南贡德尔，阿姆哈拉地区太阳能光伏发电的网站适用性分析

Abstract

埃塞俄比亚政府看向可再生energy resources to generate electrical power for the current demand of the country. 85% of the total population of the country lives in rural areas and uses fossil fuel for their domestic uses. Using fossil fuel poses a danger for users and the environment. And the government of Ethiopia planned to electrify 85% of the rural community with abundant available renewable resources around the community. Therefore, identifying potential locations for solar PV with GIS is a decision support tool for proposing suitable sites to the government. The solar PV suitability analysis provides optimal locations for solar PV power plant installations. To find suitable locations for solar PV, factors that affect suitability were identified and weighted using analytical hierarchy processes. Then, the weighted values and reclassified values were multiplied together to produce the final suitability map for solar PV. Due to site unsuitability, solar PV generation efficiency drops and may malfunction. By identifying the most suitable locations, a solar PV power plant is optimally located. Therefore, the objective of this study was to find the most suitable sites in the South Gondar Zone for generating power from solar PV. The suitability of the study area for a solar PV power plant is 86.5%. Eighty-six (86%) of the criteria considered in the study area were found to be suitable for optimal location of solar PV power plant. Most of the suitable areas were found in the western part of the zone. The nature of topography is a key factor in generating solar energy; it affects the solar irradiance coming to the solar PV panel surface.

1. Introduction

网站寻找潜在适用性分析手段locations for a solar PV installation. ArcGIS is a decision support tool for proposing suitable sites to government. Finding suitable locations for a solar PV installation enables increasing the efficiency of a solar PV power system. Thus, finding a suitable location for solar PV is one of the measurement strategies to improve the efficiency of solar power plants. Due to site unsuitability, solar PV efficiency drops and may malfunction. This is the reason why researchers have formulated recommendations for the optimal location of solar PV installations. In Ethiopia, more than 85% of the rural community benefits from the practice of finding suitable locations [1–3]。

Renewable energy is derived from natural processes constantly. It is economically feasible, easy to use, less polluting, and abundant in nature. Therefore, renewable energy is more important than nonrenewable sources of energy. Using renewable energy maintains the environment and does not contribute to global warming [4,5]。In the context of Ethiopia, it is woefully, since most of the families use wood and fossil fuel for their domestic uses. This will affect the development of the country and the health of the community. The development of the country depends on exploiting renewable energy resources and using it optimally. Ethiopia is blessed with an abundance of renewable energy resources like solar, wind, hydro, and others. Amhara is the region where these renewable energies are available throughout the year [6,7]。

Ethiopia has a higher potential to exploit renewable energies for domestic applications. This vast renewable energy resource potential was not exploited sufficiently in the country, primarily due to the lack of scientific and methodological know-how with regard to planning, site selection, and technical implementation. A further constraint prohibiting their utilization was that the real potential of these resources’ location is not well-known, because of the lack of research emphasis [6,7]。

In Ethiopia, most solar power plants have low efficiency, and the cause for this efficiency drop is site unsuitability. This can be addressed through site suitability analysis. Currently, the practice of finding a suitable location in Ethiopia is low, and solar PV has been installed without considering a detailed site suitability analysis.

Even though the government of Ethiopia recently took steps towards renewable energy resources, there is a problem in identifying the exact resource locations. The efforts were not organized, and this is due to a low number of researches to explore suitable locations. Therefore, it is important to conduct short and long-term energy planning. The renewable energy resources need to be evaluated in a systematic way, and the technoeconomic issues must be considered in renewable energy resource evaluation. Therefore, this study is aimed at finding suitable locations for a solar PV for sustainable energy planning in four different districts (Dera, Estie, Farta, and Fogera) of the South Gondar Zone, Amhara Region, Ethiopia (Figure1)。层次分析法和ArcGIS被整合为优先顺序。这就决定尝试有望激发政府相关部门使用系统的规划过程中对可再生能源资源的选择，开发可再生能源发电厂。

2。文献综述

有些情况下，多目标决策已被用于不同的角度不同的站点适用性相关的研究。多目标决策已用于能源规划和不同层次的决策，长期能源计划的风险评估，最好的可再生能源技术和能源管理问题的选择和合适的地点火电厂安装的选择。

The multicriteria weight methodology has been used to rank land suitability evaluation for crop production in Morocco [8]。大多数研究人员[9–11] have agreed that MCDM approaches are well-suited to address strategic decision-making problems. MCDM methods provide a systemic and apparent way to enclose multiple conflicting objectives. MCDM based on multiattribute value functions is used to support energy planning and to select and prioritize the suitable location for renewable energies.

Azmi et al.’s [11] review provided an application of MCDM with GIS methods in photovoltaic site suitability analysis and suggested that AHP is the most popular method.

Ammar et al. [12] used the AHP method to map a suitable area for a photovoltaic water pumping system in Algeria (Table1)。

This study contributes to the existing literature by proposing AHP with ArcGIS methodology for solar PV site suitability analysis. The AHP approach was used to identify the weights of the criteria, whereas ArcGIS was used to map the suitable location for solar power plants.

3. Study Area and Data Settings

Suitability analysis for solar power installation was conducted in the South Gondar Zone, Amhara regional state. The Amhara Region is located in the northwestern part of Ethiopia between 9°20和14°20north latitude and 36°20and 40°20east longitude. Its area is estimated to be about 170000 square kilometers [25]。Ethiopia’s largest inland body of water, Lake Tana, which is the source of the Blue Nile River, is located within Amhara (Figure1)。The region also contains the Semien Mountains National Park, which includes Ras Dashan, the highest point in Ethiopia. The Amhara Region is bordered by the state of Sudan to the west and northwest, and in other directions by other regions of Ethiopia: Tigray to the north, Afar to the east, Benishangul Gumuz to the west and southwest, and Oromia to the south. The region has twelve zones, and there was no more electricity access to the town and the surrounding community in these zones.

South Gondar is a zone in the Ethiopian Amhara Region. This zone is named for the city of Gondar, which was the capital of Ethiopia until the mid-19th century, and has often been used as a name for the local province.

South Gondar is located at a latitude/longitude of 11°5019N / 38°558E and bordered on the south by East Gojjam, on the southwest by West Gojjam and Bahir Dar, on the west by Lake Tana, on the north by North Gondar, on the northeast by Wag Hemra, on the east by North Wollo, and on the southeast by South Wollo; the Abbay river separates South Gondar from the two Gojjam zones [25]。

In finding suitable areas for solar power plants, ArcGIS10.4.1 was used. To analyze site suitability, nonsuitable sites were excluded like towns, water bodies, and schools/protected areas, and the weight of the criteria was formulated for decision making [26–29]。Data set rasterizing and reclassifying have been conducted on vectors and raster layers like solar irradiance, distance from roads, towns, soil, slope, land use land cover, forest, stream, and distance from school areas. Finally, reclassified data were combined by using the ArcGIS overlay tool [三十–32]。

The data sets were obtained from different sources in vector formats, and then vector data were converted to raster with the conventional tool of ArcGIS10.4.1. Thus, the raster outputs of data sets were reclassified and finally overweighed to show results for finding suitable sites [27,33]。

There are three tasks in finding suitable areas for solar power plant installations:(1)Data preparation and criteria formulation as the nature of the study areas, then data insertion into ArcGIS10.4.1 software as layers(2)Reclassification of the data sets(3)Overlaying the reclassified data

3.1. Analytic Hierarchy Process (AHP) for Criteria Evaluation

The analytic hierarchy process (AHP) is one of the multicriteria decision-making methods. There were nine criteria in this study, and this was the reason why the analytic hierarchy process (AHP) was used to analysis site suitability. It was used to make complex decision problems. The pairwise comparison method was used to evaluate the weights of the criteria. First, the weights of criteria analysis were done to examine important criteria in finding suitable location practice. Thus, ArcGIS can prioritize the sites according to the weights of the criteria. The input was obtained from subjective opinion like satisfaction feelings and preference. The pairwise comparison of the attributes makes it easy for complex problems decision. It compares the importance of two attributes at one time (Table2)。

3.2. Selecting Criteria for Solar PV Installation

在寻找用于太阳能发电站的潜在位点，位点选择取决于每层的权重。专家的意见（表3) were used to determine each site selection criterion for locating solar PV [31,32,36]。Solar irradiance, roads, town, soil, slope, land use, land cover, forest, stream, and schools were used to create a model to identify suitable locations for solar PV as per the nature of the study area (Table4)。

通过总结列，并将其分割给每个小区值（获得的太阳能光伏电站的归一化矩阵的决定表5)。The normalized values were calculated from the decision-making matrix ( )as follows: 哪里is a normalized value,is the column of the matrix,is the value of the column of the decision.

In identifying the potential sites for solar PV power plants, site selection was dependent on the weights of each layer (Table6)。The weights of the criteria were calculated from a normalized matrix ( )as follows: 哪里is the weight of the criteria,is the row value of the normalized matrix, and是的适用性分析标准的数目。

3.3. Site Suitability Analysis of Solar PV

Geographic information systems (GIS) and ArcGIS were used to indicate the appropriate sites for solar PV power plants. ArcGIS can prioritize the site to determine the most suitable sites. GIS was modeled to store data, analyze data, and display spatial data on a map [6,37]。

To determine the most suitable areas for solar PV placement, nine data sets were taken as a layer. Thus, the data sets are solar irradiance, roads, forest, stream, schools, town, soil, slope, and land use land cover. The study area was ranked to determine the most suitable sites, and the potential sites for solar PV placement were prioritized as highly suitable, suitable, moderately suitable, and unsuitable.

3.3.1. Solar Irradiance Reclassification

太阳能辐照度是要找到太阳能发电厂最合适的位置是最主导因素。太阳辐射数据集合是从代表以kWh /米全球水平太阳辐照度的每日总计的平均的NASA表面计量采取²[27,三十,38–43]。

For twenty years, the yearly average solar irradiance layer was downloaded from NASA.

According to the national renewable energy laboratory report [44], areas with 3.56 kWh/m²每天太阳辐照度是经济可行的. Therefore, areas with less than 3.56 kWh/m²solar irradiance per day were considered as unsuitable in the study (Table7)。The raster solar irradiance was reclassified as unsuitable (<5 kWh/m²per day), moderately suitable (5-5.5 kWh/m²per day), suitable (5.5-6 kWh/m²per day), and highly suitable (>6 kWh/m²per day) (Figure2)。

3.3.2. Road Reclassification

Transportation cost was the dominant factor for any power plant installations. Thus, the areas far from roads are not economically feasible and unsuitable. Therefore, locations with less than 500 m distance were selected as highly suitable sites for solar PV [27,33,43,45,46]。Locations with a distance of 500-1000 m are suitable, 1000-5000 m are moderately suitable, and greater than 1500 m are unsuitable (Figure3)。

The Euclidean distance was used for road data reclassifications in the ArcGIS tool and the Euclidean distance was reclassified (Table8)。

3.3.3. Reclassification of Slope

The surface of the earth was an important factor in finding suitable locations for solar power plants. The earth’s gradient affects receiving radiation from the sun. Thus, flat areas receive the most radiation and produce more energy from solar PV [25,26,47]。

Areas that have less than 3% gradient were reclassified as highly suitable and greater than 10% gradient were unsuitable (Figure4)。The slope reclassification data sets have shown that the western part of the zone was flatter (Table9)。On the other hand, the eastern part had higher slope areas.

3.3.4. Land Use, Land Cover

土地利用土地覆盖was a key factor in finding the optimal location for solar PV power plants. Cultivated/agricultural areas, forest areas, and urban areas were excluded in this study [25]。The open areas were considered as highly suitable areas for solar power plant installations (Figure5)。

3.3.5。从镇距离

The towns will expand, and there will be a shading effect due to large buildings. Thus, solar PV efficiency drops. Therefore, the distance from towns was another important factor in finding suitable sites for solar PV power plants [25,48]。The farthest distance from the town was considered as highly suitable and the shortest was considered as unsuitable for solar PV in this study (Table10)。

The town data set was reclassified as highly suitable (>6 km), suitable (4-6 km), moderately suitable (2-4 km), and unsuitable (<2 km) (Figure6)。

3.3.6。从学校的距离

Historical places, recreation areas, and schools were excluded in indicating optimal locations for a solar PV power plant [5,43]。Based on the nature of the study area, the distances from schools were reclassified into four categories, i.e., less than 300 m, 300-400 m, 400-1000, and greater than 1000 m (Table11)。The farthest distance from schools was considered as a highly suitable area, and the shortest distance was considered as an unsuitable site for solar PV power plants (Figure7)。

3.3.7. Distance from the Stream

The solar power plant locations were affected by streams and water bodies. The farthest locations were the most suitable locations [32]。The streams were reclassified into four main categories depending on the distance from the sites. The farthest locations were taken as more suitable, and the nearest locations were taken as unsuitable (Figure8)。作为highl距离大于2000米y suitable, 1000-2000 m as suitable, 500-1000 as moderately suitable, and less than 500 m as unsuitable (Table12)。

3.3.8。从森林的距离

Distance from the forest was the dominant factor for solar PV power plant site selection. The solar radiation is highly affected by the forest shadow [4,三十,38,39,47]。Thus, the distance far from the forest was considered as the most suitable and the nearest distance was considered as unsuitable locations (Table13)。

The forest data set was reclassified into four classes in this paper, with greater than 120 m as highly suitable, 80-100 as suitable, 30-60 m as moderately suitable, and less than 30 m unsuitable (Figure9)。

3.3.9. Soil Reclassifications

土壤也是在寻找最佳位置的太阳能光伏电站的重要因素。因此，luvisol被认为是最适合于太阳能光伏和laptosol的支持勃起被认为是不适合于支持勃起[25,28]。

Based on the nature of the study area, the soil type was reclassified into four main categories, and these were (luvisol) highly suitable, (fluvisol) suitable, (vertisol) moderately suitable, and (laptosol) unsuitable (Figure10)。

3.4. Weighted Overlays of Solar PV Suitability Analysis

All weights of the criteria were combined by using the ArcGIS10.4.1 weight overlay tool. The reclassified data set of the criteria which includes solar irradiance, distance from roads, distance from schools, distance from town, slope, distance from forest, soil type, and land use land cover were overlaid to the aggregate result [三十,37,43]。

适用性的最终映射得到的多plying each reclassified value with each weight value and summing up all layer products. The range of solar PV suitability was from one to four. Thus, the study area was separated into four main categories for placing solar PV.

3.5. Sensitivity Analysis Concepts in ArcGIS

Sensitivity analysis is used to evaluate the multicriteria model output on small changes in the input. Criteria weight is the most important attribute in sensitivity analysis. In this study, a suitability map is produced for solar PV-suitable sites. The criteria weights were changed with a certain percent from that of original weights to show the change in the suitability map of solar PV [三十,48–53]。

The sensitivity analysis was performed in such a way that when one priority criterion is increased by 1 percent, the other criteria are decreased with the same percent. This was done with different changes in criteria weights (Table14)。

4.结果与讨论

4。1. Suitability Map of Solar PV

The western and southwestern parts of South Gondar were suitable for solar PV from the candidate locations. This is mainly due to a high solar irradiance, a low slope, a short distance from roads, and at a distance far from the town, far from the forest, and far from the stream. The northwestern areas were also suitable in addition to the western and southwestern parts of South Gondar, and there were some potential areas in the southeast of the zone. The most unsuitable areas are found in the northeastern parts of the zone due to the near distance to the forest and Gumara stream (Figure11)。

From a total of 243, 353, 600 km²candidate areas taken in this study, 62078674.8941 km²was highly suitable which represented 25.5% of the study area, 86790957.7469 km²areas (35.7%) were suitable, and 25.3% of the areas were moderately suitable. Around 13.5% of the study area was unsuitable for solar power plant placement (Table15)。

4。2. Sensitivity Analysis

Sensitivity analysis was done to show the uncertainties of the criteria and to minimize bias of criteria selection of the experts. For analyzing the changes by which a decision maker can be able to get a better idea for a decision-making process, sensitivity analysis was done. When solar irradiation criteria weight was increased by one percent, the other criteria decreased by one percent. Thus, there is a small change in the suitability area (Table16)。A one percent change in solar irradiation has increased the suitability area by one percent, and the rest remains the same. And a suitability map was done for a one percent change in priority weight’s criteria (Figure12)。

Similarly, sensitivity analysis was done for a 5% change in solar irradiance weight (Figure13)。Thus, solar irradiance weight was reduced by 5 percent and the remaining criteria were increased by 5 percent to detect the change (Table17)。

The suitable areas were changed to unsuitable or moderately suitable which provides that sensitivity analysis is required for such uncertainties.

5. Conclusion

有在南贡德尔地区的西部和西南部地区太阳能发电的高潜力。这种潜在的有利于填补国内的需求和供给之间的能隙。它也可以用来当国家开始使用这个绿色的高潜力的太阳能发电的需求，以消除农村和城市社区之间的能隙。

The majority of the areas fulfilled the suitability analysis criteria. Solar irradiance, slope, soil type, land use, land cover, and distances from roads, forest, town, stream, and schools were the determinant factors for solar PV power site suitability analysis.

Data Availability

本研究的数据将不会被公开共享due to sensitive participant informations.

Conflicts of Interest

The authors declare that there are no conflicts of interest with regard to the publication of this paper.

Acknowledgments

我们要感谢我们的朋友谁在这个研究项目颇有兴趣，并鼓励谁，帮助我们做这个研究项目。我们也感谢巴赫达尔市工商行政管理局他们的许可和社区成员是谁给了必要的信息来进行研究。

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Copyright © 2020 Abraham Hizkiel Nebey et al. This is an open access article distributed under theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.