The document proposes a flexible optimization framework to maximize the annual energy production of wind farms by simultaneously optimizing turbine layout and turbine type selection. It develops models for wind distribution, power generation, cost, and uses a mixed-discrete particle swarm optimization method. The framework is tested on a case study wind farm and results show up to a 6% increase in energy production over a conventional array layout approach by allowing optimization of turbine types.

1. Developing a Flexible Platform for Optimal
Engineering Design ofCommercial Wind Farms
Souma Chowdhury*, Jie Zhang*, Achille Messac#, and Luciano Castillo*
# Syracuse University, Department of Mechanical and Aerospace Engineering
* Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering
ASME 2011 5th International Conference on Energy Sustainability & 9th Fuel
Cell Science, Engineering and Technology Conference
August 7 – 10, 2011
Grand Hyatt Washington
Washington, DC

2. Wind Farm Optimization
 Farm Layout Planning: The net power generated by a wind farm is reduced
by the wake effects, which can be substantially regained by optimizing the
farm layout.
 Turbine Type Selection: Optimally selecting the turbine-type(s) to be installed
can further improve the power generation capacity and the economy of a wind
farm.
 Wind Distribution Modeling: In order to accurately quantify the farm energy
production, it becomes critically important to determine the expected long-term
distribution of wind conditions and integrate it within the optimization
www.wind-watch.org 2
Turbine
Rated
Power
Rotor
Diameter
Hub
Height
Power
Curve
model.

3. Motivation
 Farm Layout Planning: The net power generated by a wind farm is reduced
3
by the wake effects, which can be offset by optimizing the farm layout.
 Turbine Type Selection: Optimally selecting the turbine-type(s) to be
installed can further improve the power generation capacity and the economy
of a wind farm.
 Wind Distribution Modeling: In order to accurately quantify the energy
production from a wind farm, it also becomes critically important to
efficiently determine the expected long-term distribution of wind conditions
and integrate it within the optimization model.
An effective wind farm optimization method must
account for the complex interactions among
these three factors

4. Presentation Outline
• Wind Energy and Existing Farm Optimization Methods
• Research Objectives
• Wind Distribution Model
• Power Generation and Turbine Selection Model
• Annual Energy Production and Cost of Energy
• Application of the Wind Farm Optimization Framework
• Concluding Remarks
4

5. Wind Energy - Overview
 Currently wind contributes 2.5% of the global electricity consumption.*
 The 2010 growth rate of wind energy has been the slowest since 2004.*
 Large areas of untapped wind potential exist worldwide and in the US.
 Among the factors that affect the growth of wind energy, the state-of-the-
art in wind farm design technologies plays a prominent role.
5
www.prairieroots.org
*WWEA, 2011 NREL, 2011

6. Existing Wind Farm Optimization Methods
6
Grid based approach
Allows the exploration of different farm
configurations.
Results might be undesirably sensitive
to the pre-defined grid size#
Array layout approach
Computationally less expensive.
Restricts turbine locating and introduces
a source of sub-optimality*
Prevailing Challenges
• Simultaneously optimization of the wind turbine selection
• Integration of the joint distribution of wind speed and direction in AEP model
*Sorenson et al., 2006; Mikkelson et al., 2007;
#Grady et al., 2005; Sisbot et al., 2009; Gonzleza et al., 2010

7. Research Objectives:
Unrestricted Wind Farm Layout Optimization (UWFLO)
7
 Avoid limiting restrictions on the layout pattern of the wind farm.
 Develop a computationally inexpensive analytical power generation
model and a response surface based wind farm cost (RS-WFC) model.
 Model the use of turbines with differing features and performance
characteristics.
 Integrate a multivariate and multimodal wind distribution model to
accurately estimate the AEP and the corresponding COE.
 Maximize the AEP by simultaneously optimizing the farm layout and the
selection of the turbine-type to be installed. To this end, we apply an
advanced mixed-discrete PSO algorithm.
AEP: Annual energy Production; COE: Cost of Energy; PSO: Particle Swarm Optimization

8. Components of the UWFLO framework
UWFLO
Framework
Wind
Distribution
Model
Power
Generation
Model
Wind Farm
Cost Model
Optimization
Methodology
8

9. Wind Distribution Model
In this paper, we use the non-parametric model called the Multivariate and
Multimodal Wind Distribution (MMWD).
• This model is developed using the multivariate Kernel Density
Estimation (KDE) method.
• This model is uniquely capable of representing multimodally distributed
wind data.
• This model can capture the joint variations of wind speed, wind direction
and air density.
• In this paper, we have only used the bivariate version of this model (for
wind speed and direction)
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10. Case study
• In this paper, we use 10-year wind data for a class 3 site at Baker, ND*.
• The optimization framework is applied to design a commercial scale 25
turbine wind farm at this site.
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*N. Dakota agricultural weather network: http://ndawn.ndsu.nodak.edu/

11. UWFLO Power Generation Model
 Dynamic co-ordinates are assigned to the
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turbines based on the direction of wind.
 Turbine-j is in the influence of the wake
of Turbine-i, if and only if
 Effective velocity of wind
approaching Turbine-j:
 Power generated by Turbine-j:
Avian Energy, UK
 This approach allows us to consider turbines with differing rotor-diameters
and hub-heights

12. Wake Model
12
 We implement Frandsen’s velocity deficit model
Wake growth Wake velocity
a – topography dependent wake-spreading constant
 Wake merging: Modeled using wake-superposition principle
developed by Katic et al.:
Frandsen et al., 2006; Katic et al.,1986

13. Turbine Selection Model
• Every turbine is defined in terms of its rotor diameter, hub-height, rated
power, and performance characteristics, and represented by an integer
code (1 – 66).
• The “power generated vs. wind speed” characteristics for GE 1.5 MW xle
turbines (ref. turbine) is used to fit a normalized power curve Pn().
• The normalized power curve is scaled back using the rated power and the
rated, cut-in and cut-out velocities given for each turbine.
   
   
   
   
 

• However, if power curve information is available for all the turbines
being considered for selection, they can be used directly.
13
if <
1 if
0 if
in
n in r
r in
out r
r
out
U U
P U U U
U U
P
U U U
P
U U


14. Annual Energy Production
• Annual Energy Production of a farm is given by:
Wind Probability Distribution
• This integral equation can be numerically expressed as:
Wind Farm Power Generation
14
Kusiak and Zheng, 2010; Vega, 2008

15. UWFLO Cost Model
• A response surface based cost model is developed using radial basis
functions (RBFs).
• The cost in $/per kW installed is expressed as a function of (i) the
number of turbines (N) in the farm and (ii) the rated power (P) of those
turbines.
• Data is used from the DOE Wind and Hydropower Technologies
program to develop the cost model.
15
farm Cost
COE
AEP


16. Problem Definition
16
Farm Boundaries
Inter-Turbine Spacing
COE Constraint

17. Application of the UWFLO Framework
 Case 1: Optimize farm layout for a fixed turbine type (GE 1.5 MW xle)
 Case 2: Optimize the farm layout and the types of the turbines to be
installed, thereby allowing a combination of multiple turbine types.
 Reference Wind Farm: A 5x5 array layout of GE 1.5 MW xle turbines
Parameter Case 1: Fixed
Turbine Type
Case 2: Variable
Turbine Types
Reference Farm
Normalized AEP 0.623 0.933 0.597
Overall Farm Efficiency 0.623 0.635 0.597
COE ($/kWh) 0.023 0.023 0.024
66 commercial onshore turbines are used to form the selection pool
AEP: Annual energy Production; COE: Cost of Energy 17

18. Optimized Layout: Case 1 (Fixed Turbine Type)
18

19. Optimized Layout: Case 2 (Variable Turbine Types)
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Power Generated Rated Power
Rotor Diameter Hub Height

20. Concluding Remarks
 We developed a flexible wind farm layout planning framework that
accounts for the joint variation of wind speed and direction.
 In this framework, wind turbines are allowed to be selected during the
optimization process.
 Optimally selecting the turbine types produced a farm efficiency 2%
higher than when a specified wind turbine was used, and a significant 6%
higher than that produced by array layout-based reference farm.
 Interestingly, we also found that the larger wind turbines (generally with
higher rated powers) were placed away from the prominent wind directions
to minimize their shading effects on the other turbines, and their ability to
be relatively more efficient at lower wind speeds.
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21. Acknowledgement
• I would like to acknowledge my research adviser
Prof. Achille Messac, and my co-adviser Prof.
Luciano Castillo for their immense help and
support in this research.
• I would also like to thank my friend and colleague
Jie Zhang for his valuable contributions to this
paper.
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22. Questions
and
Comments
22
Thank you

23. Mixed-Discrete Particle Swarm Optimization (PSO)
 This algorithm has the ability to
deal with both discrete and
continuous design variables, and
 The mixed-discrete PSO presents
an explicit diversity preservation
capability to prevent premature
stagnation of particles.
 PSO can appropriately address the
non-linearity and the multi-modality
of the wind farm model.
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24. Annual Energy Production
• Annual Energy Production of a farm is given by:
• This integral equation can be numerically expressed as:
Wind Farm Power Generation
• A careful consideration of the trade-offs between numerical errors and
computational expense is important to determine the sample size Np.
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Wind Probability Distribution
Kusiak and Zheng, 2010; Vega, 2008