Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain

Accepted Manuscript Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain J.M. Sánchez-Lozano, M.S. García-Cascales, M.T. Lamata PII:

S0959-6526(16)30246-3

DOI:

10.1016/j.jclepro.2016.04.005

Reference:

JCLP 7010

To appear in:

Journal of Cleaner Production

Received Date: 19 November 2014 Revised Date:

5 March 2016

Accepted Date: 3 April 2016

Please cite this article as: Sánchez-Lozano JM, García-Cascales MS, Lamata MT, Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain, Journal of Cleaner Production (2016), doi: 10.1016/j.jclepro.2016.04.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 1

Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar

2

farms. Case study in Spain.

3

J. M. Sánchez-Lozano(1) , M. S. García-Cascales(2), M. T. Lamata(3)

4

(1)

5

Cartagena, San Javier, Murcia, Spain

6

(2)

7

de Cartagena (UPCT), Murcia, Spain

8

(3)

9

Granada.18071 Granada, Spain

RI PT

Centro Universitario de la Defensa. Academia General del Aire. Universidad Politécnica de

SC

Depto de Electrónica, Tecnología de Computadoras y Proyectos. Universidad Politécnica

10

M AN U

Depto de Ciencias de la Computación e Inteligencia Artificial. Universidad de

*Corresponding author: María Teresa Lamata Jiménez

12

E-mail: [email protected]

13

Telephone: +34 958240593

14

Fax: +34 958243317

EP

TE D

11

AC C

15 16

Abstract

17

This paper is to select the best locations to build solar photovoltaic farms (large grid-

18

connected photovoltaic systems which have more than 100kWp of installed capacity), with the

19

coast of Murcia in the southeast of Spain being used as an example. In order to solve the

20

problem, the suitable locations to implant such facilities will be identified by a Geographical

21

Information System (GIS). To obtain the weights of the criteria which influence the proposed

22

problem, the Analytic Hierarchy Process (AHP) will be employed. Then, the suitable

1

ACCEPTED MANUSCRIPT locations will be evaluated and classified using two different multi-criteria decision methods,

24

the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and

25

ELimination and Choice Expressing Reality (ELECTRE), in this case the version TRI. We

26

are thus also able to establish a comparison between the two methods. This comparison

27

demonstrates how although the results do not completely coincide, some similarity can be

28

seen between the two methods.

RI PT

23

29

Keywords: Solar photovoltaic farms; GIS; Restrictions; Criteria; AHP; TOPSIS; ELECTRE

31

TRI.

32

1. Introduction

33

Nowadays, the commitment to carrying out sustainable development to satisfy the present

34

needs of the population without compromising those of future generations is a difficult

35

challenge to achieve. From an energy point of view, forecasts indicate that world energy

36

consumption will grow by 56 % between 2010 and 2040, although a gradual increase in prices

37

of both oil and natural gas is expected (U.S. Energy Information Administration, 2013). The

38

required containment of growth in emissions of greenhouse gases (Arrhenius, 1896),

39

established by the World Meteorological Organization and United Nations (Working Group I-

40

II-III 1990; United Nations, 1992/1997/2013) in compliance with the objectives set out in the

41

various energy policies of the European Union (European Commission, 1996, 1997; European

42

Parliament, 2009), were the main reasons that sustainable development strategies were

43

promoted (Jegatheesan et al, 2009; Engelbrecht et al, 2013; Dovì et al, 2009) and the

44

implementation of renewable energy (RE) installations was endorsed (Espey, 2001; Menz and

45

Vachon, 2006; Foxon et al, 2005). The current economic and financial crisis affecting a large

46

number of countries has led to the reduction in the support for renewable energy installations,

AC C

EP

TE D

M AN U

SC

30

2

ACCEPTED MANUSCRIPT causing significant negative aspects (Avril et al, 2012). However, economies of scale and the

48

development of these technologies have helped reduce production costs so that, although

49

governments have failed to support and stimulate this type of facility, private investors have

50

taken the reins with implementation in order to continue with renewable energy installations

51

(Wünteshagen and Menichetti, 2012).

52

In Spain, in order to meet the objectives set by the European Union and to promote the

53

implementation of renewable energy facilities, the government energy plans were developed

54

to cover two periods of action between the years 2005-2010 (IDAE, 2005) and 2011-2020

55

(IDAE, 2010). In the latter period renewable energy was earmarked to represent at least 20%

56

of final energy consumption by 2020. Among the various renewable sources which grew most

57

as a result of energy policies elaborated in the first period (Royal Decree 436/2004; Royal

58

Decree 661/2007) solar photovoltaic (PV) stood out above the rest (Bürer and Wüstenhagen,

59

2009); its growth was such that in 2009 Spain was ranked as the second country in the world

60

in terms of photovoltaic power regarding overall cumulative installed capacity, with 3.5 GW

61

(European Commission, 2010). Although subsequent energy policies have not encouraged its

62

expansion (Royal Decree-Law 14/2010; Royal Decree-Law 1/2012), the decrease in

63

production costs of photovoltaic technology as well as the excellent climatic conditions of the

64

country have allowed investors to continue supporting the implementation of solar

65

photovoltaic farms in Spain (EPIA, 2013).

66

The Spanish PV potential is huge because Spain receives on average, in the horizontal plane,

67

a global radiation of 1,600 kWh/m2 per year, with the Mediterranean coast being the area with

68

the highest PV potential, hence the interest in studying this particular area to implement such

69

technology (Figure 1). It is thus appropriate to continue promoting and encouraging the

70

implementation of solar photovoltaic energy in order to comply with the international

AC C

EP

TE D

M AN U

SC

RI PT

47

3

ACCEPTED MANUSCRIPT legislative framework, as well as to exceed the 7 GW cumulative PV power marked as a

72

specific target for 2020 (IDAE, 2010).

73

Among the several advantages of solar technology, it should be recalled that the sun is an

74

inexhaustible source of energy. It is therefore a technology that could provide significant

75

support to current energy technologies allowing to reduce consumption of fossil fuels. In

76

addition, an adequate and responsible implementation of this technology not only allows new

77

jobs to be created but also promotes the economic and industrial development of the zones

78

where they are located. However, this technology is not without drawbacks. The

79

indiscriminate implementation of large grid-connected photovoltaic systems (more than 100

80

kWp of installed capacity), also called “Solar farms”, can lead to environmental problems

81

such as the movement of migratory birds, deforestation, creation of physical barriers which

82

damage the earth’s wildlife, etc. It is therefore necessary to choose very carefully which areas

83

are suitable for implementing this technology, because it must not only seek the maximum

84

energy efficiency (areas with high solar radiation potential) but it must also be situated in

85

places where they may cause less damage to the environment, to ensure balanced and

86

sustainable development.

89

SC

M AN U

TE D

EP

88

Figure 1. Global irradiation and solar electricity potential of Spain (European Commission, 2012; Huld et al, 2012; Súri et al, 2007)

AC C

87

RI PT

71

90

The Region of Murcia, located in the southeast of Spain, has become one such area where

91

many solar photovoltaic farms have been introduced. One of the main reasons that PV

92

promoters prefer this region is because it has one of the highest levels of solar radiation

93

potential in the country. According to the Solar Radiation and Atmospheric Temperature

94

Atlas of the Autonomous Community of the Region of Murcia (Vera et al, 2007), the majority

95

of its territory has more than 5.0 kWh/m2·day. However, it should be noted that it is not easy

96

to implement such facilities anywhere in the Region of Murcia, and in areas far from the coast

4

ACCEPTED MANUSCRIPT the level of urban and residential occupancy is low compared with the level reached in areas

98

near the sea (Gómez-López et al, 2010) which further increases the difficulty facing any

99

promoter of renewable energy installations to find suitable areas which are nearshore. In

100

addition to technical factors such as solar radiation or land use, it is also necessary to take into

101

account economic factors (grid proximity, land slope, etc.) or environmental factors (sensitive

102

areas, nature reserves, etc.) which affect the optimum location for PV farms (Charabi and

103

Gastli, 2011). Among the environmental factors worth mentioning is the impact of PV farms

104

on birds, which is why in this paper restrictions will be taken into account, such as the areas

105

of special protection for birds which are protected through the Directive 92/43/EEC of 21

106

May 1992 on the conservation of natural habitats and of wild fauna and flora (European

107

Parliament, 2009). One of the main points of that Directive is the designation of special

108

conservation areas in order to create a coherent European ecological network. In this way the

109

restoration or maintenance of natural habitats and species of Community interest at a

110

favourable conservation status can be ensured. Therefore, performing an in-depth analysis of

111

the coast of Murcia, allowing to locate areas that are not subject to any restrictions and are

112

thus feasible to implement photovoltaic solar farms is extremely important, and it is precisely

113

for this reason that the management of visualization tools and cartographic editing such as

114

Geographic Information Systems (GIS) is useful. GIS offer the possibility of finding viable

115

locations to implement this type of facility, and are able to show the earth’s surface through

116

thematic layers which provide maps (visual analysis), and alphanumeric information in a

117

database form (qualitative and / or quantitative values) of these locations such as their

118

designation, area, slope, distance to cities, etc. This alphanumeric information can be

119

extracted in a spreadsheet format. Therefore, it can be useful to make any subsequent analysis

120

of decision-making.

AC C

EP

TE D

M AN U

SC

RI PT

97

5

ACCEPTED MANUSCRIPT Among the numerous applications of GIS analysis special mention should be made to

122

territorial planning of any kind (Kaijuka, 2007), managing available resources (Wallsten et al,

123

2013), environmental analysis (Yousefi-Sahzabi et al, 2011) and studies to implement

124

renewable energy facilities. The use of GIS to solve the localization of renewable energy

125

facilities began to develop in the late twentieth century (Voivontas et al, 1998; Sorensen and

126

Meibom, 1999) and it has become more widespread since then (Yue and Wang, 2006; Byrne

127

et al, 2007; Domínguez Bravo et al, 2007).

128

The literature contains numerous decision-making methods and particularly multi-criteria

129

ones that can be applied to problems in general and more specifically to those that consider

130

renewable energy (Kahraman et al, 2009; Kaya and Kahraman, 2010; Cavallaro, 2010;

131

García-Cascales et al, 2012; Khalili-Damghani and Sadi-Nezhad, 2013).

132

However, one of the main advantages offered by the GIS are their excellent ability to perform

133

analysis of optimal locations for renewable energy facilities (Van Haaren and Fthenakis,

134

2011; Janke, 2010) since through their multiple edition tools (buffer, difference, filter, logical

135

operators, etc.) complex location problems can be solved.

136

The literature provides a large number of examples where GIS are combined with multi-

137

criteria decision-making methods (MCDM), multi-objective optimization, or probabilistic

138

approaches. In this way (Zhang et al, 2010) evaluated the productivity and sustainability of

139

biofuel crop production systems through GIS and an evolutionary multi-objective

140

optimization algorithm. In order to optimize the design and strategic operation of district

141

energy systems, GIS were not only combined with these types of optimization model

142

(Fazlollahi et al, 2013), but also with k-means clustering techniques (Fazlollahi et al, 2014).

143

Recently, and from an energy point of view, the renewable energy potential has been assessed

144

in Romania using k-means clustering algorithm and GIS (Grigoras and Scarlatache, 2015).

AC C

EP

TE D

M AN U

SC

RI PT

121

6

ACCEPTED MANUSCRIPT On occasions, the decision maker does not have enough references or models to follow and

146

the criteria which involve the decision problem are multiple. In such cases, combining GIS

147

with MCDM allows for very precise and exhaustive analysis, since the alphanumeric

148

information, which is provided by the GIS software, can be extracted in a spreadsheet format

149

which can then be used in order to apply multi-criteria decision methods (Al-Yahyai et al,

150

2012; Zubaryeva et al, 2012; Uyan, 2013).

151

Although recently studies have been carried out to evaluate the best locations of solar

152

facilities in the southeast of Spain combining, on the one hand GIS with TOPSIS and AHP

153

methods (Sánchez-Lozano et al, 2013a), and through the ELECTRE TRI method on the other

154

(Sánchez-Lozano et al, 2014), there are important differences that make its application in the

155

present study especially novel. The main differences between this paper and the cited

156

references are not only in the methodologies applied but also in the goal. (Sánchez Lozano et

157

al, 2014) applied the pessimistic ELECTRE TRI procedure; however in the proposed paper

158

the optimistic ELECTRE TRI procedure will be applied. The goal to reach is not the same;

159

this study seeks to carry out a comparison between two multi-criteria decision methods. There

160

are also differences in the way of applying the methodologies. In (Sánchez Lozano et al,

161

2014) an iterative process of the IRIS software was carried out in which an expert classifies a

162

small number of alternatives according to his/her opinion. Another difference is the software

163

used, in this paper a calculation process through Excel spreadsheets has been carried out to

164

apply the TOPSIS method, the ELECTRE TRI methods, and in order to obtain the weight of

165

the criteria. In this way it is possible to analyze a large number of alternatives.

166

Through this short review of the applications of GIS and MCDM to renewable energy, it

167

becomes clear that they are very useful tools for solving problems of location and their

168

subsequent evaluation. Thus, in this paper a GIS and a comparison of MCDM will be used in

169

order to find the most feasible locations for a solar photovoltaic farm (Figure 2). Section 2

AC C

EP

TE D

M AN U

SC

RI PT

145

7

ACCEPTED MANUSCRIPT details the models and techniques that were employed in the work to obtain the desired

171

results. In Section 3, the surfaces obtained were evaluated by the two methods of multi-

172

criteria decision making (TOPSIS and ELECTRE TRI); the weights of the criteria were

173

previously calculated using the analytic hierarchy process (AHP). Finally, Section 4 will

174

reflect both the conclusions and possible limitations of the study.

175

RI PT

170

2. Methodology

177

As has been noted, the first goal is to obtain the suitable locations and this will be done using

178

a geographical information system. Subsequently a multi-criteria decision model should

179

evaluate these alternatives. In our case and due to the importance of the project we will

180

compare two differently designed methods: TOPSIS and ELECTRE TRI. All the above is

181

reflected in Figure 2.

M AN U

SC

176

182 183

TE D

Figure 2. Process scheme

2.1. GIS

185

The first part of the problem will be solved using a GIS in which thematic layers that

186

represent and define the study area and those areas where it is impossible to implement solar

187

photovoltaic farms will be introduced (restrictions). Such restrictions may be due to either the

188

current state of the ground preventing it or due to the legislative framework in force

189

prohibiting it. Once this surface has been obtained, GIS thematic layers such as the criteria

190

that influence the selection of the best locations are inserted so as to complete the database.

191

This will then serve as the starting point for the subsequent decision analysis, which allows

192

the best locations to be determined. The selected GIS is called gvSIG (www.gvSIG.org)

193

which has been driven by the Regional Ministry of Infrastructure and Transport of Valencia

194

(Spain).

AC C

EP

184

195

8

ACCEPTED MANUSCRIPT 196 2.1.1. Obtaining feasible locations. Restrictions layers

198

Once the study area is known (Coast of the Region of Murcia), the next step is to identify

199

constraints (Table 1), i.e., those areas where, due to the laws in force on the one hand

200

(European standards, national, regional and local laws) and the current status of the territory

201

on the other hand (roads, railways, towns, etc...), make it impossible to implement solar

202

photovoltaic farms.

RI PT

197

SC

203 Table 1. Legal Restrictions

205

Once the thematic layers of restrictions have been inserted in GIS they will be deducted from

206

the initial surface area occupied by the restrictions imposed by the legislative framework with

207

the editing commands of the software. Finally, to obtain viable locations, it will require only a

208

filter to be performed that removes those parcels of less than 1000 m2 or those which have

209

some buildings inside. Once the editing process has been conducted with GIS, it will have

210

created a thematic layer which will display the locations to implement any feasible solar

211

photovoltaic farms (Figure 3) and which will also provide alphanumeric information as an

212

attribute table with the cartographic and cadastral information for such locations. This

213

cadastral information is the way of identifying the rural properties in Spain, each of these

214

properties is designed as plots.

216 217

TE D

EP

AC C

215

M AN U

204

Figure 3. Suitable locations

218

2.1.2. Criteria Layer

219

To finalize the database, it will be necessary to define the criteria that influence the decision;

220

that is to say, those that will opt for one location rather than another. These criteria are

221

defined not only through the study of the literature (Janke, 2010; Gastli and Charabi, 2010; Jo

9

ACCEPTED MANUSCRIPT and Otanicar, 2011; Sánchez-Lozano et al, 2014), but they have also been agreed upon with

223

experts in solar photovoltaic farms.

224

In order to do this, the participation of three experts was received, specifically with a doctor

225

of physics, who is an expert on photovoltaic technologies with more than 10 years of

226

experience; a doctor of engineering also specialising in photovoltaic systems and technologies

227

and a promoter of renewable energy facilities with over 8 years of experience in the sector.

228

The experts defined the main criteria which must be taken into account to find optimal

229

locations of solar farms in this study area. These criteria are briefly described

SC

RI PT

222

• g1: Agrological capacity (Classes): Suitability of land for agricultural development, if

231

a zone has excellent agrological capacity, it will not be ideal to host the facility, and

232

vice versa.

234 235 236

• g2: Slope (%): Land slope, the higher percentage of having a surface inclination, the worse aptitude to hold a solar plant.

• g3: Area (m²): surface contained within a perimeter of land that can accommodate an RE plant.

TE D

233

M AN U

230

• g4: Field Orientation (Classes): Position or direction of the ground to a cardinal point.

238

• g5: Distance to main roads (m): Space or interval between the nearest road and the

240 241 242 243 244 245

different possible sites.

AC C

239

EP

237

• g6: Distance to power lines (m): Space or interval between the nearest power line and the different possible sites.

• g7: Distance to cities (m): Space or interval between cities (cities or towns) and the different possible sites. • g8: Distance to electricity transformer substations (m): Space or interval between transformer substations of electric power and the different possible sites.

10

ACCEPTED MANUSCRIPT 246 247 248 249

• g9: Potential solar radiation (kJ·m²/day): This corresponds to the amount of solar energy a ground surface receives over a period of time (day). • g10: Average temperature (ºC): Average temperatures measured on the ground in the course of one year.

RI PT

250 2.2. Multi-criteria Decision Making

252

Once the database has been extracted in Excel spreadsheet format, it is necessary to analyze

253

and evaluate it. This problem can be considered as a multi-criteria decision making problem

254

MCDM (Chen and Hwang, 1992; Hwang and Yoon, 1981; Keeney and Raiffa, 1976; Luce

255

and Raiffa, 1957), where it is sought to choose the best alternative Ai, i=1,2,…,n with n≥2

256

when considering the criteria gj, j=1,2,…,m with m≥2 and experts Ek, k=1,2,…,r with r≥2; it is

257

considered that n, r and m are finite.

258

To solve the problem proposed a comparison between TOPSIS (Hwang and Yoon, 1981) and

259

ELECTRE TRI (Roy and Bouyssou, 1991; Yu, 1992a; Yu, 1992b) methodologies is

260

performed. Prior to doing so it is necessary to determine the weight of criteria and for this the

261

AHP methodology is applied (Saaty, 1980). These methods have been chosen because their

262

focuses differ. Whereas TOPSIS works as a continuous model, other methods such as

263

ELECTRE TRI work in a discreet manner. In order to demonstrate this, a comparative

264

between both methods will be developed in this paper. The weights of the criteria are

265

unknown since there are no similar studies. For this purpose and due to its simplicity, the

266

AHP method will be applied. Furthermore, the TOPSIS method is used because its logic is

267

rational and understandable, the process is simple and organized in an algorithm. It is able to

268

seek the best alternatives through simple mathematical operations in which the process of

269

calculation takes into account the values of the weights of each criterion and if the criterion is

270

a cost or a profit.

AC C

EP

TE D

M AN U

SC

251

11

ACCEPTED MANUSCRIPT Although there are other methods based on overcoming relationships (or overrating), the

272

ELECTRE TRI method is a helpful method for multi-criteria decisions, specially designed to

273

address classification or segmentation problems. Acceptance of any alternative is based on

274

comparing it with alternative reference through overcoming relations. This is one of the main

275

reasons why it was decided to make the comparison between TOPSIS and ELECTRE TRI

276

methods, since the former compensates the lack or excesses in the criteria in a continuous

277

manner, whereas the latter works in a discreet manner. Therefore, carrying out a comparative

278

between both methods could be of great interest.

SC

RI PT

271

279 2.2.1. Analytical Hierarchy Process

281

This is a pairwise comparisons method, development by Saaty 1980. The criteria will be

282

denoted by “g1, g2, …, gn”, their actual weights by “w1, w2, …, wn” and the matrix of the ratios

283

“W = [wi / wj]”. The matrix of pairwise comparisons “A = [aij]” represents the expert’s

284

preference between individual pairs of alternatives. The elements “aij” are considered to be

285

estimates of the ratios “wi / wj”. The values aij € [1/9,..,1,…,9], are positive and satisfy the

286

reciprocity property: aij = 1/aji (i,j = 1, 2, …, n).

287

The vector of weights is the eigenvector corresponding to the maximum eigenvalue “λmax” of

288

the matrix A. The traditional eigenvector method of estimating weights in AHP yields a way

289

of measuring the consistency of the referee’s preferences arranged in the comparison matrix.

290

The consistency index (CI) is given by CI = (λmax – n)/(n-1).

291

If the referee shows some minor inconsistency, then λmax > n and Saaty proposes the

292

following measure of the consistency index: CR = CI / RI where RI is the average value of CI

293

obtained in Alonso and Lamata (2006).

AC C

EP

TE D

M AN U

280

294 295 296

Table 2. Random index for different matrix orders.

Based on these comparisons, AHP computes the importance of the criteria. 12

ACCEPTED MANUSCRIPT 2.2.2. TOPSIS Method

298

The TOPSIS method, which was proposed by Hwang and Yoon (1981), is one of the best

299

known classical MCDM. It is based upon the concept that the chosen alternative should have

300

the shortest distance from the positive ideal solution (PIS), and the farthest from the negative

301

ideal solution (NIS).

302

The computational steps of the TOPSIS method are the following:

303

Step 1.- Establish a performance decision matrix

304

Step 2.- Normalize the decision matrix by means of

305

nij = xij

j =1

ij

2

SC

m

, j = 1, …, n, i = 1, …, m.

M AN U

∑(x )

RI PT

297

306

Step 3.- Calculate the weighted normalized decision matrix

307

vij = w j ⊗ nij , j = 1,K , n, i = 1,K , m,

308

Step 4.-Determine the positive ideal solution (PIS) and negative ideal solution (NIS)

309

A+ = v1+ ,K , vn+ =

310

A− = v1− ,K , vn− =

311

Step 5.- Calculate the separation measures.

312

n 2 2 d = ∑ ( vij − v +j )  , i = 1,…, m  j =1 

313

Step 6.- Calculate the relative closeness to the ideal solution

314

The ranking score Ri is calculated using the equation:

315

Ri =

316

Such that: If Ri = 1 → Ai = A+ and if Ri = 0 → Ai = A−

317

Step 7.- Rank the preference order

{

}

{( max v , j ∈ J ) ( min v , j ∈ J ')}

i = 1, 2,..., m

{

}

{( min v , j ∈ J ) ( max v , j ∈ J ')}

i = 1, 2,..., m

1

ij

i

(2)

(3)

1

 n 2 2 d = ∑ ( vij − v −j )  , i = 1,…, m  j =1  − i

(4)

AC C

+ i

ij

ij

i

EP

i

ij

TE D

i

(1)

di− , i = 1,K , m d + di− + i

(5)

318

13

ACCEPTED MANUSCRIPT 2.2.3. The ELECTRE TRI Method

320

ELECTRE TRI (Roy and Bouyssou, 1991; Yu, 1992a; Yu, 1992b) is a method that assigns a

321

set of alternatives to previously defined categories. In this context a category is defined as a

322

way of classifying the different alternatives between two limits (upper and lower limit),

323

according to some aptitude or capacity. The assignment of an alternative a to one category or

324

another is obtained by comparing the alternative with the limits of the predefined categories.

325

ELECTRE TRI builds an outranking relationship S i.e., to validate or invalidate the assertion

326

aSbh (and bhSa) whose meaning is “alternative a is at least as good as bh”.

327

Step 1.- Definition of reference actions

328

Actions referred to as the limits of the various categories for classifying potential actions are

329

defined, and preference thresholds pj(bh) and indifference qj(bh) such that qj(bh) represents the

330

greatest difference gj(a)-gj(bh) that maintains indifference between a and bh to the criterion gj

331

and, pj(bh) represents the smallest difference gj(a)-gj(bh) compatible with a preference a on the

332

criterion gj.

333

Step 2.- Determination of concordance indexes by criteria

334

cj(ai,bh) = 0
pj ≤ gj(bh)-gj(ai)

335

0 < cj(ai,bh) <1
qj < gj(bh)-gj(ai) ≤ pj ⇒ c j (ai , bh ) = g j (ai ) + p j − g j (bh )

336

cj(ai,bh) = 1
gj(bh)-gj(ai) ≤ qj

337

Step 3.- Calculation of the overall concordance

SC

M AN U

TE D

EP

AC C

338

RI PT

319

C (ai , bh ) =

pj − qj

∑ j∈F

k j ⋅ c j (ai ⋅ bh )

(6)

(7)

∑ j∈F k j

339

Step 4.- Determination of the discordance indexes by criteria

340

dj(ai,bh) = 0
gj(ai) ≥ gj(bh) - pj

341

0 < dj(ai,bh) < 1
gj(bh) – vj< gj(ai) ≤ gj(bh) - pj ⇒ d j (ai , bh ) = g j (bh ) − g j (ai ) − p j vj − pj

(8)

14

ACCEPTED MANUSCRIPT 342

dj(ai,bh) = 1
gj(bh) - vj(bh) ≥ gj(ai)

343

Step 5.- Obtaining the degree of credibility

344

σ s ( ai , bh ) = C ( ai , bh ) ⋅ ∏ j∈F

1 − C ( ai , bh )

where F = { j ∈ F / d j ( ai , bh ) > C ( ai , bh )}

(9)

Step 6.- Determination of the “outranking” relationship

RI PT

345

1 − d j ( ai , bh )

346

3. Results and Discussion

348

After identifying the criteria that influence the location of these types of facilities as stated, it

349

is necessary to know their weights, and for this the AHP method is used. These weights were

350

obtained (table 3) through a survey of a group of three experts and using the AHP method.

M AN U

SC

347

351

Table 3. Weight vector for the location problem for solar installations

353

Knowing the weights of the criteria it is possible to assess the alternatives using the

354

methodologies described. The problem is to assess locations (alternatives) obtained by GIS,

355

they are divided by municipalities so that the database is divided into 13 decision matrices

356

(Table 4). These matrices would have been defined in a single matrix, nevertheless in order to

357

facilitate the development of the methodology described previously; a municipality will be

358

selected which contains an intermediate number of alternatives.

EP

TE D

352

360

AC C

359

Table 4. Locations available by municipality

361

TOPSIS

362

Applying the TOPSIS method to the alternatives of each of the municipalities that are

363

included in the study area through a spreadsheet, a ranking is obtained, based on the value of

364

R. The best alternatives are those whose values of R are closer to unity, i.e. closer to the

365

positive ideal solution.

15

ACCEPTED MANUSCRIPT 366

To develop the model, it has been applied to the municipality of San Javier, which has an

367

intermediate number of plots (alternatives).

368

The decision matrix is represented by 3,114 rows for total alternatives, which in Table 5

369

represent only the top 10 according to the final ranking.

RI PT

370 Table 5. Decision Matrix of the 10 best locations in the municipality of San Javier

372

The steps of the TOPSIS method allow to obtain the weighted normalized decision matrix.

373

This theoretical development has not been included herein due to a lack of space. The next

374

step consists in determining the positive ideal solution (PIS) and negative ideal solution

375

(NIS), their values are obtained through the expression 3 (Table 6).

M AN U

SC

371

376

Table 6. Ideal solution (PIS) and anti-ideal solution (NIS) in the municipality of San Javier

378

The final steps of TOPSIS provide the separation of each alternative with respect to the PIS

379

and NIS values (d+ and d- respectively) and a ranking score of alternatives (Table 7). The best

380

alternative must have the closest value to 1, therefore in this case it corresponds to alternative

381

A2147.

383 384

Table 7. Measure of PIS and NIS distances and relative closeness to the ideal solution

EP

382

TE D

377

Figure 4 shows all the alternatives to be assessed in the municipality of San Javier (3,144),

386

with the top 10 being indicated in blue, according to the TOPSIS method.

387

AC C

385

388

Figure 4. Representation of the 3,144 alternatives of the municipality of San Javier and the top 10 according to

389

TOPSIS

390

16

ACCEPTED MANUSCRIPT Proceeding similarly with other municipalities that comprise the study area, all of the maps

392

representing the evaluation of locations available will be obtained according to the TOPSIS

393

method.

394

In order to enable comparisons with other multi-criteria methodology, the alternatives were

395

also assessed by the ELECTRE TRI method.

RI PT

391

396

ELECTRE TRI.

398

The first step is to define the references actions that will be defined in a similar way as

399

described in Sánchez-Lozano et al (2014) i.e., by an expert who is a promoter of renewable

400

energy facilities with over 10 years of experience in the sector. All necessary parameters are

401

defined in order to apply the ELECTRE TRI method (Table 8).

M AN U

SC

397

402

Table 8. Reference actions

404

Developing each of the ELECTRE TRI method steps for each municipality through a

405

spreadsheet, each of the alternatives to evaluate will be classified into categories (category 1,

406

2, 3 and 4). As an example, the values of the degrees of credibility and the category on the top

407

10 alternatives of the municipality of San Javier are represented in Table 9 and the indicative

408

map (Figure 5) shows the classification of all the alternatives according to the ELECTRE TRI

409

method for this municipality.

411 412

EP

AC C

410

TE D

403

Table 9. Top 10 alternatives degrees of credibility and categories

413 414

Figure 5. Classification by categories in the municipality of San Javier according to ELECTRE TRI

415 416

17

ACCEPTED MANUSCRIPT Comparative analyses

418

Although upon simple observation of Figures 5 and 6 it can seem that the results are similar

419

for the best alternatives, for a more exhaustive comparison of the top 10 alternatives in the

420

municipality chosen, these will be selected according to the TOPSIS method by identifying

421

and showing the locations (UTM Zone 30 coordinate system). These will then be compared

422

with the results obtained with the ELECTRE TRI method (Table 10). Furthermore, since the

423

ELECTRE TRI method allows a categorization without actually providing a review thereof,

424

as a measure of comparison, the value of the degree of credibility (σs) of each alternative will

425

be compared by the category to which it belongs (specified values will be shown in brackets)

426

i.e., by the value it is known to what extent each alternative exceeds the limit of the category

427

to which it belongs.

M AN U

SC

RI PT

417

428

Table 10. Comparative between TOPSIS and ELECTRE TRI methods

430

(*) This is the score obtained through the value of the degree of credibility for each category

431

Considering the 3,144 alternatives of the municipality, according to calculations made by

432

ELECTRE TRI there are seven alternatives located in the best category (category 4). When

433

comparing the results (Table 5 and Table 10) it is seen that although the values obtained by

434

TOPSIS do not totally coincide with the classification provided by ELECTRE, the seven

435

alternatives classed in the best category by ELECTRE are located among the ten best rated by

436

TOPSIS. The best alternative rated according to ELECTRE TRI (A2147) coincides with the

437

highest score according to TOPSIS, while the second best alternative according to ELECTRE

438

TRI (A1989) is in sixth position according to TOPSIS. To a certain extent, it is logical that

439

the results obtained for the best alternatives are similar because both methods take into

440

account all the criteria that influence the decision of such location problems.

441

The same thing happens with the rest of the better alternatives, i.e., they have good scores in

442

the criteria which have high weight and, if they have some poor value for those criteria, this

AC C

EP

TE D

429

18

ACCEPTED MANUSCRIPT value is compensated by the values of the rest of better criteria. For example, the second best

444

alternative according to ELECTRE TRI (A1989) has a poor value for the best criterion

445

(criterion g7), however this value is compensated by the good scores in the following better

446

criteria (criteria g6 and g8).

447

The results show that, although the ELECTRE TRI method works in a discreet manner and

448

benefits those alternatives which are slightly above each category and is detrimental for those

449

alternatives which are slightly below each category, the weights of the criteria are

450

fundamental to sort any alternative into its corresponding category. Alternative A1989 is a

451

clear example.

452

Then, a deep analysis will be carried out with the rest of the alternatives located in category 3.

453

It should be pointed out that there is only one alternative (A994) which has veto to achieve

454

the category 4 in criteria g1 and g6, however this alternative does not have the lowest

455

credibility degree (its value is 0.90). In comparison with alternative A2674, it is worth noting

456

that in spite of not having veto to achieve the best category; the credibility degree value of

457

alternative A2674 is lower than that of alternative A994. Therefore, it is demonstrated that

458

although there is veto for some criteria of one alternative, the weights of the rest of the criteria

459

are able to compensate this alternative and thus allow it to improve its credibility degree

460

value.

461

4. Conclusions

462

From the study conducted it was found that the GIS software are not only supporting tools

463

that can help to address a PV farm location problem (in particular, site selection), but they can

464

also generate databases in spreadsheet format which provide an ideal starting point to address

465

any issues of territorial nature.

AC C

EP

TE D

M AN U

SC

RI PT

443

19

ACCEPTED MANUSCRIPT In our example it has been concluded that the coast of the Region of Murcia is an optimal

467

place to implement solar photovoltaic farms because, once all constraints have been

468

considered, it has obtained a high percentage of suitable surface available (21.25 %). In

469

addition, a very useful database has been obtained for solving complex locations such as the

470

evaluation and selection of viable locations, obtained using multi-criteria decision making

471

methodologies.

472

A comparison has been made between two methods of multi-criteria decision making

473

(ELECTRE TRI and the TOPSIS method) and, although the results do not completely

474

coincide, some similarity can be seen between the best alternatives ranked with the TOPSIS

475

method and the best classified with the ELECTRE TRI method.

476

It has been demonstrated that although the ELECTRE TRI method assigns a set of

477

alternatives to previously defined categories, and the TOPSIS method provides a ranking of

478

these alternatives, it is possible to make a more exhaustive comparison between both methods

479

through the value of the degree of credibility defined by ELECTRE TRI for each of the

480

alternatives.

481

Regarding future work of this study, economic studies could be considered such as a viability

482

analysis which allows the alternatives to be assessed not only from the technical and

483

environmental point of view but also from an economic point of view. Certain limitations of

484

this study could also be countered by increasing the number of renewable technologies to

485

implement (biomass, biogas, etc.) or by applying other decision methodologies.

486

Acknowledgement

487

This work is partially supported by FEDER funds, the DGICYT and Junta de Andalucía

488

under projects TIN2014-55024-P and P11-TIC-8001, respectively.

AC C

EP

TE D

M AN U

SC

RI PT

466

20

ACCEPTED MANUSCRIPT References

490

[1] U.S. Energy Information Administration. (2013). International Energy Outlook 2013.

491

With Projections to 2040. Office of Energy Analysis U.S. Department of Energy Washington,

492

DC 20585. pp. 1-312.

493

[2] Arrhenius, S., 1896. On the Influence of Carbonic Acid in the Air upon the Temperature

494

of the Ground. Philosophical Magazine and Journal of Science, 5 (41), pp. 237-276.

495

[3] Working Group I, 1990. Climate Change. The IPCC Scientific Assessment. Ed. J.T.,

496

Houghton, G.J. Jenkins & J.J. Ephraums, Cambridge University Press, Cambridge.

497

[4] Working Group II, 1990. Climate Change. The IPCC Impacts Assessment, Ed. W.J. McG.

498

Tegart, G.W. Sheldon and D.C. Griffiths, Australian Government Publishing Service,

499

Camberra.

500

[5] Working Group III, 1990. Climate Change. The IPCC Response Strategies. World

501

Meteorological Organization/United Nations Environment Program. Island Press.

502

[6] United Nations, 1992. Report of the United Nations. Conference on environment and

503

development. Rio Declaration on Envorinment and Development. Rio de Janeiro

504

[7] United Nations, 1997. Framework convention on climatic change: Report of the

505

conference of the parties on its third session. Adoption of the Kyoto Protocol, Kyoto

506

[8] United Nations Framework Convention on Climate Change, 2013. Message to Parties:

507

Early submission of information and views. United Nations Climate Change Secretariat,

508

Bonn.

509

[9] European Commission, 1996. Energy for the Future: Renewable Sources of Energy -

510

Green Paper for a Community Strategy, Brussels.

511

[10] European Commission, 1997. Energy for the future: renewable sources of energy. White

512

Paper for a Community Strategy and Action Plan. Brussels.

AC C

EP

TE D

M AN U

SC

RI PT

489

21

ACCEPTED MANUSCRIPT [11] European Parliament, 2009. Directive 2009/28/EC of the European Parliament and of the

514

Council of 23 April 2009 on the promotion of the use of energy from renewable sources and

515

amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official

516

Journal of the European Union. Brussels.

517

[12] Jegatheesan, V, Liow, J.L., Shu, L., Kim, S.H., Visvanathan, C., 2009. The need for

518

global coordination in sustainable development. Journal of Cleaner Production 17, 637–643.

519

[13] Engelbrecht, D., Biswas, W.K., Ahmad, W., 2013. An evaluation of integrated spatial

520

technology framework for greenhouse gas mitigation in grain production in Western

521

Australia. Journal of Cleaner Production 57, 69-78.

522

[14] Dovì, V.G., Friedler, F., Huisingh, D., Klemes, J.J., 2009. Cleaner energy for sustainable

523

future. Journal of Cleaner Production 17, 889–895.

524

[15] Espey, S., 2001. Renewables portfolio standard: a means for trade with electricity from

525

renewable energy sources. Energy Policy 29, 557-566.

526

[16] Menz, F.C., Vachon, S., 2006. The effectiveness of different policy regimes for

527

promoting wind power: Experiences from the states. Energy Policy 34, 1786–1796.

528

[17] Foxon, T.J., Gross, G., Chase, A., Howes, J., Arnall, A., Anderson, D., 2005. UK

529

innovation systems for new and renewable energy technologies: drivers, barriers and systems

530

failures. Energy Policy 33, 2123–2137.

531

[18] Avril, S., Mansilla, C., Busson, M., Lemaire, T., 2012. Photovoltaic energy policy:

532

Financial estimation and performance comparison of the public support in five representative

533

countries. Energy Policy 51, 244–258.

534

[19] Wünteshagen, R., Menichetti, E., 2012. Strategic choices for renewable energy

535

investment: Conceptual framework and opportunities for further research. Energy Policy 40,

536

1–10.

AC C

EP

TE D

M AN U

SC

RI PT

513

22

ACCEPTED MANUSCRIPT [20] Institute for Energy Diversification and Saving IDAE, 2005. Renewable Energies Plan

538

(PER) 2005-2010. Ministry of Industry, Tourism and Commerce, Madrid.

539

[21] Institute for Energy Diversification and Saving IDAE, 2010. Renewable Energies Plan

540

(PANER) 2011 – 2020. Ministry of Industry, Tourism and Commerce, Madrid.

541

[22] Royal Decree 436/2004, dated March 12th, establishing the methodology for the

542

updating and systematization of the legal and economic regime for electric power production

543

in the special regime. BOE nº 75, Ministry of Industry, Energy and Tourism 2004, Madrid.

544

[23] Royal Decree 661/2007, dated 25 May, regulating the production of electricity in the

545

special regime. (BOE n1 126, Ministry of Industry, Energy and Tourism 2007, Madrid).

546

[24] Bürer, M.J., Wüstenhagen, R., 2009. Which renewable energy policy is a venture

547

capitalist’s best friend? Empirical evidence from a survey of international cleantech investors.

548

Energy Policy 37, 4997–5006.

549

[25] European Commission, 2010. PV Status Report 2010: Research, Solar Cell Production

550

and Market Implementation of Photovoltaics. DG Joint Research Centre. Institute for Energy,

551

Renewable Energy Unit, ISBN 978-92-79-15657-1, doi:10.2788/87966, EUR 24344 EN –

552

2010. Italy.

553

[26] Royal Decree- Law 14/2010, dated 23 December, establishing urgent measures to correct

554

the tariff deficit in the electricity sector. BOE nº 312. Ministry of Industry, Energy and

555

Tourism 2010, Madrid.

556

[27] Royal Decree-Law 1/2012, dated 27 January, in which proceeded to suspend pre-

557

allocation procedures and the removal of economic incentives for new facilities of electricity

558

production from cogeneration, renewable energies and waste. BOE nº 24, Ministry of

559

Industry, Energy and Tourism 2012, Madrid.

560

[28] EPIA, 2013. Global Market Outlook for Photovoltaics 2013-2017, European

561

Photovoltaic Industry Association, Renewable Energy House, Brussels, 12-39.

AC C

EP

TE D

M AN U

SC

RI PT

537

23

ACCEPTED MANUSCRIPT 562

[29] European Commission, 2012. Solar radiation and photovoltaic electricity potential

563

country and regional maps for Europe. PVGIS © European Union, 2001-2012, Joint Research

564

Centre,

565

http://re.jrc.ec.europa.eu/pvgis/ (accessed 19.11.13).

566

[30] Huld, T., Müller, R., Gambardella, A., 2012. A new solar radiation database for

567

estimating PV performance in Europe and Africa. Solar Energy 86, 1803–1815.

568

[31] Súri, M., Huld, T.A., Dunlop, E.D., Ossenbrink, H.A., 2007. Potential of solar electricity

569

generation in the European Union member states and candidate countries. Solar Energy 81,

570

1295–1305.

571

[32] Vera, F., García, J.R., Hernández, Z., 2007. Solar Radiation and Atmospheric

572

Temperature Atlas of the Autonomous Community of the Region of Murcia. Universidad

573

Politécnica de Cartagena, Agency for Energy Management in the Region of Murcia-ARGEM.

574

[33] Gómez-López, M.D., García-Cascales, M.S., Ruiz-Delgado, E., 2010. Situations and

575

problems of renewable energy in the Region of Murcia, Spain. Renewable and Sustainable

576

Energy Reviews 14, 1253–1262.

577

[34] Charabi, Y., Gastli, A., 2011. PV site suitability analysis using GIS-based spatial fuzzy

578

multi-criteria evaluation. Renewable Energy 36, 2554-2561.

579

[35] European Parliament, 2009. Directive 92/43/EEC of 21 May 1992 on the conservation of

580

natural habitats and of wild fauna and flora. Official Journal of the European Union. Brussels.

581

[36] Kaijuka, E., 2007. GIS and rural electricity planning in Uganda. Journal of Cleaner

582

Production 15, 203-217.

583

[37] Wallsten, B., Carlsson, A., Frändegård, P., Krook, J., Svanström, S., 2013. To prospect

584

an urban mine e assessing the metal recovery potential of infrastructure “cold spots” in

585

Norrköping, Sweden. Journal of Cleaner Production 55, 103-111.

for

Energy

and

Transport,

Renewable

Energy

Unit.

AC C

EP

TE D

M AN U

SC

RI PT

Institute

24

ACCEPTED MANUSCRIPT [38] Yousefi-Sahzabi, A., Sasaki, K., Yousefi, H., Pirasteh, S., Sugai, Y., 2011. GIS aided

587

prediction of CO2 emission dispersion from geothermal electricity production. Journal of

588

Cleaner Production 19, 1982-1993.

589

[39] Voivontas, D., Assimacopoulos, D., Mourelatos, A., 1998. Evaluation of renewable

590

energy potential using a GIS decisión support system. Renewable Energy 13 (3), 333-344.

591

[40] Sorensen, B., Meibom, P., 1999. GIS tools for renewable energy modelling. Renewable

592

Energy 16, 1262-1267.

593

[41] Yue, C.D., Wang, S.S., 2006. GIS-based evaluation of multifarious local renewable

594

energy sources: a case study of the Chigu area of southwestern Taiwan. Energy Policy 34,

595

730-742.

596

[42] Byrne, J., Zhou, A., Shen, B., Hughes, K., 2007. Evaluating the potential of small-scale

597

renewable energy options to meet rural livelihoods needs: A GIS- and lifecycle cost-based

598

assessment of Western China’s options. Energy Policy 35, 4391-4401.

599

[43] Domínguez Bravo, J., García Casals, X., Pinedo Pascua, I., 2007. GIS approach to the

600

definition of capacity and generation ceilings of renewable energy technologies. Energy

601

Policy 35. 4879-4892.

602

[44] Kahraman, C., Kaya, I., Cebi, S., 2009. A comparative analysis for multiattribute

603

selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy

604

analytic hierarchy process. Energy 34, 1603–1616.

605

[45] Kaya, T., Kahraman, C., 2010. Multicriteria renewable energy planning using an

606

integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy 35, 2517-2527.

607

[46] Cavallaro, F., 2010. Fuzzy TOPSIS approach for assessing thermal-energy storage in

608

concentrated solar power (CSP) systems. Applied Energy 87, 496–503.

AC C

EP

TE D

M AN U

SC

RI PT

586

25

ACCEPTED MANUSCRIPT [47] García-Cascales, M.S., Lamata, M.T., Sánchez-Lozano, J.M., 2012. Evaluation of

610

photovoltaic cells in a multicriteria decision making process. Annals of Operations Research

611

199, 373-391.

612

[48] Khalili-Damghani, K., Sadi-Nezhad, S., 2013. A hybrid fuzzy multiple criteria group

613

decision making approach for sustainable project selection. Applied Soft Computing 13, 339–

614

352.

615

[49] Van Haaren, R., Fthenakis, V., 2011. GIS-based wind farm site selection using spatial

616

multi-criteria analysis (SMCA): Evaluating the case for New York State. Renewable and

617

Sustainable Energy Reviews 15, 3332– 3340.

618

[50] Janke, J.R., 2010. Multicriteria GIS modeling of wind and solar farms in Colorado.

619

Renewable Energy 35, 2228-2234.

620

[51] Zhang, X., Izaurralde, R.C., Manowitz, D., West, T.O., Post, W.M., Thomson, A.M.,

621

Bandaru, V.P., Nichols, J., Williams, J.R., 2010. An integrative modeling framework to

622

evaluate the productivity and sustainability of biofuel crop production systems. GCB

623

Bioenergy 2, 258–277.

624

[52] Fazlollahi, S., Becker, G., Guichard, M., Maréchal, F., 2013. Multi-objective, multi-

625

period optimization of district energy systems: Networks design. Andrzej Kraslawski and

626

Ilkka Turunen (Ed.) Proceedings of the 23 European Symposium on Computer Aided Process

627

Engineering - ESCAPE 23, June 9-12, 2013, Lappeenranta, Finland.

628

[53] Fazlollahi, S., Girardin, L., Maréchal, F., 2014. Clustering Urban Areas for Optimizing

629

the Design and the Operation of District Energy Systems. Jiří Jaromír Klemeš, Petar Sabev

630

Varbanov and Peng Yen Liew (Ed.) Proceedings of the 24th European Symposium on

631

Computer Aided Process Engineering – ESCAPE 24. June 15-18, 2014, Budapest, Hungary.

632

[54] Grigoras, G., Scarlatache, F., 2015. An assessment of the renewable energy potential

633

using a clustering based data mining method. Case study in Romania. Energy 81, 416-429.

AC C

EP

TE D

M AN U

SC

RI PT

609

26

ACCEPTED MANUSCRIPT [55] Al-Yahyai, S., Charabi, Y., Gastli, A., Al-Badi, A., 2012. Wind farm land suitability

635

indexing using multi-criteria analysis. Renewable Energy 44, 80-87.

636

[56] Zubaryeva, A., Zaccarelli, N., Del Giudice, C., Zurlini, G., 2012. Spatially explicit

637

assessment of local biomass availability for distributed biogas production via anaerobic co-

638

digestion- Mediterranean case study. Renewable Energy 39, 261-270.

639

[57] Uyan, M., 2013. GIS-based solar farms site selection using analytic hierarchy process

640

(AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews 28,

641

11–17.

642

[58] Sánchez-Lozano J.M., Teruel-Solano J., Soto-Elvira P.L., García-Cascales, M.S., 2013a.

643

Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM)

644

Methods for the evaluation of solar farms locations: case study in south-eastern Spain.

645

Renewable and Sustainable Energy Reviews 24, 544-556.

646

[59] Sánchez-Lozano, J.M., Antunes, C.H., García Cascales, M.S., Dias, L.C., 2014. GIS-

647

based Photovoltaic Solar Farms site selection using ELECTRE-TRI: Evaluating the case for

648

Torre-Pacheco, Murcia, Southeast of Spain. Renewable Energy 66, 478-494.

649

[60] Regional Ministry of Infrastructure and Transport of Valencia, gvSIG Association,

650

https://gvsig.org/web/catalog [accessed 11.10.13].

651

[61] Gastli, A., Charabi, Y., 2010. Siting of Large PV Farms in Al-Batinah Region of Oman.

652

2010 IEEE International Energy Conference. 978-1-4244-9380-7/10.

653

[62] Jo, J.H., Otanicar, T.P., 2011. A hierarchical methodology for the mesoscale assessment

654

of building integrated roof solar energy systems. Renewable Energy 36, 2992-3000.

655

[63] Chen, S.J., Hwang, C.L., 1992. Fuzzy Multiple Attribute Decision Making: Methods and

656

Applications, Springer-Verlang (Ed). Berlin.

657

[64] Hwang, C.L. Yoon, K., 1981. Multiple Attribute Decision Methods and Applications.

658

Springer- Heidelberg (Ed). Berlin.

AC C

EP

TE D

M AN U

SC

RI PT

634

27

ACCEPTED MANUSCRIPT [65] Keeney, R., Raiffa, H., 1976. Decisions with Multiple Objectives: Preferences and Value

660

Tradeoffs, Wiley (Ed). New York.

661

[66] Luce, R.D., Raiffa, H., 1957. Games and Decisions: Introduction and Critical Survey.

662

John Wiley and Sons (Ed). New York.

663

[67] Roy, B., Bouyssou D., 1991. Aide à la décision fondée sur une PAMC de type

664

ELECTRE. Université Paris-Dauphine. Document du LAMSADE; 69; 118 p.

665

[68] Yu, W., 1992a. Aide multicritère à la décision dans le cadre de la problématique du tri.

666

Concepts, méthodes et applications, Thèse de doctorat, UER Sciences de l’organisation,

667

Université Paris-Dauphine; 201 p.

668

[60] Yu, W., 1992b. ELECTRE TRI. Aspects méthodologiques et manuel d’utilisation.

669

Université Paris-Dauphine. Document du LAMSADE; 74; 80 p.

670

[70] Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw Hill (Ed).

671

[71] Alonso, J.A., Lamata, M.T., 2006. Consistency in the analytic hierarchy process. a new

672

approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14,

673

4, 445-459.

AC C

EP

TE D

M AN U

SC

RI PT

659

28

ACCEPTED MANUSCRIPT Table 1. Legal Restrictions

N.

2

AC C

EP

TE D

M AN U

SC

3 4 5 6 7 8

Urban, protected and undeveloped lands Areas of high landscape value, water infrastructure, military zones and cattle trails Watercourses and streams Archaeological, paleontological and cultural heritage sites Roads and railroad network Community interest sites (LICs) Areas of special protection for birds (ZEPAs) Mediterranean coast and mountains

RI PT

1

Denomination of the restrictions

ACCEPTED MANUSCRIPT Table 2. Random index for different matrix orders.

4

5

6

7

8

9

10

0,00

0.5247

0.8816

1.1086

1.2479

1.3417

1.4057

1.4499

1.4854

EP

TE D

M AN U

SC

RI PT

3

AC C

RI

1-2

ACCEPTED MANUSCRIPT Table 3. Weight vector for the location problem for solar installations

Criteria to implement solar photovoltaic plants w2

w3

w4

w5

w6

w7

w8

w9

w10

0.0419

0.0586

0.1271

0.0513

0.0493

0.1449

0.1855

0.1680

0.1195

0.05384

AC C

EP

TE D

M AN U

SC

RI PT

w1

ACCEPTED MANUSCRIPT Table 4. Locations available by municipality

SC

5,308 437 6,733 391 286 25,396 148 9,243 5,634 4,371 3,144 538 5,216 66,845

AC C

EP

TE D

M AN U

Águilas Alcantarilla Cartagena Fuente Alamo La Unión Lorca Los Alcázares Mazarrón Murcia Puerto Lumbreras San Javier San Pedro del Pinatar Torre Pacheco TOTAL

RI PT

Suitable locations Municipalities Alternatives

ACCEPTED Table 5. Decision Matrix of the best 10 MANUSCRIPT locations in the municipality of San Javier

g2

g3

g4

g5

g6

g7

g8

g9

g 10

(m²)

(m)

(m)

(m)

(m)

(KJ/m²·día)

(%)

(Classes)

(ºC)

a2147

1.85

397301.24

25.00

1.00

1951.40

1230.51

2040.69

0.44

4.00

17.42

a1060

4.00

245835.68

268.38

2.95

5036.51

1133.57

2048.16

1.52

5.00

17.66

a1266

3.00

139810.84

63.17

1.00

4343.95

1302.96

2045.04

0.94

6.00

17.70

a445

3.00

132862.28

153.11

1.00

5041.24

1450.61

2042.97

0.38

6.00

17.70

a2674

1.33

123041.29

176.74

94.98

2027.28

209.53

a1989

5.67

117771.77

25.00

1.00

465.84

48.74

a2754

1.50

118080.17

361.88

1.00

361.94

539.58

a705

2.00

118220.92

82.45

1.00

2402.73

574.02

a800

2.00

114530.21

120.63

31.57

2418.71

410.66

a994

0.67

122049.28

425.17

1162.65

2961.10

…

…

…

…

…

…

RI PT

g1 (Classes)

0.72

4.00

17.70

2043.97

0.49

6.00

17.60

2043.25

0.78

4.00

17.59

2042.38

0.13

8.00

17.61

2041.46

0.64

4.00

17.61

395.06

2042.59

1.08

8.00

17.70

…

…

…

…

…

AC C

EP

TE D

M AN U

SC

2043.33

ACCEPTED MANUSCRIPT Table 6. Ideal solution (PIS) and anti-ideal solution (NIS) in the municipality of San Javier 0.05409

0.00002

0.00000

0.00007

0.00713

0.00214

0.00000

0.00195

0.00097

0.00000

0.00013

0.00347

0.00883

0.00632

0.00000

0.00211

0.00413

0.00024

0.00093

EP

TE D

M AN U

SC

RI PT

0.00210

AC C

A+ A-

ACCEPTED MANUSCRIPT Table 7. Measure of PIS and NIS distances and relative closeness to the ideal solution

a1266 a445 a2674 a1989 a2754 a705 a800

0.055206

0.021591

0.034959

0.035491

0.021870

0.036460

0.021141

0.038009

0.019810

0.038703

0.019918

0.038483

0.019803

0.038485

0.019561

0.039066

0.018913

0.038281

0.018305

R 0.9249 0.6182 0.3813 0.3670

AC C

EP

TE D

M AN U

a994

0.004481

RI PT

a1060

d-

SC

a2147

d+

0.3426 0.3398 0.3398 0.3370 0.3262 0.3235

ACCEPTED MANUSCRIPT Table 8. Reference actions

AC C

EP

TE D

M AN U

SC

RI PT

g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 b1 2 -30 25000 5 -1000 -10000 100 -6250 1200 16.00 4 -20 50000 8 -500 -1000 500 -2500 1700 18.00 b2 b3 7 -10 100000 10 -25 -100 750 -500 2000 20.00 Pj 0.0419 0.0586 0.1271 0.0513 0.0493 0.1449 0.1855 0.1680 0.1195 0.05384 1 5 3 4 100 100 100 150 0 17.50 qj(b) pj(b) 4 15 1000 7 200 300 300 3000 1500 17.60 6 40 25000 9 650 500 800 10000 2050 17.70 vj(b)

MANUSCRIPT Table 9. TopACCEPTED 10 alternatives degrees of credibility and categories

σs(ai,b2) 0.98 0.86 0.90 0.86 0.80 0.81 0.98 0.99 0.99 0.90

σs(ai,b3) 0.85 0.74 0.79 0.78 0.28 0.81 0.77 0.78 0.63 0.00

Category Category 4 Category 4 Category 4 Category 4 Category 3 Category 4 Category 4 Category 4 Category 3 Category 3

RI PT

σs(ai,b1) 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

AC C

EP

TE D

M AN U

SC

A2147 A1060 A1266 A445 A2674 A1989 A2754 A705 A800 A994

ACCEPTED MANUSCRIPT Table 10. Comparative between TOPSIS and ELECTRE-TRI methods

Coord. Y

Polygon

Plot

Subplot

A2147 A1060 A1266 A445 A2674 A1989 A2754 A705 A800 A994

691494.73 684650.28 685340.94 684587.94 689323.43 689594.93 688625.94 690543.93 692313.95 688005.95

4184050.73 4190765.84 4190072.15 4190307.66 4190124.10 4185160.10 4188111.15 4183132.10 4187014.07 4191037.62

017 001 001 001 008 019 021 016 012 003

29 13 33 13 71 22 178 28 32 25

a a a b a a a a a a

Ranking TOPSIS 0.9249 0.6182 0.3813 0.3670 0.3426 0.3398 0.3398 0.3370 0.3262 0.3235

M AN U TE D EP AC C

Category ELECTRE-TRI Category 4 (0.85) Category 4 (0.74) Category 4 (0.79) Category 4 (0.78) Category 3 (0.80) Category 4 (0.81) Category 4 (0.77) Category 4 (0.78) Category 3 (0.99) Category 3 (0.90)

RI PT

Coord. X

SC

Alternatives

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT Highlights: Evaluation of optimal sites to implant optimal sites for photovoltaic solar farms. Combination of Geographic Information Systems (GIS) and Multicriteria Decision Making Method (MCDM).

RI PT

MCDM applied: Analitical Hierachy Process (AHP) method, TOPSIS method and ELECTRE TRI method.

SC

Comparative TOPSIS-ELECTRE TRI methods

GIS

TOPSIS Method Vs ELECTRE-TRI Method

M AN U

Criteria

Suitable Locations

Coast of the Region of Murcia

AC C

EP

TE D

- Restrictions

AHP

Optimal Locations