Wind power plant site selection: A systematic review

Rediske, Graciele; Burin, Heloisa Pereira; Rigo, Paula Donaduzzi; Rosa, Carmen Brum; Michels, Leandro; Siluk, Julio Cezar Mairesse

doi:10.1016/j.rser.2021.111293

Cited by 66 publications

(40 citation statements)

References 73 publications

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“…Yahyai et al 91 developed a wind farm site suitability index and classification in an Oman GIS environment by combining MCDM with an analytical hierarchy process (AHP) aggregation function and an Ordered Weigh Averaging (OWA) aggregation function. Rediske et al 92 evaluated the suitable location for wind farms and addressed the factors that restrict the choice of location. Azizi et al 93 explored land suitability evaluation for wind power plant site selection in the Ardabil province of Iran using analytic network process DEMATEL in a GIS environment.…”

Section: Introductionmentioning

confidence: 99%

A wind power plant site selection algorithm based on q-rung orthopair hesitant fuzzy rough Einstein aggregation information

Attaullah

Ashraf

Rehman

et al. 2022

Sci Rep

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Wind power is often recognized as one of the best clean energy solutions due to its widespread availability, low environmental impact, and great cost-effectiveness. The successful design of optimal wind power sites to create power is one of the most vital concerns in the exploitation of wind farms. Wind energy site selection is determined by the rules and standards of environmentally sustainable development, leading to a low, renewable energy source that is cost effective and contributes to global advancement. The major contribution of this research is a comprehensive analysis of information for the multi-attribute decision-making (MADM) approach and evaluation of ideal site selection for wind power plants employing q-rung orthopair hesitant fuzzy rough Einstein aggregation operators. A MADM technique is then developed using q-rung orthopair hesitant fuzzy rough aggregation operators. For further validation of the potential of the suggested method, a real case study on wind power plant site has been given. A comparison analysis based on the unique extended TOPSIS approach is presented to illustrate the offered method’s capability. The results show that this method has a larger space for presenting information, is more flexible in its use, and produces more consistent evaluation results. This research is a comprehensive collection of information that should be considered when choosing the optimum site for wind projects.

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Section: Introductionmentioning

confidence: 99%

A wind power plant site selection algorithm based on q-rung orthopair hesitant fuzzy rough Einstein aggregation information

Attaullah

Ashraf

Rehman

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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“…The goal of the former is to optimize turbine or wind farm site selection under certain constraints. These constraints can be diverse; among them are the average wind speed, geological and geographical properties of the surrounding area, and political guidelines that, for example, restrict the closeness of turbines to cities [124]. Furthermore, while site selection has to rely on pure wind and weather data only, wind power grid integration models usually also consider wind power data.…”

Section: Evaluation Of the Datasets' Propertiesmentioning

confidence: 99%

“…For completeness, it has to be mentioned that the maximum and average predicted capacity are not the only relevant considerations for site selection. Additionally to meteorological components, environmental, economic, and societal factors can further restrict site selection [124].…”

Section: Wind-based Datamentioning

confidence: 99%

A Collection and Categorization of Open-Source Wind and Wind Power Datasets

Effenberger¹,

Ludwig²

2022

Preprint

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Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open-source datasets, as many currently used datasets are non-disclosed and make reproducibility of research impossible. This unavailability of open-source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up-to-date overview of existing open-source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open-source datasets and compare their methods on them.

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“…Numerous researchers have proposed optimization-based solutions in the evaluation and selection of wind-power plants around the world: Thailand [25], Turkey [26], Nigeria [27], Saudi Arabia [28], Spain [29,30], Europa [31], Mexico [32], United Kingdom [33], etc. In general, the profitability of wind-power installations is associated with a set of factors from different categories, with wind speed being the most determining factor [34]. Therefore, it is crucial to use reliable data campaigns, usually involving several year periods, to characterize both seasonal and annual wind-speed variations, aiming to evaluate accurately the wind-resource potential [11].…”

Section: Introductionmentioning

confidence: 99%

Online Wind-Atlas Databases and GIS Tool Integration for Wind Resource Assessment: A Spanish Case Study

et al. 2022

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Renewable energy sources are becoming increasingly integrated into the electricity-generation sector, being eco-friendly solutions, decreasing global warming, and improving the energy transition process. Among the different renewables, wind energy is considered a mature, clean, renewable, and inexhaustible technology as well, becoming one of the main resources in a sustainable framework. Aiming to evaluate the wind resource, scientific contributions have mostly presented a common basis: historical data campaigns of the wind resource mainly considering wind speed—including the module, direction, standard deviation, etc. However, online wind-atlas databases are becoming tools widely used for both wind-resource assessment and optimal wind-power locations. Under this framework, this study analyzed and compared such online wind data sources and their integration with GIS tools for optimal wind-resource-assessment purposes. The proposed methodology identified the corresponding wind-atlas databases directly on their websites and indirectly through the wind data used in relevant contributions about the optimal location of wind sites. Our contribution to the scientific community is thus the review and comparison of these atlas databases for reducing the barrier to access wind data—including GIS-tool-integration analysis. The limitations raised by civil societies, particularly regarding environmental and bird concerns, were not included in this study. Nevertheless, the authors are aware of these concerns and limitations. A Spanish case study was also included in this work, comparing both estimated and collected wind-atlas databases in terms of wind-resource assessment.

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Wind power plant site selection: A systematic review

Cited by 66 publications

References 73 publications

A wind power plant site selection algorithm based on q-rung orthopair hesitant fuzzy rough Einstein aggregation information

A wind power plant site selection algorithm based on q-rung orthopair hesitant fuzzy rough Einstein aggregation information

A Collection and Categorization of Open-Source Wind and Wind Power Datasets

Online Wind-Atlas Databases and GIS Tool Integration for Wind Resource Assessment: A Spanish Case Study

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