European Utility Week 2015: Data Analytics and Wind Power Forecasting

Stéphanie LakkisFranz Winterauer

The power of data analytics to drive more effective wind power forecasting

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The wind power challenge

Source: Wind in power 2014 European Statistics, EWEA

The importance of wind power is growing

Total installed capacity in 2000 in the EU

12,9 GW




The importance of wind power is growing

Total installed capacity in 2014128.8 GW



Source: http://energinet.dk/EN/El/Nyheder/Sider/Vindmoeller-slog-rekord-i-2014.aspx

Wind power penetration in Denmark in 2014

In 2014, wind power provided Denmark almost

40% of its electricity




What we already achieved in 2014…

EU consumption met by wind10,2%


2020 Targets:

20% of RESin the EU


Source: https://ec.europa.eu/energy/en/topics/renewable-energy

…what we’re supposed to reach



In 2014, wind power provided Denmark almost 40% of its electricity

Better wind power forecasting is the key

128.8 GW Total installed capacity in 2014

12,9 GW Total installed capacity in 2000 in the EU

10,2% EU consumption met by wind

2020 Targets: 20% of RES in the EU


Who needs a wind power forecast?

Distribution System Operators (DSOs)

Generation companies and Independent Power

Producers (IPPs)Transmission System

Operators (TSOs)


Wind power management today

Forecast algorithm

Online measurements of wind power and meteorological data

Numerical Weather Prediction (NWP)

δ Prediction of future MW production

Congestion management Reserve monitoring Expected energy

balance Market trading

Applications that are or can be impacted by wind power forecasting

δ


….with additional wind power forecasts… and additional data

Online measurements of wind power and meteorological data

Numerical Weather Prediction (NWP)

δ1Prediction of future MW production

Congestion management Reserve monitoring Expected energy

balance Market trading

Forecast algorithm

Applications that are or can be impacted by wind power forecasting

δ

Additional weather data

Smart combination of different wind power forecasts

δ2 δ3 δn


How can utilities make the best out of their existing solutions for

a more accurate final forecast and better insight into related

data?


Smart combination of different forecasts helps to harvest their real “hidden” potential

First finding: Different data sequences leading to best FC from A, B or C

Primary wind direction

Secondary wind direction? ? ?

Q: How to get a Smart Combination?A: By looking into the details!


Finding the needle in the haystack

Q: Is Forecast “A” always good?A: Certainly not! Good: 45%

Bad: 25%

Avg: 30%



Q: When is FC “A” not good?A. Forecast “B” and “C” are better with negative fast ramps! FC “C”

FC “A”

FC “B”



FC “A” is in most cases the best (as shown earlier),but slope is much better at FC “B” from 0-30hrs)

Smart combination of best value (from FC “A”) and best Slope(from FC “B”) immediately led to a better day-ahead forecast


The benefits

+2

5

3

4

1Enables higher accuracy

Better forecasting of unusual situations

Optimize use of existing wind power forecast solution

Better decision making

Reduces complexity through solution rationalization


Thank you!

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European Utility Week 2015: Data Analytics and Wind Power Forecasting

Data & Analytics

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