A High-Resolution Wind Farms Suitability Mapping Using GIS and Fuzzy AHP Approach: A National-Level Case Study in Sudan

Abstract

Wind energy is one of the most attractive sustainable energy resources since it has low operation, maintenance, and production costs and a relatively low impact on the environment. Identifying the optimal sites for installing wind power plants (WPPs) is considered an important challenge of wind energy development which requires careful and combined analyses of numerous criteria. This study introduces a high-resolution wind farms suitability mapping based on Fuzzy Analytical Hierarchy Process (FAHP) and Geographic Information System (GIS) approaches considering technical, environmental, social, and spatial aspects, representing eight different criteria. First, a multi-criteria decision-making analysis based on the FAHP method is employed to assign appropriate weights for the addressed criteria with respect to their relative importance. Since the traditional AHP method, which was found employed in the majority of the relative case-studies, is not efficient in dealing with uncertainty when experts use a basic scale (0 to 1) for their assessments, the FAHP provides more flexible scales through the utilized fuzzy membership functions and the natural linguistic variables. Consequently, this helps to facilitate the assessments made by experts and increases the precision of the obtained results (weights). Next, the high-resolution GIS is used to carry out a spatial analysis and integrate various factors/criteria throughout the proposed index to produce the final suitability map and identify the unsuitable areas. The presented study emphasizes investigating the lightning strike flash rate due to its significant influences on the wind turbine’s safety and operation, yet this crucial factor has been seldomly investigated in previous studies. The obtained findings revealed that the wind speed, the land slope, and the elevation had the highest weighted criteria with 33.1%, 24.8%, and 12.2%, respectively. Besides, the final-developed suitability map revealed that 23.22% and 8.31% of the Sudanese territory are of high and very high suitability, respectively, for wind farms installation which are considered sufficient to cover the electricity needs. The difficulty of acquiring real data and resources for the addressed location was the main challenge of the presented work. The work outlook addresses the suitability mapping of hybrid photovoltaic-wind turbine energy systems, which will require addressing new and significant criteria in the applied methodology.

Standardizing the factors used in wind farm site suitability models: A review

“…Eighty-five (73%) of the 116 reviewed articles summarized siting factors in table form (see Supplementary Material), with table columns typically detailing each factor's description [ 76 , 201 ], dataset source [ 163 , 181 ], citations [ 81 , 135 ], and implementation for constraint or evaluation [ 74 , 78 ]. Older WiFSS studies, such as Baban and Parry [ 161 ] and Rodman and Meentemeyer [ 71 ], along with recent, high-impact studies [ 104 , 155 ], are often cited to justify siting factor choices, establishing a frequently emulated style of factor selection and tabular presentation [ 143 , 148 , 150 , 213 ].

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“…Table 1 a and 1b shows that most studies in this review utilized secondary datasets, particularly those with GIS-based MCDA approaches, usually in the form of downloaded geospatial data [ 81 , 90 , 124 , 145 , 181 ] and previously recorded observations [ 80 , 125 , 147 , 162 , 206 ]. Some secondary datasets were enlisted by multiple studies, such as road information obtained from OpenStreetMap [ 130 , 136 , 144 ], Digital Elevation Models from the United States Geological Survey [ 71 , 80 , 133 ], and wind speed information from the Global Wind Atlas [ 79 , 143 , 148 ]. The use of such datasets for WiFSS studies exemplifies the value of free resource access for public sector model development [ 228 ], because developers are thereby encouraged to use a common set of siting factors, facilitating standard language and comparisons of modeling approaches that are less biased by siting factor choices.…”

“…Acronyms for the listed MCDA methods: Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multicriteria Optimization and Compromise Solution (VIKOR, in Bosnian), Ordered Weighted Averaging (OWA), Best-Worst Method (BWM), ELimination Et Choice Translating REality (ELECTRE, in French), Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE), Multicriteria Interactive Decision Making (TODIM, in Portuguese). MCDA Approach Study Context Data Type (Secondary/Primary) Weighting/Ranking Method References GIS-based Onshore Secondary AHP [ [46] , [74] , [75] , [78] , [79] , [80] , [82] , [83] , [94] , [104] , [122] , [123] , [124] , [125] , [126] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] ] ANP [ [149] , [150] , [151] ] …”

Application of Fuzzy Analytic Hierarchy Process in Environmental Economics Education: Under the Online and Offline Blended Teaching Mode

“…Firstly, the uncertain linguistic variables, proposed by experts, were transformed into mathematical variables and corresponding indicators by the fuzzy set method, and then the students' achievement evaluation system was constructed by the AHP, and the weights of the indicators were assigned to calculate the final grades. Although the AHP has been widely used and proved, it has still relied on the subjective evaluation of experts and could not be 100% objective [20,21], so the fuzzy set method was introduced to eliminate the subjective interference as much as possible and characterize uncertain factors. The fuzzy set method was to solve the subjective interference in the AHP [22].…”

Legal Conditions for Investments in Renewable Energy in the Overburden Disposal Areas in Poland

“…For the purpose of searching for locations for renewable power plants, geoinformatic tools can be used. Geoinformatic studies try to evaluate spatial effects of adopting different criteria for renewable power plant siting: environmental, social, technical criteria and energy resource availability [25][26][27][28][29]. Nevertheless, these studies can only play a role in the initial phase of the search for the location.…”