DNV GL © 3rd June SAFER, SMARTER, GREENERDNV GL ©

ENERGY

Moving Beyond Unvalidated Wake Models

1

EWEA Resource Assessment Technology Workshop

Helsinki

3rd June, 2015

Jean-François Corbett, Global Head of CFD

standing in for Richard Whiting

DNV GL © 3rd June

The meta-message

No point having a super-

sophisticated wake model if you

don’t know whether the end result is

right or wrong

Large wind farm (LWF) wake

validation is challenging: difficult to

separate out wake effects from free-

stream wind speed variations and

turbine performance issues

2

Offshore and onshore LWF wake model validations are now possible

We now have much better free-stream flow models than we did just a few

years ago, with more physics in them

Pragmatic measures to further isolate out the wake effects

Objective: walk through examples of validations exercises, show the results of

those validations, and have a standard emerge for the substantiation needed

DNV GL © 3rd June

Overview

Methods for validating wind flow & wake models

Validation examples

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Method Quantity of data Validates which models?

Measurement-based, pre-construction

Low (few masts)

Free-stream wind flow models only

SCADA-based, operational pattern of production

High (many turbines)

Both wind flow andwake, combined (with other issues mixed in as well…)

SCADA-based, operational row-by row

Medium(many turbines)

Both, but focused on wakes

DNV GL © 3rd June

The challenge of convolution

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Production vs model differences

Wakes? Flow modelling error? Individual turbine performance?

Time-dependent factors?

DNV GL © 3rd June

Mast-based directional validations - Mesa

WAsP sort of gets the average right — for the wrong reasons — pretty much

useless for wake model validation (are wakes 8% or 9% at 150°? Who knows!)

CFD model captures dominating physics; diurnally varying stability, in this case

5

0

5

10

15

20

25

30

35

40

0.70

0.80

0.90

1.00

1.10

1.20

1.30

0 30 60 90 120 150 180 210 240 270 300 330

% T

ime

Mast-

to-M

ast

Ratio

Wind Direction (degrees)

Wind direction distribution

Measured

WAsP

DNV GL CFD

DNV GL © 3rd June

DNV GL CFD is improving – validation on 212 sites

In 2014:

Gap between CFD and WAsP has

widened

– In absolute and relative terms

– Incremental methodology

improvements working

Absolute errors down for CFD

and WAsP

– CFD usage is spreading to a

wider range of sites, including

less complex ones

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4,19%

2,85%

5,11%

3,87%

0,93% 1,02%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

Period 2012-

2013

n=117 sites

p<1E-3

Year 2014

n=75 sites

p<1E-4

Averag

e e

rro

r L

T H

H M

WS

CFD WAsP Difference

DNV GL © 3rd June

Directional errors:Atmospheric stability

Typically, these sites…

–Were flatter than average

–Had smaller mean wind

speed variations

You might expect that CFD

would add less value, but…

–Nearly universal

improvement over models

that ignore stability

– Stable CFD captures real

physics, which are missing

from neutral models

7

0%

5%

10%

15%

20%

25%

0% 5% 10% 15% 20% 25%

RMS directional speed-up errorWAsP, stability not modelled

RM

S d

irectional speed-u

p e

rror

CFD

wit

h s

tab

ilit

yen

han

cem

en

t

DNV GL CFD error is lower

n = 311 mast pairs

on 60 sites using Stable CFD

DNV GL © 3rd June

Operational data validations – hypothetical wind farm

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Lots of spatial information

SCADA

Production – not wind speed

DNV GL © 3rd June

Operational data validations – pattern of production

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> Average production

< Average production Individually normalised to

100% availability

DNV GL © 3rd June

Pattern of Production by turbine – Great Plains

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92%

94%

96%

98%

100%

102%

104%

106%

108%

Pro

duction r

ela

tive t

o a

vera

ge

Turbines

SCADA

WindFarmerHappens to be low-wake WF

CFD captures the trend

DNV GL © 3rd June

Operational data validations – row by row

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Row 2

Row 1

Row 3

…….

> Average production

< Average production

DNV GL © 3rd June

Validations: A Midwest Project – All atmospheric conditions

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1 2 3 4 5 6 7 8

Pro

du

ctio

n R

elat

ive

to R

ow

1

Row Number

Actual Output

1st Quartile

3rd Quartile

WindFarmer

DNV GL © 3rd June

Validations: A Midwest Project – Neutral conditions

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1 2 3 4 5 6 7 8

Pro

du

ctio

n R

elat

ive

to R

ow

1

Row Number

Actual Output

1st Quartile

3rd Quartile

WindFarmer

DNV GL © 3rd June

Validations: A Midwest Project – Stable conditions

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1 2 3 4 5 6 7 8

Pro

du

ctio

n R

elat

ive

to R

ow

1

Row Number

Actual Output

1st Quartile

3rd Quartile

WindFarmer

DNV GL © 3rd June

Validations: A Midwest Project – Stable conditions

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1 2 3 4 5 6 7 8

Pro

du

ctio

n R

elat

ive

to R

ow

1

Row Number

Actual Output

1st Quartile

3rd Quartile

WindFarmer

Adjusted WindFarmer

DNV GL © 3rd June

Validations: A Midwest Project – All atmospheric conditions

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1 2 3 4 5 6 7 8

Pro

du

ctio

n R

elat

ive

to R

ow

1

Row Number

Actual Output

1st Quartile

3rd Quartile

WindFarmer

DNV GL © 3rd June

Validations: A Midwest Project – All atmospheric conditions

0,70

0,75

0,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1 2 3 4 5 6 7 8

Pro

du

ctio

n R

elat

ive

to R

ow

1

Row Number

Actual Output

1st Quartile

3rd Quartile

WindFarmer

Adjusted WindFarmer

DNV GL © 3rd June

Recent detailed wake validations

Revised approach validated on

– 14 projects in the US, many consisting of multiple phases

– 3400 MW total, 250 MW average, 4 projects are 400 MW or greater

– Variety of terrains and climates: Offshore, high stability, pass flows, etc.

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0%

10%

20%

30%

40%

30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140% 150% 160% 170%

Percen

t o

f P

ro

jects

Actual Energy/P50 Estimate

Projects

Expected Distribution

Median = 99% This feeds into

overall energy

validation work

undertaken and

published:

DNV GL © 3rd June

Summary

No one is going to build a perfect wake validation wind farm for us

Wake model validation only possible in conjunction with high fidelity flow models

able to capture the physics dominating the free-stream flow in different regimes

Need to isolate other complicating factors

Optimum model parameter settings will vary with type of site

– e.g. base roughness, additional roughness in large WF models

Incremental improvements using established wake models made possible only

with improved flow modelling; and results remain impressive

So what's next in wake modelling?

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DNV GL © 3rd June SAFER, SMARTER, GREENERDNV GL ©

ENERGY

Simulated Wake Effects Platform for Turbines (SWEPT2)

GPU accelerated CFD [80 million cells]

Horns Rev Turbine Array Analysis

Able to supply validation data?

Interested in following developments?

Get in touch!

DNV GL © 3rd June

SAFER, SMARTER, GREENER

www.dnvgl.com

Thanks to Carl Ostridge, Taylor Geer, Melissa Elkinton

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Jean-François Corbett

Jean-Francois.Corbett@dnvgl.com

+45 3945 7071