نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی بیوسیستم، دانشکده علوم کشاورزی، دانشگاه گیلان، گیلان، ایران.
2 گروه مهندسی بیوسیستم، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران
3 گروه مهندسی بیوسیستم، دانشکده علوم کشاورزی، دانشگاه گیلان، گیلان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The depletion of fossil fuels, environmental issues, and climate change make the development of renewable energy, especially wind energy, essential. The main challenge in developing wind energy is selecting suitable locations for power plants, where land roughness plays a significant role. This study aimed to prioritize suitable areas based on land roughness using remote sensing in Kiashahr. Land use classification results by the SVM algorithm from 2000 to 2020 showed changes in 2,957.66 hectares of the region. Predicted maps from Markov Cellular Automata models for 2030 were used to ensure practical application of results for future years. The simulated map was gridded based on Wieringa roughness length data to generate maps of roughness length and classes. Results showed that 84 cells, equivalent to 1,363.98 hectares, in the first and second classes have the best potential for wind power plants. Additionally, land use maps for 2030 indicated that a large part of the region is used for agriculture, mostly rice cultivation. These areas have a roughness length of 0.25 m for only two months of the year, and for the rest of the year, they have a roughness length of 0.1 m (class 4) and 0.03 m (class 3). Overall, considering a roughness length of up to 0.25 meters, 552 cells, equivalent to 8,963.36 hectares, were identified as suitable for wind power plants. The findings of this research can help identify suitable areas for wind power plant construction and assist in modeling wind speed near the hub of tall wind turbines.
کلیدواژه‌ها [English]
Zoning land surface roughness for wind turbine installation using satellite remote sensing: a case study of kiashahr county
EXTENDED ABSTRACT
Energy is considered one of the key factors in economic advancement and wealth creation for countries. Due to the scarcity of fossil fuel resources and environmental damage, government support policies for investment in renewable energy have become increasingly important. Wind energy, as a clean and inexhaustible source, is a suitable option for exploitation. However, one of the major challenges in wind energy development is selecting the appropriate location for establishing power plants, where land roughness plays a very significant role. Unfortunately, in Iran, due to the vast reserves of oil and gas, there has been less attention given to renewable energies, and despite the high potential in this field, sufficient development has not occurred. This research aims to prioritize suitable areas in terms of land roughness using satellite images within the Kiashahr study area.
In this research, land use changes in the region between 2000 and 2020 were detected using supervised classification with the Support Vector Machine (SVM) algorithm. Then, the CA-Markov model was used to simulate land use changes for the year 2030, and the predicted map was utilized to create maps of roughness length and roughness classes. The predicted map was gridded based on Wieringa roughness length information to produce zoning maps of roughness length and roughness classes.
Between 2000 and 2020, forests decreased by 553.55 hectares. Contributing factors to this reduction include forest encroachment, excessive tree cutting, and forest destruction for villa construction. Residential areas expanded by 809.94 hectares between 2000 and 2020. Most of these changes resulted from the conversion of agricultural land edges into residential areas, driven by rural migration, population growth, and increased demand for new housing. This construction boom in the region has led to an increase in roughness length. Based on the results obtained from roughness maps, 84 cells, equivalent to 1363.98 hectares, in Kiashahr fall into classes 1 and 2. These areas are very suitable in terms of roughness for establishing a wind energy site. Most of these cells are concentrated in the northern and eastern parts of the region, exposed to coastal and northern winds, making them the highest priority areas for establishing a wind energy site, irrespective of other influencing parameters. Additionally, 10666.89 hectares of Kiashahr are agricultural lands, of which 5277.34 hectares have suitable roughness potential for installing wind turbines. These areas experience a roughness length of 0.25 meters for only two months of the year, while the rest of the year they experience roughness lengths of 0.1 meters (class 4) and 0.03 meters (class 3). Overall, considering a roughness length of up to 0.25 meters, 552 cells, equivalent to 8963.36 hectares, have been identified as suitable for establishing a wind power plant.
The findings of this research showed that using classification algorithms and modeling methods, large areas can be studied for the potential of wind power plants. This method not only identifies suitable areas for wind farm construction for decision-makers but also serves as a database for modeling wind speed near the hubs of tall wind turbines, which require roughness as an input parameter.
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