renewable energy database and the south african … energy database and the south african wind ......
TRANSCRIPT
Renewable Energy Database and
the South African Wind Atlas Colloquium on Energy Planning
Dept of Energy
29-30 March 2012
Gallagher Convention Centre
2
Outline
3 key Questions
Potential, Cost
Reliability
Technologies
Wind Atlas
Users
Conclusion
Recommendations
3
Questions
What is the renewable energy (practical) potential?
What is the cost of renewable energy technologies?
How reliable is renewable energy?
Renewable Energy
Resource (SARERD)
4
Wind Resource
Studies
5
DME; R. Diab 1995 SARERD, 2001 K Hagemann, University
of Cape Town (2008)
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 programme needs to be undertaken to confirm the true wind energy potential in SA
Renewable Energy
Cost
6
Reliability of Wind
Impact of Wind Generation in South Africa on Capacity Planning & System Operation
Capacity Credit
Wind is a variable energy resource. It is therefore important to know what is the capacity contribution of wind energy i.e. what is
the capacity credit of wind energy which can be defined as the additional load that the system can carry when wind power is
added, maintaining the same reliability level.
Capacity Credit of Wind Generation in South Africa (GTZ, DoE, Eskom, 2011)
Scenario 1: Year 2015, 2000MW installed wind generation capacity: CC=26,8 %
Scenario 2: Year 2020, 4800MW installed wind generation capacity: CC=25,4%
Scenario 3: Year 2020, 10000MW installed wind generation capacity: CC=22,6%
As overall conclusion, it can be stated that the capacity credit of wind generation in South Africa will be between 25% and 30%
for installed wind generation of up to 10 000MW.
Impact of Wind Energy in South Africa on System Operation
The main impact on system operation will result from the limited predictability of wind speeds and not from absolute wind speed
variations.
It can further be concluded that it is very likely that it will be possible to operate the system safely, without increased dynamic
performance requirements for the conventional power plants of South Africa.
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Renewable Energy
Technologies
No lack of commercial, viable and proven
renewable energy technologies
Localisation issues, standards, testing,
certification, “carrot and stick” balance, industry
growth and sustainability, net metering
regulations, grid code
Emerging market: decentralised, urban, rural
(min-hybrid), stand alone and integrated (e.g.
roof mounted PV, wind turbine, net metering)
renewable energy systems, smart grid
R&D opportunities: customising and integration
of renewable energy technologies for “Africa”
and similar conditions (logistics, distances, e.g.
smaller, higher efficient, robust (gearless,
permanent magnet generator), easy
transportable wind turbines, lighter, stronger
composite materials etc)
8
Wind Atlas, Why?
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Energy in wind
P = ½ U3 [W/ m2]
Wind speed U [m/s]
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
Wind, besides solar, makes out the biggest contribution of
the renewable energy mix with the 2nd highest cost (based
on RFP Bid ceiling price R1150/MWh)
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 3.725
GW of which 1.85 GW wind, 1.45 GW PV, 0.2 GW CSP
GHC mitigation 30 885.51 tons CO2/MW (20 year lifetime,
0.27 capacity factor)
Water savings of 229 l/MWh (compared to coal fired
power stations)
New industry, job creation
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Wind Atlas Method
Inputs
measured time-series of wind speed and direction –
observed wind climate (Observational Wind Atlas)
terrain topography – elevation, roughness and obstacles –
from digitised maps, SRTM data, Google Earth
Outputs
generalised regional wind climate for the specific location
Applications
energy production estimates for wind farms in the region
near the meteorological station
Wind Power Shanghai 2007 Risø National Laboratory • Technical University of Denmark
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Process
G
Global
Local wind
Global wind resources
Regional wind climate
Mesoscale modeling Microscale modeling
Measurements wind farm
Presentations and links to
information are available at the
SANERI web site
http://www.wasaproject.info
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Verification
10 minutes data and graphs available online on the project web site at
CSIR: http://www.wasa.csir.co.za
Data download
http://wasadata.csir.co.za/wasa1/WASAData
Date must be accurate, representative >1 year
> 90% data recovery, reliable
IEC and Measnet Wind Measurement Standards
WASA Data
recovery
(%)
Umean @ 61.5m
(m/s)
WM01 Alexander Bay 100.0 5.83
WM02 Calvinia 100.0 6.19
WM03 Vredendal 100.0 7.13
WM04 Vredenburg 100.0 6.68
WM05 Napier 95.8 8.58
WM06 Sutherland 100.0 7.00
WM07 Beaufort West 100.0 6.95
WM08 Humansdorp 100.0 7.36
WM09 Noupoort 89.6 7.55
WM10 Butterworth 92.4 6.52
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WM01 WM02 WM03 WM04
WM05 WM06 WM08WM07
WM09 WM10
Wind speed at 80 m above ground level
WAsP resource grids from Observational Wind Atlas
10 x 10 km2 grid
100 meter grid spacing
5.0 10.0
Microscale Modeling
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Verified Numerical
Wind Atlas
Met mast OBS Wind Climate NUM Wind Climate Error [%]
1 6.16 5.33 -13.47
2 6.62 7.01 5.89
3 7.19 6.63 -7.79
4 7.33 7.19 -1.91
5 8.99 8.35 -7.12
6 7.44 7.24 -2.69
7 7.45 6.61 -11.28
8 7.71 7.66 -0.65
9 7.5 7.58 1.07
10 6.32 6.09 -3.64
mean error -4.16
mean absolute error 5.55
Uncertainties assessed Public domain Industry-standard Traceable and transparent Platform for future development
First Verified Numerical Wind Atlas for South Africa - Generalized climatological (30-year) annual mean wind speed [m/s] 100 m above ground level, flat terrain, 3 cm roughness everywhere
First Verified Numerical Wind Atlas for South Africa - Generalized climatological (30-year) annual mean
wind speed [m/s] 100 m above ground level, flat terrain, 3 cm roughness everywhere
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Resolution is key
Numerical Wind Atlas Microscale modeling
• Grid cell size 5120 m
• Wind farm of five 2 MW turbines
• Estimated AEP = 39 GWh
• Grid cell size 20 m
• Wind farm of five 2 MW turbines
• Estimated AEP = 55 GWh
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Verified Numerical
Wind Atlas Database
The Numerical Wind Atlas Database contains the
generalised1 wind climate data sets (.lib files) for every 5
km × 5 km, corresponding to approximately 15000 data
points (“virtual masts”) that can be employed directly
with WAsP (licensed version) for wind farm planning and
wind resource assessment
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Authorities Policies, regulations, plans
Planners Resource and development planning
Investors, owners and banks Financial planning, risk assessment and decisions
Developers (small and large) Project development
Industry (small and large) Project design and implementation,
Wind turbine design and development
Power sector Power system planning, development and operation
Consultants Independent expertise and tools development
Academic community Research, methods and tools development
Users
All need the Wind Atlas, using WAsP or similar micro-scale model to calculate estimated energy production
from wind farms as part of project and planning decisions.
13
Mar
1
7
WASA Wind Atlas
Launch
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Extreme Wind Climate
Information on extreme winds essential in the design of wind farms – situated in areas with relatively
strong winds
Estimations from observations
Long measuring periods and density of measurements should be adequate.
Some Results
Dominance of
strong wind
mechanisms on
gust time-scale.
1:50 year gust
quantiles from
observed data.
• Work in progress:
Use of global reanalysis data, mesoscale modeling, SAWS data, WASA data and microscale
modeling
19
Averaged diurnal cycle October 2010 – WM01
October 2010
WRF Wind Forecasts
http://veaonline.risoe.dk/wasa/
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Wind atlas values @ 100 m a.g.l. (z0 = 0.03 m)
SA Wind Potential
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Conclusion
Several attempts have been made to quantify SA’s Renewable Energy Resource (SARERD,
SAREBC)
Solar and Wind contribute biggest share of SA’s Renewable Energy Resource base and highest cost
State of the Art Verified and Traceable Wind Atlas in place
Launched 13 March 2012
Public domain
Level playing field
Save time and money
Not a substitution for mandatory wind measurements
Application of the Verified Wind Atlas in e.g. wind farm planning, layout and resource assessment
demonstrated
Reliability of Wind Energy quantified
Commercial, viable Renewable Energy Technologies available
Opportunity for SA to leapfrog Renewable Energy (wind) R&D
Opportunity for decentralised rural/urban application of Renewable Energy technologies, lacking
policy and regulatory framework
22
Recommendations
Undertake a National Audit of SA’s Renewable Energy Resources e.g. Update
SAREBC
Develop a Verified and Traceable Solar Atlas for South Africa
Expand Capacity Credit studies to other Renewables and investigate combined
Capacity Credit
Update Renewable Energy Macro-economic study (localisation, job creation,
spin offs etc)
Coordinate and direct appropriate Renewable Energy R&D
Support decentralised rural/urban renewable energy demonstration projects
and develop supporting policy, legal and regulatory framework
Support local, regional expansion and international collaboration e.g..
IRENA/CEM Global Solar and Wind Atlas
23
Acknowledgement
The Wind Atlas for South Africa (WASA) is an initiative of the Department of Energy (DoE) and the project is co-
funded by UNDP/GEF through the South African Wind Energy Programme (SAWEP) and the Royal Danish
Embassy
SANERI (South African National Energy Research Institute)
executing partner – 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
* the original DTU partner (Risø DTU) is part of DTU Wind Energy established Jan 2012
Thank You
Andre Otto
+27 (0)82 877 0128
http://www.wasa.csir.co.za (online graphs, Guide)
http://wasadata.csir.co.za/wasa1/NWA_downloads.html (downloads)
WRF http://veaonline.risoe.dk/wasa
SANERI WASA http://www.wasaproject.info/