matthew bechly garrad hassan

The world’s largest renewable energy consultancy

August 2010

Experts in renewable energy

Onshore & Offshore Wind Wave & Tidal Solar PV & CSP

Geographical reach

750 staff, in 41 locations, across 22 countries

Vancouver

Ottawa

Portland

San Diego

Montreal

Peterborough

Austin

Monterrey

Porto Alegre

Beijing

Tokyo

Shanghai

Mumbai

Bangalore

Newcastle

Melbourne

Wellington

HeerenveenSint Maarten

Kaiser-Wilhelm-Koog

BristolDublin

Paris

Izmir

CopenhagenHinnerupOldenburgHamburgPoland

Lisbon BarcelonaZaragozaMadrid

Imola

Glasgow

London

Slough

GL Garrad Hassan supports stakeholders at all stages of a project

Common activities in Australia

• Wind farm design, wind monitoring, greenfield services;

• Wind speed and energy assessments;

• Wind energy forecasting;

• Planning technical assessments:

• ZVI, shadow flicker, noise assessments, EMI,

photomontages, aviation;

• Due diligence for developers, owners & investors;

• Independent Engineering services for owners & banks;

Other Activities

• O& M, Engineering, Design, Testing, Marine & Tidal

power, Solar, SCADA, Market, Strategy ...

Why care about wind speed?

Power = ½ ρ A U3

ρ – air density

A – swept area

U – wind speed

10% error in wind speed gives 33% error in power

Wind speed has the greatest impact on the viability of

a wind project

Wind Speed Monitoring

Making Wind Data Bankable

Wind speed monitoring - Overview

• Meteorological masts

• Monitoring equipment

• Anemometers

- Calibration

• Data handling

Wind speed monitoring

Siting guidelines

If the wind farm is going to look like this…..

there is no point in putting the mast here…..

(even though it may be the windiest location)

or here…..

(where obstacles or local surface roughness variation give unrepresentative conditions)

The mast should be sited at a representative location

Siting guidelines

• Site the mast at a representative location

• Aim – to relate the wind speeds at the turbine locations to those at

the mast

• Rely on a wind flow model – eg WAsP

• Accuracy of modelling depends on:

• complexity of the terrain

• roughness variations

• obstacles

• separation

• Several masts may be needed for large and/or complex sites

How many masts do I need?

Terrain Maximum recommended distance

between any proposed turbine location

and nearest mast

SIMPLE

Quite flat with some surface roughness

changes

2-5 km

MODERATELY COMPLEX

Rolling hills or gross surface roughness

effects such as forestry

1-2 km

VERY COMPLEX

Mountain ridges<1 km

Moving meteorological masts

• Must keep at least one

mast in same location

• Can move other masts

after 6 months to save

money

Mast height and type

• Mast height > 3/4 hub height (generally)

• Mast height = hub height (if wind profile is non-standard)

• eg flow separation, thermal effects, obstacles

• Mast type economic choice but IEC guidelines should be

followed

• tubular with guy wires

• lattice, generally with guy wires, but sometimes without

• Factors may be access to site; icing possibility

Tubular masts

Advantages

• Cheaper than lattice mast

• Simpler to erect, no foundations

• Access more difficult terrain

• Smaller footprint

Disadvantages

• Need to drop mast to fix any

issues up mast – then re-erect

• Typically shorter than lattice

masts – currently 70m MAX

Lattice masts

Advantages

• Can climb mast to fix any issues

• Can erect masts > 70m

• Better long term solution

Disadvantages

• More expensive than tubular masts

• More difficult to erect, foundations

required

• Larger footprint

Impacts of Mast installation

on property

• Foundations – generally at

mast base and sometimes

at guys anchor points

• Livestock fences may be

required (electric OK)

• Guy wires in 3 directions –

need protection

• Low flying aircraft – crop

dustings, weed control

• Lighting may be required

• Inner guys – 25-35m

• Outer guys – 40-50m

Numbers of instruments

Need several sets of wind instruments

per mast

- To measure wind shear

- In case of failure

- Use highest instruments for main

analysis

Cost of additional instruments is much

lower than cost to replace

Value of the extra information is high

Mounting of instruments

• Set up should follow IEC recommendations

• Instruments should avoid influence from mountings and from one another

• Consider dominant wind directions

• 2 examples of poor setup:

- boom effect:

distortion of wind flow, depending on direction

- stub mount effect:

can lead to over prediction of wind speeds

Good

Poor

Instrument guidelines - Anemometers

Almost always a cup anemometer

- rugged and reliable

- accurate

- low power

Alternatives:

Sonic

- high power, high cost

- ice free- very detailed

Remote Sensing

SODAR LIDAR

Advantages

• No mast needed

• no planning permission needed

• measurements at more and higher

heights

Disadvantages

• expensive

• use more power

• installation and calibration issues

• still need calibration against on-site

conventional mast

Equipment guidelines

Other equipment

• wind vane

• temperature sensor

• electric supply?

• solar panel?

• Battery

• data logger

• 3G Modem

• Lightning conductor

•

Data handling

May have to convince a third party of

accuracy

- calibration of equipment

- traceability of records

Processing to detect and remove data

affected by malfunction, icing etc

Do not read too much into monthly

statistics!

Analysis and Interpretation

of Wind Speed Data

Formats of wind statistics

5 % 10% 15%

0-3 3-6 6-9 >9 m/s wind speed

pro

ba

bilit

y

Measured

Weibull fit

• Frequency table .TAB file

• Wind rose

• Histogram

Data required

• Ideally:

10+ years of data recorded on site

In reality:

Measure-Correlate-Predict method with reference station off site, to reproduce long

term site wind regime

Site data required for MCP

• 1+ year of data close to hub height

• Interim analysis possible

with less data

• Long-term mean 7.0 m/s

• Maximum mean 8.0 m/s

• Minimum mean 6.5 m/s

5

6

7

8

9

1990 1995 2000 2005

Mean w

ind s

peed [

m/s]

Example wind speed correlations

• Fairly close reference station

0 2 4 6 8 10 12 14 16 18 20

Mast Ref at XX m wind speed (m/s)

0

2

4

6

8

10

12

14

16

18

20

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24

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28

30

Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

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30

Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

10

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Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

10

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Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

120 degrees 150 degrees


Analysis and interpretation of wind speed data

Example wind speed correlations

• More distant reference station

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data

0 2 4 6 8 10 12 14 16 18 20


0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

Mast

Site

at X

X m

win

d s

peed (

m/s

)

PCA fit

Data




Example results: long term wind statistics at mast

5 % 10% 15%

0-3 3-6 6-9 >9 m/s

Wind rose


Probability distribution of mean wind speeds

Weibull frequency distribution is found to conform well to many

observed distributions

Described by: A (scale parameter) and k (shape parameter)

Wind flow prediction at turbines

• We have derived long term wind speed statistics at the site mast location

• Now need to extrapolate from mast

• to hub height

• to turbine locations

• How?

• Measurement?

• Modelling

• In practice, computer modelling of wind flow behaviour is used to predict wind regime

at each turbine location


Mean wind speed profile in surface

layer

• Log Law

• Modified Log Law for heights up to

200m

• Power Law

Assumptions

– neutral atmospheric stability

– fully developed profile

Wind flow over hills

Maximum speedup

over the crest

Wind flow over hills

Separation bubble

Maximum speedup

over the crest

Linear models are reliable only for slopes less than ~0.3 (~17°)

..

Predicting wind flow behaviour at real sites

• Micro scale wind modelling - wind farm site

• WAsP is common industry tool

Predicting wind flow behaviour at real sites

• Meso scale wind modelling – site finding/prospecting

• Eg. Anemoscope

Predicting Long Term Mean Wind Speed & Energy

• The final piece of the puzzle to make a potential wind farm project bankable

• The Long Term Mean Wind Speed & Energy Prediction

Net energy 50 GWh/annum

Mean wind speed 7.3 m/s

Long-term historic data 14 years

Energy sensitivity 12 GWh/annum/(m/s) derived from energy

calculations at different mean wind speeds

Anemometer uncertainty 2.0% 0.15 m/s 1.8 GWh/yr

Correlation standard error 2.2% 0.16 m/s 1.9 GWh/yr

Variability of long-term 1.6% 0.12 m/s 1.4 GWh/yr

Topographic and wake modelling 4.0% 2.0 GWh/yr

Combined standard error (historic) 3.6 GWh/yr

Future wind variability (1 yr) 6.0% 0.44 m/s 5.3 GWh/yr

Future wind variability (10 yrs) 1.9% 0.14 m/s 1.7 GWh/yr

Combined standard error (historic + 1 yr future) 6.4 GWh/yr

Combined standard error (historic + 10 yr future) 4.0 GWh/yr

Predicting Long Term Mean Wind Speed & Energy

Mean of distribution = 50 GWh/annum

Standard deviation of distribution, s = 6.4 GWh/annum

50

P90 = 41.8 GWh/yr

Good practice gives accurate predictions

Thank you

Dr Matthew [email protected]

Suite 5A, Level 2, OTP House

10 Bradford Close

Kotara, Newcastle, NSW 2289

www.gl-garradhassan.com

mailto:[email protected]



matthew bechly garrad hassan

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