02/03/2009

102/03/2009 Fluid Dynamics and Thermodynamics Research Group : Erasmushogeschool Brussel - Vrije Universiteit Brussel

Improved resource assessment assessment for small wind turbines in

rural and urban areas

http://www.microwindturbine.be

http://www.microwindturbine.be/


Outline

• Introduction

• Feasibility study for small wind turbines

• Shape of the power curve

• Representation of the wind speed distribution

• Wind shear

• Conclusion

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Introduction

• Wind energy in Flanders:‣ Approximately 200 large wind turbines installed (0,5 - 5 MW)

‣ Roughly 4 times less small wind turbines (< 100 kW)

• Small wind turbine market is deadlocked‣ Performance gap with large wind turbines

Technological improvement is necessary

‣ No market Very little investment in research and development

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Outline

• Introduction




• Wind shear

• Conclusion

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• Are small wind turbines suitable to provide sustainable energy for SME’s in Flanders?‣ wind speed measurements at lower heights in Flanders

• Supplemented by measurements from KMI and KNMI

‣ an exhaustive review of small wind turbines

‣ prediction of the annual energy yield

‣ prediction of the payback time• Using an updated tool developed by Apère

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Feasibility study for small wind turbines


• Results of the feasibility study ‣ Payback time for 2 turbines and 18 sites

‣ Results for SME’s with local subsidies

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Measurement station

Turbine 1 Turbine 2

Bierset 10 year 10 year

Diepenbeek 37 year 25 year

Gosselies 12 year 11 year

Koksijde 8 year 8 year

Retie 89 year 34 year

Zelzate 13 year 12 year

Spa 16 year 12 year

Zeebrugge 6 year 6 year



• Conclusions‣ Small wind turbines can be profitable if:

• Installed on a proper location and

• a good wind turbine is selected

‣ Due to the young market • Wide range on the performance of the turbine (very bad - good)

• Identification of key factors that determine the reliability of the resource assessment:‣ Shape of the power curve

‣ Representation of the wind distribution

‣ Extrapolation of wind speed using wind shear laws

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Outline

• Introduction




• Wind shear

• Conclusion

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• Prediction of annual energy yield for different turbines and different measurement stations ‣ Rated power and cut-in wind speed can be misleading factors

• Example

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Shape of the power curve


• Annual energy yield table:

• Turbine 2 produces more power for wind speed interval 5-10 m/s‣ 62 % of the energy is captured in interval for data set 1

‣ 54 % of the energy is captured below 5 m/s for data set 2

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Turbine 1 Turbine 2

Data set 1 5458 kWh/year 6344 kWh/year

Data set 2 819 kWh/year 715 kWh/year

Shape of the power curve


Outline

• Introduction




• Wind shear

• Conclusion

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• Use of Weibull distribution can give rise to substantial errors‣ Error on the annual energy production of 13 %

‣ Inability to cope with non-zero probability of very low wind speeds

‣ Skewing of distribution for high wind speeds

• To alleviate this problem:‣ We use the wind speed data from the feasibility study to calculate:

• Probability distribution

• Power density distribution

• Annual energy production (AEP)

‣ This process is repeated for different distribution methods

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Representation of the wind distribution


• We compare:‣ Weibull method with moment estimate of parameters

‣ Weibull method with maximum likelihood estimate of parameters

‣ Maximum entropy principle (MEP) with variation of pre-exponential term

‣ Combination of methods above with hybrid method

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• The different distributions are used to calculate the power density distributions‣ For 13 different measurement stations

• We compare the methods by calculating RMSE and COD with the wind speed data set‣ Table shows comparison of Weibull and MEP method

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Measurement station

Beit. Diep. St-K-W Zelz. Midd.

Weibull 0.9778 0.9797 0.9890 0.9072 0.9656

MEP r = 1 0.9825 0.9848 0.9924 0.9484 0.9762



‣ Comparison of the RMSE for 2 measurement station shows:

• We improve the accuracy of the power density prediction for 8 of the 13 measurement stations compared to the conventional Weibull method

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Measurement station

WeibullMM

WeibullML

MEPr = 1

MEP r = 2

MEP r = 4

Deur. 0.25 0.32 0.29 0.55 1.02

Zelz. 0.98 1.03 0.73 0.49 0.29



• Using the probability distributions we can estimate the AEP for one particular turbine‣ We compare this estimate with the estimate using the data histogram

• We improve the accuracy of the AEP prediction for 9 of the 13 measurement stations compare to the conventional Weibull method

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Measurement Station

WeibullMM

WeibullML

MEPr = 0

MEP r = 4

MEP_H r = 4

Kl-Br. 0.97% 13.27% 0.32% 5.15% -10.02%

Zelz. -3.07 -1.93% -8.57% -0.33% -0.67%

Koks. -0.77% 4.52% 1.03% 4.08% 0.19%



• Accuracy of prediction of power density distribution and AEP differ

• MEP r = 4 higher probability for lower wind speeds‣ Compensated for higher wind

‣ Better prediction of AEP



Outline

• Introduction




• Wind shear

• Conclusion

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• Wind shear describes the variation of wind speed with elevation

• Different methods in literature to describe the wind shear:

‣ Linear log law:

‣ Log law:

‣ Log law high roughness:

‣ Power law:

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Wind shear


• Hub height no always equal to measurement height

‣ 2/3 rule is bankable

• Way of extrapolation to higher altitude will have an impact on the prediction of the annual energy yield‣ Extrapolation from 15 m to 22.5 m

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Shear law Wind speed Shear parameter

AEP (kWh/year)

LLog 5.83 m/s 1.82 m 6064

Log 5.80 m/s 1.17 m 5987

Power 5.94 m/s 0.42 6308

Wind shear


• Typical error in wind speed measurements 0,05 m/s

• Extrapolation from 15 m to 22,5 m‣ For each type of error

‣ For each type of shear law

• Table represent the difference ‣ Smallest and largest extrapolated wind speed

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Shear law Wind speed Shear parameter

AEP (kWh/year)

LLog 0.37 m/s 1.79 m 843

Log 0.33 m/s 0.79 m 751

Power 0.41 m/s 0.12 948

Wind shear


• We use CFD to compare different shear laws

• Siting study of flat terrain with obstacles ‣ Determination of most suitable location to install windturbine

‣ Validated by field measurements

• On the validated location we attract the vertical wind profile‣ Influence of inlet wind profile is minimal

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Wind shear


• Fit of wind shear laws on vertical wind profile

• Results

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Shear law RMSE COD

LLog 0.1589 m/s 0.9907

Log 0.0881 m/s 0.9964

Power 0.2523 m/s 0.9705

Wind shear


Outline

• Introduction




• Wind shear

• Conclusion

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• Our feasibility showed that small wind turbines are suitable to provide sustainable energy for SME’s if they are well chosen and installed on a good location

• Rated power is not a good predictor of AEP in typical conditions for small wind turbines

• Using more complex wind speed distributions can improve the accuracy on annual yield prediction

• Use of different shear laws to extrapolate wind speed from one height to another can have an impact on the estimation of the AEP

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Conclusion

02/03/2009

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