The Global Wind Atlas: The New Worldwide Microscale Wind Resource Assessment Data and Tools

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ICEM 2015, Boulder, Colorado USA The Global Wind Atlas: The New Worldwide Microscale Wind Resource Assessment Data and Tools Jake Badger, Neil Davis, Andrea Hahmann, Bjarke T. Olsen Xiaoli G. Larsén, Mark C. Kelly, Patrick Volker, Merete Badger, Tobias T. Ahsbahs, Niels Mortensen, Hans Jørgensen, Erik Lundtang Petersen, Julia Lange, DTU Wind Energy Nicolas Fichaux, IRENA EUDP 11-II, Globalt Vind Atlas, 64011-0347

Open access to global wind atlas data Use the data and extend its application Invite feedback and new ideas 2

Outline Project context Model chain Input data Output Web user interface, walk through Future plans Global assessments of the technical potential 3

Project context - International collaboration Energy Technology development and Demonstration (EUDP) Global Wind Atlas by DTU Wind Energy Coordinated by International Renewable Energy Agency (IRENA) Lead countries are Denmark, Germany and Spain.+ 11 countries and EC 23 participating CEM governments account for 80 percent of global greenhouse gas emissions 4

International collaboration What is IRENA s Global Atlas? It is a high-level prospector for renewable energy opportunities builds on publicly available information information released by the private sector data released by institutions, i.e. EUDP Global Wind Atlas New European Wind Atlas 5 http://globalatlas.irena.org/

International collaboration IRENA s Global Atlas It supports countries in prospecting their renewable energy opportunities companies to approach new markets the general public in gaining interest in renewable energy 6 http://globalatlas.irena.org/

The global wind atlas objective provide wind resource data accounting for high resolution effects use microscale modelling to capture small scale wind speed variability (crucial for better estimates of total wind resource) use a unified methodology ensure transparency about the methodology verify the results in representative selected areas For: Aggregation, upscaling analysis and energy integration modelling for energy planners and policy makers Not for: Not for wind farm siting 7

Model chain Downscaling GWA NWA SWAsP 10

Model chain Global Wind Atlas implementation Military Grid Reference System (MGRS) form basis of the job structure MRGS zones are divided into 4 pieces (total 4903) 2439 jobs required to cover land and 30 km offshore Frogfoot system runs WAsP-like microscale modelling. Inputs Generalized reanalysis winds High resolution elevation and surface roughness data

Model chain What is Frogfoot? core Frogfoot-server components ancillary components run on user PC data that is input into the system result outputs Like WAsP this is developed in partnership with World In A Box based in Finland 12

Frogfoot components Job Creation Job Management Console 13 Results Exporter WAsP Worker

Model chain How to work with Frogfoot? WAsP Worker(s) 14

Microscale Orographic speed-up Streamlines closer together means faster flow Winds speed up on hills Winds slow down in valleys Modification of the wind profile 15

Microscale Surface roughness length 16

Microscale Surface roughness change Accounted for by roughness speed-up and meso roughness parameters from WAsP flow model Unchanged profile Transition profile New log-profile Rule of thumb: 1:100 17

Datasets: atmospheric data Reanalysis Product Model system Horizontal resolution Period covered Temporal resolution ERA Interim reanalysis T255, 60 vertical levels, 4DVar ~0.7 0.7 1979- present 3-hourly NASA GAO/MERRA GEOS5 data assimilation system (Incremental Analysis Updates), 72 levels 0.5 0.67 1979- present hourly NCAR CFDDA MM5 (regional model)+ FDDA ~40 km 1985-2005 hourly CFSR NCEP GFS (global forecast system) ~38 km 1979-2009 (& updating) hourly 18

Datasets terrain: elevation and roughness Topography: surface description Elevation Shuttle Radar Topography Mission (SRTM) Viewfinder, compiles SRTM and other datasets resolution 90-30 m resolution 90-30 m ASTER Global Digital Elevation Model (ASTER GDEM) resolution 30 m Land cover ESA GlobCover Modis, land cover classification resolution 300 m resolution 500 m 19

Challenges in determining surface roughness GLOBCOVER 20

Challenges in determining surface roughness Roughness lengths used in the GWA 21

Example output 250 m calculation node spacing 22

Web user interface, walk through 23

Roughness length 24

Orography 25

WAsP Mesoroughness per sector 26

Orographic speed-up per sector 27

Annual mean wind climate 28

Selection of aggregation area 30

Wind rose 31

Windiest fractile plot 32

Wind speed distribution 33

Distribution of mean wind speed over area 34

Mean annual cycle over area 35

Still to complete Global runs with alternative reanalyses (1000 m) Complete verification Integration into IRENA global atlas Launch IRENA-coordinated web event, September 2015 36

Future plans Following projects Framework agreement led by ECN (NL) to supply renewable resource data to JRC TIMES-EU energy model. Foundation for data inputs and concepts for server platform for the New European Wind Atlas Roughness mapping improvements Elevation data verification would be of value Model chain development Many possibilities for post processing of data 37

Global assessments of the technical potential IPCC Special Report on Renewable Energy Sources and Climate Change: range tech. pot. 19 125 PWh / year (onshore and near shore) 38

Global assessments of the technical potential We can use the EUDP Global Wind Atlas to determine global potential accounting for high resolution effects and get a better spatial breakdown. The challenge is to create a consistent approach, with range of tested assumptions, available for the community to scrutinize. The Global Wind Atlas makes this easier via Transparency of methodology Providing data to allow annual energy production calculation GIS integration of datasets 39

Complex terrain (RIX> 5%) Near offshore (D<30km) Simple terrain (RIX<5%) Power density 200 0.00 400 0.25 600 0.32 800 0.36 1000 0.39 1200 0.42 1400 0.43 1600 0.45 1800 0.46 Capacity factor Assume 5 MW per km**2 capacity density Annual production from wind 1 PWh = 1e15 Wh All Exclude complex terrain Simple terrain 581 PWh 528 PWh 344 PWh 40

Thank you for your attention Open access to global wind atlas data Use the data and extend its application Invite feedback and new ideas Funding: EUDP 11-II, Globalt Vind Atlas, 64011-0347 41