bespoke climate services for the wind power industry: from ... · – weather dependent :...
TRANSCRIPT
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Dave MacLeodwith thanks to Melanie Davis & Paco Doblas-Reyes, IC3,
Met Office seminar, 11th June 2014
Bespoke climate services for the wind power industry:
from decisions to (seasonal) forecast products
European Provision Of Regional Impacts Assessments on Seasonal and Decadal
Timescales
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The “problem”:• Medium-range and seasonal climate forecasts apparently not widely
used in the wind industry
– Insufficient skill / lack of knowledge / timescales not relevant for decisions
– “Why scientific information is – or is not – used in decision making for the management of natural hazards and resources” – Oxford Martin project; Ana Lopez
Off the shelf vs. Bespoke climate services:
• Offer a standard forecast product (e.g. P(UT 10m wind) ) to “the users”
• Understanding the the industry to find potential decisions with w&c links, tailor information and feedback
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Climate services for the wind power industry
• Exploring weather & climate related decisions (thoughts & ideas post-EWEA)
• Tailoring climate variables for the industry
• Identifying promising regions for seasonal forecasting
• Creating impact surfaces for promising regions
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Weather & climate related decisions in the wind power industry
Pre-construction –> construction –> post-construction
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Weather & climate related decisions in the wind power industry
• Pre-construction– Site selection - sufficient power output
– Project financing: “interannual variability in wind energy production is the single most important factor in debt-sizing” [DNV GL]
– Useful climate information: historical record of (downscaled) mean and variability in power
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• Construction (offshore)
– MPI specialist offshore vessels
– €150,000/day at sea
– Typically, ~12 hours to site, 24 hours to install 1 turbine
– Capacity for 6 turbines per trip
– Per trip: ~1 week at ~€1M.
– Weather dependent : ‘Jack-up/down’ & installation only when wave height < threshold (relation to wind speed)
– Currently, decision to leave port based only on current wave height @port & 12 hour trend. Potential for use of medium-range forecasts.
Weather & climate related decisions in the wind power industry
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Post-construction
• Scheduled (annual) & unscheduled maintenance
– 24 hours per turbine
– Good weather necessary
– Losses due to turbine downtime
– Seasonality important, medium-range forecast potentially useful
Weather & climate related decisions in the wind power industry
• Financial support: reducing volatility due to interannual variability– Power purchase agreement (PPA)– Variable contract length: UK multi-year agreement common, in Brazil
year-by-year basis – Seasonal climate forecasts can inform which market to sell into,
can also reduce volatility by informing insurance premiums (P(PPA default)).
• Electricity grid management & trading: skillful forecasts on any timescale
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Weather & climate related decisions in the wind power industry - timescales
Pre-construction –> construction –> post-construction
Site selection, insurance & financing:Historical record of mean and variability
Minimizing construction time (on & offshore):Short - medium range (<30 days)
Planned maintenance: Historical record, plus short – medium rangeFinancial (insurance, grid management, trading):Forecast on any timescale (incl. seasonal)
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Climate services for the wind power industry
• Exploring weather & climate related decisions
• Tailoring climate variables for the industry
• Identifying promising regions for seasonal forecasting
• Creating impact surfaces for promising regions
![Page 10: Bespoke climate services for the wind power industry: from ... · – Weather dependent : ‘Jack-up/down’ & installation only when wave height < threshold (relation to wind speed)](https://reader033.vdocuments.net/reader033/viewer/2022043021/5f3cf8783956af436419f696/html5/thumbnails/10.jpg)
Making climate variables useful
• Tailored variables likely more useful than raw 10m wind speed
• Alternative variables
– Wave height (not considered further)
– Average turbine power output by a turbine under specified wind conditions (turbine model dependent)
– Capacity factor: average power / max power
– (turbine independent, scalable by # turbines in farm).
• Relating 10m daily wind speed to capacity factor:
– Technical turbine limitations
– Diurnal variability in wind speed
– Power generated higher than 10m
– Transmission/distribution losses
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Relating 10m daily wind speed to capacity factor – theoretical maximum wind power
• Wind energy:
ρ = air density
v = instantaneous wind speed
A = area of turbine perpendicular to wind direction
• Ideal gas law (relating density to temperature):
p = air pressure
R = ideal gas constant (287Jkg-1)
T = air temperature
• Theoretical limitations for extracting energy:
Betz’s law: only 60% extraction of KE possible (100% means wind behind turbine has 0 KE)
Inefficiency in design…practical limitations…
P pRT
Av3
2
E mv2
2 (Avt)v2
2 Atv3
2
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Power output curve from technical turbine specifications: Renewegy 20kW & Vestas 2.0 & 1.8 MW.
Relating 10m daily wind speed to capacity factor – real-world wind power
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Making climate variables useful – diurnal variability in wind speed
The Rayleigh distribution
– Commonly used to model wind speed distributions
– Naturally arises when overall magnitude of a vector is related to directional components.
– Assuming that magnitudes of zonal and meridional wind speed are:
– uncorrelated
– normally distributed
– equal variance
– zero mean…
…overall wind speed will be characterized by a Rayleigh distribution.
Map mean daily wind speed to a rayleigh distribution (via the median – high tails)
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Making climate variables useful – power is generated above 10m
The wind profile power lawu: speedz: heightr: speed/height at reference levelα: empirically derived coefficient related to atmospheric stability
Converting 10m to 100m winds:α0.143 neutral stability over land0.110 ± 0.03 neutral stability over deep/shallow ocean (Hsu et al. 1994)??? non-neutral stability or rough surface: log wind profile preferred
Land
Ocean (low alpha)
Ocean (mid alpha)
log wind profile:
20m/s @ 10m = 25.7 ± 0.2 m/s @ 100m over ocean27.8m/s @ 100m over land
Assuming: gives a systematic error of around 5%
Unknown error from neutral stability / smoothness assumption
uur
zzr
u100m u10m10
uzu*
ln
zdz0
(z,z0,L)
u100m 1.25u10m
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Making climate variables useful – bringing it all together
Assuming: • kickin/out 4,
25m/s• Rayleigh
distribution for diurnal wind speed
• u100m=1.25u10m
• overall reduction to 87.5%, incorporating transmission/distribution losses (following Frandsen 2007)
Curve above 4m/s is scaled so that at for T=10C (middle of operating temperature range) capacity factor =100% at around 12m/s (typical real-world rated wind speed)
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Making climate variables useful – bringing it all together
Assuming: • kickin/out 4,
25m/s• Rayleigh
distribution for diurnal wind speed
• u100m=1.25u10m
• overall reduction to 87.5%, incorporating transmission/distribution losses (following Frandsen 2007)
Curve above 4m/s is scaled so that at for T=10C (middle of operating temperature range) capacity factor =100% at around 12m/s (typical real-world rated wind speed)
![Page 17: Bespoke climate services for the wind power industry: from ... · – Weather dependent : ‘Jack-up/down’ & installation only when wave height < threshold (relation to wind speed)](https://reader033.vdocuments.net/reader033/viewer/2022043021/5f3cf8783956af436419f696/html5/thumbnails/17.jpg)
Making climate variables useful – bringing it all together
Assuming: • kickin/out 4,
25m/s• Rayleigh
distribution for diurnal wind speed
• u100m=1.25u10m
• overall reduction to 87.5%, incorporating transmission/distribution losses (following Frandsen 2007)
Curve above 4m/s is scaled so that at for T=10C (middle of operating temperature range) capacity factor =100% at around 12m/s (typical real-world rated wind speed)
![Page 18: Bespoke climate services for the wind power industry: from ... · – Weather dependent : ‘Jack-up/down’ & installation only when wave height < threshold (relation to wind speed)](https://reader033.vdocuments.net/reader033/viewer/2022043021/5f3cf8783956af436419f696/html5/thumbnails/18.jpg)
Climate services for the wind power industry
• Exploring weather & climate related decisions
• Tailoring climate variables for the industry
• Identifying promising regions for seasonal forecasting
• Creating impact surfaces for promising regions
![Page 19: Bespoke climate services for the wind power industry: from ... · – Weather dependent : ‘Jack-up/down’ & installation only when wave height < threshold (relation to wind speed)](https://reader033.vdocuments.net/reader033/viewer/2022043021/5f3cf8783956af436419f696/html5/thumbnails/19.jpg)
Where in the world are seasonal forecasts potentially useful?
Wind energy viable (high average CF)
Significant interannual
variability in CF
Skilful seasonal
prediction of wind speed
(& temperature?
)
Estimate with ERA-Interim reanalysis + Capacity Factor curve
Estimate from System 4 hindcasts
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• CF curve used to calculate CF for every day of 1979-2010 ERA-Interim reanalysis wind and 2mT temperature
• Calculate annual & seasonal means & std dev
Where in the world are seasonal forecasts potentially useful?
Wind energy viable (high average CF)
Significant interannual
variability in CF
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Where is wind power potential greatest?
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Seasonal average capacity factor
DJF
JJA
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Where is interannual variability in seasonal average capacity factor greatest?
DJF
JJA
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Where are seasonal forecasts potentially useful?
Wind energy viable (high average CF)
Significant interannual
variability in CF
Skilful seasonal
prediction of wind
speeds (& temperature
?)
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Estimating skill in seasonal prediction
• ECMWF, System 4: IFS atmosphere, NEMO ocean• Atmospheric resolution T255 (60km x 60km at equator)• Forecasts initialized beginning every month, 1981-2010• 25 member ensemble
– Initial conditions from singular vectors– Atmosphere perturbed with SPPT
• Looking at lead 2-4 for standard seasons. i.e. DJF, JJA, initialised in November, May, respectively
F’cast probabilities
EventsNon-events
1: perfect, 0: skill no better than climatology
Event is: average 10m wind speed next season will fall in the upper/lower tercile of the historical distribution
Significance from 1,000 boostrap resampling
Measuring probabilistic skill with the ROC skill score: the ability of a forecasting system to
discriminate events & non-events
ROCss 2(ROCareaf 'cast ROCareaclim )1
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System 4 ROC SS: Lower tercile 10m wind speed, lead 2-4
DJF
JJA
?
[Similar results for upper tercile]
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Alternative routes to seasonal wind prediction
Gridpoint wind
GloSea5 correlation with ERA-I DJF windspeed (Scaife et al 2014)
NAO-based wind
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Climate services for the wind power industry
• Exploring weather & climate related decisions
• Tailoring climate variables for the industry
• Identifying promising regions for seasonal forecasting
• Creating impact surfaces for promising regions
![Page 29: Bespoke climate services for the wind power industry: from ... · – Weather dependent : ‘Jack-up/down’ & installation only when wave height < threshold (relation to wind speed)](https://reader033.vdocuments.net/reader033/viewer/2022043021/5f3cf8783956af436419f696/html5/thumbnails/29.jpg)
Creating impact surfaces
• Motivation:
– Linking (3 month average) seasonal climate forecast to expected power output
– Calculating power from seasonal average ignores subseasonal variability. This is not acceptable due to nonlinearity of the power curve.
– Visualizing the climatology in 2D (multivariate – wind speed & temperature), using ‘familiar units’ (i.e. capacity factor)
• Promising regions:
– Central USA, DJF
– Brazil, JJA
– North sea, DJF
– Borneo, JJA
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Creating impact surfaces
• Method– Subset all grid points within domain (either all land
or ocean)
– Calculate capacity factor for every day in every ERA-Interim year, based on daily temp & 10m mean daily wind speed.
– Calculate seasonal averages in temp, 10m mean wind speed and capacity factor
– Plot results in discretized “climate space”
– Stratifying by ENSO
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Impact surface – East Brazil, JJA
Key:
X/Y coordinate: average seasonal temperature and 10m wind speed
Colour: average capacity factor calculated for seasons of that particular wind/temp average
Size (area): relative frequency of that climate average state in the sample
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Impact surface – East Brazil, JJA
El Niño La Niña
Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)
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Impact surface – Central USA, DJF
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Impact surface – Central USA, DJF
El Niño La Niña
Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)
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Impact surface – North Sea, DJF
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Impact surface – North Sea, DJF
El Niño La Niña
Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)
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Impact surface – Borneo, JJA
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Impact surface – Borneo, JJA
El Niño La Niña
Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)
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• Predictability of power from temperature forecasts is dwarfed by that from wind speed
• Slight shift in distribution from ENSO state, for North Sea power prediction (observed link between ENSO and NAO, Brönnimann et al. 2007)
• What about second order wind characteristics (i.e. sub-seasonal variability?)
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Impact surface – North Sea, DJF
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Impact surface – North Sea, DJF
El Niño La Niña
Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)
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• Generally higher power for less variable seasons (less time spent above kick-out)
• Susbseasonal wind speed forecast arguably gives more predictability for power than temperature
• Is there skill for this?
• Potential ensemble climate forecast:
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• How accurate is this model?
• “Parameterized” with reanalysis data
• Average CF in USA 33% (CCEE)
• Reanalysis grid-box wind likely an underestimate of wind at an optimal wind farm location
• However, multiple (i.e. several gridboxes) & long timeseries allow fuller exploration of ‘impact space’
Careful!Central USA
0.7x0.7 degree grid (ERA-Interim)
CF F(w,t)
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Summary
• Potential untapped application of seasonal forecasts for wind energy sector Post-construction, finance
• Method for transforming 10m daily wind speed to “user-friendly” seasonal average capacity factor described
• Identification of most likely regions for useful seasonal forecasts
USA, Brazil, North Sea, Southeast Asia
• Description of impact surfaces
Variation in air density due to seasonal temperature fluctuations gives little predictability of power compared to wind speed.
Forecast of subseasonal variability potentially useful
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Thanks for listening
Outstanding questions:• Validity of Rayleigh distribution?• Predictability of sub seasonal variability in windspeed?• Best seasonal prediction? (other models are available…
indirect methods)
• Integrating forecasts with impact surfaces (multivariate bias correction – ongoing work at IC3)
Feedback to users• Potential contribution as part of the EUPORIAS RESILIANCE
prototype
Further work• Case study for a single region• Station data vs reanalysis• Use hi-temporal resolution wind data to test Rayleigh
assumption• Validation with historical power data? (company secrets?)
Location specific