Most areas of the Iran enjoy more than 300 days of good sunshine for photovoltaic energy production and the potential for wind energy production is also immense. In this paper we evaluated the suitability of Mashhad County in Iran for the installation of photovoltaic panels. Mashhad is a mega city with a growing population due to its religious importance and the travelling pilgrims coming to the city to visit the Holy Shrine each year. We evaluated the area based on several factors including including topographic features (slope gradient, slope direction and height), roads and infrastructure, built-up areas, environmentally protected areas, solar photovoltaic output potential, air temperature, land cover and active fault lines. Since most of the area is flat with minor elevations, there is no limitation for energy production. The largest share of the area has slope gradient of less than 15% (3303 km2), this factor also imposes no limitation. In terms of elevation also 42% (4335 km2) of the area below 2000m which is suitable for wind farm construction. There are some 72 active faults in the area. In total, 7.3% of the total area is covered with protected areas accounting for ~ 720 km2 which is not suitable for solar farms. In terms of land cover, 72% of the area is available for constructing solar farms. There is also no limitation in terms of air temperature and potential PV output. The final suitability map was obtained by combining these layers and then divided into five classes. Based on our results, the suitable class had the largest share of the final map. Highly suitable areas comprise 14.1% of the total area. The excluded lands from the analysis due to the limiting factors made up approximately 56% equal to 5811 km2. In total, Mashhad County is considerable potential for solar energy production.
Research Article
GIS application in identifying suitable areas for solar panel installation in Mashhad County of Iran
https://doi.org/10.21203/rs.3.rs-1970568/v1
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Photovoltaic
Khorasan
renewable
desert
climate change
Iran
The global population is surging at an alarming rate and is believed to reach 7.8 billion as of 2020, to level out around 2100 at 10.9 billion (Hoornweg et al., 2017). This significant population growth will have a considerable environmental footprint. As of now, global warming is torching the earth and maximum temperatures are making a new records each year (Change, 2018). However, non-renewable energy sources such as fossil fuels are estimated to fall short of global energy demand in 30-40 years (Pimentel et al., 2008). Fossil fuels and the surge in their demand and consumption have adverse effects on the environment such as air and soil resources contamination and serious health issues related to pollution (Ebhota et al., 2020). This concern over energy demand in the future as well as environmental concerns and global raising awareness has led countries to push towards renewable sources and especially solar energy. The mid-latitude countries like Iran enjoy a great number of sunny days and sunshine every year. This has made them an ideal candidate for solar energy production (El Ouderni et al., 2013; Musembi et al., 2016).
Iran is a major oil and gas-producing country as well as a major emitter of greenhouse gases (Shahsavari et al., 2019). Its fossil-fueled power plants annually emit nearly 180 million tons of carbon dioxide Iran’s total area is around 1600,000 km2 or 1.6×1012 m2 with about 300 clear sunny days in a year and an average 2200 kW-h solar radiation per square meter. Considering only 1% of the total area with 10% system efficiency for solar energy harness, about 9 million MW h of energy can be obtained in a day. The government’s goal on 2012 was to install 53,000 MW capacity plants for electricity generation. It is expected that by 2030 solar energy production capacity will reach 2.8 GW in Iran (Najafi et al., 2015).
Despite this huge solar energy production potential, little research has been done surrounding it. Among the studies, one would mention Shahsavari et al. (2019) who evaluated Iran’s country-wise production. Based on their results, the installed capacity of solar energy was around 17.3 megawatts (MW) by 2015, while each kilowatt-hour of solar electricity could save around 715 g of CO2. Alamdari et al. (2013) studied the feasibility of using solar energy in different regions of Iran. They reported annual average horizontal radiation of above 500 W/m2 in most areas of Iran, which shows its potential for photovoltaic applications. Kashani et al. (2014) determine the theoretical solar irradiation potential in Iran by using the Niroo Research Institute irradiation model based on geographical and meteorological data. They found solar energy as a viable and reliable source of power in Iran.
Most of the literature has focused on the large-scale statistical analysis of solar power, and yet ignores the local variation of energy distribution. The introduction of geographical information systems (GIS) into scientific fields has made it possible to quickly and reliably evaluate site suitability for different purposes (Charabi et al., 2011). GIS is per-se a tool in identifying suitable areas and still lacks a trust-worthy decision-making system to turn evaluation endeavors automatic. The application of multicriteria decision-making systems (MCDM) has made it possible to use both visual interpretations and informed decision-making in site suitability analysis (Jafari et al., 2018; Jafari Shalamzari et al., 2019). However, there is a paucity of research in terms of local evaluation of solar energy potential using GIS. Among the few studies concerning the application of GIS and MCDM systems in site suitability analysis for solar energy, production one would mention the work of Asakereh et al. (2017) who attempted to prioritize the land of Khuzestan province in Iran to install solar photovoltaic farms, based on techno-economic and environmental aspects. 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. In another study, Sadeghi et al. (2017) determined suitable sites for solar farms and wind turbines using GIS and AHP in Tehran, to generate a distributed network to increase power network stability. Farajzadeh et al. (2013) have found the combination of MCDM and GIS a promising methodology for site selection.
As mentioned, the application of GIS and decision-making methods in identifying suitable locations for solar panel installations is still an immature science and requires further studies in Iran. Therefore, we have applied a combination of these two techniques in identifying suitable areas for solar farms in Mashhad County of Iran which is a major energy consumer and a mega city in Iran with considerable projected urban expansion in the future. This urban expansion would require major enhancement of energy production. To comply with global warming emission reduction requirements for Iran, solar energy is an important energy alternative. Therefore, the main objective of this research will be to identify land suitability for energy production from solar panels in this part of the country.
Study Area
The study area of this research is the Mashhad County of Khorasan Razavi Province of Iran located between 35º 43′ 9″ to 36º 58′ 4″ N and 59º 3′ 48″ and 60º 36′ 21″ E, with a total area of 10326 km2 (Figure 1). Elevation ranges between 950 m and 1150 m above sea level. The region is characterized by a semi-arid climate going under annual precipitation of 1509 mm with an annual average temperature of 15.7 ºC. The temperature reaches its maximum of 43ºC in July and its minimum of -23 ºC in January. Most of the precipitation is concentrated in the Winter and Early Spring, indicating a Mediterranean Climate. Mashhad county has a complex geological setting with several active faults within its boundaries. Mashhad is bounded to the north by the Kalat and Dargaz, to the east by the Sarakhs plains, to the south by the Torbat Heidarieh, and to the west by Neishabour. Based on the results of the census conducted in 2010, Mashhad is home to 3070000 people mostly concentrated in cities. Mashhad plain is a surrounded valley 100 km in length and 25 km in width as part of the Kashafroud Watershed. Geologically, the region is mostly covered with the Karstic and evaporative Mozdouran Sedimentary Carbonates with suitable underground reservoirs. The water table has been dropping over the past decade and most of the rivers and streams have dried out. The location of the study area is illustrated in Figure 1.
Iran is geographically located in a suitable area for photovoltaic power generation. The data from the global solar energy atlas https://globalsolaratlas.info/ for the monthly and hourly distribution of solar energy in the study area in figures 2 and 3 shows that most months of the year enjoy 12-15 hours of sunshine which make them ideal for energy production.
The monthly distribution of solar energy output for the study area also shows that spring and summer are the ideal months for energy production. The highest solar energy output belongs in August by 181.9 MW.h solar energy output.
Methodology
Site Suitability Analysis
To locate suitable areas for solar panel installation, the following set of indicators (table 1) and thresholds were adopted. In this research, we have developed a tool for solar panel installation in Mashhad County of Iran. The first stage of the work includes removing unsuitable areas for the installation. These areas included built-up and environmentally protected areas. Since there is no special water body in the study area, we excluded this indicator from the data analysis. We also excluded slopes greater than 10% and elevations above 2000m since they induce limitations for site preparation and installation. The remaining area is evaluated using a multi-criteria decision-making method.
Table 1. Criteria and indicators for selecting suitable sites for wind energy production According to (Sánchez-Lozano et al., 2013)
|
|
Criteria |
Range |
|
Environmental |
Residential areas |
>2000 m |
|
Highways and roads |
>500 |
|
|
Railways |
>300 |
|
|
Rivers |
>500 |
|
|
Environmental Protected Areas |
>2000 |
|
|
Faults |
>500m |
|
|
|
Land Cover |
Bare lands/Rangelands |
|
Physiographic |
Elevation |
<2000 |
|
Slope |
<15% |
|
|
|
Direction |
Flat areas, southern and eastern aspects |
|
Climatic |
Solar Irradiation |
No limitation |
|
|
Temperature |
Lower average maximum temperatures |
Data Collection
To combine different criteria and indicator layers, data were processed as indicated in the diagram below. Data for topography was downloaded from Alos DEM 12.5 m from Alaska State Data Hub at asf.alaska.edu. Data for environmentally protected areas was obtained as shapefiles from the local environment protection office. Data for railways and roads and residential areas were downloaded from https://mapcruzin.com/free-iran-arcgis-maps-shapefiles.htm. Data for fault lines were obtained from the Department of Geology Surveys and Mineral Exploration of Iran. Data for windspeed was obtained from Global Wind Atlas available at https://globalwindatlas.info/ for the altitudes of 50 m and 80 m. To generate buffer layers for thematic layers, the Euclidean Distance Tool in ArcGis 10.3 was used.
Methodology
We used a combination of the MCDM-AHP model for locating suitable spots for solar panel installation. The following diagram was used for suitability analysis. The thematic layers were separated into limiting and factor layers. The decision-making criteria were defined based on expert knowledge and group discussions. The layers were standardized, weighted, and combined to find the final suitable areas. The detail of each step is given in the following sections.
Normalization
To combine the RS thematic layers, they must have a common scale of measurement. The process of converting data into a comparable range of values is called standardization. There are several standardization procedures such as Min-Max, Z-score, Median Normalization, Fuzzy Transform, etc. (Jain et al., 2005). We used the Fuzzy transform method for data normalization into a range of 0 and 1. The Fuzzy Transform was first introduced by Zadeh (1996) to convert verbal expressions into mathematical equations. The fuzzy set X has its fuzzy subset A which is defined by a membership function 
which maps each element in A onto a real number between 0-1. Since we were only interested in the lower and upper part of the data, indicating severe conditions conducting for desertification, the Large and Small fuzzy membership functions in ArcGIS were applied. The Large fuzzy membership function is defined as:
Where
is the spread parameter defining the shape and character of the transition zone and 
is the midpoint, after which numbers have a higher possibility of becoming a member of the set (Jafari Shalamzari et al., 2019).
And the Small fuzzy membership function is defined as:
In the small function, numbers after the midpoint have a lower possibility of becoming a member of the set. Here, TCI, SDI, VCI, and salinity layers were standardized using the large function, while EVI and Precipitation layers were standardized using the small function.
Combination
The final desertification intensity was calculated using the weighted overlay combination as (eq. 7):
Where
is the weight assigned to each layer, and
is the fuzzy layer.
Topographic features of the study area
Figure 4 shows the distribution of elevation, slope gradient, and slope direction in the study area. The elevation of the area ranges between 671m and 3100m, with an average of 1520m. slope gradient map in figure 4b also indicates that flat lands are the dominant slope gradient class. Most of the heights are distributed along the northern and south-western sections imposing little limitation for the installation of solar panels. As for the slope direction and based on the results of the slope gradient, the majority of spots are flat with a minor southern and western inclination and impose no significant limitation on land suitability for solar farm construction.
Faultline and Protected areas
Faults are a major concern for the installation of wind turbines. Among the faults, active faults are the greatest source of concern. Therefore, we used the data on the location of active fault lines for finding suitable areas for wind farms. Another major concern is the legal procedure of land-use change in Iran which prohibits altering land uses in environmentally sensitive areas and wildlife reserves. Therefore, we excluded those areas from the final suitability map. In total, 7.3% of the total area is covered with protected areas accounting for ~720 km2. The location of wildlife reserves and fault lines in Mashhad county is shown in figure 6.
Land cover
Not all land covers are suitable for installing wind farms. The best candidates for this purpose are the bare lands and rangelands. The land cover of the study area which is derived from the sentinel 2 dataset and available at https://livingatlas.arcgis.com/landcover/ is shown in Figure 7. There are four categories of land cover in the study area including croplands, built-up areas and artificial surfaces (roads and railways), bare lands, and rangelands. Rangelands with sparse vegetation are the dominant land cover in the area accounting for 59% (6000 km2) of the area. The second largest land cover is bare land with approximately 21% of the total area (2167km2). Croplands are covering 13% of the area totaling Builtup areas and roads are 1341 km2. Built-up areas are merely 7% of the total area (1032 km2).
Solar Radiation
One of the factors necessary for site suitability evaluation for solar panels is the Total photovoltaic power output. The data which was obtained from the global solar atlas in kWh units is shown in figure 8. As shown, total photovoltaic output ranges between 1348 and 2082 kW.h-1 per year. Most suitable areas are therefore located in the southern, northern, and a small spot in eastern sections of the study area. These values indicated a very promising condition for solar energy production in the area.
Temperature
Temperature is important for the proper performance of solar panels. The data for surface temperature obtained from the global solar energy atlas is shown in figure 8. Based on the map, the average annual temperature in the area ranges between 2.4 and 16.5 C which is a suitable condition for the performance of solar panels. As expected, highlands are characterized by the lowest temperatures while plains in the middle section of the area show the highest values. But, there is another major limitation for areas with lower temperatures which is the elevation, slope gradient, and slope direction of the site which make these spots less appealing for the construction of solar farms.
Suitability analysis
The results of the final suitability for solar farm installation in Mashhad county are shown in figures 10 and 11. Figure 10 is the suitability without imposing the limiting factors and figure 11 is the final map after including all the binary factors into consideration. Based on the results of the classification, suitability ranged between 0.15 to 0.86 indicating both the lower and upper ranges of suitability. The range between 0 and 1 was divided into 4 classes with an interval of 0.25 to show areas unsuitable (lowest scores) to highly suitable for solar farm construction. Our results showed that the narrow strip to the south and north are the most suitable areas for solar energy production, while the middle areas had the lowest suitability scores. After imposing the limiting factors, the remaining area only covers 58.5 % of the county, totaling 6047 km2.
The areal distribution of the county into the four suitability categories is brought into table 2. Based on the results, the suitable class had the largest share of the final map. Highly suitable areas comprise 14.1% of the total area. The excluded lands from the analysis due to the limiting factors made up approximately 56% equal to 5811 km2.
Table 2. Results of the classification of the final suitability map in Mashhad County after imposing the limiting factors
|
Suitability |
Unsuitable |
Low suitability |
Suitable |
Highly suitable |
Total |
|
Area % (km2) |
14 (1455.2) |
8.3(846.5) |
20.1(2074.9) |
16.2 (1672) |
6047 |
Iran is spending a relatively great amount of budget on renewable energies especially wind and solar energies. Iran has great potential for solar and winds energy production given its geographic location, and climatic conditions. Most areas of the country enjoy more than 300 days of good sunshine for photovoltaic energy production and given the constant winds in the eastern wing of the country which lasts for at least 120 days a year, the potential for wind energy production is also immense (Bahrami et al., 2013). However, since land suitability analysis requires accurate and iterative results with the ability to update the data layers, GIS application has received great interest in recent years' suitability analysis. Therefore, in this paper, we evaluated the suitability of Mashhad County in Iran for the installation of photovoltaic panels. We evaluated the area based on several factors including topographic features (slope gradient, slope direction, and height), roads and infrastructure, built-up areas, environmentally protected areas, solar photovoltaic output potential, air temperature, land cover, and active fault lines. Based on the topographic features, there is no significant limitation for the installation of solar farms. The only problem with the topography is the slope direction which in the best condition should be southeast and south for maximum energy production. Since most of the area is flat with minor elevations, there is no limitation on energy production. The largest share of the area has a slope gradient of less than 15% (3303 km2), this factor also imposes no limitation. In terms of elevation also 42% (4335 km2) of the area below 2000m is suitable for solar farm construction. Topography affecting temperature, the difficulty of access to the site, and slope direction have considerable influence on the total amount of power producible from the area. Olson et al. (2019) also argue that topographic shading is an important factor contributing to surface energy balance. Lai et al. (2010) reported that topography could influence received solar radiation in both local, meso, and macroscales. Apart from solar radiation, the risk of landslides and rock slides is a major threat to the sites located in valleys and close to high elevations and northern slopes. This is in accordance with the results of Marques-Perez et al. (2020).
Another important feature that could impact photovoltaic energy production is the geological activity of the area. We decided that the location of active fault lines is the best measure of analyzing the impact of geology on site suitability. Our results indicated some 72 active faults in Mashhad County which could severely limit land suitability. Most of these lines are aligned with the elevations in the north and south, leaving the middle and southern plains intact for PV farm construction. Günen (2021) also found active fault lines to be a major threat to solar farms in Turkey. Sites closer to fault lines are prone to a higher risk of earthquakes and consequent damage. The transmission lines are also at risk of subsidence due to geological activities. Fault lines also impose the project activities and personnel at risk and therefore, the areas farther from the faults are preferred for PV panels. The same results are reported in the work of Ibrahim et al. (2021). Another limitation in locating suitable areas for solar farms is the presence of environmentally sensitive sites. We excluded those lots falling into the protected area according to the national regulations. Any industrial and mining activities are banned inside the protected areas by law in Iran. The total share of these areas is 572 km2. We also included a 2000 m buffer around these areas to best observe the regulations. Nonetheless, solar panels have both beneficial and adverse impacts on these sites if they were allowed inside the protected areas. Fthenakis et al. (2011) believe that solar panels could provide new habitats for local wildlife. Turney et al. (2011) also believe that solar farms are beneficial to local wildlife and general environmental condition. However, there are other researchers opposed to this idea and believe that solar farms adversely impact the site’s condition for vegetation and animals due to the destruction of their natural habitats (Dhar et al., 2020; Hamed et al., 2022).
Land cover is one of the largest limiting factors for PV farm construction. Most land uses should be excluded from the total area because of their property rights, human life security, remoteness, surface features, and costs. We only included rangelands and bare lands for the analysis. The rangelands themselves, however, should be evaluated from the traditional utilization rights perspective yet we did not include this into consideration due to the lack of access to the data on ranchers and local owners. Some of the bare lands are also saline with high water tables but we were unable to obtain the water table data for the measurement wells of the county. However, if one would consider these two land covers suitable, there is considerable potential for finding suitable areas for the construction of solar farms. We did not include built-up areas, roads and infrastructure, and croplands in our analysis. A buffer was also included surrounding the residential areas and roads (highways, railways, and roads) for human safety. Croplands were also excluded for not impacting the food security of the region. In total, 72% of the area is available for constructing solar farms. The importance of land-cover on-site suitability analysis for PV farms is emphasized in the works of Guaita-Pradas et al. (2019) and Lu et al. (2021) who believe that large-scale solar farms could benefit the vegetation cover of rangelands with poor cover conditions.
Two other important features affecting the suitability of the site for solar energy production are air temperature and potential PV power output. Air temperature is a major determiner of the panel’s performance. Most panels experience a major reduction in performance at high temperatures. Kawajiri et al. (2011) similarly reported that air temperature negatively affects panel performance and hence lands at middle earth latitudes by having higher temperatures face a great challenge in reaching optimal panel performance. Peng et al. (2017) reported that a solar panel that is cooled down, can have a significant increase in the electric output. They also argued that the Life cycle assessment of these panels suggests the cost payback time of cooled PV can be reduced. Therefore, areas with lower average temperatures are more suitable for PV farms. We also included the total PV power output into our analysis and found no limitation in this regard. Therefore, the county has both optimal condition in terms of potential power output and average air temperature. By combining all the limiting layers and factor layers into the final integrating equation, we found suitable sites for solar energy production. Our results indicated that using equal weights for factor layers doesn’t significanty influence site suitability for PV farms.
In this research, the suitability of Mashhad county of Iran for solar energy production has been evaluated. We included several limiting and factor layers in our analysis. However, other major driving factors should be included in future analysis. Since we did not have access to the data on transmission lines for security reasons, the results might change in a similar study. One unique feature of this study is the excluding environmentally sensitive areas from our analysis. Since the final intention for using renewable energies is to protect the environment, it would be the wisest decision not to disturb sensitive areas for installing PV panels. Most areas in Iran are hard to recover once the damage is done given their low precipitation, high temperature, unsuitable geology, and current pressure from human activities. The results of our work could be the beginning of a series of research for also evaluating the economic aspect of solar energy production and locating the sites with the most cost-effective chance of investment. Therefore, the topic that we would be pursuing in our future work, will be locating the economic spots for constructing solar farms relying on the results of the suitability analysis in the current work.
Author’s Contribution
All authors contributed to the study conception and design. MD collected the data, conducted data curation and visualization as well as data analysis. MJS helped with drafting the manuscript, data analysis, and interpretations. The first draft of the manuscript was revised with MD and MJS submitted the paper.
Competing interests
The authors declare that there is no conflict of financial or non-financial interests in doing this research.
Availability of Data and Materials
All data will be available upon request from the corresponding author via email.
Funding
No funding was obtained for this study
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No competing interests reported.