Assessing the Impact of Climate Variability on Wind Energy Potential in Decarbonization Scenarios

1. Energy
System
Modeling
Analytics
ESMA
Energy System Model Analytics | ESMA
Center on Global Energy Policy | CGEP
Columbia University | SIPA
By Bryn Stecher & James Glynn • ESMA • CGEP
IEA-ETSAP Workshop, Golden, CO • June 2023
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2. Coupling ESOMs & GCMs;
Assessing the Impact of Climate Variability on Wind
Energy Potential in Decarbonization Scenarios
By Bryn Stecher & James Glynn • ESMA • CGEP
IEA-ETSAP Workshop, Golden, CO • June 2023
4
Preliminary Unpublished Work – feedback most welcome

3. 1. What decarbonization scenario will affect wind
farm capacity the most in a given region or
country?
2. Where will wind farms do well or do worse in
10, 30, 50 years from now?
3. Analyze how different decarbonization
pathways will affect wind energy production
Importance and
Implications
Capacity Factor: The capacity factor of a wind turbine is its
average power output divided by its maximum power capability
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

4. 6
Wind Capacity in IPCC Paris scenarios
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

5. SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5 SSP Used
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University
Methods: Decarbonization Pathways (SSP and RCP)

6. SSP Scenarios
SSP 1-2.6, 3-7.0, and 5-8.5 were used as the
decarbonization scenarios in this research.
The use of SSPs enables the incorporation of
interdisciplinary considerations and the evaluation of the
effects of different decarbonization pathways on
atmospheric variables, such as wind patterns and
speeds, which directly affect wind energy production,
As temperatures increase, the air becomes less dense,
which reduces the speed of the wind and therefore the
energy output of the turbines
SSP Scenarios using CMIP6 model projection on surface wind speed changes (%)
compared to the 1981-2010 average (Iturbide, Maialen et al., 2021).
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

7. Methods – Data pipeline process
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

8. Methods
Annual Average Surface
Wind Speed 80m Floor
Value (m/s)
IEC Class
< 7.5 1
7.5 < X < 8.5 2
> 8.5 3
Annual Average
Surface Wind Speed
80m Floor Value (m/s)
IEC Class
1 Capacity
Factor
IEC
Class 2
Capacity
Factor
IEC
Class 3
Capacity
Factor
3 0.193936 0.164475 0.162758
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

9. Find the longitude and latitude of the wind farm(s) you are looking for historical or future wind turbine
capacity factors
Data source: https://github.com/WRIglobal-power-plant-database
Methods – find the wind farm of interest

10. 2020 SSP5-RCP8.5 - Daily wind speeds 2090 - SSP5-RCP8.5 –Daily wind speeds
Wind Speed Variability in SSP-RCP future
• ISIMIP - state of the art climate impact
simulation data.
– https://protocol.isimip.org/protocol/ISIMIP3b/
• Energy Theme relevant data
– Grided wind speedsat daily resolution for
SSP1-RCP2.6, SSP3 – RCP7, SSP5-RCP8.5
• Also Explore Extreme weather events and their
impacts on the Energy system
– Starting with flooding impacts.
– Annual Maximum Head height and discharge events
– www.isimip.org
– https://protocol.isimip.org/protocol/ISIMIP3a/water_g
lobal.html
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

11. The trends of the average annual anomaly capacity factor values for each region by SSP. The anomaly values are
compared with the individual regions’ historical (1981-2010) average capacity factor
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

12. The trends of the average annual anomaly capacity factor values for each region by SSP. The anomaly values are
compared with the individual regions’ historical (1981-2010) average capacity factor
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

13. Methods:
US Wind Farm
Coordinates
Location of all operating wind turbines in the
United States of America as of January 2023
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

14. Results: Season Variations
Spring and Winter are the best months for wind energy in terms of capacity factor. Overall decreases
overtime in capacity factor for 7 of the 12 scenarios of season/SSP.
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Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

15. Results: SSP Variations
Capacity Factors roughly stay the same on average, with SSP 3-7.0 leading in most regions.
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Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

16. Results: Location Variations
SSP1-2.6 saw an increase in central Texas but mostly decreases, especially in the West and eastern part of
the Midwest.
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Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

17. Results: Location Variations
SSP3-7.0 saw an highest increases of the study in the Northeast, increase in central Texas and Midwest, and
decrease in the West.
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Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

18. Results: Location Variations
SSP5-8.5 saw an increase in offshore in the Atlantic ocean but mostly decreases, especially in the West.
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Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

19. Suggestions for Policy + Deployment
Northeast
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Offshore
Texas
Energy System Modeling Analytics | Center on Global Energy Policy | Columbia University

20. Coupling ESOMs & GCMs;
Assessing the Impact of Climate Variability on Wind
Energy Potential in Decarbonization Scenarios
By Bryn Stecher & James Glynn,
IEA-ETSAP Workshop, Golden CO, June 2023

21. Thank you.
Dr James Glynn (he/him)
Senior Research Scholar of Energy Systems Modeling and Analytics (ESMA)
Center on Global Energy Policy | Columbia University SIPA
jg4434@columbia.edu | @james_glynn
Executive Assistant & Scheduling: Kelly Banegas kb3290@columbia.edu

22. WRI or GEM Global Power Plant database
Find the longitude and latitude of the wind farm(s) you are looking for historical or future wind turbine
capacity factors
Data source: https://github.com/WRIglobal-power-plant-database

23. ISIMIP data.isimip.org
Extract the time series surface wind speed <sfcwind> from the relevant historical ISIMIP3a or future
ISIMIP3b data sets.
*Note sfcwind is assumed to be 10 metres above the ground surface
Data source: data.isimip.org

24. Navigate & Configure ISIMIP data.isimip.org
Extract the time series surface wind speed <sfcwind> from the relevant historical ISIMIP3a or future
ISIMIP3b data sets.
*Note sfcwind is assumed to be 10 metres above the ground surface
Data source: https://github.com/NREL/turbine-models.git

25. ISIMIP data.isimip.org
Configure your download for the correct time range and location
Data source: data.isimip.org

26. ISIMIP data.isimip.org
Configure your download for the correct time range and location
Data source: data.isimip.org

27. ISIMIP data.isimip.org
Power scale sfcwind (m/s) from 10 metres ground speed to an 80 metre hub height wind speed
Data source: data.isimip.org
https://github.com/NREL/turbine-models.git

28. NREL Normalized IEC Class 1, 2 & 3 Wind power curves
The National renewable Energy Labs (NREL) maintains a database of power curves for wind turbines of
various vintages, including normalized power curves for IEC classes
Data source: https://github.com/NREL/turbine-models.git

29. NREL Turbine Models Database
Lookup Wind spend against normalized power curve to get capacity factor (CP) or availability factor (AF)
for that specific longitude and latitude for that specific time slice
Data source: data.isimip.org
https://github.com/NREL/turbine-models.git

30. Case Study in TIMES IRELAND Model - TIM
Find the longitude and latitude of the wind farm(s) you are looking for historical or future wind turbine
capacity factors
Data source: https://github.com/WRIglobal-power-plant-database

31. Match TimeSlice resolution from ISIMIP to your Model
Match the time slice resolution from your model to the time resolution of the available capacity factor
data from ISIMIP.
Data source: Authors Calculations
data.isimip.org
https://github.com/NREL/turbine-models.git

32. Match Cf to technologies in your Energy System Model
Match the technology resolution to the spatial resolution of your data extract from ISIMIP
TIMES IRELAND Model Example
TIMES – Declare additional process/technologies in the base year template
TIMES – Add additional SubAnnual availability factors in a Scenario file
Data source: Authors Calculations
https://github.com/MaREI-EPMG/TIMES-IRELAND-MODEL.git
data.isimip.org
https://github.com/NREL/turbine-models.git

33. Thank You
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