tansu fİlİk spatial short-term wind speed estimation_final

8/18/2019 TANSU FİLİK Spatial Short-Term Wind Speed Estimation_final

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Assist. Prof. Dr. Tansu Filik

Department of Electrical and Electronics EngineeringAnadolu Üniversitesi

Spatial Short-Term Wind Speed

Estimation

Renewable Energy Systems Winter School



• Wind Energy

–Wind Speed Prediction

• Spatial Wind Speed/Direction Estimation– Space-Time Statistical Models

• Case Study: Bozcaada, Gonen, Ipsala

•

Conclusion• References

Content




• The European Union 20/20/20 target [1]:

–Reduce greenhouse gas emission by 20% (as compared to 1990)

– Increase the amount of renewable Energy to 20% of the Energy supply

– Reduce the Energy consumption by 20% through improved Energy efficiency

by 2020.

• The wind energy has become very attractive.

– sustainable

– emission-free

– cost-effective

• The penetration of wind energy on power systems across the world is inc

– In UK, the wind penetration target is high: 30% by the year 2030

– In Turkey, currently, approximately 5% (2.700 MW)

– by the year 2023, it is aimed to 30% (20.000 MW wind power)

Wind Energy




• It is critically important to accurately forecast wind power generation for

and economic power system operation.

Wind Power Forecast


Three power curves with different capacity ranges from low tohigh from three manufacturers: 0.3 MW from Nordex, 1.5 MW

from GE, and 2.5 MW from Bonus.

International Statistical Review Volume 80, Issue 1, pages 2-23, 10 APR 2012 DOI: 10.1111/j.1751-5823.2011.00168.xhttp://onlinelibrary.wiley.com/doi/10.1111/j.1751-5823.2011.00168.x/full#f3

• Accurate wind power output predictionis rely heavily on accurate wind speedprediction.

• So, wind speed forecasting is a betterapproach than predicting wind poweroutput directly.

http://onlinelibrary.wiley.com/doi/10.1111/insr.2012.80.issue-1/issuetoc

http://onlinelibrary.wiley.com/doi/10.1111/j.1751-5823.2011.00168.x/full





http://onlinelibrary.wiley.com/doi/10.1111/insr.2012.80.issue-1/issuetoc



• The reliable short-term wind speed prediction is an importan

challenging problem for the future grid operations [1].

• Short-Term versus Long-Term Wind Speed Prediciton– Short-term: a hour or less ahead

– Long-term: 6-72 hours ahead

• In literature, the numerical whether prediction (NWP) mode

widely used for the day ahead (long-term) wind forecast.

• On the other hand, NWP model based methods, which havecomputational complexity, are not suitable for the short-termhour or less ahead) wind speed prediction [2].

• We need some simpler techniques to improve short-term for

Wind Speed Prediction




• The wind speed is a random signal and the temporal correla

not often can be tracked for the accurate prediction.

• There are two major approaches to forecast wind speed:– Point Forecasting:

– gives a single value as the forecast of future wind speed

– Probabilistic Forecasting:

– models a probabilistic density function for future wind speed

– more informative and useful than point forecasting

– provides information about the uncertainty

• Nowadays, the spatio-temporal methods are come on stage– which gives more accurate and reliable results according to the tempor

correlation based methods

Wind Speed Prediction




• Wind is a horizontal movement in the atmosphere near the sdriven by air pressure, it usually covers a large area.

• Wind speed is positively correlated in a wide area.

• Wind speed and direction are significantly affected by local t

• Flat ground allow wind to blow uninterrupted.

• On the other hand, complex terrains can slow down the wineven change the wind direction.

• The information from neighborhoods of target location is veuseful for accurate wind speed forecasting.

Spatial Wind Speed/Direction Estimatio




• In this short course, the space-time models for short-term wispeed forecasting is reviewed.

• According to the traditional time series forecasting models, stime statistical models take both the spatial and time correlainto account.

Space-Time Statistical Models




Regime-Switching Space-Time Diurnal (RSTD) Model [3, 10

• RSTD model is for predicting 2-h ahead average wind speedStateline Wind Energy Center in Vansycle, Oregon, U.S.

• The wind speed data collected in 2002 and 2003 from 3 state






• These 3 locations are along the Colombia River Gorge whichfrom east to west.

• Due to high terrains to the north and south

• the airflow runs parallel to the channel of walls and results

westerly or easterly winds.• Two regimes are defined: a westerly regime and an easterly

• The forecasting models are built separately for each of them






The 2-h ahead wind speed is denoted as

It is modelled as a truncated normal distribution on the positive domain

The key is modelling center and scale parameter: For the westerly regime

Here Ds, s=1,…,24 are linear combinations of trigonometric functions of thethe day, fitting the diurnal pattern of the wind speed






After removing a diurnal pattern, the residual is fitted by a linear function ocurrent and past residuals at the 3 locations

where the residual wind speeds V, K and G represents the capital letters of stations (Vansycle, Kennewick and Goodnoe Hills).

The scale parameter is fitted with the same model

where the volatility value is calculated as;





Trigonometric Direction Diurnal (TDD) Model [3,11]:

• This model eliminates the regimes by incorporating wind ddirectly into the predictive mean function of the RSTD mod

• It treads wind direction as a circular variable

• and achieves similar forecast accuracy as the RSTD model

•

TDD model did not need any prior geographic information the target data

• for some areas there are no significant wind patterns or the are too complex to model

• So, TDD model is more powerful than RSTD model





•





Multichannel Adaptive Filtering for short term prediction [5

• Low complexity predictor is proposed for hourly mean winspeed and direction from 1 to 6h ahead at multiple sites distaround to UK.



−8 −6 −4 −2 0 2

50

51

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59

1

2

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longitude / [degrees]

l a t i t u d e

/ [ d e g r e e s

]

1

2

3

4

5

6

7

8

9

10

11

12

13

1. Boulmer

2. Chivenor

3. Coningsby

4. Gorleston

5. Hawarden Airport

6. Langdon Bay

7. Leuchars

8. Machrihanish

9. Peterhead Harbour

10. Rhoose

11. Shawbury

12. Tain Range

13. West Freugh



−8 −6 −4 −2 0 2

50

51

52

53

54

55

56

57

58

59

1

2

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longitude / [degrees]

l a t i t u d e

/ [ d e g r e e s

]

1

2

3

4

5

6

7

8

9

10

11

12

13

1. Boulmer

2. Chivenor

3. Coningsby

4. Gorleston

5. Hawarden Airport

6. Langdon Bay

7. Leuchars

8. Machrihanish

9. Peterhead Harbour

10. Rhoose

11. Shawbury

12. Tain Range

13. West Freugh

Multichannel Adaptive Filtering for short term prediction [5], [9]:

• The wind speed and direction are modeled via the magnitude anof a complex-valued time series.

• A multichannel adaptive filter is set to predict this signal on the bits past values and the spatio-temporal correlation between windmeasured at numerous geographical locations.

• The adaptive filter coefficients are determined by minimizing thesquare prediction error.

• a cyclo-stationary Wiener solution is proposed

• an iterative (adaptive) solution provides lower complexity, increarobustness and ability to tract time variations in the underlying sy





• In this case study:

–the real wind speeds and directions of the Bozcaada station is used.

– 7 year hourly measurements are used (between 2007 and 2013)

Wind Speed Prediction: Case Study


Station# Year Month Day

Hour(UTC)

Wind speed (m_sec) /Direction (°)

17111 2013 6 10 0 2.5 /9

17111 2013 6 10 1 2.1 /15

17111 2013 6 10 2 1.6 /110

17111 2013 6 10 3 1.7 /8417111 2013 6 10 4 2.9 /91

17111 2013 6 10 5 1.3 /9317111 2013 6 10 6 1.3 /359

17111 2013 6 10 7 2.0 /352

17111 2013 6 10 8 3.3 /350

17111 2013 6 10 9 3.8 /338

17111 2013 6 10 10 3.9 /347

2 5 E

30 E

40 N

2nd

stationx2[n]

IPS

BOZGON

1st

stationx1[n]

3rd stationx3[n]



• In this case study:

–the real wind speeds and directions of the Bozcaada station is used.

– 7 year hourly measurements are used (between 2007 and 2013)



bl E h l



•



R bl E S Wi S h l



•



R bl E S t Wi t S h l



Results for real wind data from Bozcaada



1 2 3 4 5 6 7 81.76

1.77

1.78

1.79

1.8

1.81

1.82

1.83

1.84

1.85

number of p (coefficients)

RMSE

(m/s)

7600 7650 7700 7750-5

0

5

10

15

20

hours

W i n

d s p e e d ( m / s )

Bozcaada Station




• Multi-channel Case:



40 N

2nd

stationx2[n]

IPS

BOZGON

1st

stationx1[n]

3rd stationx3[n]

200 400

600 800

30

210

60

240

90

EAST

270

WEST

120

300

150

330

180

SOUTH

0

NORTH

200 400

600 800

1000

30

210

60

240

90

EAST

270

WEST

120

300

150

330

180

SOUTH

0

NORTH

200 400

600

30

210

60

240

90

EAST

270

WEST

120

300

150

330

180

SOUTH

0

NORTH




40 N

2nd

stationx2[n]

IPS

BOZGON

1st

stationx1[n]

3rd stationx3[n]

200 400

600 800

30

210

60

240

90

EAST

270

WEST

120

300

150

330

180

SOUTH

0

NORTH

200 400

600 800

1000

30

210

60

240

90

EAST

270

WEST

120

300

150

330

180

SOUTH

0

NORTH

200 400

600

30

210

60

240

90

EAST

270

WEST

120

300

150

330

180

SOUTH

0

NORTH

• Multi-channel Case:

• The primitive wind directions are along the Çanakkale Strait– more frequently wind is from NNE through SSW (called northerly)

– wind also blow from SSW through NNE (called southerly)

•

For the northerly winds the linear multichannel speed and destimate models works very well.– Because the selected two stations namely IPS, GON gives very high co

for the future BOZ speed value.



Re e able E e y Sy te Wi te S hool



•






• It is critically important to accurately forecast wind power generation for

and economic power system operation.• wind speed forecasting is a better approach than predicting wind power

directly.

• The reliable short-term (1-4 h ahead) wind speed prediction is an importchallenging problem for the future grid operations

• The challenge is to develop (propose) accurate, robust, simpler and

computationally efficient techniques to improve short-term forecast.

• The spatio-temporal (multichannel) methods give more accurate and relresults according to the temporal time correlation based methods

• In case study, inserting to 2 more stations (IPS, GON) improved the resul

– RMSE prediction error is 58% lower than the single channel time sermodel for the westerly winds

Conclusion





[1] Xie, L., Carvalho, P.M.S., Ferreira, L.A.F.M., Liu, J., Krogh, B., Popli, N. and Ilic, M.D., Wind energy integration in power soperational challenges and possible solutions, Proceedings of IEEE: Special Issue on Network Systems Engineering for MeetiEnergy and Environment Dream, 99 (2011) 214-232.,

[2] Xie, L., Gu, Y., Zhu, X., Genton, M. G., Short-term spatio-temporal wind power forecast in robust look-ahead power system

IEEE Transactions on Smart Grid, 5 (1), (2014) 511-520.

[3] Xinxin Zhu and M. G. Genton, Short-Term Wind Speed Forecasting for Power System Operations, International StatisticaVolume 80, Issue 1, pages 2–23, April 2012

[4] D. C. Hill, D. McMillan, K. R. W. Bell and D. Infield, Application of Auto-Regressive Models to U.K. Wind Speed Data foSystem Impact Studies, Sustainable Energy, IEEE Transactions on (Volume:3 , Issue: 1 )

[5J J. Dowell, S. Weiss, D. Hill and D. Infield, Short-term spatio-temporal prediction of wind speed and direction, Wind Ener

[6] Miao He, L. Yang, J. Zhang and V. Vittal, A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm GeIEEE Transactions on Power Systems, 2014

[7] J. Tastu, P. Pinson, P.J. Trombe and Henrik Madsen, Probabilistic Forecasts of Wind Power Generation Accounting forGeographically Dispersed Information, IEEE Transaction on Smart Grid, Vol. 5, No:1 January 2014

[8] F. Onur Hocaoglu, Omer Nezih Gerek and Mehmet Kurban, A novel wind speed modeling approach using atmospheric pobservations and hidden markov models, Journal of Wind Engineering and Industrial Aerodynamics, 98 (2010).

[9] J. Dowell, S. Weiss, D. Hill and D. Infield, A Cyclo-stationary complex multichannel wiener filter for the prediction of winand direction,

[10] Gneiting et al., Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime Switching Space TimMethod, Journal of American Statistical Association, 2006

[11] Xi i Zh K th P B d M G G t I ti t hi i d i f ti f i d

References:


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