Bespoke climate services for the wind power industry: from decisions to (seasonal) forecast products

Transcription

1 Bespoke climate services for the wind power industry: from decisions to (seasonal) forecast products Dave MacLeod with thanks to Melanie Davis & Paco Doblas-Reyes, IC3, Met Office seminar, 11th June 2014 European Provision Of Regional Impacts Assessments on Seasonal and Decadal

2 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

3 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

4 Weather & climate related decisions in the wind power industry Pre-construction > construction > postconstruction

5 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 debtsizing [DNV GL] Useful climate information: historical record of (downscaled) mean and variability in power

6 Weather & climate related decisions in the wind power industry 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 & 12 hour trend. Potential for use of medium-range forecasts.

7 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, mediumrange forecast potentially useful 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

8 Weather & climate related decisions in the wind power industry - timescales Minimizing construction time (on & offshore): Short - medium range (<30 days) Pre-construction > construction > postconstruction e selection, insurance & financing: torical record of mean and variability Planned maintenance: Historical record, plus short medium range Financial (insurance, grid management, trad Forecast on any timescale (incl. seasonal)

9 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

10 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

11 Relating 10m daily wind speed to capacity factor theoretical maximum wind power Wind energy: ρ = air density mv2 (Avt )v2 Atv3 E v = instantaneous wind speed A = area of turbine perpendicular to wind direction Ideal gas law (relating density to temperature): p = air pressure p Av3 P RT 2 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

12 Relating 10m daily wind speed to capacity factor real-world wind power Power output curve from technical turbine specifications: Renewegy 20kW & Vestas 2.0 & 1.8 MW.

13 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)

14 Making climate variables useful power is generated above 10m u z The wind profile power law r ur z u: speed z: height r: speed/height at reference level α: empirically derived coefficient related to atmospheric stability Converting 10m to 100m winds: u100m u10m 10 α neutral stability over land ± 0.03 neutral stability over deep/shallow ocean (Hsu et al. 1994)??? non-neutral stability or rough surface: log wind profile preferred log wind profile: uz u* z d ln (z, z0, L) z0 10m = 25.7 ± m over ocean 100m over land Assuming:u100m 1.25 u10 m gives a systematic error of around 5% Unknown error from neutral stability / smoothness assumption Land Ocean (low alpha) Ocean (mid alpha)

15 Making climate variables useful bringing it all together Assuming: kickin/out 4, 25m/s Rayleigh distribution for diurnal wind speed u100m=1.25u10 m overall reduction to 87.5%, incorporating transmission/distri bution 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 realworld rated wind

16 Making climate variables useful bringing it all together Assuming: kickin/out 4, 25m/s Rayleigh distribution for diurnal wind speed u100m=1.25u10 m overall reduction to 87.5%, incorporating transmission/distri bution 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 realworld rated wind

17 Making climate variables useful bringing it all together Assuming: kickin/out 4, 25m/s Rayleigh distribution for diurnal wind speed u100m=1.25u10 m overall reduction to 87.5%, incorporating transmission/distri bution 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 realworld rated wind

18 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

19 Where in the world are seasonal forecasts potentially useful? Wind energy viable (high average CF) Estimate with ERA-Interim reanalysis + Capacity Factor curve Significant interannual variability in CF Skilful seasonal prediction of wind speed (& temperature? ) Estimate from System 4 hindcasts

20 Where in the world are seasonal forecasts potentially useful? Wind energy viable (high average CF) Significant interannual variability in CF CF curve used to calculate CF for every day of ERA-Interim reanalysis wind and 2mT temperature Calculate annual & seasonal means & std dev

21 Where is wind power potential greatest?

22 Seasonal average capacity factor DJF JJA

23 Where is interannual variability in seasonal average capacity factor greatest? DJF JJA

24 Where are seasonal forecasts potentially useful? Wind energy viable (high average CF) Significant interannual variability in CF Skilful seasonal prediction of wind speeds (& temperature?)

25 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, 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 Non-events F cast probabilities Events 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 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

26 System 4 ROC SS: Lower tercile 10m wind speed, lead 2-4 DJF? JJA [Similar results for upper tercile

27 Alternative routes to seasonal wind prediction Gridpoint wind NAO-based wind GloSea5 correlation with ERA-I DJF windspeed (Scaife et al 2014)

28 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

29 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

30 Creating impact surfaces Method Subset all grid points within domain (either all land or ocean) Calculate capacity factor for every day in every ERAInterim 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

31 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

32 Impact surface East Brazil, JJA Stratifying on ENSO (when 3-month average ONI < or > than ±0.5) El Niño La Niña

33 Impact surface Central USA, DJF

34 Impact surface Central USA, DJF Stratifying on ENSO (when 3-month average ONI < or > than ±0.5) El Niño La Niña

35 Impact surface North Sea, DJF

36 Impact surface North Sea, DJF Stratifying on ENSO (when 3-month average ONI < or > than ±0.5) El Niño La Niña

37 Impact surface Borneo, JJA

38 Impact surface Borneo, JJA Stratifying on ENSO (when 3-month average ONI < or > than ±0.5) El Niño La Niña

39 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. subseasonal variability?)

40 Impact surface North Sea, DJF

41 Impact surface North Sea, DJF Stratifying on ENSO (when 3-month average ONI < or > than ±0.5) El Niño La Niña

42 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:

43 Careful! How accurate is this model? Central USA CF F(w, t) 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 0.7x0.7 degree grid (ERA-Interim

44 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

45 Outstanding questions: Validity of Rayleigh distribution? Location specific 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 Thanks for listening Validation with historical power data? (company secrets?)