Current best practice of uncertainty forecast for wind energy

Transcription

1 Current best practice of uncertainty forecast for wind energy Dr. Matthias Lange Stochastic Methods for Management and Valuation of Energy Storage in the Future German Energy System 17 March 2016

2 Overview Company profile Wind power forecasting Facts on forecasting errors Approaches to estimate uncertainty

3 Company profile Integration of renewables into grids and markets Service provider for energy meteorology since 2004 Wind and solar power predictions forecasting service worldwide for traders and grid operators Virtual power plants energy trading, remote control of decentralized units, reserve power Software as a Service National and international research projects 60 people

4 Areas with operational forecasting experience 85 GW of installed wind power currently predicted Previento wind power forecasting for grid operators for traders

5 How does a wind power prediction look like? Schedule containing 24 or 96 values per day Single plants or aggregate Uncertainty forecast included Delivered prediction Measurement Confidence bands

6 Previento wind power forecasting system Previento Physical Model: Spatial refinement Thermal stratification Site-specific power curve Forecast uncertainty energy & meteo systems

7 Input: Wind speed forecast from numerical weather models

8 Combination: Do not trust in one weather model only!

9 Types of forecasting error prediction measurement phase error: fronts not in time NWP amplitude error: wind speed at hub height wrongly predicted NWP or wind profile

10 Types of forecasting error prediction measurement shifted diurnal cycle: thermal stratification wrongly predicted wind profile machine dependent errors: cut-off, curtailment, availability machine data

11 How accurate are wind power predictions in general? Different metrics Mean absolute error (MAE) Root mean square error (RMSE) Bias Correlation Skill scores Contingency tables CSI (ramps) Different normalizations Installed power Average output Actual forecast value Visusal / subjective Scatter plots Time series plots (ramps)

12 RMSE forecasting error for real sites (day-ahead) Long-term results from wind farms and aggregations over regions in Europe, North America and Australia

13 Increase of forecasting error with prediction horizon Previento prediction of total aggregate of German wind farms 2014

14 Forecast errors in scatter plot underestimated Scatter over 1 year overestimated

15 Distribution of wind power prediction error (dayahead) Large region installed power: 2250 MW

16 Distribution of wind power prediction error (dayahead) Small region installed power: 37 MW

17 Transformation of wind speed errors to power errors power curve

18 Artificial forecasting errors Wind farm availability below 100 % Wind farm shut down due to grid congestion

19 Quantile forecasts as uncertainty estimate Statistical training based on historical errors Quantile forecast P85, i.e. with probability of 85% observed value is lower combination prediction Measurement Quantile forecast P15, i.e. with probability of 15% observed value is lower

20 Now for something completely different: Weather Exploit that atmosphere is chaotic.

21 Sensitive dependence on initial conditions Lorenz System Very small differences in starting point of simulation.

22 Sensitive dependence on initial conditions Lorenz System

23 Solution jumps chaotically between two states Lorenz System

24 Ensemble uncertainty tries to exploit chaotic behaviour COSMO-DE-EPS Multimodel-Ensemble Not enough spread Spread covers error range

25 Different NWP models represent different future scenarios combination of NWP

26 Different kinds of uncertainty estimates Spread of ensemble Quantiles with fixed confidence level Prediction of January 24, 2016, 8 UTC

27 Different kinds of uncertainty estimates Spread of ensemble Quantiles with fixed confidence level Prediction of January 15, 2016, 8 UTC

28 Spread of ensemble updates 19/07/2015 to 22/07/2015 Prediction of 20/07/2015, 08 UTC

29 Spread of ensemble updates 19/07/2015 to 22/07/2015 Spreads are more weather specific! Prediction of 21/07/2015, 08 UTC

30 Approaches to estimate uncertainty Quantile prediction Based on historical timeseries of forecasting error, i.e. really observed deviations Conditional error distributions can be considered, e.g. foreast horizon or production level Advantage: well-defined confidence levels Disadvantage: only weak dependence on current weather situation Spread of ensemble Based on deviation between several predicted scenarios for the upcoming weather situation Ensemble members represent realistic weather situations with equal probability Better results for intraday and day-ahead if poor mans ensemble is used instead of ensemble model Advantage: clear relation to current weather situations Disadvantage: so far no well-defined confidence levels

31 Thanks for your attention! Contact: Dr. Matthias Lange