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Renewable Energy 36 (2011) 2554e2561

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Renewable Energy

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PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation

Yassine Charabi a,*, Adel Gastli b,1

aDepartment of Geography, College of Arts, Sultan Qaboos University, P.O. Box 42, Al-Khodh, Muscat 123, OmanbDepartment of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khodh, Muscat 123, Oman

a r t i c l e i n f o

Article history:Received 19 June 2010Accepted 6 October 2010Available online 15 March 2011

Keywords:FLOWAProspectPhotovoltaicSolar mapSolar powerSolar radiation

* Corresponding author. Tel.: þ968 24142003; fax:E-mail addresses: yassine@squ.edu.om (Y. Charabi),

1 Tel.: þ968 24141373; fax: þ968 24413454.

0960-1481/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.renene.2010.10.037

a b s t r a c t

This paper presents some preliminary results from a research study conducted on solar energy resourceassessment in Oman. GIS-based spatial multi-criteria evaluation approach, in terms of the FLOWAmodule was used to assess the land suitability for large PV farms implementation in Oman. The tool usedapplies fuzzy quantifiers within ArcGIS environment allowing the integration of a multi-criteria decisionanalysis. Land suitability analysis for large PV farms implementation was carried out for the case study ofOman. The overlay results obtained from the analysis of the resultant maps showed that 0.5% of the totalland area demonstrate a high suitability level. Different PV technologies were considered for imple-mentation. It was found that the CPV technology provides very high technical potential for implementinglarge solar plants. In fact, if all highly suitable land is completely exploited for CPV implementation, it canproduce almost 45.5 times the present total power demand in Oman.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Despite the cascade effects of the financial crisis that haveaffected every sector, in varying degree and geography, theinvestment in renewable energy continues growing witha sustainable trend. According to the new report of the UNEP(United Nation Environment Programme) [1], the investment inrenewable energy rose 5% in 2008 proving definitely the estab-lishment of new methods of electric power generation andconfirms that this sector represents now a mainstream energyinvestment [2]. The climate of the good health of renewable energyis the fruit of the interactions of the governmental and societalengagement towards tangible actions tomitigate climate change byreducing Green House Gases (GHG), reducing their dependency onfossil fuel supply and making energy security a strategic priority.Certainly, the current financial and economical crisis may haveslowed down the demand on the fossil fuel energy and drivendown prices. But, the world opinion is still convinced, that is onlya temporary pause. It seems that there is a latent threat formenergy crisis, and will constitute a good stimulus for the emergenceof the renewable energy era.

To face this threat from resources depletion, solar energy isrecognized as a robust alternative to unsustainable energy use indeveloped and developing countries. During the last two decades,

þ968 24141851.gastli@squ.edu.om (A. Gastli).

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the rhythm of the implementation of solar farm using Photovoltaic(PV) panels or Concentrated Solar Power (CSP) technologies hasaccelerated in the countries situated in the solar energy belt,despite their prohibitive costs.

According to the International Energy Agency (IEA) solar elec-tricity will grow up to 20e25% by 2050 [1]. The IEA has also fore-seen that, by 2050, the PV and CSP systems will be able to generate9000 TWh of electricity and reduce the yearly CO2 emissions byalmost 6 billion tones [3].

Comparing the CSP and PV technologies, the CSP necessitatelarger amounts of water for cooling and mirror washing than thePV. Therefore, for arid countries with scarce fresh water resources,the PV technology is more suitable, environment friendly, andeconomical. Besides, the implementation of PV plants is muchfaster than the CSP ones, which gives it more flexibility to copeeasily with the development of the grid system. To enable thedevelopment of the PV solar technologies long-term orientedstrategies with predictable incentives are needed to ensure thesuccessful deployment of PV systems to competitiveness in themost suitable locations and times.

The Geographical Information System (GIS) reached a high levelof maturity and emerged as a powerful tool to build solar energystrategies and to integrate large amounts of PV into flexible, effi-cient and smart grid. GIS is able to handle, processing, analyzinga large quantities of spatial data and underpinning decision-making for the spatial deployment of PV. Using GIS and Multi-Criteria Analysis (MCA) together provide a fine lens for the optimalsite selection for plants. GIS-based MCA is commonly used to solvethe conflicts of location suitability and harmonizing the tradeoffs

Table 1Classification of factors affecting optimum locations for large PV farms.

Technical Economical Environmental

Solar Radiation Grid proximity Sensitive areasLand Accessibility Land slope Hydrographic lineLand use Load poles Sand/dust risk

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and risks related to various experts’ judgment engaged in theimplementation of different applications [4e6]. However, verylittle was published on solar applications.

This paper presents a study that aimed at developing the firstgeographical mapping models to locate the most appropriate sitesfor different PV technologies in Oman using MCA.

2. Overview of multi-criteria analysis

The principal of the MCA is to condense complex problemswith multiple criteria into finest ranking of the best scenarios fromwhich an option is selected [7e10]. In GIS-based MCA and for solarenergy purpose, this might include a set of geographically definedcriteria, such as solar radiation, Digital Elevation Model (DEM),residential area, sensitive area, transmission lines, load demand,road accessibility etc. Weights can be attributed to the criteriaaccording to the importance of each variable in deriving theoptimal alternative and each of the variable and their weights mayhave a more or less favorable in the final decision than another[11].

The GIS-based multi-criteria analysis relies basically on twomain approaches: Boolean overlay operators and weightedsummations procedures. Both approaches are considered asdecision algorithms based on the Ordered Weighted Averaging(OWA) methodology [12e17] which is an ensemble of multi-criteria aggregation procedures using fuzzy set theory. The OWAincorporate both the criterion importance and order weights. Ithas enough flexibility to generate a large variety of decisionstrategies.

The analytical hierarch process (AHP) [18] is anotherapproach used in decision-making strategies. It is a robuststructured approach dealing with complex decisions. The AHP isbased on the additive weighting model and has been used withinGIS in two different modes [19]. The first technique can be usedto derive the importance weights coupled with criterion maplayers. In second step, the weights are aggregated with thecriterion map layers in a manner identical to weighted combi-nations approach [19]. This Method offers an important advan-tage for a spatial decision problem with a wide range ofalternatives making it impossible to complete pair-wisecomparisons of the alternatives. The second technique of AHPcan be used to combine the priority for all levels of the hierar-chical structure, including the level representing alternatives.According to this approach a small number of alternatives can beevaluated [19,20].

OWA and AHP algorithms were incorporated in the GIS plat-forms since 1995. They were refined afterward with the integrationof the AHP_OWA procedures using fuzzy quantifiers in GIS solu-tions [21]. AHP_OWA have been used around the world in a widevariety of decision situations [22e25]. However, few applicationswere conducted on renewable energies. In this paper we proposeto use the AHP_OWA using fuzzy quantifiers in GIS environmentsto develop an index for land suitability for PV and CSP farmsimplementations.

3. Application methodology to PV farms siting

3.1. PV site suitability

Solar energy resource assessment and site suitability for largePV farms implementations is affected by different factors which canbe classified in three main categories: Technical, Economical andEnvironmental. These factors depend on the geographical location,biophysical attributes and socio-economical infrastructure of thecountry under study.

For a country like Oman, which is situated astride the tropic ofcancer and characterized by an arid and very hot climate, thetypical parameters that affect most the optimum location of largePV farms are shown in Table 1. Notice that the dust and sand riskfactors are only specific to the region and may not apply for othercountries with temperate climate.

The suitability of the location of a PV farm is determined basedon the combination with different weights of all the factors listedabove. The most insolated areas are predisposed to high suitability.

Proximity to roads avoids additional cost of infrastructureconstruction and consequential damage to the environments.Lands that have minimal value due to past use and presentconditions should be evaluated for potential PV farms deployment.PV farms are particularly suitable where the connection to theexisting electric grid is effortless. The arrangement to implementPV farms in close proximity to the existing grid and loads polereduce significantly transmission losses.

Large-scale PV farms require flat terrain or fairly steep slope thatis facing southwith less than a 5% graded slope. The deployments ofthe PV at large scale were adopted in the perspective of sustainabledevelopment and mitigation of climate change, because it operatesfor long periods with low maintenance. PV systems were recog-nized as technologies that have virtually no environmental impact,because, they are clean and silent. From this standpoint, theimplementation of PV farms, should respect the sensitive areasunder landscape and monument protection due to estheticrequirements. Zone of influences identified as critical risk zone forPV farms such as floods and windy area, should be avoided. Also,area with abundance of dust, combined with the occurrence of fogand mist, will affect the efficiency (revenue) of PV farms. Forinstance, if a solar collector surface is maintained at a cleanlinesslevel of 90%, the estimated annual loss in revenue reach up to 10%[26]. Furthermore, washing with water (conventional cleaningmethod) may well involve prohibitive costs especially in an aridcountry like Oman.

3.2. Selection criteria

In this study, the evaluation criteria were selected based onstudy goals, spatial scale, and accessibility to the geo-referencingdata base. For instance, the resolution of the digital elevationmodelis selected based on the capacity of the computer machine which isused. Besides, the complete transmission lines spatial dataset werenot available and thus were not used. This omission will also allowidentifying potential pathways for future transmission linesdevelopment to make them pass nearby most suitable locations forlarge PV farms implementations.

3.3. Analysis tool

The tool used in this analysis is the Fuzzy Logic Ordered WeightAveraging (FLOWA) module developed by Boroushaki and Malc-zewski [19] that was integrated within ESRI ArcMap 9.3. Itincorporates the concept of fuzzy (linguistic) quantifiers into theGIS-based land suitability analysis via ordered weighted averaging(OWA). OWA is defined as a multi-criteria evaluation procedure (orcombinationoperator). Thenatureof theOWAevaluationprocedure

Y. Charabi, A. Gastli / Renewable Energy 36 (2011) 2554e25612556

depends on some selected parameters, which can be specified bymeans of fuzzy (linguistic) quantifiers. OWA can produce a numberof decision strategies or scenarios by changing the parameters.

3.4. Hierarchical structure

A hierarchical structure is governing the correlation betweenthe objectives and attributes [19]. The overall goal here is evalu-ating land suitability for a large PV farm implementation on thebasis of solar radiation topography, land usage and sensitive areasobjectives. Thus, the objectives are measured in terms of thecriteria including solar radiation, sensitive areas, soil type (sandy),land slope, land usage, land accessibility, load poles, hydrographiclines. Each criterion is represented by a spatial layer.

To simplify the analysis and minimize the number of layers andcomputing cost, some of the above-mentioned criteria are merged

Fig. 1. Spatial distribution of annu

together in one spatial layer. For instance the flood pathways,rivers, dams, urban and village areas, sensitive areas, sand typesoils, and roads are merged all together in one layer and were givenequal weight. This makes sense because it is not possible to builda large PV farm on buildings, sandy soil (Sahara) or land with highrisk of floods.

In order to identify the most suitable lands for PV farmsimplementation, the following three criteria are considered: (i)solar radiation (ii) constraint areas, and (iii) proximity to majorroads. The first two criteria are to bemaximized and the third factoris a minimization criterion.

3.5. Generation of criteria weights

Based on the above defined hierarchical structure, a pair-wise comparison matrix at each level of the hierarchy can be

al solar radiation (MWh/m2).

Y. Charabi, A. Gastli / Renewable Energy 36 (2011) 2554e2561 2557

developed, beginning at the top by assigning 1 for equalimportance and going downward by giving number 9 forextreme importance [27]. This procedure greatly reduces theconceptual complexity of a problem since only two componentsare considered at any given time. This approach requires usuallyexperts to provide their best judgment to the relative intensityof importance of one evaluation factor (objective and criterion)against another.

3.6. Order weights

The computing of the overall evaluation is performed by meansof the OWA combination function [19]. The FLOWAmodule permitsspecifying a linguistic quantifier to the levels of objectives in orderto generate a set of ordered weights. It employs fuzzy statements

Fig. 2. Constraints laye

such as “all”, “most”, “many”, “half”, “some”, “few”, “at least one”.The FLOWAmodule only considers a class of the relative quantifiersknown as the regular increasing monotone quantifiers [19].

4. Study-case implementation & results

To find out themost appropriate locations for PV farms in Oman,an important data base was collected from different sources toshape factors affecting optimal locations for large PV farms in Omanas stated in III. Five steps compose the achievement of the spatialdistribution of the land suitability level:

Step 1: the collected geo-referenced data base was convertedfrom vector files to raster format with a pixel of 40 m in order tokeep uniformity with the Digital Elevation Model (DEM) ofOman.

r including slopes.

Y. Charabi, A. Gastli / Renewable Energy 36 (2011) 2554e25612558

Step 2: use of solar radiation analyst module of ArcGIS tocalculate the total solar radiation map. This module incorporatesslope, hill shade and aspect to produce an accurate solar radiationmap [28] and it allows to modify the coefficient of the atmo-spheric transmissivity. In the published literatures, there are nostudies about atmospheric transmissivity in Oman. A trans-missivity value of 0.65 was adopted, according to study conductsin arid region comparable to Oman [29]. The calculation of solarradiation with a resolution of 40 m for whole Oman consumesa lot of time and incalculable with the available computers.Therefore, the size of the pixel size was enlarged to 500 m toreduce the time consuming for the different GIS calculationprocess (Fig. 1).

Step 3: a constraint layer regrouping all the unsuitable areas wascreated. This layer is composed from dams, flood area, land use,village boundary, historical and touristic monuments, wadis(rivers), sand dunes, roads and area with slopes more than 5�. The

Fig. 3. Spatial distribution of

map algebra function of ArcGIS was used to merge and reclassify allthis layers. The unsuitable areas were attributed by 0 and thesuitable with 1 (Fig. 2).

Step 4: Land accessibility was recognized as an importantcriterion in the process of PV farms siting. As the objective was tominimize distance from roads, the straight-line distance tool ofArcGIS is used to measure distances from each location to theclosest road (Fig. 3).

Step 5: The above listed layers (Solar radiation, constraint layerand straight-line distance to roads) were used to run the FLOWAmodule. The FLOWA module process is mainly composed of thefollowing four phases:

� Phase 1: Selection and standardization of the raster criteria. Atthis stage of the process, solar radiation and constraint layerwere selected for maximization and straight-line distance toroads was selected for minimization.

distance to major roads.

Table 2Pair-wise comparison matrix of objectives and calculated weights.

Objective Solarradiation

Constraintlayer

Distance tomajor roads

Weight

Solar radiation 1 2 3 0.545Constraint layer 0.5 1 2 0.287Distance to major roads 0.333 0.5 1 0.168

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� Phase 2: Hierarchical structure of decision problem. Inthis phase one objective was considered, which is related tothe optimization of site suitability for the PV farmsdeployment.

� Phase 3: Pair-wise comparison for objective. The specificweights criteria were calculated against each other. Table 3shows the pair-wise and judgment to the relative intensity ofimportance of one evaluation factor (objective) against another(see Table 2).

� Phase 4: Fuzzy logic quantifiers which are linguistics state-ments such as “all”, “most”, “many”, “half”, “some”, “few”, “atleast one”, etc. In this phase “all”was selected tomake sure thatall objectives are met simultaneously. This is the last phase ofthe FLOWA analysis, after which results are displayed. Fig. 4shows the results obtained.

Fig. 4. Spatial distribution o

5. Discussions

The annual electric power generation potential for the selectedareas can be estimated based on the calculated average annual solarradiation per unit surface, the total suitable area, and the efficiencyof the PV technology. Eq. (1) can be used to estimate the yearly solarelectric power generation potential [28].

f land suitability levels.

Table 3Total generation potential on highly suitable lands.

PV Technology Efficiency, h (%) Highly suitableland area, CA (km2)

Mean annual solar radiationfor highly suitable land,SR (GWh/km2/year)

Generation potential,GP (GWh/year)

c-Si 13.1 1508.2 2623 362,717a-Si 7.9 218,738CdTe 8.8 243,657CIGS 8.4 232,582CPV 26.3 728,204

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GP ¼ SR� CA� AF � h (1)

where:GP Electric power generation potential per year (GWh/year).SR Annual solar radiation received per unit horizontal area

(GWh/km2/year).CA Calculated total area of suitable land (km2).AF The area factor, indicates what fraction of the calculated areas

can be covered by solar panels.h The efficiency with which solar system converts sunlight into

electricity.From Fig. 4, it can be noticed that a small portion of the country

exhibit a high suitability level however, a very large portion exhibitsmoderate suitability. The potential generation capacity (in GWh/year), for highly suitable level, using different PV technologies arepresented in Table 3. An area factor of AF ¼ 70% was selected basedon maximum land occupancy of PV panels with minimum shadingeffect. The efficiency h of each PV technology was considered fora typical high temperature and low DNI region application such isthe case for Oman.

It is clear that for highly suitable lands, all types of PV tech-nologies can produce huge amount of electricity in Oman. Forinstance, if the CPV technology (3rd generation of PV cells) isconsidered, there is a potential to generate many multiples of thecurrent demand for electricity in Oman. Note that the total annualgross electricity production for Oman as of 2008 was roughly16,000 GWh [30].

6. Conclusion

This paper has demonstrated the application of a GIS-basedspatial multi-criteria evaluation approach, in terms of the FLOWAmodule to assess the land suitability for large PV farms imple-mentation in Oman. The FLOWA module applies fuzzy quantifierswithin ArcGIS environment. It incorporates uncertainty of expertopinions on the criteria and their weights, and provides a mecha-nism for guiding the decision-making through the multi-criteriacombination procedures.

Land suitability analysis for large PV farms implementation wascarried out for the case study of Oman. The overlay results obtainedfrom the analysis of the resultant maps showed that about 0.5% ofthe land area demonstrate high suitability for PV farm imple-mentation. Different PV technologies were considered and it wasfound that the CPV technology provides very high technicalpotential for implementing large solar plants. In fact, if all highlysuitable land is exploited for CPV farms, it can produce almost up to45.5 times the current total power demand in Oman.

Acknowledgment

Authors would like to acknowledge Sultan Qaboos Universityand The Research Council in Oman for providing the financialsupport to conduct this research study.

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