During wind farm planning, the farm layout or turbine arrangement is generally optimized to minimize the wake losses, and thereby maximize the energy production. However, the scope of layout design itself depends on the specified farm land-shape, where the latter is conventionally not considered a part of the wind farm decision-making process. Instead, a presumed land-shape is generally used during the layout design process, likely leading to sub-optimal wind farm planning. In this paper, we develop a novel framework to explore how the farm land-shape influences the output potential of a site, under a given wind resource variation. Farm land-shapes are defined in terms of their aspect ratio and directional orientation, assuming a rectangular configuration. Simultaneous optimizations of the turbine selection and placement are performed to maximize the energy production capacity, for a set of sample land-shapes with fixed land area. The maximum farm capacity factor or farm output potential is then represented as a function of the land aspect ratio and land orientation, using quadratic and Kriging response surfaces. This framework is applied to design a 25 MW wind farm at a North Dakota site that experiences multiple dominant wind directions. An appreciable 5% difference in capacity factor is observed between the best and the worst sample farm land-shapes at this wind site. It is observed that among the 50 sample land-shapes, higher energy production is accomplished by the farm lands that have aspect ratios significantly greater than one, and are oriented lengthwise roughly along the dominant wind direction axis. Subsequent optimization of the land-shape using the Kriging response surface further corroborates this observation.

References

1.
Fronk
,
B. M.
,
Neal
,
R.
, and
Garimella
,
S.
,
2010
, “
Evolution of the Transition to a World Driven by Renewable Energy
,”
ASME J. Energy Res. Technol.
,
132
(
2
), p.
021009
.10.1115/1.4001574
2.
Prasad
,
B. J. S.
,
2010
, “
Energy Efficiency, Sources, and Sustainability
,”
ASME J. Energy Res. Technol.
,
132
(
2
), p.
020301
.10.1115/1.4001684
3.
Sorensen
,
P.
, and
Nielsen
,
T.
,
2006
, “
Recalibrating wind turbine wake model parameters—validating the wake model performance for large offshore wind farms
,”
European Wind Energy Conference and Exhibition
,
EWEA
.
4.
Beyer
,
H. G.
,
Lange
,
B.
, and
Waldl
,
H. P.
,
1996
, “
Modelling Tools for Wind Farm Upgrading
,”
European Union Wind Energy Conference
,
AIAA
.
5.
Jensen
,
N. O.
,
1983
, “
A Note on Wind Turbine Interaction
,”
Risoe National Laboratory
,
Roskilde, Denmark
, Technical Report No. M-2411.
6.
Katic
,
I.
,
Hojstrup
,
J.
, and
Jensen
,
N. O.
,
1986
, “
A Simple Model for Cluster Efficiency
,”
European Wind Energy Conference and Exhibition
,
EWEA
.
7.
Frandsen
,
S.
,
Barthelmie
,
R.
,
Pryor
,
S.
,
Rathmann
,
O.
,
Larsen
,
S.
,
Hojstrup
,
J.
, and
Thogersen
,
M.
,
2006
, “
Analytical Modeling of Wind Speed Deficit in Large Offshore Wind Farms
,”
Wind Energy
,
9
(
1–2
), pp.
39
53
.10.1002/we.189
8.
Larsen
,
G. C.
,
Hojstrup
,
J.
, and
Madsen
,
H. A.
,
1996
, “
Wind Fields in Wakes
,”
EUWEC
.
9.
Ishihara
,
T.
,
Yamaguchi
,
A.
, and
Fujino
,
Y.
,
2004
, “
Development of a New Wake Model Based on a Wind Tunnel Experiment
,” Global Wind, Technical Report, http://windeng.t.u-tokyo.ac.jp/ishihara/posters/2004_gwp_poster.pdf.
10.
Grady
,
S. A.
,
Hussaini
,
M. Y.
, and
Abdullah
,
M. M.
,
2005
, “
Placement of Wind Turbines Using Genetic Algorithms
,”
Renewable Energy
,
30
(
2
), pp.
259
270
.10.1016/j.renene.2004.05.007
11.
Sisbot
,
S.
,
Turgut
,
O.
,
Tunc
,
M.
, and
Camdali
,
U.
,
2009
, “
Optimal Positioning of Wind Turbines on Gokceada Using Multi-objective Genetic Algorithm
,”
Wind Energy
,
13
(
4
), pp.
297
306
.10.1002/we.339
12.
Gonzalez
,
J. S.
,
Rodriguezb
,
A. G. G.
,
Morac
,
J. C.
,
Santos
,
J. R.
, and
Payan
,
M. B.
,
2010
, “
Optimization of Wind Farm Turbines Layout Using an Evolutive Algorithm
,”
Renewable Energy
,
35
(
8
), pp.
1671
1681
.10.1016/j.renene.2010.01.010
13.
Kusiak
,
A.
, and
Song
,
Z.
,
2010
, “
Design of Wind Farm Layout for Maximum Wind Energy Capture
,”
Renewable Energy
,
35
, pp.
685
694
.10.1016/j.renene.2009.08.019
14.
Chowdhury
,
S.
,
Zhang
,
J.
,
Messac
,
A.
, and
Castillo
,
L.
,
2012
, “
Unrestricted Wind Farm Layout Optimization (UWFLO): Investigating Key Factors Influencing the Maximum Power Generation
,”
Renewable Energy
,
38
(
1
), pp.
16
30
.10.1016/j.renene.2011.06.033
15.
Chowdhury
,
S.
,
Zhang
,
J.
,
Messac
,
A.
, and
Castillo
,
L.
,
2013
, “
Optimizing the Arrangement and the Selection of Turbines for a Wind Farm Subject to Varying Wind Conditions
,”
Renewable Energy
,
52
, pp.
273
282
.10.1016/j.renene.2012.10.017
16.
Elia
,
S.
,
Gasulla
,
M.
, and
Francesco
,
A. D.
,
2012
, “
Optimization in Distributing Wind Generators on Different Places for Energy Demand Tracking
,”
ASME J. Energy Res. Technol.
,
134
(
4
), pp.
041202
.10.1115/1.4007656
17.
DuPont
,
B. L.
, and
Cagan
,
J.
,
2012
, “
An Extended Pattern Search Approach to Wind Farm Layout Optimization
,”
ASME J. Mech. Design
,
134
(
8
), p.
081002
.10.1115/1.4006997
18.
Mustakerov
,
I.
, and
Borissova
,
D.
,
2010
, “
Wind Turbines Type and Number Choice Using Combinatorial Optimization
,”
Renewable Energy
,
35
(
9
), pp.
1887
1894
.10.1016/j.renene.2009.12.012
19.
Gu
,
H.
, and
Wang
,
J.
,
2013
, “
Irregular-Shape Wind Farm Micro-Siting Optimization
,”
Energy
,
57
, pp.
535
544
.10.1016/j.energy.2013.05.066
20.
Chowdhury
,
S.
,
Messac
,
A.
,
Zhang
,
J.
,
Castillo
,
L.
, and
Lebron
,
J.
,
2010
, “
Optimizing the Unrestricted Placement of Turbines of Differing Rotor Diameters in a Wind Farm for Maximum Power Generation
,”
ASME 2010 International Design Engineering Technical Conferences (IDETC)
, No. DETC2010-29129,
ASME
.
21.
Chowdhury
,
S.
,
Tong
,
W.
,
Messac
,
A.
, and
Zhang
,
J.
,
2013
, “
A Mixed-Discrete Particle Swarm Optimization With Explicit Diversity-Preservation
,”
Struct. Multidisciplinary Optimization
,
47
(
3
), pp.
367
388
.10.1007/s00158-012-0851-z
22.
Chen
,
L.
, and
MacDonald
,
E.
,
2012
, “
Considering Landowner Participation in Wind Farm Layout Optimization
,”
ASME J. Mech. Design
,
134
(
8
), p.
084506
.10.1115/1.4006999
23.
Sahin
,
A. D.
,
Dincer
,
I.
, and
Rosen
,
M. A.
,
2006
, “
New Spatio-Temporal Wind Exergy Maps
,”
ASME J. Energy Res. Technol.
,
128
(
3
), pp.
194
202
.10.1115/1.2213271
24.
Kuvlesky
,
W. P.
,
Brennan
,
L. A.
,
Morrison
,
M. L.
,
Boydston
,
K. K.
,
Ballard
,
B. M.
, and
Bryant
,
F. C.
,
2010
, “
Wind Energy Development and Wildlife Conservation: Challenges and Opportunities
,”
J. Wildlife Manag.
,
71
(
8
), pp.
2487
2498
.10.2193/2007-248
25.
Windustry, 2010, “Landowner Guide
:
Evaluating a Wind Energy Development Company
,”
Minneapolis, MN
, http://www.windustry.org/sites/windustry.org/files/Landowner%20Guide%20to%20Evaluating%20a%20Developer.pdf.
26.
Christie
,
D.
, and
Bradley
,
M.
,
2012
, “
Optimizing Land Use for Wind Farms
,”
Energy Sustainable Dev.
,
16
(
4
), pp.
471
475
.10.1016/j.esd.2012.07.005
27.
Chowdhury
,
S.
,
Zhang
,
J.
,
Messac
,
A.
, and
Castillo
,
L.
,
2012
, “
Characterizing the Influence of Land Area and Nameplate Capacity on the Optimal Wind Farm Performance
,”
ASME 2012 6th International Conference on Energy Sustainability
, No. ESFuelCell2012-91063,
ASME
.
28.
Graves
,
A.
,
Harman
,
K.
,
Wilkinson
,
M.
, and
Walker
,
R.
,
2008
, “
Understanding Availability Trends of Operating Wind Farms
,”
AWEA Wind Power Conference
,
AWEA
.
29.
Kusiak
,
A.
, and
Zheng
,
H.
,
2010
, “
Optimization of Wind Turbine Energy and Power Factor With an Evolutionary Computation Algorithm
,”
Energy
,
35
, pp.
1324
1332
.10.1016/j.energy.2009.11.015
30.
Sobol
,
M.
,
1976
, “
Uniformly Distributed Sequences With an Additional Uniform Property
,”
USSR Comput. Math. Math. Phys.
,
16
, pp.
236
242
.10.1016/0041-5553(76)90154-3
31.
Zhang
,
J.
,
Chowdhury
,
S.
,
Messac
,
A.
, and
Castillo
,
L.
,
2011
, “
Multivariate and Multimodal Wind Distribution Model Based on Kernel Density Estimation
,”
ASME 2011 5th International Conference on Energy Sustainability
,
ASME
.
32.
Zhang
,
J.
,
Chowdhury
,
S.
,
Messac
,
A.
, and
Castillo
,
L.
,
2013
, “
A Multivariate and Multimodal Wind Distribution Model
,”
Renewable Energy
,
51
, pp.
436
447
.10.1016/j.renene.2012.09.026
33.
Cressie
,
N.
,
1993
,
Statistics for Spatial Data
,
Wiley
,
New York
.
34.
Lophaven
,
S.
,
Nielsen
,
H.
, and
Sondergaard
,
J.
,
2002
, “
Dace—A MATLAB Kriging Toolbox, Version 2.0
,”
Technical University of Denmark
, Informatics and Mathematical Modelling Report No. IMM-REP-2002-12.
35.
NDSU
, “
North Dakota Agricultural Weather Network
,” http://ndawn.ndsu.nodak.edu/, accessed, December 2010.
36.
Denholm
,
P.
,
Hand
,
M.
,
Jackson
,
M.
, and
Ong
,
S.
,
2009
, “
Land-Use Requirements of Modern Wind Power Plants in the United States
,” Technical Report No. NREL/TP-6A2-45834, NREL.
You do not currently have access to this content.