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Transcript of 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)
3 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
,
Pout : Exctractable power output (W)
U: Wind speed (m/s) Cp: Power coefficient ρ: air density R: diameter blades
4 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Figure: Simulated temperture change with ECHAM5 MPI-OM (IPCC AR5)
5 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Global Climate Model (GCM)
Reality
6 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
7 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
8 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
Main Objective Estimation of the change in wind power (Pout) in Europe under future climate conditions
9 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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!
10 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Figure: Probability density function (PDF) scores of the wind speed PDF at ~80 m (1979 - 2005).
11 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
12 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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).
13 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
Main Objective Estimation of the change in wind power (Pout) in Europe under future climate conditions
14 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
15 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Tra
nsfe
rfunction
Yi=β
i,1.X
1+
βi,2.X
2+ε
=
Set up
Past (period 1)
Validation
Past (period 2)
?
16 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Figure: GCM PDF – Observed PDF
without downscaling
with downscaling
OBSERVATIE PDF GCM PDF
Winter day Winter night
Summer day Summer night
17 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
18 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
Main Objective Estimation of the change in wind power (Pout) in Europe under future climate conditions
19 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
20 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
21 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Past (1979-2005) Future (2020-2049)
Ensemble mean change in power output
68 %
22 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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 %
23 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Change in Pout(kW)
WIN
TER D
AY
(1979-2005 to 2020-2049) (for a 2300kW turbine)
SUM
MER
DA
Y
68 %
24 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Pout : Exctractable power output (W)
U: Wind speed (m/s) Cp: Power coefficient ρ: air density R: rotor diameter
25 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
U Cp
Figure: Effect of varying λ and k parameters on Weibull PDF
26 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
27 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Conceptual example
28 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
30 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
Is the change in Pout dependent on the turbine type?
WIN
TER D
AY
31 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions
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
34 1. Introduction 4. Downsc. Development 3. GCM evaluation 5. Downsc. Application 2. Research Objectives 6. Conclusions