states. The beauty of the approach is that the potentiality indices and corresponding ranks are relative to the top performing state. This reason also makes this ranking valid for being compared with the installed capacity ranking and to correspondingly proceed further for determining the potentiality improvement index (PII). The PII provides an idea about the overall improvement required to alter the obtained ranking. In order to determine the potentiality improvement index (PII) the sensitivity analysis is performed corresponding to each criterion. Based on PII values obtained for a criterion corresponding state governments can take necessary actions to promote the effective utilization of available solar resource. Fig. 2 shows the flow chart for the proposed methodology. This clearly indicates that initially evaluation criteria need to be decided. This can be done based upon various studies documented in literature and discussions with various officials, society representatives and common citizens. All these can be collectively called as ‘decision makers’. Thereafter, a ‘decision compiler’ based on the understanding gained fills the decision matrix for both MDL and Fuzz-AHP so as to obtain the Potentiality Index. The states can be ranked accordingly and later a sensitivity analysis could be performed to depict the PII. Mathematically, the employed methodology is comprised of the following steps: Step 1: MDL weights calculation. As discussed in section 3.1, MDL weights (Wj) are calculated for all the parameters. This provides the weights of different criteria. These weights help us in judgment of inter-criteria (parameters) examination for every alternative and ranking is done according to the weights. Step 2: Characterize linguistic variables, fuzzy numbers and their membership function. A set of fuzzy rates is required in order to compare all the alternatives for each criterion. These fuzzy terms are assigned by the decision makers and responsible for intra criterion comparisons of the alternatives. Step 3: Construction of decision matrix. Let p be the parameters and q be the alternatives for k number of decision makers in the proposed model. The aggregated fuzzy rating for Cj criterionare represented as xijk ¼{xijk1, xijk2, xijk3, xijk4}. For i¼1,2,….p; j¼ 1,2,….q and k¼ 1,2,….k, xijk is calculated as [44,45]: 8   xij1 ¼ min bijk1 > > > k > > > x ¼ b > ij3 ijk3 k > >   > > : xij4 ¼ max bijk4

ð10Þ

The feasible δh;i;j' values gives the priority improvement index (PII) for a state with respect to the corresponding evaluation criterion. Further, these values are utilized (Eqs. (9) and (10)) to calculate Dk' and overall sensitivity of the system.

Thus the obtained decision matrix (M) is shown as: 2 3 x11 x12 ⋯x1p 6 x x ⋯x 7 2p 7 6 21 22 6 7 ⋮⋮⋱⋮ 7 M¼6 6 7 6 ⋮⋮⋱⋮ 7 4 5 xq1 xq2 ⋯xqp Step 4: Defuzzification

A. Singh et al. / Renewable and Sustainable Energy Reviews 58 (2016) 397–406

401

Fig. 2. Flow chart for the proposed methodology.

Defuzzification is performed to obtain the crisp values for each criterion corresponding to the states. This provides a quantitative value for the linguistic variables and fuzzy numbers assigned based on the verbal reasoning of the decision makers. Following equation lead to the crisp values: R  μðxÞ:xdx f ij ¼ Defuzz xij ¼ R μðxÞ:dx R 

xij2

¼

xij1

x  xij1 xij2  xij1

R 

xij2 xij1

¼



:xdx þ

x  xij1 xij2  xij1



R

xij3

xdx þ

xij2

dx þ

R

xij3

xij3 xij2

R 

xij4

R 

xij4

dxþ

xij3

xij4  x xij4  xij3

xij4  x xij4  xij3

 :xdx



dx

  2   2  xij1 xij2 þxij3 xij4 þ 13 xij4  xij3 þ 13 xij2  xij1 xij1  xij2 þ xij3 þ xij4

4. Parameters for evaluation As discussed in the previous section that the first step towards potentiality analysis of solar energy is identification of evaluation parameters. In this context, based on various studies documented in literature and our discussions with various government officials, society representatives and common citizens (decision makers) we (decision compilers) have identified six prime factor that greatly influence the potentiality indices for tapping solar energy in India [7,15,46,47]. It is to be noted that the effect of these parameters may vary from region to region because of diversity in social, geographical as well as environmental factors [6,8,46,48–50]. The detailed explanation of these parameters is as follows:

ð12Þ

The crisp values obtained are integrated with MDL weights to calculate final ranking using AHP approach. Step 5: Determination of AHP overall priority vectors or potentiality index. Firstly, priority vectors for different evaluation criterion are calculated using the crisp values rather than on a scale of 1–9 (as illustrated in Tables 2 and 3). Thereafter, the sum product of priority vectors gives the overall priority vector for each attribute (state in present case). This is termed as the potentiality index of a state. Step 6: Calculation of potentiality improvement index (PII)  PII δ0k;i;j is obtained using sensitivity analysis and tells about the percentage improvement in a particular parameter required to improve the existing ranking of a state. Mathematically, PII is given as:



Dj  Di 100

 ð13Þ δ0k;i;j ¼

bjk  bik W k

It is to be noted that only feasible values of δh;i;j' tells about PII. Non-feasible value of δh;i;j' for any criterion interprets that for that particular state there is no more improvement possibility in criterion corresponding to the non-feasible value.

4.1. Existing energy demand and availability (P1) Firstly, it becomes essential to understand the energy needs of a particular area before one starts to look for the alternative sources of energy. It depends on the available energy supply from distinct resources and the estimated consumption. If available energy is already in surplus then there is no point in installing additional energy units. However, such a case warrants careful examination of the problems caused by exploitation of available energy resources. If these problems have a serious impact on mankind and society; these needed to be replaced immediately. At the same time, a shortage in availability in neighbor states but with well connected grids (as the presence of grid and distance covered can cause a huge variation in the final costs of the power delivered [51]) can simultaneously resolve many issues. It will result in better exploitation of the energy resources of a state and will parallelly sort out the energy needs of neighbors. All this will not only result in an additional income but will also promote better social as well as political relations. Contemporary, in case of shortage it is a must to look for the best solutions and in Indian scenario it is the solar energy. Therefore, energy demand and availability become an important factor for the present study.

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4.2. Availability of solar radiation (P2)

4.6. Environment issues (P6)

Availability of solar radiation flux is among the most essential factors for implementation of solar energy. It is important to note that the solar radiation flux available rarely exceeds 1 kW/m2 on earth. This radiation flux is very low for technological utilization. Another major problem associated with the exploitation of solar energy is heavy fluctuations in the radiation flux density throughout the year and the day as well. It is highly dependent on geographical conditions and weather of the area [52]. Ideally, the site should be flat or on a slight south facing in the northern hemisphere. Such topography makes installation simpler and diminishes the expense of technical modifications [53]. Another important consideration is Tilt angle. This is the angle of the photovoltaic (PV) modules from the horizontal plane for a fixed mounting. The maximum radiation can be obtained by tilting the surface at an optimum angle, which is determined by the latitude of the location [54].

Environmental issues can severely affect the implementation potential of solar energy. A few of them are highlighted below:

4.3. Government policy (P3) Solar units are expensive projects and needs huge financial support. Generally, private house owners and industries are not willing to bear such high costs. This factor is a reported roadblock in tapping of solar energy [55]. In this context, government of India is making sincere efforts for promoting the use of solar energy through various appealing policies. Recently, government has offered 5% relaxation on custom duty of solar panels. Moreover, the government of India has provided different subsidies and benefits for solar equipment users [56]. For example, the “Ministry of New and Renewable Energy” announced 70% subsidy on the installation photovoltaic units in North-East states while this is merely 30% in other regions. Another example is a mega solar power plant installed in Mysore with 50% concession offered by the Mysore City Corporation [57]. Additionally, the state governments also have launched many schemes such as low interest loan, long-term credit, land usage compensation and funds for grid connection [7]. Also, some states specifically spend a considerable amount of money in publicity and awareness programs to promote utilization of renewable sources of energy. 4.4. Social acceptability (P4) Social acceptability is among the major challenges faced for the implementation of any new technology. Solar power plant produces electricity by converting solar radiation using mirrors and reflectors. The use of mirrors and reflectors increases the temperature of site. The increased temperature has an adverse affect on the environment. This factor thus also becomes a reason for social conflicts. Consequently, people raise a strong objection to such projects. It is found that there are strong objections for implementation of solar plants or panels in rural areas in India. These objections are because of lack of awareness, variation in available infrastructure due to absence of appropriate norms, social inequality and economical constraints. 4.5. Land availability (P5) The availability and cost of land are major factors concerned with implementation of solar units. These are supposed to be built on low value land so as to reduce the overall cost of the project. Moreover, other factors such as local weather, land type, environmental conditions and human as well as wildlife disturbances are the constraints towards the suitability of a site. The areas close to natural reserves, bird breeding sites and lakes should be carefully assessed and normally avoided [58].

4.6.1. High wind speeds The high wind speeds cause damage or root out the foundation of solar panels. Therefore, depending on the wind speed fixed frameworks or tracking framework (in the areas where wind speed exceeds 16–20 m/s or more) systems are used [58]. 4.6.2. Snow Settling snow on modules can cause reduction in annual energy yield. The small impacts of snow can be overcome by using high tilt angle. 4.6.3. Floods Floods are the common natural disaster in North India. It increases the risk of erosion of support structure and foundations. So, the areas free from floods and far away from rivers are preferred in general. 4.6.4. Earth quakes Larger earthquakes can damage the plant and structure may fall. All these factors have their own way of influencing the available solar resource in a particular area and hence must be taken in account while performing the potentiality indices.

5. Results and discussion Present study aims to determine the potentiality indices for better exploitation of available solar resource in Indian states. In this context, shortlisted Indian states are ranked based on crucial factors that influence the implementation of solar energy in India. Afterwards, a comparison in predicted and existing ranking helps to determine the states with high potential but relatively low installed capacity. Furthermore, in order to improve the ranking of lagging states sensitivity analysis is performed so as to identify pivotal parameters to be worked on. Finally, the study depicts PII which tells about the required percentage improvement in a particular criterion for up-gradation in the present ranking. Fig. 3 shows a schematic hierarchy of the present problem. Level 0 indicates the objective of our study that has to be achieved from the six shortlisted parameters (existing energy demand and availability (P1), availability of solar radiation (P2), government policy (P3), social acceptability (P4), land availability (P5), environment issues (P6)) which are explained in Section 2 (level 1). These have been shortlisted on the bases of various reports documented by different government agencies and understanding gained by us [59–61]. Further, Level 2 shows the eleven states (Andhra Pradesh(S1), Gujarat(S2), Jammu and Kashmir(S3), Jharkhand(S4), Karnataka(S5), Kerala(S6), Madhya Pradesh(S7), Maharashtra(S8), Rajasthan(S9), Tamil Nadu(S10) and Uttar Pradesh(S11)) that are considered in the present study. These have been shortlisted on the bases of solar radiation zones with more than 5.6 kWh/m2 radiation density [16]. Interdependency of these parameters demonstrates the complexity of the problem. Once the prime parameters are identified the next question is to prioritize these parameters. In order to prioritize these MDL approach has been employed. Table 4 summarizes the decision matrix for MDL and the contributions of these parameters are highlighted in Fig. 4. Availability of solar radiation is founded to be the most influential parameter while the environment issues are found to be of least importance in Indian scenario.

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403

Fig. 3. Schematic hierarchy for potentiality index of solar energy in India.

Table 4 Subjective weights using MDL and corresponding ranks of the evaluation criteria. Parameters

P1

P2

P3

P4

P5

P6

Positive decisions

Weights

Ranks

Existing energy demand & availability (P1) Availability of solar radiation (P2) Government policy (P3) Social acceptability (P4) Land availability (P5) Environment issues (P6)

2 3 1 1 1 1

1 2 1 1 1 1

3 3 2 1 3 1

3 3 3 2 2 2

3 3 1 2 2 1

3 3 3 2 3 2

13 15 9 7 10 6

0.210 0.242 0.145 0.113 0.161 0.097

2 1 4 5 3 6

Table 5 Linguistic variables and corresponding fuzzy numbers.

Fig. 4. Contribution of various parameters towards the potentiality index of solar energy in India (calculated using MDL).

Linguistic variable

Fuzzy number

Exceptionally high (EH) High (H) Above average (AA) Average (A) Below average (BA) Low (L) Extremely low (EL)

(0.8, 0.9, 1.0, 1.0) (0.7, 0.8, 0.8, 0.9) (0.5, 0.6, 0.7, 0.8) (0.4, 0.5, 0.5, 0.6) (0.2, 0.3, 0.4, 0.5) (0.1, 0.2, 0.2, 0.3) (0.0, 0.0, 0.1, 0.2)

Table 6 Linguistic decision matrix for all evaluation parameters and corresponding Indian states evaluation parameters. States

The next step is to determine the potentiality index and the corresponding ranking of the shortlisted states. In this context, fuzzy approach is used as it works well for the problems where discussion in the form of verbal reasoning with the ‘decision makers’ needs to be converted into subjective values. Table 5 accommodates the linguistic variables and their corresponding fuzzy ratings. The best range is termed exceptionally high (EH) while the worst is termed extremely low (EL). Initially a primary decision matrix (Table 6) is filled by the ‘decision compilers’ on the bases of discussion with various ‘decision makers’. Consequently, a single decision matrix is formed rather than having a separate decision matrix for each decision maker. However, this matrix may be changed depending on the requirements and circumstances. Afterwards, the linguistic variables and their corresponding fuzzy values are aggregated and defuzzified (Eq. (12). Table 7 augments the calculated crisp values obtained after normalization of the aggregated fuzzy ratings. The crisp values thus obtained are

Andhra Pradesh (S1) Gujarat (S2) Jammu and Kashmir (S3) Jharkhand (S4) Karnataka (S5) Kerala (S6) Madhya Pradesh (S7) Maharashtra (S8) Rajasthan (S9) Tamil Nadu (S10) Uttar Pradesh (S11)

Evaluation parameters P1

P2

P3

P4

P5

P6

AA H BA H A A AA AA H A EH

A H H A BA A AA AA EH H A

AA EH L L BA L H H EH AA EL

BA EH L L AA A AA AA EH AA L

BA H L EL A A AA A EH A EL

BA BA H A A A BA A AA BA AA

utilized to calculate the priority vectors/potentiality index (using AHP approach) of distinct states in contrast to the evaluation parameters. Table 8 shows the potentiality index and

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corresponding ranks of the respective states. The consistency of ranks is confirmed with the TOPSIS ranks. A correlation of 88% is depicted between both the rankings. This shows the robustness of the methodology used. It clearly indicates that Rajasthan is at the top while Uttar Pradesh is at the bottom podium. Uttar Pradesh also comes in 5.6 kWh/m2 radiation density zone but got lowest ranking because of 1.) high population density leading to poor land availability [62]; 2.) lack of government initiatives and 3.) lack of awareness. This clearly indicates that Uttar Pradesh government needs to make some sincere efforts. Another important observation is the narrow gap in the priority of Jharkhand, Jammu and Kashmir and Uttar Pradesh; which means that these can be classified in the same category and a small fluctuation in evaluation criteria could adversely affect the ranking. The next question comes to the mind of policy makers is how to improve the ranking of states with higher potential but less installed capacity. As the availability of solar resource and environment issues are natural criteria so one can merely focus on the remaining four criteria for improvement in the depicted ranks. But, the question is how much improvement is required in these criterions to have better rank of the respective state. The answer to this is the sensitivity analysis. It tells about the percentage change in the present weightage of a parameter that can cause an interchange in the current ranking of the two states. This percentage change in the rating of parameters helps the policy makers to decide the working domain which is required to promote effective utilization of solar resources. In this context, firstly all values of δ are computed and feasible and non-feasible solutions are distinguished. Table 9 shows the feasible and non-feasible values. It is Table 7 Calculated crisp values for assigned fuzzy rating evaluation parameters. States

Andhra Pradesh (S1) Gujarat (S2) Jammu and Kashmir (S3) Jharkhand (S4) Karnataka (S5) Kerala (S6) Madhya Pradesh (S7) Maharashtra (S8) Rajasthan (S9) Tamil Nadu (S10) Uttar Pradesh (S11)

Evaluation parameters P1

P2

P3

P4

P5

P6

0.667 0.833 0.367 0.833 0.533 0.533 0.667 0.667 0.833 0.533 0.944

0.533 0.833 0.833 0.533 0.367 0.233 0.667 0.667 0.944 0.833 0.533

0.667 0.944 0.233 0.233 0.367 0.367 0.833 0.833 0.944 0.667 0.078

0.367 0.944 0.233 0.233 0.667 0.533 0.667 0.667 0.944 0.667 0.233

0.367 0.833 0.233 0.078 0.533 0.533 0.667 0.533 0.944 0.533 0.078

0.367 0.367 0.833 0.533 0.533 0.533 0.367 0.533 0.667 0.367 0.667

to be noted that the tables shows the interchangeability possibility of all the states only with the first and second ranking states as the present study aims is to have the best utilization of available solar resource. Consequently, a percentage change in the feasible values of all evaluation parameters are obtained and shown in Table 10. These values of percentage change in the rating of different parameters are the potentiality improvement index (PII). It reveals that the government of the state with last rank (Uttar Pradesh) needs to concentrate on awareness, land availability and better government policies in order to have better utilization of available solar resources. Simultaneously, the table shows that it is not wise to work on non-feasible parameters for interchanging the ranking of certain states. For example the states with rank 1 (Rajasthan (S9)) and 2 (Gujarat (S2)) has non-feasible values of PII for four evaluation parameters. This clearly interprets that both the states are already on the verge of maximum improvement limit for these evaluation parameters. Contemporary, the feasible PII values exists for solar radiation (P2) and land availability (P5). Therefore, it is very clear that the Gujarat government needs to work on better solutions to provide land availability. Similar observations can be noticed for other states and necessary actions can be taken accordingly. Finally, Table 11 enlists all the sensitivity values that show the least change required in any parameter to make any Table 9 Calculated feasible and non-feasible (NF) δk,j,I values. States

S1–S2 S1–S9 S2–S3 S2–S4 S2–S5 S2–S6 S2–S7 S2–S8 S2–S9 S2–S10 S2–S11 S3–S9 S4–S9 S5–S9 S6–S9 S7–S9 S8–S9 S9–S10 S9–S11

Evaluation parameters P1

P2

P3

P4

P5

P6

0.194 0.202 0.101 NF 0.130 0.146 0.097 0.129 NF 0.067  0.431 0.104 NF 0.135 0.150 0.105 0.137 0.072  0.443

0.101 0.077 NF 0.146 0.079 0.069 0.091 0.122 0.011 NF 0.150 0.411 0.110 0.066 0.060 0.059 0.078 0.183 0.113

0.097 0.101 0.055 0.054 0.056 0.063 0.121 0.161 NF 0.061 0.046 0.057 0.056 0.058 0.065 0.131 0.172 0.065 0.047

0.046 0.048 0.055 0.054 0.117 0.088 0.048 0.064 NF 0.060 0.056 0.057 0.056 0.121 0.091 0.052 0.068 0.064 0.057

0.050 0.042 0.057 0.044 0.094 0.105 0.070 0.052 0.009 0.048 0.046 0.049 0.040 0.071 0.079 0.045 0.040 0.038 0.041

NF  0.084 0.094 0.166 0.140 0.157 NF 0.077  0.003 NF 0.119 0.493  0.389  0.329  0.367  0.044  0.186  0.054 NF

Table 8 Potentiality index of the states and their corresponding ranks. States

Evaluation parameters

Priority vectors (MDL)

P1 0.210

P2 0.242

P3 0.145

P4 0.113

P5 0.161

P6 0.097

Andhra Pradesh (S1) Gujarat (S2) * Jammu and Kashmir (S3) Jharkhand (S4) Karnataka (S5) * Kerala (S6) * Madhya Pradesh (S7) * Maharashtra (S8) * Rajasthan (S9) * Tamil Nadu (S10) * Uttar Pradesh (S11)

0.090 0.112 0.049 0.112 0.072 0.072 0.090 0.090 0.112 0.072 0.127

0.076 0.119 0.119 0.076 0.053 0.033 0.096 0.096 0.135 0.119 0.076

0.108 0.153 0.038 0.038 0.059 0.059 0.135 0.135 0.153 0.108 0.013

0.060 0.153 0.038 0.038 0.108 0.087 0.108 0.108 0.153 0.108 0.038

0.069 0.156 0.044 0.015 0.100 0.100 0.125 0.100 0.177 0.100 0.015

0.121 0.121 0.053 0.083 0.083 0.083 0.121 0.083 0.066 0.121 0.066

*

Overall priority/potentiality Index

AHP rank

TOPSIS rank

Installed capacity rank

0.085 0.133 0.063 0.064 0.075 0.068 0.109 0.101 0.135 0.103 0.062

6 2 10 9 7 8 3 5 1 4 11

6 2 8 9 7 11 3 5 1 4 10

5 1 11 9 7 10 4 3 2 6 8

The state with higher potential to exploit solar energy in contrast to current installed capacity.

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Table 10 Possible δ0k;i;j values with non-feasible values (Note: S9 and S2 are states with ranks

References

1 and 2 respectively). Table shows the percentage change required in the evaluation parameter to interchange the ranking of corresponding states.

[1] Fuselli D, De Angelis F, Boaro M, Squartini S, Wei Q, Liu D, et al. Action dependent heuristic dynamic programming for home energy resource scheduling. Int J Electr Power Energy Syst 2013;48:148–60. [2] Rosenbloom E. A problem with wind power. 〈http://wwwaweoorg/pro blemwithwindhtml〉; 2010. [3] Shahi R. Hydroelectric projects development: challenges and response; June 2013. [4] Sicchio M. Dam demolition background. 〈http://webmitedu/12000/www/ m2012/finalwebsite/problem/damsshtml〉;1999. [5] Ramachandra TV, Jain R, Krishnadas G. Hotspots of solar potential in India. Renew Sustain Energy Rev 2011;15:3178–86. [6] Ansari MF, Kharb RK, Luthra S, Shimmi S, Chatterji S. Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique. Renew Sustain Energy Rev 2013;27:163–74. [7] Luthra S, Kumar S, Garg D, Haleem A. Barriers to renewable/sustainable energy technologies adoption: Indian perspective. Renew Sustain Energy Rev 2015;41:762–76. [8] Khare V, Nema S, Baredar P. Status of solar wind renewable energy in India. Renew Sustain Energy Rev 2013;27:1–10. [9] Dave U. Total state wise solar power installed under various solar policies in India along with deployment of total grid off grid renewable energy systems. ed: 〈http:// urvishdave.wordpress.com/2014/03/11/total-state-wise-solar-power-installedunder-various-solar-policies-in-india-along-with-deployment-of-total-grid-offgrid-renewable-energy-systems-in-india-till-january-2014/〉; 11 March 2014. [10] Vats S, Vats G, Vaish R, Kumar V. Selection of optimal electronic toll collection system for India: a subjective-fuzzy decision making approach. Appl Soft Comput 2014;21:444–52. [11] Saaty TL, Vargas LG. Models, methods, concepts & applications of the analytic hierarchy process, Springer. [12] Rao RV. A material selection model using graph theory and matrix approach. Mater Sci Eng: A 2006;431:248–55. [13] Vats G. Multi-attribute decision making for ferroelectric materials selection. Saarbrucken, Germany: Lambert Academic Publishers; 2014. [14] Vats G, Vaish R. Piezoelectric material selection for transducers under fuzzy environment. J Adv Ceram 2013;2:141–8. [15] Uyan M. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renew Sustain Energy Rev 2013;28:11–7. [16] Sanschagrin PC, Kuhn LA. Cluster analysis of consensus water sites in thrombin and trypsin shows conservation between serine proteases and contributions to ligand specificity. Protein Sci 1998;7:2054–64. [17] Anojkumar L, Ilangkumaran M, Sasirekha V. Comparative analysis of MCDM methods for pipe material selection in sugar industry. Expert Syst Appl 2014;41:2964–80. [18] Kumar R, Ray A. Optimal selection of material: an eclectic decision. J Inst Eng: C 2014:1–5. [19] Haq AN, Boddu V. Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach. J Intell Manuf 2014:1–12. [20] Mokhtari SM, Alinejad-Rokny H, Jalalifar H. Selection of the best well control system by using fuzzy multiple-attribute decision-making methods. J Appl Stat 2014;41:1105–21. [21] Parameshwaran R, Kumar SP, Saravanakumar K. An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria. Appl Soft Comput 2014;26:31–41. [22] Vats G, Sharma M, Vaish R, Chauhan VS, Madhar NA, Shahabuddin M, et al. Application oriented selection of optimal sintering temperature from user perspective: a Study on K0.5Na0.5NbO3 ceramics. Ferroelectrics 2015;481:64–76. [23] Vats G, Vaish R. Selection of optimal sintering temperature of K0.5Na0.5NbO3 ceramics for electromechanical applications. J Asian Ceram Soc 2014;2:5–10. [24] Vats G, Vaish R. Phase change materials selection for latent heat thermal energy storage systems (LHTESS): an industrial engineering initiative towards materials science. Adv Sci Focus 2014;2:140–7. [25] Vats G, Vaish R. Selection of lead‐free piezoelectric ceramics. Int J Appl Ceram Technol 2014;11:883–93. [26] Vats G, Vaish R, Bowen CR. Selection of ferroelectric ceramics for transducers and electrical energy storage devices. Int J Appl Ceram Techno 2015;12:E1–7. [28] Goyal S, Grover S. Manufacturing system's effectiveness measurement by using combined approach of ANP and GTMA. Int J Syst Assur Eng Manag 2013;4:404–23. [29] Yadav S, Srivatava AK, Singh R. Selection and ranking of multifaceted criteria for the prioritization of most appropriate biomass energy sources for the production of renewable energy in Indian perspective using analytic hierarchy process; 2015. [30] Parsons W. Renewable energy siting and feasibility. California, US. [31] Dehghan-Manshadi B, Mahmudi H, Abedian A, Mahmudi R. A novel method for materials selection in mechanical design: combination of non-linear normalization and a modified digital logic method. Mater Design 2007;28:8–15. [32] Dengfeng L, Chuntian C. New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions. Pattern Recognit Lett 2002;23:221–5. [33] Zadeh LA. Fuzzy sets. Inf Control 1965;8:338–53. [34] Ribeiro RA. Fuzzy multiple attribute decision making: a review and new preference elicitation techniques. Fuzzy Sets Syst 1996;78:155–81.

States

S1–S2 S1–S9 S2–S3 S2–S4 S2–S5 S2–S6 S2–S7 S2–S8 S2–S9 S2–S10 S2–S11 S3-S9 S4–S9 S5–S9 S6–S9 S7–S9 S8–S9 S9–S10 S9–S11

Evaluation parameters P1

P2

P3

P4

P5

P6

92.398 96.248 48.124 NF 62.027 69.513 46.199 61.599 NF 32.083 205.009 49.499 NF 64.165 71.651 50.049 65.449 34.222 210.784

41.940 31.880 NF 60.289 32.578 28.397 37.746 50.328 4.718 NF 62.036 169.857 45.270 27.221 24.697 24.535 32.084 75.492 46.545

66.809 69.592 38.058 37.515 38.811 43.495 83.511 111.348 NF 41.755 31.673 39.146 38.602 40.150 44.834 90.470 118.307 44.539 32.566

41.141 42.855 48.748 48.052 103.401 78.298 42.787 57.049 NF 53.484 49.444 50.141 49.444 106.967 80.707 46.352 60.615 57.049 50.837

30.975 26.061 35.134 27.502 58.222 65.249 43.365 32.122 5.421 30.115 28.299 30.491 24.671 43.951 49.079 28.187 24.906 23.441 25.366

NF 86.327 97.118 171.549 144.201 161.604 NF 79.559 3.453 NF 122.585 508.546 401.186 339.030 378.584 44.890 192.117 55.249 NF

Table 11 Values of Dk' and Sens Ck. Evaluation parameters

Dk Sens Ck

P1

P2

P3

P4

P5

P6

0.555 1.802

0.874 1.144

2.485 0.402

6.603 0.151

1.936 0.517

3.139 0.319

alternation in the predicted ranking. Mathematically, it can be suggested social acceptability is the most sensitive criterion while the existing demand and availability is the least sensitive. Also, it is revealed that a minimum alteration of 15% in social acceptance can change the overall ranking of the model. Finally, we sum up our study with a hope that such analysis can be proven extremely helpful for the state as well as central governments in fund allocation and re-evaluation as well as formulation of their energy and investment policies.

6. Conclusions Present study proffers a novel application of MADM approaches. In this context, a potentiality index for better exploitation of available solar resources in Indian states is depicted under six techno-socio-economic constraints. These parameters are weighted using MDL method and it is found that the availability of solar radiation is the most influential parameter while environment issues being the least influential among all the parameters under consideration. Thereafter, MDL weights incorporation with fuzzyAHP approach are employed to calculate potentiality index and ranking of states. Rajasthan is found to be at the top and become the first choice for investment in solar power. Contemporary, Jharkhand, Jammu and Kashmir and Uttar Pradesh are classified as the members of analogues league. Finally, sensitivity analysis is performed to determine the percentage change in different parameters that leads to improvement in the current ranking of a state. Such approaches can be highly helpful for states governments in re-evaluation of their current policies and to take further action to promote effective utilization of available solar resources.

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[35] Bevilacqua M, Ciarapica F, Giacchetta G. A fuzzy-QFD approach to supplier selection. J Purch Supply Manag 2006;12:14–27. [36] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 1975;8:199–249. [37] Bellman RE,, Zadeh LA. Decision-making in a fuzzy environment. Manag Sci 1970;17:B-141–64. [38] Laukkanen S, Kangas A, Kangas J. Applying voting theory in natural resource management: a case of multiple-criteria group decision support. J Environ Manag 2002;64:127–37. [39] Saaty TL. Fundamentals of decision making and priority theory with the analytic hierarchy process. Rws Publications; 2000. [40] Kangas J. The Analytic Hierachy Process (AHP): standard version, forestry application and advances. In: Multiple use of forests and other natural resources. Springer; 1999. p. 96–105. [41] Saltelli A, Tarantola S, Campolongo F, Ratto M. Sensitivity analysis in practice: a guide to assessing scientific models. John Wiley & Sons; 2004. [42] Homma T, Saltelli A. Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 1996;52:1–17. [43] Triantaphyllou E, Sánchez A. A sensitivity analysis approach for some deterministic multi‐Criteria decision‐making methods. Decis Sci 1997;28:151–94. [44] Kahraman C, Cebeci U, Ulukan Z. Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag 2003;16:382–94. [45] Kwong C, Bai H. Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Trans 2003;35:619–26. [46] Yun-na W, Yi-sheng Y, Tian-tian F, Li-na K, Wei L, Luo-jie F. Macro-site selection of wind/solar hybrid power station based on Ideal Matter-Element Model. Int J Electric Power Energy Syst 2013;50:76–84. [47] Vafaeipour M, Zolfani SH, Varzandeh MHM, Derakhti A, Eshkalag MK. Assessment of regions priority for implementation of solar projects in Iran: new application of a hybrid multi-criteria decision making approach. Energy Convers Manag 2014;86:653–63.

[48] Xiao J, Yao Z, Qu J, Sun J. Research on an optimal site selection model for desert photovoltaic power plants based on analytic hierarchy process and geographic information system. J Renew Sustain Energy 2013;5:023132. [49] Reddy S, Painuly JP. Diffusion of renewable energy technologies—barriers and stakeholders' perspectives. Renew Energy 2004;29:1431–47. [50] Amer M, Daim TU. Selection of renewable energy technologies for a developing county: a case of Pakistan. Energy Sustain Dev 2011;15:420–35. [51] Hirmer S, Cruickshank H. Making the deployment of pico-PV more sustainable along the value chain. Renew Sustain Energy Rev 2014;30:401–11. [52] C D. Energy technology handbook. In: Considine D (Ed.). Energy technology handbook. New York: McGraw-Hill; 1977. [53] Sharma DBD. Performance of solar power plants in India. New Delhi; 2011. [54] Mandal A. Detail project report SPV power plant. New Delhi, India; 2013. p. 28. [55] Painuly JP. Barriers to renewable energy penetration; a framework for analysis. Renew Energy 2001;24:73–89. [56] Prabhu G.N. Evaluating the future of Indian solar industry. January 2011 ed. IIMB; 2011. [57] Herald D. MCC plans to burn midnight oil the solar way. May 29 2012 ed. Mysore; 2012. [58] Alasdair Miller BL. Utility scale solar power plants. New Delhi: International Finance Corporation; 2012. [59] Shah A. Solar panels in India – complete guide on buying low cost PV panels from solar energy system manufacturers; October 13 2010 ed. 2010. [last updated June 12 2012]. [60] Shahan Z. Solar power costs 50% lower than last year; 2009. [61] David Frankel KO, Dickon Pinner. The disruptive potential of solar power. US; 2014. [62] Sharma DBD. Performance of solar power plants in India. In: Commission CER, editor. New Delhi; 2011.