moving beyond unvalidated wake models - the european …...large wind farm (lwf) wake validation is...
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DNV GL © 3rd June SAFER, SMARTER, GREENERDNV GL ©
ENERGY
Moving Beyond Unvalidated Wake Models
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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
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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
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The challenge of convolution
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Production vs model differences
Wakes? Flow modelling error? Individual turbine performance?
Time-dependent factors?
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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
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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
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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
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Operational data validations – hypothetical wind farm
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Lots of spatial information
SCADA
Production – not wind speed
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Operational data validations – pattern of production
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> Average production
< Average production Individually normalised to
100% availability
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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
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Operational data validations – row by row
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Row 2
Row 1
Row 3
…….
> Average production
< Average production
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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
+45 3945 7071