Introduction

The increase in population has led to a steady increase in energy demands. Energy has played a critical role in the development and sustenance of global economies in almost every sector (Wang et al. 2020). In addition, the use of renewable energy sources has gained importance because of the diminishing fossil-based fuel reserves and their more severe adverse environmental effects. In this context, solar energy is a modern, growing renewable energy source (Wang et al. 2020), which shows great potential as a reliable alternative to traditional energy sources that can reduce greenhouse gas emissions as well as mitigate the adversities of climate change (Mensour et al. 2019; Ibrahim et al. 2021). Thus, the number of solar photovoltaic (PV) power plants has been increasing rapidly.

Because of its geographical location, Turkey has considerable solar radiation exposure. According to the Turkey Solar Energy Potential Atlas prepared by the General Directorate of Energy Affairs, the annual total sunlight duration is 2741 h (7.5 h per day), and the total annual solar energy is 1527 kWh/m2 (daily average 4.18 kWh/m2) (Uyan 2017b; Colak et al. 2020; Arca and Keskin Çıtıroğlu 2022). However, power plants that utilize imported fossil-based fuels constitute a significant part of the existing installed power, whereas solar power plants account for only 6.1% (Yolcan and Köse 2020). Therefore, considering the high potential of solar energy generation and the substantial incentives offered by the government for solar energy investments, the number of solar PV power plant projects has recently increased in Turkey. In addition, Turkey’s 2023 vision aims to further increase electricity generation from solar energy (Colak et al. 2020).

However, the amount of energy generated plays an important role in the development of a country, along with the preservation of cultural and natural wealth and its transfer to future generations. As a result, the development of a country must be shaped by adopting a holistic approach, considering the geographical structure of the country, all living creatures within its territory, and its culture, nature, and urban and social structure. Therefore, consideration of the installation and generation costs, land use impact, and potential environmental effects are of great significance. What also matters in this regard is the exclusion of natural and cultural areas when choosing the location of solar PV power plants (Şenlik 2017). Wang et al. (2020) stated that environmental, social, and economic factors should also be taken into account in site selection together with the main limitations of solar energy such as geographical conditions, high dependence on technological developments, and large installation areas with a high initial costs. However, because many technical and environmental parameters must be evaluated, determining an optimal site that can fully utilize the maximum solar energy potential is a difficult and complex task (Mokarram et al. 2020). Thus, a suitable solar PV power plant site needs to be selected on the basis of the installation expenses associated with such an endeavor (Yalçın and Yüce 2019). In addition, suitable sites must be determined such that environmental damage is minimized and sustainable, efficient, and rational economic investments are realized (Gül et al. 2017).

As with environmental or site selection applications for other infrastructure facilities (Şener et al. 2010; Duarte et al. 2016; Arabameri et al. 2019; Rezaeisabzevar et al. 2020; Zhang et al. 2021), suitable site selection for solar PV power plants is inherently a multi-criteria decision-making (MCDM) practice. Therefore, approaches that integrate geographic information systems (GIS) and MCDM methods are highly effective for solving the solar PV power plant site selection problem. Among MCDM methods, the analytic hierarchy process (AHP) (Tahri et al. 2015; Al Garni and Awasthi 2017; Colak et al. 2020; Günen 2021a; Kırcalı and Selim 2021), fuzzy AHP (Eroğlu 2018; Mokarram et al. 2020), fuzzy analytic network process (ANP) (Lee et al. 2017; Wang et al. 2020), the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) (Fang et al. 2018; Nazari et al. 2018), Boolean logic (Jahangiri et al. 2016), weighted linear combination (WLC) (Barzehkar et al. 2021), and fuzzy logic (Yousefi et al. 2018; Dhunny et al. 2019) are used to determine the most suitable locations for solar PV power plant installation. A review of the literature shows that although many studies have been conducted to determine the most suitable areas for solar PV power plant installation in different regions of the world, they differ in terms of both the parameters considered and the methods used in the suitability analysis. In this context, Uyan (2013) evaluated five parameters for solar PV power plant site selection via the AHP method. Tahri et al. (2015) used seven parameters with the AHP; Eroğlu (2018) used 14 parameters with the fuzzy AHP; Fang et al. (2018) used 11 parameters with the TOPSIS; Yalçın and Yüce (2019) used four parameters with the AHP; Colak et al. (2020) used 11 parameters with the AHP; Mokarrem et al. (2020) used 11 parameters with the fuzzy Dempster–Shafer and fuzzy AHP; Uzar and Koca (2020) used six parameters with the fuzzy AHP; Pour et al. (2021) used 12 parameters with the Delphi, GIS, and fuzzy data envelopment analysis; and Arca and Keskin Çıtıroğlu (2022) used 10 parameters with the AHP. All these methods successfully determined the most suitable sites for solar PV power plant installation in the study areas.

On the other hand, Al Garni and Awasthi (2017) conducted a study aimed at evaluating and selecting the best location for utility-scale solar PV projects using GIS and the MCDM technique. In that study, solar radiation, air temperature, slope, aspect, proximity to power lines, proximity to roads, and proximity to urban areas were taken into account, and the AHP method was applied for weighting the criteria. Günen (2021a) determined suitable areas for solar power plants in Kahramanmaraş (Turkey) using the AHP method with three main criteria (geography, climate, and location) and fourteen sub-criteria. Global horizontal irradiance (GHI), aspect, and distance from power line networks were determined as the most important sub-criteria, respectively. Distance from railway transportation networks, distance from lakes and dams, and distance from rivers were determined as the least weighted sub-criteria. Settou et al. (2021) carried out a site selection application for a large-scale grid-connected PV system in Algeria using the AHP method, taking into account the criteria of GHI, distance to power lines, distance to major roads, distance from cities, land use, slope, and soil. Rediske et al. (2018) analyzed 27 articles selected from the literature, determined the factors affecting the selection of the most suitable sites for solar PV power plants, and ranked them as solar radiation, distance to substations, slope, distance to roads, distance from urban areas, and land use factors. Although there were differences in the parameters used depending on the characteristics of the study area, it was also stated that these parameters had been used in most of the studies.

Artvin, located between 40° 34′ 19.55′′–41° 31′ 29.62′′ north latitudes and 41° 09′ 25.41′′–42° 35′ 47.16′′ east longitudes, holds an important position in Turkey due to its rich water sources as well as its natural wealth. There are three national parks, two nature parks, two nature reserves, two natural monuments, and one wildlife sanctuary within the province. Four dams with hydroelectric power plants (i.e., Muratlı Dam, Borçka Dam, Deriner Dam, and Artvin Dam) were built on part of Çoruh River passing through the provincial borders of Artvin. The total installed power of these four dams is 1417 MW (GDSHW, 2021). However, there is no active solar power plant in Artvin. Furthermore, no study thus far has been conducted to determine suitable sites for solar PV power plant installation in the region. Therefore, this study aimed to determine the optimal sites for solar PV power plant installation in the province of Artvin. Artvin was evaluated for solar energy potential, and the Ardanuç district was chosen as the study area because of its suitable solar energy radiation potential. Considering the literature review, the objective of the study, data availability, and the geo-environmental characteristics of the study area, 12 parameters were determined for the most suitable site selection for solar PV power plant installation. Rediske et al. (2018) stated that natural disasters in a certain region should be taken into account when choosing the location for a power plant, as its structure could be susceptible to damage. Natural disasters such as earthquakes, landslides, and floods can damage the electrical infrastructure and energy system components (solar panels, substations, transmission lines, etc.) in solar power plants, resulting in power outages. It is thus necessary to take into account the risks of natural disasters in site selection. Artvin is among the provinces in Turkey that experience the most natural disasters. Meteorological-character natural disasters such as floods and landslides frequently occur throughout the province (Akinci and Yavuz Ozalp 2021). The most important point that distinguishes this study from previous studies is that it considers more natural disaster risk factors compared to previous research and offers a broader perspective to the community. In this context, the distance to fault lines and landslide and flood susceptibility were also taken into account as site selection criteria in the study.

The contributions of this paper can be summarized as follows. (i) Currently, there is no solar power plant installed in Artvin. In addition, no study has been conducted to determine the optimum solar power plant areas in Artvin. This research is the first contribution in this direction. (ii) As a result of the study, stakeholders or investors will be encouraged to establish solar power plants in developing cities such as Artvin. (iii) The study will contribute to boosting solar energy power in Turkey. (iv) Considering the literature review, a comprehensive evaluation was carried out for institutional requirements, geo-environmental characteristics of the study area, and accessibility to spatial data, and the most appropriate criteria for site selection were determined. (v) Finally, the study will assist in efficient site selection in other regions with rugged topography similar to that of Artvin.

The remainder of the paper is structured as follows. The study area is described in the “Study area” section. A detailed description of the site selection criteria is given in the “Site selection criteria” section. The site selection with fuzzy overlay analysis for a solar PV power plant is explained in the “Site selection for solar photovoltaic power plant using fuzzy overlay analysis” section. The “Results and discussion” section presents and discusses the results, and the “Conclusions” section consists of the concluding remarks.

Study area

This study was performed in the Ardanuç district of Artvin, which is located on the Georgian border in the northeast of Turkey, between north latitudes of 41° 59ʹ 59.78ʺ to 42° 22ʹ 46.51ʺ and east longitudes of 40° 55ʹ 03.78ʺ to 41° 15ʹ 27.77ʺ (Fig. 1). The surface area of 852 km2 generally consists of mountainous and rugged terrain, with altitudes varying between 330 and 3050 m, the average altitude being 1833.77 m, and the average slope being 19.89° (36%).

Fig. 1
figure 1

Location maps of the study area: a Artvin Province in Turkey, b districts and neighboring areas of Artvin Province, c altitude changes and settlements in the study area

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According to CORINE 2018 land cover data (CLMS 2021), the study area consists of bare rocks (3%), plant change areas (9%), agricultural land (16%), forests (30%), and natural grasslands (33%) (Akinci and Zeybek 2021). According to the Köppen climate classification, the climate of Artvin is warm in winter and hot and dry in summer (Csb) (GDM, 2021). According to the data of the General Directorate of Meteorology, the total annual precipitation in the Ardanuç district is 455.9 mm, and the average temperature is 13.2 °C. In the study area, the average minimum and maximum temperatures are in February (− 12.2 °C) and July (31.2 °C), respectively. The average monthly sunlight duration of the study area varies between 3.23 h and 8.45 h, and the average annual sunlight duration is 6.04 h. The sunlight duration is a minimum of 7 h for 6 months of the year, i.e., between April and September. Given these values, the study area is generally suitable for solar PV power plant installation (GDEA 2021).

According to the data of the Address Based Population Registration System, which is a database managed by the Turkish Statistical Institute (TURKSTAT), the total population of the Ardanuç district was 11,344 in 2020. Of the total population, 5501 people reside in the district center, whereas 5843 live in the villages (TURKSTAT 2021).

Site selection criteria

Suitable site selection for solar PV power plants directly affects both the installation and operation process and the electricity generation costs (Yolcan and Köse 2020). Suitable site selection is important for reducing the cost of solar PV power plant installation, maximizing efficiency, and minimizing any possible adverse environmental effects. Suitable site selection is carried out by determining and evaluating the correct and sufficient number of factors, including universal values, technical information, legal infrastructure, and local characteristics. Although there are no clear rules for selecting the most suitable sites for the installation of solar PV power plants (Song et al. 2015; Şenlik 2017; Hazaymeh et al. 2018; Yalçın and Yüce 2020), the practical experience gained and some of the literature studies conducted have led to the standardization of the parameters. However, Song et al. (2015), Eroğlu (2018), and Colak et al. (2020) stated that the parameters and sub-parameters used in the literature were insufficient and emphasized that all parameters that may be effective in suitable site selection should be considered in order to minimize any adverse environmental, economic, technical, or social impacts. Examination of the related literature revealed that the basic parameters that have been used include (i) solar energy potential, (ii) meteorological data (such as average temperature, relative humidity, sunshine duration), (iii) land structure, (iv) land use/cover, (v) land use capability class, (vi) slope, (vii) aspect, (viii) altitude, (ix) distance to transmission lines, (x) distance to residential areas, (xi) distance to roads, (xii) water resources, (xiii) distance to protected areas, (xiv) geological structure, (xv) distance to fault lines, (xvi) distance to lake areas, (xvii) ownership status, and (xviii) land prices (Song et al. 2015; Tahri et al. 2015; Şenlik 2017; Uyan 2017b; Colak et al. 2020; Yalçın and Yüce 2020; Pour et al. 2021). The literature reviewed to determine the site selection criteria is given in Table 1.

Table 1 Review of studies on optimum site selection for solar PV power plants
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According to ILBANK, an incorporated company affiliated with the Ministry of Environment, Urbanization, and Climate Change of the Republic of Turkey, the following points should be considered in the selection of a suitable site in Turkey for the installation of a solar PV power plant with a capacity of 1 MW: (i) the location should not be an agricultural or a forestry land, (ii) the location should be excluded from the irrigation program of the General Directorate of State Hydraulic Works (GDSHW), (iii) the distance from the power transmission lines should not be too great, (iv) the land slope should not be high, (v) it should preferably face south, and (vi) it should have a surface area of at least 1.8 to 2 hectares (ILBANK 2022). However, ILBANK has not clarified the limit values for slope and distance from power transmission lines. On the other hand, in the report of the General Directorate of Geographic Information Systems (GDGIS 2017), the criteria and sub-criteria concerning the location of solar power plants were determined considering the factors affecting the site selection, sub-factors, and geographical data within the scope of the “Project for the improvement of the investment environment with geographic information systems.” According to this report, the main criteria were established as solar radiation, distance to residential areas, distance to rivers, distance to major roads, distance to protected sites, slope, aspect, land use/cover, land use capability class, surface waters (lakes, dams, etc.), distance to landslide areas, distance to fault lines, distance to transmission lines, distance to flora/fauna areas, and distance to aquifer areas. However, in the report, no evaluation was made for the rank, degree of importance, or weight of these parameters.

In the light of the literature review (Table 1), the site selection requirements of the ILBANK, the report of the GDGIS (2017), data availability, and the geo-environmental characteristics of the study area, it was decided to use the following 12 criteria: solar radiation, slope, aspect, distance to transmission lines, distance to roads, distance to faults, distance to residential areas, land use, landslide susceptibility, flood susceptibility, distance to protected areas, and distance to lakes (Fig. 2). The characteristics of the spatial data provided for these criteria are given in Table 2.

Fig. 2
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Hierarchical representation of the decision criteria used in this study

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Table 2 Characteristics of the datasets used in the study
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Since the GDSHW does not have an irrigation program in Ardanuç district, that site selection criterion of the ILBANK was not taken into account. The criteria of distance to flora/fauna areas and distance to aquifer areas could not be used in the study because relevant spatial data could not be obtained from the relevant institutions. Since there is only a single meteorological station and a single substation in the study area, the parameters regarding the distance to a substation and climate data (e.g., average temperature, sunshine duration, relative humidity, and cloudiness) could not be used in the study.

The factors used in solar PV power plant site selection can be considered to be either constraints or criteria, as in the case with suitable site selection for solid waste landfill sites. Factors that are considered to be constraints (such as distance to lakes or protected sites) limit alternatives and reclassify suitable sites for selection as 1, and those that are not suitable as 0, and are represented in the form of Boolean maps (Rezaeisabzevar et al. 2020). According to Rezaeisabzevar et al. (2020), a criterion allocates a value to the factor by using a fuzzy membership function based on its nature. Fuzzy membership functions reclassify the raster-based spatial data of the factors on a scale of 0–1, depending on the probability of being a member of a specified set. Pixels that are members of a certain set are assigned a value of 1, whereas those that are not members of a certain cluster are assigned a value of 0. Pixels are assigned a value between 0 and 1 according to their membership levels (or strength of membership). Linear or sigmoidal functions would be sufficient for solar PV power plant site selection, as in site selection applications for solid waste landfill areas (Akinci and Demirarslan 2020). In this study, the factors were standardized using the fuzzy linear membership function in ArcGIS 10.5 (ESRI 2021). The fuzzy linear membership function implements a linear conversion function between user-specified minimum and maximum values. It converts the input values linearly on a scale of 0 to 1 by assigning 0 to all values below the minimum input value and 1 to all values above the maximum input value (ESRI 2021). The minimum and maximum values are shifted to establish a negative or decreasing linear relationship. The standardization process for the spatial data of the factors used in the study is explained in the following sections.

Solar radiation

Solar radiation, which directly affects the energy obtained from the plant to be established, is the most important and decisive criterion used in determining suitable areas for solar PV power plants (Song et al. 2015; Al Garni and Awasthi 2017; Fang et al. 2018; Hazaymeh et al. 2018; Khemiri et al. 2018; Rediske et al. 2018; Hashemizadeh et al. 2020; Arca and Keskin Çıtıroğlu 2022). In general, the higher the solar radiation value in an area, the more suitable the area is for solar PV power plants (Kereush and Perovych 2017; Hazaymeh et al. 2018). In terms of the efficiency of the system, the annual solar radiation value should reach 1100 kWh/m2/year according to Kereush and Perovych (2017) and Yolcan and Köse (2020), 1300 KWh/m2/year according to Hazaymeh et al. (2018), and over 1500 kWh/m2/year according to Gül et al. (2017). In this study, a solar radiation map of the study area for the years 2016–2020 was produced using the area solar radiation function in ArcGIS 10.5. Accordingly, the solar radiation value in the study area varies between 149 and 1860 KWh/m2/year (Fig. 3a). In this study, locations where the annual solar radiation values were over 1100 KWh/m2/year were considered suitable for solar PV power plants. The solar radiation map produced in ArcGIS 10.5 was standardized using the fuzzy linear increasing membership function (Table 3).

Fig. 3
figure 3

Factor maps: a solar radiation, b slope, c aspect, d distance to transmission lines

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Table 3 Scoring values for factors used in the study
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Slope

Slope is also considered to be one of the most important factors in determining suitable sites for solar PV power plants (Uyan 2017b; Khemiri et al. 2018; Rediske et al. 2018; Uzar and Koca, 2020; Yalçın and Yüce, 2020). A steep slope causes self-shading of the solar panels, reducing their efficiency, and increases the installation cost of the plant (Khemiri et al. 2018; Uzar and Koca, 2020; Arca and Keskin Çıtıroğlu, 2022). However, it seems that there is no consensus in the literature regarding the most suitable land slope for solar PV power plant installation. For example, a slope is not suitable for site selection if it is above 20% according to Colak et al. (2020) and Kocabaldır and Yücel (2020), above 15% according to Kereush and Perovych (2017), 11% according to Hazaymeh et al. (2018) and Uzar and Koca (2020), 10% according to Yousefi et al. (2018), 9% according to Al Garni and Awasthi (2017), 7% according to Mokarram et al. (2020), 5% according to Khemiri et al. (2018), and 3% according to Uyan (2017b), Yalçın and Yüce (2020), and Arca and Keskin Çıtıroğlu (2022). In this study, first, a digital elevation model (DEM) of the study area was produced using contour lines in 1/25.000-scale topographic maps. After the produced DEM was converted to a raster format with 30-m resolution, a slope map of the study area was produced by using this DEM. The slope in the study area varies between 0 and 75.39° (Fig. 3b). In this study, given the very rough topography, a slope above 20% (11.31°) was considered unsuitable for solar PV power plant installation, and the slope map was standardized using the fuzzy linear membership function in ArcGIS 10.5 (Table 3).

Aspect

Aspect is an important parameter in solar PV power plant site selection since it affects the sunlight duration (Yalçın and Yüce 2020; Arca and Keskin Çıtıroğlu 2022). When selecting a suitable site for solar PV power plants, the southern aspects should be given priority in order to get the greatest benefit from the sun during the day (Tahri et al. 2015; Al Garni and Awasthi 2017; Kereush and Perovych 2017; Sharma and Singh 2018; Yalçın and Yüce 2020). An aspect map of the study area was produced from the DEM and divided into nine classes: flat (–1), north (0–22.5° and 337.5–360°), northeast (22.5–67.5°), east (67.5–112.5°), southeast (112.5–157.5°), south (157.5–202.5°), southwest (202.5–247.5°), west (247.5 –292.5°), and northwest (292.5–337.5°). In this study, the north-facing aspects (north, northeast, and northwest) were considered unsuitable and assigned a value of 0 (Colak et al. 2020). The flat areas and south-facing aspects (south, southeast, and southwest) were considered the most suitable for solar PV power plant site selection and assigned a value of 1. The areas with east- and west-facing aspects were assigned a value of 0.5, and the aspect parameter was standardized (Fig. 3c).

Distance to transmission lines

Considering the installation cost of energy transmission lines, the distance to transmission lines appears as one of the important criteria in determining the sites suitable for solar PV power plant installation because longer transmission lines are more expensive (Uyan 2017b; Eroğlu 2018; Rediske et al. 2018; Uzar and Koca 2020). A short distance to transmission lines provides an advantage in reducing energy loss (Kereush and Perovych 2017; Sharma and Singh 2018; Colak et al. 2020). Although there is a consensus among many studies in the literature regarding the necessity and importance of this parameter, considerable differences have been observed in the way this parameter is handled, i.e., in determining the sub-parameters. For example, Eroğlu (2018) and Uzar and Koca (2020) suggested that a solar PV power plant should not be installed more than 6000 m from an energy transmission line, whereas Gül et al. (2017) stated that a distance to transmission lines that exceeds 4000 m is not suitable. By contrast, a distance exceeding 10,000 m was considered as less suitable by Uyan (2017b), whereas Mokarram et al. (2020) considered a distance exceeding 15,000 m as less suitable. In this study, a distance of up to 2000 m was considered as the most suitable and exceeding 10,000 m as unsuitable, and the parameter was standardized using the fuzzy linear membership function (Table 3; Fig. 3d).

Distance to roads

The presence of a road network in and around a region is important for directing investments toward that region. If the solar power plant is close to existing roads, the plant’s maintenance and repair costs will be lower (Tahri et al. 2015; Uyan 2017b; Eroğlu 2018; Guaita-Pradas et al. 2019; Colak et al. 2020). However, the pollution created by the vehicles passing over the roads may adversely affect the solar panels and pose threats to safety (Uzar and Koca 2020; Arca and Keskin Çıtıroğlu 2022). In addition, solar panels located close to roads may cause visual pollution. Therefore, it is generally accepted in the literature that solar PV power plants should not be closer than 100 m to roads (Eroğlu 2018; Colak et al. 2020; Uzar and Koca 2020). In this study, it was assumed that the areas closer than 100 m to roads were not suitable for solar PV power plant installation. Data on the roads in the study area were provided digitally from Başarsoft Information Technologies, which is a producer of navigation maps in Turkey. Road buffers were generated in ArcGIS 10.5 using the Euclidean distance function. A value of 0 was assigned to the pixels below 100 m. The pixels above 100 m were standardized using the fuzzy linear membership function so that the suitability value decreased as the distance increased (Table 3; Fig. 4a).

Fig. 4
figure 4

Maps of the study area: a distance to roads, b distance to faults, c distance to residential areas, d landslide susceptibility

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Distance to faults

Areas with low earthquake risk should be selected when choosing a solar power plant installation location (Colak et al. 2020; Arca and Keskin Çıtıroğlu 2022). The earthquake risk is higher in the areas close to the fault lines and accordingly, there would be more structural damage in such areas. Solar power plants installed close to fault lines may be seriously affected by earthquakes and lose their functionality (Günen 2021b). Therefore, areas that are far from fault lines are more suitable for solar power plant construction (Soydan 2021). Given the fact that Turkey is in an earthquake zone, it is very important to install solar PV power plants away from fault lines to prevent possible negative impacts on the plant caused by earthquakes. Thus, the distance to fault lines has been used as a parameter in many studies and in selecting regions with low earthquake risk when determining sites suitable for solar PV power plants (Güçlüer 2010; Gül et al. 2017; Colak et al. 2020; Sarsıcı 2020; Pour et al. 2021; Arca and Keskin Çıtıroğlu 2022). Like with many other site selection parameters, there is no consensus in the literature regarding distance to faults; however, it is generally observed that as the distance to fault lines increases, the site becomes more suitable for solar PV power plants. For example, Gül et al. (2017) stated that areas closer than 25,000 m to fault lines are not suitable for solar PV power plant installation, whereas Colak et al. (2020) stated that areas closer than 5000 m to fault lines are not suitable for such installations. In this study, the areas closer than 1000 m to fault lines were considered unsuitable for solar PV power plant site selection, whereas areas at a distance of at least 5000 m from fault lines were considered suitable (Table 3; Fig. 4b).

Distance to residential areas

As stated by Rediske et al. (2018), distance to residential areas is a determining factor when it comes to selecting the most suitable site for solar PV power plants and one that is widely used in the literature. When solar PV power plants are close to residential areas, energy transmission costs are reduced. However, this may have adverse environmental effects and can lead to solar PV power plants being too close to rapid growing residential areas. For these reasons, many studies have emphasized that solar PV power plants should be at an ideal distance from residential areas (Uyan 2017b; Eroğlu 2018; Uzar and Koca 2020; Colak et al. 2020; Pour et al. 2021; Arca and Keskin Çıtıroğlu 2022). However, it seems that there is no consensus in the literature regarding the distance to residential areas. Hence, the distance of solar PV power plants to residential areas should be at least 500 m according to Uyan (2017b), Uzar and Koca (2020), and Colak et al. (2020), whereas it should be at least 2000 m according to Tahri et al. (2015) and Hazaymeh et al. (2018), or 5000 m according to Mensour et al. (2019). In this study, the sites closer than 500 m to residential areas were considered unsuitable for solar PV power plants, and the distance to residential areas was standardized using the fuzzy linear membership function in ArcGIS 10.5 (Table 3; Fig. 4c).

Landslide susceptibility

Selecting areas without landslide risk for solar PV power plants is very important for both life and property safety and economic reasons. Although some researchers (GDGIS 2017; Eroğlu 2018; Guaita-Pradas et al. 2019) have stated that it is important and necessary to perform landslide investigations in regions deemed suitable for solar PV power plants, this parameter has not been addressed in many studies. Eroğlu (2018) considered active landslide areas to be unsuitable for solar PV power plants, whereas Sarsıcı (2020) assumed that areas at a distance of up to 1000 m from landslide areas were unsuitable based on landslide inventory data. However, instead of landslide inventory maps, landslide susceptibility maps produced using inventory maps, which show possible future landslide locations, should be used for location selection applications. Artvin is one of the Turkish provinces where landslides most commonly occur. According to the landslide inventory map produced by the General Directorate of Mineral Research and Exploration and updated by the Artvin Provincial Directorate of Disaster and Emergency, the study area has 93 landslide polygons, 66 of which are active. The Artvin Çoruh University Natural Hazards Application and Research Center produced a 1/25.000-scale landslide susceptibility map of the study area based on this landslide inventory data and machine learning techniques (Akinci and Zeybek, 2021). The landslide susceptibility map includes five susceptibility classes: very low, low, moderate, high, and very high. According to the landslide susceptibility map, 5.8% of the Ardanuç district is highly susceptible to landslides, and 6.97% is very highly susceptible. Areas that are highly or very highly susceptible to landslides are unsuitable for solar PV power plant installation. For this reason, the landslide susceptibility map was standardized by assigning a value of 0 to the areas highly or very highly susceptible to landslides, 0.5 to those moderately susceptible, and 1 to those with low and very low susceptibility (Fig. 4d).

Flood susceptibility

To avoid any negative effects of water overflow, solar farms should not be located in areas with high flood risk. In Turkey, the most common natural disaster following landslides is flooding. Therefore, it is essential to examine the investment areas in terms of flood susceptibility when determining a suitable site for large investment projects. In this context, some researchers (Charabi and Gastli 2011; Guaita-Pradas et al. 2019; Uzar and Koca 2020) have emphasized that the flood risk should be taken into account when selecting suitable sites for solar PV power plants and the establishment of plants in risky areas should be avoided. Sarsıcı (2020) stated that the compaction and leveling processes that are performed in solar PV power plant installation areas disrupt the natural structure and topography of the land; therefore, because of deterioration of the drainage channels, installation of power plants should be avoided in areas where the flood risk is high. However, instead of flood susceptibility maps showing flood-susceptible areas in a region, some studies considered that a certain distance from a river (400 m or 1000 m) could help prevent the solar PV power plant from being affected by floods (Kocabaldır and Yücel 2020; Uzar and Koca 2020; Arca and Keskin Çıtıroğlu 2022). Unlike other studies where the distance from a river criterion was used (Yousefi et al. 2018; Mensour et al. 2019; Günen 2021a; Soydan 2021), this study used a 1/25.000-scale flood susceptibility map produced by the Artvin Çoruh University Natural Hazards Application and Research Center. The study area was divided into five classes (very low, low, moderate, high, and very high) on the flood susceptibility map. It was found that 4.55% of the study area is highly susceptible to floods and 1.71% is very highly susceptible. These areas were considered unsuitable for solar PV power plant installation and assigned a value of 0, whereas those with low or very low flood susceptibility were assigned a value of 1 and those with moderate flood susceptibility a value of 0.5. The flood susceptibility map was then standardized (Table 3; Fig. 5a).

Fig. 5
figure 5

Maps of the study area: a flood susceptibility, b land use, c distance to protected areas, d distance to lakes

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Land use

Land use is another important and determining parameter used for selecting the most suitable sites for solar PV power plant installation (Rediske et al. 2018; Colak et al. 2020). However, the land use parameter was addressed by the studies in different ways, depending on the local characteristics of the study. Some areas such as mountains, forests, water bodies, wetlands, urban areas, and agricultural lands are limited for use owing to their economic, environmental, and ecological significance (Kereush and Perovych 2017; Eroğlu 2018; Hazaymeh et al. 2018; Colak et al. 2020; Pour et al. 2021). Uyan (2017b) and Colak et al. (2020) stated that agricultural and forest lands are generally not preferred for solar PV power plants. On the other hand, according to USDA (1973) guidelines, land capability classes range from I to VIII. A land capability class between I and IV is suitable for agriculture, whereas capability classes between V and VIII are generally not suitable for agriculture, but are suitable for controlled grazing, pasture, forest, woodland, and wildlife purposes. Uyan (2016) considered class I, II, and III agricultural lands unsuitable areas for solar PV plants, whereas Kocabaldır and Yücel (2020) considered class I, II, III, and IV agricultural lands unsuitable. When examined in terms of the relevant legislation, the Regulation on the Implementation of Articles 17 and 18 of the Forest Law (2011) states that, if there is a public interest and necessity, the Ministry of Agriculture and Forestry may allow the installation of some plants such as power generation plants and transformers on forest lands. However, the sensitivity of these areas must be taken into account, and as a general principle, the authorized institutions have adopted a conservative approach by not allowing the installation of solar PV power plants on these areas. Law No. 5403 on Soil Conservation and Land Use states that lands that are completely agricultural, special crop lands, cultivated agricultural lands, and irrigated agricultural lands cannot be used for purposes other than agricultural production; however, provided that there is no alternative area and if the board deems it suitable, there may be room for some exceptions. In light of all this information, this study assumed that forestlands and pasture areas as well as class I, II, III, and IV agricultural lands are not suitable for solar PV power plant installations (Fig. 5b).

Distance to protected areas

In the report published by the GDGIS (2017) on the factors affecting the selection of suitable sites for solar PV power plants, the distance to protected areas was defined as one of the parameters to be considered in the site selection. Hence, Tahri et al. (2015), Uyan (2017a), and Kereush and Perovych (2017) did not recommend the installation of solar PV power plants in protected areas because of their archaeological or ecological significance. Şenlik (2017) emphasized that any sites on or near regions affected by biodiversity and ecosystem sensitivity should not be chosen. It was also emphasized that suitable sites for solar PV power plants should be at a certain distance from protected areas, since their installation may adversely affect the natural life in the region (Hazaymeh et al. 2018; Rediske et al. 2018; Uzar and Koca 2020; Arca and Keskin Çıtıroğlu 2022). From a legal point of view, in the context of protection and usage conditions, Law No. 2863 on the Protection of Cultural and Natural Assets restricts the installation and operation of solar PV power plants in natural protected areas (Şenlik 2017). The protection and usage conditions in Policy decision No. 658 on Archaeological Sites stipulates that no construction work is allowed in 1st and 2nd degree archaeological sites, whereas solar power plants can be installed in 3rd degree archaeological sites if it is in line with public interest, contributes to national energy generation, and is deemed appropriate by the Conservation Board (Sarsıcı 2020). Uyan (2017b), Hazaymeh et al. (2018), and Uzar and Koca (2020) stated that solar PV power plants cannot be installed closer than 500 m to protected areas, although Al Garni and Awasthi (2017) suggested that this distance should be 1000 m. The study area encompasses three protected areas—Adakale District Urban Archaeological Site, Gevhernik Castle (1st Degree Archaeological Site), and Cehennem Deresi Canyon Qualified Natural Protection Area (2nd largest canyon in the world). Therefore, it was accepted that areas closer than 500 m to these sites are not suitable for solar PV power plant installations (Table 3; Fig. 5c).

Distance to lakes

In the studies on suitable site selection for solar PV power plants, some researchers (Uyan 2017b; Colak et al. 2020; Uzar and Koca 2020) considered the distance to lakes and dams as a parameter. It is important to install the power plants at a certain distance from lakes in order to prevent the possible adverse effects of coastal floods caused by occasional changes in the volume of the lakes and environmental pollution. Furthermore, Coastal Law No. 3621 does not allow installation of solar power plants on natural and artificial lake shores and at a depth of 100 m from the shore border line on the coastal lines that are under the influence of these areas. Hence, the minimum distance of solar power plants to lakes should be 400 m according to Uzar and Koca (2020), 100 m according to Colak et al. (2020), and 500 m according to Kocabaldır and Yücel (2020). In this study, considering the local characteristics of the site, it was accepted that areas at a distance of 500 m from lakes are unsuitable for solar PV power plants (Table 3; Fig. 5d).

Altitude

Altitude is also an important factor in selecting a suitable site for solar power plants. A strong correlation is known to exist between altitude and meteorological parameters (e.g., temperature, precipitation, humidity, and frost) (Yousefi et al. 2018; Badi et al. 2021). Because regions with higher altitudes receive more solar radiation, they have greater solar energy generation potential compared to those at lower altitudes. However, as altitude above sea level increases, the transportation of personnel and equipment and construction of solar PV power plants become more difficult, resulting in increased costs (Yousefi et al. 2018; Badi et al. 2021). Therefore, installation of solar PV power plants at high altitudes is not recommended (Georgiou and Skarlatos 2016; Noorollahi et al. 2016; Hazaymeh et al. 2018; Yousefi et al. 2018; Badi et al. 2021). However, no clear altitude limit has been established. In this context, Kereush and Perovych (2017), Yousefi et al. (2018), Noorollahi et al. (2016), and Wang et al. (2016) stated that, depending on the study area, altitudes above 1500 m, 2000 m, 2200 m, and 5000 m, respectively, were not suitable for solar PV power plant installation. In addition, in their study, Mokarrem et al. (2020) stated that the sites with an altitude above 750 m were not suitable. Considering the geographical and topographic characteristics of this study area, an altitude of over 2000 m was considered to be unsuitable for solar PV power plant installations. However, altitude was not used as a direct site selection criterion in this study. Instead, altitude was taken into account in the evaluation of alternative areas and in the determination of the most suitable area.

Site selection for solar photovoltaic power plant using fuzzy overlay analysis

A GIS analysis performed to determine the most suitable site to be used for a particular purpose is called a suitability analysis, or, more generally, a site selection analysis (Briney 2014). In suitability analysis applications, users set various application-specific criteria to determine the most suitable or ideal sites, collect spatial data to create criterion layers, reclassify layers to obtain subclasses of criteria and score sub-criteria, assign relative significance values (or weights) to each criterion using various methods, and determine the suitable sites by using an overlay function (such as a weighted overlay). The product obtained at the end of these steps is a suitability index map with a certain value range, usually in raster format. In such a map, the pixels with the greatest value show the most suitable sites, whereas the pixels with the lowest values indicate the least suitable sites (Akinci and Demirarslan 2020). In methods commonly used in suitability analyses, such as the weighted overlay, the factor with the highest weight or importance has a greater effect on the result map. For this reason, from the criteria with the highest weight in the resulting suitability index map, the subclasses with the highest score, point, or weight are deemed as the best sites (Baidya et al. 2014). Different weighting of the same criteria (and subclasses) in the same study area can yield varying and inconsistent results.

One of the widely used approaches in site selection applications is fuzzy logic. Similar to other suitability analysis methods, fuzzy logic has a standard workflow that enables all necessary steps to be followed. This standard workflow includes the steps of defining the problem and site selection criteria, collecting spatial data of the layers of criteria, assigning fuzzy membership values, implementing the fuzzy overlay, and verifying and executing the results (Çakıt and Karwowski 2018).

After the appropriate fuzzy membership value is assigned to the criteria, the reclassified layers with values ranging from 0 to 1 are produced. The next step in implementing fuzzy logic is to overlay these layers. Although, in general, this step is similar to a weighted overlay, it is different because different fuzzy overlay operators, such as fuzzy AND, fuzzy OR, fuzzy PRODUCT, fuzzy SUM, and fuzzy GAMMA, are used in the overlay process. Raines et al. (2010) stated that overlays using fuzzy logic provide more flexible weighting of evidence and combinations of evidence than the traditional weighted overlay. The fuzzy AND operator is used to find the best sites that meet all criteria. In this study, the fuzzy AND operator was used to incorporate the fuzzy membership values, and a suitability index map of the study area for solar power plants was produced (Fig. 6).

Fig. 6
figure 6

Land suitability index map (LSI) for solar PV power plant sites

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Results and discussion

The index values vary between 0 and 0.85 on the suitability map created as a result of the fuzzy overlay operation (Fig. 6). The map produced was divided into five classes using the natural breaks classification algorithm as unsuitable, low suitable, moderately suitable, highly suitable, and very highly suitable (Fig. 7). As stated by Akinci and Demirarslan (2020), the classification method most commonly used in site selection applications is the Jenks natural breaks classification (Jenks 1967). The fact that the natural breaks classification method minimizes the intra-class variance and maximizes the interclass variance is the most important reason for choosing this method. However, examination of the literature revealed that different scales have been used in the classification of sites in solar PV plant suitability maps and there is no consensus on which scale should be used for classification purposes. In their study, Tahri et al. (2015) established five classes as unsuitable, marginally suitable, suitable, moderately suitable, and highly suitable. Al Garni and Awasthi (2017) established five classes as least suitable, marginally suitable, moderately suitable, highly suitable, and most suitable. Yalçın and Yüce (2020) established four classes as unsuitable, marginally suitable, suitable, and highly suitable, and Arca and Keskin Çıtıroğlu (2022) established four classes as high, medium, low, and very low suitability.

Fig. 7
figure 7

Degrees of suitability for solar PV power plant sites

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The literature reveals the fact that the weights or importance levels of the parameters used in site selection for solar power plants vary from study to study. For example, Uzar and Koca (2020) found the most important parameter to be “distance to transmission lines,” whereas Kereush and Perovych (2017) found it to be “aspect,” Fang et al. (2018) found “sunlight duration and policy support,” Yalçın and Yüce (2019, 2020) found “slope,” Colak et al. (2020) found “solar energy potential,” and Tahri et al. (2015), Mokarrem et al. (2020), and Arca and Keskin Çıtıroğlu (2022) found “solar radiation” to be the most important parameter. By contrast, some studies (Yalçın and Yüce 2019, 2020; Uzar and Koca 2020) did not consider solar radiation as a parameter because the selected study area contained sufficient amounts of solar radiation. Instead of assigning a significance level to parameters, this study adopted the method of fuzzification of parameters using continuous values between 0 and 1. Continuous values were used to increase the consistency of the study and ensure that it better reflected reality (Uzar and Koca 2020).

At this point, the reclassified solar PV power plant suitability map showed that 89.82% of the study area is not suitable for solar PV power plant installation. However, it was determined that 2.07% of the study area is less suitable, 4.71% is moderately suitable, 1.85% is highly suitable, and 1.55% is very highly suitable for solar PV power plant installation (Table 4).

Table 4 Land suitability degrees and distribution
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The reason for the high rate of unsuitable sites in the study area might be the steep slopes as well as the abundance of restricted areas having the status of forests, agricultural land, and protected areas. This might also be due to the fact that a site was deemed unsuitable (or not allowed) based on a single parameter, even though it was suitable according to other parameters. Some studies in the literature reported that a majority of the selected areas were not suitable for solar PV power plants for similar reasons. In this context, areas found unsuitable for solar PV power plant installation included 83% of the study area in the Limassol district in Cyprus (Georgiou and Skarlatos 2016), 65.91% of the study area in Izmir/Menemen (Uzar and Koca 2020), 96% of the study area in Çanakkale (Kocabaldır and Yucel 2020), and 75.47% of the study area in Karabük (Arca and Keskin Çıtıroğlu 2022).

The second part of the study examined the sizes and distributions of polygons in the moderately suitable, highly suitable, and very highly suitable classes (Table 5). According to the practices in Turkey, approximately 20,000 m2 (2 ha) of land is required for installing a 1-MW solar power plant (TENVA 2017; Sadıkoğlu 2018; Colak et al. 2020). In this regard, since power plants that generate less than 1 MW of power using renewable resources are entitled to generate electricity without a license, solar PV plants are generally established as Unlicensed Power Plants (Akar 2016). Furthermore, the 1-MW limit was increased to 5 MW with the amendment adopted in 2019. Therefore, the sizes of the sites suitable for solar PV power plant installation are given in Table 5, which shows that there are a total of 3708 polygons in the moderately suitable class, with 258 of them having a surface area of over 2 ha; a total of 2002 polygons in the highly suitable class, with 125 having a surface area of over 2 ha; and a total of 854 polygons in the very highly suitable class, with 86 polygons having a surface area of over 2 ha.

Table 5 Size and distribution of suitable areas
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In the next step of the study, polygons with a surface area of more than 2 ha were analyzed as suitable for solar PV power plant installation in terms of altitude. In this context, according to the solar PV power plant suitability map, it was determined that the altitudes of 86 polygons, which were in the very highly suitable class based on the index value and whose surface area was more than 2 ha, varied between 2367 and 2892 m, and their solar radiation values were between 1574 and 1705 kWh/m2. Although the solar radiation value is high, the altitudes (above 2000 m) in these designated areas make the installation of solar PV power plants difficult, rendering them unsuitable. Similarly, 125 polygons with a surface area greater than 2 ha were determined in the highly suitable class. The altitudes of these polygons are between 2126 and 2967 m, and their solar radiation values between 1536 and 1711 kWh/m2. Therefore, although the solar radiation value is high, the altitude above 2000 m negatively affects the suitability of these polygons. Finally, for 258 polygons with a surface area of more than 2 ha in the moderately suitable class, it was determined that only one polygon has an altitude below 2000 m (Fig. 6). This polygon, with a surface area of 3.24 ha, is 821 m from the nearest residential area and 28 m from an energy transmission line, its average altitude is 1559 m (1548–1572 m), its average slope is 4.90%, its average aspect is SW, and its solar radiation value is between 1314 and 1510 kWh/m2 (avg. 1411 kWh/m2). It also has very low susceptibility to landslides and flooding, and its land use capability is class VI agricultural land. Therefore, considering all these characteristics, the polygon in question could be very highly suitable for solar PV power plant installation.

In this regard, as stated by Arca and Keskin Çıtıroğlu (2022), although the Solar Energy Potential Atlas produced for the whole of Turkey provides a general idea about suitable sites for solar PV power plant installation, this alone is not enough. By considering many important and necessary parameters, more accurate, better, and more feasible decisions can be made regarding site selection. Given future threats such as climate change and drought in the province of Artvin, where electricity generation based on hydroelectric power plants is common, in terms of future investment, it is essential to suggest suitable sites for alternative energy sources. It has been stated that drought caused by global warming reduces the efficiency of hydroelectric power plants and thus, until 2023, the plan is to use all the hydraulic potential in electrical energy production (Akar 2016).

The validation of the site selection analyses for solar power plants is verified either by literature studies conducted in the same region (Dhunny et al. 2019) or by checking to which susceptibility class the locations of existing installed power plants correspond on the suitability maps (Günen 2021a, 2021b; Soydan 2021). The above verification methods could not be used in this study as no study has been conducted for the site selection of solar power plants in Artvin and there is no solar power plant installed in Artvin. Therefore, the results of this study were validated with the reference data obtained from the Global Solar Atlas (http://globalsolaratlas.info/). A point that is approximately in the middle of the suitable area determined in this study was marked on the global solar atlas, and the basic parameters for the solar energy potential of the area (such as direct normal irradiation, GHI, diffuse normal irradiation, global tilted irradiation at optimum angle) were obtained (Fig. 8).

Fig. 8
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Global solar atlas information on the suitable area for a solar power plant

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If a ground-mounted large-scale PV system with a capacity of 1000 kWp were to be installed in this area, the average hourly, monthly, and annual photovoltaic power outputs that would be observed are shown in Fig. 9. These outputs confirm that the area selected in the study is suitable for a solar power plant installation.

Fig. 9
figure 9

Total photovoltaic power output capacity of the selected area

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Conclusions

Multiple parameters must be considered and evaluated in a holistic manner when determining suitable sites for solar PV power plant installation. The use of multiple parameters also contributes to minimizing negative economical, technical, and ecological impacts. This study adopted the determinant parameters in the literature as well as other essential parameters that were not used in the literature, such as susceptibility in terms of natural disasters. In this context, a suitability map for a solar PV power plant installation in the Ardanuç district of Artvin was produced using GIS and fuzzy logic methods based on a total of 12 parameters, solar radiation, slope, aspect, distance to transmission lines, distance to roads, distance to faults, to residential areas, land use, landslide susceptibility, flood susceptibility, distance to protected areas, and distance to lakes. By using the fuzzy AND operator, the most suitable sites meeting all criteria were determined. Examination of the solar PV power plant suitability map revealed that 3.4% of the study area is highly and very highly suitable for solar PV power plant installation, whereas 89.82% is unsuitable. These estimates might be the result of limiting factors, such as high slopes and the status of forests, agricultural land, and protected areas. The lack of suitable sites for a solar PV power plant installation makes it necessary to use these areas more effectively. Therefore, the findings of this study with regard to the optimal solar PV power plant installation sites can provide a resource for investors, decision makers, and planners.

Future studies will seek to apply other MCDM techniques (e.g., AHP, Fuzzy AHP, ANP, ELECTRE, TOPSIS, and VIKOR) to select suitable locations for renewable energy facilities and to carry out a comparative analysis of these techniques. Research will also be conducted on using machine learning techniques in the selection of suitable locations for solar PV power plants. In addition, studies are planned to determine the most suitable locations for wind, solar, biomass, and hybrid wind-solar power plants in Artvin. On the other hand, in order to improve and enrich the model used in this study, it would be beneficial to include other criteria such as air temperature, land surface temperature, sunshine duration, wind speed, cloudiness, and relative humidity in the site selection.