wind power prediction risk indices based on numerical weather prediction ensembles

Wind power prediction risk indices based on numerical weather prediction ensembles

Erik Holmgren, Nils Siebert, George Kariniotakiserik.holmgren@sserenewables.com, nils.siebert@mines-paristech.fr

Renewable Energies TeamMINES ParisTech / ARMINES

European Wind Energy Conference 2010, Warsaw, Poland

Outline

• Introduction and objectives

• Definition and evaluation of risk indices

• Risk indices in decision making

• Conclusions

2

Introduction: ensemble forecasting

• Typical Wind Power Forecasting (WPF) modelling scheme• NWP can also be provided as meteorological ensembles

– Alternative forecasts representing different scenarios– Computed by perturbing initial conditions– Control forecast + ensemble forecast members

3

NWP ensemblesMeasurements ModelMeasurements ModelModel

+1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

0

0.5

1

1.5

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2.5

3

3.5

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1.5

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2.5

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3.5

+1h +6h +12h +18h +24h +30h +36h … +48ht0 +1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

Production [MW]

ForecastMeasured production

Numerical Weather Predictions (NWP)

Control forecastEnsemble forecastsMeasured production

Objectives

• Investigate relationship between ensemble spread and forecast error

• Quantify the ensemble spread through risk indices

4

ModelModel

+1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

0

0.5

1

1.5

2

2.5

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3.5

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1.5

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3.5

+1h +6h +12h +18h +24h +30h +36h … +48ht0 +1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

Production [MW]

Control forecastEnsemble forecastsMeasured production

Period with smaller ensemble spread

Period with larger ensemble spread

Outline

• Introduction and objectives

• Definition and evaluation of risk indices

• Risk indices in decision making

• Conclusions

5

Notation: for

Production [MW]

Present Future

0

0.5

1

1.5

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+1h +6h +12h +18h +24h +30h +36h … +48ht0 +1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

+1h +6h +12h +18h +24h +30h +36h … +48ht0

Quantify ensemble spread through risk indices

• Definition: the Normalized Prediction Risk Index (NPRI)– Weighted standard deviation

of ensemble members

– Average value over a

look-ahead time window

6

ModelModel

Control forecast

Ensemble forecasts

Ensemble mean

Measured production

Relationship between risk indices and forecast errors

• Definition: Energy Imbalance for a look-ahead time window– Sum of absolute errors

• Compute relative imbalances by dividing by long-term average :

7

ModelModel

+1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

0

0.5

1

1.5

2

2.5

3

3.5

0

0.5

1

1.5

2

2.5

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3.5

+1h +6h +12h +18h +24h +30h +36h … +48ht0 +1h +6h +12h +18h +24h +30h +36h … +48ht0

Present Future

Control forecast

Measured production

Production [MW]

Evaluation of relationship risk index - forecast error

• Validation of approach on more test cases– Three French onshore wind farms with different characteristics

• Different prediction models

• Temporal scales– Size and position of look-ahead time window

• Spatial scales– Aggregate of wind farms

• Alternative definitions of risk indices– e.g. Range of ensemble members

8

Evaluation

• NWPs from ECMWF*– 51 members

• 1 control member• 50 perturbed members

– Temporal resolution: 6 hours (0 - 240 hours)– Spatial resolution: 1o longitude, 1o latitude

– 2 prediction runs per day (midnight & noon)– 1.5 year of data

• WPF model

* European Centre for Medium-range Weather Forecasts

• Wind Farm– French onshore farm

• Flat terrain• Installed capacity: 10.1 MW

Model Type Explanatory variables Procedure Accuracy

Random Forest (RF)

Statistical • Predictions of wind speed• Predictions of wind direction• Last available power measurement

• Model learning on control member• Model testing on all ensemble members

• NMAE = 7 – 9 % for 6 – 48 hours ahead

9

NPRI

rela

tive e

nerg

y im

bala

nce

[%

]

NPRI [%]

rela

tive e

nerg

y im

bala

nce

[%

]

1 2 3 4 5

NPRI Class

1 2 3 4 5

Evaluation: NPRI results

Steps1: Estimate Normalized Prediction Risk Indices (NPRI)

2: Compute energy imbalances

3: Derive NPRI – energy imbalance pairs

4: Group into classes based on NPRI

5: Calculate imbalance distributions

Results for day 2 ahead

10

Outline

• Introduction and objectives

• Definition and evaluation of risk indices

• Risk indices in decision making

• Conclusions

11

• Operational context: – Give alerts when high risk for large energy imbalance

• Alert function:

• Parameters:x = 1.5

y = 0.2

Exploration: risk indices for decision making - proposals

12

Energy imbalance

Mean energy imbalance

“Make an alert when the probability of an energy imbalance larger than x times the average is greater than y”

Exploration: risk indices for decision making - results

• Evaluation: – Confusion matrix

• Results – Day 2 ahead

– Interesting approach• Alert function parameters x and y can be tuned depending on risk tolerance• More investigation needed to validate results

13

Alert needed Alert not needed Total

Alert made 4 1 5

Alert not made 15 70 85

Total 19 71 90

Alert needed Alert not needed

Alert made True alert False alert

Alert not made False non alert True non alert

“Make an alert when the probability of an energy imbalance larger than 1.5 times the average is greater than 0.2”

Conclusions

• Evaluation• Larger ensemble spread indicates

– Larger average forecast error– Larger uncertainty in the predictions – Higher risk of large forecast error

• Risk indices useful to extract and display this information• Better understanding and extended validation of the concept of risk indices

• Value of risk indices for decision making• Useful in giving alerts for large energy imbalances

• Future work• More focus on the use of risk indices in operational contexts

14

Thank you for your attention!

Acknowledgement: European R&D project SafeWind (FP7)