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.

7

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?

9

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

10

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

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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

freenrg@afrihost.co.za

+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/