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

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

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

Weather & climate related decisions in the wind power industry

Pre-construction –> construction –> post-construction

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

• 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

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

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)

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

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

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

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

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)

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

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)

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)

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)

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

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

• 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

Where is wind power potential greatest?

Seasonal average capacity factor

DJF

JJA

Where is interannual variability in seasonal average capacity factor greatest?

DJF

JJA

Where are seasonal forecasts potentially useful?

Wind energy viable (high average CF)

Significant interannual

variability in CF

Skilful seasonal

prediction of wind

speeds (& temperature

?)

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

System 4 ROC SS: Lower tercile 10m wind speed, lead 2-4

DJF

JJA

?

[Similar results for upper tercile]

Alternative routes to seasonal wind prediction

Gridpoint wind

GloSea5 correlation with ERA-I DJF windspeed (Scaife et al 2014)

NAO-based wind

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

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

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

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

Impact surface – East Brazil, JJA

El Niño La Niña

Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)

Impact surface – Central USA, DJF

Impact surface – Central USA, DJF

El Niño La Niña

Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)

Impact surface – North Sea, DJF

Impact surface – North Sea, DJF

El Niño La Niña

Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)

Impact surface – Borneo, JJA

Impact surface – Borneo, JJA

El Niño La Niña

Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)

• 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?)

Impact surface – North Sea, DJF

Impact surface – North Sea, DJF

El Niño La Niña

Stratifying on ENSO (when 3-month average ONI < or > than ±0.5)

• 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:

• 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)

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

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