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Uncertainty in Wind Energy Yield Predictions
With Sgurr Energy
Sustainable Engineering MSc Project
• Robin Odlum• Sheikh M. Ali• Vijay Dwivedi• Antonio Sanchez
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
- 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.
Our Project:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
• Data Acquisition / Processing• Modelling (WAsP, Windfarm etc)• Losses in Energy Production• Long Term Prediction – MCP Method
In predicting wind energy yield from a wind farm, there is uncertainty in:
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 orrandom errors reduce the reliability of data
• Research in this area is not of high interest to our group, so excluded from our project
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
• 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.
Modeling (WAsP, Windfarm etc)
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
•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.
Losses:
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 speedand direction measurements from a target and a reference site are
correlated and thecorrelation parameters(m,c) are applied to longterm historic data ofreference site to predictlong term wind resourceat target site.
Reference Site
Target Site
Wind SpeedWind Direction
Wind SpeedWind Direction
Correlation Parameters:slope m: it represents the change in velocity of target site with respect to the reference siteintercept c: it gives the velocity of target site when the velocity of reference site is zero
Correlation
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.
Reference Site
Target Site
Wind SpeedWind Direction
Wind SpeedWind Direction
Correlation Parameters:slope m: it represents the change in velocity of target site with respect to the reference siteintercept c: it gives the velocity of target site when the velocity of reference site is zero
Correlation
Plot of Wind Speed for Lynemouth (pseudo wind farm) and Dumfries (met site)
0
5
10
15
20
25
30
35
0 5 10 15 20 25
Wind Speed, m/s (Dumfries, met site)W
ind S
peed,
m/s
(Lyn
em
outh
, pre
dic
ted) Y_1979
Y_1983
Y_1984
Y_1985
Y_1986
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
Uncertainty in Wind Energy Yield Predictions
Data Acquisition
Long Term Prediction
Modelling
Losses in Energy Production
Sources of Uncertainty
Scope of Project
Uncertainty in Wind Energy Yield Predictions
Data Acquisition
Long Term Prediction
Modelling
Losses in Energy Production
Sources of Uncertainty
Traditional MCP Method
- 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
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. . . . .
An Interesting Case Study AnalyzingPower Curve Performance
Scope of Project
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
Study of Variation in Linear MCP Correlation Parameters (Linear Regression Parameters)
Traditional MCP Method
Modified MCP Method
- 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
- 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 dataof pseudo wind farm for next 10 years
Results
AB
CD
Name Location Latitude Longitude
1634-01 Turnhouse Near Edinburgh Airport 55.95 -3.35 Complex1646-01 Edinburgh 55.93 -3.19 Complex
2083-01 Lynemouth 55.2 -1.54 Flat
6620-02 Dumfries 55.05 -3.64 Flat
Met Station Number
Terrain category
Blackford HillVillage in
Northumberland, England
Drungans
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.
Variation in Linear MCP Parameters
We have started evaluating the variation in MCP parameters bymaking four pairs from the given sites in the following fashion:
We make pairs of site on the basis of their terrain which areclassified as ‘complex’ or ‘flat’
Pair No Met reference Site Pseudo wind farm site Terrain comparison
1 Blackford Hill Turnhouse Complex : Complex
2 Lynemouth Turnhouse Flat : Complex
3 Turnhouse Lynemouth Complex : Flat
4 Dumfries Lynemouth Flat : Flat
Variation in Linear MCP Parameters
Regression Analysis
MCP Parameters Study
We can see clearly from this graph that the slope and intercept are varying with the passage of time.
Flat-Flat site combination
- Flat Reference Site- Flat Target Site
Plot of Wind Speed for Lynemouth (pseudo wind farm) and Dumfries (met site)
0
5
10
15
20
25
30
35
0 5 10 15 20 25
Wind Speed, m/s (Dumfries, met site)
Win
d Sp
eed,
m/s
(Lyn
emou
th,
pred
icte
d)
Y_1979
Y_1983
Y_1984
Y_1985
Y_1986
MCP Parameters Study
0 5 10 15 20 25
0
2
4
6
8
10
12
14
16
Plot of Wind Speed for Turnhouse (pseudo wind farm)
and BlackfordHill (met site)
Y_1979
Y_1981
Y_1983
Y_1984
Y_1986
Wind Speed, m/s (Blackford Hill met site)
Win
d S
peed,
m/s
(T
urnh
ous
e P
redic
ted)
- Complex Reference Site- Complex Target Site
- Flat Reference Site- Complex Target Site
Plot of Wind Speed for Turnhouse (pseudo wind farm) and Lynemouth (met site)
0
2
4
6
8
10
12
14
16
0 5 10 15 20 25
Wind Speed, m/s (Lynemouth met site)
Win
d S
peed
, m/s
(Tur
nhou
se
Pre
dict
ed)
Y_1978
Y_1979
Y_1982
Y_1984
Y_1985
MCP Parameters Study
Estimation of Deviation in Energy output for various Terrain Catagories
23%
31%25%
8%
31%
52%
58%
23%
0%
65%
Flat-Flat Complex-Flat Flat-Complex Complex-Complex
Deviation in energy outputbased on modified MCPmethodDeviation in energy outputbased on traditional MCPmethod
Benchmark data for energy output
Modified MCP method predicts well in comparison with traditional one.
MCP Parameters Study
Significant improvement is there in 4th year.
Uncertainty in Estimation of Energy output for Flat-Complex Terrain Category by Traditional/Modified
MCP Method
58%53%
39%
31%
25%20%
30%
40%
50%
60%
1 2 3 4 5
Year
MCP Parameters Study
Complex Reference Site Complex Target Site
Flat Reference SiteComplex Target Site
Complex Reference SiteFlat Target Site
Flat Reference SiteFlat 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.
Uncertainty in Wind Energy Yield Predictions
Data Acquisition
Long Term Prediction
Modelling
Losses in Energy Production
Sources of Uncertainty
Traditional MCP Method
- 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
Our Modified MCP Method
- 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. . . . .
An Interesting Case Study AnalyzingPower Curve Performance
Scope of Project
It is important to realise how vital long term wind prediction is today, with theincreasing 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.
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
The Power Curve Performance
Power curve performance is used to analyse the energy production in a wind farm.
Case Study:
Case Study: A real wind Farm in UK
• 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
Technical Details of the Wind Farm:
Case Study: Performance of Wind Turbines
Expected Performance Unexpected Performance
kWkW
m/s m/s
Winter
During the first three months of the year, a turbine was showing an unexpected performance
Power Curve Performance
Summer & Spring
Power Curve Performance
During summer & spring time, a turbine was showing expected performance
• Wind Direction No wind direction data from wind farm
• Wind speed • Predicted & Actual Power Output comparison• Alarm codes
Parameters under study:
Power Curve Performance
2007
Turbine X has excess power in March, 2006 and power loss in March 2007
2006
Power Curve Performance
In March 2006, Turbine Y has excess power and power loss in March 2007
2006 2007
Power Curve Performance
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
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
• 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.
KEY FINDINGS:
• 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 temperatureson electronic devices in a wind farm.
FUTURE WORK:
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS
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