UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS

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:


• 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:


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


• 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)


•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:


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 SiteWind Speed

Wind DirectionWind Speed

Wind 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


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 SiteWind Speed

Wind DirectionWind Speed

Wind 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

Spe

ed, m

/s (

Lyne

mou

th,

pred

icte

d) Y_1979Y_1983Y_1984Y_1985Y_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


Data Acquisition

Long Term Prediction

Modelling

Losses in Energy Production

Sources of Uncertainty

Scope of Project


Data Acquisition


Modelling



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)


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 Spe

ed, m

/s (Ly

nem

outh

, pr

edic

ted)

Y_1979Y_1983Y_1984Y_1985Y_1986


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

(Tu

rnho

use

Pre

dict

ed)

- 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_1978Y_1979Y_1982Y_1984Y_1985


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.


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


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.


Data Acquisition


Modelling




- 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.


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


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:


2007

Turbine X has excess power in March, 2006 and power loss in March 2007

2006


In March 2006, Turbine Y has excess power and power loss in March 2007

2006 2007


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



• 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:



Documents

UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS