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September 2016 Michael Osmann Model developer
Day ahead forecasting September 2016 Michael Osmann Model developer September, 2016
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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
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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
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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
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The wind speed in an area
Grid points in area 12 September, 2016
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The wind speed in an area
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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
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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
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Aligning wind speed data with production
relative production = production installed capacity September, 2016
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Input data for power curve
Wind speed in 100m and corresponding relative production during 3 months September, 2016
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Creating the power curve for normal wind speeds
Remove outliers and observations with high wind speed September, 2016
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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
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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
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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
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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
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Creating a power curve for high wind speeds
High wind speed data during 3 years September, 2016
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The full power curve September, 2016
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Producing a wind power forecast
September, 2016
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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
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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
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Combination forecasts
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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
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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
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Thank you for your attention
Horns Reef September, 2016
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