Land Suitability Analysis for Solar Farms Exploitation Using GIS and Fuzzy Analytic Hierarchy Process (FAHP)—A Case Study of Iran

Autor: Ehsan Noorollahi, Dawud Fadai, Mohsen Akbarpour Shirazi, Seyed Hassan Ghodsipour
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Zdroj: Energies, Vol 9, Iss 8, p 643 (2016)
Druh dokumentu: article
ISSN: 1996-1073
DOI: 10.3390/en9080643
Popis: Considering the geographical location and climatic conditions of Iran, solar energy can provide a considerable portion of the energy demand for the country. This study develops a two-step framework. In the first step, the map of unsuitable regions is extracted based on the defined constraints. In the next step, in order to identify the suitability of different regions, 11 defined criteria, including solar radiation, average annual temperatures, distance from power transmission lines, distance from major roads, distance from residential area, elevation, slope, land use, average annual cloudy days, average annual humidity and average annual dusty days, are identified. The relative weights of defined criteria and sub-criteria are also determined applying fuzzy analytical hierarchy process (FAHP) technique. Next, by overlaying these criteria layers, the final map of prioritization of different regions of Iran for exploiting solar photovoltaic (PV) plants is developed. Based on Iran’s political divisions, investigation and analysis of the results have been presented for a total of 1057 districts of the country, where each district stands in one of the five defined classes of excellent, good, fair, low, and poor level. The obtained data indicate that 14.7% (237,920 km2), 17.2% (278,270 km2), 19.2% (311,767 km2), 11.3% (183,057 km2), 1.8% (30,549 km2) and 35.8% (580,264 km2) of Iran’s area are positioned as excellent, good, fair, low, poor and unsuitable areas, respectively. Moreover, Kerman, Yazd, Fars, Sisitan and Baluchestan, Southern Khorasan and Isfahan are included in the regions as the most excellent suitable provinces for exploiting solar PV plants.
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