In order to calculate the future Annual Energy Production (AEP) of a potential wind power project, a three-step methodology is normally used to calculate the long-term wind speed of a proposed wind farm site.
The AEP of the proposed wind power station can then be determined using this long-term data set, in combination with wind flow tools (like WAsP or CFD programs), wind turbine power curves, thrust curves, electrical layouts and so on.
But the first task is to establish the long-term wind resource at the proposed site, a calculation often based on the Measure, Correlate and Predict (MCP) methodology.
The three steps of the methodology are briefly described below, with our major focus on the second step, namely the correlation process:
- Measure wind speed at the site and weather stations simultaneously (over a period of approximately 12-months)
- Correlate the data sets to find a relationship between the two short-term data sets (site and weather station)
- Predict long-term site wind resource with the long-term data set from the weather station together with the established relationship between the weather station and site data to calculate the long-term site wind resource.
The correlation process (Step 2)
Approximately one year of wind resource data, simultaneously measured at the proposed wind farm site and at a suitable weather station is correlated to establish a relationship between the two data sets.
A long-term site record can then be calculated based on this mathematical relationship using the long-term wind resource record of the weather station as input data.
The resultant long-term wind speed data set for the proposed wind farm site forms the basis of the AEP calculations.
There are several mathematical functions that can be used to describe the relationship between the “met” station data and the site data:
- A numerical analysis typically using straight lines to depict the relationship.
- An analytical analysis uses a mathematical function that describes the physical properties of the wind – this is the SKM methodology that connects the correlation function to IEC 61400.
Numerical analysis
The objective of a correlation analysis is to identify a mathematical function that describes the relationship between the wind characteristics at the meteorological site and the proposed wind farm site. It is noted that this is different from “finding a curve that fits the measured data best”. The curve that describes the relationship should be able to forecast site wind speed values from weather station data that has not been included in the establishment of the correlation curve – it should not just provide the highest “goodness of fit” relationship.
Analytical analysis – describing the wind
The IEC 61400 series of standards describe the wind resource at sites with Weibull distribution functions; these functions can be used to model a relationship between site and weather station .
These Weibull functions are widely accepted in the wind industry, with numerous papers that depict the distribution of wind with Weibull distribution curves, confirming their ability to describe a wind resource and establish a correlation between measured “met” station data and site data.
Correlation using Weibull curves
The SKM correlation methodology is based on IEC 61400, which describes the wind as a Weibull curve.
The wind resource at both the “met” station and the site can be described in terms of cumulative distribution functions that describe the cumulative wind speed distribution at the site and weather station1.
The correlation analysis establishes a one-to-one relationship between weather station data and site data, with the two Weibull functions being used to describe the relationship between the meteorological wind speed and the site wind speed.
By taking Weibull curves from both weather station and site data, verifiable and transparent correlation curve is produced based on simultaneous measurements. This approach is consistent with the IEC 61400 set of standards.
Verifying the results
Rigorous analysis using ‘prediction and observation comparison techniques’ of the different correlation methodologies verifies the Weibull correlation methodology.
The original measured data file containing synchronized weather station and site data has been randomly split forming two separate data sets. Set 1 was used to establish all three correlation curves (prediction). Set 2, which like Set 1 contains synchronised site and weather station data, was used for the evaluation of the three correlation curves (observation).
The weather station data from Set 2 was used together with the three different correlation functions to calculate three different synthesised site wind speed data sets. These sets were then compared with the actual site data from Set 2 – comparing prediction with observed values.
The comparison of the synthesized/predicted data set and the actual/observed data set provides a measure of how well the correlation functions describe the measured data, but more importantly is the comparison of the energy production based on the predicted wind data with the energy production based on the observed wind data.
During the verification the process was repeated to construct 40 sets of four wind speed traces (40 random Set1 data sets used to predict the correlation curves and Set2 data sets resulting in 40 sets of one actual (observational) data and three synthesised wind speed data sets based on the different correlation curves). These wind speed data sets (observed and predicted) were than translated to AEP values using a wind turbine generator (WTG) power curve.
The AEP of the actual (observed) dataset was then normalised to one (straight vertical black line in Graph 1 below).
Graph 1 – AEP distribution
The mean AEP value and associated standard deviation based on the three correlation curves are shown as Gaussian distribution curves (RED based on the Weibull correlation curve, GREEN based on the straight line forced through zero correlation and BLUE based on the straight line correlation).
It shows that the AEP based on the Weibull distribution curve is closest to the actual AEP value thus validating this methodology.
Conclusion
In assignments to calculate AEP values for developers, investors and financial institutions for both on-shore and offshore projects, SKM has found that the above Weibull correlation methodology yields a higher accuracy and associated lower uncertainty whenever the site and particularly the weather station data was of high enough quality.
This has been valid for on-shore projects with different topographical and environmental characteristics as well as off-shore projects.
Note: This article is a summary of the technical paper, Improvements in AEP calculations using IEC 61400.
1 IEC 61400-1 section 3.63, Wind turbines - Part 1: Design requirements, International Electrotechnical Commission (IEC), 2005.
© Sinclair Knight Merz
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Who does this affect?
Those with an interest in wind power stations.
What do I need to do?
Understand the methodology used to calculate Annual Energy Production (AEP) for wind farms.
Author: Paul van Lieshout
Paul van Lieshout, Wind Power Practice Leader, has been involved in wind farm developments for 25 years. During his career he has completed assignments in the UK, New Zealand, Australia the Americas and Europe with an increasing emphasis on offshore wind farms
© Sinclair Knight Merz
Requests to re-publish achieve articles should be made here