SafeWind Advances in Short-Term Wind Power Forecasting with Focus on "Extreme" Situations
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Transcript of SafeWind Advances in Short-Term Wind Power Forecasting with Focus on "Extreme" Situations
SafeWindAdvances in Short-Term Wind Power Forecasting with Focuson "Extreme" SituationsGeorge KariniotakisHead of Renewables & SmartGrids R&D GroupMINES ParisTech - [email protected]
EWEA 2012,16-19 April 2012, Copenhagen, Denmark
Introduction
2002
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2020
20 GW
74.7 GW
230 GW
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Source: EWEA
• Ambitious targets for wind integration in EU
• Challenges for the power system management
• Short-term forecasting of wind generation is recognized as a prerequisite for an efficient wind integration
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measurementprediction
Introduction
• The actual wind power forecasting technology is quite mature
• However, in some situations large forecast errors may have an important impact on power system operation
Example in Germany:
Path of low-pressure system was different than predicted,
Maximum error: 5500 MW…
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Introduction
• The actual wind power forecasting technology is quite mature
• However, in some situations large forecast errors may have an important impact on power system operation
• The SafeWind project was developed to improve wind power predictability in challenging and extreme situations.
– at various temporal scales (very short to longer term)
– at various spatial scales (local/regional/continental)
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The SafeWind Project
9 countries22 partners
2008-2012
Coordination ARMINES/
MINES ParisTech
End-users
http://www.safewind.eu
Research
Industry
MeteorologistsINT
ER
NA
TIO
NA
L, I N
DIA
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Scientific & Technical Objectives (1)
• New forecasting methods for wind generation focusing on uncertainty and challenging situations/extremes:
o Probabilistic models
o Event forecasting models (ramps, cut-offs)
o Spatio-temporal correction models
o Regime switching models
o Prediction scenarios
o …
– Methodologies for communicating probabilistic forecasts & risks
Highlight: Ramps forecasting using ensembles
New approach for probabilistic prediction of ramp events:• Prediction of ramps occurrence and timing. • Based on ensembles. • Ramps detection through a derivative filtering based on edge detection theory.
Results:• Controllable tradeoff between
ramp capture and forecast accuracy
• Reliable probabilistic forecasts with sharpness
• 5-15% performance increase with respect to climatology
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Highlight: Scenarios and their verification
Scenarios of wind power generation are a must-have for high-value decision-making, either• Using meteorological ensembles as input,
• Or based on probabilistic forecasts and spatio-temporal statistical models
Results :• A methodology for the
comparative verification of scenarios
• Methods to produce scenarios based on ECMWF ensemble forecasts or state-of-the-art probabilistic predictions
• Readily operational and used in practice
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Highlight: Spatio-temporal modelling for forecasting
New spatio-temporal forecasting approaches for the shorter term:• Wind farms are considered as measurement points• A spatio-temporal model is fitted on each wind farm using off site measurements• Improvement of wind power forecasts up to 18% (red when east, blue for west).
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Case: Denmark
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Scientific & Technical Objectives (2)
• Develop a "European vision" for wind power forecasting– Prepare the way for the coordinated management of 200+ GW
wind generation at European Scale.
Data from Synoptic Stations in Europe
Monitor the Wind Energy Weather over Europe in real-time with observation data from many different sources
• More than 2000 weather stations• 120 single wind farms• 19 regions• 1 met mast
SafeWindData Management System
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Scientific & Technical Objectives (3)
• Models for "alarming" for very short-term (0-6 h). Monitor/assess the wind energy weather situation over Europe Detect severe deviations in forecasts due to extreme events Issue alerts to users that a forecast error is occurring Produce improved updates of the prediction in the short-term
Alarming in Forecast Time Series (23/1/2009)for Eastern part of Germany (50 Hertz)
Pressure gradient deviation (forecast - AdHoc-Analysis)
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Scientific & Technical Objectives (4)
• Models for "warning" on the level of predictability in the medium-term (next day(s)).
Approaches based on ensembles and weather pattern identification
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Scientific & Technical Objectives (5)
• Develop research in meteorology oriented to wind power forecasting: – Deliver the meteorological component for skilful
probabilistic wind power forecasts:• look at various products specifically suited for wind energy and
extremes (i.e. new product - 100m winds) (next talk)• evaluate various ensemble forecasting configurations• wind energy oriented verification of ECMWF products• improving ensemble forecasts (wind & wind power)
– Stimulate the interaction between meteorologists and wind power communities
Highlight: Recalibrated ECMWF wind ensembles
Ensemble forecasts of wind speed and direction are of great value as input to wind power prediction• They need advanced post-processing (due to bias, lack of spread, etc.)
• They can be recalibrated with adaptive statistical techniques
Results:• ECMWF ensemble forecasts of
(u,v)-winds recalibrated for the whole Europe over the period 2007-2009
• Substantial improvement of various skill scores and diagnostics
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Scientific & Technical Objectives (6)
• Evaluate the impact of short-term wind predictability in the resource assessment phase:– when wind farms participate in an electricity market. – study if a tradeoff between wind potential and predictability can be
beneficial when selecting a site or expanding a portfolio
Terrain complexity
Flat Medium Complex
Wind potential
Predictability
A B
1 & 2 day-ahead wind power forecast error for Central Europe• Computed from COSMO-EU Forecasts and Analysis (German Weather Service)• Normalization with estimated wind energy yield RMSE [MWh/MWh]• Spatial fc error smoothing considered (here: size of a DSO control zone)
Highlight: Forecast Error Maps
day-ahead day-ahead
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2day-ahead 2day-ahead
See Posters PO 51 & PO109
Highlight: Extreme Wind Atlases-from global data to site use
New approach to obtain turbine design winds • Downscale the global data to obtain extreme winds for turbine sites. • Use mesoscale modelling to simulate the yearly strongest storms over areas of
different terrain complexity.• Use a post-processing to prepare the data for microscale modelling.
Results :• Extreme wind atlases for places of
various extreme weather mechanisms and terrain complexities.
• Reasonable agreement with measurements.
• Output ready for microscale modelling for particular turbine sites.
Generalized extreme wind atlas for Navara region in Spain
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m/s
• Analyse how new measurement technologies like Lidars can be beneficial for better evaluation of external conditions, resource assessment and forecasting purposes.
Høvsøre Large Wind Turbine Test Facility
Scientific & Technical Objectives (7)
Measurement campaigns at flat (DK) and complex (ES) terrains
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Anemos.eXtreme: On-line demo of new approaches
• On-line demonstration of SafeWind modules:– Integration of 8 new modules into the ANEMOS wind
power prediction platform– Demonstration /evaluation for various TSOs
• Test cases:SONI (N. Ireland/U.K.) EirGrid (Ireland)
EDF (France) RTE (France)
PPC (Crete, Greece)
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Conclusions
• Successful scientific production: >75 papers so far
• New approaches for forecasting in challenging situations
• The work methodology designed to enable quick transfer of results for operational use by industrial stakeholders
• Final project workshop: 31 August 2012, Paris
What comes next :
• Improving predictability requires continuous collaborative R&D
• Need to better integrate forecasts into power system management tools
• R&D in forecasting is promoted as a priority (i.e. through TPWind).
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Thank you for your attention