public defense phd

promotor: Prof. Dr. Nicole van Lipzig co-promotor: Dr. Matthias Demuzere

Wind energy in Europe under future climate conditions

The statistical downscaling of

a CMIP5 model ensemble

Annemarie DEVIS Sept 2014

2 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions

Figure: Installed power (MW/km²) in 2012 (Vautard et al., 2014)


,

Pout : Exctractable power output (W)

U: Wind speed (m/s) Cp: Power coefficient ρ: air density R: diameter blades


Figure: Simulated temperture change with ECHAM5 MPI-OM (IPCC AR5)


Global Climate Model (GCM)

Reality


Main Objective Estimation of the change in wind power (Pout) in Europe under future climate conditions

GCM

Downscaling

Wind power (Pout)

Wind speed (U) at rotorheight (at climate time scales for past & future)

GCM1

GCM2

GCM4 GCM5

GCM6

PDF


GCM

Downscaling

Wind power (Pout)

GCM1

GCM2

GCM4 GCM5

GCM6

• Evaluate all GCMs - past

• Apply the downscaling on GCM ensemble

- past -future

• Develop downscaling to go from one GCM to rotorheight wind climate

- past

Sub Objectives

PDF



GCM5

Reanalysis - in which real observations are

assimilated - only available for present

= ? GCM

GCM1 GCM2

GCM4

GCM6

reanalysis

GCMs

If PDF score > 0.7 ok!


Figure: Probability density function (PDF) scores of the wind speed PDF at ~80 m (1979 - 2005).


PDF score

Figure: Lowest altitude for which all GCMs have PDF scores > 0.7 and remain >0.7 up to ~1500 m in representing the wind speed PDF during summer day. White: no GCM has a level with a PDF score > 0.7. Gray: PDF score at 1500 m <0.7 and layers underneath have PDF scores > 0.7. The surrounding graphs show on the left axis the probability density for the reanalysis minus the probability density for the GCM at each bin for MIROC (green), CanESM (blue), NorESM (yellow), ISPL (pink), HADGEM (red) and CNRM (grey). ERA-Interim reanalysis wind speed histograms are plotted in grey, and their frequency values are shown on the righthand y-axis. x-axes show wind speed (m s–1).

Wind speed

Wind speed

Den

sity

D

ensi

ty


Reanalyse PDF GCM PDF

Figure: Lowest altitude for which all GCMs have PDF scores > 0.7 and remain >0.7 up to ~1500 m in representing the wind speed PDF during winter day. White: no GCM has a level with a PDF score > 0.7. Gray: PDF score at 1500 m <0.7 and layers underneath have PDF scores > 0.7. The surrounding graphs show on the left axis the probability density for the reanalysis minus the probability density for the GCM at each bin for MIROC (green), CanESM (blue), NorESM (yellow), ISPL (pink), HADGEM (red) and CNRM (grey). ERA-Interim reanalysis wind speed histograms are plotted in grey, and their frequency values are shown on the righthand y-axis. x-axes show wind speed (m s–1).


GCM

Downscaling

Wind power (Pout)

GCM1

GCM2

GCM4 GCM5

GCM6

1. Evaluate all GCMs - past

3. Apply the downscaling on GCM ensemble - past -future

2. Develop downscaling to go from one GCM to rotorheight wind climate in Cabauw - past

Sub Objectives

PDF



Small scale wind climate

Large scale information

Statistical relationship between small-scale and large-scale

Yi=βi,1.X1+ βi,2.X2+ε

Possible predictors (X)

PDF is defined by λ and k

GCM

Do

wn

scal

ing

PDF


Tra

nsfe

rfunction

Yi=β

i,1.X

1+

βi,2.X

2+ε

=

Set up

Past (period 1)

Validation

Past (period 2)

?


Figure: GCM PDF – Observed PDF

without downscaling

with downscaling

OBSERVATIE PDF GCM PDF

Winter day Winter night

Summer day Summer night


Winter day

Figure: GCM PDF – Observed PDF

without downscaling

with downscaling

Winter day Winter night

Summer day Summer night

Yi=βi,1.X1+ βi,2.X2+ε

Predictors: •wind speed from ~ 1000m

Predictors: •wind speed from ~ 1000m •temperature gradient between in and out ABL


GCM

Downscaling

Wind power (Pout)

GCM1

GCM2

GCM4 GCM5

GCM6

1. Evaluate all GCMs - past

3. Apply the downscaling on GCM ensemble - past -future

2. Develop downscaling to go from one GCM to rotorheight wind climate in Cabauw - past

Sub Objectives

PDF



Past (1979-2005) Future (2020-2049)

Do

wn

scal

ing

Pout (GCM1) Pout

(GCM2)

Pout (GCM3)

Pout (GCM4)

Pout (GCM5)

GCM1

GCM2

GCM3

GCM4

GCM5

Pout (GCM1) Pout

(GCM2)

Pout (GCM3)

Pout (GCM4)

Pout (GCM5)

GCM1

GCM2

GCM3

GCM4

GCM5


Do

wn

scal

ing

Pout (GCM1) Pout

(GCM2)

Pout (GCM3)

Pout (GCM4)

Pout (GCM5)

GCM1

GCM2

GCM3

GCM4

GCM5

Pout (GCM1) Pout

(GCM2)

Pout (GCM3)

Pout (GCM4)

Pout (GCM5)

GCM1

GCM2

GCM3

GCM4

GCM5

Change in Pout = Pout future - Pout past


Past (1979-2005) Future (2020-2049)

Ensemble mean change in power output

68 %


Lower bound of ensemble

Upper bound of ensemble

Conceptual example

Change in Pout(kW)

WIN

TER D

AY

(1979-2005 to 2020-2049) (for a 2300kW turbine)

68 %


Change in Pout(kW)

WIN

TER D

AY

(1979-2005 to 2020-2049) (for a 2300kW turbine)

SUM

MER

DA

Y

68 %


Pout : Exctractable power output (W)

U: Wind speed (m/s) Cp: Power coefficient ρ: air density R: rotor diameter


U Cp

Figure: Effect of varying λ and k parameters on Weibull PDF


Is the change in Pout different when only mean wind is taken into account?

U

Ensemble mean change in λ Ensemble mean change in k Ensemble mean change in Pout

WIN

TER D

AY


Conceptual example


1. Evaluate all GCMs

3. Apply the downscaling on GCM ensemble

2. Develop downscaling to go from one GCM to rotorheight wind climate in Cabauw

Sub Objectives Summer: Small-scale bias in GCMs Winter: Little small-scale bias in GCMs

Summer: Added value Winter: Little added value

Possible changes in power output by 2020-2049: •Significant decrease in Mediterranean •Insignificant small increase in Northwestern Europe

Sub Conclusions

Bedankt voor jullie aandacht

Arenberg Doctoral School of Science, Engineering &Technology Faculty of Science Earth & Environmental Science

Agentschap voor wetenschap en technologie

• Added value of downscaling on representation of rotorheight windclimate:

– Summer: Small-scale bias in GCMs added value

– Winter: No small-scale bias in GCMs little added value

• Possible changes in power output by 2020-2049:

– Significant decrease in Mediterannean (~16%)

– Insignificant small increase in Northwestern Europe


Is the change in Pout dependent on the turbine type?

WIN

TER D

AY


Is there still an added value of downscaling?

Representation of past rotor height wind speed Yes, during summer Representation of change (future-past) in power output Impossible to check …

WIN

TER D

AY

With downscaling

Without downscaling

SUM

MER

DA

Y 32 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions

Change in Pout(kW) (for a 2300kW turbine)

Do

wn

scal

ing

Do

wn

scal

ing

Do

wn

scal

ing

=

Set up

Present

Application

Future

Validation

Present

+CO2

+CO2 ?

• Effect of downscaling

– Representation of present climate

• Summer: Small-scale bias in GCMs added value

• Winter: No small-scale bias in GCM little added value

– Climate change signal

• No effect

• Possible changes in power output by 2020-2049

– Significant decrease in Mediterannean (~16%)

– Insignificant small increase in Northwestern Europe


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