期刊
ENERGY
卷 197, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.117167
关键词
Solar energy; Restrictive factors; Determinant factors; Geographic Information System (GIS); Multi-criteria decision making (MCDM)
资金
- National Institute of Science and Technology in Distributed Generation (INCTGD)
- CNPq [465640/2014-1]
- CAPES [23038.000776/2017-54]
- FAPERGS [17/2551-0000517-1]
The decision of the location for the installation of photovoltaic power plants is a critical issue for large-scale projects due to climatic aspects, the proximity to other facilities, and the presence of protected environmental areas. The objective of this study is to propose a model capable of indicating the best location for the implantation of large-scale photovoltaic plants. According to the proposed modeling, the relevant factors were identified to be considered in the choice of the ideal place, which were combined with the GIS-MCDM tools. The analysis of the areas was processed by the gvSIG software, using AHP methods for the weighting of the factors and TOPSIS for ranking the alternatives. In application of this model, a total of 453 areas were identified that could receive a large photovoltaic installation, equivalent to 1823.35 km(2) of the study area. Among these study areas, according to our model rating, 67.23% of them are inadequate, 0.52% is good, 12.34% are very good, and only 19.91% are excellent. Since the areas considered excellent are concentrated in the extreme south of the analyzed region, a place of low index of slope and near the substation. In order to validate the quality of the solution obtained by the proposed model was applied a detailed analysis with two scenarios. In the first one are used weights equal to all the criteria and in the second, the MAUT method was applied to rank the alternatives. The modeling proposed in this paper combining with GIS-MCDM is applicable to similar investigations in other regions and also to evaluate the spatial adequacy of other renewable energy technologies. (C) 2020 Elsevier Ltd. All rights reserved.
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