Wind Atlas for South Africa (WASA)

Transcription

1 Wind Atlas for South Africa (WASA) Andre Otto (SANEDI), Jens Carsten Hansen (DTU Wind Energy) 7 November 2014

2 Outline Why Wind Resource Assessment Historical South African Wind Atlases Wind Atlas for South Africa (WASA) Project WASA method Wind Measurements for Verification Verified Numerical Wind Atlas WRF Time Series Time Series Application High Resolution Wind Resource Map High Resolution Wind Resource Map Application Extreme Wind Atlas Extreme Wind Atlas Application WASA User Statistics Conclusion

3 Historical South African Wind Atlases DME; R. Diab 1995 SARERD, 2001 Tripod Review of Wind Energy Resources in South Africa (2002) concluded: These studies are inconclusive and under estimate the true wind energy potential as weather measurement stations at 10 m were used and in may cases these stations are shaded by buildings etc from measuring the true wind potential; and Recommended that a dedicated wind energy measurement program be undertaken to confirm South Africa s true wind energy potential

4 Why Wind Resource assessment? Wind energy contributes a significant share of the IRP 2010 and REBID IRP 2010 to 2030, 42%, 17.8 GW of which 20%, 8.4 GW wind new build to come from renewable energy (8.4 GW PV, 1 GW CSP) Renewable Energy IPP Procurement Programme GW of which 1.85 GW wind, 1.45 GW PV, 0.2 GW CSP IRP 2010 is a living document Energy in wind P = ½ U 3 [W/ m 2 ] GHC mitigation tons CO2/MW (20 year lifetime, 0.27 capacity factor) Water savings of 229 l/mwh (compared to coal fired power stations) Wind speed U [m/s] New industry, job creation Wind provides the income Wind measurements are in one point in space Wind varies significantly across the terrain Spatial distribution needed for planning and projects Accuracy is essential (ΔU of 5% ΔP of 15%) Modelling is necessary and challenging

5 Wind Atlas for South Africa (WASA) : The Wind Atlas for South Africa (WASA) project ( ) is a Capacity Development and Research Cooperation Initiative of the South African Department of Energy (DoE). Principal funders are: the Royal Danish Embassy DKK 9,998,441 and Global Environmental Facility (GEF) R8 million through the South African Wind Energy Programme (SAWEP). SANEDI (South African National Energy Development Institute) executing agency contracting the implementing partners coordination and dissemination UCT CSAG (Climate System Analysis Group, University of Cape Town) mesoscale modelling CSIR (Built Environment, Council for Scientific and Industrial Research) Measurements, microscale modelling, application SAWS (South African Weather Service) extreme wind assessment DTU Wind Energy* (Dept of Wind Energy, Technical University of Denmark) partner in all activities

6 WASA Method Global Measurements wind farm G Mesoscale modeling Regional wind climate Microscale modeling Local wind Global wind resources Presentations and links to information are available at the SANERI web site :

7 Wind Measurements for Verification 10 minutes data and graphs available online: Cannot manage what you cannot measure Data download: U 61.9 m U 61.9 m WASA - 1 YEAR - 3 YEARS* U Data recovery [m/s] [m/s] [%] [%] WM WM WM WM WM WM WM WM WM09* WM10* U mean mean wind speed * 2-year periods for WM09 and WM10: WM09: to minus the year WM10: to plus to x 60m masts Date must be accurate, representative >1 year, > 90% data recovery, reliable IEC and Measnet Wind Measurement Standards Quality Assurance checked

8 Observational Wind Atlas WM01 WM02 WM03 WM04 WM05 WM06 WM07 WM Wind speed at 80 m above ground level WM09 WM10 WAsP resource grids from Observational Wind Atlas 10 x 10 km 2 grid 100 meter grid spacing

9 Numerical Wind Atlas

10 Numerical Wind Atlas cont.

11 Numerical Wind Atlas cont.

12 Numerical Wind Atlas cont.

13 New research wind atlas: WRF Model Configuration Very large (309 x 435) inner grid (3km grid spacing) Changes to standard WRF land use and roughness ERA-Interim forcing, 1/12 degree SSTs; MYJ PBL; 41 vertical levels (further details in meso report) Simulations: 8 years for (27/9/3 km) ; Highresolution SSTs; 24 years (27/9 km) ; 11/7/

14 Weather, Research and Forecast (WRF) model Complex model with many options that need to chosen by the user Mesoscale & Microscale Meteorology Division / NCAR Best configuration not found by chance: Extensive set of year-long simulations were performed to optimize domain size and location and various parameterizations /7/201 4

15 Difference in MODIS Landcover Difference in land cover classes between what is currently used in WRF (1 year) and the new MODIS climatology ( ) MODIS satellite-derived land cover 11/7/

16 Numerical wind atlases WRF vs KAMM Generalized wind speed, h=100 m, z0=0.03 m KAMM (old) WRF (new) 11/7/2014 EWEA, Barcelona,

17 Validation: Example: WASA site 1, far northwest Observed wind atlas Weighted (solid) Re-fit (dashed) Numerical wind atlas KAMM Numerical wind atlas WRF 11/7/

18 Validation summary

19 Example: Seasonal and diurnal cycles in the observations and the WRF simulations Chris Lennard and Brendan Argent 11/7/2014 WASA Final Univ. Cape Town Wind Seminar 19

20 Summary: Seasonal and diurnal cycles in the observations and the WRF simulations

21 WRF Time series D1 D2 D3 Available WASA time series: D1 (covers all South Africa), 27 km x 27 km, 3-hourly, Oct Sep 2013 (could be rerun for with hourly output) D2, 9 km x 9 km, hourly, Jan Sep 2013 (could be extended to 2014) D3 (WASA 1 domain), 3 km x 3 km, hourly and 10-minutes (only winds), Oct Sep 2013

22 Time Series Application Study the annual, seasonal and diurnal variations in wind resources As input to power system modelling Study the geographical cross correlation of wind across South Africa Used for long-term correction of the wind resources given by the WRF wind climate files ( lib files)

23 NWA Summary and conclusions Results from a new verified numerical wind atlas for South Africa are presented and compared to the first verified wind atlas Production of the new wind atlas required a large amount of work many knowledge and software was not available at the inset of the project KAMM/WAsP method, numerically very cheap, gives good results underestimation of mean wind speed at most sites; specially at sites influenced by thermal processes resulted in a quite conservative wind resource atlas WRF method, numerically very expensive, gives excellent results Excellent comparison between wind roses in model and observations Stability conditions should be taken into account at generalization 11/7/

24 Microscale vs Mesoscale: effect of resolution

25 Available input data for detailed Verified Numerical Wind Atlas resource mapping WRF mesoscale model, 3 km Elevation 100-m elevation grids from Space Shuttle Topography Mission, SRTM version 3. Land cover and roughness USGS Global Land Cover Characteristics database. Transformation table for z 0 WASA Final Wind Seminar 25 8 April 2014

26 High Resolution Wind Resource Map Large Scale High Resolution Wind Resource map WRF based (local wind climate, 250 m resolution, April 2014)

27 High Resolution Wind Resource Map Application The High Resolution Wind Resource Map depicts the local wind climate that a wind turbine would encounter and offers the following important benefits for developers, policy makers, utilities and industry: Cost and timing savings as the bankability of a potential wind farm site can be estimated with known and traceable accuracy*; Levels the playing field between small or large industry players to identify and develop project sites for wind farms*; Assists the South African Government in estimating the potential yield of the wind energy resources; Identification of potential wind development zones in line with the strategic environmental framework or assessments (SEA) studies (e.g. Dept Environmental Affairs SEA for Solar PV and Wind Long-term grid planning. *Physical wind measurements are still required by financiers to confirm the bankability of a wind farm project at the identifiedsite(s).

28 Extreme Wind Atlas 1:50 yr 10 min wind speed 10 m above ground level (standard conditions)

29 Extreme Wind Atlas Application Wind constitutes most critical environmental loading affecting structural design of built environment in South Africa and information on extreme winds is also essential in the design of wind farms situated in areas with relatively strong winds. Providing input data (1:50 yr 10 min average wind speed (standard conditions) in calculating the 10 minute average wind speed at hub height which is a parameter in determining the appropriate Wind Turbine Class (IEC turbine classification scheme)

30 WASA User Statistics (May 2014) registered users 60 - countries downloads All users by Affiliation-type 20 - Non-SA Governmental/Provincial/Municipal Agencies 21 - Non-SA Non-Profit Non-SA Private Companies 79 - Non-SA Universities and Schools 12 - Non-SA Other SA Government/Provincial/Municipal Agencies 65 - SA Non-Profit SA Private Companies SA Universities and Schools 40 - SA Other IRENA Global Atlas World Bank ESMAP

31 Conclusion State of the Art Wind Atlas method Public domain through websites Transparent methodology no black box Verified with proper wind measurements, traceable Data + Application, Guidelines, Training Level playing field Save time and money Not a substitution for mandatory wind measurements South Africa has an excellent wind resource that compares with the best in the world.

32 SAWEA and its members have argued and measured for many years that there is a significant wind resource in South Africa, enough to support a significant sized Wind Energy industry. This view has now been validated by the independent WASA team, consisting of DoE, SANEDI, CSIR, SAWS, UCT and DTU, through the latest developments in the Wind Atlas. SAWEA would also like to congratulate the WASA team on further developing an internationally recognised methodology for compiling such a wind map that is already being adopted by other countries Frank Spencer - On behalf of SAWEA Technical Working Group

33 WASA Websites (information, presentations etc) (wind measurement mast online graphs) (final reports, data downloads) WRF (WRF wind forecasting) Numerical Wind Atlas database access through Tadpole (Google Earth)

34 Thank you Andre Otto Jens Carsten Hansen SANEDI DTU Wind Energy +27 (0)