انرژی خورشیدی از پاکترین، قابل دسترسترین و ارزانترین انرژیهای جهان است که استفاده از آن اثرات منفی کمتری بر محیط زیست میگذارد. تعیین مکان مناسب برای احداث و استفاده از تکنولوژیهای خورشیدی از اهمیت بالایی برخوردار است. بنابراین هدف از این تحقیق، انتخاب مناطق بهینه احداث نیروگاههای خورشیدی با لحاظ کردن مفهوم ریسک در تصمیمگیری با استفاده از مدل OWA برای استان خراسان رضوی میباشد. مدل OWA قادر است تا میزان ریسکپذیری و ریسکگریزی گزینههای تصمیمگیران را در انتخاب گزینه نهایی لحاظ کند. در پژوهش حاضر، برای وزندهی به معیارها از مدل وزندهی AHP، جهت استخراج مکانهای مناسب با درجات ریسک مختلف از مدلOWA و برای آنالیز حساسیت وزن معیارها از روش OAT استفاده شده است. نقشههای حاصل از مدل OWA در پنج کلاس خیلی نامناسب، نامناسب، متوسط، مناسب و خیلی مناسب طبقهبندی گردیدند به طوری که در ORness=0 و ORness=1 مساحت طبقه خیلی مناسب (1-8/0) برای استان خراسان رضوی به ترتیب برابر با 6 و 82 درصد از مساحت کل منطقه میباشد. در استان خراسان رضوی، شهرستانهای فردوس، گنابادو بردسکن دارای بیشترین مساحت از طبقه خیلی مناسب برای احداث نیروگاههای خورشیدی میباشند. نتابج تجزیه و تحلیل حساسیت معیارها نشان داد که تغییر وزن معیارهای شیب و گسل به ترتیب دارای بیشترین و کمترین تأثیر بر مساحت طبقه خیلی مناسب جهت احداث نیروگاههای خورشیدی هستند.
عنوان مقاله [English]
Mapping the potential of solar power plants based on the concept of risk Case study: Razavi Khorasan Province
There is a huge potential in the usage of renewable energy sources because these natural resources are inexpensive and harmless to the environment. Solar, wind, and geothermal energies are among the renewable energies. Solar photovoltaic (PV) technology is one of the fastest growing renewable energy technologies across the world. Solar energy is a practical and suitable technology, especially in arid areas with high solar energy potential. The first step in using renewable energy in Iran was in 1994, and since then, much attention has been paid to this type of energy in the society and the government. In Iran, 850 million tons of greenhouse gases are produced annually. Consequently, renewable energy sources such as solar energy can have a significant impact on reducing the greenhouse gas emissions.
The integration of GIS and MCDA helps the decision maker to perform decision analysis functions such as ranking the options to select a suitable location so that the GIS is used as a powerful and integrated tool for storing, manipulating and analyzing the solar energy criteria. The use of the MCDA method can facilitate the evaluation and selection of the most appropriate location (s), taking into account the key criteria in the decision-making process.
In this study, the optimal areas for the construction of the solar power plants have been identified in five highly optimistic, optimistic, moderate, pessimistic, and highly pessimistic levels using the spatial criteria and the OWA model. One of the most prominent features of this research in relation to the other articles is the inclusion of the concept of risk into the solar power plant site selection process to determine the optimum areas for the construction of solar power plants using the OWA model.
Materials and methods
The primary data used in this study include the Digital Elevation Model (DEM) derived from the Aster satellite data for the extraction of solar radiation and the region slope, the extraction of the mean land surface temperature for 2017 using the Terra Sensor MOD11A1, the preparation of the average map of the vegetation for 2017 using MODRA13A2 Terra sensor, the 1.250000 fault map prepared by the geological organization, the statistics and data of the rainfall prepared by the Meteorological Organization of Chahar mahal-o-Bakhtiari province, shapefile of road network prepared by the Organization of Roads and Urban Development, the climaticshapefile of
the country prepared by the Iran Meteorological Organization, the shapefile of urban areas generated by the National Cartographic Center (NCC).The proposed methodology works by employing AHP to obtain the appropriate weights for each criterion, and utilizing OWA to extract suitable locations to varying degrees of risk. Sensitivity analysis for the criteria weights were conducted by virtue of the OAT method.
Results and discussion
The northern sectors of Razavi Khorasan province are endowed with cold temperatures and cold mountainous climate, which has had a substantial contribution to the increased cloudy and rainy days as well as the relatively extensive vegetation cover in this area. In this light, with respect to all ‘ORness’s, the target areas fall within the ‘very unsuitable’ and ‘unsuitable’ classes for construction of solar power plants. Moreover, the high slope factor in these areas has contributed to high levels of surface radiation, albeit, as the slope criterion is considered a constraint, the target areas are, in fact, not suitable for the construction of solar power plants. Moving southwards, the suitability of the regions, in terms of construction of solar power plants, tends to shift in the positive direction (very suitable class), which is most likely the result of the low rainfall and vegetation cover in conjunction with high surface temperatures in these areas, as opposed to their counterparts in the north. Areas falling within the very suitable class for construction of solar power plants in Razavi Khorasan can be realized by dint of calculating the percentage of area attributed to each class at ORness = 0.5 per city. The findings show that cities located towards the south and southwest of the province contribute to the highest area in the suitable class, while counties in the northern regions have the lowest share of area in the very suitable class.
The highest sensitivity in locating suitable areas in Razavi Khorasan province were observed among the factors of slope, road, and urban criteria. Alterations in the weights assigned to these criteria would entail a significantly strong impact on the extent of the very suitable class. This highlights the significance of accurately determining the weights for these three criteria in Razavi Khorasan Province. Based on the findings, the rate of change in weight assigned to the of fault criteria ranges from 0 to 0.2, which in turn causes substantial change in the area of regions in the very suitable class extent. However, setting the criteria weight at between 0.2 and 1 appears to have no significant effects in the area of this class.
The results of this research indicate that the northern parts of Razavi Khorasan province are highly unsuitable and unsuitable for all of ‘ORness’ values, while a significant extent of the highly suitable class for the construction of solar power plants is comprised of sectors of the southern regions. Areas within the very suitable class corresponding to an ORness=1 comprise 5% of the class, whereas those with an ORness=0 have a 74% share. The three cities of Ferdows, Bardaskan, and Gonabad, had the highest share of the area attributed to the very suitable class (0.8-1), as maintained by a per city analysis of the area for each class. However, the cities of Dergas, Quchan, Mashhad, and Kalat had no share of the areas within the very suitable class. This most probably stems from the high geographic latitudes of said regions, which has engendered unsuitable climatic conditions in these areas. Finally, results from sensitivity analysis of the criteria showed that increases in the weights assigned to the factors of slope, road, and urban criteria, would cause a further increase in the area of the very suitable class. Stated differently, the selection of suitable locations for the establishment of solar power plants is highly sensitive to these criteria. Changes in the weight of the surface temperature criterion had no considerable effect on the area of the very suitable class. Moreover, shifts in the weight allotted to solar radiation and precipitation in the province, ranging from 0 and 0.6, brought about substantial changes in the area of the very suitable class. Whereas, shifts within the 0.6–1 range had no significant effects on the area of the very suitable class.