Site Suitability Assessment for the Development of Wind Power Plant in Wolaita area, Southern Ethiopia: An AHP-GIS Model

doi:10.21203/rs.3.rs-2474836/v1

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Site Suitability Assessment for the Development of Wind Power Plant in Wolaita area, Southern Ethiopia: An AHP-GIS Model

https://doi.org/10.21203/rs.3.rs-2474836/v1

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published 13 Nov, 2023

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The engine behind any economy's growth is energy. The majority of energy sources are derived from fossil fuels, and the demand for energy produced using fossil fuels has increased since the Industrial Revolution. It is now obvious that the energy generated by these fossil fuels led to significant levels of toxic gases, which harmed the environment. Moreover, Ethiopia still lacks electricity despite having a vast amount of renewable energy resources. As a result, resources must be assigned to using renewable energy sources, like wind energy, as an alternative to the country's current power grid. This study aimed to conduct a site suitability assessment for the potential location of a wind power plant in the Wolaita area using geospatial methods. Many factors and criteria are considered in the process of selecting a suitable site. Geographic Information System (GIS) was employed to develop a multi-criteria approach for determining the most suitable locations for wind power plants. In this study, seven criteria were used. The Analytical Hierarchy Process (AHP) was used to evaluate these criteria, and all of these criteria were assigned a weighted score. These criteria were also given weights using pairwise comparison. The result or suitability map is scored in four categories, ranging from most suitable to unsuitable. According to the findings, 0.628% of the total area (28.00 km²) is deemed highly suitable for establishing wind farms. From a total area of 2433.96 km², 54.61% of the land is considered to be moderately suitable. The remaining 0.85% (37.85 km²) of the total area is deemed least suitable for wind turbines, leaving 43.91% (1956.94 km²) not suitable. It has been identified that the study area's central, northwestern, and southern regions are superb locations for the establishment of wind farms. The results of this investigation will assist the Ethiopian government or any other organization working on the development of sustainable and renewable energy to speed up the investment process in renewable energy and increase the certainty of resource development. The results of this study could be used as a benchmark for the sector of wind energy.

Physical sciences/Energy science and technology/Energy infrastructure

Physical sciences/Energy science and technology/Renewable energy

AHP

Ethiopia

GIS

Renewable energy

Wind farms

With today's rapidly increasing population and industrialization, the magnitude and nature of world energy needs have altered and increased in unprecedented ways [1]. Since the Industrial Revolution, traditional fossil fuels have been frequently consumed, resulting in the release of various greenhouse gases (e.g., CO₂, CH₄, etc.) into the atmosphere. This has resulted in an unusual rise in the Earth's average atmospheric temperature as well as other environmental concerns [2–4]. The use of traditional fuels has detrimental impacts on the environment, the economy, and human health, including deforestation, pollution of the environment, and rising use of agricultural waste and animal dung, which prevents the soil of essential nutrients needed for soil fertility. Moreover, the smoke produced when using dung and wood as fuel for cooking increases the risk of acute respiratory infections. Environmentally friendly energy sources will therefore be given priority [5].

Eco-friendly energy sources have been suggested as a primary energy resource replacement and are now generally accepted to be able to meet most of the expanding energy demand [4]. These energy sources, which are also referred to as renewable sources of energy, are abundant, free, and clean in contrast to finite fossil fuel reserves [6–9]. Renewable energy is widely recognized as important not only because of the limited fossil fuel resources but also because of the main causes of air pollution associated with fossil fuel combustion. Therefore, in terms of environmental acceptability, renewable energy resources outperform conventional energy systems. Wind, hydro, solar, biomass, and geothermal are some of the most common renewable energy sources. Solar and wind resources have reached commercial maturity, both technologically and economically. Wind energy is an emerging renewable energy source because it has low production, operation, and maintenance costs and also because efficient multi-megawatt wind turbines are readily available [9].

Wind energy has been successfully installed for power production in over 95 countries worldwide, with a global wind capacity estimated to be 539.58 GW by the end of 2017 [10]. With a 40% market share, Denmark has the highest percentage of wind power penetration, followed by Uruguay, Ireland, Portugal, Cyprus, and Spain, all of which have rates well above 20%, Germany, at 16%, and Canada, China, and the United States, at 6, 5.5, and 4%, respectively [10]. South Africa, Egypt, and Morocco have the largest share of African wind energy production, with total capacities of 2094MW, 810MW, and 787MW, respectively, as of the end of 2017 [10].

Although Ethiopia has a variety of alternative energy sources, like wind, solar, hydropower, geothermal, and biomass, only a small portion of them have been utilized. The majority of the country's electricity comes from hydropower, which provides 86% of all the country's power needs [11]. The country plans to diversify its energy sources, with the expansion of wind energy being one of them, to lessen its reliance on hydropower. This is because hydro and wind energy complement one another and work well together. Ethiopia plans to build a number of wind energy projects. The proposed wind farm projects had respective output targets of 970 MW, 1750 MW, and 4000 MW over the short, medium, and long terms. However, the country only installed 324 MW of wind energy, which is comprised of three wind farms: Adama I (51 MW), Adama II (153 MW) and Ashegoda (120 MW) [11, 12].

Implementing a wind energy project necessitates carefully taking a variety of variables into account. When choosing a location for a wind power plant, environmental, economic, and land use necessities all need to be taken into account. The best locations must be chosen based on a number of economic, ecological and physical factors rather than always being the windiest [13]. Multi-criteria decision-making (MCDM) methods are used to select locations for wind farms. The ranking method, weighted sum method (WSM), AHP, weighted linear combination (WLC), Boolean overlay operation, Order Weighted Average (OWA), trade-off analysis method, analytic network process (ANP), concordance analysis, and Elimination Et Choice Translating Reality (ELECTR) are MCDM methods that can be combined with the GIS environment [14]. The WSM is frequently used to resolve decisions involving a single dimension, but its use in situations involving multiple dimensions is surprisingly challenging [15]. The ELECTR method is also useful when there are many variables to take into account and a wide range of options to select from. The provided method for choosing the best alternative is not, however, foolproof [16]. In comparison, the AHP is considered among the most popular MCDM approach in the previous studies for evaluating renewable energy sites since it is straightforward, easy to use, and able to assess the consistency of the decision. It helps in the simplification of complex decision-making situations that demand a high level of consistency and adaptability. Both qualitative and quantitative criteria can be used with the schema. AHP is an algebraic technique for comparing paired criteria that assigns each criterion a relative weight based on the decision-makers' professional judgment [17, 18]. To find the ideal locations for the development of wind energy on the Greek island of Lesvos, Tegou, et al. (2010) used GIS and AHP [19]. With the aid of a set of economic, environmental, social, and technical restrictions based on current Greek law, they were able to pinpoint potential locations for the establishment of wind power. The potential for wind power, the type of land cover, the demand for electricity, the land value, the visual impact, and the distance from the power grid are all factors that are considered when evaluating the area under consideration. Similarly, Chikoto et al. (2015) used a GIS-based AHP approach in their work, asking a group of regional experts to pair-wise compare the integrated criteria to establish the relative importance of each criterion [20]. Islam et al (2022) also used GIS overlay analysis to look into the suitability of a potential wind farm site in Bangladesh. They used the following factors to get the desired outcome: wind speed, slope, elevation, lightning strikes flash rates, closeness to town, highways/railways, airport and transmission network [21]. The financial success of wind energy operations thus depends on finding suitable locations for wind power installation.

Although there have been a few studies on the potential for wind energy, there have been none on the process of site selection in the Wolaita area of Ethiopia. This is, as far as we are aware, the first comprehensive analysis that has used AHP and GIS to identify potential locations for wind farm siting in the Wolaita area. Due to this, we are incorporating a thorough literature review and the opinions of local experts into the current study to provide a solid foundation for AHP estimation. Because its spatial scope for wind farm siting spans the entire country, it is expected that this study would provide insightful information for wind energy applications on a national scale.

2.1. Description of the study area

The Wolaita Zone is located in Southern Ethiopia between the coordinates 6.4-7.1^o N and 37.4-38.2^o E, with the lowest elevation of 1500 m above sea level and the highest elevation of over 3000 m at Damota Mountain. The Zone is found about 300 km south of Addis Ababa (Fig. 1). It is bordered on the south by Gamo Gofa, on the west by the Omo River, which divides it from Dawro, on the north by Kembata Tembaro and Hadiya, on the east by the Bilate River, which separates it from Sidama, and on the south-east by the Lake Abaya, which separates it from Oromia region [22]. The area experiences a subtropical highland climate with year-round average temperatures of 24^o to 30^o C during the day and 16^o to 20^o C at night. The major rainy season is from June to September, with an annual average rainfall of 1350 mm [22].

2.2. Parameters for Assessing Land Suitability

In order to conduct a geographic analysis and select the best locations for Wolaita's wind farm, seven assessment factors were developed and used as input to ArcMap 10.5. These factors included wind speed, distance to settlements, distance between power grids, distance to transmission systems, distance from rivers, slope, and land use/land cover.

2.2.1. Wind Speed

Wind speeds were the most crucial factor in deciding where to build a wind power plant. The majority of wind turbines start up with an efficiency of 3 m/s and stop up at a speed of 25 m/s. Wind speeds of at least 3.5 m/s were considered to be good wind speeds. As a result, the first step was to avoid any locations where a wind speed of 3.5 m/s was not anticipated [23].

2.2.2. Distance to Settlement

The local population may suffer negative effects from wind power plants located close to settlements. Due to factors like wind turbine noise pollution, the unpleasant impact of shadow flickers on inhabitants, a decrease in wind speed, and the development of future residential sections, it is crucial to maintain a suitable distance from human settlements. The maximum possible distance (7.5 km) between wind power plants and settlement areas is advised in order to reduce environmental harm [24].

2.2.3. Distance from Transmission Lines

The wind farm's main objective is to generate electricity and then use transmission lines to feed that electricity into the main grid. As a result, when choosing a wind farm site, the proximity between potential wind farm sites and existing transmission lines and the power system is crucial. A bare minimum distance should be established in order to minimize the detrimental effects of power transmission on the public health [25]. This study determined that 1.67 km should be the minimum distance between power transmission lines and the sites [26].

2.2.4. Distance from rivers

Rivers, lakes, and wetlands are examples of water bodies that have been deemed unsuitable for use as wind farm sites due to the ecological services they provide [27]. Water bodies have significant ecological and economic value. They frequently serve as habitats for a variety of very rich and diverse species of flora and fauna. The wind farms' distance from the riverbed will increase the safety of the facilities because the river routes are dynamic and constantly changing, and there is also a risk of flooding [21]. Within 300 m of water bodies, renewable energy projects should not be constructed [21]. A 0.6 km buffer is built around the water bodies in this study. The buffered water course places were subsequently left out of the study area.

2.2.5. Distance from transportation lines

The location of wind farms should ideally be as near as possible to main roads in order to minimize multiple discomforts, such as the negative impact on road mobility due to loud noises and changes in the visual scene due to the rotation of the wind turbines caused by wind turbine operation [23]. Furthermore, wind farms should be located as close as possible to main roads in order to reduce transportation costs and make access easier for various employees. Therefore, 1.67 km should be the minimum distance between the wind farm projects and the main roads [28].

2.2.6. Slope

The slope is a crucial technical factor that must be taken into consideration when selecting wind farms. Because these locations are difficult to access due to steep slopes, maintenance and equipment installation costs increase. For wind farm establishment, flat and low-slope areas are frequently advised to avoid the hurdles of wind farm construction. On the other hand, regions with a slope of more than 10% are excluded from the final suitability map [25].

2.2.7. Land use/land cover (LULC)

One of the key considerations for energy investments is land use. In areas where wind turbines barely impact current land use, wind energy should be installed. Land use has an impact on the choice of a wind farm because there are some locations where wind farms cannot be built even though there is sufficient wind speed, like in a forest, wetland, aviation zone, archaeological site, etc. Therefore, it can be generally said that the most suitable types of land are agricultural land, grassland, barren land, and shrubland, while forest land is considered to be less suitable [19, 29, 30]. This study did not include wetlands, water sources, or settlements because it would not be appropriate to construct wind farms there [19].

2.3. Multi-Criteria Decision-Making

The Analytic Hierarchy Process (AHP) has received a lot of attention because it is thought to possess robust mathematical properties and has been applied in a wide range of fields [31]. The AHP has been used by researchers from a wide range of fields in numerous different contexts because of its ability to resolve issues involving the use of multiple criteria in decision-making (MCDM). In a MCDM problem, the AHP gives users the option to specify the weights of the influencing factors in order to get closer to optimal solutions. The AHP method uses a hierarchical model with goals, criteria, sub-criteria, and potential solutions to solve any problem [32]. The hierarchy can then be calculated once the problem's formation has been established. A pairwise comparison matrix is built based on the preference scale so that the criteria from one hierarchy level can be compared to the criteria from the next hierarchy level [33].

2.3.1. AHP methods

The AHP is an idea of pairwise comparison in measurement. A scale of absolute judgments that expresses how much more one element dominates another with regard to a particular attribute is used to make the comparisons. In order to create the derived priority scales, they are multiplied by the priority of their parent nodes and added for all such nodes. AHP compares pairs of criteria using a reciprocal matrix and an absolute judgment scale to determine how much one criterion dominates the other. The procedure consists of two steps: 1) determination of the relative importance of each criterion, and 2) determining the relative weight.

The AHP method has the potential to introduce some degree of inconsistency when performing pairwise comparisons of criteria. The consistency of the reasoning underlying pairwise comparisons must therefore be evaluated as a result. Saaty (1980) created the consistency ratio (CR) to measure how consistent pairwise comparison-based evaluations are with one another [17, 32]. For the pairwise comparison matrix, calculations are made to find the CR. The matrix is considered consistent (CR = 0.1) when the total number of contradictions between pairwise comparisons is less than a predetermined threshold [34]. Otherwise, the ratings for the decision-making process will need to be reassessed. Eq. (1) can be used to determine the CR.

$$CR= \frac{CI}{RI} ;CI= \frac{{}_{m}-n}{n-1}$$

where λ_m is the primary eigenvalue of the comparison matrix, CI is the consistency index, and RI is the random index, which depends on the size of the matrix (n) [32].

For this ratio, Saaty suggests an upper limit of "0.10." It is assumed that the decisions are sufficiently consistent and that the evaluation can proceed when the consistency ratio for the judgments is less than 0.10. The judgments are regarded as being inconsistent if the CR is greater than 0.10. The decisions in this situation need to be of higher quality. Examining the judgments can help to reduce the CR [34].

2.3.2. Data Types and sources

In the current work, we attempt to integrate an AHP-GIS spatial suitability analysis study for the location of wind farms in Ethiopia's Wolaita province. The suitability map was produced using ArcGIS 10.5, and the geo-information data were gathered from various organizations (Table 1). This study uses seven evaluation criteria to identify the best locations for wind farms and the worst locations.

Table 1

Data types and sources
Data	Data type	Purpose	Data source
Boundary	Shape file/polygon	To evaluate a focus area	Ethiopian Central Statistics Agency (CSA)
Wind Speed	Statistical data	To determine the potential for wind energy	Global wind atlas
Settlements	Shape file/polygon	To lessen the visual effect and noise pollution	Central Statistical Agency (CSA)
Power lines	Shape file/polyline	To decreases in the cost of energy transfer to the grid	Ethiopian Electric Power (EEP)
Road Network	Shape file/polyline	To place the wind farm close to the road	Ethiopian Roads Authority (ERA)
Rivers	Shape file/polygon	To protect environmentally vulnerable areas	Ethiopian Roads Authority (ERA)
Slope	Raster	To determine whether a wind power construction is feasible	United States Geological Survey (USGS)
LULC	Raster Image	To reduce the environmental impact of the development	European Space Agency (ESA)

The methodological framework for wind farm siting assessment used in this study is broken down into a number of steps, which are described and depicted in Fig. 2.

3.1. Land Suitability Mapping

3.1.1. Wind Speed

Wind speed is an important consideration when selecting the ideal site for new wind farms. Wind turbines' energy output rises as wind speeds increase till the nominal wind speed is reached, which refers to the speed that enhances power generation. Therefore, places with greater wind speeds are more ideal than places with low wind speeds.

However, in this case the suitability of the Wolaita zone for the installation of wind farms was assessed using the criteria for wind speed evaluation. Depending on the frequency of the value and statistical data, land classes were divided into four degrees (Most Suitable, Moderately Suitable, Least Suitable, and Not Suitable) (Table 2). According to the wind speed map, about 65.16% of the entire area is ideal for the development of wind farms (Fig. 3). 4.69% of the area has high wind speeds (> 3.5 m/s), 60.47% experiences moderate wind speeds (1.75–3.5 m/s), and approximately 29.05% and 5.79% of the area experiences least suitable and not suitable for wind power plant installation, respectively (Table 2).

Table 2

Suitability score and areas coverage for the selcted parameters
Evaluation Factor	Suitability Class	Score	Range	Area (km²)	Area Coverage (%)
	Most Suitable	1	> 3.5	208.99	4.69
Wind Speed (m/s)	Moderately Suitable	2	1.75 to 3.5	2694.98	60.47
	Least Suitable	3	0.88 to 1.75	1294.89	29.05
	Not Suitable	4	< 0.875	257.87	5.79
	Most Suitable	1	> 7.5	1773.98	39.80
Distance to Settlement (km)	Moderately Suitable	2	5 to 7.5	1049.50	23.55
	Least Suitable	3	2.5 to 5	966.79	21.69
	Not Suitable	4	< 2.5	666.47	14.95
	Most Suitable	1	< 1.67	807.38	18.12
Distance to power lines (km)	Moderately Suitable	2	1.67 to 3.34	696.28	15.62
	Least Suitable	3	3.34 to 5	599.47	13.45
	Not Suitable	4	> 5	2353.61	52.81
	Most Suitable	1	> 0.6	3815.96	85.62
Distance to rivers (km)	Moderately Suitable	2	0.4 to 0.6	232.51	5.22
	Least Suitable	3	0.2 to 0.4	238.98	5.36
	Not Suitable	4	< 0.2	169.29	3.80
	Most Suitable	1	< 1.67	922.78	20.71
Distance to transportation network (km)	Moderately Suitable	2	1.67 to 3.34	757.17	16.99
	Least Suitable	3	3.34 to 5	645.63	14.49
	Not Suitable	4	> 5	2131.15	47.82
	Most Suitable	1	< 10	3373.76	75.70
Slope (^o)	Moderately Suitable	2	10 to 20	741.24	16.63
	Least Suitable	3	20 to 30	276.74	6.21
	Not Suitable	4	> 30	65.00	1.46
	Most Suitable	1	Open Areas & Bare ground	963.39	21.62
LULC	Moderately Suitable	2	Cropland	2770.74	62.17
	Least Suitable	3	Trees & Flooded vegetation	216.28	4.85
	Not Suitable	4	Waterbody & Built Area	506.33	11.36

Therefore, locations with stronger winds are better suited than those with weaker winds; Fig. 3 illustrates the geographic ranges of wind speed suitability. The central, northeastern, northwestern, southeastern, southwestern, and southern tip regions of the study area are better suited for wind farms than other regions.

3.1.2. Distance to settlements

Wind farms' nearness to settlements may pose significant issues. The major issues in wind farm development are noise, shadow flicker, visual aspects, and aesthetics. To lessen noise and visual pollution, wind farms must be erected a certain distance from urban areas. In this assessment, the distance from the settlement was reclassified into four classes using data from the central statistics agency (CSA), zonal capitals, and woreda capitals (Fig. 4 and Table 2) [9, 23]. Therefore, a distance of < 2.5 km was deemed inappropriate, whereas a distance of more than 10 km was deemed most suitable (Table 2). Furthermore, the categories of less suitable, moderately suitable, and suitable have been given to ranges of 2.5–5, 5-7.5, and 7.5–10 km (Table 2). In general, the study site is ideal for wind farming because more than half of it is farther away than 10 km (Fig. 4). This might be a result of the study area's distinct lack of more urbanized settlements.

3.1.3. Distance to power lines (PL)

The proximity between the transmission lines and a potential wind farm site is another essential factor to take into account because it has an immense effect on both the development and operation of a plant. In order to figure out the best course of action in this regard, all existing transmission lines were taken into account in this analysis. As a result, areas close to the transmission line are considered to be highly suitable, while those farther away are deemed unsuitable. As shown in Table 2, highly suitable places in this study are those that are less than 1.67 km from PLs. Distances of more than 5 km were deemed inappropriate. Likewise, the PLs were moderately suitable, less suitable, and unsuitable at distances of 1.67 to 3.34 km, 3.34 to 5 km, and > 5 km, respectively. According to Table 2 and Fig. 5, nearly 33.74% of the total area is suitable, while the remaining is unsuitable from the perspective of PLs.

3.1.4. Distance to rivers

Water bodies are not suitable for wind farm sites, because of the ecological services they provide [27]. Therefore, wind turbines must be located far away from bodies of water to ensure the safety of both the bodies of water and the turbines. Szurek (2014) formed a minimum distance of 250m from river and surface water [35]. Baban and Parry (2001) used a 400-meter zoning setback from bodies of water [36]. In this study, a 1km buffer is created around the lake and a 200m buffer is created around the river. Therefore, areas greater than 600 m from a water body were deemed suitable for the construction of a wind farm. Figure 6 depicts a map of the distance from a water body. This map shows that more than 90% of the land area is less than 600 m from a water body, making it an ideal location for wind farms. In contrast, 3.80% of the land area is deemed unsuitable for the development of wind farms.

3.1.5. Distance to transportation network

The choice of wind farm sites requires consideration of the area's accessibility to major transportation networks [29]. The cost of building a wind farm was reduced by the use of already constructed roads. While existing roads make it easier to access the site, building new ones significantly increased the cost of production [30]. For the transportation of the turbines and other equipment, it is necessary to confirm that road access exists or that it can be established at an affordable price. Distance from roads was reclassified into four categories in this study using the Euclidean distance technique in the spatial analyst methods (Table 2 and Fig. 7).

According to Table 2, locations that were 1.67 km from a street were deemed to be highly suitable, while locations that were more than 5 km far were deemed to be unsuitable. This is due to constructing new roads and transporting wind turbines are more expensive the farther they are from existing roads [29, 30]. Figure 7 depicts the road connectivity map in this case. This map shows that 20.71% of the total area is less than 1.67 km from a road, making it the most likely location for wind farms. Furthermore, 16.99% of the total area is located between 1.67 and 3.34 km from streets, making it moderately suitable for the development of wind energy. However, it is determined that 47.82% of the area is unsuitable for the construction of wind farms.

3.1.6. Slope

The terrain's slope has a serious influence on how easily wind turbines can be installed and maintained, so it is an important consideration when choosing a suitable site. Because wind turbine construction and maintenance are more difficult on steep terrain than they are on gentle slopes, construction costs increase as the study area's slope increases [23]. Furthermore, it is challenging to transport cranes and turbines to the site in areas with steep slopes. In this study, a steep slope was found to be inappropriate, while a gentle slope was found to be suitable [29, 30]. In general, the digital elevation model of the SRTM with a resolution of 30 m was used to generate a slope map in the GIS environment. In accordance with the degree of the slope, the slope raster was then split into four classes. In this instance, it was determined that a slope of less than 10^o was highly suitable and that a slope of more than 10^o was unsuitable (Table 2) [29, 30]. The slope map in Fig. 8 illustrates how the majority of the land in the study area, which comprise 75.70% of the site's total land area, is highly suitable for the development of wind farms.

3.1.7. Land use/Land cover (LULC)

The type of dominant land cover also affects whether a location is suitable for the setup of wind turbines. In terms of social acceptance, several types of land cover may be viewed as more desirable than others. The current land use in an area with a sufficient amount of wind resource is crucial when choosing a location for a wind farm. For instance, some areas do not permit the construction of wind farms despite having adequate wind resources there. These places consist of wetlands, historical sites, aviation areas, and army areas [29, 30]. As a result, it can generally be said that agricultural land, grassland, barren land, and shrub land are the most suitable types of land, whereas forest land is thought to be less suitable [29, 30]. Wetlands, water sources, and settlements were not included in this study because it would be inappropriate to build wind farms there [19]. Croplands and bare land were considered to be highly and moderately suitable land classes, respectively. Wetland/settlement/waterbody and forest were deemed less suitable and unsuitable, respectively (Table 2). Apart from that, 62.17% of the total area was cropland, and the remaining 21.62% was bare land. While other portions of the total area—forest and wetlands/settlements/waterbodies—respectively make up 4.85% and 11.36% of it (Table 2 and Fig. 9).

3.2. Determining factors weight and suitability analysis

3.2.1. AHP results

The overall weights of the wind energy suitability variables were determined using the ArcMap mapping program. This was done by integrating AHP tools for figuring out each factor's weights. In order to express a relative preference among factors, a pair-wise comparison matrix was used to evaluate each factor. Each factor was given a weight, signifying a priority or evaluation of the importance of one factor to the other. However, several criteria pairs may not be entirely consistent during comparison. The accuracy of the criteria comparison is therefore assessed using the consistency ratio (CR). The ratio displays a plausible level of consistency in pairwise comparisons when CR is less than 0.1 [21]. If the CR is much larger than 0.1, the activity is either useless or needs to be redone since the considerations are uncertain due to being too close to randomness. To ensure unrealistically perfect accuracy, it is simple to establish a few assumptions and then evaluate the remaining data. Table 3 summarizes the AHP weight result for each element. It was determined that the variable wind speed criteria had the highest priority weight (43.29%), which is in accordance with studies conducted in Adama, and that the proximity to settlement and slope area each had a minimal impact on wind farm establishment with 5% weight (Table 3). The consistency ratio, according to the study's findings, is CR = 0.06, which is acceptable since it is less than 0.10.

Table 3

Weighing scores for each criterion
	AHP (%)	Weighted overlay (%)
Reclassified wind speed	43.29	43
Reclassified existing electric grid lines	19.75	20
Reclassified existing roads	13.57	14
Reclassified distance to settlement	4.76	5
Reclassified slope	5.32	5
Reclassified river	6.87	7
Reclassified LULC	6.44	6

3.2.2. Wind Farm Suitability Categories

Using a weighted overlay on ArcMap, the full wind farm suitability map for the study area was generated (Table 4). There are four classes in the final map (Fig. 10). Table 4 demonstrates that 0.628% of the total area (28.00 km²) is deemed to be highly suitable for establishing wind farms. With an area of 2433.96 km², a total of 54.61% of the land is moderately suitable. The remaining 0.85% (37.85 km²) of the total area is deemed unsuitable for wind turbines, leaving 43.91% (1956.94 km²) less suitable. Table 4 and Fig. 10 show that the majority of Wolaita (54.61% of the area) is moderately suitable. The promising areas are found in the study area's central, northwestern, and southern regions. Based on these findings, there will be enough space for the deployment of wind farms in the study area if sites with highly suitable and moderately suitable areas are utilized.

Table 4

Final suitability score and area coverage for Wolaita
Suitability score	Classes	Area (km²)	Area Coverage (%)
Most Suitable	1	28.00	0.628192
Moderately Suitable	2	2433.96	54.61289
Least Suitable	3	1956.94	43.90959
Not Suitable	4	37.85	0.849328
		4456.74	100.00

Ethiopia has launched a climate-resilient green economy initiative to increase electric power supply and achieve near-zero greenhouse gas emissions by 2030. As a result, this study helps to realize the goals of climate resilience and a green economy by diversifying energy sources and reducing the impact of rainfall variability and the frequent appearance of severe drought on hydropower. The location of potential wind energy projects has become a contentious issue among developers, environmentalists, and local residents. In this regard, using GIS and the AHP approach, this study identified feasible ideal sites for wind farm developments by taking into account social, economic, and environmental factors and constraints. The results show that integrating AHP with GIS for spatial decision-making is a worthwhile technique for dealing with large and contradictory criteria in wind farm site selection processes. Important factors were considered in the analysis when selecting potential wind farm sites. Land use type, slope, distance from road, distance from transmission lines, settlement, wind speed, and distance from river are all determining factors in locating an appropriate location for a wind turbine. As a result, a wind farm suitability map was generated in GIS for each of the factors. The factor maps were combined based on their importance, with wind speed factors being more important in order to create an overall suitability map. The study found that highly suitable areas are located in the study area's central, northeastern, northwestern, southeastern, southwestern, and southern regions. In total, 0.628% of the total area (28.00 km²) is found highly suitable for wind farm construction. With a total area of 2433.96 km², 54.61% of the land is moderately suitable. The remaining 0.85% of the total area (37.85 km²) is deemed unsuitable for wind turbines, leaving 43.91% (1956.94 km²) less suitable. Based on these findings, if sites with highly suitable and moderately suitable areas are used, there will be enough space for establishing wind farms in the study area. This study suggests additional research that takes into consideration the unique geological character traits. Finally, the results may persuade interested parties to make more certain contributions in the energy sector in order to aid Ethiopia's efforts to cut carbon emissions.

Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

This work was supported by a thematic research project funded by Wolaita Sodo University.

Author contributions

Conceptualization, Y.B.A, and N.E.B; writing—original draft preparation. Y.B.A, N.E.B, M.D.C A.G.S and Y.S.E.; writing—review and editing; All authors have read and agreed to the final version of the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Correspondence and requests for materials should be addressed to N.E.B.

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No competing interests reported.

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You are reading this latest preprint version

Site Suitability Assessment for the Development of Wind Power Plant in Wolaita area, Southern Ethiopia: An AHP-GIS Model

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Description of the study area

2.2. Parameters for Assessing Land Suitability

2.2.1. Wind Speed

2.2.2. Distance to Settlement

2.2.3. Distance from Transmission Lines

2.2.4. Distance from rivers

2.2.5. Distance from transportation lines

2.2.6. Slope

2.2.7. Land use/land cover (LULC)

2.3. Multi-Criteria Decision-Making

2.3.1. AHP methods

2.3.2. Data Types and sources

3. Results And Discussion

3.1. Land Suitability Mapping

3.1.1. Wind Speed

3.1.2. Distance to settlements

3.1.3. Distance to power lines (PL)

3.1.4. Distance to rivers

3.1.5. Distance to transportation network

3.1.6. Slope

3.1.7. Land use/Land cover (LULC)

3.2. Determining factors weight and suitability analysis

3.2.1. AHP results

3.2.2. Wind Farm Suitability Categories

4. Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1