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ISGAN –
International Smart Grid
Action Network
Matthias Lange, Quentin Libois, Remco Verzijlbergh
23 March 2021
ISGAN Academy webinar #27
Recorded webinars available at: https://www.iea-isgan.org/our-work/annex-8/
ISGAN in a Nutshell
Created under the auspices of:
the Implementing
Agreement for a
Co-operative
Programme on Smart
Grids
2
Strategic platform to support high-level government
knowledge transfer and action for the accelerated
development and deployment of smarter, cleaner
electricity grids around the world
International Smart Grid Action Network is
the only global government-to-
government forum on smart grids.
an initiative of the
Clean Energy
Ministerial (CEM)
Annexes
Annex 1
Global
Smart Grid
Inventory Annex 2
Smart Grid
Case
Studies
Annex 3
Benefit-
Cost
Analyses
and
Toolkits
Annex 4
Synthesis
of Insights
for
Decision
Makers
Annex 5
Smart Grid
Internation
al
Research
Facility
Network
Annex 6
Power
T&D
Systems
Annex 7
Smart Grids
Transitions
Annex 8:
ISGAN
Academy
on Smart
Grids
ISGAN’s worldwide presence
3
Value proposition
4
ISGAN
Conference
presentations
Policy
briefs Technology
briefs
Technical
papers
Discussion
papers
Webinars
Casebooks
Workshops
Broad international
expert network
Knowledge sharing,
technical assistance,
project coordination
Global, regional &
national policy support
Strategic partnerships
IEA, CEM, GSGF,
Mission Innovation, etc.
Visit our website:
www.iea-isgan.org
Advanced weather forecasting for RES
applications
Smart4RES developments towards high-resolution
and Numerical Weather Prediction solutions to
improve RES forecasting models
23 March 2021
5
Matthias Lange, Quentin Libois, Remco Verzijlbergh
Agenda
• Smart4RES in a nutshell
• Challenges in forecasting of renewables
• Towards Numerical Weather Prediction models dedicated to Renewables Energy
Sources
• High-resolution weather models
6
ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS
Smart4RES in a nutshell
ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS 7
Smart4RES in a nutshell
ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS 8
• RES forecasting is a mature technology with operational
tools and commercial services used by different actors
• However, we want to make progress to improve the
forecasting accuracy and to reduce costs of RES
integration
Smart4RES vision
Science and industry closely co-operate to achieve
outstanding improvements of RES forecasting by
considering the whole model and value chain.
Episode 3
Episode 2
Episode 4
Smart4RES webinar series
9
#1
June 2020
Introduction
to
Smart4RES -
Data science
for
renewable
energy
prediction
#2
Dec 2020
Extracting
value from
data
sharing for
RES
forecasting
#3
March & April
2021
#4
May 2021
#5
July 2021
Optimizing the
value of
storage in
power systems
and electricity
markets
#6
September
2021
Modelling tools
for integrating
RES
forecasting in
electrical grids
Optimising
participation of
RES
generation in
electricity
markets: new
opportunities
and the role of
forecasting
Season1: Towards a new Standard for the entire RES forecasting value chain
Replay on
Youtube
Replay on
Youtube
Advanced
weather
forecasting for
RES applications
3.1 NWP & High-
resolution models
3.2 Data
observations &
assimilation
Smart4RES consortium
ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS 10
6 countries
12 partners
11/2019-4/2023
Funds: H2020
programme
Budget: 4 Mio€
Duration: 3.5 years
End-users
Universities
Research
Industry
Meteorologists
Follow us!
www.smart4res.eu
Challenges in forecasting of renewables
11
Challenges in forecasting of renewables
• Solar power prediction: the fog situation
12
fog seen from satellite
impact of fog on solar power forecast for Germany:
individual weather models with large forecasting errors
NWP based forecast
corrected forecast
production
Depending on market situation and
the trading strategy such a single
forecasting error can be very
expensive. For example: up to 0.25
Mio € in the most expensive hour
and up to 0.75 Mio € over the day.
source: energy-charts.info
Challenges in forecasting of renewables
• Wind power prediction: fluctuations and ramps
13
measurement
prediction
State-of-the-art weather models provide a time resolution of 1 hour and do not predict fast changes
Towards Numerical Weather Prediction
(NWP) models dedicated to Renewable
Energy Sources (RES)
14
Innovative weather forecasts for RES –
Challenges and Solutions
• atmosphere is chaotic (initial state is critical)
• NWP models have coarse resolution while physical processes
occur at very small scales (clouds, rain, wind gusts, etc.)
→ Parameterizations needed
Why is weather forecasting so difficult ?
• using ensembles to explore all possible states
• increasing spatial resolution to get rid of
parameterizations
• improving parameterizations
What are the solutions ?
Many physical processes occur at scales smaller than the model grid,
and are not explicitly simulated. They are parameterized based on
explicitly simulated large scale variables
Schematic illustration of the use of ensembles in weather forecasts
(UK Met Office)
Towards NWP models dedicated to RES
Comparison between 1-hour (operational) and 5-min resolution
outputs for wind speed forecasts (courtesy B. Alonzo)
 evaluating and calibrating NWP models accounting for RES scores and developing specific forecasters capabilities
 extracting additional relevant variables for RES
(cloud optical thickness, direct/diffuse partition, spectral distribution of radiation, rapid wind fluctuations)
 providing higher temporal resolution outputs (NWP models have a ~1 min time step but standard output is 1 hour)
Paths to improved RES forecasts
Direct solar radiation in 6 spectral bands simulated by AROME
around Toulouse (France)
High temporal resolution outputs
• High frequency fluctuations in simulated wind speed are consistent with observations
• Slight underestimation of these fast fluctuations in the ensemble mean
but some members do capture them better
• Autocorrelation demonstrates the forecast capability
Low frequency variations and intra-hourly standard deviation of wind
speed. Comparison between ensemble simulations and observations
(courtesy B. Alonzo)
Autocorrelation functions of low and high frequency wind fluctuations
(courtesy B. Alonzo)
Ensembles (perturbed initial conditions, lateral forcing, surface or parameterizations)
highlight how the atmospheric state can vary a lot from a member to another
Different members can have very distinct behaviours
Fog prediction (maps of liquid water content at level 1, 10 m high) around
Paris for 6 members of an ensemble (courtesy T. Bergot)
Paris airports
The power of ensembles – fog
Fog predictions (operational 12 members ensemble simulations)
and observations (12 measurements) at CdG airport (Paris) in
December 2018 (courtesy T. Bergot, R. Lestringant)
LWC
(g
kg
-1
)
• Ensembles are useful to estimate the uncertainty associated with a forecast
• Uncertainties in RES relevant variables can be significant
Relative standard deviation (standard deviation/mean) for wind (left) and global horizontal
irradiance (right) across an ensemble of 25 members (courtesy B. Alonzo)
The power of ensembles – wind, solar
The power of ensembles –
Pseudo-deterministic simulations
• Ensembles can be used to build pseudo-deterministic
simulations (that look like deterministic simulations to the end-
user)
• These pseudo-deterministic simulations can be built in various
ways, and can be very specific to the end-user needs
• Pseudo-deterministic simulations can exceed ensemble mean
forecasting performances
Performances of various simulations with respect to
observations. Here, the dedicated pseudo-deterministic
simulation performs best (courtesy B. Alonzo)
Definition of categories for wind speed, used to build pseudo-
deterministic runs for wind power forecasts. Over each period, the
most represented category across the ensemble is used.
Increasing spatial resolution
• Fog formation is very sensitive to the state of the atmosphere close to the surface
• Increasing vertical and horizontal resolution modifies turbulence and dynamics
Higher vertical resolution better reproduces fog daily cycle
near Paris airport (Philip et al., 2016)
Differences in fog prediction for different spatial resolutions
(2,5 km, 60 levels vs 1 km, 156 levels ; Ragon, 2020)
Fog
cover
(%)
Time along one night
High-resolution weather models
Large Eddy Simulation (LES): the future
22
Operational LES model of the HornsRev
offshore wind farm
https://vimeo.com/whiffle/hornsrev
Gilbert, C., Messner, J. W., Pinson, P., Trombe, P., Verzijlbergh, R., Dorp, P. Van, & Jonker, H. (2019). Statistical Post-
processing of Turbulence-resolving Weather Forecasts for Offshore Wind Power Forecasting. Wind Energy, 1–16.
https://doi.org/10.1002/we.2456
Resolved motions
Sub-grid motions
Large Eddy Simulation
Model grid in physical space showing
schematically the turbulent motions (eddies)
In spectral space the energy of different eddies peaks around a
certain size (small wavenumber k are the largest eddies)
A turbulent spectrum: energy as function of wavenumber
Fourier
transform
dx = grid size
dx
The energy spectrum of turbulent flow in
Large Eddy Simulation (LES)
Flow dependent Flow independent
LES resolves all relevant physical processes and only parametrizes homogeneous turbulence
Wavenumber k
Energy
The energy spectrum of turbulent flow in
Large Eddy Simulation (LES)
High-resolution allows to resolve the
surface in detail
Higher resolution and more explicit modelling of:
• Forest and vegetation resolved in more detail (canopy
parameterization)
• Explicitly model obstacles like buildings and wind turbines
Tiggelen, M. Van. (2018). Towards improving the land- surface-
atmosphere coupling in the Dutch Atmospheric Large- Eddy
Simulation model (DALES). MSc Thesis, Delft University of
Technology.
Land use Terrain and buildings Turbines
15 km
Surface scheme controls momentum,
heat and moisture exchange
Aerial & satellite
images
Land use and
vegetation
properties
Obstacles, terrain
and orography
(mountains)
Turbine types and
locations
Large-scale
weather forecast
GPU-Resident Atmospheric Simulation Platform (GRASP)
Operational
forecasting
with LES
Static boundary conditions
A schematic representation of different inputs to a the LES forecasting model
Parametrization: expressing the sub-grid processes in terms of resolved quantities
See for example https://www.ecmwf.int/en/elibrary/18714-ifs-documentation-cy45r1-part-iv-physical-processes
resolved
resolved
resolved
parametrized parametrized
Typical processes that are
parameterized in NWP:
• Turbulence
• Large-scale clouds
• Convective clouds
• Surface drag
• Radiation
• Precipitation
• Surface energy balance
Schematic view on transport by sub-grid processes
Physics parameterizations in NWP
Parametrization: expressing the sub-grid processes in terms of resolved quantities
See for example https://www.ecmwf.int/en/elibrary/18714-ifs-documentation-cy45r1-part-iv-physical-processes
resolved
parametrized
Typical processes that are
parameterized in LES:
• Turbulence
• Large-scale clouds
• Convective clouds
• Surface drag
• Radiation
• Precipitation
• Surface energy balance
(in high resolution)
The LES grid is fine enough to resolve turbulence, clouds
and the surface. “Assume less, compute more”
Explicit modelling of:
• Wind turbines
• Canopies
• Buildings
• Turbulence
• Clouds / fog
Physics parameterizations in LES
Traditional weather forecast
Satellite image
Whiffle forecast
Country scale LES already possible on
multi-GPU systems
Smart4RES use case: Rhodes
Important forecast characteristics for smarter
grid management:
• Fast fluctuations in wind power
• Spatio-temporal patterns
• Good forecast accuracy on complex terrain
A nested LES forecast is produced
• Outer domain captures Rhodes ~ 100m resolution
• 4 inner domains on wind farms: ~ 40m resolution
• Figure shows variability in individual wind turbine
forecasts
• Compare with smooth signal of large-scale
weather model (era5)
Planned innovations in Smart4RES
Local observations
• Clouds
• Wind
• Radiation
• Pressure
• Power
• SCADA wind
• SCADA temp
• …
Static boundary conditions
Large-scale weather model
data
GPU-Resident Atmospheric Simulation Platform (GRASP)
New in Smart4RES: assimilation of local observations in LES
Data assimilation : estimating the state of a system
(here: the atmospheric model) given a set of
observations and the model dynamics
minimize obj = f (modeled state – observed state)
subject to model dynamics
The future of numerical weather prediction
Wind farm level
EU level
Global
Forecast
domain
Time
2030
2019
Country level
LES: scaling up the domain size NWP: increasing the resolution
2030
2019
Grid
cell
size
Next generation weather model:
- Turbulence and cloud resolving ( = LES ! )
- Uses big data and massive computational power
- Supports energy transition and climate adaptation
Synoptic scale
Cloud system
Convection Turbulence
Who will be first?
Take-away messages
• Numerical models contain much more than operational outputs
• The natural evolution of NWP is to increase spatial resolution
→ Now at a stage called grey-zone turbulence in between
parameterizations and LES (~ 500 m resolution)
→ LES might be the future of NWP
• Ensembles are becoming the norm to explore uncertainties
• Increasing amount of observations, including non-conventional,
are likely to improve forecasts via data assimilation
Attend our next webinar!
→ for very short-term, direct observations remain very powerful because models give a statistical
view of the atmosphere, not perfectly punctual in space and time
→ combination of various inputs is promising, in particular with increased use of artificial intelligence
 Follow us!
www.smart4res.eu
Evolution of Météo-France
computation power
Further reading
[1] Gilbert, C., Messner, J. W., Pinson, P., Trombe, P. J., Verzijlbergh, R., van Dorp, P., & Jonker, H.
(2020). Statistical post-processing of turbulence-resolving weather forecasts for offshore wind
power forecasting. Wind Energy, 23(4), 884–897. https://doi.org/10.1002/we.2456
[2] Lindsay, N., Libois, Q., Badosa, J., Migan-Dubois, A., Bourdin, V. (2020). Errors in PV power
modelling due to the lack of spectral and angular details of solar irradiance inputs. Solar
Energy,197, 266-278 https://doi.org/10.1016/j.solener.2019.12.042
[3] Schalkwijk, J., Jonker, H. J. J., Siebesma, A. P., & Van Meijgaard, E. (2015). Weather
forecasting using GPU-based large-Eddy simulations. Bulletin of the American Meteorological
Society, 96(5), 715–723. https://doi.org/10.1175/BAMS-D-14-00114.1
[4] E.J. Wiegant, P. Baas, R.A.Verzijlbergh, B.Reijmerink, and S.Caires. The new frontier in
numerical metocean modelling: coupled high-resolution atmosphere wave interactions. In Wind
Europe Offshore, 2019.
[5] R.A.Verzijlbergh, H.J.J. Jonker, P. van Dorp, E.J. Wiegant, P. Baas, B.M. Meijer, and J. Coeling.
Breakthrough weather prediction technology enables wind turbine resolving resource assessments.
In Wind Europe, 2019.
35
Smart4RES webinar series
36
#1
June 2020
Introduction
to
Smart4RES -
Data science
for
renewable
energy
prediction
#2
Dec 2020
Extracting
value from
data
sharing for
RES
forecasting
#3
March & April
2021
#4
May 2021
#5
July 2021
Optimizing the
value of
storage in
power systems
and electricity
markets
#6
September
2021
Modelling tools
for integrating
RES
forecasting in
electrical grids
Optimising
participation of
RES
generation in
electricity
markets: new
opportunities
and the role of
forecasting
Advanced
weather
forecasting for
RES applications
Season1: Towards a new Standard for the entire RES forecasting value chain
Replay on
Youtube
Replay on
Youtube
3.1 NWP & High-
resolution models
3.2 Data
observations &
assimilation
Thank you
info@smart4res.eu
matthias.lange@energymeteo.de
quentin.libois@meteo.fr
remco.verzijlbergh@whiffle.nl
Back-up slides
https://vimeo.com/whiffle/cloudstreetswinter

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Advanced weather forecasting for RES applications: Smart4RES developments towards high-resolution and Numerical Weather Prediction solutions to improve RES forecasting models

  • 1. ISGAN – International Smart Grid Action Network Matthias Lange, Quentin Libois, Remco Verzijlbergh 23 March 2021 ISGAN Academy webinar #27 Recorded webinars available at: https://www.iea-isgan.org/our-work/annex-8/
  • 2. ISGAN in a Nutshell Created under the auspices of: the Implementing Agreement for a Co-operative Programme on Smart Grids 2 Strategic platform to support high-level government knowledge transfer and action for the accelerated development and deployment of smarter, cleaner electricity grids around the world International Smart Grid Action Network is the only global government-to- government forum on smart grids. an initiative of the Clean Energy Ministerial (CEM) Annexes Annex 1 Global Smart Grid Inventory Annex 2 Smart Grid Case Studies Annex 3 Benefit- Cost Analyses and Toolkits Annex 4 Synthesis of Insights for Decision Makers Annex 5 Smart Grid Internation al Research Facility Network Annex 6 Power T&D Systems Annex 7 Smart Grids Transitions Annex 8: ISGAN Academy on Smart Grids
  • 4. Value proposition 4 ISGAN Conference presentations Policy briefs Technology briefs Technical papers Discussion papers Webinars Casebooks Workshops Broad international expert network Knowledge sharing, technical assistance, project coordination Global, regional & national policy support Strategic partnerships IEA, CEM, GSGF, Mission Innovation, etc. Visit our website: www.iea-isgan.org
  • 5. Advanced weather forecasting for RES applications Smart4RES developments towards high-resolution and Numerical Weather Prediction solutions to improve RES forecasting models 23 March 2021 5 Matthias Lange, Quentin Libois, Remco Verzijlbergh
  • 6. Agenda • Smart4RES in a nutshell • Challenges in forecasting of renewables • Towards Numerical Weather Prediction models dedicated to Renewables Energy Sources • High-resolution weather models 6 ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS
  • 7. Smart4RES in a nutshell ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS 7
  • 8. Smart4RES in a nutshell ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS 8 • RES forecasting is a mature technology with operational tools and commercial services used by different actors • However, we want to make progress to improve the forecasting accuracy and to reduce costs of RES integration Smart4RES vision Science and industry closely co-operate to achieve outstanding improvements of RES forecasting by considering the whole model and value chain. Episode 3 Episode 2 Episode 4
  • 9. Smart4RES webinar series 9 #1 June 2020 Introduction to Smart4RES - Data science for renewable energy prediction #2 Dec 2020 Extracting value from data sharing for RES forecasting #3 March & April 2021 #4 May 2021 #5 July 2021 Optimizing the value of storage in power systems and electricity markets #6 September 2021 Modelling tools for integrating RES forecasting in electrical grids Optimising participation of RES generation in electricity markets: new opportunities and the role of forecasting Season1: Towards a new Standard for the entire RES forecasting value chain Replay on Youtube Replay on Youtube Advanced weather forecasting for RES applications 3.1 NWP & High- resolution models 3.2 Data observations & assimilation
  • 10. Smart4RES consortium ADVANCED WEATHER FORECASTING FOR RES APPLICATIONS 10 6 countries 12 partners 11/2019-4/2023 Funds: H2020 programme Budget: 4 Mio€ Duration: 3.5 years End-users Universities Research Industry Meteorologists Follow us! www.smart4res.eu
  • 11. Challenges in forecasting of renewables 11
  • 12. Challenges in forecasting of renewables • Solar power prediction: the fog situation 12 fog seen from satellite impact of fog on solar power forecast for Germany: individual weather models with large forecasting errors NWP based forecast corrected forecast production Depending on market situation and the trading strategy such a single forecasting error can be very expensive. For example: up to 0.25 Mio € in the most expensive hour and up to 0.75 Mio € over the day. source: energy-charts.info
  • 13. Challenges in forecasting of renewables • Wind power prediction: fluctuations and ramps 13 measurement prediction State-of-the-art weather models provide a time resolution of 1 hour and do not predict fast changes
  • 14. Towards Numerical Weather Prediction (NWP) models dedicated to Renewable Energy Sources (RES) 14
  • 15. Innovative weather forecasts for RES – Challenges and Solutions • atmosphere is chaotic (initial state is critical) • NWP models have coarse resolution while physical processes occur at very small scales (clouds, rain, wind gusts, etc.) → Parameterizations needed Why is weather forecasting so difficult ? • using ensembles to explore all possible states • increasing spatial resolution to get rid of parameterizations • improving parameterizations What are the solutions ? Many physical processes occur at scales smaller than the model grid, and are not explicitly simulated. They are parameterized based on explicitly simulated large scale variables Schematic illustration of the use of ensembles in weather forecasts (UK Met Office)
  • 16. Towards NWP models dedicated to RES Comparison between 1-hour (operational) and 5-min resolution outputs for wind speed forecasts (courtesy B. Alonzo)  evaluating and calibrating NWP models accounting for RES scores and developing specific forecasters capabilities  extracting additional relevant variables for RES (cloud optical thickness, direct/diffuse partition, spectral distribution of radiation, rapid wind fluctuations)  providing higher temporal resolution outputs (NWP models have a ~1 min time step but standard output is 1 hour) Paths to improved RES forecasts Direct solar radiation in 6 spectral bands simulated by AROME around Toulouse (France)
  • 17. High temporal resolution outputs • High frequency fluctuations in simulated wind speed are consistent with observations • Slight underestimation of these fast fluctuations in the ensemble mean but some members do capture them better • Autocorrelation demonstrates the forecast capability Low frequency variations and intra-hourly standard deviation of wind speed. Comparison between ensemble simulations and observations (courtesy B. Alonzo) Autocorrelation functions of low and high frequency wind fluctuations (courtesy B. Alonzo)
  • 18. Ensembles (perturbed initial conditions, lateral forcing, surface or parameterizations) highlight how the atmospheric state can vary a lot from a member to another Different members can have very distinct behaviours Fog prediction (maps of liquid water content at level 1, 10 m high) around Paris for 6 members of an ensemble (courtesy T. Bergot) Paris airports The power of ensembles – fog Fog predictions (operational 12 members ensemble simulations) and observations (12 measurements) at CdG airport (Paris) in December 2018 (courtesy T. Bergot, R. Lestringant) LWC (g kg -1 )
  • 19. • Ensembles are useful to estimate the uncertainty associated with a forecast • Uncertainties in RES relevant variables can be significant Relative standard deviation (standard deviation/mean) for wind (left) and global horizontal irradiance (right) across an ensemble of 25 members (courtesy B. Alonzo) The power of ensembles – wind, solar
  • 20. The power of ensembles – Pseudo-deterministic simulations • Ensembles can be used to build pseudo-deterministic simulations (that look like deterministic simulations to the end- user) • These pseudo-deterministic simulations can be built in various ways, and can be very specific to the end-user needs • Pseudo-deterministic simulations can exceed ensemble mean forecasting performances Performances of various simulations with respect to observations. Here, the dedicated pseudo-deterministic simulation performs best (courtesy B. Alonzo) Definition of categories for wind speed, used to build pseudo- deterministic runs for wind power forecasts. Over each period, the most represented category across the ensemble is used.
  • 21. Increasing spatial resolution • Fog formation is very sensitive to the state of the atmosphere close to the surface • Increasing vertical and horizontal resolution modifies turbulence and dynamics Higher vertical resolution better reproduces fog daily cycle near Paris airport (Philip et al., 2016) Differences in fog prediction for different spatial resolutions (2,5 km, 60 levels vs 1 km, 156 levels ; Ragon, 2020) Fog cover (%) Time along one night
  • 22. High-resolution weather models Large Eddy Simulation (LES): the future 22
  • 23. Operational LES model of the HornsRev offshore wind farm https://vimeo.com/whiffle/hornsrev Gilbert, C., Messner, J. W., Pinson, P., Trombe, P., Verzijlbergh, R., Dorp, P. Van, & Jonker, H. (2019). Statistical Post- processing of Turbulence-resolving Weather Forecasts for Offshore Wind Power Forecasting. Wind Energy, 1–16. https://doi.org/10.1002/we.2456
  • 24. Resolved motions Sub-grid motions Large Eddy Simulation Model grid in physical space showing schematically the turbulent motions (eddies) In spectral space the energy of different eddies peaks around a certain size (small wavenumber k are the largest eddies) A turbulent spectrum: energy as function of wavenumber Fourier transform dx = grid size dx The energy spectrum of turbulent flow in Large Eddy Simulation (LES)
  • 25. Flow dependent Flow independent LES resolves all relevant physical processes and only parametrizes homogeneous turbulence Wavenumber k Energy The energy spectrum of turbulent flow in Large Eddy Simulation (LES)
  • 26. High-resolution allows to resolve the surface in detail Higher resolution and more explicit modelling of: • Forest and vegetation resolved in more detail (canopy parameterization) • Explicitly model obstacles like buildings and wind turbines Tiggelen, M. Van. (2018). Towards improving the land- surface- atmosphere coupling in the Dutch Atmospheric Large- Eddy Simulation model (DALES). MSc Thesis, Delft University of Technology. Land use Terrain and buildings Turbines 15 km Surface scheme controls momentum, heat and moisture exchange
  • 27. Aerial & satellite images Land use and vegetation properties Obstacles, terrain and orography (mountains) Turbine types and locations Large-scale weather forecast GPU-Resident Atmospheric Simulation Platform (GRASP) Operational forecasting with LES Static boundary conditions A schematic representation of different inputs to a the LES forecasting model
  • 28. Parametrization: expressing the sub-grid processes in terms of resolved quantities See for example https://www.ecmwf.int/en/elibrary/18714-ifs-documentation-cy45r1-part-iv-physical-processes resolved resolved resolved parametrized parametrized Typical processes that are parameterized in NWP: • Turbulence • Large-scale clouds • Convective clouds • Surface drag • Radiation • Precipitation • Surface energy balance Schematic view on transport by sub-grid processes Physics parameterizations in NWP
  • 29. Parametrization: expressing the sub-grid processes in terms of resolved quantities See for example https://www.ecmwf.int/en/elibrary/18714-ifs-documentation-cy45r1-part-iv-physical-processes resolved parametrized Typical processes that are parameterized in LES: • Turbulence • Large-scale clouds • Convective clouds • Surface drag • Radiation • Precipitation • Surface energy balance (in high resolution) The LES grid is fine enough to resolve turbulence, clouds and the surface. “Assume less, compute more” Explicit modelling of: • Wind turbines • Canopies • Buildings • Turbulence • Clouds / fog Physics parameterizations in LES
  • 30. Traditional weather forecast Satellite image Whiffle forecast Country scale LES already possible on multi-GPU systems
  • 31. Smart4RES use case: Rhodes Important forecast characteristics for smarter grid management: • Fast fluctuations in wind power • Spatio-temporal patterns • Good forecast accuracy on complex terrain A nested LES forecast is produced • Outer domain captures Rhodes ~ 100m resolution • 4 inner domains on wind farms: ~ 40m resolution • Figure shows variability in individual wind turbine forecasts • Compare with smooth signal of large-scale weather model (era5)
  • 32. Planned innovations in Smart4RES Local observations • Clouds • Wind • Radiation • Pressure • Power • SCADA wind • SCADA temp • … Static boundary conditions Large-scale weather model data GPU-Resident Atmospheric Simulation Platform (GRASP) New in Smart4RES: assimilation of local observations in LES Data assimilation : estimating the state of a system (here: the atmospheric model) given a set of observations and the model dynamics minimize obj = f (modeled state – observed state) subject to model dynamics
  • 33. The future of numerical weather prediction Wind farm level EU level Global Forecast domain Time 2030 2019 Country level LES: scaling up the domain size NWP: increasing the resolution 2030 2019 Grid cell size Next generation weather model: - Turbulence and cloud resolving ( = LES ! ) - Uses big data and massive computational power - Supports energy transition and climate adaptation Synoptic scale Cloud system Convection Turbulence Who will be first?
  • 34. Take-away messages • Numerical models contain much more than operational outputs • The natural evolution of NWP is to increase spatial resolution → Now at a stage called grey-zone turbulence in between parameterizations and LES (~ 500 m resolution) → LES might be the future of NWP • Ensembles are becoming the norm to explore uncertainties • Increasing amount of observations, including non-conventional, are likely to improve forecasts via data assimilation Attend our next webinar! → for very short-term, direct observations remain very powerful because models give a statistical view of the atmosphere, not perfectly punctual in space and time → combination of various inputs is promising, in particular with increased use of artificial intelligence  Follow us! www.smart4res.eu Evolution of Météo-France computation power
  • 35. Further reading [1] Gilbert, C., Messner, J. W., Pinson, P., Trombe, P. J., Verzijlbergh, R., van Dorp, P., & Jonker, H. (2020). Statistical post-processing of turbulence-resolving weather forecasts for offshore wind power forecasting. Wind Energy, 23(4), 884–897. https://doi.org/10.1002/we.2456 [2] Lindsay, N., Libois, Q., Badosa, J., Migan-Dubois, A., Bourdin, V. (2020). Errors in PV power modelling due to the lack of spectral and angular details of solar irradiance inputs. Solar Energy,197, 266-278 https://doi.org/10.1016/j.solener.2019.12.042 [3] Schalkwijk, J., Jonker, H. J. J., Siebesma, A. P., & Van Meijgaard, E. (2015). Weather forecasting using GPU-based large-Eddy simulations. Bulletin of the American Meteorological Society, 96(5), 715–723. https://doi.org/10.1175/BAMS-D-14-00114.1 [4] E.J. Wiegant, P. Baas, R.A.Verzijlbergh, B.Reijmerink, and S.Caires. The new frontier in numerical metocean modelling: coupled high-resolution atmosphere wave interactions. In Wind Europe Offshore, 2019. [5] R.A.Verzijlbergh, H.J.J. Jonker, P. van Dorp, E.J. Wiegant, P. Baas, B.M. Meijer, and J. Coeling. Breakthrough weather prediction technology enables wind turbine resolving resource assessments. In Wind Europe, 2019. 35
  • 36. Smart4RES webinar series 36 #1 June 2020 Introduction to Smart4RES - Data science for renewable energy prediction #2 Dec 2020 Extracting value from data sharing for RES forecasting #3 March & April 2021 #4 May 2021 #5 July 2021 Optimizing the value of storage in power systems and electricity markets #6 September 2021 Modelling tools for integrating RES forecasting in electrical grids Optimising participation of RES generation in electricity markets: new opportunities and the role of forecasting Advanced weather forecasting for RES applications Season1: Towards a new Standard for the entire RES forecasting value chain Replay on Youtube Replay on Youtube 3.1 NWP & High- resolution models 3.2 Data observations & assimilation