Reanalysis for wind and solar electricity simulations: challenges and lessons learned in the Renewables.ninja project - 2018-07-17.key
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Reanalysis for wind and solar
electricity simulations: challenges
and lessons learned in the
Renewables.ninja project
Stefan Pfenninger 1st International Symposium on Regional Reanalysis
Dept of Environmental Systems Science 17.7.2018
Bonn, Germany
With Iain Staffell, Imperial College London
1. 2. 3. 4.
Why weather How we use Comparing The ninja
matters reanalysis reanalyses project
2
1.
Why weather
matters
3
Goal: eliminate greenhouse gas
emissions from the energy sector
Cho (2010). Science 4
100 GW
Why variability is problematic
GW 8080
GW
Electricity
Power generation and consumption
60
60 GW
Demand
Conventional
(coal, gas)
40
40 GW
Germany, May 2014
PV
20
20 GW
Wind
0 10. May
0 GW
04:00 08:00 12:00 16:00 20:00 11. May 04:00 08:00 12:00 16:00 20:00 12. May 04:00 08:00 12:00 16:00 20:00
10. May 11. May 12. May
Conventional power plants Solar Wind Wind Offshore Water Biomass Electricity Consumption Hard coal Lignite Nuclear Pumped hydro
Natural gas Other
• Demand and generation must match second by second Agora Energiewende; Current to: 16.07.2016, 19:10
• Electricity is difficult to store
• Demand is not very controllable
• Power stations are not like light bulbs
• Everything must be carefully coordinated and scheduled
https://www.agora-energiewende.de/en/topics/-agothem-/Produkt/produkt/76/Agorameter/ 5
Power System Localised Long-term
Dispatch Wind and Solar
Modelling Resource Profiles
Long-term
Multi-scenario
European
Power System
Model
x5
70 Five-fold
increase in variability
Total Generation Costs
65 Calm/Dull
of CO2 emissions
(€bn per year)
60 and total generation
55
Windy/Sunny
costs by 2030
50 Calm/Dull
Power system
45
Windy/Sunny functions well with
40 high penetrations
2015 2030 of renewables
Collins et al. (2018). Joule [press embargo until 11:00am EST 26 July 2018] 6
Variable weather = variable curtailment
Curtailment in ENTSO-E vision 3
(~35% of EU electricity from
solar and wind)
Collins et al. (2018). Joule [press embargo until 11:00am EST 26 July 2018] 7
Variability of CO2 emissions will rise
0%
Emissions intensity
variability in 2030
under ENTSO-E
vision 3 (30% VRE)
15%
Collins et al. (2018). Joule [press embargo until 11:00am EST 26 July 2018] 8
2.
How we use
reanalysis
9
Virtual Wind Farm Model
25 m/s
20 Take hourly
Interpolate
wind speeds
15 from grid
from a
points to site
10 reanalysis
of actual wind
(originally
5 farms
MERRA)
0
100
100%
Height above ground (m)
80 Extrapolate Convert
NASA data 80%
wind speeds from wind
Load factor
60 Log wind profile
60%
to hub height speed to
Extrapolated speed
40 at hub height with place- 40% power output
and time- 20%
Farm using whole-
20
Turbine
specific farm power
0%
0 parameters 0 10 20 30 curve
0 2 4 6 8 10 12
Wind speed (m/s)
Wind speed (m/s)
Staffell and Pfenninger (2016) 10Bias correction: not optional
Average wind capacity factors in Europe
Original model
Actual data
(uncorrected MERRA-1)
Bias
correction
Staffell and Pfenninger (2016) 11Bias correction using electric generation data
Hourly weather conditions Bias-correct
(wind speeds, sunlight, …)
Solar and wind electricity Measured electricity
simulation models generation data
Hourly estimates of
solar/wind power generation Validate
Pfenninger and Staffell (2016), Staffell and Pfenninger (2016) 12Bias-corrected wind simulations
Simulating the UK wind fleet with the MERRA reanalysis:
R² = 0.95
6
British Fleet Output (GW)
5
Actual Simulated
4
3
2
1
0
Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14
Staffell and Pfenninger (2016); actual data from Elexon & National Grid 1325 years of daily variability
Simulated UK wind and PV fleets, 1991-2015
143.
Comparing
reanalyses
(solar PV only,
for now)
Image source: http://xarray.pydata.org/ 15lower resolution
Reanalysis and satellite datasets more coverage
higher resolution
less coverage
Urraca et al., 2018: “We conclude that ERA5 and
COSMO-REA6 have reduced the gap between
reanalysis and satellite-based data” (for solar
irradiance data)
Urraca et al. (2018). Solar Energy
Pfenninger and Staffell. Work in progress. 16Germany: PV capacity factors (2012)
7-day mean capacity factor
Pfenninger and Staffell. Work in progress. 17Single PV system:
SARAH best, COSMO-REA6 not bad
Pfenninger and Staffell. Work in progress.
Post-processed COSMO-REA6 data from Frank et al. (2018). Solar Energy 184.
The ninja
project
19www.renewables.ninja
Goal: provide easy access to
validated wind and PV
simulations worldwide.
>1500 users from >250
institutions in 65 countries.
Pfenninger and Staffell (2016). Energy; Staffell and Pfenninger (2016). Energy 20renewables.ninja
21renewables.ninja
22External validation — National level
PV
Wind
Moraes et al. (2018). Applied Energy 23External validation — NUTS-2 level Moraes et al. (2018). Applied Energy 24
External validation
“The lack of a trust-worthy source of data for
comparison makes the evaluation of data quality
challenging in many countries.”
“[…] the chain of methods used to convert wind
speeds and solar radiation into power outputs
are decisive in this process, and the use of
reanalysis data is promising”
Moraes et al. (2018). Applied Energy 25Renewables.ninja:
Upcoming improvements
• NUTS-2 level data for Europe should be online
by end of this week
• Global country and sub-country level data (e.g.
U.S. states, Chinese provinces) later this year
• New generation of reanalyses and better bias
correction
261. 2. 3. 4.
Why weather How we use Comparing The ninja
matters reanalysis reanalyses project
Discussion
• What is ground truth? Reference energy datasets →
better reanalysis validation and bias correction.
• Which reanalyses have which strengths? Or, how to
choose the right reanalysis for a particular task?
• What improvements for energy applications are
easily possible within existing / next-gen reanalyses?
stefan.pfenninger@usys.ethz.ch | www.renewables.ninja | www.callio.pe
Own publications available on www.pfenninger.org/publications
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