This is my paper presentation from the ASME Energy Sustainability and Fuel Cell conference, 2011 in Washington DC

1. Developing a Flexible Platform for Optimal
Engineering Design of
Commercial 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 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.
Turbine
Rated Rotor Hub Power
Power Diameter Height Curve
www.wind-watch.org 2

3. Motivation
 Farm Layout Planning: The net power generated by a wind farm is reduced
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
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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
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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.
www.prairieroots.org
*WWEA, 2011 NREL, 2011 5

6. Existing Wind Farm Optimization Methods
Array layout approach Grid based approach
Computationally less expensive. Allows the exploration of different farm
Restricts turbine locating and introduces configurations.
a source of sub-optimality* Results might be undesirably sensitive
to the pre-defined grid size#
• Do not simultaneously optimize the selection of wind turbines
• Do not consider the joint distribution of wind speed and direction
*Sorenson et al., 2006; Mikkelson et al., 2007;
#Grady et al., 2005; Sisbot et al., 2009; Gonzleza et al., 2010
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7. Research Objectives:
Unrestricted Wind Farm Layout Optimization (UWFLO)
 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
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8. Components of the UWFLO framework
UWFLO
Framework
Wind Power
Wind Farm Optimization
Distribution Generation
Cost Model Methodology
Model Model
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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.
*N. Dakota agricultural weather network: http://ndawn.ndsu.nodak.edu/
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11. UWFLO Power Generation Model
 Dynamic co-ordinates are assigned to the
turbines based on the direction of wind.
 Turbine-j is in the influence of the wake
of Turbine-i, if and only if
Avian Energy, UK
 Effectiveapproach allows us to consider turbines with differing rotor-
 This velocity of wind  Power generated by Turbine-j:
approaching Turbine-j:
diameters and hub-heights
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12. Wake Model
 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 12

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.
  U  U in 
 Pn   if U in <U  U r
  U r  U in 
P 
 1 if U out  U  U r
Pr 
0 if U  U out



• However, if power curve information is available for all the turbines
being considered for selection, they can be used directly.
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14. Annual Energy Production
Wind Probability Distribution
• Annual Energy Production of a farm is given by:
Wind Farm Power Generation
• This integral equation can be numerically expressed as:
• A careful consideration of the trade-offs between numerical errors and
computational expense is important to determine the sample size Np.
Kusiak and Zheng, 2010; Vega, 2008 14

15. UWFLO Cost Model
• A response surface based cost model is developed using radial basis
functions (RBFs).
• The cost in $/per kW installed is expressed as a function of (i) the
number of turbines (N) in the farm and (ii) the rated power (P) of those
turbines.
• Data is used from the DOE Wind and Hydropower Technologies
program to develop the cost model.
Cost farm
COE 
AEP
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16. Problem Definition
Farm Boundaries
Inter-Turbine Spacing
COE Constraint
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17. Application of the UWFLO Framework
 Case 1: Optimize farm layout for a fixed turbine type (GE 1.5 MW xle)
 Case 2: Optimize the farm layout and the types of the turbines to be
installed, thereby allowing a combination of multiple turbine types.
 Reference Wind Farm: A 5x5 array layout of GE 1.5 MW xle turbines
Parameter Case 1: Fixed Step 2: Variable Reference Farm
Turbine Type Turbine Types
Normalized AEP 0.623 0.933 0.597
Overall Farm Efficiency 0.623 0.635 0.597
COE ($/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)
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19. Optimized Layout: Case 2 (Variable Turbine Types)
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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. Thank you
Questions
and
Comments
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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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