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Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Combining Droop Curve 

Concepts with Control 

Systems for Wind Turbine 

Active Power Control

Preprint

A. Buckspan, J. Aho, and L. Pao

University of Colorado at Boulder

P. Fleming

National Renewable Energy Laboratory

Y. Jeong

Colorado School of Mines

To be presented at the IEEE Symposium on Power Electronics and 

Machines in Wind Applications

Denver, Colorado

July 16-18, 2012

Conference Paper

NREL/CP-5000-55211

June 2012
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Combining Droop Curve Concepts with Control
 

Systems for Wind Turbine Active Power Control
 

Andrew Buckspan, Jacob Aho, Paul Fleming, Yunho Jeong, Lucy Pao 

Abstract—Wind energy is becoming a larger portion of the 

global energy portfolio, and wind penetration has increased dramatically in certain regions of the world. This increasing wind 

penetration has driven the need for wind turbines to provide 

active power control (APC) services to the local utility grid, as 

wind turbines do not intrinsically provide frequency regulation 

services that are common with traditional generators. Large scale 

wind turbines are typically decoupled from the utility grid via 

power electronics, which allows the turbines to synthesize APC 

commands via control of the generator torque and blade pitch 

commands. Consequently, the APC services provided by a wind 

turbine can be more flexible than those provided by conventional 

generators. 

This paper focuses on the development and implementation of 

both static and dynamic droop curves to measure grid frequency 

and output delta power reference signals to a novel power set 

point tracking control system. The combined droop curve and 

power tracking controller is simulated and comparisons are made 

between simulations using various droop curve parameters and 

stochastic wind conditions. The tradeoffs involved with aggressive 

response to frequency events are analyzed. At the turbine level, 

simulations are performed to analyze induced structural loads. At 

the grid level, simulations test a wind plant’s response to a dip in 

grid frequency. 

NOMENCLATURE 

β blade pitch angle 

Ωg generator speed 

τg generator torque 

AGC automatic generation control 

APC active power control 

BPF band-pass filter 

CART3 3-bladed Controls Advanced Research Turbine 

DDC dynamic droop curve 

DEL damage equivalent load 

ERCOT Electric Reliability Council of Texas 

LPF low-pass filter 

NREL National Renewable Energy Laboratory 

Andrew Buckspan is a doctoral student in the Dept. of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, e-mail: andrew.buckspan@colorado.edu. 

Jacob Aho is a doctoral student in the Dept. of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, e-mail: jacob.aho@colorado.edu. 

Paul Fleming is a research engineer at the National Wind Technology Center, 

Boulder, e-mail: paul.fleming@nrel.gov. 

Yunho Jeong was a graduate student in the Division of Engineering, Colorado 

School of Mines, Golden, e-mail: yunho3600@gmail.com. 

Lucy Pao is the Richard & Joy Dorf Professor in the Dept. of Electrical, 

Computer, and Energy Engineering, University of Colorado, Boulder, e-mail: 

pao@colorado.edu 

This paper is produced as part of the NREL Active Power Control from 

Wind Power Project. This work was supported in part by the U.S. Department 

of Energy’s Office of Energy Efficiency and Renewable Energy Wind and Hydropower Technologies Program and also by a University of Colorado Boulder 

College of Engineering and Applied Science Dean’s Graduate Assistantship. 

PI proportional-integral 

RL rate limit 

ROCOF rate of change of frequency 

SDC static droop curve 

TSO transmission systems operator 

I. INTRODUCTION 

Wind energy is a growing renewable energy technology, 

with a 27.7% mean annual growth rate in global installed 

capacity over the past decade [1]. While capacity levels in 2010 

had wind energy providing only 2.5% of the global electrical 

energy supply, countries such as Denmark, Portugal, Spain, 

and Germany produced 21%, 18%, 16%, and 9% of their 

annual energy from wind, respectively [1]. The higher wind 

penetrations in these countries have driven the interest in wind 

energy providing active power control (APC) services to help 

stabilize grid frequency. 

Transmission systems operators (TSOs) require conventional 

utilities to provide frequency regulation services in order to 

maintain the necessary balance between generation and load, 

which in turn regulates grid frequency. Conventionally, grid 

frequency response to a large disturbance is divided into separate control regimes: inertial, primary frequency response, and 

secondary frequency response or automatic generation control (AGC). The various phases of grid frequency response can 

be seen in Fig. 1. These classifications are based on methods 

for providing each service with conventional generators: synchronous generator inertia for inertial response, turbine governors for primary response, and finally output control responding 

to system operator power level demands for AGC response. For 

further information and exact definitions of inertial, primary, and 

secondary response, see [2] and [3]. 

Fig. 1. Inertial, primary frequency, and secondary (AGC) frequency response 

phases. Figure reproduced from image provided by Pouyan Pourbeik of EPRI. 

1
Fig. 2. A block diagram showing the general interconnection for APC commands. The wind plant controller can measure the frequency of the utility grid 

and receive an AGC power command signal from the grid operator and in turn produce a power reference for each turbine in the wind plant. From [4] with 

permission. 

Wind power has not historically been required to provide 

APC services, as most modern wind turbines do not intrinsically 

provide any of the frequency regulation services that are available with conventional generators because utility-scale wind 

turbines are usually decoupled from the grid via their power 

electronics. However, new requirements and regulations put on 

wind plants by TSOs in the aforementioned countries have lead 

to new research and development to enable wind turbines to 

meet APC requirements. These regulations require that (1) new 

wind plants provide an inertial response when grid frequency 

deviates from the normal operating point and (2) wind plants 

meet a power set point provided by the TSO at a specified rate 

of change [5]-[9]. The interconnection of a wind plant controller 

with the utility grid, TSO, and individual turbines can be seen in 

Fig. 2. The new TSO requirements have forced manufacturers 

to meet these minimum requirements to provide inertial and 

AGC response. 

This paper provides an overview of prior research in wind 

turbine primary response and describes a new methodology 

of using droop curve concepts with a novel wind turbine 

control system. Droop curves typically relate fluctuations in grid 

frequency to a corresponding change in power in a governor 

for a conventional synchronous generator. Augmenting a wind 

turbine control system designed to modify active power output 

with a droop curve can allow for participation in primary frequency response, as the APC system can automatically respond 

to changes in grid frequency in addition to active power set 

points requested by the grid operator. This paper focuses on 

analyzing the costs and benefits of using droop curves with 

various parameters on a single turbine, as well as on the utility 

grid as a whole. 

The paper is organized as follows: Section II provides an 

overview of previous relevant work on APC of wind turbines 

to provide primary frequency response. Section III develops 

the combined droop curve and novel active power control 

methodology. Section IV presents selected simulation results 

using the droop curve augmented controller. Finally, Section V 

discusses future work and provides concluding comments. 

II. PRIOR RELEVANT RESEARCH IN WIND TURBINE
 

PRIMARY RESPONSE
 

In this section, we present the most relevant prior research in 

implementing primary frequency control in wind turbines [10]­

[15]. A feature common to these methodologies is the modification of generator torque based on measurements of the change in 

grid frequency and possibly the rate of change of grid frequency 

(ROCOF). In [10] and [11], the turbine is operated at a higher 

than optimal tip-speed ratio, allowing for an overhead power 

reserve. In the event that grid frequency deviates too far below 

nominal, generator torque is increased, extracting some inertia 

from the over-speeding rotor, and reducing the tip-speed ratio 

closer to optimal. A similar methodology is developed in [12]. 

Combined torque and blade pitch methodologies have also been 

developed, such as in [15]. Here, a proportional torque controller 

is used, where the gain can be variably adjusted according to 

grid frequency deviations. A blade pitch controller assists in 

primary response by decreasing pitch angle as necessary to 

ensure torque actuation during frequency transients does not 

cause large drops in mechanical power. In [13], a proportionalintegral (PI) control loop generates a torque command that is 

used to operate the turbine on the optimal power-rotor speed 

trajectory. This control loop is augmented by loops that change 

the torque command proportionally to grid frequency deviations 

and ROCOF, with the former providing primary frequency 

control. 

The use of droop curves to supplement wind turbine APC has 

also been investigated, such as in [13], [16], and [17]. In these 

studies, static linear droop curves, like those associated with 

governors for conventional generators, are used to generate a 

power reference based on frequency deviations, which is then 

an input to the control system. While [16] is generally more 

focused on voltage regulation through reactive power control, 

the use of droop curves in APC is briefly introduced. The use 

2
of droop curves in frequency regulation is considered more 

thoroughly in [17]. Here, a supervisory controller generates a 

power set point, while deviations in grid frequency are passed 

through a droop curve, generating an offset in the power 

reference. In an under­frequency event, pitch angle is reduced 

to capture power that was previously being shed by the blades. 

Simulations have been conducted to evaluate the performance 

of some of the proposed strategies for providing primary frequency response with wind turbines. In [13], two small­scale 

grid simulations were analyzed to determine the effect on grid 

frequency caused by losing a conventional generator. Primary 

frequency support was provided by the wind turbines in only 

one of the simulations. In a similar study [18], simulations 

analyzed an electric grid with varying levels of penetration 

by wind turbines with primary frequency control capabilities. 

Here, the grid was modeled as a single electrical bus, and the 

effect of loss of a generator on grid frequency was monitored. 

These simulations indicate that wind turbines can help arrest 

grid frequency dips and reduce maximum frequency deviations 

by participating in primary response. These simulations are, 

however, limited by their use of simplified wind turbine models. 

These simulations also neglect to analyze possible increases 

in turbine structural loads that can be induced when APC is 

employed. 

III. OVERVIEW AND IMPLEMENTATION OF DROOP CURVES 

In this section, we discuss a wind turbine controller designed 

for power set­point tracking combined with droop curves that 

generate the power reference commands. 

A. Control System Description 

The controller used in this study is adapted from one provided 

in [19], where a number of controllers were designed to 

implement APC on wind turbines by tracking a power reference 

signal provided to the turbine control system. The reference 

signal is generally an absolute AGC power reference signal [19]. 

The APC torque controller tracks a power reference for a given 

rotor speed by changing torque proportionally to the power set 

point but only works well in above­rated wind speed when the 

blade pitch controller is regulating rotor speed. The APC pitch 

controller is a gain­scheduled PI controller, similar to the one 

described in [20], which uses the power reference command to 

determine the speed set point along the baseline torque­speed 

trajectory, as seen in Fig. 3. 

The APC torque and pitch controllers are combined in [19] 

using various methods, with the most promising method using 

filters to divide the control action between the torque and pitch 

controllers. The power reference signal is band­pass filtered and 

delivered to the torque controller, as the APC torque controller 

offers much faster response to changing power reference than 

does the pitch controller. The power reference input is low­pass 

filtered before being passed to the APC pitch controller, as the 

blade pitch motor cannot actuate as quickly as the torque can 

but performs much better in regulating the steady­state power 

output. This methodology is the basis of the APC controllers 

developed for this study. 

Fig. 3. The baseline torque­speed trajectory for the turbine simulated in this 

study is shown with an overlay of constant power curves. The APC controller 

used determines the speed set point for the blade pitch controller to be the point 

on the baseline trajectory that intersects the desired power reference. The torque 

controller will nominally follow the baseline trajectory but will track band­pass 

filtered power reference fluctuations given the speed set point. 

B. Droop Curve Description 

The previously described controller was originally designed 

to provide secondary services by tracking an AGC power reference signal. However, it can be adapted to participate in primary 

grid response if the power reference signal is augmented to 

respond to variations in grid frequency. This can be achieved 

through the use of a droop curve, which relates deviations in 

grid frequency to changes in power reference. 

Droop curves are traditionally used to characterize the response of the governor of a synchronous generator to grid 

frequency deviations. These droop curves are parameterized 

based on their slope, usually given in percentage. For example, a 

governor with a 5% droop curve would vary the power output of 

the generator by 100% of rated power for a 5% deviation from 

nominal grid frequency. A deadband is often used to prevent the 

governor from actuating unnecessarily when small amplitude 

fluctuations in grid frequency are detected. Fig. 4 shows the 

static droop curves (SDCs) used in this study. 

Fig. 4. The SDCs shown here are used in simulations both at the individual 

turbine and utility grid level. 

Providing primary response using a droop curve differs from 

the methods described in Section II, as these methods treat 

frequency and ROCOF as direct control inputs. Using a droop 

curve to augment the existing APC controller simply modifies 

the power reference signal, allowing power reference to still 

be the only input into the control system. This means that the 

control system itself does not have to be redesigned or re­tuned 

to provide primary response capabilities. This is a potential 

advantage, as a great deal of work in industry, such as that 

3
Fig. 5. A block diagram of the APC controller augmented with a droop curve to provide inertial/primary response. Features of note include the band-pass 

filtered (BPF) input into the torque controller, low pass filtered (LPF) input into the blade pitch controller, and saturation and rate limited (RL) torque input into 

the plant. 

in [21] and [22], has been to develop APC systems that provide 

secondary regulation by tracking a reference power signal. 

The use of droop curves to generate a power reference signal 

for a wind turbine or wind plant has a number of possible advantages over using frequency and ROCOF as direct inputs into 

the wind turbine APC system. For one, implementing a droop 

curve with a deadband can prevent the control system from 

persistantly actuating in reaction to small amplitude fluctuations 

in grid frequency. Additionally, droop curve slopes can be set 

at different values in different frequency regions. For example, 

the droop curve can be shaped so that it has a more aggressive 

slope for overfrequency events than underfrequency events. 

Further, a wind turbine droop curve is implemented synthetically, and serves only to generate a power reference into 

the APC system. This differs from a traditional synchronous 

generator, where the droop curve characterizes the physical 

response of the governor. Therefore, droop curve characteristics 

can be dynamically changed online in response to ROCOF. For 

example, if a large ROCOF is detected, the droop curve slope 

can be increased and deadband decreased in anticipation of 

an underfrequency event. Dynamically shaping the droop curve 

can potentially allow for aggressive response to large changes 

in frequency, effectively adding extra inertial response, without 

inducing excessive loads on the turbine components. 

The dynamic droop curves (DDCs) used in this study were 

set to have a nominal slope and deadband width, which was then 

changed to a more aggressive value if ROCOF exceeded a minimum threshold. At a maximum ROCOF, a maximum slope and 

minimum deadband width were reached, which would then be 

kept for increasing ROCOF. For ROCOF between the minimum 

and maximum thresholds, the droop curve characteristics were 

linearly interpolated between the nominal and most aggressive 

values. Further, droop curve ”shaping” is only permitted when 

ROCOF and the frequency deviation have the same sign; that is, 

only if the frequency deviation is above nominal and ROCOF 

is serving to increase frequency or if the deviation is below 

nominal and ROCOF is serving to decrease frequency. The 

droop curves used in this study are summarized in Table I. 

TABLE I
 

DROOP CURVE PARAMETERS
 

Droop Curve Slope Range Deadband Range ROCOF Thresholds 

SDC1 

SDC2 

DDC1 

DDC2 

DDC3 

5% 

2.5% 

5-2.5% 

5-2.5% 

7.5-5% 

50 mHz 

0 mHz 

50-0 mHz 

50-0 mHz 

50-0 mHz 

n/a 

n/a 

5-10 mHz/sec 

1-5 mHz/sec 

5-10 mHz/sec 

IV. SIMULATION RESULTS 

A. Individual Turbine Simulation Results 

Fig. 6. This frequency event was measured on the ERCOT grid on 

Nov. 29, 2011. The total load on the grid was 30,071 MW. The frequency drops 

to 59.694 Hz after 1,365 MW of generating resource abruptly goes offline. Data 

courtesy of Vahan Gevorgian (NREL). 

Simulations at the individual turbine and grid level were 

carried out to evaluate the performance of multiple droop 

curves combined with the controller described in Section III. 

The combined droop curve and APC system is depicted in 

Fig. 5. Individual turbine tests were carried out using the 

FAST wind turbine response simulation code developed by the 

National Renewable Energy Laboratory (NREL) [23]. The APC 

controller was augmented using the droop curves described in 

Table I. The simulations used five stochastic wind fields, each 

with a mean wind speed of 18 m/s, and results were averaged 

over the five simulations for each droop curve considered. Time 

series data from a frequency dip, measured during a generator 

fault on the Electric Reliability Council of Texas (ERCOT) grid, 

is passed through the static, linear droop curves shown in Fig. 4. 

4
Fig. 7. The DELs induced during simulations with a stochastic wind field and the reference frequency input from Fig. 6. 

This frequency dip is shown in Fig. 6. The effect of the droop 

curve and controller on the turbine was measured in terms of 

the damage equivalent loads (DELs) that were induced on the 

turbine components during the simulations, as seen in Fig. 7. 

A description of DELs can be found in [24]. 

As can be seen in Fig. 7, varying the parameters of the static 

droop curves affected the intensity of the induced structural 

loads. The baseline case is a constant power reference, which 

was set to 40% of rated power. Compared to this baseline, 

augmenting the power reference with a droop curve increases 

the structural loads on the turbine when using any of the droop 

curves. This is due to increased actuation of both the blade 

pitch and torque controllers in response to the rapidly changing 

power reference supplied by the droop curve in response to 

the variation in grid frequency. Further, the more aggressive 

SDC2 response to the frequency event, characterized by the 

2.5% droop curve with no deadband, had the effect of increasing 

all of the measured DELs relative to SDC1, as the SDC2 droop 

curve causes the power reference to change more dramatically 

compared to the SDC1 case. 

The effects on DELs of using the dynamic droop curves 

are more complex than those caused by the two static droop 

curves. Compared to the SDC1 case, increases in both of the 

tower bending moments and both of the blade root bending 

moments can be seen in all of the DDC cases. The low-speed 

shaft flex is improved in all of the three DDC cases. Further, 

with the exception of the tower fore aft-bending moment, the 

DELs increased or were unchanged between the DDC1 and 

DDC2 cases, which indicates that decreasing ROCOF thresholds 

as a means to achieve more aggressive frequency can change 

induced structural loads. Additionally, the DDC3 case had 

similar DELs to those in the DDC1 case, with the exception 

of a slight increase in the low-speed shaft flex, which would 

indicate that changing the slope range does not have as a great 

of an effect as changing the ROCOF thresholds with the ROCOF 

ranges considered in this study. 

The induced DELs caused by using the dynamic droop curves 

are also shown in comparison to the SDC2 curve simulation 

in Fig. 7. The SDC2 droop curve represents the worst case 

of induced structural loads in the static droop curve cases. It 

can be seen that, with the exception of the tower side-to-side 

bending moment in the DDC2 simulation, the dynamic droop 

curve cases resulted in generally lower DELs than this worstcase static droop curve simulation. Hence, the loads induced by 

dynamic droop curves are in a middle ground between the best 

and worst cases observed using only static droop curves. 

B. Grid Simulation Results 

It is also desirable to test out the effects of a droop curve in 

combination with a wind turbine APC system at the utility grid 

level. A simple simulation of a grid represented as a single 

bus was used to analyze the effects of the APC controller 

in coordination with conventional generation when there is a 

frequency event. The grid model used in this study has hydro, 

wind, reheat and non-reheat steam turbine plants producing 

40%, 15%, 40%, and 5% of power, respectively. The models for 

the conventional units in this simulation are based on control 

diagrams that can be found in [25]. Fig. 8 shows the grid 

frequency for simulations where the non-reheat steam turbine 

(5%) abruptly goes offline at time t = 1000 seconds. In the 

”no wind” case, the wind plant is replaced with a reheat steam 

turbine. It can be seen that when the wind plant is operating 

with its normal ‘baseline’ control system set to simply track 

a constant power reference, the frequency response is worse 

due to the reduced amount of conventional generation providing 

frequency response. It should be noted that this effect assumes 

that wind fully decommits another utility (a reheat steam turbine 

in this case), whereas [26] shows that in the case where some 

of the generators are derated rather than all decommitted, this 

effect is much more limited or possibly reversed. 

The aforementioned APC system was simulated using the 

same droop curves used for the individual turbine simulations. 

It can be seen in Fig. 8 that the frequency response is affected 

when the APC system is augmented with the static droop curves. 

The smallest deviation from nominal frequency is achieved 

with the SDC2 case, but this also causes large, undesirable 

5
Fig. 8. Simulation results from a single bus grid. At 1000 seconds 5% of generating capacity goes offline. The system response with all conventional generation 

is compared to the cases when there is a wind plant at 15% penetration with a baseline control system or the droop curve and APC system configurations. 

oscillations as frequency recovers to nominal. The SDC1 case 

results in improved frequency response over both the ”no wind” 

and baseline wind cases. At the nadir, the grid frequency has 

deviated further from nominal than in the SDC2 case, but there 

are no oscillations in the secondary response in the SDC1 case. 

It appears from these simulations that using a more aggressive 

droop curve can help improve the frequency nadir, but can also 

cause oscillations and grid instability, as well as increase loads 

on the turbine (as shown in Fig. 7). Hence, there is a level at 

which providing inertial/primary APC using static droop curves 

can be a detriment to both the grid and the individual turbines 

themselves. 

Fig. 8 shows that the use of dynamic droop curves results 

in improved performance in all three cases over both the ”no 

wind” and baseline simulations. Additionally, in the DDC1 and 

DDC2 simulations, the grid frequency recovery is improved 

compared to the SDC1 simulation and is comparable to the 

initial recovery of the SDC2 simulation. In both cases, the 

frequency nadir occurs earlier and the maximum deviation 

from nominal frequency is reduced. An interesting result is 

that the DDC1 and DDC2 results in Fig. 8 are essentially 

indistinguishable until the secondary recovery phase. This is 

likely due to the fact that during the inertial phase, ROCOF is 

fairly high, and in both cases the maximum ROCOF threshold 

is reached fairly quickly, so the droop curve shaping ”saturates” 

quickly for both cases. The frequency makes a slight dip around 

1010 seconds, and as the DDC2 case is more sensitive to low 

values of ROCOF than is the DDC1 case, this dip is smaller 

for the DDC2 simulation. Therefore the steady­state primary 

response frequency value is slightly higher than in the DDC1 

simulation. 

Another interesting feature is that both the DDC1 and DDC2 

cases do not exhibit the large oscillations in grid frequency seen 

when an aggressive static droop curve is used. This is likely 

Fig. 9. Zoomed in plot showing ROCOF shortly after the grid fault occurs. 

Note that the DDC2 case is not shown, as at this time interval it is essentially 

identical to the DDC1 case. 

due to the fact that the dynamic droop curve ‘shaping’ only 

occurs when the frequency is below nominal and ROCOF is 

negative. This causes an aggressive response in the first few 

seconds after the frequency event occurs, helping to arrest the 

decline in grid frequency. However, when the frequency starts 

to return to nominal, the response of the droop curve is not as 

aggressive. This is due to the fact that ROCOF is positive in 

this response phase, even though grid frequency is still below 

nominal. 

Fig. 9 shows a zoomed­in view shortly after the frequency 

event occurs in the simulations to allow initial ROCOF to be 

seen more clearly. The DDC2 simulation is not shown, as it 

is again essentially indistinguishable from the DDC1 case in 

this time interval. The DDC1 case can be seen to have the 

shallowest initial ROCOF, while the wind baseline case has the 

steepest initial ROCOF. The DDC3 case has a steeper ROCOF 

than either of the SDC cases, but it is shallower than either the 

”no wind” or baseline cases. 

The improved grid response to loss of generation achieved 

6
in the DDC1 and DDC2 cases comes at the cost of generally 

higher induced structural loads on the turbine compared to 

the SDC1 case. However, these dynamic droop curve cases 

offer a general decrease in structural loads when compared to 

the SDC2 simulation. Furthermore, the dynamic droop curve 

simulations do not create the same undesirable oscillations in 

grid frequency seen with this static droop curve simulation. 

These results demonstrate that a desirable middle ground of 

performance in terms of both structural loads and overall grid 

frequency performance can be achieved by using dynamic droop 

curves. 

The results from the grid simulations are summarized in 

Table II. The frequencies are given in terms of percent deviation 

from 60 Hz, the time to the nadir is given as the elapsed time 

after the frequency event at time t=1000 seconds, and the steadystate frequency is given as the frequency 25 seconds after the 

frequency event occurs. 

TABLE II
 

GRID SIMULATION RESULTS
 

Simulation Case Nadir Freq. Time to Nadir Steady-State Freq. 

No Wind 

Wind Baseline 

SDC1 

SDC2 

DDC1 

DDC2 

DDC3 

-0.417% 

-0.450% 

-0.383% 

-0.348% 

-0.345% 

-0.343% 

-0.383% 

4.62 sec 

5.51 sec 

4.28 sec 

3.70 sec 

3.70 sec 

3.70 sec 

4.17 sec 

-0.215% 

-0.253% 

-0.183% 

n/a 

-0.198% 

-0.198% 

-0.208% 

V. CONCLUSIONS AND FUTURE WORK 

In this study, a previously designed APC system was augmented using various static and dynamic droop curves. The 

pre-existing APC system was designed to track an AGC power 

reference signal, and hence was only capable of providing 

secondary frequency response services. Through the use of a 

droop curve, this control system was able to provide inertial 

and primary frequency response as well. This provides a general 

methodology to adapt control systems designed for AGC power 

reference tracking to provide inertial and primary frequency 

response services. 

The tradeoffs between aggressive frequency response and 

induced structural loads on an individual turbine were also 

analyzed. Several prior studies have simulated APC at the grid 

level, and one prior study simulated APC at the turbine level 

to analyze loads, but both have not previously been studied 

simultaneously. Using static droop curves, there is a clear 

tradeoff between using a more aggressive frequency response 

and increased loads. This tradeoff is most apparent when using 

a steeper droop curve slope. 

Based on the grid simulations performed in Section IV-B, 

a relatively high penetration of wind not providing any APC 

capabilities has the effect of degrading overall grid frequency 

response, given that this wind plant entirely replaced another 

generating unit. Additionally, it is apparent that a noteable 

penetration of wind providing primary frequency response services can help improve overall grid recovery when a generator 

fault occurs. In all of the cases simulated using a droop curve 

to allow the APC system to participate in primary frequency 

response, overall frequency deviation was minimized, and the 

time to begin the return back to nominal frequency was reduced. 

However, grid instability was also observed when the wind plant 

response was too aggressive, and too much wind power was 

pushed onto the grid in response to a grid underfrequency event. 

Two of the dynamic droop curves provided the best overall 

results. The concept of dynamically changing droop curve 

parameters online in response to grid ROCOF does not seem 

to appear in any existing literature on the subject, especially as 

it pertains to wind turbine APC. These dynamic droop curves 

offer a middle ground between aggressive frequency regulation and induced structural loads. When the APC system was 

augmented with two of the dynamic droop curves considered, 

grid frequency response was greatly improved over the static 

droop curve cases. Frequency recovery time, frequency nadir, 

and steady-state frequency were all improved in two of the 

dynamic droop curve cases, without inducing oscillations as 

seen with an aggressive static droop curve. While these dynamic 

droop curves had the tendency to generally increase individual 

turbine structural loads compared to a relatively non-aggressive 

static droop curve, the dynamic droop curves did not generally 

increase structural loads as much as was observed with the most 

aggressive static droop curve. 

The turbine model used in Section IV to test performance 

on an individual turbine is the three-bladed Controls Advanced 

Research Turbine (CART3), located at the National Wind 

Technology Center. One of the preliminary controllers found 

in [19] has been tested on the CART3. As the controllers 

developed in [19] form the basis for the droop curve augmented 

control systems developed in this study, an area of future 

work emerged, which is to evaluate the performance of the 

combined droop curve and APC system developed in this paper 

on the CART3. In particular, these field tests will be useful 

in validating the individual turbine simulations and can help 

confirm the general trend that responding more aggressively 

to frequency deviations tends to increase structural loads as 

observed in these simulations. 

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