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Renewable Energy 66 (2014) 478e494

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Renewable Energy

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GIS-based photovoltaic solar farms site selection using ELECTRE-TRI:Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain

Juan M. Sánchez-Lozano a,*, Carlos Henggeler Antunes b,e, M. Socorro García-Cascales c,Luis C. Dias d,e

aCentro Universitario de la Defensa de San Javier (University Centre of Defence at the Spanish Air Force Academy), MDE-UPCT, C/ Coronel López Peña s/n,30720 Santiago de la Ribera, Murcia, SpainbDepartment of Electrical Engineering and Computers, University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, PortugalcDepartment of Electronics, Computers Architecture and Projects Engineering, Universidad Politécnica de Cartagena, C/Dr Fleming s/n, 30202 Cartagena,Murcia, Spaind Faculty of Economics, University of Coimbra, Av. Dias da Silva 165, 3004-512 Coimbra, Portugale INESC Coimbra, Rua Antero de Quental 199, 3000-033 Coimbra, Portugal

a r t i c l e i n f o

Article history:Received 11 June 2013Accepted 27 December 2013Available online

Keywords:Solar farmsDecision support systems (DSS)Geographic information systems (GIS)Multicriteria decision analysis (MCDA)ELECTRE-TRI methodInteractive robustness analysis andparameters’ inference for multicriteriasorting problems (IRIS)

* Corresponding author. Tel.: þ34 968 189914; fax:E-mail address: juanmi.sanchez@cud.upct.es (J

0960-1481/$ e see front matter � 2014 Elsevier Ltd.http://dx.doi.org/10.1016/j.renene.2013.12.038

a b s t r a c t

The Region of Murcia has one of the highest percentages of potential solar radiation in Spain, which putsit in an excellent position to host electricity generation plants through photovoltaic solar systems,commonly known as solar farms. This paper proposes the use of a Geographic Information System (GIS)in order to identify the best plots suitable for installing photovoltaic solar farms in the Municipality ofTorre Pacheco, in the southeast of Spain. The plots are classified according to multiple evaluation aspects,by developing a multicriteria model and applying the ELECTRE-TRI method using the Decision SupportSystem IRIS. The combination of GIS and IRIS offers the user the possibility of using the informationprovided by the GIS mapping leading to an assignment of the feasible courses of action (the plots) tocategories of merit according to multiple, conflicting and incommensurate evaluation criteria.

The GIS provides a cartographic and alphanumeric database, including two factors of distinct nature:restrictions and criteria. The restrictions are entered into the GIS using layers defined from the currentlegislation (urban land, undeveloped land, special protection areas for birds, community sites, in-frastructures, etc.), which reduce the study area by eliminating those areas in which photovoltaic solarfarms cannot be implemented. The criteria are organized into a tree to be used for assessing the greateror lesser capacity to install photovoltaic solar farms. These criteria are introduced into the GIS, taking intoaccount weather, environmental, location, and terrain evaluation aspects.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The European Photovoltaic Industry Association (EPIA), in itsreport on the global market outlook for photovoltaic energy until2016 [1], indicates that although there was a major setback in Spainin the year 2012 it is expected that it will continue to expandmoderately as economic conditions improve and energy policystabilizes (Fig. 1).

The southeast of Spain, and specifically the Region of Murcia,has become one of the main areas inwhich more solar photovoltaicpower plants have been implemented. Many factors are responsiblefor this trend, not least the fact that Murcia has one of the highestlevels of potential solar radiation in the country; specifically in the

þ34 968 189914..M. Sánchez-Lozano).

All rights reserved.

study area, the Municipality of Torre Pacheco, the average annualglobal radiation in most of its territory exceeds 5 kWh/m2 perday [2].

As a result of the excellent climatic characteristics offered by thisterritory, it has become an attractive area to implant photovoltaicsolar farms. In order to achieve higher returns on their premises,developers and investors need to use decision support models andmethods that enable them to maximize the efficiency of solarfarms. Since these problems involve the appraisal of possiblecourses of action/alternatives according to multiple, generallyconflicting and incommensurate, evaluation aspects, multicriteriadecision analysis (MCDA) approaches are the most adequate meansfor providing decision support.

The ELECTRE (Elimination and Choice Translating Reality)method family is a well-known MCDA approach of the so-calledEuropean MCDA school [3e5]. Among the ELECTRE methods,

Fig. 1. Market evolution in Spain [1].

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494 479

ELECTRE-TRI is devoted to the sorting problem, which consists ofassigning each alternative to predefined ordered categories ofmerit. In this paper the ELECTRE-TRI method is used to classify thedifferent alternatives into categories using the Decision SupportSystem IRIS (Interactive Robustness analysis and parameters’Inference for multicriteria Sorting problems), which implementsthe most common variant of the ELECTRE-TRI method (pessimisticvariant) [6,7].

MCDA approaches have been used to provide decision supportin several problems in the area of renewable energies, recognizingthe multiple and conflicting aspects at stake for the appraisal ofdifferent courses of action [8]. Beccali et al. [9] used the ELECTRE IIImethod to evaluate a plan of action for the dissemination ofrenewable energy technologies at regional level. Haralambopoulosand Polatidis [10] applied PROMETHEE II to investigate and assessthe exploitation of geothermal energy sources in the island of Chios(Greece). San Cristóbal [11] evaluated the efficiency of 13 renewableenergy technologies through a Multiple Criteria Data EnvelopmentAnalysis model. Lee et al. [12] applied a new MCDA method basedon the Analytic Hierarchy Process (AHP) for the selection of stra-tegies for wind farm installation. Jo and Otanicar [13] developed ahierarchical methodology for the meso-scale assessment of build-ing integrated roof solar energy systems. More recently, studieshave conducted a multicriteria evaluation of photovoltaic tech-nologies using the TOPSIS and the AHP methods [14].

Geographic information systems (GIS) are a valuable tool forassisting decision making in problems with environmental impli-cations on a territorial base. As GIS have developed, their applica-tion has been extending to various fields, including that ofrenewable energy [15e20].

GIS provide an ideal complement between technical systemsand decision support by offering a collection of procedures, tech-niques and algorithms to structure data to instantiate decisionproblems dealing with the design, evaluation and prioritization ofdecision alternatives [21].

In recent years, GIS coupled with MCDA have been applied toproblems in renewable energy facilities. In the state of Colorado(USA), Janke [22] studied multicriteria decision models of wind andsolar farms using GIS. In Oman, Charabi and Gastli [23] studied thelocation of solar power plants using GIS and a multicriteria fuzzymethodology. In Andalusia, in the south of Spain, Arán-Carriónet al. [24] carried out research into the choice of optimal site se-lection for grid-connected photovoltaic power plants combiningGIS with AHP. In the northeast of Brazil, Tiba et al. [25] analyzed the

development of a management and planning system on a GISplatform for administrators, planners or consultants in renewableenergies. In Italy, Gemelli et al. [26] used a GIS-based approach toobtain a regional model of the low temperature geothermal po-tential and its economic exploitability.

In the present article, a combined approach using GIS and theELECTRE-TRI method is described to classify the possible locationsfor solar farms in the Municipality of Torre Pacheco, in the south-east of Spain, into ordered categories of merit according to multipleevaluation criteria. There are two main distinguishing features ofthis approach as regards the existing literature. First, it does notseek to find a best location in the context of a relative evaluation(among competing alternatives), but to perform a classification ofeach location based on its absolute merits and drawbacks. Themodel can therefore be applied to assess other locations besides theones considered in this work and it does not assume that only one(the best) location would be adequate for a solar farm. Second, itdoes not require setting a precise numerical value to express theimportance of each criterion, which can be a difficult task for adecision maker (DM).

The rest of the paper is organized as follows. In Section 2, GIS arebriefly reviewed, bearing in mind the problem at hand. Themethodology proposed and the Decision Support System IRIS aredescribed in detail in Sections 3 and 4. GIS-based photovoltaic solarfarms site selection using ELECTRE-TRI is applied to the case studyin southeast of Spain in Sections 5 and 6. Finally, the results andconclusions of this work are drawn in Section 7.

2. Geographic information systems (GIS)

According to Star and Estes [27], a GIS can be defined as an in-formation system that is designed to work with data referenced byspatial or geographic coordinates. In other words, a GIS is both adatabase system with specific capabilities for spatially referenceddata, as well as a set of operations for working with data. GIS areused for the storage, management, analysis and display ofgeographically referenced data, being valuable tools for assistingplanning and decision making in multiple contexts in which geo-referenced information plays a relevant role.

In the present work a Spanish free software application calledgvSIG (Generalitat Valenciana Geographical Information System)has been used, which was developed by the Ministry of Infra-structure and Transport of the Generalitat Valenciana [28]. ThegvSIG package allows the information to be processed both as

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494480

raster (image) and vector (shapefile) formats. This application of-fers the ability to access interactive maps based on Spatial DataInfrastructure of Spain (IDEE), which aims at integrating data,metadata, services and geographic information produced in Spain,using the Internet. gvSIG also provides access to map servers basedon INSPIRE (Infrastructure for Spatial Information in Europe),which is a European Commission initiative aimed at makingavailable relevant, coordinated and quality geographic information,enabling the formulation, implementation, monitoring and evalu-ation of policy or territorial impacts in the European Community.

3. The ELECTRE-TRI method

The ELECTRE methods have been widely used by researchersand professionals in several areas of application, including in en-ergy problems [8,29,30]. ELECTRE methods are based on the con-struction and exploitation of an outranking relation S (a$S$bdenotes that alternative a outranks alternative b, meaning that a isat least as good as b). The construction of outranking relations isbased on two principles: (1) the concordance principle requiresthat a sufficient majority of criteria agree that a is at least as good asb; (2) the non-discordance principle requires that, when theconcordance condition holds, none of the criteria in the minority isstrongly opposed to the assertion a$S$b.

Several members of the ELECTRE method family have beenproposed according to the problem to be addressed, i.e. choice,ranking and sorting problems: I [31], II [32,33], III [34], IV [35,36], IS[37], and TRI [38e40]. ELECTRE-TRI [38e40] is devoted to thesorting problem, which consists of assigning each alternative topreviously defined categories of merit bounded by lower and upperprofiles (reference actions). The main steps of ELECTRE-TRI aredepicted in Fig. 2.

STEP 1 Definition of reference actions

The assignment of an alternative to a given category is deter-mined by establishing an outranking relation between the

Fig. 2. Main steps of the ELECTRE-TRI method.

alternative and the boundary actions (reference actions) definingthe categories. Let g1, g2,.gm denote the set of criteria. Eachboundary action bh is the upper limit of the category Ch and thelower limit of category Chþ1 (Fig. 3). The boundaries b0 and bpþ1may correspond to the anti-ideal and ideal solutions, respectively.

STEP 2 Determination of concordance indices by criteria

The criterion concordance indices indicate how much each cri-terion agrees with the assertion ai$S$bh, taking into account indif-ference (qj) and preference (pj) thresholds, which characterize theacceptance of imprecision in the judgment. If gj(ai), the performanceof ai on criterion gj, is equal to or better than gj(bh), the performanceof bh on the same criterion, then this criterion fully agrees thatai$S$bh. The criterion may also fully agree even if gj(ai) is slightlyworse than gj(bh), since ai and bh are considered indifferent for cri-terion gj if the difference between their individual performances isless than qj. If gj(ai) is worse than gj(bh) by a difference that is greaterthan qj but less than pj, then gj agrees that ai$S$bh only partially. Thetransition between indifference and preference is linear.

The concordance index for a given criterion cj(ai,bh) is defined asfollows:

cjðai;bhÞ ¼ 05pj�gjðbhÞ�gjðaiÞ

0<cjðai;bhÞ ¼gjðaiÞþpj�gjðbhÞ

pj�qj<15qj<gjðbhÞ�gjðaiÞ<pj

cjðai;bhÞ ¼ 15gjðbhÞ�gjðaiÞ�qj(1)

STEP 3 Calculation of the overall concordance

The global concordance indices quantify the relative importanceof the coalitions of criteria that are in favor of the assertion ai$S$bh.The weight assigned to each criterion (denoted kj) may be inter-preted as a true importance coefficient, in the sense of its votingpower for those coalitions. This means that in ELECTRE-TRI weightsare scale-independent and they do not play the role of coefficientsto convert the performances of the criteria into a common valuescale. The global concordance indices are determined from theconcordance indices for each of the criteria:

Cðai;bhÞ ¼Pm

j¼1kj$cjðai;bhÞPmj¼1kj

(2)

STEP 4 Determination of the discordance indices by criteria

The concordance relation is complemented with a discordancerelation that uses the veto (vj) and the preference (pj) thresholds todefine howmuch each criterion disagrees with the assertion ai S bh.The discordance indices are calculated as:

djðai;bhÞ¼05gjðaiÞ�gjðbhÞ�pj

0<djðai;bhÞ¼gjðbhÞ�gjðaiÞ�pj

vj�pj<15gjðbhÞ�vj<gjðaiÞ<gjðbhÞ�pj

djðai;bhÞ¼15gjðbhÞ�vjðbhÞ�gjðaiÞ(3)

STEP 5 Obtaining the degree of credibility

The degree of credibility is an indicator that considers jointly theoverall concordance indices C(ai,bh) and the discordance indicesdj(ai,bh) so it expresses inwhat degree “a outranks bh”. The followingexpression can be used to compute the degree of credibility [7]:

Fig. 3. Definition of categories using limit profiles [41].

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494 481

ssðai;bhÞ ¼ Cðai;bhÞ$�1� max

j˛f1;.;mgdjðai; bhÞ

�(4)

STEP 6 Determination of the outranking relation

The outranking relation between a potential action a and areferenceactionbh is basedonthedegreeof credibilityanda constantcutting level lwhich corresponds to the lowest value of the degree ofcredibility fromwhich the assertion “a outranks bh” is valid, i.e., suchassertion is corroborated (ai$S$bh) only when ss(ai,bh)� l.

STEP 7 Assignment of alternatives to different categories.Once an alternative ai has been compared to the action or

reference profiles bh, in ELECTRE-TRI there are two ways toassign an alternative ai to one of the predefined categories.The comparison of alternative ai with the reference profilemay be done according either to a pessimistic or optimisticprocedure:

(a) Pessimistic procedure (or conjunctive): it consists of

assessing ai$S$bh successively for the different profilesstarting with the best profile to find the bh profile forwhich ai$S$bh is verified, and once found ai is assignedto the category Chþ1.

(b) Optimistic procedure (or disjunctive): it consists ofcomparing the alternative ai successively with thedifferent profiles starting with the worst profile to findthe bh profile for which w(ai S bh) ^bh S ai, and oncefound ai is assigned to the category Ch.

The IRIS software, described in the next section, implements thepessimistic procedure, which is more intuitive and more used inpractice: if ai is sorted in category Ch, then this means it is goodenough to outrank this category lower bound bh�1 but it is not goodenough to outrank its upper bound bh.

4. The decision support system IRIS

IRIS [7] is a DSS based on ELECTRE-TRI (pessimistic procedure)that implements the interactive methodology proposed inRef. [6]. IRIS enables to exploit an ELECTRE-TRImodel in case the useris a DMwith no expertise in the method or an analyst mediating thecommunication between the software and the DM. Rather thandemanding precise values for the ELECTRE-TRI weights and cutting

level, IRIS allows the DM to enter constraints on these values,including assignment examples that it tries to restore.

Using IRIS theuser can state precise values (e.g. k1, theweightof thefirst criterion, is equal to 0.2), intervals (e.g. k1˛ [0.1, 0.3]), linear con-straints (e.g. k1� k2), or indirect constraints (e.g. a1 should be sorted incategory C3,). Sources of inconsistency among these constraints areidentifiedwhenever it isnotpossible to satisfy themsimultaneously. Insuch cases the combination of parameter values that minimizes theconstraintviolations is computed. IRISalso infers robustconclusionsbyindicating the range of assignments for each alternative that does notcontradict any user-defined constraint. Whenever those constraintsare consistent, IRIS infers a “central” combination of parameter valuesstating the category each alternative is sorted into corresponding tothatcombination,andtherangeof categorieseachalternativemightbeassigned to while satisfying all constraints.

IRIS offers DMs an interactive environment in which they maygather information in an interactive and progressive manner,exerting a critical analysis on the results displayed to guide theelicitation of parameter values. This enables a step-by-step approachin which further knowledge is gathered and preferences are refinedthrough a learning process, thus reducing the scope of the searchuntil the DM is confident about the results to make a final decision.

In this paper, a sample of the information provided by the GIS isused as input into the DSS IRIS to produce an evaluation of potentiallocations for solar farms in the region under study, which is framedas an MCDA sorting problem.

5. A GIS-based methodology for obtaining suitable surfaces toimplant photovoltaic solar farms

The gvSIG program is used in two stages. In the first one, re-strictions that prevent a solar plant from being implanted in aparticular area are represented. These areas unsuitable for im-plantation are removed, leaving only the areas that are feasible forthis purpose. In the second stage, all the information pertaining toall retained alternative locations for all evaluation criteria isselected to be supplied to the DSS IRIS.

The Municipality of Torre Pacheco covers an area of 189.60 km2,where the territory is divided into different types of land accordingto its Urban Municipal General Plan. This classification is added tothe gvSIG program as a vector layer, as represented in Fig. 4.

Then the layers relating to the restrictions are added using thegvSIG application commands to obtain the suitable surface toimplant photovoltaic solar farms in the municipality.

Fig. 4. Addition of layer of the Municipality of Torre Pacheco in gvSIG.

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494482

The restrictions to be inserted into gvSIG are obtained throughthe Administrations and Public Bodies of the Region. Theseconstitute the technical and environmental restrictions of the areaunder study and are entered into the software in the form of avector layer (Table 1).

The layers in Table 1 are added to gvSIG, and their representa-tions are shown in Appendix A.

Once introduced in vector format layers, a gvSIG commandenables to remove the area affected by the restrictions and then theresulting surface should be assessed by the decision supportmethodology.

To classify and enumerate the resulting surface the classificationmade by the General Direction of Cadastre of the Region of Murciais used, which divides the surface of any land into zones, plots andsubplots. The cadastral layer of the Municipality of Torre Pacheco isthus drawn in vector format (Fig. 5).

Besides indicating the zones, plots and subplots, the cadastrallayer provides information on the area of each parcel and whetherit is a building. Since to establish a photovoltaic solar farm largesurfaces are required, the minimum area of each suitable plot is1000 m2. It also seems logical that those parcels containing abuilding will be the least suitable to host a solar plant. Therefore,taking into account these considerations, filtering is carried out toremove those parcels that are smaller than 1000 m2 or containbuildings, using a gvSIG command. A new layer of cadastral infor-mation is obtained (classified in zones, plots and subplots) for theMunicipality of Torre Pacheco to implant photovoltaic solar farms.About 35% of the total area of the municipality (189.60 km2) is then

Table 1Layers of restrictions.

No. Denomination of the layers of restrictions

1 Urban lands2 Protected and undeveloped lands3 Areas of high landscape value4 Water infrastructure, military zones and cattle trails5 Watercourses and streams6 Archaeological sites7 Paleontological sites8 Cultural heritage9 Roads and railroad network10 Community interest sites (LICs)

found to be suitable to implant solar farms; this percentage cor-responds to 65.36 km2. Its distribution in the municipality can beobserved using an orthophoto or raster layer (Fig. 6).

In the second stage the information about the criteria is insertedinto the gvSIG program (in vector or raster format). The criteria tobe considered are derived from a review of existing literature [22e24,42], which has been agreed with experts in the field of renew-able energy sources. The criteria are used to assess themerit of eachplot to implant a photovoltaic plant therein. This information isobtained through public and private institutions (Fig. 7, Table 2).The above layers are added to gvSIG and their representations areshown in Appendix B.

5.1. Vector thematic layer processing and attribute table

Once all the criteria influencing the decision process have beendefined, the layers of criteria are linked spatially with the vectorlayer obtained in stage 1 (Fig. 6). This process is carried out in twodifferent ways depending onwhether the criterion layer is vector orraster.

In the gvSIG program, vector layers are structured by rows(registers) and columns (fields) and each register corresponds to aspatial object (e.g., a plot) referenced by an identifier which isusually defined in the first column, while the remaining columnsrepresent the attributes associated with each spatial object. Theserows and columns are shown in a table called an attribute table.

If the criterion is of vector type (agrological capacity, plot area,distance to power lines, electricity transformer substations, townsor villages and main roads), the transfer of attributes is made andthe distances from each plot to power lines, electricity transformersubstations, towns or villages and main roads are calculated usingthe appropriate processes in the gvSIG program. If the criterion isexpressed in raster information, an extension of gvSIG called SEX-TANTE is used. It processes the raster layers of criterion information(orientation, solar radiation, average temperature and slopes),which links with the layer obtained by applying spatial links to thepreviously obtained vector layers.

The final table obtained (Table 2) contains the alternativesdefined by zone, plot and subplot and the criteria indicated forassessing each alternative.

Fig. 5. Addition of layer of the Cadastral information in gvSIG.

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6. Using the ELECTRE-TRI method for assigning plots tocategories of merit

To carry out the evaluation of alternatives an expert in solarphotovoltaic facilities was involved in the model building process.This expert, who will be the DM, is a promoter of renewable energyfacilities with more than 10 years of experience in the industry. Hewill be able to apply his judgment about a small number of alter-natives according to his knowledge and experience. Thus, from thetable of criteria obtained using gvSIG, only a few alternatives (20plots in this case) are extracted in order to provide the information

Fig. 6. Suitable areas after

to perform the multicriteria decision support process using the DSSIRIS. The decision problem is structured using the criterion treedisplayed in Fig. 7 and the plots selected by the DM are shown inTable 3.

In addition to selecting the alternatives (ai) and criteria (gj), theapplication of the ELECTRE-TRI method requires the definition ofthe different categories with their upper and lower reference pro-files (b0,b1,.,b4), as well as indifference (qj(bh)), preference (pj(bh))and veto (vj(bh)) thresholds. The values for the bounds of the cat-egories and the indifference, preference and veto thresholds havebeen provided by the DM according to his personal knowledge and

cadastral restrictions.

Fig. 7. Criterion tree resulting from the problem structuring phase.

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494484

experience. The table of reference actions delimiting the four cat-egories is displayed in Table 4.

The alternatives to be evaluated and categories are displayed inFig. 8.

This information is entered into the DSS IRIS. Fig. 9 displays theevaluation matrix, i.e. the performance of each alternative in eachcriterion.

Table 2Attribute table of the shape with the information of criteria and attributes.

Attribute table

Alternatives PlotCadastral information Zone and subplotCriteria Agrological capacity (g1)

Slope (g2)Field orientation (g3)Plot area (g4)Distance to main roads (g5)Distance to power lines (g6)Distance to town or villages (g7)Distance to electricity transformer substations (g8)Solar radiation (g9)Average temperature (g10)

In the next step the reference profiles bounding each categoryfor each criterion (also indicating whether the criterion is to bemaximized or minimized), as well as the preference, indifference,and veto thresholds are entered (Fig. 10).

The upper and lower bounds of the cutting level l ðl˛½0:5;1�Þ,which states the exigency of the classification into categories ofmerit, and the weights (kj refers to the weight of criterion gj), whichstate the “voting power” of each criterion for establishing theoutranking relation, are introduced. IRIS does not require the userto indicate precise values for the criterion weights (k1,.,k10) andthe cutting level l; rather it allows to obtain such values through aninference procedure [43]. The initial bounds for the criterionweights have been obtained through a system of surveys tophotovoltaic solar energy experts [44], so that the maximum andminimum values are consistent with those they provided. In orderto obtain a higher degree of credibility and consistency in the re-sults the interval for l˛½0:67; 0:80� has been considered, thusrequiring a “qualified majority” of criteria, and the extreme values(l ¼0.67 and l ¼0.80) are analyzed (Fig. 11).

Finally, further constraints on the range of the parameters canbe added; the DM may edit at any time the constraints that theweights and the cutting level should respect.

Table 3Examples selected for evaluation by an expert.

Alternatives Criteria

Zone Plot Subplot g1 g2 g3 g4 g5 g6 g7 g8 g9 g10

(1e8) 0e30% 1e10 m2 m m m m kJ/m2 day �C

Alternatives a1 1 16 c 1.00 17.70 7.26 13559.32 68.82 126.21 1846.93 4265.78 2047.04 17.60a2 1 19 a 4.00 10.50 7.10 37855.87 562.86 1.25 1668.60 4869.39 2049.17 17.69a3 1 22 c 4.00 16.02 7.45 8691.09 1154.72 106.17 1738.34 5782.16 2048.65 17.60a4 1 22 f 5.00 11.76 7.89 49659.87 1473.29 59.99 1977.82 6025.01 2050.18 17.60a5 1 22 g 4.00 7.60 7.00 3037.95 1529.19 15.64 1851.09 6444.00 2051.64 17.60a6 1 26 a 5.00 8.26 8.99 7891.86 982.63 197.90 1043.22 6662.57 2050.48 17.60a7 1 27 a 2.00 5.11 8.30 4484.85 391.99 481.15 519.97 6158.91 2051.18 17.60a8 1 32 c 1.00 7.69 4.37 9623.20 55.06 540.65 491.80 5798.14 2048.73 17.60a9 1 33 a 4.00 20.88 5.96 193639.32 24.38 82.27 568.40 5619.33 2050.53 17.60a10 1 33 b 5.00 32.50 6.30 57522.34 493.70 51.28 1014.88 5802.09 2050.81 17.60a11 1 35 a 1.00 15.52 3.92 79125.52 22.69 252.74 828.56 4956.58 2049.89 17.69a12 1 47 a 5.00 8.21 7.79 4102.16 323.52 459.22 587.23 5997.43 2051.03 17.60a13 1 56 b 8.00 21.78 6.86 1979.96 730.25 373.08 811.91 6602.30 2051.78 17.60a14 1 58 a 5.00 30.26 8.23 266207.77 577.19 202.73 1255.21 5251.43 2047.96 17.61a15 1 58 i 5.00 18.76 6.08 5988.98 264.70 539.39 992.95 5429.40 2050.00 17.66a16 1 61 a 2.00 16.78 3.25 5203.00 531.85 442.27 599.25 6328.74 2050.20 17.60a17 1 76 a 1.00 12.88 6.59 7994.66 687.46 384.65 751.04 6529.83 2051.42 17.60a18 1 89 a 2.00 13.99 6.11 6577.69 978.06 6.46 1179.66 6348.33 2052.02 17.60a19 1 90 b 5.00 12.81 5.25 12170.21 1157.92 132.57 1355.16 6449.19 2052.21 17.60a20 1 91 e 2.00 19.35 6.09 11984.62 741.60 236.24 813.25 6414.66 2050.60 17.60

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6.1. First iteration

Once all the parameters have been introduced, the IRIS programis run providing a graph as shown in Fig. 12; we refer to these inputparameters and output results as of the first iteration.

Fig. 12 shows the results (with l˛½0:67;0:80�) that indicate therange of possible assignments for each alternative, i.e. the cate-gories to which alternatives may be assigned without violatingany constraints and assignments examples; these ranges appearin green. In each range, one of the cells has a darker shade ofgreen, meaning it is the category assignment recommended byIRIS. The corresponding l and kj values are presented in the lastrow of Fig. 12. If the DM selects any cell in a range, the penultimateline in Fig. 12 shows a combination of parameter values that assignthe action in the cell’s row to the category in the cell’s column(e.g., Fig. 12 shows a combination of values that assign a14 to C3).

6.2. Second and successive iterations

Analyzing the results provided by IRIS, the DM observes thatalternative a9 is classified in the best category. This alternative(plot) is very attractive because it occupies a large area of territory(criterion g4); however, according to the DM it should not be in thebest category as it is very close to towns and village (criterion g7). Sothe DM considers that alternative a9 should be at most very good(category C3) but not excellent (category C4). Using IRIS it is possibleto modify the input parameters in order to establish the best

Table 4Reference actions.

g1 g2 g3 g4 g5

b1 2 �30 5 25,000 �1000b2 4 �20 8 50,000 �500b3 7 �10 10 100,000 �25q1(b) 1 5 4 3 100p1(b) 4 15 7 1000 200v1(b) 6 40 9 25,000 650q2(b) 1 5 4 15 75p2(b) 4 15 7 1000 200v2(b) 6 40 9 25,000 650q3(b) 1 5 4 100 20p3(b) 4 15 7 1000 200v3(b) 6 40 9 25,000 650

category for this alternative, so that at most it is situated in categoryC3 (Fig. 13).

With this second iteration, besides assigning alternative a9 tocategory C3, IRIS states that alternatives a7 and a16 would no longerbe positioned in the worst category (C1) to be classified as goodalternatives (category C2). However, the DM considers that thesealternatives deserve to be in the worst category because, besidesnot presenting large areas (criterion g4), the distance of these plotsto electricity transformers substations (criterion g8) is excessive.Therefore, a third iteration with IRIS is performed to place alter-natives a7 and a16 in the worst category according to the DM’sexperience and preferences (Fig. 14).

Once the third iteration is made it is observed that not only it hasbeen possible to assign alternatives a7 and a16 to the worst categorybut also the categories of alternatives a2, a9, a10, a11 and a14 havebeen reduced from very good (category C3) to good (category C2).The DM considers that such alternatives should not be situated incategory 2, and he even considered that alternative a14 should besituated in the best category because not only it has the greatest area(criterion g4), but it is also quite far from towns and villages (crite-rion g7). Therefore, a fourth iteration is performed to reflect thesepreferences indicated by the DM in an interactive manner (Fig. 15).

With this fourth iteration in IRIS, the DM is satisfied with theresults obtained since in this last classification all plots are placed ina single category and match his expert judgment. Indeed, eachrequest of the DM consisted of excluding the vectors of parametersthat were incompatible with the corresponding preference

g6 g7 g8 g9 g10

�10,000 100 �6250 1200 16.00�1000 500 �2500 1700 18.00�100 750 �500 2000 20.00100 100 150 0 17.50300 300 3000 1500 17.60500 800 10,000 2050 17.7010 100 50 0 17.50

300 300 3000 1500 17.60500 800 10,000 2050 17.70

1 100 5 0 17.50300 300 3000 1500 17.60500 800 10,000 2050 17.70

Fig. 8. Alternatives to be evaluated and definition of categories using reference profiles.

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494486

expression. The reduction of the set of parameter vectors acceptedin each iteration led also to reducing the range of categories inwhich other alternatives can be placed.

The parameter values displayed in the last line of Fig. 15 (on theright) correspond to the cutting level l and the criterion weights

Fig. 9. Introduction of the ev

inferred from the DM’s judgment, through an indirect process thatrevealed more meaningful for the expert than directly providingsuch numerical values. It is possible to observe that the criterion“distance to town or villages” (g7) is considered to be the mostimportant for this classification.

aluation matrix in IRIS.

Fig. 10. Introduction of reference profiles for each criterion and thresholds.

Fig. 11. Upper and lower bounds of the l-cut and kj.

Fig. 12. Results output in IRIS (first iteration).

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Fig. 13. Results output in IRIS (second iteration).

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494488

7. Results

The classification associated with the fourth iteration (Fig. 15) isshown in the gvSIG program (Fig. 16) in order to obtain a carto-graphic visualization; this figure shows the sample plots with thecategories in different colors.

Fig. 14. Results output in

The analysis of the map displaying the categorization obtainedby the DSS IRIS with selected plots enables to draw useful conclu-sions to reach a final recommendation. In the 20 alternatives (plots)chosen, it is observed that one of them (the blue colored plot) hasexcellent capability to host the implementation of a photovoltaicsolar farm; four plots (colored in yellow) have very good capability;

IRIS (third iteration).

Fig. 15. Results output in IRIS (fourth iteration).

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494 489

10 plots (green colored plots) have good capability; and theremaining plots (red plots) have poor capability. The remainderplots, shown in light blue, have not been evaluated by the DM,although the inferredmodel parameters can now be used to classify

Fig. 16. Classification and represe

these plots. (For interpretation of the references to color, the readeris referred to the web version of this article.)

Although more alternatives could have been added, the DM hasconsidered preferable to use a limited number of them in order to

ntation of the sample plots.

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494490

run the decision process in greater depth and also to view theirresults more clearly and concisely.

8. Conclusions

In the early stages of the study it was verified through aGeographic InformationSystem(gvSIG) that themunicipalityof TorrePacheco is an optimal zone to implement photovoltaic solar farms.

The main contribution of this paper is the combination of thisgeographic information system with a multicriteria decision anal-ysis Method (ELECTRE-TRI, which is based on the exploitation of anoutranking relation devoted to the sorting problem) by developinga multicriteria model to be tackled by the DSS IRIS to provide de-cision support.

The main advantage offered by this integrated approach is usinga GIS to gather and organize the information to be supplied to theDSS, which in turn provides results that can be meaningfully dis-played using the GIS. IRIS indicates for each alternative the categoryof merit where it is classified according to a set of criteria, as well asit provides interactive features that enable a progressive shaping of

Appendix A. Maps used for each restriction

a final recommendation. This allows the DM to obtain robust con-clusions, i.e. conclusions that hold true for all the acceptable com-binations of parameter values, which do not need to be preciselyspecified (thus not imposing an excessive burden on the DM).

Future research lines include improving the combination be-tween GIS and MCDA tools at the methodological level, and alsoanalyzing larger extensions of territory and studying otherrenewable energy technologies (wind farms, solar thermoelectric,biomass, etc.).

Acknowledgments

This work is partially supported by FEDER funds, the DGICYTandJunta de Andalucía under projects TIN2011-27696-C02-01 and P11-TIC-8001, respectively.

C. H. Antunes and L. Dias acknowledge the support of EMSUREe

Energy and Mobility for Sustainable Regions Project (CENTRO-07-0224-FEDER-002004) and Portuguese Foundation for Science andTechnology (FCT) under project grants MIT/SET/0018/2009 andPEst-C/EEI/UI0308/2011.

Appendix B. Maps used for each criterion

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J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494492

J.M. Sánchez-Lozano et al. / Renewable Energy 66 (2014) 478e494 493

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