Transcript Document
Sustainable Engineering MSc Project
Uncertainty in Wind Energy Yield Predictions With Sgurr Energy
• • • •
Robin Odlum Sheikh M. Ali Vijay Dwivedi Antonio Sanchez
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.
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
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
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.
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.
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 correlated and the correlation parameters
Reference Site Wind Speed Wind Direction Target Site Wind Speed Wind Direction
(m,c) are applied to long term historic data of reference site to predict long term wind resource
Correlation slope m: it represents the change in velocity of target site with respect to the reference site intercept c: Correlation Parameters:
at target site.
it gives the velocity of target site when the velocity of reference site is zero
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 It was a reasonably parameters with time.
and Dumfries (met site) as: 35 We were interested in 30
Reference Site Wind Speed Wind Direction Target Site
15 Sgurr Energy also 10
Wind Speed Wind Direction
showed its interest 5 0 0 5 10 Y_1979 Y_1983 Y_1984 Y_1985 Y_1986 15 20 25 Wind Speed, m/s (Dumfries, met site)
Correlation slope m: intercept c: Correlation Parameters: it represents the change in velocity of target site with respect to the reference site it gives the velocity of target site when the velocity of reference site is zero
Scope of Project
Uncertainty in Wind Energy Yield Predictions
Sources of Uncertainty Data Acquisition Long Term Prediction Modelling Losses in Energy Production
Scope of Project
Uncertainty in Wind Energy Yield Predictions
Sources of Uncertainty Data Acquisition Long Term Prediction Modelling Losses in Energy Production An Interesting Case Study Analyzing Power Curve Performance 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 target site data parameters (m,c) for each concurrent year years ref site data to predict next 10 years velocity
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
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 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 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
Variation in Linear MCP Parameters Met Station Number
1634-01 1646-01 2083-01 6620-02
D A Name
Turnhouse Edinburgh Lynemouth Dumfries
B C 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.
Location
Near Edinburgh Airport Blackford Hill Village in Northumberland, England Drungans
Latitude Longitude Terrain category
55.95
55.93
-3.35
-3.19
Complex Complex 55.2
55.05
-1.54
-3.64
Flat Flat
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 1 2 3 4
Blackford Hill Lynemouth Turnhouse Dumfries
Pseudo wind farm site
Turnhouse Turnhouse Lynemouth Lynemouth
Terrain comparison
Complex : Complex Flat : Complex Complex : Flat Flat : Flat
Regression Analysis
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.
35 30 25 20 15 10 Plot of Wind Speed for Lynemouth (pseudo wind farm) and Dumfries (met site) Y_1979 Y_1983 Y_1984 Y_1985 Y_1986 5 0 0 5 10 15 20 Wind Speed, m/s (Dumfries, met site) 25
MCP Parameters Study
Plot of Wind Speed for Turnhouse (pseudo wind farm) and BlackfordHill (met site) 16 14 12 10 8 6 4 2 0 0 5 10 15 20 Wind Speed, m/s (Blackford Hill met site) 25 Y_1979 Y_1981 Y_1983 Y_1984 Y_1986
- Complex Reference Site - Complex Target Site
Plot of Wind Speed for Turnhouse (pseudo wind farm) and Lynemouth (met site) 16 6 4 2 0 0 14 12 10 8 5 10 15 20 Wind Speed, m/s (Lynemouth met site) 25 Y_1978 Y_1979 Y_1982 Y_1984 Y_1985
- Flat Reference Site - Complex Target Site
MCP Parameters Study
65% Estimation of Deviation in Energy output for various Terrain Catagories 52% 58% Deviation in energy output based on modified MCP method Deviation in energy output based on traditional MCP method 31% 31% 25% 23% 23% 8% 0% Benchmark data for energy output Flat-Flat Complex-Flat Flat-Complex Complex Complex M odified MCP method predicts well in comparison with traditional one.
MCP Parameters Study
Uncertainty in Estimation of Energy output for Flat Complex Terrain Category by Traditional/Modified MCP Method 60% 58% 50% 53% 40% 39% 30% 31% 20% 1 2 3
Year
4 Significant improvement is there in 4 th year.
5 25%
MCP Parameters Study Complex Reference Site Complex Target Site Flat Reference Site
There is significant impact in assessment of energy yield if the number of years are increased from
Flat Target Site Flat Reference Site Flat Target Site
Scope of Project
Uncertainty in Wind Energy Yield Predictions
Sources of Uncertainty
An Interesting Case Study Analyzing Power Curve Performance
- 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 Data Acquisition It is important to realise how vital long term wind prediction is today, with the Traditional MCP Method increasing diversification of energy production. Long Term Prediction 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.
Modelling Our Modified MCP Method 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 Losses in Energy Production from expected values.
. . . . .
target site data parameters (m,c) for each concurrent year years ref site data to predict next 10 years velocity
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.
Case Study: A real wind Farm in UK
Technical Details of the Wind Farm:
• • • • • • • Location Total Wind Turbines Cut in speed Rated Speed Cut out Speed Hub height Rating : Northern Part of UK : 15 : 4 m/s : 16 m/s : 25 m/s : 40 m : 850kW
Case Study: Performance of Wind Turbines
kW kW m/s
Expected Performance
m/s
Unexpected Performance
Power Curve Performance
Winter
During the first three months of the year, a turbine was showing an unexpected performance
Power Curve Performance
Summer & Spring
Power
900 800 700 600 500
kW
400 300 200 100 0 0 P.Output
P.expect
200 400 600
Hours
800 1000 1200 1400
During summer & spring time, a turbine was showing expected performance
Power Curve Performance
Parameters under study:
• Wind Direction No wind direction data from wind farm • Wind speed • • Predicted & Actual Power Output comparison Alarm codes
Power Curve Performance
Turbine X has excess power in March, 2006 and power loss in March 2007
2006 2007
Power Curve Performance
In March 2006, Turbine Y has excess power and power loss in March 2007
2006 2007
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
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.
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.
UNCERTAINTY IN WIND ENERGY YIELD PREDICTIONS