September 2016 Michael Osmann Model developer Day ahead forecasting September 2016 Michael Osmann Model developer September, 2016 www.energinet.dk

Outline The Danish setup Setting up the data Making a power curve for normal windspeeds Making a powercurve for high windspeeds Combination forecast September, 2016 www.energinet.dk

Power balance Two synchronous areas West East 1.5 GW 0.7 GW 1.3/1.7 GW Primary power station Local CHP plant Wind turbines Power balance Two synchronous areas 1.5 GW 0.7 GW West Consumption 1.4 – 3.6 GW Primary power stations 2.5 GW Local CHP plants 1.7 GW Wind turbines 3.8 GW PV 0.5 GW 1.3/1.7 GW 0.6 GW East Consumption 0.9 – 2.7 GW Primary power stations 3.1 GW Local CHP plants 0.7 GW Wind turbines 1.0 GW PV 0.3 GW 1.5/1.8 GW 0.6 GW import/export September, 2016 www.energinet.dk

Danish wind areas A wind power forecast is provided for each area 1 2 3 1 4 5 6 7 8 10 11 15 14 12 13 9 A wind power forecast is provided for each area September, 2016 www.energinet.dk

The wind speed in an area Grid points in area 12 September, 2016 www.energinet.dk

The wind speed in an area September, 2016 www.energinet.dk

The wind speed in an area The most common ways to calculate the wind speed in an area are The average value from all grid points in the area Simple approach Works well in areas where turbines are evenly spread Use only one grid point – the one that had previously had the best performance Fairly simple approach Weighted average from all grid points in the area Requires more input data, but is potentially the best approach Can be done in several ways Use weights based on distance to turbines Use weights that historically would have been optimal September, 2016 www.energinet.dk

Historical weather data Weather forecast starting at 00:00 Weather forecast starting at 06:00 Weather forecast starting at 12:00 Weather forecast starting at 18:00 Weather forecast starting at 00:00 6 hours 6 hours 6 hours 6 hours 6 hours Time series of historical weather data Time September, 2016 www.energinet.dk

Aligning wind speed data with production relative production = production installed capacity September, 2016 www.energinet.dk

Input data for power curve Wind speed in 100m and corresponding relative production during 3 months September, 2016 www.energinet.dk

Creating the power curve for normal wind speeds Remove outliers and observations with high wind speed September, 2016 www.energinet.dk

Creating the power curve for normal wind speeds 5’th degree polynomial Any method to describe relative power from wind speed which look satisfactory will work! September, 2016 www.energinet.dk

Training issues Production data cleaned before power curve training Separate training for each wind area Weighted mean of NWP grid points Availability factor Separate training for each NWP provider Separate handling of high wind speed Special handling of “low” spot prices September, 2016 www.energinet.dk

High wind shutdown Contrary to popular belief it’s more complicated than wind speed > 25 m/s causes turbine shutdown Sustained wind speed e.g. 25 m/s for 5 minutes Gust wind speed e.g. 28 m/s for 30 seconds Peak gust speed e.g. 30 m/s for 3 seconds Hysteresis e.g. average wind speed < 22 m/s for 5 minutes Depends on manufacturer specifications September, 2016 www.energinet.dk

Different requirements for high and normal wind speeds For high wind speeds much more data is required, e.g. 3 years Due to the reasons on previous slide, there is a high variability in the production when wind speeds are high The power curve for normal wind speeds needs to be re-calibrated frequently, in case of altered production patterns The power curve for high wind speeds only needs to be re-calibrated whenever high wind speeds have been observed September, 2016 www.energinet.dk

Creating a power curve for high wind speeds High wind speed data during 3 years September, 2016 www.energinet.dk

The full power curve September, 2016 www.energinet.dk

Producing a wind power forecast September, 2016 www.energinet.dk

Combination forecasts Providers of weather forecasts to Energinet DMI ECMWF ConWx Wind speed parameters from each provider 10m wind speed 100m wind speed Gives a total of 6 weather forecasts Each weather forecast needs its own power curve! September, 2016 www.energinet.dk

Combination forecasts DMI 10m Power curve Power forecast DMI 100m Power curve Power forecast ECMWF 10m Power curve Power forecast Combined power forecast ECMWF 100m Power curve Power forecast ConWx 10m Power curve Power forecast ConWx 100m Power curve Power forecast September, 2016 www.energinet.dk

Combination forecasts September, 2016 www.energinet.dk

How to make combination forecasts Simple model: Use the average value of all available forecasts It is simple! Works well, especially if all weather forecasts perform similarly Advanced model: Use a weighted average, where the weights are determined in such a way that the errors of the combination forecast is minimized historically Model: 𝐹 combination = 𝑖=1 𝑁 𝑤 𝑖 𝐹 𝑖 , where 𝑖=1 𝑁 𝑤 𝑖 =1 and 𝐹 𝑖 are the available forecasts Is more complicated Has potential to be a better approach, especially if the weather forecasts do not perform similarly September, 2016 www.energinet.dk

Advantages of combination forecasts If a weather forecast fails to be delivered, you can still provide a power forecast Combination forecasts are usually more precise than single power forecasts September, 2016 www.energinet.dk

Thank you for your attention Horns Reef September, 2016 www.energinet.dk