Creation of Wind Speed Maps and Determination of Wind Energy Potential with Geographic Information Systems: The Case of Kırklareli Province, Türkiye

Abstract

The intensive use of fossil fuels for energy production harms the environment. The adoption of sustainable energy systems can reduce the damage. Wind energy is one of the most widely used renewable sources. The most important problem in establishing new wind power plants (WPPs) is estimating the wind energy potential (WEP) in potential installation locations where there are no measured data. Many geographic information system (GIS)-based studies have been conducted on this subject. In this study, based on the technical specifications of a wind turbine selected for the Kırklareli Province of Türkiye, wind speed maps at 125 m height were created using many station points with known locations and wind speeds and the WEP of Kırklareli was calculated. In addition, the WEP map of Kırklareli was created by first determining the areas where WPPs cannot be installed and creating the wind speed map. After removing exclusion areas where wind turbines cannot be installed, the wind speeds at 125 m ranged between 3.12 m/s and 8.51 m/s. The wind speed was found to be higher in the south of the province, and the total WEP in areas with wind speeds higher than 6 m/sec was 6628.21 MW.

1. Introduction

Energy plays an important role in social and economic development [1]. This was recognized centuries ago in the age of industrialization, and it continues to be valid in the Industry 4.0 era [2]. There is a linear relationship between social and economic development and energy consumption, where energy consumption increases with growing development [3,4,5]. The continuing growth of the world population, rising living standards, and the increasing digitalization of modern societies have resulted in the need to provide more energy with more economical methods [6,7]. By 2040, energy needs are expected to increase by 65% in developing countries and 35% worldwide compared to those in 2010 [8].

The dependence on limited and depleting energy resources in the world has created many difficulties in achieving sustainable development goals and caused environmental problems due to greenhouse gas emissions, compelling countries to search for new energy sources [9]. However, according to the International Energy Agency (IEA), fossil fuel sources, such as natural gas, oil, and coal, remain the energy sources with the most wide-spread production and consumption in the world. [10]. More than 80% of the world’s energy production is based on fossil fuels [11]. The primary energy consumption of the world is shown in Figure 1.

The use of fossil fuels in the world has had devastating effects on the ecological system, causing global warming and climate change. For this reason, countries are seeking reliable and environmentally friendly new energy sources [2,4].

Renewable energy sources, which are defined as naturally occurring energy sources that are regenerated over time, include solar energy, wind energy, hydroelectricity, tidal energy, and geothermal energy [9]. Wind energy is the one of the most important renewable energy sources and is rapidly developing because like other renewable energy sources, it is clean, renewable and has a low impact on people and the environment [13]. In 2021, 6.7% of the world’s electricity was produced from wind energy [14]. In recent years, significant progress has been made in the deployment of wind energy in both developed and developing countries due to its renewability, sustainability, relatively low impact on people and the environment, and cost advantages [14,15].

More than 100 countries in the world are producing electricity from wind energy. According to the 2024 report of the Global Wind Energy Council (GWEC), the total installed power of wind power plants was determined to be 945.5 GW onshore and 75.2 GW offshore. China, the USA, and Germany are the top three countries in the world in terms of onshore wind energy capacity. The total onshore wind power capacities of countries are shown in

Table 1. Total onshore wind power capacities of countries [16].

Rank	Country	New Installation in 2022 (MW)	Total Capacity in 2022 (MW)	New Installation in 2023 (MW)	Total Capacity in 2023 (MW)
1	China	32,579	333,998	69,327	403,325
2	USA	8612	144,356	6402	150,433
3	Germany	2403	58,106	3567	61,139
4	Spain	1659	29,800	762	30,562
5	France	1590	20,653	1400	22,003
6	Canada	1006	15,267	1720	16,986
7	Sweden	2441	14,278	1973	16,249
8	UK	502	14,575	553	14,866
9	Türkiye	867	1945	397	12,342
10	Mexico	158	7317	96	7413

[16].

The use of wind for the production of electrical energy in Türkiye first started in 1986 with the generation of 55 kW of electricity in Altın Yunus Facilities in the Çeşme district of İzmir Province. In the international arena, the first wind turbine was installed in 1998 to generate electricity in this city [17].

According to the January 2023 reports of the Turkish Wind Energy Association (TUREB), the installed power of WPPs in Türkiye increased by 842.9 MW in 2022. Thus, the total installed power of WPPs reached 11,944.72 MW, playing an important role in meeting the electricity demand. According to TUREB reports, in January 2022, 9.84% of the electricity generated in Türkiye was generated from wind energy, while it increased by 11.11% in January 2023 [18]. The increase in the share of wind energy in electricity production is shown in Figure 2.

The installed power of wind energy in Türkiye was 10,886 MW as of June 2022. Türkiye ranks 7th in Europe and 13th in the world in terms of WPPs’ installed power [19]. In addition, 20 WPPs, with a total power of 803.08 MW, are under construction [20].

Considering the regional distribution of RES in Turkey, the Aegean Region ranks first, the Marmara Region ranks second, and the Mediterranean Region ranks third. The province of Kırklareli, which was selected as the study area, is located in the Marmara Region. It is also among the top 10 provinces in Turkey in terms of installed WPP capacity. The wind energy capacities of the top 10 cities in Turkey according to installed WPP capacity are shown in Figure 3, and the locations of these cities are shown in Figure 4 [21,22].

According to the data in the GWEC and TUREB reports, the energy obtained from wind through newly established WPPs globally and in Türkiye continues to increase every year [16,18,20,21,22]. Feasibility studies are of great importance in determining the locations of new WPPs to be established in order to reach the targeted installed power potential required to obtain more electrical energy from wind. In this context, investors need decision support systems that will make the most realistic production predictions and choose the most suitable location for newly planned WPPs yet to be established [23]. Geographic information systems (GISs) are among the most important tools of in location-based decision support systems that can work with large-scale spatial and attribute data collected from various sources and provide accurate results in appropriate location selection applications [11]. Location selection analyses can be performed with GIS by considering economic, environmental, social, and physical factors in the selection of suitable locations for WPPs [4,24,25].

Many studies involving GIS-based applications have been conducted to determine and develop WEP. Many spatial factors are used in studies to determine suitable areas for the installation of WPPs [5,26]. The most important of these factors is considered to be WEP, which affects wind energy production [25,27,28,29,30,31,32,33,34,35]. An important step in obtaining the data needed for the selection of suitable locations for new WPPs is the creation of wind speed maps and the determination of the WEP. Since many studies conducted to determine wind energy potential in the literature are performed by starting from a station point that measures wind speed data, wind energy potential cannot be determined at a wider spatial scale. In this study, unlike other studies, since WEP calculation and map creation are required for the installation of many WPP facilities in large region (province), it is necessary to use many station points with known locations and wind speeds. This is possible with spatial interpolation, a GIS function. By using the generated wind speed map and the properties of the determined wind turbine model, wind energy potential can be calculated and mapped in the GIS environment.

2. Data and Methods

In this study, the WEP of Kırklareli Province was determined, and a WEP map was created with GIS in order to be used in the selection of suitable locations for newly established WPPs in the province. Wind speed data from 12 meteorological stations operated by the General Directorate of Meteorology (MGM) were used to determine the WEP of Kırklareli Province. With the GIS, the study area was divided into cells of 200 × 200 m. A wind speed map was created by calculating the wind speed in each cell at the turbine height specified for the selected wind turbine model, and the wind energy to be produced was calculated. The results were compared with the wind energy potential atlas of Türkiye (WEPA).

2.1. Study Area and Wind Energy Characteristics

The Marmara region is the best region in Türkiye in terms of annual average wind speed and wind density [17]. In this study, Kırklareli Province in the Marmara region was chosen as the study area. The area of the province is 6650 km², and the altitude of the city center is 203 m [36]. The location of the study area is shown in Figure 5.

Preliminary wind speed information was obtained from WEPA data for the calculation of the wind speed and potential required to determine the adequacy of new WPPs to be installed in Kırklareli.

According to WEPA data, the WEP of Kırklareli Province is 3079.36 MW at a height of 50 m [37]. According to TUREB’s July 2022 report, the installed WPP power in Kırklareli Province is 481.68 MW and ranks 6th in Türkiye [38]. The existing WPPs in Kırklareli Province are located in the north of the province and their locations are shown in Figure 6 [20]. The first WPP in Kırklareli Province started to generate electricity in 2014. List of WPPs in operation in Kırklareli and their characteristics are shown in

Table 2. List of WPPs in operation in Kırklareli and their characteristics [20].

Project	Installed Power (MW)	Turbine Brand	Turbine Model	Date of Operation
Kıyıköy RES	28.0	Siemens Gamesa	G90/G97	2014
Karadere RES	16.0	Ge	GE1.6-100	2014
Karadere RES	3.2	Ge	GE1.6-100	2016
Zeliha RES	25.6	Siemens Gamesa	SWT-3.2-113	2016
Air RES -4	60.8	Siemens Gamesa	SWT-3.2-113	2017
Kıyıköy RES Ext	64.8	Vestas	V136-3.6	2020
Evrencik RES	9.6	Nordex	N149	2020
Vize-II RES	57.6	Nordex	N149	2020

.

2.2. Data

In this study, data of 12 MGM stations in different location in Kırklareli Province were used. The locations of the MGM stations in Kırklareli are shown in Figure 7. The wind speed data used between 2014 and 2019 were measured daily. The average of all daily data for each station was taken to obtain the average wind speed data at 10 m height.

The Hellmann equation shown in Equation (1) was used to calculate the wind speeds at the determined wind turbine height with the wind speed data from the MGM stations [39], as follows:

$${\displaystyle \frac{V}{V_0}}={\left({\displaystyle \frac{H}{H_0}}\right)}^{\alpha }$$

(1)

where H₀ is the height at which the wind measurement was carried out (10 m); V₀ is the wind speed; H is the height at which the wind speed will be determined; α is the coefficient of friction; and V is the wind speed at the height of interest.

The coordination of information on the environment (CORINE 2018) land cover inventory map from Copernicus Services was used to determine the friction coefficient used in the Hellman equation [40]. The friction coefficients for land characteristics in the locations of the MGM stations used in this study vary between 0.10 and 0.40. Friction coefficients for different land characteristics are shown in

Table 3. Friction coefficients for different land characteristics [41].

Land Use Characteristic	Friction Coefficient (α)
Smooth, hard ground or calm water	0.10
Tall grass on level ground	0.15
High crops, hedges, and shrubs	0.20
Wooded countryside, many trees	0.25
Small town with trees and shrubs	0.30
Large city with tall buildings	0.40

* Adapted from [41]

[41].

To create the wind speed map, the wind speed data obtained from the 12 MGM stations in Kırklareli Province were converted to wind speeds at the tower height of the selected turbine, and these wind speeds at the turbine height were used in further analyses.

For the calculation of the wind energy potential of Kırklareli Province, land use data provided by the General Directorate of Spatial Planning of the TR Ministry of Environment, Urbanisation and Climate Change were used to determine areas that are not suitable for WPP installation. With these data, data layers were created for the study area. Characteristics of the data layers used are shown in

Table 4. Characteristics of the data layers used.

Land Use	Data Layer	Data Type
Villages	Vector	Point
Nature and National Parks	Vector	Polygon
Power transmission line	Vector	Line
Natural gas pipeline	Vector	Line
Slope	Raster	Cell (pixel)
Digital elevation model	Raster	Cell (pixel)
Flood area	Vector	Polygon
Protected area	Vector	Polygon
Railway	Vector	Line
Highway	Vector	Line
Water surfaces	Vector	Polygon
Urban areas	Vector	Polygon
University area	Vector	Polygon
Industrial area	Vector	Polygon

.

2.3. Methods

2.3.1. Turbine Model Used in Wind Energy Potential Calculation

To calculate the estimated wind energy potential of Kırklareli Province accurately, it is necessary to select a turbine model that is a product of current wind technology. In this context, WPPs in operation and under construction in the province were examined. The Nordex N149 turbine model, with a tower height of 125 m and a power of 4.8 MW, was used based on the Vize 2 power plant, the most recent of the operating WPPs, and the Evrencik power plant, which is under construction. The switch-off speed of the wind turbine model used in this study is 26 m/s. In windy weather, i.e., when the wind exceeds this speed, the wind turbine automatically stops. The wind turbine characteristics based on the study area are shown in

Table 5. Wind turbine characteristics based on the study area [42].

Nordex Turbine Technical Specifications
Model	N149
Power	4.8 MW
Tower height	125 m
Turbine diameter	149.1 m
Turbine swept area	17.460 m2
Maximum power	5 MW
Switch-on speed	3 m/s
Switch-off speed	26 m/s

.

2.3.2. Creation of Wind Speed Map

To create the wind speed map of Kırklareli Province, the wind speeds recorded by the MGM stations at a height of 10 m were used to calculate the wind speeds at 125 m, which is the height of the turbine model to be used in the wind energy potential calculation using the Hellman equation. The results are shown in

Table 6. Wind speeds of MGM station points at 125 m height.

Station Name	Corine ID	Friction Coefficient (α)	Wind Speed at 10 m (V10)	Wind Speed at 125 m (V125)
Pehlivanköy	211	0.25	3.0	5.57
Lüleburgaz TIGEM	121	0.40	2.3	6.21
Babaeski	121	0.40	3.1	8.51
Kırklareli	111	0.40	1.7	4.75
Kofcaz	321	0.20	3.3	5.44
Pınarhisar	333	0.20	4.6	7.57
Vize Yumurtatepe	243	0.25	3.4	6.45
Kırklarelı Üniversitesi	321	0.20	4.2	7.03
Vize/Midye Kıyıköy Batı Mendirek Fener (Ana)	123	0.10	4.9	6.28
Demirköy	311	0.30	2.1	4.46
Vize Kıyıköy	231	0.25	1.7	3.12
Demirköy Begendik Köyü	242	0.30	2.9	6.08

.

While creating the wind speed map, the number of samples from which data were taken was determined to correspond to the number of 12 MGM station points. The interpolation method was used to estimate unknown wind speed values at other points in the study area from the point-based wind speed data obtained from MGM stations [43]. Wind speed maps can be created with the interpolation method and GIS using points with known wind speeds in a specified area.

With the GIS software ArcGIS 10.5, wind speed maps can be created. By examining studies in the literature, it was determined that inverse distance weighting (IDW) is the most suitable interpolation method for the creation of a wind speed map using point wind speeds [43,44]. With the IDW technique, surface interpolation is performed according to the weighted average of the sample points, where the weight decreases as the distance from the interpolated point increases. Although different types of weighted functions have been used, IDW has been the most common form used in GIS. IDW is a full intermediate value generator, thus reinforcing the value of the data [45].

In this study, the study area was divided into 200 × 200 m cells, which is the cell size used by WEPA, in order to compare the results with WEPA when creating the wind speed map. The calculated point wind speed values at 125 m were transferred to ArcGIS software, and the wind speed map was created using the IDW interpolation method.

2.3.3. Determination of Areas Unsuitable for WPP Installation

Since wind speed data alone are not sufficient to calculate the energy potential of the study area, it is also necessary to determine the areas that are not suitable for WPP installation. The exclusion criteria for WPPs were determined by taking into account the criteria used in previous studies on WPP location selection and in the preparation of WEPA, which includes wind source information [27,28,29,30,31]. Criteria used for areas where WPPs cannot be installed are shown in

Table 7. Criteria used for areas where WPPs cannot be installed.

Criteria	Exclusion Criterion
Distance to Residential areas (province, district, town)	<500 m
Distance to Residential area (Village)	<500 m
Nature and National Parks	Whole area
Distance to Energy transmission line (ETL)	<500 m
Distance to Natural gas pipeline	<500 m
Slope	20%<
Flood area	Whole area
Protected area	Whole area
Distance to Railway line	<500 m
Distance to Highway	<500 m
Distance to Water surfaces (lakes)	<500 m
Distance to Rivers	<500 m
Distance to University area	<500 m
Distance to Industrial area	<500 m

.

The Regulation on the Technical Assessment of Applications for Electricity Generation Based on Wind Source (20.10.2015/29508) was used to determine the exclusion distance to identify unsuitable locations for WPPs [46]. According to this regulation, ellipses with a major axis length of 14 × D (the value of the rotor blade diameter of the turbine under evaluation in meters) and a minor axis length of 6 × D were drawn. The impact area ellipse of a wind turbine is shown in Figure 8. There should not be more than one wind turbine within an ellipse so that the wind turbines to be installed would not adversely affect each other’s wind.

When the ellipse defined in the Regulation on the Technical Assessment of Applications for Electricity Generation Based on Wind Source is used for the Nordex N149 turbine selected for the study area, the major radius of the ellipse is 1043.7 m, and the minor radius is 447.3 m. Placement of turbines according to the impact area ellipse is shown in Figure 9.

In this study, the minor semi-axis of the impact area ellipse was chosen to determine the approach distance for areas where WPPs cannot be installed. The calculated value of 447.3 m was used to define an approach distance of 500 m to further reduce the impact of WPPs on some natural and artificial areas and to ensure a safe distance. Areas that are unsuitable for WPPs were determined by performing a buffer analysis with the ArcGIS 10.5 program.

Data on the villages in Kırklareli Province, which are among the areas that are not suitable for WPP installation and need to be excluded, should be area-based, but only point-based data are available. Therefore, village settlement areas were created by determining the approximate radii of the village settlements using the Google Earth program. The numbers of villages and the village residential area radius are shown in

Table 8. Village settlements and drawn circles.

No.	Village Residential Area Radius (m)	Number of Villages
1	200	17
2	300	36
3	400	46
4	500	49
5	600	11
6	700	6
7	800	4
8	1000	2

.

Sloping regions prevent the regular wind regime required for wind energy generation [47]. Since only 3% of wind energy production can be provided in areas where the slope is more than 20% in Türkiye, these areas are unfavorable for WPPs due to the installation and maintenance costs [48]. Therefore, a slope map was created using digital elevation model (DEM) data for Kırklareli Province. The areas unsuitable for WPP installation due to slopes greater than 20% were determined.

2.3.4. Calculation and Mapping of Wind Energy Potential

In determining the wind energy potential, it is necessary to calculate the power that each turbine can produce according to the wind flow moving at speed V, which can be determined by using Equation (2), as follows:

$$P={\displaystyle \frac{1}{2}}\rho A{C}_p{V}^3$$

(2)

where P is the power (watt); $\rho$ is the air density(kg/m³); A is the turbine swept area (m²); V is the wind speed (m/s); and C_p is the efficiency coefficient.

The value used for $\rho$ in Equation (2) is 1.225 kg/m³, which is the specific mass of air under standard atmospheric conditions at sea level [49]. The efficiency coefficient Cp was calculated to be 0.26 using Equation (3) for the selected turbine type, as follows:

$$C_P={\displaystyle \frac{\mathrm{P}}{\frac{1}{2}\mathsf{\rho }\mathrm{A}{V}^3}}$$

(3)

In order to calculate the total wind energy potential of the study area, all exclusion areas were removed from the wind speed map. After this process, the wind speed was divided into 4 ranges, and the number of cells in each speed range was determined. The total amount of suitable area for WPP installation in the relevant range was calculated from the number of cells in each speed range. Impact area distances were used to determine the number of wind turbines that could be installed in the calculated area for each speed class. For this purpose, an equation to determine the number of turbines that could be installed in a square kilometer area was produced in accordance with the principles in the “Regulation on the Technical Assessment of Applications for Electricity Generation Based on Wind Source” and the WPP layout plan along the route in two rows. The WPP layout plan was designed so that the nearest turbine could be installed at a distance of 3 times and 7 times the turbine diameter in two perpendicular directions, taking the turbine center as a reference, within the framework of these principles. The number of turbines that could be installed in each unit area was calculated for different unit areas. Equation (4) was obtained with the calculated values, as follows:

$$\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{T}\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{s}=\left(4.2841\times \mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a}\right)+2$$

(4)

Considering the wind speed ranges used in WEPA, the speed ranges used in this study were divided into the following 4 classes: 6.0–7.0 m/s, 7.0–7.5 m/s, 7.5–8.0 m/s, and 8.0–8.5 m/s. The power that a turbine can generate in each specified class was calculated using the average speed in the class range. The total area corresponding to the class range was calculated from the number of cells in each class range. The wind energy potential for the class range was calculated by multiplying the total area of each class in the study area by the number of turbines that could be installed.

In addition, in order to evaluate the distribution of wind energy potential in Kırklareli Province on a cell basis, a coefficient expressing the number of turbines that could be installed in each cell was obtained. To determine this coefficient, calculations were performed using the number of turbines that could be installed in a cell in the study area, which was divided into 200 × 200 m cells, the total area suitable for turbine installation in Kırklareli Province. By averaging the calculated values, a coefficient of 0.1714 was obtained as the average number of turbines that could be installed in a cell. With this coefficient, the wind energy potential in each cell was calculated, and the wind energy potential map for Kırklareli Province was created.

3. Findings

In addition to wind speed, which is the most important criterion in determining suitable areas for WPPs, many other criteria were used, including natural and artificial areas such as settlements that are not suitable for WPP installation, sensitive areas that need to be protected, and transport networks. After the exclusion areas were identified with these criteria and removed from the study area, suitable areas for WPPs were determined for the remaining study area. The most important criterion in determining suitable areas for WPPs is wind speed, and therefore, the first map created is a wind speed map.

When creating wind speed maps, the distribution of reference points with known wind speeds is of great importance for calculating values that are close to the real values. The most appropriate interpolation method should be determined, and maps should be created to obtain the most accurate result based on the distribution of the reference points. Examining studies in the literature revealed that inverse distance weighting (IDW) was the appropriate interpolation method, as when the squared mean error values obtained with spatial interpolation methods were compared, the IDW interpolation method had the lowest error value [43,44,50,51]. A wind speed map was created for the study area using the IDW interpolation method. The wind speed map of Kırklareli Province for 125 m height is shown in Figure 10.

In Kırklareli Province, the wind speed at a height of 125 m varies between 3.12 m/s and 8.51 m/s. The regions with the highest wind speed, 8.51 m/s, are concentrated in the south of the province. Babaeski and Pınarhisar districts stand out in terms of district-based wind speed distribution in the wind speed map of Kırklareli Province at 125 m height. The highest wind speed in Babaeski district is between 8 m/s and 8.5 m/.

In determining the areas where WPPs cannot be installed and the criteria for calculating the wind energy potential, studies on WPPs and the Regulation on the Technical Assessment of Applications for Electricity Generation Based on Wind Source were used. The exclusion areas were determined by performing buffer analyses for energy transmission lines, natural gas lines, railways, highways, water surfaces, rivers, universities, industry, and urban and village settlements. The areas to be excluded based on slope, i.e., where the slope was more than 20%, were determined using the DEM [52].

In addition, protected areas, national parks, nature parks, and floodplains in the study area were transferred to the WEP map. Thus, areas that are not suitable for WPP installation in Kırklareli Province were determined. The exclusion areas in Kırklareli Province are shown in Figure 11.

After the areas where WPPs could not be installed were removed from the WEP map, the areas where WPPs could be installed were determined. The determined area was divided into cells of 200 × 200 m and divided into four class ranges according to wind speeds.

The wind energy potential was calculated using the number of cells for each wind speed class determined for the area. The results are shown in

Table 9. Wind energy potential of Kırklareli Province.

Wind Speed (m/s)	Number of Cells (n)	Area (n × 0.04 km2)	Power Produced by a Turbine (P) (MW)	Power That Can Be Produced P × [(4.2841 × Area) + 2] (MW)
6.0–7.0	39,961	1598.44	0.76	5230.54
7.0–7.5	4784	191.36	1.06	870.78
7.5–8.0	1538	61.52	1.29	343.71
8.0–8.5	673	26.92	1.56	183.18

.

After the exclusion areas were removed from the generated wind speed map, the wind energy potential that can be generated for all cells was calculated with Equation (2) using the wind speed data of the cells in the remaining area. A wind energy potential map was created with the determined WEP values. Existing WPP locations were added to the created wind potential map to determine the wind energy potential at these locations. WPPs in operation and wind energy potential map are shown in Figure 12. Thus, a wind energy potential map was created for existing and potential WPPs in the study area.

4. Conclusions

The energy potential was calculated for different speed ranges with the help of a wind speed map created using 12 MGM station points with known wind speeds and locations. The study area was divided into 200 × 200 m cells, and the wind energy potential was determined on an area basis.

Although WEPA states that the speed limit for an economical WPP is 7 m/s, various studies on selecting suitable WPP locations have shown economic WPP investments with wind speed thresholds of 5, 6, and 6.5 m/s [53,54,55,56]. Considering that the wind speed threshold for WPP investment in the study area is ≥6 m/s, 53.4% of all speeds are suitable for energy production.

After removing the exclusion areas where wind turbines cannot be installed, the wind speeds at 125 m range between 3.12 m/s and 8.51 m/s. The wind speed is higher in the south of the province. The total WEP in areas with wind speeds higher than 6 m/sec and higher than 7 m/sec was determined to be 6628.21 MW and 1397.67 MW, respectively.

In WEPA, the total wind energy potential in areas with speeds above 6.8 m/s at a height of 50 m in Kırklareli Province is 3079.36 MW. In this study, the total wind energy potential in areas with speeds above 6.8 m/sec at 125 m height was calculated to be 1832.47 MW.

According to the wind energy potential map created for Kırklareli Province, the wind energy potential is higher in Babaeski district than in other districts. In addition, Pınarhisar, Lüleburgaz, Merkez, and Vize districts are also evaluated as favorable in terms of wind potential.

When the locations of existing wind power plants and the wind energy potential map were examined, it was seen that the existing WPPs are located in regions with low energy potential.

5. Discussion

It is necessary to determine the wind energy potential when selecting suitable locations for the installation of WPPs, which are required to obtain electrical energy from wind, a renewable energy source. In the studies conducted to determine the wind energy potential, analyses were carried out using wind speed data measured by stations at specific points. In single-point-based studies, the wind energy potential was determined only for the point studied [37,57,58]. However, wind energy potential cannot be determined at a larger spatial scale in these studies. With spatial interpolation, which is a GIS function, a wind speed map can be produced for a specific area using a large number of meteorological station points with known locations and wind speeds. Using the produced wind speed map and the characteristics of the determined wind turbine model, the wind energy potential can be calculated and mapped in the GIS environment.

Within the scope of this study, wind speed data measured by 12 MGM stations were analyzed to determine the wind energy potential of Kırklareli Province more accurately. With these data, a wind speed map at the height of the wind turbine was created. Using the wind speed map and the features of the selected wind turbine model, an accurate wind energy potential map of the study area was generated.

According to the created wind energy potential map, the WPPs in operation are located in areas with low energy potential. The wind energy potential criterion has the greatest weight in determining suitable locations for WPPs. However, the wind energy potential is not the only criterion in determining the locations of installed WPPs; many other criteria are taken into consideration.

When the study was compared with WEPA, the fact that the potential is higher than in WEPA despite the lower height is due to the potential calculation approach used in WEPA. In WEPA, it is generalized that in all wind speed ranges, each km² area has a potential of 5 MW in areas with speeds above 6.8 m/s. In this study, the wind speed class range values were kept the same to enable a comparison with WEPA, and the areas corresponding to the wind speed ranges were determined. The wind energy potential was calculated with a more accurate approach by using the wind speeds of these areas and the selected turbine’s characteristics.

Kırklareli Province is located in the Marmara region. According to the calculations performed using the address-based population registration system (ADNS) data published by the Turkish Statistical Institute (TÜİK), 30.94% of Türkiye’s population lived in the Marmara region as of 31.12.2023 [59]. According to the 2023 market development report of the Energy Market Regulatory Authority (EPDK), 35.57% of the country’s electricity consumption and 25.15% of its electricity production occur in the Marmara region [60]. In addition, according to 2023 TÜİK data, 64.51% of Türkiye’s exports are from the Marmara region [61]. The share of this region in Türkiye’s total gross domestic product (GSYİH) was 45.18% in 2022 [62]. According to these data, when electricity consumption in the Marmara region is compared to other regions, for reasons such as intensive industrial activity, agricultural activities, production, residential areas, and lighting, electricity consumption is 10.42% higher than production, while it is −1.57% in Kırklareli Province [60]. It is possible to transfer the electrical energy produced by WPPs to the nearest existing or newly established transformer center and the national electricity system to be distributed to all of Türkiye, especially to Kırklareli Province.

In this study, the methodological discussion presents the process steps that should be followed for WPP installation in any region from a different perspective. When comparing the study results with the currently installed WPPs, it was concluded that they are located in regions with relatively low potential rather than regions with high WEP. This shows that there are WPP areas that will provide more economic benefits. It is seen that more effective and efficient use will be achieved if the installations are made by applying the preliminary survey procedures as in this study.