WFES 2014

EUDP
Global Wind Atlas:

New, Unique, and Dedicated
dataset for the
Global Atlas
DTU Wind Energy, Technical Uni...
Context
The global wind atlas objectives are to:
• provide wind resource data accounting for high resolution effects
• use...
Importance of resolution and microscale modelling
Wind resource (power density) calculated at different resolutions
50 km
...
Importance of resolution

Mean wind power density for windiest half of area
4

DTU Wind Energy, Technical University of De...
Importance of resolution
Note:
Even for Danish
landscape effect can
give 25 % boast in
wind resource at the
windiest 5 per...
Input: newly available global dataset
Reanalysis: atmospheric data
Product

Model system

Horizontal
resolution

Period co...
Reanalysis data from NCEP DOE II 1980-2009
mean wind at 10 m direct from dataset

Wind speed shows variation in part due t...
Reanalysis data from NCEP DOE II 1980-2009
generalized mean wind speed at 10 m and z0 = 10 cm

Wind speed shows less varia...
The GWA jobs
• MGRS grid zones form basis
of the job structure
• MRGS grid zones are divided
into 4 pieces (total 4903)
• ...
Example jobs

DTU Wind Energy, Technical University of Denmark
Example jobs

DTU Wind Energy, Technical University of Denmark
EUDP Global Wind Atlas Output
Heights:
50, 100, 200 m
Weibull A and k for 12
direction sectors
Aggregated products based
o...
Application of high resolution resource data
Wind Atlas for South Africa (WASA) experience in planning

Credit: Cornelius ...
Application of high resolution resource data

• DTU PhD working on advanced GIS applications of Global Wind Atlas
– Conver...
Summary
To discover the true global wind resource and make it available for all
•
•
•
•

provide wind resource data accoun...
Thank you for listening
jaba@dtu.dk

Acknowledgement
This work is undertaken in collaboration with the Danish Energy Agenc...
Project overview

17

DTU Wind Energy, Technical University of Denmark
Calculation of local wind climates at microscale
Job Management Console
Job Creation

WAsP Worker
Results Exporter
DTU Win...
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Upcoming Datasets: Global wind map, Jake Badger ( Risoe DTU)

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Upcoming Datasets: Global wind map. A presentation by Jake Badger ( Risoe DTU) during the Global Atlas side event which held at the World Future Energy Summit in 2014

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Upcoming Datasets: Global wind map, Jake Badger ( Risoe DTU)

  1. 1. WFES 2014 EUDP Global Wind Atlas: New, Unique, and Dedicated dataset for the Global Atlas DTU Wind Energy, Technical University of Denmark Presented by Jake Badger EUDP is a Danish fund for development and demonstration projects from the Danish Energy Agency
  2. 2. Context The global wind atlas objectives are to: • provide wind resource data accounting for high resolution effects • use microscale modelling to capture small scale wind speed variability (crucial for better estimates of aggregated wind resource) Suitable for aggregation and upscaling analysis and energy integration analysis for energy planners and policy makers WARNING: Not suitable for developers and site resource assessment IPCC SRREN report: range tech. pot. 19 – 125 PWh / year (onshore and near shore) 2 DTU Wind Energy, Technical University of Denmark
  3. 3. Importance of resolution and microscale modelling Wind resource (power density) calculated at different resolutions 50 km 10 km 5 km 50 km 324 W/m2 378 W/m2 2.5 km 323 W/m2 410 W/m2 mean power density of total area 3 DTU Wind Energy, Technical University of Denmark 328 W/m2 398 W/m2 0.1 km 505 W/m2 641 W/m2 mean power density for windiest 50% of area
  4. 4. Importance of resolution Mean wind power density for windiest half of area 4 DTU Wind Energy, Technical University of Denmark
  5. 5. Importance of resolution Note: Even for Danish landscape effect can give 25 % boast in wind resource at the windiest 5 percentile. Mean wind power density for 10% of area 5 DTU Wind Energy, Technical University of Denmark
  6. 6. Input: newly available global dataset Reanalysis: atmospheric data Product Model system Horizontal resolution Period covered Temporal resolution ERA Interim reanalysis T255, 60 vertical levels, 4DVar ~0.7° × 0.7° 1989-present 3- and 6hourly NASA – GAO/MERRA GEOS5 data assimilation system (Incremental Analysis Updates), 72 levels 0.5° × 0.67° 1979-present 3-hourly NCAR CFDDA MM5 (regional model)+ FDDA ~40 km 1985-2005 hourly CFSR NCEP GFS (global forecast system) ~38 km 1979-2009 (& updating) hourly Topography: surface description Elevation Shuttle Radar Topography Mission (SRTM), version 2.1, released 2009 ASTER Global Digital Elevation Model (ASTER GDEM), version 1, released 2009 resolution 90 m resolution 30 m Land cover ESA GlobCover, version 2.1, released 2008, resolution 300 m 6 DTU Wind Energy, Technical University of Denmark
  7. 7. Reanalysis data from NCEP DOE II 1980-2009 mean wind at 10 m direct from dataset Wind speed shows variation in part due to changing surface roughness length. • Tendency for lower winds over land, higher winds over sea. • Sub-grid scale variation of orography and roughness will lead to marked variation in wind 7 DTU Wind Energy, Technical University of Denmark
  8. 8. Reanalysis data from NCEP DOE II 1980-2009 generalized mean wind speed at 10 m and z0 = 10 cm Wind speed shows less variation, roughness length is now 10 cm everywhere • Less contrast between land and sea • Generalized wind climate is the link to downscaling models • described sectorwise, for different heights and different roughness lengths (WAsP libfile) 8 DTU Wind Energy, Technical University of Denmark
  9. 9. The GWA jobs • MGRS grid zones form basis of the job structure • MRGS grid zones are divided into 4 pieces (total 4903) • 2439 jobs required DTU Wind Energy, Technical University of Denmark
  10. 10. Example jobs DTU Wind Energy, Technical University of Denmark
  11. 11. Example jobs DTU Wind Energy, Technical University of Denmark
  12. 12. EUDP Global Wind Atlas Output Heights: 50, 100, 200 m Weibull A and k for 12 direction sectors Aggregated products based on calculations at 250 m grid spacing Verification against mesoscale existing national wind atlases Verification against SAR offshore resource estimation 12 DTU Wind Energy, Technical University of Denmark
  13. 13. Application of high resolution resource data Wind Atlas for South Africa (WASA) experience in planning Credit: Cornelius van der Westhuizen, CSIR, South Africa See also: www.windaba.co.za/wp-content/uploads/2013/10/Cornelius-van-derWesthuizen-Methodolody-and-initial-results-of-the-DEA-wind-SEA.pdf 13 DTU Wind Energy, Technical University of Denmark
  14. 14. Application of high resolution resource data • DTU PhD working on advanced GIS applications of Global Wind Atlas – Conversion of high resolution wind climate data to technical potential incorporating optimization. • EU JRC project developing technical potential data for TIMES-EU, derived from Global Wind Atlas – Conversion and aggregation to formats for integrated assessment modelling (IAM). 14 DTU Wind Energy, Technical University of Denmark
  15. 15. Summary To discover the true global wind resource and make it available for all • • • • provide wind resource data accounting for high resolution effects use a unified methodology using newer higher reanalysis datasets verification and publication of the methodology are important be applied for aggregation and upscaling analysis and energy integration analysis for energy planners and policy makers • Look out for 2nd end-user workshop late 2014. 15 DTU Wind Energy, Technical University of Denmark
  16. 16. Thank you for listening jaba@dtu.dk Acknowledgement This work is undertaken in collaboration with the Danish Energy Agency and funded by grant EUDP 11-II, Globalt Vind Atlas, 64011-0347 16 DTU Wind Energy, Technical University of Denmark
  17. 17. Project overview 17 DTU Wind Energy, Technical University of Denmark
  18. 18. Calculation of local wind climates at microscale Job Management Console Job Creation WAsP Worker Results Exporter DTU Wind Energy, Technical University of Denmark

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