A GIS-based Fuzzy-AHP method for the evaluation of solar farms locations: Case study in Khuzestan province, Iran

Solar Energy 155 (2017) 342–353

Contents lists available at ScienceDirect

Solar Energy journal homepage: www.elsevier.com/locate/solener

A GIS-based Fuzzy-AHP method for the evaluation of solar farms locations: Case study in Khuzestan province, Iran Abbas Asakereh, Mohsen Soleymani ⇑, Mohammad Javad Sheikhdavoodi Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, 61357-8315, Iran

a r t i c l e

i n f o

Article history: Received 6 February 2017 Received in revised form 13 April 2017 Accepted 26 May 2017

Keywords: AHP Iran Land suitability Photovoltaic Solar power Combating desertification

a b s t r a c t This study was conducted to prioritize the land of Khuzestan province in Iran to install solar photovoltaic farms, based on techno-economic and environmental aspects. Fuzzy logic and fuzzy membership functions were used to create criteria layers in the environment of GIS to draw the map of the suitability of lands. Also the Analytic Hierarchy Process (AHP) technique was used to weigh the techno-economic and environmental criteria and to draw the final map of the suitability of lands as solar farms. Results showed that Khuzestan province has a great potential to generate solar electricity via photovoltaic arrays. Based on the results, the potential of solar electricity generation in Khuzestan through the worst scenario is approximately 1.75 times more than the gross electricity produced in Iran in 2013. On the other hand, installing solar farms on the lands located at the south and the southwest of the province which are in danger of turning to desert, is a good opportunity to combat with the spread of desertification. By using solar power facilities for multipurpose application (generating solar power and combating with desertification), the costs of solar power generation will be amortized and the overall cost of solar power will be reduced. Based on the map of the suitability of the lands as solar farm, the desert and semi-desert areas of Khuzestan province have the excellent potential of electricity generation. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Energy plays a vital role in economic activities so that accessing to competitive and sustainable energy resources is an essential key for economic growth and social development. In fact, energy consumption per capita is one of the major index to evaluate society’s development (Singh, 2002). The global economy is largely depended on fossil energy carriers such as coal, petroleum, and natural gas. But the natural reservations of fossil fuels are limited and it is expected to be depleted within the next century, if their consumption is continued by the current rate (Mostafaeipour et al., 2011). Also, widespread use of fossil energy resources has been seriously degrading the earth’s environment (Dickmann, 2006; Shao and Chu, 2008). There are many immediate adverse effects such as greenhouse gases and other polluting particles imposed to the environment by the burning of fossil fuels (Li et al., 2009). Fossil fuels burning (especially incompletely) emits a significant amount of CO2, SO2, NOx and other kinds of greenhouse gases into the atmosphere (Kalogirou, 2004).

⇑ Corresponding author. E-mail address: [email protected] (M. Soleymani). http://dx.doi.org/10.1016/j.solener.2017.05.075 0038-092X/Ó 2017 Elsevier Ltd. All rights reserved.

On the other hand, energy demand is growing rapidly by world population and economic growth (especially in developing countries), so that the existing energy resources cannot meet this increasing demand. By 2050, demand for energy may be doubled or even tripled. So it is very essential to attain clean, renewable, sustainable and environmentally friendly alternative energy resources (Mirhosseini et al., 2011). Renewable energy is generally defined as the energy that is obtained from resources which are naturally replenished in a short timescale. Nowadays and as a result of the rapid technological development, many forms of renewable energies have become economically available. Expanding the use of renewable energy along with enhancing the efficiency of the energy converting devices, may significantly lead to improve energy security, reduce climate change, and earn economic benefits. Some types of renewable energies have unique capacity to provide cost-competitive energy for remote communities (Mirhosseini et al., 2011). Also compared with fossil and nuclear energy, renewable energy resources have the least environmental impacts. Nowadays, replacing nonrenewable energy resources with renewable ones is one of the most important challenges and in most countries has become the major goal in the field of energy. The growing demand of electricity and heat in the world has become the major challenge in the field of energy and researchers are trying to deal with this challenge by

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

developing safe and sustainable energy converting systems (Fadai, 2007). Solar energy is a type of clean and renewable energy that can be converted safely to the other forms of energy without emitting air pollutants. In this energy conversion method, Volatile Organic Compounds (VOCs) and emissions of nitrogen and sulfur won’t be released. Therefore it can be effectively contributed to reduce the probability of occurring the acidic rain. Although solar radiations can be utilized in a wide range of applications, solar engineering is mainly focused on thermal processes and photovoltaic (PV) (Asakereh et al., 2014). Solar power plants convert the solar radiation into electricity, directly with PV or indirectly with concentrated solar powers (CSP). PV solar power is one of the best options to supply the world future energy demand sustainably (Razykov et al., 2011). The International Energy Agency (IEA) has reported that solar electricity will increase by 20–25% by 2050, so that it will be possible to generate 9000 TWh electricity by PV and CSP systems. This amount of clean electricity can contribute to reduce CO2 emissions by about 6 billion tons per year (IEA, 2010). Solar energy which is an inexhaustible and the cleanest form of energy with remarkable source available all over the world (Aman et al., 2015; Charabi and Gastli, 2011), has been widely used by mankind for thousands of years, in different ways (light, heat and power) and by applying different types of evolving and improving technologies. It is a renewable resource with a large potential to reduce dependency on limited reserves of fossil fuels and to mitigate impacts of climate change (Shafiee and Topal, 2009). However, related technologies are no longer cost prohibitive (Bazilian et al., 2013), there are continual advances to increase the efficiency and to reduce production cost. On the other hand, solar energy systems can contribute to reduce greenhouse gas (GHG) emissions and air pollution, to enhance energy security, to create local jobs, accelerate rural electrification and to improve the quality of life in remote societies. It can be safely converted to other forms of energy without any environmental adverse effects (Charabi and Gastli, 2011; Burney et al., 2010; Tsoutsos et al., 2005). However the construction of solar PVs emits some GHG gasses to the environment but air pollution comes from Solar PVs during the electricity generation is zero. Also some toxic materials are widely used in solar cells structure. Nevertheless these environmental tolls are negligible compared to those of conventional energy resources (Aman et al., 2015). Solar energy can be harnessed in almost all locations (sunny) and thereby it can decentralize energy supply, enhance energy security (IEA, 2013) and avoid from additional relevant costs including monetary costs, transport related pollution costs, and roading wear and tear costs. Another benefit of solar PV is the reduction of water consumption which is very substantial in conventional power plants. Water consumption of PV in operation phase is very little and it can be preserved and be effectively utilized for other purposes (Aman et al., 2015). This issue is particularly very critical for countries such as Iran that is located in the arid and semi-arid areas. For instance ‘‘Kazeroon Combined Cycle Power Plant” in the vicinity of the ‘‘Parishan Lake” has had an important role in drying up the lake. Repair and maintenance costs of solar energy facilities are very low as well. Once a solar panel is installed and worked at its maximum efficiency there is only a little proceedings required yearly to ensure of remaining in working order. Also solar energy installations don’t use raw materials such as oil or coal and require significantly lower operational labor than conventional power plants (Bhandari and Stadler, 2009). Moreover, since there are no moving parts in solar panels structure, these parts don’t wear, live longer and don’t make noise at work (Timilsina et al., 2012; Ibrahim et al., 2011). Solar PV systems can be grid-tied or off-grid. Off grid

343

or battery backup systems are very important for remote, isolated and rural areas. So solar PVs are very attractive in diverse regions worldwide (Hernandez et al., 2014). Solar thermal or photovoltaic electricity without subsidies from the government is not still cost-competitive with fossil based electricity. The levelized cost of solar PV electricity is predicted to be about 125 $/MWh in 2020 (Eia, 2013) and this is very comparable with this cost of electricity from Conventional Coal based plants or Conventional Combined Cycle Natural Gas-fired plants which will be about 95 and 75 $/MWh respectively. About 110 $ of the cost of electricity generating by solar PVs is capital cost (Eia, 2013). But if solar power plants facilities will also be used for other purposes, these costs are amortized and reduced the overall cost of solar power. In recent years Khuzestan province has been grappling with environmental problems especially desertification and micro dust crisis. Common methods of combating desertification are mulching, growing the deserts, increasing vegetation cover and so on. Photovoltaic arrays specially when used on a large scale cover the land so that it doesn’t need to other measures to combat desertification. Many studies have been conducted on the various fields of solar energy for last decade. Site selection for solar power plants and investigation the available energy in those places, was one of the main fields of these studies (Carrion et al., 2008; Hott et al., 2012; Li, 2013). Geographic Information Systems (GIS) has been used widely in this kind of studies. Multi-Criteria DecisionMaking (MCDM) methods have been used widely as well, and these methods have been integrated frequently with GIS to evaluate qualitative and quantitative spatial criteria simultaneously. GIS is a powerful tool which is able to handle, process and analyze a large number of spatial data and it can draw together and analyze data from disparate sources. It has been used frequently to select solar energy sites, at local, regional, or national scale (Arnette and Zobel, 2011). Charabi and Gastli (2011) conducted a study to specify locations suitable to stablish PV sites in Oman. In this study GIS was integrated with multiple criteria decision making methods to prioritize different regions to install PV farms in Oman. The main considered criteria were availability and intensity of solar radiation, natural limitation of land, and availability of major roads and infrastructures. Maximum and minimum weight was given to the intensity of solar radiation and availability of major roads, respectively. The analytic hierarchy process (AHP) technique along with GIS was used to evaluate the environmental, locational, orographic, and climatic factors in order to select the most suitable place to install a grid-connected PV power plant in Granada, Spain (Carrion et al., 2008). Multicriteria GIS modeling technique was used to identify suitable areas for wind and solar farms in Colorado, USA. The GIS criteria used in this study, were including distance from electricity transmission lines, cities and roads; solar radiation potential; population density and land covering condition. Each criterion had its own weight to model suitable location for PV site. Mondino et al. (2014) developed a tool in GIS environment that merges the qualitative and quantitative criteria together in order to obtain the final indicator to evaluate the land suitability for large PV farm installation. Other studies have also been conducted on the field of solar site selection in which GIS combined with MCDM and provided a powerful tool to merge the qualitative, quantitative and spatial criteria to select suitable site (Aydin et al., 2013; Brewer et al., 2015; Sánchez-Lozano et al., 2014; Sánchez-Lozano et al., 2013; Sun et al., 2013). Wind and solar energy are more accessible than any other forms of renewable energy in the Middle East. Iran, a country which is located in the Middle East, not only has enormous resources of fossil fuels, but also can obtain significant amount of

344

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

energy from many kinds of renewable resources (Fadai, 2007). Iran has the second largest energy resources in the Middle East. Although, the latest Iranian energy balance sheets has shown a balance between supply and demand of electric power, but such success in power generation are mainly based on fossil energy sources. In order to reduce the adverse environmental effects of fossil fuels, especially in urban areas, and also to increase energy security, government of Iran has planned to replace part of current using fossil fuels with renewable fuels (Ghorashi and Rahimi, 2011; Ghorashi, 2007). Nevertheless, due to some economic and technical limitations, renewable energy market in Iran is still in its early stages and Iran energy supply is heavily depended on fossil resources. The potential of solar energy in Iran is more than the potential of any other kinds of renewable energy. The Organization of Renewable Energy of Iran, has done or being carried out many projects in the field of solar energy, including photovoltaic electricity generation in remote areas and villages, installation of photovoltaic farms, and agricultural water pumping with PV systems. Several studies also have been conducted on the field of solar energy in Iran, for instance disinfection of drinking water (Mahvi, 2007), using of solar energy in buildings (Alipour, 2011) and feasibility and potential of solar energy (Azadeh et al., 2009; Behrang et al., 2010; Dehghan, 2011; Moghaddamnia et al., 2009; Rahimikhoob, 2010; Sabziparvar, 2008). Nevertheless, Iran is in its early stages of using solar power and only 0.0002% of generated electricity in 2013 was came from solar energy (Anonymous, 2013). Khuzestan is a province in the southwest of Iran with an area of 64,019 km2 and it is located within 29°580 and 32°580 N latitude and 47°420 and 50°390 E longitude. The province can be basically divided into two regions, mountainous in north and east, and plains and marshland in south and west. The climate of Khuzestan is generally very hot and occasionally humid particularly in the south, while in winters it is mild and dry. Khuzestan population in 2011 was about 4.53 million people, approximately 71.0% of them were living in urban areas and the rest were living in rural areas (Statistical Centre of Iran, 2011). Khuzestan province is the largest energy genarator and the pole of crude oil extraction in Iran. On the other hand, this province is the second largest electricity consumer in the country (especially in summer days its consumption raise dramatically). Household electricity consumption in Khuzestan province in 2013 was 10428.4 GWh and it was the first rank among all 31 provinces in Iran (Anonymous, 2013). This level of electricity consumption imposes the heavy load on the power grids and power outages at the peak time of domestic electricity consumption in Khuzestan province (summer days) Asakereh et al., 2015. The potential of solar energy in Khuzestan is large enough to become the largest solar energy generator in Iran (Maleki et al., 2012). The main object of this study was to implement a special decision support system (SDSS) by integrating MCDM and GIS techniques to prioritize Khuzestan land, in order to select the most suitable site for large-scale solar farm installations in Khuzestan province.

2. Methodology 2.1. Estimation the solar insolation Solar insolation is a measure of the solar radiation received on the Earth’s surface. This value is an indicator to calculate the potential of solar energy generation in a given site. The greater the amount of solar insolation in a given area, the greater the potential of electricity generation (Arnette and Zobel, 2011). The information of solar insolation of 20 stations in Khuzestan and adjacent provinces was obtained from Meteorological Organi-

zation of Iran and Renewable Energy Organization of Iran. Reliable long-term sunshine data in Iran are rare and only available in the regions where meteorological stations have been installed for a long time. Solar insolation varies with geographical location, season, time of day, and weather conditions. However, monthly and yearly average of local insolation is relatively constant. Generally, in large scale analysis like this study, a certain level of approximation is rather acceptable (Bergamasco and Asinari, 2011). Therefore the insolation of the regions without available data, was estimated by interpolation in data obtained from meteorological stations with similar climate. The average monthly and annual amount of solar insolation of each station from the installation year to the year of 2010, was gathered. These average values were plotted on a map of studied area, based on their longitude and latitude in Universal Transverse Mercator (UTM) coordinate system, and then, spatial interpolation technique (Kriging interpolation) was used for predicting insolation values for un-sampled locations, using ArcGIS 9.3. Kriging is an advanced interpolation technique that estimates a value for an unmeasured location, using measured values surrounded it (Gormally et al., 2012; Ruelland et al., 2008). The data was provided at a resolution of 50 m in a raster format. 2.2. Criteria selection Site selection for a PV farm is affected by some factors which can be classified in three main categories: Technical, Economical and Environmental. These factors depend on the geographical location, the biophysical attributes and the socio-economical infrastructure of the studied areas (Charabi and Gastli, 2011). In this study, the evaluation criteria were selected based on the study goals, spatial scale, and accessibility to the geo-referencing database. This is why three layers were created in the environment of GIS, two of them (solar radiation potential and accessibility of transport links) to consider techno-economic factors and one layer (human and environmental limitation) to consider environmental limitations. Fuzzy logic and fuzzy membership functions were used to create all layers. Each layer has a value between zero and one. The value of zero represents the full restriction and the worst suitability while the value of one shows the most suitable site to install a solar farm. Finally, by using Analytic Hierarchy Process and based on the previous studies, all three layers, solar radiation potential, accessibility of transport links, and the layer of human and environmental limitations were weighted and combined together to create the ultimate unique layer. Accordingly the final layer of land suitability for solar farms in Khuzestan province were obtained. 2.2.1. Human and environmental considerations As mentioned, criteria to select suitable places for installing solar farms was divided into two main categories: environmental and techno-economical. Environmental criteria to install a solar farm at a specific location, are the criteria which consider human and environmental limitations. The places where not possible to install solar farms, or if possible causes harmful effects on the environment or human life, were excluded to be selected as suitable locations as solar farm. These locations include cities, villages, riparian, verge, roads, borders of protected areas, agricultural lands, rivers, swamps, wetlands, pastures and so on. For more simplicity and also to avoid excessive calculation, all these criteria ultimately were combined together and formed a raster layer to represent the layer of human and environmental limitations (Constraint layer). These limitations will be addressed separately in the following sections. A summary of these criteria is given in Table 1. 2.2.1.1. Constraint areas. Constraint areas include water bodies, ecologically sensitive areas, wildlife conservation areas, flood

345

A. Asakereh et al. / Solar Energy 155 (2017) 342–353 Table 1 Human and environmental considerations in some studies. Criterion

Limitations

Reference

Slope

Slope up to 4° Less than 3%: suitable between 3 and 10%: just SE to SW aspects is suitable and greater than 10%: unsuitable Slope up to 3%, is acceptable but 1% is most economic Less than 3% To 3%: completely suitable 3% to 7%: Suitability is decreasing and more than 7%: unsuitable Slope up to 10°

Sun et al. (2013) Bravo et al. (2007)

Slope up to 5% 0–30% is the best, more than 50% is the worst Slope up to 15% Water body, Wetlands, River, . . .

Water bodies are restricted areas Lakes, rivers, mountains and other zones considered in regional planning instruments (like zones of rivers overflow) are unsuitable 500 m buffer zone for wetlands 400 m buffer zone for wetlands 300 m buffer zone for wetlands 500 m buffer zone for rivers 400 m buffer zone for rivers River: access zone (5 m) + patrolled area (100 m)

Hang et al. (2009) Carrion et al. (2008), Aydin et al. (2013) Aydin et al. (2013) Baban and Parry (2001), Watson and Hudson (2015) Gastli and Charabi (2010), Charabi and Gastli (2011) Sánchez-Lozano et al. (2013) Mondino et al. (2014) Sun et al. (2013), Janke (2010), SánchezLozano et al. (2013) Mondino et al. (2014) Arnette and Zobel (2011) Baban and Parry (2001) Alabi (2010) Arnette and Zobel (2011) Baban and Parry (2001) Carrion et al. (2008)

Agriculture land

Agriculture land is unsuitable Non-irrigated arable land, precipitation less than or equals to 400 mm/yr: Unsuitable Cultivable land must be protected

Sun et al. (2013) Bravo et al. (2007)

Natural reserve, protected natural areas, natural reserve, all areas with environmental protection

Natural reserve is unsuitable Wildlife designations: 1000 m buffer zone

Sun et al. (2013) Watson and Hudson (2015), Baban and Parry (2001) Bravo et al. (2007), Sun et al. (2013), Mondino et al. (2014), Carrion et al. (2008) Arnette and Zobel (2011)

All areas with environmental protection or natural reserve are restricted areas 1000 m buffer around conservation areas

Aydin et al. (2013)

Residential areas

Urban: maximize (1–5 km) Residential areas: 500 m buffer zone

Aydin et al. (2013) Watson and Hudson (2015), Baban and Parry (2001)

Roads and railroad network

Road: 500 m buffer zone Road: 150 m buffer zone Roads and railroad network areas: restriction

Al-Yahyai et al. (2012) Dunsford et al. (2003) Sánchez-Lozano et al. (2013)

areas, village and city boundary, roads, railroad and areas with steep slope. One of the main criteria applied to determine the potential of a location to install a solar farm, is the current land use. The allocated land for solar farms, cannot be used for other purposes. As a result, only barren land or land with poor vegetation cover which is not suitable for other useful purposes should be considered as potential land for solar energy farms. In study in the greater southern Appalachian Mountains (USA), land with vegetation cover less than 15% was considered as appropriate site for solar farm installation (Arnette and Zobel, 2011), but in Colorado, short vegetation, logged areas, or barren land, that cannot reduce solar insolation, were considered as ideal locations (Janke, 2010). Large scale installations of PV systems may damage the arable land (Tsoutsos et al., 2005). On the other hand, protection of arable land is one of the main environmental objective. Thus fertile arable land mustn’t be used for solar farms. Based on Aydin et al. (2013), agricultural areas must be excluded from being selected as solar farms. For greater certainty a buffer zone with radius of one km should be considered around these areas. That is, the acceptability of land for solar power plant within the buffer zone increases from zero to one, respectively for the border of a farmland and a distance of one km from that border (Aydin et al., 2013). Fig. 1 shows the fuzzy membership value of land (l) for solar farm installation, based on the distance from agricultural areas. This figure shows that the agricultural land is excluded to be selected as a solar farm

1 0.8 0.6

µ

0.4 0.2 0 0

0.5

1

1.5

2

Distance from agricultural areas (km) Fig. 1. Limitation in term of agricultural areas.

(l = 0), land inside the buffer zone is partially suitable (0 < l < 1) and land outside the buffer zone (l = 1), in the absence of other barriers and restrictions, is completely suitable for this purpose. Rivers, wetlands, lakes and wildlife conservation areas are the sensitive areas that must be kept away from toxic materials. On the other hand, PV modules consist of some toxic and hazardous materials that must be avoided to be released into the environment. Therefore solar farms should be installed at a safe distance from the scope of these sensitive areas. This distance from rivers, wetlands, lakes and wildlife conservation areas was different in previous studies (Table 1). So based on previous studies (Table 1),

346

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

a fuzzy membership function (Eq. (1)) was designed to consider this restriction:

lRWC ¼

8 > < 0

x 6 100

x100 > 300

:

1

100 6 x 6 400

ð1Þ

x P 400

where: x, is the distance from rivers, wetlands and conservation areas in meter; and lRWC ; is the fuzzy membership value of land for solar farm installation (coefficient of rivers, wetlands and conservation areas). Acceptability of land around the lakes was determined by Eq. (2) as follows:

lL ¼

8 > < 0

x300 > 200

:

1

x 6 300 300 6 x 6 500

ð2Þ

x P 500

where: lL , is the fuzzy membership value of land around the lakes for solar farm installation (coefficient of lakes). Clearly, it is not possible to install a large PV farm among the buildings. Also to install a solar power plant, economic issues related to the other profitable uses of land should be considered. For instance, land near urban areas may be more profitable for other commercial applications than to allocate to a solar farm. Eqs. (3) and (4) were designed based on Table 1, to exclude land near urban and rural areas from selecting as the potential land for solar farms:

lU ¼

8 > <

0

:

1

x1000 > 4000

lW ¼

8 > < 0

x300 > 400

:

1

x 6 1000 1000 6 x 6 5000 x P 5000

ð3Þ

x 6 300 300 6 x 6 700

ð4Þ

x P 700

where: lU and lW are the fuzzy membership values of urban and rural areas, respectively. As mentioned before, it is better to install solar farms on land with poor vegetation cover. Forest areas are not applicable to install solar farms, because of shading of trees on collectors, photovoltaic modules and other devices. Forest areas also must be protected from possible harmful effects of solar farms. The forests of Khuzestan province can be divided into three categories: dense (forest with more than 50% canopy cover), semi-dense (forest with 25–50% canopy cover) and low-dense (forest with 5–25% canopy cover). Different minimum safe distances from forests have been considered in different studies (Baban and Parry, 2001; Yue and Wang, 2007). Based on similar studies and the condition of Khuzestan forests, the possibility of land next to the forests to select as solar farm is represented by a fuzzy set, as Eq. (5):

lF ¼

8 > < 0

x100 > 400

:

1

x 6 100 100 6 x 6 500

ð5Þ

x P 500

This function indicates that dense forest areas and 100 m buffer zone around them is considered as non-exploitable area, and land acceptability for PV farms increases from zero to one respectively from 100 m to 500 m next to the dense forest area. Also, semi dense forest areas were considered as non-exploitable areas but the buffer zone was not considered for them. Ultimately the fuzzy membership value for low-dense forest areas, was considered to be 0.5. Rangelands of Khuzestan province, as forests, can be divided into three categories: dense, semi-dense and low-dense. Areas

with short vegetation such as shrubs, prairies, grasslands, scrub and steppe, were considered as ideal locations for solar farm installation by Janke (Janke, 2010). The fuzzy membership value for lowdense, semi-dense and dense rangelands, was considered to be 1, 0.5 and 0, respectively. Iran is a country with poor vegetation cover and protection from this vegetation cover especially in Khuzestan province where desertification and micro-dust phenomena has been caused many environmental problems in the recent decade, is very essential. One of the important type of land cover in Khuzestan province is shrubbery and reed-bed of water or swamp margins. These areas not only are the habitat of various birds and animals, but also play an important role in combating desertification. Eq. (6) shows the fuzzy membership functions of acceptability of land as solar farm, considering shrubberies and reed-bed. These function was created based on the data of Table 1.

lY ¼

8 > < 0

x100 > 300

:

1

x 6 100 100 6 x 6 400

ð6Þ

x P 400

Roads, railroads, sand dune, and flood zones are the areas where not possible or not economic to install a large solar farm. There are different types of roads and railroads in Khuzestan province. In Iran like many countries, depending on the type of the roads, any construction from the centerline of roads and railroads to a certain distance, is prohibited. This distance, besides 100 m (as buffer zone) was considered as non-exploitable area and was excluded from being selected as potential area to install a solar farm. Also, the following fuzzy set (Eq. (7)) was applied to consider the limitation of flood zones:

lF ¼

8 > < 0

x100 > 300

:

1

x 6 100 100 6 x 6 400

ð7Þ

x P 400

Slope is a topographic feature which can strongly affect the project costs. So to select a proper location for a solar power plant, the slope of that location can play an important role. In a steep land, panels can create the shadow over the neighboring panels and hereby the efficiency of energy conversion decreases. Also setting up the infrastructure in a flat land is a lot easier and as a result, the overall construction costs decrease. In a flat terrain, on the other hand, changes such as excavation or embankment and consequently manipulation of the natural landscape can be minimized. As shown in Table 1, different maximum slope are recommended for a PV farm installation. The Digital elevation model (DEM) map of Khuzestan Province with a pixel of 10 m was obtained from Iran National Cartographic Center. The value of each pixel represents the elevation which can be used to calculate slope with ArcMap. So fuzzy membership functions were determined (Eq. (8)) based on the maximum gradients expressed in previous studies. The value of one was given to the cells (in raster format) with a slope of 0–3%, and the fuzzy value of the cells with a slope between 3 and 10% were reduced from one to zero respectively. Ultimately the area with slop more than 10% was considered as nonexploitable areas.

lS ¼

8 > < 1

x3 > 7

:

0

x63 3 6 x 6 10

ð8Þ

x P 10

Finally each mentioned criterion was modeled as a layer (sublayers) and was drawn as a map (in raster format) in GIS environment, with 50-m spatial resolution. Ultimately, all layers were multiplied together and formed the final restriction layer. Loca-

347

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

tions with value of zero in each limitation layer also would be zero in the final restriction layer. For example, in a location with the high risk of flood where it is completely unsuitable for a solar farm (fuzzy membership value is zero), the final restriction layer is completely unsuitable and fuzzy membership value will be zero too, even if fuzzy membership value of all other layers be one. Locations with value of one in all sublayers, also have the value of one in the final restriction layer. Since the other locations in sublayers have the fuzzy membership values between zero and one, so the fuzzy membership value of the final restriction layer in these locations will be lower than that of the sublayers. Locations in the final restriction layer which have the fuzzy membership value of one are the most suitable locations without any limitation to install a solar farm. 2.2.2. Techno-economic criteria The amount of solar radiation received on the earth’s surface determines the amount of solar energy can be converted to the electricity. The condition of the solar radiation in a location is a key indicator to qualify that location to install a solar power plant. If all criteria are in good condition except the solar radiation, that location will not be qualified as a good location to install a solar power plant. US National Renewable energy Laboratory (NREL) classified solar radiation qualities into four categories including: moderate (less than 4 kWh/m2/day), good (4–5 kWh/m2/day), very good (5–6 kWh/m2/day) and excellent (>6 kWh/m2/day) (Phuangpornpitak and Tia, 2011). Based on Aydin et al. (2013), generation of solar energy economically, requires solar radiation at least 4.5 kWh/m2/day. This amount is slightly different in various studies and regions (for example: 4.19 kWh/m2/day in the south of USA (Arnette and Zobel, 2011) and 5 kWh/m2/day in the south-east of Spain (Sánchez-Lozano et al., 2013). The minimum annual solar radiation in Oman was calculated to be 1522 kWh/ m2/year (4.17 kWh/m2/day) and it was found too low to be suitable for a solar PV development (Charabi and Gastli, 2011). The minimum and maximum solar radiation in England was calculated to be 536 (1.47 kWh/m2/day) and 1076 (2.95 kWh/m2/day) kWh/ m2/year, respectively (Watson and Hudson, 2015). Brewer et al. (2015) conducted a study on the site suitability for large-scale solar power installation in the southwest of the United States and found that the minimum and maximum common radiation was 3 and 8 kWh/m2/day respectively. According to the classification of National Renewable energy Laboratory and based on similar studies, a fuzzy membership equation was created by using of Gaussian function, to determine the solar radiation potential of Khuzestan province (Fig. 2). In this equation, locations with 6 kWh/m2/day and more was valued by 1 (Excellent for solar farm installation, provided that other suitable conditions) and the values of the locations with the solar intensity

in the range of [0, 6] kWh/m2/day change in the interval of [0, 1]. Solar intensity of 4 kWh/m2/day was valued by 0.5 (moderate). Proximity to the transport links (roads and railroads) is another essential factor that determines the level of difficulty to carry PV cells into the site. It is easier to establish PV utilities at locations close to the roads or railroads. Proximity to the roads or railroads avoids additional cost of infrastructure construction and consequently reduces probable damages to the environment (Brewer et al., 2015). The maximum acceptable distance from the roads and railroads depends on the circumstances of each region. So different distances are recommended in different studies (for example: 10 km Baban and Parry, 2001, 40 km (Asakereh et al., 2014; Dahle et al., 2008) or 80 km (Assessing the Potential for Renewable Energy on Public Lands, 2003). Some studies haven’t considered the maximum suitable distance from the roads or railroads. These studies valued the locations suitability based on the distance of that location from the roads, so that the worst value is assigned to the farthest locations and the best value is assigned to the nearest locations from the roads or railroads (Brewer et al., 2015; Charabi and Gastli, 2011; Janke, 2010; Sánchez-Lozano et al., 2014; Watson and Hudson, 2015). All these recommendations were considered to define a fuzzy membership function to determine Khuzestan land proximity to the transport links, as Fig. 3. 2.3. AHP The aim of a MCDM method is to investigate a number of alternatives in the light of multiple criteria and conflicting objectives (Sánchez-Lozano et al., 2013). AHP is one of the well-known MCDM method invented by Saaty in 1970s as a decision making tool to resolve unstructured problems (Saaty, 1980). It is the powerful and useful MCDM approach tool for dealing with complex decision problems. AHP can handle the decision making problems which require a high degree of flexibility and reliability (Carrion et al., 2008). AHP is based on the pairwise comparisons. In this method, a decision-maker forms a hierarchical decision tree and determines its indices and options. It provides a comprehensive and rational framework for structuring a decision problem, for representing and quantifying its elements, for relating those elements to overall goals and for evaluating alternative solutions. AHP is a technique that widely used in studies which is applying GIS-MCDM methods in the field of sustainable energy (Pohekar and Ramachandran, 2004). In this study, it is found that the combination of GIS and AHP will be useful to select the suitable location for a PV solar farm. The basic theory of AHP may be summarized as follows (Saaty, 2008):

1 0.9 1

0.8 0.7

0.8

µ

0.6

0.6

0.5

µ

0.4 0.3

0.4

0.2 0.2

0.1 0

0

0

1

2 3 4 5 Solar energy potential (kWh/m2/day)

Fig. 2. Fuzzy set for solar energy potential.

6

7

0

5

10

15

20

25

30

35

40

45

Distance from transport links (km) Fig. 3. Fuzzy set for transport links adjacency.

50

348

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

1. Modeling the problem as a hierarchy which is containing the decision goal, the alternatives to reach this goal, and the number of criteria to evaluate these alternatives. 2. Defining priorities among the elements of the hierarchy by making a series of judgments based on pairwise comparisons of the elements. The values used in the pair-wise comparison, as shown in Table 2, are in the interval of 1–9. 3. Synthesize these judgments to yield a set of overall priorities for the hierarchy. 4. Check the consistency of the judgments. 5. Come to a final decision based on the results of this process. 2.4. Solar energy conversion The annual potential of solar electricity generation, at a selected site can be calculated based on the average annual solar radiation intensity, site area and the efficiency of solar energy conversion system. Eq. (1) was used to calculate the potential of solar electricity generation (Charabi and Gastli, 2011).

efficiency (which include efficiency resulting in dust and dirt on the PV array (94%), DC electrical panel (98%), inverter efficiency (90%) and reflection of the sunbeams of the array (97%)). The value of ginst was estimated 80%. PV module efficiency is expressed at the Standard Test Conditions (STC) which means at 1000 W/m2 of solar irradiance, an air mass of 1.5 spectrum and with a module temperature of 25 °C. The efficiency of PV modules has been improving under the continuous development of PV module technology. Different module efficiencies have been applied in similar studies (Charabi and Gastli, 2011; Bergamasco and Asinari, 2011; Gormally et al., 2012; Wakeyama and Ehara, 2010). Therefore three scenarios were considered based on three efficiency values of 10, 15 and 20% (Table 3). According to Bergamasco and Asinari (2011), efficiency reducing over the lifetime of the modules is negligible and can be ignored. Also the loss of efficiency resulted from the temperature variations is about 10% (gT = 0.9). Module installation angle is the angle of the panel toward the south (in the northern hemisphere). To consider

GP ¼ SR  CA  AF  gTot 9

kWh/m2/day

where: GP is the annual potential of solar electricity generation (kWh/year), SR is the annual average solar radiation intensity (kWh/m2/day), CA is the total area of the selected site (m2), AF is the area factor and g is the efficiency of solar energy conversion system. AF shows what fraction of the site area can be covered by solar panels. The value of AF was selected based on the maximum land can be covered by PV panels with minimum shading effect. The value of AF in a similar study at the greater southern Appalachian mountains (in USA) Arnette and Zobel, 2011 and Oman (Charabi and Gastli, 2011) was considered to be 75% and 70% respectively. One of the most important technical issues in solar electricity generation is the efficiency of PV system that converts the solar energy into the electrical energy. The total system efficiency is influenced by the module efficiency, losses of dust and dirt on the PV array, DC electrical panel, temperature variations, module installation angle, inverter efficiency and the reflection of the sunbeams from the array. Considering all above factors, the total system efficiency (gTot ) is calculated by the following equation (Bergamasco and Asinari, 2011):

8

Max

7

Min

6 5 4 3 2 1 0

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

Fig. 4. Maximum and minimum solar insolation for each month of the year.

gTot ¼ gMod  gT  gAZ  gInst where: gMod , is the module efficiency; gT , temperature variations; gAZ , module installation angle efficiency and gInst is the installation Table 2 The values that used are in the pair-wise comparison. Verbal judgements of preferences between alternative

Numerical rating

Extremely preferred Very strongly preferred Strongly preferred Moderately preferred Equally preferred Intermediate values

9 7 5 3 1 2, 4, 6, 8

Table 3 Total system efficiency for the modules with three efficiencies. H

A

B

C

gMod gTH gAZ gInst gTot

0.10 0.90 0.90 0.80 0.0648

0.15 0.90 0.90 0.80 0.0972

0.20 0.90 0.90 0.80 0.129

Fig. 5. Fuzzy value of solar energy potential.

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

Fig. 6. Constraint fuzzy layers.

349

350

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

reducing the efficiency of photovoltaic system due to the incorrect angle of the installation, gAZ was introduced. So this coefficient was assumed 0.9 (gAZ =90) based on Bergamasco and Asinari (2011). Total system efficiency of the modules with three efficiencies are shown in Table 3, where gTot is the total efficiency of the system. 3. Results and discussions 3.1. Solar energy potential The results of kriging interpolation of annual solar insolation, with an approximate resolution of 50 m, were plotted in Arc GIS 9.3. The amount of solar insolation ranges from 5.09 to 5.49 kWh/m2/day annually, which following to the classification of NREL, belongs to the class of very good for solar electricity generation. Therefore, apart from other constraints such as techno-economic or environmental limitations, all areas of Khuzestan province can be potentially used as much suitable solar farm site (Phuangpornpitak and Tia, 2011) (Fig. 4). Fig. 4 shows the monthly solar insolation in Khuzestan province. The highest amount of solar insolation which is greater than 7 kWh/m2/day occurs during the summer months. Generally, the average solar insolation in the province increases from January to June and then reduces to Dec (Fig. 4). Maximum insolation intensity which is approximately 7.73 kWh/m2/day occurs in Jun. On the other hand the maximum electricity consumption in Khuzestan province, occurs during the hot summer simultaneously with this maximum insolation intensity (April to September). Average solar insolation in these months is at least 6 kWh/m2/day. Therefore it is the best fortune to alleviate the load on the power grid and to reduce the risk of blackouts, by generating solar electricity as a good alternative to the current fossil based electricity. The intensity of solar insolation in the southern areas of Khuzestan province in seven months of the year (Mar to Sep) is higher than 5 kWh/m2/day which is very good to generate electricity based on the NREL classification. Minimum solar insolation is about 2.64–3.10 kWh/m2/day and occurs in December. A map based on the fuzzy membership values of solar energy potential was plotted on the map of Khuzestan province in raster format with a 50 m resolution (Fig. 5). The fuzzy membership values of solar energy potential, range from 0.866 to 0.956, and are increasing from the north to the south. The fuzzy membership values of 42.30% of land of Khuzestan province is greater than 0.9. Therefore it is obvious that all areas of Khuzestan province have the great theoretical potential for solar energy generation but due to various constraints and low efficiency of solar energy conversion systems, only a small portion of this potential can be harnessed. In recent years Khuzestan province has been grappling with environmental problems especially desertification and micro dust crisis. Although the majority of micro-dusts comes from the neighboring country, Iraq and is originated from dried marshes of rivers Tigris and Euphrates (Hamidi et al., 2013), but there are several internal active hotspots on the South and West of the province. By installing solar farms in these areas where there is a great potential of solar energy (Fig. 5), it is not only possible to generate a great deal of renewable and sustainable energy but also to combat with desertification crisis in Khuzestan province by covering land with PV arrays.

the plains of the province cause the greatest constraints to install solar power plants. About 24.46% of all land in Khuzestan province have a slope more than 10% which is unsuitable to install a solar farm and its fuzzy membership value was considered to be zero. Land with a slope of 3% or less is completely suitable for solar farm installation and its fuzzy membership value was considered to be one. This type of land covers 61.65 of the area of the province. Only 13.89% of the province land has a slope between 3 and 10%. About 21% of the total area of the province is fertile agricultural land. So it is unsuitable for a solar farm and the fuzzy membership value of this type of land was considered to be zero. In this point of view, 63% of the total land of Khuzestan with the fuzzy membership value of one is completely suitable for solar farm installation and the others are relatively suitable with the fuzzy membership value between zero and one. Urban and rural infrastructures stand in the fourth place of restrictions and 9.42% of province area is non exploitable (fuzzy value equal to zero) because of this type of constraint. Also the condition of other restrictions such as rivers, wetlands, lakes, conservation and protected areas, rangelands, shrub lands, reed bed, roads and railroads, sand dune, and flood zones are shown in Fig. 6. Totally, 71.47% of the areas of Khuzestan province with the fuzzy value of zero is non exploitable and in return 8.32% is completely exploitable with the fuzzy value of one. The rest has fuzzy value between zero and one. The most exploitable areas are located in plain and flat land, generally in the southern part of the province. Installation of solar power plants in these areas, by covering earth surface can prevent the creation and expansion of deserts. Total annual solar insolation of locations with the fuzzy value of one and between zero and one is about 10,105 and 24,658 TWh, respectively. Assuming the efficiency of 6.48% for energy converting system and regardless of techno-economic aspects, the potential of electricity generation in areas with the fuzzy value of one (without any constraint), will be 458.4 TWh/year that is about 15 times more than the electricity consumption of the province or approximately 1.75 times more than the gross electricity produced

3.2. Human and environmental constraints Fig. 6 shows the fuzzy layers of all constraints. As this figure shows, many restrictions are overlapping. Steep terrains and forested areas in the mountainous areas and agricultural land in

Fig. 7. Access to transport links in the province.

351

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

in Iran in 2013. Total annual gross electricity production in Iran was 262433.2 GWh in 2013 (Anonymous, 2013). 3.3. Availability of transport links The fuzzy layer of the access to the transport links in Khuzestan province is shown in Fig. 7. Almost all areas of the province have good access to the roads or railroads so that about 84% of the land of the province has a fuzzy value more than 0.9 and only 0.57 of the province was non-exploitable (fuzzy value was zero). Another important techno-economic indicator is availability of electricity transmission lines. But unfortunately the map of these lines was not publicly available. So it was no possibility to review this factor. 3.4. Determining suitable land Finally the three main criteria (solar energy potential, environmental constraints and availability of transport links) were compared pair-wise and the weight of each criterion was estimated according to the guidelines of AHP method (Table 4). The importance and weight of each criterion was selected based on the professional experts (the results of similar studies). Therefore the greatest weight was given to the solar energy potential as the most important criterion, while the lowest weight was given to the availability of transport links as the least important criterion. Table 4 shows the result of estimated weights for each one of three main criteria. For determining suitable sites, non-exploitable areas which have fuzzy value of zero in sublayers (solar energy potential, environmental constraints and availability of transport links) were

eliminated at first. The layers, then were combined based on their estimated weights in Table 4. Ultimately, as shown in Fig. 8, the final layer which is determining the land suitability for PV farms installation was created in raster format based on AHP methods. Changing the color on the map reflect the variation of fuzzy values which vary from zero to 0.976 and represent non-exploitable locations and the best locations to install solar farms respectively. Five suitability levels were defined based on the fuzzy membership values of locations, namely unsuitable, poor, moderate, good and excellent with the fuzzy membership value of [0, 0.6], (0.6, 0.7], (0.7, 0.8], (0.8, 0.9] and (0.9, 0.976], respectively. Fig. 8(b) shows the condition of this classification on the map of Khuzestan province. It should be emphasized again that the high potential of solar energy in desert areas or areas with high risk of desertification (South and West regions of the province) provides a good opportunity to combat with desertification by installing solar farms on these areas and covering land surface with photovoltaic arrays. Fig. 8 shows that only a small portion of the province area (8.87%) has an excellent potential to install solar farms. However these areas are quite sufficient to generate large amounts of solar energy. Areas with good and moderate suitability level are included 4.98 and 9.87% of total areas, respectively. The potential of solar electricity generation, using PV systems with different efficiencies of modules is presented in Table 5. The potential of electricity generation from the areas with excellent suitability level, assuming 6.48% for PV system efficiency, will be 480 TWh/year. This amount is 1.83 times more than the total annual gross electricity produced in Iran and also 7.46 times more than household electricity consumption in Iran in 2013. Also the cumulative potential of electricity generation from good and excellent suitability

Table 4 Pair-wise comparison matrix of objectives and estimated weights. Objective

Solar energy potential

Environmental constraints

Transport links adjacency

Weight

Solar energy potential Environmental constraints Availability of transport links

1 0.513 0.333

1.95 1 0.556

3 1.8 1

0.539 0.291 0.170

Fig. 8. Land suitability for solar farm installation.

352

A. Asakereh et al. / Solar Energy 155 (2017) 342–353

Table 5 Solar electricity potential by using PV system with different module efficiencies in different classes. Class name Area of province (Percent) Electricity Generation Potential (TWh/year)

a b c

Aa Bb Cc

Unsuitable

Poor

Moderate

Good

Excellent

71.47 3904 5856 7772

4.90 258 388 515

9.87 530 795 1055

4.98 271 406 539

8.78 480 721 956

The efficiency of the system of solar energy conversion, 6.48% (the efficiency of panel, 10%). The efficiency of the system of solar energy conversion, 9.72% (the efficiency of panel, 15%). The efficiency of the system of solar energy conversion, 12.9% (the efficiency of panel, 20%).

levels, in the same conditions, will be 2.86 times more than the total annual gross electricity produced in Iran in 2013. Therefore it is clear that there is a huge potential for electricity generation using solar farms in Khuzestan province. 4. Conclusion In this study, the suitability of the lands of Khuzestan province in Iran has studied regarding to the techno-economic and environmental aspects. Results showed that Khuzestan province has a great potential to generate solar electricity via photovoltaic arrays. Based on the results, the potential of electricity generation of the province land in the worst case scenario is more than the gross electricity produced in Iran. On the other hand, installing solar farms on the lands located at the south and the southwest of the province which are in danger of turning to desert, is a good opportunity to combat with the spread of deserts. Based on the map of the suitability of the lands as solar farm, these areas have the excellent potential of electricity generation. Acknowledgements This research was supported by Shahid Chamran University of Ahvaz. The authors also would like to thank Iran Meteorological Organization, Renewable Energies Organization of Iran (SUNA), Jihad-e Agriculture Organization of Khuzestan province and Iran National Cartographic Center for providing the data for this research study. References Alabi, O.O., 2010. An investigation on using GIS to prospect for renewable energy in Nigeria (Phd Thesis). University of Missouri-Kansas City. Alipour, V., 2011. A Test Reference Year for Ahvaz, Iran (M.Sc Thesis). University of Manchester, UK. Al-Yahyai, S., Charabi, Y., Gastli, A., Al-Badi, A., 2012. Wind farm land suitability indexing using multi-criteria analysis. Renew. Energy 44, 80–87. Aman, M.M., Bakar, A.H.A., Solangi, K.H., Hossain, M.S., Badarudin, A., Jasmon, G.B., Mokhlis, H., Kazi, S.N., 2015. A review of Safety, Health and Environmental (SHE) issues of solar energy system. Renew. Sustain. Energy Rev. 41, 1190– 1204. Anonymous, 2013. Energy balance sheet, Iran Ministry of Energy Deputy of Electricity and Energy Affairs. Arnette, A.N., Zobel, C.W., 2011. Spatial analysis of renewable energy potential in the greater southern Appalachian mountains. Renew. Energy 36, 2785–2798. Asakereh, A., Omid, M., Alimardani, R., Sarmadian, F., 2014. Developing a GIS-based Fuzzy AHP model for selecting solar energy sites in Shodirwan Region in Iran. IJAST 68, 37–48. Asakereh, A., Omid, M., Alimardani, R., Sarmadian, F., 2015. Investigating potential of wind energy in Mahshahr, Iran. WE 39 (4), 369–384. Assessing the Potential for Renewable Energy on Public Lands, 2003. U.S. Department of the Interior Bureau of Land Management and U.S. Department of Energy Efficiency and Renewable Energy. http://www.osti.gov/bridge (accessed 15.03.02). Aydin, N.Y., Kentel, E., Duzgun, H.S., 2013. GIS-based site selection methodology for hybrid renewable energy systems: a case study from western Turkey. Energy. Convers. Manage. 70, 90–106. Azadeh, A., Maghsoudi, A., Sohrabkhani, S., 2009. An integrated artificial neural networks approach for predicting global radiation. Energy Convers. Manage. 50, 1497–1505.

Baban, S.M.J., Parry, T., 2001. Developing and applying a GIS-assisted approach to locating wind farms in the UK. Renew. Energy 24, 59–71. Bazilian, M., Onyeji, I., Liebreich, M., MacGill, I., Chase, J., Shah, J., Gielen, D., Arent, D., Landfear, D., Zhengrong, S., 2013. Re-considering the economics of photovoltaic power. Renew. Energy 53, 329–338. Behrang, M.A., Assareh, E., Ghanbarzadeh, A., Noghrehabadi, A.R., 2010. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol. Energy 84, 1468–1480. Bergamasco, L., Asinari, P., 2011. Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: application to Piedmont Region, Italy. Sol. Energy 85, 1041–1055. Bhandari, R., Stadler, I., 2009. Grid parity analysis of solar photovoltaic systems in Germany using experience curves. Sol. Energy 83 (9), 1634–1644. Bravo, J.D., Casals, X.G., Pascua, I.P., 2007. GIS approach to the definition of capacity and generation ceilings of renewable energy technologies. Energy Policy 35, 4879–4892. Brewer, J., Ames, D.P., Solan, D., Lee, R., Carlisle, J., 2015. Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability. Renew. Energy 81, 825–836. Burney, J., Woltering, L., Burke, M., Naylor, R., Asternak, D., 2010. Solar-powered drip irrigation enhances food security in the Sudano-Sahel. Proc. Natl. Acad. Sci. U. S. A. 107 (5), 1848–1853. Carrion, J.A., Estrella, A.E., Dols, F.A., Toro, M.Z., Rodriguez, M., Ridao, A.R., 2008. Environmental decision-support systems for evaluating the carrying capacity of land areas: optimal site selection for grid-connected photovoltaic power plants. Renew. Sustain. Energy Rev. 12, 2358–2380. Charabi, Y., Gastli, A., 2011. PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renew. Energy 36, 2554–2561. Dahle, D., Elliott, D., Heimiller, D., Mehos, M., Ro-bichaud, R., Schwartz, M., Stafford, B., Walker, A., 2008. Assessing the Potential for Renewable Energy Development on DOE Legacy Management Lands. National Renewable Energy Laboratory. Dehghan, A.A., 2011. Status and potentials of renewable energies in Yazd provinceIran. Renew. Sustain. Energy Rev. 15, 1491–1496. Dickmann, D.I., 2006. Silviculture and biology of short rotation woody crops in temperate regions then and now. Biomass Bioenergy 30, 696–705. Dunsford, H., Macfarlane, R., Turner, K., 2003. The Development of a Regional Geographical Information System for the North East Renewable Energy Strategy. Centre for Environmental and Spatial Analysis. University of Northumbria. Eia, U., 2013. Levelized Cost of New Generation Resources in the Annual Energy Outlook 2011. US Energy Information Administration, Washington DC. Fadai, D., 2007. Utilization of renewable energy sources for power generation in Iran. Renew. Sustain. Energy Rev. 11, 173–181. Gastli, A., Charabi, Y., 2010. Solar electricity prospects in Oman using GIS-based solar radiation maps. Renew. Sustain. Energy Rev. 14, 790–797. Ghorashi, A.H., 2007. Prospects of nuclear power plants for sustainable energy development in Islamic Republic of Iran. Energy Policy 35, 1643–1647. Ghorashi, A.H., Rahimi, A., 2011. Renewable and non-renewable energy status in Iran: Art of know-how and technology-gaps. Renew. Sustain. Energy Rev. 15, 729–736. Gormally, A.M., Whyatt, J.D., Timmis, R.J., Pooley, C.G., 2012. A regional scale assessment of local renewable energy resources in Cumbria, UK. Energy Policy 50, 283–293. Hamidi, M., Kavianpour, M.R., Shao, Y., 2013. Synoptic analysis of dust storms in the Middle East. APJAS 49 (3), 279–286. Hang, Q., Jun, Z., Xiao, Y., Junkui, C., 2009. Prospect of concentrating solar power in China – the sustainable future. Renew. Sustain. Energy Rev. 12, 2505–2514. Hernandez, R.R., Allen, E.B., Easter, S.B., Murphy-Mariscal, M.L., Maestre, F.T., Tavassoli, M., Barrows, C.W., Belnap, J., Ochoa-Hueso, R., Ravi, S., Allen, M.F., 2014. Environmental impacts of utility-scale solar energy. Renew. Sustain. Energy Rev. 29, 766–779. Hott, R., Santini, R., Brownson, J., 2012. GIS-base spatial analysis for large-scal solar power and transmission line issues: case studt of Wyoming. In: U.S. Proceedings of the 41st American Solar Energy Society Meeting, May 13–17; 2012. Ibrahim, A., Othman, M.Y., Ruslan, M.H., Mat, Sohif, Sopian, K., 2011. Recent advances in flat plate photovoltaic/thermal (PV/T) solar collectors. Renew. Sustain. Energy Rev. 15 (1), 352–365. IEA (International Energy Agency), 2010. Sees Great Potential for Solar, Providing up to a Quarter of World Electricity by 2050. https://www.iea.org/newsroom/ news/2010/may/2010-05-11–1.html; (accessed 16.09.12).

A. Asakereh et al. / Solar Energy 155 (2017) 342–353 IEA (International Energy Agency), 2013. Energy Policy Highlights. https://www.iea. org/publications/freepublications/publication/energy-policy-highlights-2013. html; (accessed 17.4.14). Janke, J.R., 2010. Multicriteria GIS modeling of wind and solar farms in Colorado. Renew. Energy 35, 2228–2234. Kalogirou, S.A., 2004. Solar thermal collectors and applications. Prog. Energy Combust. 30, 231–295. Li, D., 2013. Using GIS and Remote Sensing Techniques for Solar Panel Installation Site Selection (M.Sc Thesis). University of Waterloo. Li, D.H.W., Lam, T.N.T., Chan, W.W.H., Mak, A.H.L., 2009. Energy and cost analysis of semi-transparent photovoltaic in office buildings. Appl. Energ. 86, 722– 729. Mahvi, A.H., 2007. Feasibility of solar energy in disinfection of drinking water in Iran. Am.-Eur. J. Agri. Environ. Sci. 2 (4), 407–410. Maleki, S., Firuzi, M.A., Ahmadi, A., 2012. The role of solar energy in sustainable development in Khuzestan province, Iran. GJMST 1 (6), 1–6. Mirhosseini, M., Sharifi, F., Sedaghat, A., 2011. Assessing the wind energy potential locations in province of Semnan in Iran. Renew. Sustain. Energy Rev. 15, 449– 459. Moghaddamnia, A., Remesan, R., Hassanpour Kashani, M., Mohammadi, M., Han, D., Piri, J., 2009. Comparison of LLR, MLP, Elman, NNARX and ANFIS models – with a case study in solar radiation estimation. J. Atmos. Sol.-Terrest. Phys. 71, 975– 982. Mondino, E.B., Fabrizio, E., Chiabrando, R., 2014. A GIS tool for the land carrying capaci. Energy Procedia 48, 1576–1585. Mostafaeipour, A., Sedaghatb, A., Dehghan-Niric, A., Kalantarc, V., 2011. Wind energy feasibility study for city of Shahrbabak in Iran. Renew. Sustain. Energy Rev. 15, 2545–2556. Phuangpornpitak, N., Tia, S., 2011. Feasibility study of wind farms under the Thai very small scale renewable energy power producer (VSPP) program. Energy Procedia 9, 159–170. Pohekar, S.D., Ramachandran, M., 2004. Application of multi-criteria decision making to sustainable energy planning – a review. Renew. Sustain. Energy Rev. 8, 365–381. Rahimikhoob, A., 2010. Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew. Energy 35, 2131–2135. Razykov, T.M., Ferekides, C.S., Morel, D., Stefanakos, E., Ullal, H.S., Upadhyaya, H.M., 2011. Solar photovoltaic electricity: current status and future prospects. Sol. Energy 85 (8), 1580–1608.

353

Ruelland, D., Ardoin-Bardin, S., Billen, G., Servat, E., 2008. Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa. J. Hydrol. 361, 96–117. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Saaty, T.L., 2008. Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. RWS Publications, Pittsburgh, Pennsylvania. Sabziparvar, A.A., 2008. A simple formula for estimating global solar radiation in central arid deserts of Iran. Renew. Energy 33, 1002–1010. Sánchez-Lozano, J.M., Teruel-Solano, J., Soto-Elvira, P.L., García-Cascales, M.S., 2013. Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: case study in south-eastern Spain. Renew. Sustain. Energy Rev 24, 544–556. Sánchez-Lozano, J.M., Antunes, C.H., García-Cascales, M.S., Dias, L.C., 2014. GISbased photovoltaic solar farms site selection using ELECTRE-TRI: evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renew. Energy 66, 478–494. Shafiee, S., Topal, E., 2009. When will fossil fuel reserves be diminished? Energy Policy 37 (1), 181–189. Shao, H., Chu, L., 2008. Resource evaluation of typical energy plants and possible functional zone planning in China. Biomass Bioenergy 32, 283–288. Singh, J.M., 2002. On Farm Energy Use Pattern in Different Cropping Systems in Haryana, India (MS. Thesis). International Institute of Management, University of Flensburg. Statistical Centre of Iran, 2011. Khuzestan province. http://www.amar.org.ir (accessed 15.03.02). Sun, Y.W., Hof, A., Wang, R., Liu, J., Lin, Y.J., Yang, D.W., 2013. GIS-based approach for potential analysis of solar PV generation at the regional scale: a case study of Fujian Province. Energy Policy 58, 248–259. Timilsina, G.R., Kurdgelashvili, L., Narbel, P.A., 2012. Solar energy: markets, economics and policies. Renew. Sustain. Energy Rev. 16, 449–465. Tsoutsos, T., Franztzeskaki, N., Gekas, V., 2005. Environmental impacts from the solar energy technologies. Energy Policy 33, 289–296. Wakeyama, T., Ehara, S., 2010. Renewable energy potential evaluation and analysis for use by using GIS -a case study of Northern-Tohoku area and Tokyo metropolis, Japan. IJESD 1 (5), 446–453. Watson, J.J.W., Hudson, M.D., 2015. Regional Scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape Plan 138, 20–31. Yue, C., Wang, S., 2007. GIS-based evaluation of multifarious local renewable energy sources: a case study of the Chigu area of southwestern Taiwan. Energy Policy 35, 383–394.