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Summary As a clean and renewable energy resource, wind energy is the most mature, environmentally friendly, and most commercially developed new energy resource in the world. Therefore, it is of great importance to determine the best location of wind farms to ensure the sustainable development of wind energy. However, since wind farm site selection often involves multiple criteria, which include qualitative and quantitative criteria, there may be conflicts between these criteria, so wind farm site selection is a complex multi‐criteria decision‐making (MCDM) problem. Therefore, the objective of this study is to propose a novel integrated MCDM approach using a fuzzy analytic hierarchy process and satisfaction degree‐based fuzzy axiomatic design (AD) to determine the optimal onshore wind farm site under a hybrid decision information environment. First, based on the literature review and experts' opinion, the evaluation index system for wind farm site selection is built from a sustainable perspective, which includes geographic, technical, economic, social, and environmental criteria. Second, fuzzy analytic hierarchy process is applied to determine criteria weights. Third, the satisfaction degree‐based fuzzy AD is employed to evaluate and rank alternatives under a hybrid decision information environment. Finally, a case study is used to illustrate the reliability and advantages of the method proposed in this paper. In addition, the information content of each alternative is calculated by aggregating the evaluation matrix of experts, and the results are IC1 = 0.058, IC2 = 0.096, IC3 = 0.16, IC4 = ∞, IC5 = ∞, and IC6 = 0.226. Then the satisfaction degree of each alternative is S1 = 0.629, S2 = 0.545, S3 = 0.501, S4 = 416, S5 = 389, and S6 = 0.463. Thus, the best wind farm site is A1. Moreover, the results show that the method proposed herein is flexible and can effectively deal with the wind farm site selection problem. Although this paper chooses China as a case study, the proposed method herein is also applicable to other countries or regions.
Summary As a clean and renewable energy resource, wind energy is the most mature, environmentally friendly, and most commercially developed new energy resource in the world. Therefore, it is of great importance to determine the best location of wind farms to ensure the sustainable development of wind energy. However, since wind farm site selection often involves multiple criteria, which include qualitative and quantitative criteria, there may be conflicts between these criteria, so wind farm site selection is a complex multi‐criteria decision‐making (MCDM) problem. Therefore, the objective of this study is to propose a novel integrated MCDM approach using a fuzzy analytic hierarchy process and satisfaction degree‐based fuzzy axiomatic design (AD) to determine the optimal onshore wind farm site under a hybrid decision information environment. First, based on the literature review and experts' opinion, the evaluation index system for wind farm site selection is built from a sustainable perspective, which includes geographic, technical, economic, social, and environmental criteria. Second, fuzzy analytic hierarchy process is applied to determine criteria weights. Third, the satisfaction degree‐based fuzzy AD is employed to evaluate and rank alternatives under a hybrid decision information environment. Finally, a case study is used to illustrate the reliability and advantages of the method proposed in this paper. In addition, the information content of each alternative is calculated by aggregating the evaluation matrix of experts, and the results are IC1 = 0.058, IC2 = 0.096, IC3 = 0.16, IC4 = ∞, IC5 = ∞, and IC6 = 0.226. Then the satisfaction degree of each alternative is S1 = 0.629, S2 = 0.545, S3 = 0.501, S4 = 416, S5 = 389, and S6 = 0.463. Thus, the best wind farm site is A1. Moreover, the results show that the method proposed herein is flexible and can effectively deal with the wind farm site selection problem. Although this paper chooses China as a case study, the proposed method herein is also applicable to other countries or regions.
Summary Energy has been considered as one of the essential needs of mankind along with air, water, and food and witnessed evolution of civilization since evidence of human life. Managing energy resources is one of the challenging problems being capital intensive. Addressing this involves critical thinking and decision making with all possible aspects, technically known as set of primary and secondary criteria. There exist a number of literature sources addressing applications of multicriteria decision‐making (MCDM) in different energy‐related areas. Some are focusing on energy policy making, few are explaining site selection of solar PV, wind farm, and hydro power plants, and a few are describing applications in load management. Moreover, a few literature in this field elaborates various MCDM methods and their applications. In this article, an extensive and exhaustive study is carried out incorporating almost all possible applications of MCDM in renewable energy area. Various energy‐intensive applications are mapped with MCDM methods along with governing sensitive parameters. Hence, this study facilitates practicing engineers, decision‐makers, academician, and researchers to identify areas and MCDM techniques researched over the past decade in energy sector for planning, managing, selecting renewable resources, etc.
Due to population growth and industrial development in the Kingdom of Saudi Arabia (KSA), energy demand has increased. On the other hand, the consumption of fossil fuels has led to adverse environmental effects. This has led to government investment in clean energy. In many sectors of this country, there is good potential for using wind energy. Production of hydrogen from wind farms is a method for reducing energy swings, and it may also be utilized as a fuel in Saudi Arabia's businesses. As a result, the purpose of this research is to discover the best location for hydrogen generation from wind energy utilizing Multi-Criteria Decision-Making (MCDM) techniques. The criteria were weighted using the Step-wise Weight Assessment Ratio Analysis (SWARA) approach. The most important criteria were determined to be "average wind speed," "number of refineries in the region," and "wind power density" with values of 0.2780, 0.2570, and 0.1570, sequentially. Then, the Weighted Aggregated Sum Product Assessment (WASPAS) approach was used to rank the locations. Finally, three other techniques, namely the Evaluation based on Distance from Average Solution (EDAS), the COmplex PRoportional ASsessment (COPRAS), and the Weighted Sum Model (WSM) were used to validate the results. Saudi Arabia's Eastern Province was recognized as the best position for hydrogen expansion in the country. The Eastern Province, with a rated capacity of 900 kW, was anticipated to produce 1863 MWh of energy and 30.16 tons of hydrogen every year. Highlights• Wind potential in Saudi Arabia was evaluated for hydrogen production.• Location planning was analyzed to rank locations.List of Symbols and Abbreviations: COPRAS, the complex proportional assessment; EDAS, the evaluation based on distance from average solution; MCDM, multi-criteria decision-making; SWARA, the stepwise weight assessment ratio analysis; WASPAS, the weighted aggregated sum product assessment; WPM, the weighted product model; WSM, the weighted sum model; c, scale parameter; C F , the capacity factor; E WT , the annual wind power generation; ec el , the power needed for electrolysis; f v ð Þ, the distribution function of Weibull probability; h 1 , height of measuring wind speed; h 2 , height of wind turbine tower; i, the alternatives for decision making problem; j, the criteria for decision making problem; k, shape parameter; M hydrogen , the amount of hydrogen production; q j , the local weight of criterion j; S j , the relative value of criterion j; w j , the final weight of criterion j; x ij , the performance of alternative i in terms of criterion j; x ij , the normalized performance of alternative i in terms of criterion j; v, average wind speed; v 1 , wind speed at the height of measurement (h 1 ); v 2 , wind speed at the height of measurement (h 2 ); v i , the cut-in speed; v r , the nominal speed; v 0 , the cut-out speed; WPS i , the generalized criterion of weighted aggregation of additive and multiplicative methods for alternative i; η conv , the rectifier performance; Γ, gamma funct...
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