global wind atlas 2.0: aiming for best value out of …...global wind atlas 2.0: aiming for best...

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

2

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

3

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

4

02 October 2017

Global Solar Atlas

5

02 October 20176

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

7

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

• …

8

02 October 2017

Exclusion map: Denmark 69.9 %

9

Capacity factor

Sambahamphe (2017)

02 October 2017

Exclusion map: Uruguay 17.2 %

10

Capacity factor

Sambahamphe (2017)

02 October 201711

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….

12

https://photius.com/rankings/2017/energy

02 October 2017

v112 Following Danish law 2647

13

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

14

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

16

Consideration of buffer zones: Uruguay

Note: increasing buffer size, increases number of turbines a little.

02 October 201717

Sambahamphe (2017)

02 October 2017

Consideration of buffer zones: Kenya

18

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.

02 October 201719

Uruguay

Proximity to transmission

02 October 201720

Required distribution of wind turbine number

• within country

• within 10 km of transmission line

Uruguay

Sambahamphe (2017)

02 October 201721

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

22

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

23

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

24

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

25

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

– …

26

02 October 2017

THANK YOU

jaba@dtu.dk

27