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

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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

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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

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• 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

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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

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• 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!

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