Energy and Acoustic Environmental Effective Approach for a Wind Farm Location

The justification for the construction of a wind farm depends primarily on two factors. The first one is the availability of the area with significant windiness; the second one is the environmental conditions in the selected location. The aim of this paper was to demonstrate the need for parallel noise and energy analyses during the design of a turbine location and selection of its type on the wind farm. The noise analyses were performed according to ISO 9613-2. A detailed analysis of wind conditions in a given location is a basic activity to determine the profitability of a wind power plant foundation. The main environmental impact of WF is noise emission. The examples of wind turbines’ selection optimally utilizing wind resources in two particular locations are presented. Six wind turbines were analyzed for each location. The choice of a wind turbine for the examined location was determined by the parameters of the device, the results of annual wind measurements, and acceptable noise levels in the environment. The three devices that met the acoustic criteria and the most energy efficient ones are indicated. We describe how a proper process of selecting a type of WT for a specific location should proceed.

Spatio-temporal Kriging of Lower Caribbean Wind Data

Planning of a wind farm location requires significant data. However, wind speed data sets in the lower Caribbean are usually incomplete. This paper considers imputation by spatio-temporal kriging using data from neighbouring locations. Temporal basis functions with spatial covariates are used to model diurnal wind speed cyclicity. The residual set of our spatio-temporal model is modelled as a Gaussian spatial random field. Fitted models may be used for spatial prediction as well as imputation. Examples of predictions are illustrated using two months of hourly data from eight Caribbean locations with prediction accuracy being assessed by cross validation and residuals.

Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.

Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study

The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the analyzes, both the positions in the final rankings, as well as the corresponding values of utility functions of the decision variants were studied. As a result of the detailed analysis of the obtained results, recommendations were presented in the field of reference criteria set for the considered decision problem, thus demonstrating the practical usefulness of the authors’ proposed approach. It should be pointed out that the presented study of criteria relevance is an important factor for objectification of the multi-criteria decision support processes.

A novel pythagorean fuzzy AHP and TOPSIS method for the wind power farm location selection problem

This paper proposes an Integrated Fuzzy Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method using Pythagorean fuzzy sets for the wind farm location selection problem. The method combines the advantages of the two methodologies to consider uncertainties and lack of information in the decision-making process (human expert capacities) and enables better representation of membership and non-membership functions, and at the same time, allows for the evaluation of large numbers of alternatives and criteria. The authors implement the method to evaluate four potential sites near cities located in the west and north-west regions of Turkey, using seven main criteria and twenty-five sub-criteria. The analyses are based on the judgements of three experts/decision-makers. Moreover, the authors compare the results (site ranking) of the methodology, with those from a Pythagorean Fuzzy AHP and an interval-valued Type-2 fuzzy AHP method. With the additional consideration of the sub-criteria, the proposed method generates slight differences in the ranking compared to the previously evaluated methods.

Elaboration of a Wind Energy Potential Map in Morocco using GIS and Analytic Hierarchy Process

The construction of a wind power generation center starts by the selection of a suitable wind farm location. The selection includes six factors, namely wind speed, slope, land use, distance from the power lines, distance from the roads, and distance from populated areas which have been integrated into QGIS by weights calculated using the Analytical Hierarchy Process (AHP) approach. As a result of this study, the areas having very high wind potentiality have been identified and a best wind farm location map has been prepared. The map, using the overlay function in GIS, exhibits the most and least suitable areas for the location of wind farms in Morocco. The approach could help identify suitable wind farm locations in other areas using their geographic information.

Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands

Improving the estimation of the power output of a wind farm enables greater integration of this type of energy source in electrical systems. The development of accurate models that represent the real operation of a wind farm is one way to attain this objective. A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. Using such information could be of interest, either to study possible improvements that could be obtained in the performance of the original model, which uses exclusively the meteorological conditions of the area where the wind farm is located, or simply because no reliable meteorological data for the area of the wind farm are available. In the study developed it is deduced that the incorporation of meteorological data from an additional weather station other than that of the wind farm site can improve by up to 17.6% the performance of the original model.