WFO_SDM_2012_Souma

Exploring the Best Performing Commercial Wind Turbines
for Different Wind Regimes
in a Target Market
Souma Chowdhury*, Jie Zhang*, Mat Catalano*, Ali Mehmani#, Samuel
Notaro, Achille Messac#, and Luciano Castillo**
# Syracuse University, Department of Mechanical and Aerospace Engineering
* Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering
** Texas Tech University, Department of Mechanical Engineering
53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and
Materials Conference
April 23-26, 2012, Honolulu, Hawaii

Broad Research Question
How to quantify:
 The demand potential of different wind turbines to aid
turbine manufacturers?
 The performance potential (COE) of a given
geographical region to aid wind energy investors?
2
COE: Cost of Energy produced by the farm

Regional Variations in a Wind Energy Potential
 The wind pattern/regime varies significantly
over a target market (e.g. onshore USA).
 overall energy available
 variation of wind with time
 A turbine that is optimally efficient for a particular wind pattern might not be
3
efficient for a significantly different wind pattern.
 Turbines with unique feature combinations are necessary to suit the
needs of different wind patterns.
http://www.ge-energy.com/wind

Corollary Research Questions Addressed in this Paper
1. Best turbine choices: What types of commercial turbines perform the best
for different wind regimes?
2. Market suitability of turbines: What is the suitability of different
available turbine-feature combinations for a target market?
3. Best performance offered by current turbines: How does the Cost of
Energy (COE) of an array of turbines vary with the wind pattern?
 COE of the best performing array of turbines
4
The Benefit
 Manufacturers: Help develop better product family of turbines
 Wind farm developers: Help gauge overall profitability of wind projects
based on site location

Turbine Selection Criteria
Conventional Methods
 Turbines that offer the best trade-off between:
1. Long term energy production capacity for the given resource conditions
2. Survivability in the given site conditions (load bearing capacity)
3. Life-cycle costs
 Other important considerations: Performance history of the specific
turbine-model and of the manufacturer.
 IEC ratings provide information regarding criteria 1 and 2.
Limitations of ConventionalMethods:
 Performance of the turbine as a part of an optimized array of turbines
is not considered.
5

Presentation Outline
Research Objectives
Characterizing Wind Map Information
Determining Optimum Turbine Choices
Quantifying the Market Suitability of
Turbine-Feature Combinations
6

Research Objectives
7

Research Objectives
 Determine the best performing turbines for different wind regimes
 Performance of turbines in terms of COE when operating as a group in an
optimal farm layout is considered in this case
 Develop and apply a metric to explore:
the suitability of available turbine-feature combinations
for a target market
Current Application
 Apply the methodology to illustrate the suitability of available turbines
(and their features) for the US onshore wind energy market
8

Research Strategy
Wind Map
Information
Properties
of available
commercial
turbines
1. Best performing turbines
and their performance
2. Suitability of commercial
turbine-feature combinations
9
A complex combination of
advanced models

Overall Framework
Optimization of farm
layout and turbine
type selection
UWFLO
10
Generate a set of N
random average wind
speed (AWS) values
Sobol’s Algorithm
AWS-1:
Minimum COE
Optimal Turbine
AWS-2:
Minimum COE
Optimal Turbine
AWS 3:
Minimum COE
Optimal Turbine
Commercial turbine
specifications:
Rated Power, Rotor-
Diameter, Tower
Height, Drive-Train
Develop a metric to
represent the
expected market
suitability of
different turbine
feature combinations
Distribution of average
wind speed in the
concerned target
market

Assumptions
 Load bearing capacity (survivability) of a turbine is not
considered in the optimum turbine selection process.
 Unavailability of maps of turbulence intensity
 The used wind map information assumes the entire US land
area is available for the development of wind energy project
development.
 Further advancement is necessary to apply the methodology
to the offshore wind energy market.
11

Research Objectives
12

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Annual average wind speed (AWS) is a measure of the resource strength/potential

Wind Map Information
 Wind map is digitized using image processing tools
 Total area under different average wind speeds (AWS) is estimated with
an interval of 0.5 m/s
 A normal distribution is fitted to represent the geographical distribution
of average wind speeds (AWS) over contiguous USA
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 = 5.6 m/s
s = 1.3 m/s

Research Objectives
15

Wind Farm Optimization
 Purpose of wind farm optimization: For a given average wind speed,
determine the turbine choice that provides the minimum Cost of Energy,
when operating as a group in an optimized layout (arrangement).
 From the average wind speed value (s), a distribution of wind speed is
estimated using the 1-parameter Rayleigh distribution.
16
Rayleigh parameter, s

Unrestricted Wind Farm Layout Optimization (UWFLO)*
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UWFLO
Framework
Wind
Distribution
Model
Power
Generation
Model
Wind Farm
Cost Model
Optimization
Methodology
Unique features of UWFLO:
Other Wind Farm Optimization Approaches
 • Array Avoids Layout limiting approach: restrictions Sorenson on et the al., layout 2006; Mikkelson pattern of et the al., wind 2007;
farm.
• Grid-based approach: Grady et al., 2005; Sisbot et al., 2009; Gonzleza et al., 2010
 Uniquely capable of simultaneously optimizing the farm layout and the
selection of the turbine-type(s) to be installed.
 Uses a distribution of wind conditions.
*Chowdhury et al. (Renewable Energy, 2012)

Wind Farm Power Generation Model
 Turbines are ranked in terms of the order in
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which they encounter the incoming 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
Standard wake velocity deficit models and wake superposition
models are used (Frandsen et al., Katic et al.)

Wind Turbine Cost Model
 We use the Wind Turbine Design Cost and Scaling (WTDCS) model by
Fingersh et al. (NREL) to represent the wind farm cost, CFT
FT C
COE
AEP
=
CMF: total manufacturing cost; CBS: balance-of-station cost; CLR: levelized replacement cost (LRC)
 All costs components are represented as functions of rated power, rotor
diameter, hub height, and type of drive train.
 Available drive-train types:
1. Three-stage gearbox with high speed generator
2. Direct-drive
19
O&M Costs are not considered, since it was expressed solely as a function of
annual energy production, and is a small fraction of the total cost for onshore
wind farms.

Problem Definition
Mixed-integer non-linear programming problem (with highly
multimodal functions and high number of design variables)
20
Inter-Turbine Spacing
Farm Boundaries
Total number of allowed
commercial turbine models
Solved by
Mixed-Discrete Particle Swarm Optimization
Land area of the circular farm:
With t = 21, we get an area of 11.7 hectares/MW (NREL’s estimate = 34.5 ± 22.4 hectares/MW)

Optimal Turbine Selection: Specifications
 Optimum turbine selection is performed for a set of 25 random values
Turbine
Manufacturers
GE
Vestas
Enercon
Siemens
Goldwind
Suzlon
Gamesa
Rated Power
Class
No. of available
Choices
No. installed in
the farm
0.60 3 42
0.80 7 31
0.85 13 29
0.90 3 28
1.25 6 20
1.50 16 17
1.60 5 16
1.80 10 14
2.00 36 13
2.30 14 11
2.60 3 10
2.75 4 9
3.00 11 8
of average wind speed (3.5-10.0 m/s)
 Optimization is performed separately for each rated power class (owing
to differing numbers of design variables involved)
21
131 commercially available turbines
are allowed to be selected

Optimal Turbine Selection: Results
 The best performing turbines among all rated power classes (best of the best)
are of direct-drive type.
 For AWS > 6.5 m/s, 2.3 MW turbines performed the best
 For AWS < 6.5 m/s, 3.0 MW turbines performed the best
22

Performance of the Optimally Selected Turbines
 Beyond average wind speeds of 7 m/s,
the decrease in the COEmin is marginal
 For high average wind speeds, there is
less than 25% difference in the COEmin
accomplished by the best performing
turbines of different rated-power
classes
23
 The capacity factor of the optimized
farm is observed to follow roughly a
linear variation with average wind speed
(wind distribution and wake effects)
 The minimized COE is expected to vary
as a inverse polynomial function of the
AWS

The Best COE Accomplished for any Given Wind Regime
24
Wind Farm
Optimization
UWFLO
Optimize Turbine
Choice - 1
Optimize Layout - 1
Optimize Turbine
Choice - 2
Optimize Layout - 2
Optimize Turbine
Choice - n
Optimize Layout - n
Average wind
speed - 1
Average wind
speed - 2
Average wind
speed - n
Regression Model
Minimum
Cost of Energy
(COE) - 1
Minimum
Cost of Energy
(COE) - 2
Minimum
Cost of Energy
(COE) - n

Regression Model: COEmin = f (average wind speed)
 An inverse polynomial regression (multiplicative surrogate) is performed to
represent the minimum COE accomplished by the best performing turbine
(of each class) as a function of AWS, s.
where c1 > 0 and c2 < 0
25
Accurate Fits obtained:
R2 > 0.96
-1.4 > c2 > -2.2
Direct-drive
NOT available

Research Objectives
26

Turbine Suitability for a Target Market
 The selection likelihood of turbine feature combinations governed by:
1. How often different feature combinations were selected during the 13x25
wind farm optimizations?
2. What level of performance (in terms of COE) was offered by the best
performing turbines (from each rated-power class)?
3. The probability of occurrence of each of the 25 sample average wind
speeds over the US onshore market.
 A metric called the Performance-based Expected Market Suitability
(PEMS) is developed to represent the likely market success of turbines.
 PEMS is expressed in total Gigawatts of likely installation of that turbine
in the concerned market.
27

Performance-based Expected Market Suitability (PEMS)
28
Probability of occurrence of the
jth sample average wind speed
Total wind power potential
of USA in GW*
COE obtained by ith turbine for the
jth sample average wind speed
* A total wind power potential value of 10,459 GW at 80 m height for the contiguous
USA (excluding Hawaii and Alaska), as estimated by NREL is used in this paper.

Market Suitability: Rotor Diameter & Rated Power Combinations
 Expectedly, “higher rated powers and larger rotor diameters” are the most
favored among available commercial variants.
29

Market Suitability: Hub Height & Rated Power Combinations
 “Higher rated-power turbines with higher tower heights” are far less
favored by the US onshore conditions than some (not all) of the “small-medium
rated power turbines with higher hub heights” (e.g. 0.8 MW and 1.5
MW)
30

Market Suitability: Hub Height & Rotor Diameter Combinations
 “Turbines with larger rotor diameter and medium sized tower heights”
(approx. 100 m) are the most popular, which again indicates that relatively
higher tower heights are not favored for the larger turbines.
 The use of shear profiles other than log law and power law should provide
further insights in this direction.
31

Concluding Remarks
 Comprehensive method developed to find the best performing turbines for
different wind regimes and quantify their performance and market demand.
 The comprehensive method is primarily made possible by the unique capabilities of the
UWFLO framework.
 Overall, higher rated-power turbines were preferred in the US market
 A majority of the US wind resource is between 4.5-7.0 m/s at 80 m height (wind classes 1-4)
 Smaller rated-power turbines remained competitive for sites with higher
average wind speeds
 Direct-drive turbines performed appreciably better than the more
conventional gearbox-based turbines
 Higher tower heights were not preferred for the larger turbines (with higher
rated power).
32

Future Work
 Consider other important aspects of turbine selection - e.g. load bearing
capacity of the turbine, site-based cost of transport and installation, and
performance history of the turbine in the concerned market.
 Use the distribution of wind resource strengths (or average wind speeds)
over the actual land-area available for wind projects in a small target
market, instead of the entire geographical region.
33

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.
• Support from the NSF Awards is also
acknowledged.
34

Questions
and
Comments
35
Thank you

Optimized Layout
36
An optimally selected 2.0 MW turbine
 Rotor Diameter: 90 m
 Tower Height: 100 m

Turbine Characterization Model
• Every turbine is defined in terms of its rotor diameter, hub-height, rated
37
power, and performance characteristics, and represented by an integer
code (1 – 131).
• The “power generated vs. wind speed” characteristics for GE 1.5 MW xle
turbines 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.
   
   
P if U <
U U
U U
n in r
U U
   =  1 if
 
 
0 if


in
r in
U U U
out r
P
P
r
out
U U

• However, if power curve information is available for all the turbines
being considered for selection, they can be used directly.

Wake Model
38
 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

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.
39
Wind Probability Distribution
Kusiak and Zheng, 2010; Vega, 2008

Features of the Best Performing Turbines
 Smaller rotor diameters and lower tower heights of the 0.90 MW turbines
restricts the amount of power these turbines can extract from the wind.
 For resources with higher average wind speeds, relatively lower tower
heights are preferred.
40

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
41
*WWEA, 2011 NREL, 2011

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.
42

The Best COE Accomplished for any Given Wind Regime
Regression Model
43
Wind Farm
Optimization
UWFLO
Optimize Turbine
Choice - 1
Optimize Layout - 1
Optimize Turbine
Choice - 2
Optimize Layout - 2
Optimize Turbine
Choice - n
Optimize Layout - n
Average wind
speed - 1
Average wind
speed - 2
Average wind
speed - n
Minimum
Cost of Energy
(COE) - 1
Minimum
Cost of Energy
(COE) - 2
Minimum
Cost of Energy
(COE) - n

• Research Objectives
• Characterizing Wind Map Information
• Exploring “Turbine - Wind Regime” Compatibilities
– Turbine Characterization Model and Cost Model
– Wind Farm Optimization
• Optimal Turbine Choices and their Performance
– Results and Discussion
– Turbine Suitability for the US Onshore Market
• Concluding Remarks
44

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