NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Combining Wind Plant Control With Systems 

Engineering

2015 Wind Energy Systems Engineering Workshop, Boulder, Colorado

Paul Fleming, Andrew Ning, Pieter Gebraad, Katherine Dykes

National Renewable Energy Laboratory

January 14, 2015

PR-5000-63584
2

Overview

• Wind plant controls research

• Combining optimization

• Case study: Princess Amalia Wind Park

• Conclusions.
3

Wind Plant Control

• Recent research has focused on improving 

wind plant performance by coordinating the 

control of individual turbines.

Photo from Uni-Fly A/s
4

Yaw-Based Wake Redirection Control

• One promising approach uses intentional yaw misalignment to 

redirect wakes away from downstream turbines.

Yaw angle (degrees)

Illustrations from Fleming, P.; Gebraad, P.; Lee, S.; van Wingerden, J.W.; Johnson, K.; Churchfield, M.; 

Michalakes, J.; Spalart, P.; Moriarty, P. (2014). “Simulation comparison of wake-mitigation control strategies 

for a two-turbine case.” Wind Energy.
5

Model Development

• Work has used high-fidelity simulation to 

study the control systems (Simulator for 

On/Offshore Wind Farm Applications, or 

SOWFA)

• Additionally, control-oriented engineering 

models have been developed for control 

design (FLOw Redirection and Induction in 

Steady-state, or FLORIS).

Illustrations from Fleming, P.; Gebraad, P.; Lee, S.; van Wingerden, J.W.; Johnson, K.; Churchfield, M.; 

Michalakes, J.; Spalart, P.; Moriarty, P. (2014). “Simulation comparison of wake-mitigation control strategies 

for a two-turbine case.” Wind Energy.

Gebraad, P. M. O.; Teeuwisse, F. W.; van Wingerden, J. W.; Fleming, P. A.; Ruben, S. 

D.; Marden, J. R.; Pao, L. Y. (2014). “Data-Driven Model for Wind Plant Power 

Optimization by Yaw Control.” Proceedings of the 2014 American Control Conference 

(ACC); June 4-6, 2014, Portland, Oregon. NREL/CP-5000-61405. Piscataway, NJ: 

Institute of Electrical and Electronics Engineers; pp. 3128-3134.
6

Model-Based Control

• Using engineering-level model:

o Optimize yaw angles within this model.

• Test selected values in SOWFA.
7

Model-Based Control

Adapted from: Gebraad, P. M. O.; Teeuwisse, F. W.; van Wingerden, J. W.; Fleming, P. A.; Ruben, S. D.; Marden, J. R.; Pao, L. Y. (2014). “Data-Driven Model for Wind Plant Power 

Optimization by Yaw Control.” Proceedings of the 2014 American Control Conference (ACC); June 4‒6, 2014, Portland, Oregon. NREL/CP-5000-61405. Piscataway, NJ: Institute of Electrical 

and Electronics Engineers; pp. 3128-3134.
8

Combined Optimization

• The previous work assumes a fixed plant 

layout, turbine design, and so on

• Perhaps the benefit of wind plant control 

could be amplified if accounted for during the 

early phase of design

• In this work, a proof-of-concept study was 

performed in which wind plant controls and 

layout were optimized.
9

Case Study: Princess Amalia Wind Park

• Given baseline layout of 

real wind plant, compare:

• Baseline: fixed (original) positions, turbines all 

yawed in mean wind direction

• Optimized yaw: fixed (original) positions, 

turbines optimally yawed for each wind 

direction

• Optimized location: position optimized, 

turbines all yawed in mean wind direction

• Combined optimization: simultaneously 

optimized position and yaw for each wind 

direction.

• Use as a proxy metric of COE the power density of the wind farm (W/m2)

• Note 1: Full paper (under review in Wind Energy) considered cable length and boundary limitations as well

• Note 2: NREL’s 5-megawatt (MW) turbines were used in place of ~2-MW turbines, making baseline spacings closer

• Note 3: All following figures from upcoming full paper.
10

Baseline

Baseline

Mean power (MW) 78.86

Area (km2) 14.53

Power density (W/m2) 5.43

Power Output Layout
11

Yaw Control

Optimized yaw angles for the 180o case
12

Yaw Control

Baseline YawOpt

Mean power (MW) 78.86 84.91

Area (km2) 14.53 14.53

Power density (W/m2) 5.43 5.84

Power Output Layout
13

Layout Optimization

Baseline YawOpt PosOpt

Mean power (MW) 78.86 84.91 78.86

Area (km2) 14.53 14.53 12.45

Power density (W/m2) 5.43 5.84 6.33

Power Output Layout
14

Combined Optimization

Baseline YawOpt PosOpt Combined

Mean power (MW) 78.86 84.91 78.86 78.84

Area (km2) 14.53 14.53 12.45 8.96

Power density (W/m2) 5.43 5.84 6.33 8.80

Power Output Layout
15

Results

• Coupling yaw control and position density 

provided a 40% increase in power density over 

layout optimization alone and 50% more than 

yaw control alone

• Proof-of-concept study demonstrated that the 

potential of wind plant control can be greatly 

expanded if included in the design phase

• Full paper includes optimizations for cable 

length and given a fixed wind plant boundary.
16

Future Work

• Current and future work considers:

o Impact on loading and inclusion of loading effects 

in optimization

o More realistic cost-of-energy optimization 

function

o More detailed and realistic constraints

o Improved optimization for faster convergence.
17

Thanks for Your Attention!

file:///C:/Users/pfleming/Downloa

ds/25915.jpg

Photo by Dennis Schroeder, NREL 25915

Paul Fleming | paul.fleming@nrel.gov