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Conference Paper

NREL/CP-5000-72935

May 2019

Best Practices for Wake Model and 

Optimization Algorithm Selection in 

Wind Farm Layout Optimization 

Preprint

Nicholas F. Baker,

1 Andrew P.J. Stanley,1

Jared J. Thomas,1 Andrew Ning,1 and Katherine Dykes2

1 Brigham Young University

2 National Renewable Energy Laboratory

Presented at American Institute of Aeronautics and Astronautics 

SciTech Forum

San Diego, California

January 7–11, 2019
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Laboratory (NREL) at www.nrel.gov/publications.

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Conference Paper

NREL/CP-5000-72935

May 2019

Best Practices for Wake Model and 

Optimization Algorithm Selection in 

Wind Farm Layout Optimization 

Preprint

Nicholas F. Baker,

1 Andrew P.J. Stanley,1

Jared J. Thomas,1 Andrew Ning,1 and Katherine Dykes2

1 Brigham Young University

2 National Renewable Energy Laboratory

Suggested Citation

Baker, Nicholas, Andrew Stanley, Jared Thomas, Andrew Ning, and Katherine Dykes. 

Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout 

Optimization: Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5000-72935. https://www.nrel.gov/docs/fy19osti/72935.pdf. 
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Best Practices for Wake Model and Optimization Algorithm

Selection in Wind Farm Layout Optimization

Nicholas F. Baker∗, Andrew P. J. Stanley†, Jared J. Thomas‡, and Andrew Ning§

Brigham Young University, Provo, Utah 84602.

Katherine Dykes¶

National Renewable Energy Laboratory, Golden, Colorado 80401

This paper presents the results of two case studies regarding the wind farm layout optimization problem. We asked a general audience to take part in the studies that we designed, and

nine individuals participated. Case study 1 considered variations in optimization strategies for

a given simple Gaussian wake model. Participants were provided with a wake model that outputs annual energy production (AEP) for an input set of wind turbine locations. Participants

used an optimization method of their choosing to find an optimal wind farm layout. Case study

2 looked at trade-offs in performance resulting from variation in both physics model and optimization strategy. For case study 2, participants calculated AEP using a wake model of their

choice while also implementing their chosen optimization method. Participants then used their

wake model to calculate the AEP all other participant submitted turbine configurations produce for cross-comparison results. for optimized turbine locations were then cross-compared

by recalculating the AEP using every other participant’s wake model. Results for case study 1

show that the best optimal wind farm layouts in this study were achieved by participants who

used gradient-based optimization methods. A front-runner emerged with the Sparse Nonlinear

OPTimizer plus Wake Expansion Continuation (SNOPT+WEC) optimization method, which

consistently discovered a higher AEP for each scenario. Results for case study 2 show that for

small wind farms with few turbines, turbine placement on the wind farm boundary is superior.

Conclusions for case study 2 were drawn from participant cross-comparison of results.

I. Introduction

O

ptimizing turbine placement within a wind farm is a complex problem characterized by many local minima. The

large number of inter-dependent variables involved in wind farm layout optimization (WFLO) create a design space

that is difficult to solve reliably. In this study, we designed and conducted a set of case studies to discover superior

practices in solving the WFLO problem.

Two approaches have been taken to simplify the WFLO problem, as described by Padrón et al. [1]. The first

approach aims at improving the quality of individual models for wind farm attributes, i.e., aerodynamics, atmospheric

physics, turbine structures, etc. The second approach is to improve formulating the optimization problem, as well as the

algorithms used to solve the optimization [1].

Complex computational methods such as direct numerical simulations (DNS) or large eddy simulations (LES) can

be applied to wind turbines in order to better model the aerodynamics of the waked airflow region. But since these

methods use the Navier-Stokes equations, the computational time they require for full simulation can be prohibitive.

Simplified engineering wake models (EWMs) respond to this weakness by making certain limiting physics assumptions,

which result in greatly reduced computational costs [2]. Yet these simpler, less accurate, approximations may lead to

inefficient recommendations for turbine placement, due to what can be inaccurate assumptions in specific wind farm

scenarios.

For a given EWM, optimization methods to select ideal turbine locations may be limited by characteristics of the

implementation. For example, EWMs that define a discontinuous wind speed behind wind turbines cannot be effectively

used with gradient-based optimization methods. Conversely, EWMs for which gradients have not been calculated are

limited to gradient-free algorithms, or gradient-based algorithms using finite difference derivatives. Furthermore, within

these limitations, different optimization strategies have varying capacity to avoid local optima in the pursuit of a global

optimum. Factors such as step size or the “temperature” tolerances in simulated annealing optimization methods can

permit algorithms to escape local concavities in a design space in order to pursue a superior optimum.

To better understand the effects of EWMs and optimization algorithms, we created two case studies. We solicited

participant involvement from different research labs and private companies in industry currently working on both

general optimization methods, as well as methods specific to solving the WFLO problem. The first case study isolates

optimization techniques for a single simplified EWM; the second case study observes the differences when combining

variations in the EWM and optimization method.

Though papers have been published that survey the state of the wind farm optimization (perhaps one of the most

notable written by Herbert-Acero reviewing the current methodologies in the field [2]), our case studies are the first time

an international collaboration has been conducted to comparatively analyze optimization methods and EWM selection

on a representative WFLO problem.

Our case studies are created in support of the International Energy Agency’s (IEA’s) Wind Task 37 (IEA37). IEA37

coordinates international research activities centered around the analysis of wind power plants as “holistic” systems [3].

Though our case studies concentrate mainly on wake modelling optimization at the farm-level scale, our results still

contribute to IEA37’s integrated approach [3] to wind energy.

∗Masters Student, Department of Mechanical Engineering

†Ph.D. Candidate, Department of Mechanical Engineering

‡Ph.D. Student, Department of Mechanical Engineering

§Assistant Professor, Department of Mechanical Engineering

¶Senior Engineer, National Wind Technology Center

1

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II. Methodology

To enable production of useful data, our case studies require a model wind farm with characteristics that are

simultaneously restrictive enough to maintain simplicity, yet general enough to maintain relevance to more complex and

realistic problems. The wind farm scenarios we selected to meet this criteria, and other details relevant to this project as

a whole, are described below in Subsection A.

Many factors affect recommendations for superior turbine placement of a proposed wind farm. The two major

factors we chose to study are 1) EWM characteristics and 2) optimization algorithm [2]. We designed two case studies

in an attempt to quantify the effects of each of these choices.

For the first case study, in which the goal was to isolate variability in the optimization method, we pre-coded a

representative wake model as a control variable and permitted participants to use any optimization strategy to alter

turbine locations that would deliver the best annual energy production (AEP) for the farm. This is called case study 1

and is described below in Subsection B.

Isolating EWM variability proves more complicated. An EWM’s compatibility with gradient-based or gradient-free

optimization methods dictates which algorithms can be applied. As such, designing a case study that restricts participants

to a single optimization algorithm would unnecessarily limit the scope of EWMs studied. For this reason, our second

case study permits not only participant selection of EWM but also implemented optimization algorithm. It is called

case study 2 and is described below in Subsection C.

A. Common to Both Case Studies

1. Wind Turbine Specifications

We used IEA’s 3.35-MW reference turbine in all wind farms. Its attributes are open source, and it is designed as a

baseline for onshore wind turbine specifications [4]. The specifics of the turbine necessary for our simplified version of

Bastankhah’s Gaussian wake model (used in case study 1) are located in Table 1:

Table 1 Attributes for NREL’s 3.35-MW onshore reference turbine

Rotor Diameter 130 m

Turbine Rating 3.35 MW

Cut-In Wind Speed 4 m/s

Rated Wind Speed 9.8 m/s

Cut-Out Wind Speed 25 m/s

Its power curve is defined as:

P(V) =







0 V < Vcut-in

Prated 

V−Vcut-in

Vrated−Vcut-in 3

Vcut-in ≤ V < Vrated

Prated Vrated ≤ V < Vcut-out

0 V ≥ Vcut-out

0 5 10 15 20 25 30

V (m/s)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

P (MW)

2. Farm Geography

To focus on optimization method and EWM variability, as well as to avoid introducing too many unnecessary

variables, the wind farms for all scenarios are on flat and level terrain. To reduce boundary impacts on farm design, we

chose a radially symmetric farm boundary. Turbine (x, y) hub locations are restricted to be on or within the boundary

radius. Turbines are further constrained to be no less than two rotor diameters apart from any other turbine.

Farm diameter sizing for each scenario needed to be restrictive enough to avoid simply placing all turbines on the

boundary yet also permit meaningful turbine movement by the optimizers. Although the participants were not required

to use the example starting layouts that we provided, we tried to provide reasonable example layouts by dispersing the

turbines as much as possible in an orderly way. This was done by placing turbines in evenly spaced concentric rings.

The boundary radii of the various wind farms we defined were selected to permit turbine placement in concentric rings

with an average turbine spacing of five rotor diameters.

3. Wind Attributes

The wind distribution frequency and wind speed are the same for all wind farm scenarios in both case studies.

Freestream wind velocity is constant in all wind directions, at 9.8 m/s, regardless of turbine location or time of day. This

wind speed is used because it is our utilized turbine’s rated wind speed. Using this incoming wind velocity increases

power production variability experienced by the farm. In setting the scenario’s freestream velocity for the turbine’s

rated wind speed, any wake effects will push air speeds down the turbine’s power curve. With greater variability in the

resultant produced power, more local optima will be experienced by participant optimizers.

2

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A lack of local optima in a design space permits even ineffective optimizers to find a “best” result. In a design space

where local optima are present, inferior designs are very likely. We strove to create such design spaces with our case

study scenarios, as they test the robustness of optimization methods.

The selection of wind rose is a major factor in the frequency and magnitude of local optima resulting from turbine

placement. We selected a wind rose with an off-axis wind frequency distribution, binned for 16 directions. When we

tested this wind rose against 1,000 randomized starting turbine locations, it gave few optimized results with relatively

high AEP values. We interpreted this to be indicative of the presence of many local optima. The wind rose we used

is depicted in Fig. 1, in polar coordinates. In this figure, a greater magnitude in the radial direction from the origin

indicates a higher wind frequency from that specific direction.

N

N-E

E

S-E

S

S-W

W

N-W

4%

12%

20%

Figure 1 The wind frequency distribution for our case studies

B. Case Study 1: Optimization Only

The purpose of this case study is to determine the best optimization practices for WFLO, using a single representative

EWM. We selected a generalized wake model that both gradient-based and gradient-free optimization algorithms could

use and that was computationally inexpensive in comparison to LES and DNS methods.

1. Wake Model

The wake model selected for case study 1 is a simplified version of Bastankhah’s Gaussian wake model [5, 6]. This

wake model is described by the following equations:

∆U

U∞

=

 

1 −

s

1 −

CT

8σ

2

y /D2

!

exp− 0.5



y − δ

σy

2



(1)

Where ∆U

U∞

is the wake velocity deficit, CT = 8/9 is the thrust coefficient, y − δ is the distance of the point of interest

from the wake center in the cross-stream horizontal direction, D is the turbine diameter, and σy is the standard deviation

of the wake deficit in the cross-stream horizontal direction as defined in Eq. (2):

σy = (ky · x) +

D

√

8

(2)

In Eq. (2), x is the downstream distance from the turbine generating the wake to the turbine of interest, and D is the

turbine diameter. The variable ky is determined as a function of turbulence intensity (I). In this case study turbulence

intensity is treated as a constant of 0.075 (reasonable for an off-shore scenario), and we therefore used a corresponding

ky of 0.0324555 [6, 7].

Increasing turbulence intensity has numerous effects and draws attention away from the main purpose of this case

study, which is to observe the differences of optimization strategies. For the wake model we use [given in Eq. (1)],

increasing the turbulence intensity widened the wake cone, but second and third order effects are unknown. As such,

this first IEA37 set of case studies uses a very low intensity in an attempt to minimize the considered variables.

2. Farm Sizes

Variability in wind farm size (and thus number of design variables) affects optimization algorithm performance. To

account for this, three wind farm sizes are specified in case study 1: 16, 36, and 64 turbines. These had farm boundary

radii of 1300 m, 2000 m, and 3000 m respectively, determined in the manner described previously in Subsection A.2.

Inclusion of three farm sizes is to observe how increased complexity correlates to algorithm performance. The turbine

numbers are selected as perfect squares that roughly double in size. Perfect squares are used to permit even grid turbine

arrangements, if desired.

3. Supplied Code

To enable participation in this case study, we provided a link to a GitHub repository,∗ which included files that had:

• Turbine characteristics, wind frequency, and wind speed in IEA 37’s .yaml schema

• Example turbine layouts for each farm size (in .yaml format)

• Python parsers of the .yaml schema

∗https://github.com/byuflowlab/iea37-wflo-casestudies

3

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• Python target function to calculate AEP (given .yaml turbine locations and farm attributes)

We selected the programming language Python, since it is widely used by researchers in the industry.

Participant alteration to our specific code implementation, or replication of our model in another language, was

permitted if needed for compatibility with participant optimization methods. This is with the understanding, however,

that final wind farm layouts would be evaluated with the original Python code that we provided.

C. Case Study 2: Combined Physics Model/Optimization Algorithm

The intent of this case study is to assess not only the optimization methods measured by case study 1 but also the

effects that different physics model approximations have on turbine location recommendations. Case study 2 differs

from the previous one in that 1) no wake model is provided and 2) only a single wind farm size is to be optimized.

Participants are free to choose their preferred EWM and optimization method combination.

Unlike case study 1, participant-reported AEP is not comparable, since different EWMs are used to calculate them.

To help with this, we conducted a cross-comparison of results between participants. For the cross-comparison, each

participant’s proposed optimal turbine layout in the standardized .yaml format was published to the other combined

case study participants. Participants then used their own wake model to calculate the AEP of the other participant’s

proposed farm layouts. From this portion of the case study, we hope to learn if any participants’ results are seen as

superior by other EWMs.

1. Farm Attributes

The wind farm size for the combined case study is limited to nine turbines. We did this to limit the computation

time requirements when assessing results in a standardized LES, discussed later in Section IV. We used the previously

described method under Subsection A.2 to determine the boundary radius, and the wind rose and wind speed are the

same as case study 1.

III. Results

A. Case Study 1: Optimization Only

Participants ran the optimization algorithm of their choosing using our supplied AEP function or a functional

equivalent in another language. Since there exists a great deal of variability in hardware, participants also reported

processor speed, function calls, number of cores used, and total Random Access Memory (RAM) installed in their

system when finding their optimized results. The AEP results and rankings are given below in Tables 2 to 4.

There were 10 submissions for case study 1. One participant submitted twice, using a different optimization method

for each submission. For anonymity, each submission is assigned a number. We will refer to each submission below by

this submission number (i.e., sub1, ..., sub10, etc.).

1. Data

Tables 2 to 4 display the final AEP data of all participant-proposed optimal turbine layouts. The Python module

we supplied, which uses the simplified Bastankhah wake model, was used for all AEP calculations. Submissions are

ranked from highest to lowest resultant AEP values, with submission number (sub#), whether using a gradient-based

(G) or gradient-free (GF) optimization method, and a percentage increase of AEP (Increase) from the provided example

layout’s AEP.

2. General Trends

As a general trend, gradient-based methods performed better in discovering a relative optima, especially for smaller

farm sizes. Some gradient-based algorithms improve in comparative AEP ranking as the number of design variables

increase (sub10, sub3), while others degrade (sub5, sub8). Simultaneously, one gradient-free algorithm increases in

effectiveness as design variables increase (sub3), while others compete for lowest comparative performance, regardless

of farm size (sub6, sub7, sub9).

Despite these multivariate results, one clear front-runner does emerge. Regardless of wind farm size, sub4’s

algorithm consistently discovered turbine placements that delivers an AEP superior to all other participants. A summary

of sub4’s method is included in a following section.

Also of note, as the number of design variables increases, the relative disparity between proposed optimal AEPs

likewise diverges. For the 16-turbine case, the highest result is 7.88% better than the lowest. For the 36 and 64 cases,

the highest result is 11.45% and 13.54% better than the lowest, respectively.

3. Analysis of Best Results

For all three farm sizes, the superior method was implemented by sub4, using a gradient-based method. Coded

in Python and FORTRAN, it combined SNOPT [8] with a method called Wake Expansion Continuation (WEC) [6].

Running 200 optimizations, sub4 had one optimization run start from the provided example layout, and the other 199

use randomized turbine starting locations within the farm boundary.

The WEC method is specifically designed to reduce the multimodality found in wind farm layout optimization. In

the cited paper [6], it is a method of converting design spaces with many local minima into curves approaching convexity,

allowing gradient-based optimizations to more easily find the better solutions. An example of such “relaxation” to

convexity is included in Figs. 2 and 3, reproduced with permission.

Figs. 2 and 3 demonstrate the effects of the WEC method on a simple design space, relaxing the local optima into a

more easily discovered global solution. As the authors Thomas and Ning state, “Larger values of ξ allow the smaller

local optima to disappear completely. Smaller values of ξ allow for more accurate wake widths but with an increase in

4

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Table 2 16 turbine scenario participant results

Rank Algorithm sub# Grad. AEP Increase

1 SNOPT+WEC 4 G 418924.4064 14.17 %

2 fmincon 5 G 414141.2938 12.86 %

3 SNOPT 8 G 412251.1945 12.35 %

4 SNOPT 1 G 411182.2200 12.06 %

5 Preconditioned Sequential Quadratic Programming 2 G 409689.4417 11.65 %

6 Multistart Interior-Point 10 G 408360.7813 11.29 %

7 Full Pseudo-Gradient Approach 3 GF 402318.7567 9.64 %

8 Basic Genetic Algorithm 7 GF 392587.8580 6.99 %

9 Simple Particle Swarm Optimization 6 GF 388758.3573 5.95 %

10 Simple Pseudo-Gradient Approach 9 GF 388342.7004 5.83 %

11 (Example Layout) - - 366941.5712 -

Table 3 36 turbine scenario participant results

Rank Algorithm sub# Grad. AEP Increase

1 SNOPT+WEC 4 G 863676.2993 17.05 %

2 Multistart Interior-Point 10 G 851631.9310 15.42 %

3 Preconditioned Sequential Quadratic Programming 2 G 849369.7863 15.11 %

4 SNOPT 8 G 846357.8142 14.70 %

5 SNOPT 1 G 844281.1609 14.42 %

6 Full Pseudo-Gradient Approach 3 GF 828745.5992 12.31 %

7 fmincon 5 G 820394.2402 11.18 %

8 Simple Pseudo-Gradient Approach 9 GF 813544.2105 10.25 %

9 Basic Genetic Algorithm 7 GF 777475.7827 5.37 %

10 Simple Particle Swarm Optimization 6 GF 776000.1425 5.17 %

11 (Example Layout) - - 737883.0985 -

Table 4 64 turbine scenario participant results

Rank Algorithm sub# Grad. AEP Increase

1 SNOPT+WEC 4 G 1513311.1936 16.86 %

2 Preconditioned Sequential Quadratic Programming 2 G 1506388.4151 16.36 %

3 Multistart Interior-Point 10 G 1480850.9759 14.35 %

4 SNOPT 1 G 1476689.6627 14.03 %

5 Full Pseudo-Gradient Approach 3 GF 1455075.6084 12.36 %

6 SNOPT 8 G 1445967.3772 11.66 %

7 Simple Pseudo-Gradient Approach 9 GF 1422268.7144 9.82 %

8 Simple Particle Swarm Optimization 6 GF 1364943.0077 5.40 %

9 fmincon 5 G 1336164.5498 3.18 %

10 Basic Genetic Algorithm 7 GF 1332883.4328 2.93 %

11 (Example Layout) - - 1294974.2977 -

2 0 2 4 6 8 10

Streamwise Location, X=Dr

6

4

2

0

2

4

6

Crosswind Location, Y=D

r

Wind Direction

Turbine Movement

Wind turbines

Figure 2 Simple design space used to

demonstrate the effects of the relaxation

factor, ξ, on the wind farm layout design

space. [6]

6 4 2 0 2 4 6

Downstream Turbine's Crosswind Location, X=Dr

10

12

14

16

18

20

22

AEP, GWh

» = 1

» = 2

» = 3

» = 4

» = 5

» = 6

» = 7

Figure 3 The impact of the wake relaxation factor, ξ.

One turbine was moved across the wakes of two upstream turbines (see Fig. 2). [6]

5

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the number and magnitude of local optima.” [6]. We suspect that the WEC method for reducing the multimodality of

the design space is why sub4’s optimizations found superior layouts to the other used methods.

4. Discussion

Though sub4 consistently found the superior AEP relative to the other participants, sub2’s results demonstrate a

trend closing the gap as the number of design variables increased. For the 16 turbine case, sub4 was 2.5% better than

sub2’s results. For the 36 and 64 cases, sub4 was 1.68% and 0.46% better, respectively. It should be noted, however,

that at the current average U.S. rate [9] of roughly $0.13 for a kWh (or $133 per MWh), the income difference between

the AEPs of sub4 and sub2 in the 64 turbine case, though only 0.46%, equates to a difference of a little under $1 million

per year.

Since sub2’s Preconditioned Sequential Programming (PSQP) method steadily closed the gap, a future study

should test even larger wind farm sizes. This could determine if the PSQP algorithm will eventually outperform the

SNOPT+WEC method when a certain number of design variables are reached, or if there is an upper limit or convergence

to this trend.

Though the majority of participants used random starts for each optimizaiton, sub2’s method of “warm starting”

performed progressively well, especially as the number of design variables increased. Taking a starting set of turbine

coordinates, sub2 rotated the layout in π/6 steps. These rotations created the starting geometry for subsequent iterations.

Though not precisely “intuitive” starts, they are more intelligently designed than pure randomized locations. As

discussed above, sub2 did perform increasingly well compared to other methods (ranking 2nd for the 64-turbine case).

Translating the provided AEP target function proved helpful in speeding up the computations and allowing for

greater exploration. At least two participants translated the target Python file into FORTRAN, one into Julia, and one

altered it within Python by converting loops into vectorized statements. In testing, these reimplementations sped up the

analysis time by at least an order of magnitude.

B. Case Study 2: Combined Physics Model/Optimization Algorithm

For case study 2, participants ran both the optimization algorithm and wake model of their choosing. There were

no restrictions on programming language for either the wake model or optimization algorithm, but results of optimal

turbine layouts were to be submitted in the .yaml format supplied in the case study 1 examples.

There were five participant submissions for case study 2. All five participants also submitted for case study 1 (though

were not required to do so). For ease of comparison, we assigned their submissions the same numbers from that case

study as well (i.e., sub1 - sub5 are from the same individual participants for both case study 1 and case study 2).

Because participants used different wake models, AEP values reported cannot be fairly compared between participants.

Results were therefore judged on cross-comparison calculations.

1. Data

The cross-comparison displays some interesting trends. Tables 5 to 9 show how each submission’s wake models

ranked the proposed optimal turbine layouts for the other 4 submissions. Each submission’s ranking of its own layout is

in bold. The penultimate column in each table is the submission number of the layout being cross-compared (cc-sub#).

So submission 4’s analysis of submission 2’s layout would be found in sub4’s table, with 2 in the cc-sub# column. The

last column is the percentage increase (Increase) from the reporting submission’s submitted layout. A negative value

here indicates a worse AEP.

2. General Trends

We expected participants to rank their own layout as superior to the others. Each wake model accounts for different

fluids phenomena, and what one wake model considers an optimal layout, another may not. An example of this is if one

EWM predicts a wake deficit due to some factor such as vorticity or turbulence. A turbine placed downstream under

such a model would, under a more simplistic wake model not accounting for this phenomena (such as the Jensen’s

model [10]), feel the full brunt of the wake and deliver a suboptimal AEP.

Unexpectedly, only sub4 and sub5 found their own layouts to be superior to the other participants. Furthermore,

all other participants also found sub4 and sub5’s layouts superior to their own, though to varying degrees. Three

participants (including sub4) found sub4 to have the highest AEP-producing layout. The other two participants found

sub5 to have the highest AEP-producing layout.

3. Analysis of Best Results

Within expectations, sub4 and sub5 ranked their own layouts superior to all other participant results. Two correlations

are important to note regarding sub4 and sub5. First, both used variations of the same wake model. From case study 1,

sub5 used the simplified Gaussian wake model previously described [5, 6]. Though sub4 also used the Gaussian wake

model [5], sub4 combined it with the model created by Niayifar and Porté-Agel [7], supplemented by the WEC method

described earlier. Furthermore, sub4 also accounted for wind shear and local turbulence intensity. Neither of these

factors were accounted for by sub5. The second factor to note is that despite using very similar wake models, sub4 and

sub5 used different gradient-based optimization algorithms that nonetheless reached very similar conclusions.

As can be seen in the visual depictions included in the Appendix, sub4 and sub5 found nearly identical optimal

turbine placements. Though appearing identical, the actual coordinates do indeed differ, enough so to result in different

AEP calculations shown in the tables above.

Without LES data, the conclusions able to be drawn from the cross-comparison analysis are limited. That both sub4

and sub5 were found by the other participant wake models to have superior placement could be a result of either a more

efficient optimization method or a better coupling between optimization method and wake model. That these minima

existed within the other wake models (resulting in a higher computed AEP by those models), yet were nevertheless

undiscovered in their optimizations, is inconclusive in telling us which it is.

6

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Table 5 Cross-comparison results of sub1

Rank Wake Model Algorithm AEP cc-sub# Increase

1 Simplified Bastankhah fmincon 262350.319 4 0.624 %

2 Bastankhah SNOPT+WEC 262282.416 5 0.598 %

3 FLORISSE 3D SNOPT 260722.295 1 -

4 Bastankhah Full Pseudo-Gradient Approach 260640.906 3 -0.031 %

5 Park2 PSQP 248215.024 2 -4.797 %

Table 6 Cross-comparison results of sub2

Rank Wake Model Algorithm AEP cc-sub# Increase

1 Simplified Bastankhah fmincon 250464.9732 4 5.975 %

2 Bastankhah SNOPT+WEC 250249.0259 5 5.884 %

3 Bastankhah Full Pseudo-Gradient Approach 247812.0522 3 4.853 %

4 FLORISSE 3D SNOPT 240309.5850 1 1.678 %

5 Park2 PSQP 236342.799 2 -

Table 7 Cross-comparison results of sub3

Rank Wake Model Algorithm AEP cc-sub# Increase

1 Bastankhah SNOPT+WEC 247109.5234 5 0.590 %

2 Simplified Bastankhah fmincon 246942.3767 4 0.522 %

3 Bastankhah Full Pseudo-Gradient Approach 245659.4124 3 -

4 Park2 PSQP 242431.5431 2 -1.314 %

5 FLORISSE 3D SNOPT 237548.6622 1 -3.302 %

Table 8 Cross-comparison results of sub4

Rank Wake Model Algorithm AEP cc-sub# Increase

1 Simplified Bastankhah fmincon 257790.1924 4 -

2 Bastankhah SNOPT+WEC 257663.4068 5 -0.049 %

3 Bastankhah Full Pseudo-Gradient Approach 255063.8201 3 -1.058 %

4 FLORISSE 3D SNOPT 251776.7157 1 -2.333 %

5 Park2 PSQP 239612.8223 2 -7.051 %

Table 9 Cross-comparison results of sub5

Rank Wake Model Algorithm AEP cc-sub# Increase

1 Bastankhah SNOPT+WEC 251771.9067 5 -

2 Simplified Bastankhah fmincon 251697.7126 4 -0.029 %

3 Bastankhah Full Pseudo-Gradient Approach 249829.2199 3 -0.772 %

4 FLORISSE 3D SNOPT 246503.8323 1 -2.092 %

5 Park2 PSQP 239482.6767 2 -4.881 %

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Both sub4 and sub5 used similar wake models but very different optimization methods. Coding in MATLAB, sub5

did 1,000 random starts and used MATLAB’s fmincon (which uses a finite difference method to find gradients) to

optimize for a minimum. Using a combination of Python and FORTRAN, sub4 ran 1 optimization with a user-selected

initial turbine configuration, and randomized the turbine starting locations for another 199 to make 200 optimizations

altogether. SNOPT’s SQP algorithm (using algorithmic differentiation to obtain gradients) was sub4’s implemented

optimizer.

Of note, from trends seen above in case study 1, sub5’s optimization methods demonstrate superior performance for

small design variable sizes but comparatively degrades as the wind farm size increases. The superior performance of

this wake model and optimization method combination for this small farm may not be representative of performance on

larger wind farms.

4. Discussion

Participants of earlier case studies were critical of wind farm scenarios where non-novel, simplistic layouts (such as

all turbines on the boundary border) are optimal. The small farm radius with few turbines given for this case study

seems to have fallen into this category. What is interesting, however, is that three of the five participants were trapped in

local optima, and proved blind to optima others found using different physics approximations and optimization methods.

Many factors could have led to these shortfalls (i.e., inferior optimization methods, lack of sufficient iterations, lack of

sufficient wall time, etc.), and further testing would need to be done to discover which factors majorly contributed to the

outcome.

IV. Conclusion

Results from case study 1 show that sub4s use of SNOPT+WEC delivers superior results for the tested wind farms

with 16, 36, and 64 turbines. Although information on this method is continuing to be produced, the initial paper

written by Thomas and Ning[6] describes this method. Regarding sub2’s PSQP method, though it shows a trend of

increased performance that may surpass SNOPT+WEC for wind farms of sizes larger than 64, further testing is required

to validate this pattern.

Case study 2 demonstrates that, for wind farms of small area with few turbines, placement on the wind farm boundary

delivers superior AEP. Shortcomings in participant pairings of optimization methods were trapped in local optima,

however. The lesson learned here is to either train researcher intuition to use such layouts as warm starts or improve

optimization methods so that automated optimizers can discover this themselves.

Though we are happy with the level of participation in the case studies, a larger participant sample size with different

methods may provide more informative data or display other novel and superior methods. To refine our data collection

process, we plan on running another round of results for these case studies in the near future.

Due to the difficulty in comparing the results of different EWMs, we will run all participant-reported optimized

turbine locations through an LES. With the inherent bias each EWM has for its own optimized locations removed,

reported turbine locations will be measured using the same high-fidelity simulation tool for a comparative AEP. Case

study 2 was constructed mainly for this LES wake model evaluation in order to gauge which simplified model is most

accurate when compared with a higher-cost computational model.

8

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Acknowledgments

This work was authored [in part] by the National Renewable Energy Laboratory, operated by Alliance for Sustainable

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided

by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office.

The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S.

Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government

retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work,

or allow others to do so, for U.S. Government purposes.

The authors gratefully acknowledge the following individuals, in alphabetical order, as submitting participants in the

case study:

• Tim Camp, Director, Turbine Engineering

• Abhinav Prakash, Ph.D. Student, Texas A&M University

• Erik Quaeghebeur, Dr.ir, Delft University of Technology

• Sebastian, Sanchez Perez Moreno, Ph.D. Student, Delft University of Technology

• Landon Wiley, M.Sc Student, Brigham Young University

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sub1

sub2 sub3

sub4 sub5

Pictorial Representations of Case Study 1’s Participant Submissions

10

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sub1 sub2 sub3

sub4 sub5 sub6

sub7 sub8 sub9

sub10

Pictorial Representations of Case Study 2’s 16-Turbine Submissions

11

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sub1 sub2 sub3

sub4 sub5 sub6

sub7 sub8 sub9

sub10

Pictorial Representations of Case Study 2’s 36-Turbine Submissions

12

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sub1 sub2 sub3

sub4 sub5 sub6

sub7 sub8 sub9

sub10

Pictorial Representations of Case Study 2’s 64-Turbine Submissions

13

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References

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Energy Production in Wind Farm Layout Optimization,” Wind Energy Science, 2018. doi:10.5194/wes-2017-56, (in review).

[2] Herbert-Acero, J. F., Probst, O., Réthoré, P.-E., Larsen, G. C., and Castillo-Villar, K. K., “A Review of Methodological

Approaches for the Design and Optimization of Wind Farms,” Energies, 2014, p. 23.

[3] McWilliam, M. K., Zahle, F., and Dykes, K., “IEA Task 37 on System Engineering in Wind Energy The Aerodynamic Only

Optimization Case Study,” , May 2017.

[4] Bortolotti, P., Dykes, K., Merz, K., Sethuraman, L., and Zahle, F., “IEA Wind Task 37 on System Engineering in Wind Energy,

WP2 - Reference Wind Turbines,” Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO., May 2018.

[5] Bastankhah, M., and Porté-Agel, F., “Experimental and theoretical study of wind turbine wakes in yawed conditions,” J. Fluid

Mech., Vol. 806, 2016, pp. 506–541.

[6] Thomas, J. J., and Ning, A., “A Method for Reducing Multi-Modality in the Wind Farm Layout Optimization Problem,” Journal

of Physics: Conference Series, Vol. 1037, The Science of Making Torque from Wind, Milano, Italy, 2018, p. 10.

[7] Niayifar, A., and Porté-Agel, F., “Analytical Modeling of Wind Farms: A New Approach for Power Prediction,” Energies, 2016.

[8] Gill, P., Murray, W., and Saudners, M., “SNOPT: an SQP algorithm for large-scale constrained optimization,” SIAM Review,

Vol. 47, 2005, pp. 99–131.

[9] “Worth of kWh per state,” , November 2018. URL https://www.chooseenergy.com/electricity-rates-by-state/.

[10] Jensen, N., “A Note on Wind Generator Interactions,” Tech. rep., Risø National Laboratory, 1983.

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