1. The document studies various wind measurement strategies combining met masts and lidars to optimize reducing uncertainties in wind resource assessment and annual energy yield estimation for wind farm projects. 2. Several measurement system combinations are modeled, including different heights and durations of met mast deployment and multiple lidar deployments both fixed and seasonally moved. 3. The results show that strategies using lidars away from met masts and with seasonal movement of lidars provide the highest reduction in estimated annual energy yield uncertainty and result in the largest decreases in required equity investment and increases in project internal rate of return.

1. Wind Measurement Strategies
to Optimize Lidar Return on Investment
Matthieu BOQUET1, Karin GÖRNER2, Kai MÖNNICH2
1LEOSPHERE (mboquet@leosphere.fr), 2DEWI (k.moennich@dewi.de)
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, drive 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 an 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.
Wind Resource Assessment and Estimation of AEP Uncertainty
Strategy Description Comment M060 M080 M100
M060 60m met mast 40m vertical difference to hub height D D D
No lidar 15.4% 14.3% 13.8%
M080 80m met mast 20m vertical difference to hub height D C A
M100 100m met mast Measurement at hub height D D
Lc1 14.6% 13.9% N/A
Reduction of vertical extrapolation C B
1 Lidar position close to
Lc1
the mast
uncertainty at mast position (in C C
combination with M060 or M080) Lc1Laf1 13.8% 13.1% N/A
C B
X lidar position(s) away Reduction of horizontal B B
LafX
from the mast and fixed extrapolation only Lc1Laf2 13.3% 12.5% N/A
Reduction of horizontal and vertical
C B
X lidar position(s) away extrapolation in the same time A A
Lc1Laf3 12.9% 12.1% N/A
LasX from the mast and intra- (representative wind profile at lidar C B
seasonally moved positions due to coverage of all C C C
seasons). Las1 13.2% 12.9% 12.7%
C B A
Hypothesis: Las2 12.5%
B
12.3%
B
12.1%
B
•Masts are fixed for the complete 1 year of assessment. B A A
•Lidar measurement period of 3 successive months for Legend: A A A
Las3 11.9% 11.8% 11.7%
B A A
fixed locations and 4 months for the “moving”
instrument (1 month per season) Uncertainty
The uncertainty of an energy yield calculation has been Legend: Class
Definition
determined with WAsP using the following: horizontal low A
A theoretical, but realistic wind farm layout with 41 wind •Measurement uncertainty mast: 2.0% (IEC conform) overall EY uncertainty medium-low B
turbine slots embedded in a medium-complex terrain •Measurement uncertainty lidar: 2.0% (pulsed lidar, uncertainty vertical medium-high C
has been used for the study. Mast and lidar positions following installation best practices) uncertainty high D
were in about 4 km distances to gain an optimum •Uncertainty in long-term correlation of mast: 3.9%
coverage of the wind farm area. •Average sensitivity factor (dE/dv): 1.85
Economic Balance: cost of the measurement strategies and resulting bank loan
The total costs of operation (TCO) of the strategies are The financial analysis is the study of debt size and M060 M080 M100
calculated with the instruments mean market value and equity investment with which the developer will cover No lidar 41.1 M€
73%
38.8M€
74.5%
37.7M€
75.2%
are summarized in the table below: the wind farm costs. This leverage effect, as well as the 12.5% 12.7% 12.8%
74% 75%
resulting Internal Rate of Return (IRR), are the very Lc1 39.6 M€ 12.6%
38 M€ 12.8%
N/A
M080-Lc1Laf1
M060-Lc1Laf1
M080-Lc1Laf2
M060-Lc1Laf2
M080-Lc1Laf3
M060-Lc1Laf3
important metrics the developer is willing to increase at 75.2% 76.2%
M100-Las1
M080-Las1
M060-Las1
M100-Las2
M080-Las2
M060-Las2
M100-Las3
M080-Las3
M060-Las3
Lc1Laf1
M080-Lc1
M060-Lc1
37.7 M€ 36.3 M€ N/A
strategy
most. The following financial parameters are used: 12.8% 13%
M100
M080
M060
75.8% 77%
Economics related to wind farm Lc1Laf2 36.9 M€ 12.9%
35 M€ 13.1%
N/A
Wind farm Total rated power 102.5 MW 76.5% 77.5%
P50 equivalent hours/NCF 2453 hours (28%)
Lc1Laf3 35.8 M€ 13%
34.3 M€ 13.2%
N/A
Capex (material and construction costs included) 1.45 M€/MW 76% 76.5% 76.8%
TCO
(k€)
101
119
105
114
102
134
123
109
Las1 36.6 M€ 35.8 M€ 35.3 M€
57
45
32
65
52
83
70
87
92
80
67
88
Revenue (for 15 years, 60€/MWh after ppa) 80 €/MWh 12.9% 13% 13.1%
O&M (15% of revenue) 12 €/MWh 77% 77.3% 77.5%
Las2 35 M€ 13.1%
34.6 M€ 13.2%
34.3 M€ 13.2%
Further assumptions: The “on site move” cost is used Inflation 2%
77.8% 78% 78.3%
when the lidar is moved from one location to another GWh price inflation (% of inflation) 60% Las3 33.8 M€ 13.3%
33.5 M€ 13.3%
33.1 M€ 13.4%
Tax rate 33%
within the same project site. If the lidar is necessary Wind farm life time 20 years The table above summarizes the Legend:
less than a year on the same site, it is sent to another Interest rate 6.5%
equity investment, debt size and IRR Debt
project to get a full year use of the instrument. The Debt period 15 years
resulting from the different projects
Equity
size
Banks require a DSCR of 1.2 with P90 revenue
installation and dismantlement costs are then applied. due diligence.
Investment
IRR
Best Measurement Strategies
The best measurement strategies is here be defined as Adding a highly accurate and mobile measurement
the highest reduction of equity investment with the system, like here a pulsed lidar, in a energy yield
lowest operational costs (highest RoI). Results, as assessment has a high RoI, increasing the wind farm
shown on the graph beside, are: value and considerably decreases the developer
financial effort.
• Lidar away from mast has higher RoI than close
References
• Every new location increases the equity savings, the
1. M. Boquet & al., “Return on Investment of a Lidar Remote Sensing Device”, DEWI
RoI however decreases with increasing number of Magazine, pp.56 to 61
locations 2. Iain Campbell & al., “A Comparison of Remote Sensing Device Performance at Rotsea
site”, RES Group
• Seasonal moves have higher RoI than fixed locations 3. A. Albers & al., “Comparison of Lidars, German Test Station For Remote Wind
• Equity savings with lidar can reach 9 millions of Euros! Sensing Devices”, Deustsche Windguard GmbH
4. M. Strack, W. Winkler, “Analysis of Uncertainties in Energy Yield Calculation of Wind
Farm Projects“, DEWI Magazine No. 22 (2003), pp. 52 to 62
CANWEA 2011, Vancouver