Wind power prediction risk indices based on numerical weather prediction ensembles

1. European Wind Energy Conference 2010, Warsaw, Poland Wind power prediction risk indices based on numerical weather prediction ensembles Erik Holmgren, Nils Siebert, George Kariniotakis erik.holmgren@sserenewables.com, nils.siebert@mines-paristech.fr Renewable Energies Team MINES ParisTech / ARMINES

2. Outline Introduction and objectives Definition and evaluation of risk indices Risk indices in decision making Conclusions 2

3. Introduction: ensemble forecasting Model t0 +1h +6h +12h +18h +24h +30h +36h … +48h Control forecast Ensemble forecasts Measured production Numerical Weather Predictions (NWP) NWP ensembles Measurements Measurements Model Model Production [MW] Forecast Measured production 3.5 3.5 3 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 t0 t0 +1h +6h +12h +18h +24h +30h +36h … +48h +1h +6h +12h +18h +24h +30h +36h … +48h Future Future Present Present • 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

4. Objectives t0 +1h +6h +12h +18h +24h +30h +36h … +48h Control forecast Ensemble forecasts Measured production Period with smaller ensemble spread Period with larger ensemble spread Model Model Production [MW] 3.5 3.5 3 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 t0 t0 +1h +6h +12h +18h +24h +30h +36h … +48h +1h +6h +12h +18h +24h +30h +36h … +48h Future Future Present Present • Investigate relationship between ensemble spread and forecast error • Quantify the ensemble spread through risk indices 4

5. Outline Introduction and objectives Definition and evaluation of risk indices Risk indices in decision making Conclusions 5

6. Quantify ensemble spread through risk indices t0 +1h +6h +12h +18h +24h +30h +36h … +48h Model Model Production [MW] 3.5 3.5 3 3 2.5 2.5 2 2 Control forecast Ensemble forecasts Ensemble mean Measured production 1.5 1.5 1 1 0.5 0.5 0 0 t0 t0 +1h +6h +12h +18h +24h +30h +36h … +48h +1h +6h +12h +18h +24h +30h +36h … +48h Notation: for Future Future Present Present • Definition: the Normalized Prediction Risk Index (NPRI) • Weighted standard deviation of ensemble members • Average value over a look-ahead time window 6

7. Production [MW] 3.5 3.5 3 3 2.5 2.5 t0 +1h +6h +12h +18h +24h +30h +36h … +48h 2 2 Model Model 1.5 1.5 1 1 0.5 0.5 0 0 t0 t0 +1h +6h +12h +18h +24h +30h +36h … +48h +1h +6h +12h +18h +24h +30h +36h … +48h Future Future Present Present 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 : Control forecast Measured production 7

8. 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

9. 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 Evaluation 9

10. Evaluation: NPRIresults Steps 1: 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 1 2 3 4 5 relative energy imbalance [%] relative energy imbalance [%] 1 2 3 4 5 NPRI Class NPRI NPRI [%] 10

11. Outline Introduction and objectives Definition and evaluation of risk indices Risk indices in decision making Conclusions 11

12. Exploration: risk indices for decision making - proposals “Make an alert when the probability of an energy imbalance larger than x times the average is greater than y” Energy imbalance Mean energy imbalance • Operational context: • Give alerts when high risk for large energy imbalance • Alert function: • Parameters: x = 1.5 y = 0.2 12

13. Exploration: risk indices for decision making - results “Make an alert when the probability of an energy imbalance larger than 1.5 times the average is greater than 0.2” • 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

14. 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

15. Thank you for your attention! Acknowledgement: European R&D project SafeWind (FP7)