1. Uncertainty in Wind Energy
Sustainable Engineering MSc Project
Uncertainty in Wind Energy
Yield Predictions
With Sgurr Energy
Robin Odlum
Sheikh M. Ali
Vijay Dwivedi
Antonio Sanchez 1

2. UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Our Project:
- To study the variation in correlation parameters for the Measure-Correlate-Predict (MCP) method between the wind speed data of pseudo wind farm site and meteorological site.
- A case study: Behaviour of power curve for a wind farm. 2

3. Data Acquisition / Processing Modelling (WAsP, Windfarm etc)
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
In predicting wind energy yield from a wind farm, there is uncertainty in:
Data Acquisition / Processing
Modelling (WAsP, Windfarm etc)
Losses in Energy Production
Long Term Prediction – MCP Method 3

4. Data Acquisition/Processing:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Data Acquisition/Processing:
Uncertainty arises from measuring instrument errors, wind shear, density correction etc.
High probability of human, systematic or random errors reduce the reliability of data
Research in this area is not of high interest to our group, so excluded from our project 4

5. Modeling (WAsP, Windfarm etc)
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Modeling (WAsP, Windfarm etc)
Use of assumptions in the modelling software reduces the reliability of energy yield prediction.
Research would require access to the source codes of the software and more resources, so excluded from this project. 5

6. Sorry, WIND TURBINE is Not Available Losses:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Losses:
There is uncertainty due to different types of losses in wind energy production on a wind farm like Wake losses, Turbine unavailability, Blade contamination etc.
Different factors are used to correct the energy output from a wind farm to make better prediction.
A detailed investigation could become commercially sensitive. However a brief case study is presented to illustrate the main issue.
Sorry,
WIND TURBINE is
Not Available 6

7. Long Term Prediction using MCP Method:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Long Term Prediction using MCP Method:
MCP (Measure Correlate Predict) is a statistical technique used for
predicting the long term wind resource at a target site. Wind speed
and direction measurements from a target and a reference site are
Reference Site
Target Site
correlated and the
correlation parameters
(m,c) are applied to long
term historic data of
reference site to predict
long term wind resource
at target site.
Wind Speed
Wind Direction
Wind Speed
Wind Direction
Correlation
Correlation Parameters:
slope m: it represents the change in velocity of target site with respect to the reference site
intercept c: it gives the velocity of target site when the velocity of reference site is zero 7

8. Long Term Prediction using MCP Method:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Long Term Prediction using MCP Method:
Different MCP techniques have been used giving different results. We found it interesting to research in the variation in correlation parameters with time.
It was a reasonably good area of research as:
We were interested in understanding the statistical nature of data
Sgurr Energy also showed its interest
Reference Site
Target Site
Wind Speed
Wind Direction
Wind Speed
Wind Direction
Correlation
Correlation Parameters:
slope m: it represents the change in velocity of target site with respect to the reference site
intercept c: it gives the velocity of target site when the velocity of reference site is zero 8

9. Uncertainty in Wind Energy Yield Predictions
Scope of Project
Uncertainty in Wind Energy Yield Predictions
Sources of Uncertainty
Data Acquisition
Long Term Prediction
Modelling
Losses in Energy Production

10. Uncertainty in Wind Energy Yield Predictions
Scope of Project
- 10 year ref site data, 1 year target site data
- Concurrent data gives Correlation parameters (m,c) applied to 10 years ref site data to predict next 10 years velocity
- Predict next 10 years energy
An Interesting Case Study Analyzing
Power Curve Performance
Uncertainty in Wind Energy Yield Predictions
Sources of Uncertainty
Data Acquisition
Traditional MCP Method
Long Term Prediction
Modified MCP Method
- 10 year ref site data, multiple years of target site data
- Concurrent data gives Correlation parameters (m,c) for each concurrent year
- Mean value of m and c is applied to 10 years ref site data to predict next 10 years velocity
- Predict next 10 years energy
Modelling
Losses in Energy Production

11. Traditional MCP Method
Study of Variation in Linear MCP Correlation Parameters (Linear Regression Parameters)
- 10 year ref site data, 1 year target site data
- Concurrent data gives correlation parameters (m,c) applied to 10 years ref site data to predict next 10 years velocity
- Predict next 10 years energy yield
Steps to carry out the Linear MCP parameters study:
Select the reference and target sites
Carry out linear regression analysis
Investigate the uncertainty in linear regression coefficients
Compare the energy output between conventional MCP method and modified MCP method
Traditional MCP Method
Modified MCP Method
- 10 years ref site data, multiple years of target site data
- Concurrent data gives correlation parameters (m,c) for each concurrent year
- Mean value of m and c is applied to 10 years ref site data to predict next 10 years velocity
- Predict next 10 years energy yield
Compare Predicted energy to Actual energy for the
next 10 years using the predicted and actual wind data
of pseudo wind farm for next 10 years
Results 11

12. Variation in Linear MCP ParametersB C D
Here we select any two met stations to take one as the reference site and other as the pseudo wind farm site. The purpose is to study the variation in correlation parameters for Measure-Correlate-Predict (MCP) method between the wind speed data of pseudo wind farm site and reference site. We have 20 years (1978~1997) wind data available for these met sites.

13. Variation in Linear MCP Parameters
We have started evaluating the variation in MCP parameters by
making four pairs from the given sites in the following fashion:
We make pairs of site on the basis of their terrain which are
classified as ‘complex’ or ‘flat’
Pair No
Met reference Site
Pseudo wind farm site
Terrain comparison 1
Blackford Hill
Turnhouse
Complex : Complex 2
Lynemouth
Flat : Complex 3
Complex : Flat 4
Dumfries
Flat : Flat 13

14. Regression Analysis 14

15. Flat-Flat site combination
MCP Parameters Study
Flat-Flat site combination
- Flat Reference Site - Flat Target Site
We can see clearly from this graph that the slope and intercept are varying with the passage of time. 15

16. MCP Parameters Study - Complex Reference Site - Complex Target Site
- Flat Reference Site - Complex Target Site 16

17. Modified MCP method predicts well in comparison with traditional one.
MCP Parameters Study
Modified MCP method predicts well in comparison with traditional one. 17

18. Significant improvement is there in 4th year.
MCP Parameters Study
Significant improvement is there in 4th year. 18

19. There is significant impact in assessment of
MCP Parameters Study
Complex Reference Site Complex Target Site
There is significant impact in assessment of
energy yield if the number of years are increased from
one to three or more for collection of wind data.
Flat Reference Site Complex Target Site
Complex Reference Site Flat Target Site
Flat Reference Site Flat Target Site 19

20. Scope of Project
- 10 year ref site data, 1 year target site data
- Concurrent data gives Correlation parameters (m,c) applied to 10 years ref site data to predict next 10 years velocity
- Predict next 10 years electrical energy
An Interesting Case Study Analyzing
Power Curve Performance
Uncertainty in Wind Energy Yield Predictions
Sources of Uncertainty
It is important to realise how vital long term wind prediction is today, with the
increasing diversification of energy production.
It has resulted in power companies investing millions of pounds on potential sites,
and made the accurate long-term wind prediction of these sites absolutely vital.
This case study using the data from a real wind farm will illustrate how the energy
output from the same site can vary dramatically from year to year and could deviate
from expected values.
Data Acquisition
Traditional MCP Method
Long Term Prediction
Our Modified MCP Method
Modelling
- 10 year ref site data, minimum 3 year target site data
- Concurrent data gives Correlation parameters (m,c) for each concurrent year
- Mean value of m and c is applied to 10 years ref site data to predict next 10 years velocity
- Predict next 10 years electrical energy
Losses in Energy Production

21. The Power Curve Performance
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Case Study:
The Power Curve Performance
Power curve performance is used to analyse the energy production in a wind farm.

22. Technical Details of the Wind Farm:
Case Study: A real wind Farm in UK
Technical Details of the Wind Farm:
Location : Northern Part of UK
Total Wind Turbines : 15
Cut in speed : 4 m/s
Rated Speed : 16 m/s
Cut out Speed : 25 m/s
Hub height : 40 m
Rating : 850kW

23. Unexpected Performance
Case Study: Performance of Wind Turbines kW kW
m/s
m/s
Expected Performance
Unexpected Performance

24. Power Curve Performance
Winter
During the first three months of the year, a turbine was showing an unexpected performance 24

25. Power Curve Performance
Summer & Spring
During summer & spring time, a turbine was showing expected performance 25

26. Power Curve Performance
Parameters under study:
Wind Direction
No wind direction data from wind farm
Wind speed
Predicted & Actual Power Output comparison
Alarm codes 26

27. Power Curve Performance
Turbine X has excess power in March, 2006 and power loss in March 2007
2006
2007 27

28. In March 2006, Turbine Y has excess power and power loss in March 2007
Power Curve Performance
In March 2006, Turbine Y has excess power and power loss in March 2007
2006
2007 28

29. Challenges in this Project:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
Challenges in this Project:
The real wind farm data has close to 1/3rd missing entries.
Missing and wrong entries in the weather data from the Met office.
Understanding statistics better to find good results 29

30. KEY FINDINGS: Variation in slope and intercept with respect to time.
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
KEY FINDINGS:
Variation in slope and intercept with respect to time.
Modified MCP method predicts well in comparison to traditional method.
There is significant impact in assessment of energy yield if the number of years are increased from one to three or more for collection of wind data.
Distance between met site and proposed wind farm site should be as small as possible in order to get better results.
The energy yield from a wind farm can vary dramatically from year to year. 30

31. UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
FUTURE WORK:
One can look into more than one met site to assess the wind energy yield of proposed wind farm.
A hybrid method could be thought of to predict the wind speed of proposed wind farm. For example for lower wind speed, linear regression and for higher wind speed a non-linear method such as quadratic regression or neural network method.
One can look into the effect of varying temperatures on electronic devices in a wind farm. 31

32. UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS