Abstract:In pursuit of green technology innovations, the energy industry is showing an interest in sustainable sources such as wind energy generation. The Saudi Arabian energy industry has a 2030 target to generate and transmit electricity to major customers nationwide and other neighboring Gulf countries. However, the selection of wind energy power plant locations is a concern because the decision process involves social, technological, economical, and environmental factors. The originality of this study lies in (1) p… Show more
“…For each indicator, a preference function must be computed to translate the difference between the evaluations obtained by two countries into a preference degree ranging from zero to one [96]. The preference function is the V-shaped function with the strict preference threshold (p); this is the maximum value of each indicator, if the indicators need to be maximized, while, in the case of indicators to be minimized, the value of this threshold will be the maximum as well, but with a negative sign [97]. In addition, this method requires the definition of weights and the selection of preference functions that convert the difference between alternatives into a ranging [22].…”
Fostering sustainability in the construction industry has been claimed; however, important barriers are hindering its implementation in public procurement. The main reason is the lack of knowledge about what sustainability criteria should be included and the high level of subjectivity in the definition of their level of importance. Both aspects should be addressed depending on the specific context of each country. Therefore, the aim of this research focused on identifying the sustainability shortcomings that exist in each European Union country in order to determine the level of importance of each sustainability category. Five environmental categories and eight social categories were established, and, to assess the sustainability performance of the 28 European countries, 42 national indicators were selected and the Promethee method was undertaken to rank the countries. Finally, through a cluster analysis, two groups of countries were identified. The first group consisted of the most economically developed European Union countries. These countries need to focus mainly on the environmental performance. However, the second group needs to make an effort in social sustainability at the same time, which controls their environmental performance. This research provides guidance on the decision-making with regard to the inclusion of sustainability in public procurement of the construction industry.
“…For each indicator, a preference function must be computed to translate the difference between the evaluations obtained by two countries into a preference degree ranging from zero to one [96]. The preference function is the V-shaped function with the strict preference threshold (p); this is the maximum value of each indicator, if the indicators need to be maximized, while, in the case of indicators to be minimized, the value of this threshold will be the maximum as well, but with a negative sign [97]. In addition, this method requires the definition of weights and the selection of preference functions that convert the difference between alternatives into a ranging [22].…”
Fostering sustainability in the construction industry has been claimed; however, important barriers are hindering its implementation in public procurement. The main reason is the lack of knowledge about what sustainability criteria should be included and the high level of subjectivity in the definition of their level of importance. Both aspects should be addressed depending on the specific context of each country. Therefore, the aim of this research focused on identifying the sustainability shortcomings that exist in each European Union country in order to determine the level of importance of each sustainability category. Five environmental categories and eight social categories were established, and, to assess the sustainability performance of the 28 European countries, 42 national indicators were selected and the Promethee method was undertaken to rank the countries. Finally, through a cluster analysis, two groups of countries were identified. The first group consisted of the most economically developed European Union countries. These countries need to focus mainly on the environmental performance. However, the second group needs to make an effort in social sustainability at the same time, which controls their environmental performance. This research provides guidance on the decision-making with regard to the inclusion of sustainability in public procurement of the construction industry.
“…The choice of this set was dictated by the properties and popularity of these methods. These methods and their modifications found an application in many different domains such as sustainability assessment [34][35][36], logistics [37][38][39], supplier selection [7,40,41], manufacturing [42][43][44], environment management [45][46][47], waste management [48,49], energy management [50][51][52][53][54][55], chemical engineering [56][57][58], and many more [59,60]. The choice of a group of TOPSIS, VIKOR, and COPRAS methods is justified, as they form a coherent group of methods of the American MCDA school and are based on the same principles, using the concepts of the so-called reference points.…”
Multi-Criteria Decision-Analysis (MCDA) methods are successfully applied in different fields and disciplines. However, in many studies, the problem of selecting the proper methods and parameters for the decision problems is raised. The paper undertakes an attempt to benchmark selected Multi-Criteria Decision Analysis (MCDA) methods. To achieve that, a set of feasible MCDA methods was identified. Based on reference literature guidelines, a simulation experiment was planned. The formal foundations of the authors’ approach provide a reference set of MCDA methods ( Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Complex Proportional Assessment (COPRAS), and PROMETHEE II: Preference Ranking Organization Method for Enrichment of Evaluations) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). This allowed the generation of a set of models differentiated by the number of attributes and decision variants, as well as similarity research for the obtained rankings sets. As the authors aim to build a complex benchmarking model, additional dimensions were taken into account during the simulation experiments. The aspects of the performed analysis and benchmarking methods include various weighing methods (results obtained using entropy and standard deviation methods) and varied techniques of normalization of MCDA model input data. Comparative analyses showed the detailed influence of values of particular parameters on the final form and a similarity of the final rankings obtained by different MCDA methods.
“…To use the function (6) to determine the wind speed distribution, a transformation of this distribution is required to make the WECS compatible, i.e., the function is transformed to no longer depend on the incidence of wind speed-it considers only its expression in relation to the power of wind generators. The Weibull probability distribution function for the WECS is hence transformed in order to be represented as ( 10):…”
Section: Economic Function Of Wind Farmsmentioning
confidence: 99%
“…This can apply to a range of strategic and operational decision problems arising throughout the lifecycle of a wind farm. Recent works in this regard include that of Rehman et al [6], who consider the location of onshore wind farms from a discrete set of alternatives. Seventeen underlying criteria across economic, environmental, social, and technical sustainability dimensions are considered, and the authors apply the Promethee MCDM methodology to investigate multiple criteria trade-offs and, hence, suggest optimal locations.…”
Wind energy is becoming an increasingly substantial component of many nations’ energy portfolios. The intermittent nature of wind energy is traded off in a multi-objective sense against its environmental benefits when compared to conventional thermal energy sources. This gives rise to the multi-criteria sustainable dispatch problem considered in this paper. A relevant multi-objective model is formulated considering both environmental and economic criteria as well as ensuring adequate production levels. The techniques of weighted goal programming (WGP) and the progressive bounded constraint method (PBC) are combined in a novel manner in order to overcome computational challenges associated with the sinusoidal nature of the model. This allows the generation of a representative set of Pareto efficient solutions. The proposed methodology is demonstrated on a test set of relevant examples, and conclusions are drawn from both methodological and application perspectives. The results provide a quantification of the economic and environmental benefits of added wind power to a solely thermal system. However, a trade-off between the levels of economic versus environmental benefits gained is also demonstrated.
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