Micrositing wind flow modeling presents one of the most relevant uncertainties in the project of wind power plants. Studies in the area indicate that the average uncertainty related to this item varies between 2.4% and 8% of the annual energy production (AEP). The most efficient form to mitigate this uncertainty is to obtain additional measurements from the site. This can be achieved by installing met masts and by applying short-term remote sensing campaigns (LIDAR and SODAR). Ideally, measurement campaigns should have at least one complete year of data to capture seasonal changes in the local wind behavior and to increase the long-term representation of the sample. However, remote sensing is frequently performed in reduced periods of measurement, coming down to months or even weeks of campaign. The main contribution of this paper is to analyze whether short-term remote sensing measurements contribute to the development of wind power projects, given the associated uncertainties due to low representativeness of the reduced data sample. This study was performed using over 60 years of wind measurement data. Its main findings indicate that the contribution of short-term remote sensing campaigns vary depending on the complexity of the local terrain, and the respective uncertainty related to horizontal and vertical extrapolation of micrositing models. The results showed that in only 30% of the cases, a 3 month measurement campaign reduced the projects overall uncertainty. This number increases to 50% for a 6 month campaign and 90% for a 10 month campaign.

References

References
1.
Global Wind Energy Council
,
2016
, “
GWEC—Global Wind Energy Council
,” Global Wind Energy Council, Brussels, Belgium, accessed Jan. 31, 2019, http://www.gwec.net/global-figures/graphs/
2.
Bailey
,
B. H.
,
2015
, “
The Financial Impact of Uncertainty on Energy Projections
,”
AWEA WINDPOWER Conference
,
Orlando, FL
,
May 18–21
.
3.
Lira
,
A. G.
,
Rosas
,
P. A. C.
,
Araújo
,
A. M.
, and
Castro
,
N. J.
,
2016
, “
Uncertainties in the Estimate of Wind Energy Production
,”
Energy Economics Iberian Conference—EEIC
,
Lisboa, Portugal
,
Feb. 4–5
.
4.
Brower
,
M.
,
Marcus
,
M.
,
Taylor
,
M.
,
Bernadett
,
D.
,
Filippelli
,
M.
,
Beaucage
,
P.
,
Hale
,
E.
,
Elsholz
,
K.
,
Doane
,
J.
,
Eberard
,
M.
,
Tensen
,
J.
, and
Ryan
,
D.
,
2010
,
Wind Resource Assessment Handbook
,
New York State Energy Research and Development Authority—NYSERDA
,
New York
.
5.
Mortensen
,
N. G.
, and
Jørgensen
,
H. E.
,
2012
, “
Comparison of Resource and Energy Yield Assessment Procedures
,”
EWEA 2012, Wind Energy Division, Risø DTU
,
Copenhagen, Denmark
,
Apr. 16–17
.
6.
Petersen
,
E. L.
,
Mortensen
,
N. G.
,
Landberg
,
L.
,
Hojstrup
,
J.
, and
Frank
,
H. P.
,
1997
, “
Wind Power Meteorology
,”
Riso National Laboratory
,
Roskilde, Denmark
.
7.
Rathmann
,
O.
, and
Mortensen
,
N. G.
,
2009
, “
Uncertainties When ‘Production-Estimating’ With WAsP
,”
Vindkraftnet Meeting
, Arhus, Denmark.http://www.gesel.ie.ufrj.br/app/webroot/files/publications/20_lira.pdf
8.
Bowen
,
A. J.
, and
Mortensen
,
N. G.
,
2004
, “
WAsP Prediction Errors Due to Site Orography
,”
Risø National Laboratory—Wind Energy
,
Roskilde, Denmark
.
9.
Courtney
,
M.
,
Wagner
,
R.
, and
Lindelow
,
P.
,
2008
, “Commercial Lidar Profilers for Wind Energy—A Comparative Guide,”
NRG White Paper
,
Roskilde, Denmark
.
10.
Schwatz
,
M.
, and
Elliott
,
D.
,
2005
, “Towards a Wind Energy Climatology at Advanced Turbine Hub-Heights,”
NREL
,
Savannah, GA
.
11.
Axel
,
A.
, and
Gerdes
,
G.
,
1999
, “
Wind Farm Performance Verification
,” DEWI Magazin Nr. 14, Rio de Janeiro, Brazil, Feb., pp. 24–35.
12.
Albers
,
A.
,
1998
, “
Wind Year
,” DEWI Magazin Nr. 14, Rio de Janeiro, Brazil, Feb., pp. 36–37.
13.
Taylor
,
M.
,
Mackiewicz
,
P.
,
Brower
,
M. C.
, and
Markus
,
M.
,
2004
, “An Analysis of Wind Resource Uncertainty in Energy Production Estimates,”
AWS Truewind
,
Albany, NY
.
14.
Gorner
,
K.
,
Westerhellweg
,
A.
, and
Brillet
,
S.
,
2010
, “Seasonal Correction of Short-Term SODAR and LIDAR Measurements for Use in Energy Yield Assessments of Wind Farms,”
DEWEK—Deutsche Windenergie Konferenz
,
Bremen, Germany
.
15.
Procedure
,
M.
,
2009
, “
Evaluation of Site-Specific Wind Conditions
,” Measuring Network of Wind Energy Institutes (MEASNET), Madrid, Spain.
16.
Botta
,
G.
,
Castagna
,
R.
,
Borghetti
,
M.
, and
Mantegna
,
D.
,
1992
, “
Wind Analysis on Complex Terrain—The Case of Acqua Spruzza
,”
J. Wind Eng. Ind. Aerodyn.
,
39
(
1–3
), pp.
357
366
.
17.
Sempreviva
,
A. M.
,
Troen
,
I.
, and
Lavagnini
,
A.
,
1986
, “
Modelling of Wind Potential in Sardinia
,”
European Wind Energy Association Conference and Exhibition
,
Rome, Italy
,
Mar. 16–19
, pp.
323
328
.
18.
Bowen
,
A. J.
, and
Saba
,
T.
,
1995
, “
The Evaluation of Software for Wind Turbine Siting in Hilly Terrain
,”
International Conference on Wind Engineering
, New Delhi, India, Jan. 9–13.
19.
Bass
,
J. H.
,
Rebbeck
,
M.
,
Landberg
,
L.
,
Cabre
,
M.
, and
Hunter
,
A.
,
2000
, “
An Improved Measure-Correlate Predict Algorithm for the Prediction of the Long Term Wind Climate in Regions of Complex Environment
,” Non Nuclear Energy Programme JOULE III, European Commission, Brussels, Belgium, UK, Final Report No.
JOR3-CT98-0295
.http://eprints.lincoln.ac.uk/3389/
20.
Lackner
,
M.
,
Rogers
,
A. L.
, and
Manwell
,
J. F.
,
2008
, “Uncertainty Analysis in MCP-Based Wind Resource Assessment and Energy Production Estimation,”
University of Massachusetts - Selected Works
,
Amherst, MA
.
21.
Rogers
,
A. L.
,
Rogers
,
J. W.
, and
Manwell
,
J. F.
,
2005
, “Uncertainties in Results of Measure-Correlate-Predict Analyses,”
AWEA Windpower
,
Denver, CO
.
22.
Bailey
,
B. H.
, and
McDonald
,
S. L.
,
1997
, Wind Resource Assessment Handbook—Fundamentals for Conducting a Successful Monitoring Program,
NREL
,
Albany, NY
.
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