News

Article

Above image: Screenshot from Global Wind Atlas/ Credit: World Bank

Above image: Screenshot from Global Wind Atlas/ Credit: World Bank

World Bank launches free web-based tool to help policymakers and investors identify potential areas for wind power generation

In February 2017, the World Bank introduced a free, web-based tool to identify potential sites for solar power generation anywhere in the world. Today, the World Bank launched a free, web-based application, the Global Wind Atlas (GWA) developed to help policymakers and investors identify potential high-wind areas for wind power generation virtually anywhere in the world, and perform preliminary calculations, prior to the installation of meteorology measurement stations on site. It is expected to serve as a useful tool for governments to get a better understanding of their wind resource potential at provincial and local levels.

The tool facilitates online queries and provides freely downloadable datasets based on the latest input data and modeling methodologies. Users can additionally download high-resolution maps showing global, regional, and country wind resource potential in the Downloads section.

The website states that the correct usage of the Global Wind Atlas dataset is for aggregation, upscaling analysis and energy integration modeling for energy planners and policy makers. It is not correct to use the data and tools for wind farm siting.

This new version of the Global Wind Atlas (GWA 2.0) is the product of a partnership between the Department of Wind Energy at the Technical University of Denmark (DTU Wind Energy) and the World Bank Group (consisting of The World Bank and the International Finance Corporation, or IFC). Work on GWA 2.0 was primarily funded by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by The World Bank and supported by 13 official bilateral donors. It is part of the global ESMAP initiative on Renewable Energy Resource Mapping that includes biomass, small hydropower, solar and wind. It builds on an ongoing commitment from DTU Wind Energy to disseminate data and science on wind resources to the international community.

GWA 2.0  is a major upgrade from the first version of the Global Wind Atlas (GWA 1.0), which was developed by DTU Wind Energy under the framework of the Clean Energy Ministerial (CEM) and, in particular, the CEM Working Group on Solar and Wind Technologies, led by Germany, Spain and Denmark.  GWA 1.0 was released in 2015, and benefitted from collaboration with IRENA (International Renewable Energy Agency) and the MASDAR institute, which brought various energy stakeholders together. Furthermore, the IRENA Global Atlas of Renewable Energy created a dedicated platform to serve GWA 1.0 data to a worldwide audience, which will now be updated to serve GWA 2.0 data.

The GWA website will continue to be owned and developed by DTU Wind Energy, and future upgrades and improvements are already planned under the partnership with the World Bank Group and ESMAP.

Methodology used

Above image: Schematic showing the methodology of the GWA is downscaling/ Credit: World Bank

The GWA uses a downscaling process, moving from large-scale wind climate data to microscale wind climate data. Large scale atmospheric data from re-analysis datasets are used as an input into medium scale mesoscale atmospheric models, which model the atmosphere’s complex flows and weather features so that weather systems and weather fronts are well described and modeled. However, mesoscale models with typical grid spacing of 3-10 km are too coarse to accurately describe the flow over hills and ridges and terrain is often oversimplified by the grid spacing in mesoscale models.

The output from the mesoscale modeling is generalised to prepare it for use in microscale modeling. The output of the microscale modeling is predicted wind climates, which account for high resolution topography, such as hills, ridges and land use, such as grasslands and forests. Factoring in such terrain features is important to estimate wind resources because wind resources are very sensitive to wind speed. Typical grid spacing in microscale models is 5–100 m.

The datasets used in the GWA were chosen from the best available global datasets for each required category. They had to be of high quality, and also have high enough resolution so that the downscaling process would not be missing large amounts of information.

As part of the development of GWA 2.0, the World Bank Group selected Vortex, a leading commercial provider of wind resource data analysis, to carry out a global mesoscale modeling simulation at 9km resolution using the latest ERA Interim reanalysis data. Nazka Mapps was the web developer.

Visit site to retreive White Paper:
Download
FB Twitter LinkedIn YouTube