global wind atlas 2.0: aiming for best value out of …...global wind atlas 2.0: aiming for best...
TRANSCRIPT
Global Wind Atlas 2.0:
Aiming for best value out of high resolution global datasets
Presented by Jake Badger, Head of Section, DTU Wind Energy
Calculations and plots from:
Bidur Subba Sambahamphe MSc
Supervised by Jake Badger and Neil Davis
02 October 2017
GWA (1.0) globalwindatlas.com
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02 October 2017
Global Wind Atlas 2.0
• Support from ESMAP World Bank
• DTU Wind Energy
• owner
• microscale modelling
In collaboration with
• Vortex providing the mesoscale modelling
• Mesocale modelling and WB services
• Nazka Mapps
• Developing of new website
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02 October 2017
What’s new
• More accurate
– 9km mesoscale simulations substitutes MERRA reanalysis dataset in model chain
• More user friendly
– revised website
• More validation
– To be coupled with national ESMAP wind mapping projects
• Vision to support more derived datasets
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02 October 2017
Global Solar Atlas
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Global Solar Atlas
02 October 2017
Country profiling
• What should be the content of a Global Wind Atlas country poster?
• Combined with other datasets to create best value…
• Such a question has been addressed in a recent MSc project
– 3 countries taken as examples: Denmark, Uruguay, Kenya
– Public data used
• Credit to MSc Bidur Subba Sambahamphe,
– Supervisors Jake Badger and Neil Davis
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02 October 2017
Returning to the GWA (1.0)
• The GWA data has been post-processed
– Capacity factor for
• V112-like power curve, 3MW, low specific power
• V90-like power curve, 3MW, medium specific power
– These global capacity factor maps combined with
• Protected areas
– national parks, …
• Physical constraints
– Wetlands, water bodies, …
• Infrastructure
– Highways, railways and transmission
• …
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02 October 2017
Exclusion map: Denmark 69.9 %
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Capacity factor
Sambahamphe (2017)
02 October 2017
Exclusion map: Uruguay 17.2 %
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Capacity factor
Sambahamphe (2017)
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Exclusion map: Kenya 25.1 %
Capacity factor
Sambahamphe (2017)
02 October 2017
How many turbines to reach annual demand?
Denmark electricity demand 31.4 TWh per year
Uruguay electricity demand 10.4 TWh per year
Kenya Electricity demand 11.3 TWh per year
Turbines must be placed according to Danish rules and:
– Best sites first
– 7D spacing
– not concerned about integration perspectives
– this must come later….
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https://photius.com/rankings/2017/energy
02 October 2017
v112 Following Danish law 2647
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Sambahamphe (2017)
02 October 2017
Consideration of buffer zones: Denmark
Wind turbine Buffer zones Total turbine
v112 Following Danish law 2647
v90 Following Danish law 3505
V112 Buffer distance increased 3220
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Note: increasing buffer size, increases number of turbines significantly.
02 October 201715
Sambahamphe (2017)
02 October 2017
Wind Turbine Buffer zone Total turbine
v112 Following Danish law 1356
v90 Following Danish law 1973
V112 Increased buffer distance 1440
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Consideration of buffer zones: Uruguay
Note: increasing buffer size, increases number of turbines a little.
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Sambahamphe (2017)
02 October 2017
Consideration of buffer zones: Kenya
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Wind Turbine Buffer zone Total turbine
v112 Following Danish law 528
v90 Following Danish law 661
v112 Increased buffer distance 531
Note: increasing buffer size, increases number of turbines very little.
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Uruguay
Proximity to transmission
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Required distribution of wind turbine number
• within country
• within 10 km of transmission line
Uruguay
Sambahamphe (2017)
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Required distribution of wind turbine number
• within country
• within 10 km of transmission line
Kenya
Sambahamphe (2017)
02 October 2017
Dominant causes of uncertainty: Denmark
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Criteria Area %Mean c.f.
“Production” index
Slope (> 30%) 0.006 0.36 0.4
Forest 11.7 0.35 713
02 October 2017
Dominant causes of uncertainty: Uruguay
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Criteria Area %Mean c.f.
“Production” index
Slope (> 30%) 0.33 0.22 53.2
Forest 2.56 0.19 362
02 October 2017
Dominant causes of uncertainty: Kenya
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Criteria Area % Mean c.f. “Production” index
Slope (> 30%) 2.4 0.194 1140
Forest 2.78 0.13 884
02 October 2017
Summary
• GWA2.0 will be launched at WindEurope conference
– mapping tools
– area analysis
– posters
• Some GIS analysis based on public open data sets were presented
– could be repeated for all countries
• Highlighted contrasting issues around
– Proportion of exclusion area to total area
– Proximity to transmission
– Resource share perspective on uncertainty
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02 October 2017
Some reflections
• How to involve more machine learning?
– Seek best power curve for wind climate at site
– Create clusters of similar development conditions: wind climate, transmission, …
– Discover relationships between actual placement of turbines and development conditions
– With measurement data, discover uncertainty relationship with siting conditions
– Discover scenarios for varying buffer size and turbine size for social engagement
– …
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