A Hybrid Multi-Criteria-Decision-Making Aggregation Method and Geographic Information System for Selecting Optimal Solar Power Plants in Iran

被引:16
|
作者
Dahooie, Jalil Heidary [1 ]
Kashan, Ali Husseinzadeh [2 ]
Naeini, Zahra Shoaei [2 ]
Vanaki, Amir Salar [1 ]
Zavadskas, Edmundas Kazimieras [3 ]
Turskis, Zenonas [3 ]
机构
[1] Univ Tehran, Fac Management, Tehran 141556311, Iran
[2] Tarbiat Modares Univ, Fac Ind & Syst Engn, Tehran 14115138, Iran
[3] Vilnius Gediminas Tech Univ, Fac Civil Engn, Inst Sustainable Construct, Sauletekio Al 11, LT-10223 Vilnius, Lithuania
关键词
solar energy; power plant location; multicriteria decision-making; MCDM; aggregation method; SITE SELECTION; MCDM MODEL; GIS-AHP; FARMS; LOCATION; TOPSIS; FRAMEWORK; REGION; DEA; OPTIMIZATION;
D O I
10.3390/en15082801
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of the potential. Although the simultaneous use of several methods increases the robustness and accuracy of the results, existing methods to integrate MCDM methods mainly consider the same weight for all methods and utilize the alternatives ranking for the final comparison. This paper presents a hybrid decision-making framework to determine the best location for SPPs in Iran using a set of criteria extracted from the literature and expert opinions. An initial list of decision-making alternatives is prepared and evaluated using GIS software in terms of criteria. Decision-makers prioritized the identified alternatives using the MCDM methods, including SWARA and different ranking methods (TOPSIS, TODIM, WASPAS, COPRAS, ARAS, and MULTIMOORA). Finally, the CCSD method aggregates the results and identifies the best location. Results highly correlate with the results of previous methods and demonstrate the robustness of the proposed approach and its capability to overcome the limitations of previous methods.
引用
收藏
页数:20
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