Global Atlas for Renewable Energy - application to Mauritania
-
Upload
irena-global-atlas -
Category
Environment
-
view
659 -
download
0
Transcript of Global Atlas for Renewable Energy - application to Mauritania
L’Atlas Mondial des Energies Renouvelables Application a la Mauritanie
Dr. Nicolas Fichaux, IRENA
Bridge the gap between nations having access to the necessary funding, technologies, and expertise to evaluate their national potentials, and those deprived of those elements.
2
Bridge the gap between nations having access to the necessary funding, technologies, and expertise to evaluate their national potentials, and those deprived of those elements.
Access to data and methods Building capacities on strategic planning Mobilizing technical assistance
3
4
Albania, Australia, Austria, Belgium, Colombia, Denmark, Egypt, Ethiopia, Fiji island, France, Gambia, Germany, Greece, Grenada, Honduras, India, Iraq, Iran, Israel, Italy, Kazakhstan, Kenya, Kiribati, Kuwait, Lithuania, Luxembourg, Maldives, Mali, Mauritania, Mauritius, Mexico, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Peru, Philippines, Poland, Portugal, Qatar, Saudi Arabia, Senegal, Seychelles, South Africa, Spain, Sudan, Swaziland, Switzerland, Tonga, Tunisia, Turkey, UAE, Uganda, UK, United Republic of Tanzania, Uruguay, USA, Vanuatu, Yemen, Zimbabwe.
Global Atlas
5
What share of my energy mix can be supplied by renewable energy?
Where are the resources located?
What is the most cost-effective combination of technologies?
What amount of investments does it represent? How many jobs ?
Is there a large enough market for sustaining a supply chain?
6
Conceptual diagram of Renewable Energy Potentials (from NREL, 2012)
How competitive is it?
How much can it cost?
Where can it be harvested? How much power?
Where is the resource?
Complexity StandardsPrivate sector
interest Risks
• COUNTRY-DRIVEN• LONG TERM PLANNING PROCESS • COMMITMENT REQUIRED
Winds in Africa. Mesoscale 5km basemap from 3TIER. Average annual wind speeds at 80 m high.
The values can not be used without validation, but the wind patterns appear clearly, and are consistent with other mesoscale sources. The boxes attempt to highlight areas with possibly strong annual average wind speeds.
This rough approximation does not exclude the possibility of good wind sites outside the red squares, due to local effects not captured by the mesoscale model.
8
Data bankability
Investor’sinterest
PUBLICSECTOR EFFORT
Local measurementsPRIVATE
SECTOR EFFORT
Existing local measurements
Data quality
Zoning
NOT ‘BANKABLE’
‘BANKABLE’
9
Global Atlas in numbers • 1 interface to access 1,100 datasets on 11 Geoservers • 67 countries, of which 47 contributed to the project
• 100,000 sessions since Jan. 2013• 1,000 registered users created 1,600 maps stored in the
system
• 150 daily visitors with peaks of 1,000+ visitors • 2-days training module for 35 policy makers in 25 countries
10
Some datasets of the Global Atlas
11
Global Atlas 2.0
Global Atlas is an Integrated Global Spatial Data Infrastructure
14
Global Atlas analysis
ApplicationCarte de Mauritanie:
http://irena.masdar.ac.ae/?map=1693
15
Solar
16
Solar time series - Nouakchott
17
PV installation simulation - Nouakchott
18
Wind
19
Winds > 7 m/s, population, grids, roads
20
21
Wind rose and frequency
Oct 2015 – Global Wind Atlas - DTU
22
Oct 2015 – Global Wind Atlas - DTU
23
Blackberry World, Windows Mobile, Google Play, soon on iOS
www.irena.org/GlobalAtlas
Calculation scheme for wind energy yield using wind speed distributions and power curves
Ei = Annual energy yield of wind class [Wh,
watthours], i = 1, 2, 3 …n ti = duration of wind speeds at wind class [h/a,
hours/year] Pi(vi) = Power of wind class vi of wind turbine
power curve [Watt] vi = wind class [m/s] PN = Nominal power of WEC [kW] at nominal
wind class vi [m/s]
hi = relative wind class frequency in %
Source: J.liersch; KeyWindEnergy, 200925
26
Calculation scheme for annual energy production
Ei = Pi(vi) * ti
E = E1 + E2 +…+ En
E = Energy yield over one year
J.lie
rsch
; Key
Win
dEne
rgy,
200
9
27
Shape of different wind speed distributions• Weibull distribution:
shape factor k=1,25 and A= 8 m/s
• Weibull distribution: shape factor k=3 and A= 8 m/s
28
Sample power curves of wind turbines(82 m rotor diameter, 2 and 3 MW)
Sou
rce:
Ene
rcon
pro
duct
info
rmat
ion
2014
29
30