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Forecasting Uncertainty Related to Ramps of Wind Power Production. European Wind Energy Conference, Warsaw, 20-23 April 2010. Arthur Bossavy , Robin Girard, Georges Kariniotakis Center for Energy and Processes MINES- ParisTech /ARMINES. Introduction. - PowerPoint PPT Presentation
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Forecasting Uncertainty Related to Rampsof Wind Power Production
Arthur Bossavy, Robin Girard, Georges KariniotakisCenter for Energy and ProcessesMINES-ParisTech/ARMINES
European Wind Energy Conference, Warsaw, 20-23 April 2010
draftIntroduction
• Need to improve wind power forecasting with focus on extreme situations– Various temporal/space scales– Focus on uncertainty and weather predictability– Distribution tail events
• To contribute to– An increased and more secure wind integration to power
grid– Lower costs (i.e: reduced imbalances)– …
2
draftIntroduction
Centered prediction intervals of coverage:
predictionsobservations
A problem with usual wind power forecasts
draftObjectives of the work
4
1. Improve the reliabilityof usual confidence
intervalsw.r.t ramp events
2. Forecast confidence intervals to estimate the
uncertainty of ramps timing
draftOutline
1. A methodology for ramps detection
2. A probabilistic model using ramps information
3. Forecast of ramps timing using ensembles
Detection of ramps
FILTERING
THRESHOLDING
Threshold
intensity
timing
Rampdetected
draftDetection of ramps
• Evolution of the ramp intensity
through Denmark
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draftOutline
1. A methodology for ramps detection
2. A probabilistic model using ramps information
3. Forecast of ramps timing using ensembles
draftA probabilistic model using ramps information
Objective:
Produce more reliable probabilistic forecasts by using information on forthcoming ramps
9
draft3-stage forecasting process using ramps information
Production of spot forecasts
SCADA
NWP
Spot
forecasting
model
Ramps detection
forecasts
spot
Thresholding
Filtering Ramps• TIMING• INTENSITY
Probabilistic processing
Probabilisticforecasting
model
NWPSCADA
TIMING
INTENSITYRamps
Information
draftCase-studies
• 1 wind farm in Ireland, 1 in Denmark
• 18 months of data (02/01-08/02 and 01/03-07/04)
• Hourly power measures
• Hourly wind speed/direction NWP forecasts (10m height).
• Probabilistic model based on the Quantile Regression Forests procedure
draftEvaluation measures
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Reliability:
Sharpness:
draftResults
• Forecasts underestimate quantiles
• Reliability improved for highest quantile forecasts
• Sharpness remains unchanged
Wind farm in Denmark Wind farm in Ireland
draftResults
14
• Estimation of the uncertainty may be improved at ramps
• Need of more tests: other quantile estimation methods
draftOutline
1. A methodology for ramps detection
2. A probabilistic model using ramps information
3. Forecast of ramps timing using ensembles
draftForecast of ramps timing using ensembles
Objective:
Aggregate ramps information provided by members of a wind power forecasts ensemble
16
draftForecast of ramps timing using ensembles
Filtering members of a forecasts ensemble
More than 35over 51
members predicting this
ramp
h1h3
h2
draftForecast ramps timing using ensembles
Proposal for a probabilistic forecast of ramps timing– Mean value for the ramp timing:
– Confidence intervals:
draftCase-studies and evaluation results
• Case-studies: 3 wind farms in France– Wind speed forecasts ensemble (51 members from the EPS
system of ECMWF)– Random Forest procedure
• Evaluation of forecast probabilities:
Brier Skill Scorew.r.t Climatology:
Brier Score:
Visualization of confidence intervals
20
• 39 members predicting the increasing ramp
• 15 members predicting the decreasing ramp
43%
70%
39%
57%
65%28%
draftConclusions
• The probabilistic model using ramps information may be valuable when estimating the highest quantiles
• The approach based on ensembles provide confidence intervals to forecast ramp occurrence.
Reliability w.r.t Climatology is improved
Need of more experiments
draftAcknowledgments
• Project SAFEWIND: « Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration »
• Industrial partners of the project for providing data
draftThank you for your attention!
29