Wind Measurement Strategies
to Optimize Lidar Return on Investment
1
2
Matthieu Boquet , Karin Görner , Kai Mönnich
2
1
LEOSPHERE SAS, 14-16 avenue Jean Rostand, 91400 Orsay, France, [email protected]
2
DEWI GmbH, Kasinoplatz 3, 26122 Oldenburg, Germany, [email protected]
Abstract
Understanding the wind resource at a prospective project site has long been considered a
critical step in the wind farm development process, and therefore wind resource experts
have become more and more sophisticated in performing the assessment of the wind
resource. The data collected from a wind resource assessment program, and the accuracy
of that data, drives the success of the wind farm project.
In the context of the constant aim to reduce project uncertainties through the design of their
wind resource estimate campaigns, consultants make use of new measurement
technologies and methods of analyzing data. Though the combination of met masts and
lidars is one approach that is gaining traction, a remaining question is which combination
strategy must be applied to reach greatest uncertainties reduction at reasonable operational
costs.
Methodology
In this paper, DEWI and LEOSPHERE propose to study various wind measurement
strategies on a representative wind farm site. Several measurement system combinations
are proposed, including met masts of different heights and lidar devices, located at one or
several locations for varying duration and seasonal periods. The resulting uncertainties on
the annual energy yield estimation are calculated and compared.
Based on a wind farm business case, the results are also shown on a financial level, taking
into account the operational costs, leveraging effect, equity investment and Internal Rate of
Return from the developer perspective. The best measurement strategies are highlighted
according to a higher Return on Investment of the costs involved in the assessment of the
wind resource.
Site description
A theoretical, but realistic wind farm layout with 41 wind turbine positions embedded in a
medium-complex terrain with an elevation ranging from 0 – 90 m has been used for the
study. Mast and lidar positions were assumed in about 4 km distances to gain an optimum
coverage of the wind farm area.
Lidar measurements, when placed away from the mast measurement, are useful to gain
additional information about the spatial distribution of the wind resource and help to reduce
model calculation uncertainty with respect to horizontal extrapolation. A lidar placed close to
the mast can reduce the uncertainty in vertical extrapolation, if the mast height is lower than
the planned hub height.
When considering site-specific conditions, a well-planned combined measurement strategy
can therefore lower the annual energy yield estimation within a study.
Figure 1 Site layout with turbines, mast and lidar positions
Measurement strategies
Different measurement strategies comprising mast and lidar system(s) in several
combinations as defined in the following table have been studied for a wind farm with
planned hub height of 100 m.
Positions of masts are fixed at one location for the complete 1 year of assessment. For costeffectiveness reasons lidar systems are usually measuring only a few months at one
location and site. But their mobility could be used to move them on site several times during
the 1 year period. Two different strategies of moving a lidar system on-site have been
considered and following lidar measurement periods have been assumed in the study:
•
•
Measuring fixed at one location during 3 successive months
Measuring at one location 1 month in each season (in sum 4 months)
In the second case the lidar measurement is more representative regarding wind speed
distribution and wind profile compared to a 1 year measurement period (assuming MidEuropean regions) and therefore more suitable for e.g. time series correlation.
Table 1
The strategy M080Lc1Laf2 means having an 80m met mast with a lidar being positioned
close to the mast for one period, and then positioned away at two locations during
successive periods.
The strategy M100Las3 means having a 100m met mast with a lidar being located away
from the mast at 3 different locations. The lidar is moved within the seasons to probe every
location once in each season.
Energy Yield Assessment Uncertainties
Based on the theoretical layout the uncertainty of an energy yield calculation (with WASP)
has been determined assuming the following:
• Measurement uncertainty mast: 2.0% (high quality, IEC and IEA conform)
• Measurement uncertainty lidar: 2.0% (WINDCUBE®v2 lidar, following installation
best practices)
• Uncertainty in long-term correlation of mast: 3.9%
• Average sensitivity factor (dE/dv): 1.85
Main focus is given on the uncertainty reduction of horizontal and vertical extrapolation
assuming additional available wind data information of same quality as mast measurement
data for energy yield modeling. It has to be noted that the remote sensing device uncertainty
can vary among the different types of sensor and according to the site complexity. The
device used here is a WINDCUBE®v2 lidar which uncertainty has universally and
independently been assessed by several wind experts ([3], [4], [7]), and is here taken same
as well calibrated cups.
Furthermore, uncertainties resulting from time series correlation with mast data or seasonal
correction of a short-term measured wind profile during 3 successive months are sitespecific and have been assumed here as low. This study is aiming at gaining sensitivity on
the uncertainty reduction by using a lidar, and accumulation of on-site measurement
experiences will help refine these uncertainties reductions.
Different uncertainty values were determined for the vertical and horizontal extrapolation
uncertainty for each measurement strategy. The results are summarized in the table below:
Table 2
Costs of the Measurement Strategies
Operational costs for conducting the different strategies are here studied with the following
mean market values in 2010.
Table 3
Further assumptions:
•
Lidar price includes options like safety and enhanced communication skills etc…
•
The “on site move” cost is used when the lidar is moved from one location to another
within the same project site. It mainly considers the working time on-site. If the lidar
is necessary less than a year on the same site, it is sent to another project to get a
full year use of the instrument. The installation and dismantlement costs are then
applied.
•
Amortization of masts and lidar is 3 years
strategy
M100
M080
M060
M080-Lc1
M060-Lc1
M080-Lc1Laf1
M060-Lc1Laf1
M080-Lc1Laf2
M060-Lc1Laf2
M080-Lc1Laf3
M060-Lc1Laf3
M100-Las1
M080-Las1
M060-Las1
M100-Las2
M080-Las2
M060-Las2
M100-Las3
M080-Las3
M060-Las3
TCO (k€)
57
45
32
65
52
83
70
101
87
119
105
92
80
67
114
102
88
134
123
109
Finally the total costs of operation (TCO) of the strategies are summarized below:
Table 4
Financial Analysis
The financial analysis is the study of debt size and equity investment with which the
developer will cover the wind farm costs. This leverage effect, as well as the resulting
Internal Rate of Return (IRR), are the very important metrics the developer is willing to
increase at most. The variation of leverage effect is studied with the AEP uncertainties
previously calculated and with the following financial parameters:
Table 5
The table below summarizes the equity investment, debt size and IRR resulting from the
different projects due diligence calculated with a financial model developed by Eurowatt
Services, France:
Table 6
This table shows for instance that from an 80m met mast alone to an 80m met mast with a
lidar planned at three locations and seasonally moved among these locations, the developer
has saved 5.3M€ of equity investment through the reduction of the investment risks, when
the wind farm project is presented to banks for obtaining the loan.
Best Measurement Strategies
The best measurement strategies can be defined as the highest reduction of equity
investment with the lowest operational costs (highest RoI). On the graph below, the 60m
mast is taken as reference and strategies are shown as further operational costs (x-axis)
and equity savings (y-axis) in comparison with the reference:
Figure 2
There are some financial conclusions that can here be drawn:
Lidar away from mast has higher RoI than lidar close to the mast
Every new location increases the equity savings, the RoI however decreases with
increasing number of locations
Lidar with seasonal moves has higher RoI than lidar at fixed locations
The equity savings can reach several millions of Euros with an extra expenditure of a few
tens of thousands Euros invested in the energy yield assessment campaign.
Conclusion
DEWI & LEOSPHERE study confirms that adding a highly accurate and mobile
measurement system, like the certified WINDCUBE®v2 lidar, in an energy yield assessment
leads to lower modeling uncertainties compared to having only one mast on site, or,
compared to several masts on site to lower costs for the measurement campaign. Using a
WINDCUBE®v2 in the assessment of the energy yield has therefore a high Return on
Investment: it increases the wind farm value and considerably decreases the developer
financial effort, saving millions of Euros in its equity investment.
References
1. M. Boquet & al., “Return on Investment of a Lidar Remote Sensing Device”, DEWI
Magazine #37, sept. 2010, pp.56 to 61
2. D. Faghani & al., Helimax - GL Garrad Hassan. “Remote Sensing: Practical Use for a
Wind Power Project”. AWEA Wind Resource Assessment Workshop 2009.
3. I. Campbell & al., “A Comparison of Remote Sensing Device Performance at Rotsea
site”, Document Reference: 01485-000090 Issue: 05 – Approved, RES Group
4. A. Albers & al., “Comparison of Lidars, German Test Station For Remote Wind Sensing
Devices”, Deutsche Windguard GmbH
5. M. Strack, W. Winkler, “Analysis of Uncertainties in Energy Yield Calculation of Wind
Farm Projects“, DEWI Magazine No. 22 (2003), pp. 52 to 62
6. C. Bezault et al., “Sensitivity of the CFD based LIDAR correction”, Meteodyn, EWEA
2011
7. D. Faghani & al., “LiDAR Validation in Complex Terrain”, GL Garrad Hassan, EWEA
2011