نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 دانشیار سنجش‌ازدور و سیستم اطلاعات جغرافیایی دانشکدة جغرافیای دانشگاه تهران
2 دانشجوی دکتری سنجش‌ازدور و سیستم اطلاعات جغرافیایی دانشکدة جغرافیای دانشگاه تهران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction
Energy as the fundamental need of mankind is essential for modern societies to achieve the interrelated goals: such as human needs for heating, cooling, lighting, mobility and for running a large diversity of appliances, it is necessary to supply power and heat to production systems. Global energy resources can be classified into three main groups, namely fossil energies (oil, gas, coal, etc.), nuclear energy, and renewable energies (wind, solar, geothermal, biomass, etc.). Most of the energy sources currently relied on finite resources and will be depleted because of the increasing demand. In addition, there have been serious local air, water, and soil pollution problems as a result of consumption of the various energy resources. It has become clear that application of fossil fuels is not a wise option not only because of its global impacts on climate system but also the short-term and very long-term impacts on society and the ecosystem. Solar photovoltaic (PV) technology is one of the fastest growing technologies in using Renewable Energy Resources (RES) worldwide. Harnessing the untapped and unmatched solar potential would not only assist in improving total energy mixture but also reduce the emission of harmful and toxic gases. Multi Criteria Analysis (MCA) can be defined as formal or structured approaches for individuals or groups to determine overall preferences among alternative options by taking account of multiple criteria and indicators. They have become increasingly popular in decision making on sustainable developments and on energy systems due to their ability to consider simultaneously a number of economic, environmental, social and technological aspects. The purpose of this study is to provide a decision support tool for decision makers using Fuzzy AHP as a Multi Criteria Decision Making (MCDM) technique and GIS tools for investigation and selection of suitable solar farm sites in East Azarbaijan province.
Methodology
Based on the experiences of the previous studies in the literature and case study subject of this research, we selected nine criteria including GHI, PVOUT, elevation, slope, aspect, annual sunshine hours, average annual relative humidity, proximity to cities and roads. GHI and PVOUT layers are downloaded from “solargis” website. For generating climatic layers, we applied IDW method using 14 meteorological stations data downloaded from “East Azarbaijan metrological website”. Elevation, slope and aspect’s layers were created using ASTER satellite Digital Elevation Model (DEM). The layers of proximity to cities and roads have also been created using raster calculator tool in ArcGIS 10.3 software. All criteria have been generated in ArcGIS 10.3 software and standardized in a [0-1] scale using different equations. In order to perform overlay analysis, all layers have been set to same cell size of 29.27 square meters. In some areas, due to legal constraints, engineering or environmental aspects, there is no possibility to deploy solar farms. Thus, three types of areas including protected areas, areas so close to cities and areas close to active faults have been defined as constraint areas. We applied fuzzy AHP technique to determine the relative weights of the criteria to each other. Finally, sensitivity analysis has been performed on the results of AHP in order to validate the outputs. In order to generate primary suitability map, the criteria and their weights have been combined using ArcGIS Raster Calculator. The highest suitability was 0.8 and the lowest was 0.0628 in the results. Then, constraint layers have been created using buffer tool and converted into a binary layer with 0 and 1 values in which the number 0 represents the places of the constraint and 1 represents those with no constraint. In order to generate final suitability map, the final binary constraint layer is applied to primary suitability map using multiplication operator by raster calculator tool.
Results and discussion
The results obtained by AHP method show that PVOUT and GHI criteria have the highest priority and annual average relative humidity and annual sunshine hours have the lowest priority. The results show that 47.99% of the study area has weak suitability, 27.10% of that has moderate suitability, 18.31% has good suitability, and 06.60% has great suitability for solar farm deployment. The west and southwest areas of the study area are the most suitable and north and northeast areas are the least suitable. This can be argued that the west and southwest areas are mostly flat areas with high values of GHI and PVOUT and north and northeast areas are not suitable. To conduct sensitivity analysis, 3 criteria of weight scenarios were considered. In first scenario, equal weights are assigned to all criteria, in second scenario the higher weights are assigned to elevation, slope, and aspect criteria and weights of other six criteria considered equal. Finally, in third scenario the higher weights are assigned to GHI and PVOUT criteria. It should be noted that in all scenarios, the total weight of all nine criteria is 1 (i.e., 100%).
Conclusion
In this study site selection for solar farms has been conducted by Multi Criteria Decision Making techniques and Geographic Information System. Nine criteria were adopted by relative priority weights using Fuzzy AHP method. The PVOUT criterion has the highest weight and annual sunshine hour criterion has the lowest weight. Layer standardization for overlay analysis was conducted in ArcGIS 10.3. Three areas as constraint zones are applied to primary suitability map. The results show that forest and mountain areas are not suitable for solar farms and on the other side smooth and flat areas are mostly located in west and southwest parts of the study area as they are more suitable for solar farms. Policy makers and planners can use the results of this study for energy supply using solar energy as one of renewable and cleanest energy sources. The methodology used in this study can be performed in other areas with similar conditions.
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