Energie braucht Impulse

Development of an Offshore-Specific Wind Power Forecasting Model Based on Ensemble Weather Prediction and Wave Parameters

Ümit Cali, B.Lange, M.Kurt, C.Möhrlen

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Aim & Content

Aim:  

The aim of this study is to investigate the role of offshore-specific parameters and ensemble weather prediction system on the accuracy of the wind power forecasting for offshore wind farms.

Content: 

› Selection of methodology: Artificial Neural Network (ANN)

› Site description (Horns Rev Wind Farm)

› Evaluation of the new approach for an offshore-specific wind power prediction system

› Day Ahead offshore wind power forecasting models

› 2 Hours Ahead Short-term offshore wind power forecasting models

› Optimization of the models (Combination methods)

› Results

› Conclusion and Outlook

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› Wind Power

› Hydropower

Strategical Activitiy Areas / Fields of the EnBW Renewables GbmH

› Hydropower

› Biomass

› Photovoltaik

› Wind Power

› Wind Power offshore

› Wind Power onshore

› Biomass

Baden-Württemberg

Deutschland

Europa› Wind Power

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General Structure of the Study

Notice: The measured wind power information from Horns Rev was available from February 2005 and July 2006. Hence, the common period for all variables is in this case from February 2005 and July 2006.

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Site Description of the Horns Rev Offshore Wind Farm

Turbine Type Vestas V80 - 2MW

Total Capacity 160 MW

Estimated Energy Annual Production 600000000 kwh

Rotor Diameter 80 m

Hub Height 70 m

Water Dept 6- 14 m

Distance Between Turbines 560 m

Total Occupied Area of Wind Farm 20 km²

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Wind Power Forecasting using ANN

› Physical Models

› Statistical Models

› Artificial Intelligence based Models (e.g. ANN)

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General Structure of the Offshore-Specific Wind Power Forecasting

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Offshore WPF: Day Ahead Forecasting

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Offshore WPF: Day Ahead Forecasting

› DA_1: Wind Speed and Wind Direction at 10m

› DA_9: All Available meteorological parameters from MS EPS

› DA_10: Like DA_9, additionally forecasted wave parameters from ECMWF

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Combination Models: Simple Avg. and 2 ANN for DA_10

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Overall Results of Day Ahead Experiments

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General Structure of MS EPS

17.6%

17.8%

18.0%

18.2%

18.4%

18.6%

18.8%

19.0%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75

Number of Ensemble Members

nRM

SE

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2 Hours Ahead Offshore Wind Power Forecasting Experiments

Measurement Forecast

Period TP, T02 MP1, MP2, MWP

Wave Height Hmax, Hm0 SWH, SHWW

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2 Hours Ahead Offshore Wind Power Forecasting Experiments

Exp. Code Description

HR_ST2_1 Short-term Wind Power Forecasting using NWP Data

HR_ST2_2WFShort-term Wind Power Forecasting using NWP and Forecasted WaveParameters

HR_ST2_3WShort-term Wind Power Forecasting using NWP and Measured WaveParameters

HR_ST2_4Short-term Wind Power Forecasting using NWP and Measured WindParameters

HR_ST2_5WFShort-term Wind Power Forecasting using NWP, Forecasted Wave andMeasured Wind Parameters

HR_ST2_6WShort-term Wind Power Forecasting using NWP, Measured Wave,Measured Wind and Forecasted Wave Parameters

HR_ST2_7WWFShort-term Wind Power Forecasting using NWP, Forecasted Wave andMeasured Wave Parameters

HR_ST2_8WWShort-term Wind Power Forecasting using NWP, Measured Wave andMeasured Wind Parameters

HR_ST2_Pers 2 hours persistent

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Results of 2 Hours Ahead (Short-term) Forecasting

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Conlusion and Outlook

Conlusion

› The new offshore day ahead wind power forecasting using additional oceanographic parameters model brings improvements in the forecast accuracy. (21.3 % of improvement)

› Beside integrating the WAM (from ECMWF) input variables, application of the combination model approaches (such as simple averaging and 2 ANN) improves the forecast accuracy up to 26.51 %.

› The integration of additional parameters such as wave and wind measurements decreases the forecasting error and increases the improvement of the accuracy up to 27.41 % (for 2 hours ahead models).

Outlook

› In future, the influence of the vertical temperature gradient shall be investigated in order to increase the forecast accuracy.

› We recommend the regulatory authorities to set some regulations and rules for the actors who are supposed to make such measurements to improve the accuracy of the wind predictions and the reliability of their operations.

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Thank You ...

Notice: The work was carried out mainly at ISET e.v. with measured data from the offshore wind farm Horns Rev in Denmark in the scope of the project “High Resolution Ensemble for Horns Rev” (HRensembleHR) funded by the Danish PSO Programme 2006-2009.

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Results of Combination Models: Simple Avg. and 2 ANN