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Contract No. DE-AC36-08GO28308

Controlling Wind Turbines for 

Secondary Frequency 

Regulation: An Analysis of AGC 

Capabilities Under New 

Performance Based 

Compensation Policy

Preprint

Jacob Aho and Lucy Y. Pao

University of Colorado Boulder

Paul Fleming and Erik Ela

National Renewable Energy Laboratory

Presented at the 13th International Workshop on Large-Scale 

Integration of Wind Power Into Power Systems as Well as on 

Transmission Networks for Offshore Wind Power Plants

Berlin, Germany

November 11-13, 2014

Conference Paper

NREL/CP-5D00-62815

February 2015
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Controlling Wind Turbines for Secondary Frequency
 

Regulation: An Analysis of AGC Capabilities Under
 

New Performance Based Compensation Policy
 

Jacob Aho and Lucy Y. Pao Paul Fleming and Erik Ela 

Dept. of Electrical, Computer, and Energy Engineering National Renewable Energy Laboratory 

University of Colorado Boulder 

Boulder, CO 80309–0425 

Abstract—As wind energy becomes a larger portion of the 

world’s energy portfolio there has been an increased interest for 

wind turbines to control their active power output to provide 

ancillary services which support grid reliability. One of these 

ancillary services is the provision of frequency regulation, also referred to as secondary frequency control or automatic generation 

control (AGC), which is often procured through markets which 

recently adopted performance-based compensation. A wind turbine with a control system developed to provide active power 

ancillary services can be used to provide frequency regulation 

services. Simulations have been performed to determine the 

AGC tracking performance at various power schedule set-points, 

participation levels, and wind conditions. 

The performance metrics used in this study are based on 

those used by several system operators in the US. Another 

metric that is analyzed is the damage equivalent loads (DELs) on 

turbine structural components, though the impacts on the turbine 

electrical components are not considered. The results of these 

single-turbine simulations show that high performance scores 

can be achieved when there is sufficient wind resource available. 

The capability of a wind turbine to rapidly and accurately 

follow power commands allows for high performance even when 

tracking rapidly changing AGC signals. As the turbine de-rates 

to meet decreased power schedule set-points there is a reduction 

in the DELs, and the participation in frequency regulation has 

a negligible impact on these loads. 

I. INTRODUCTION 

Wind energy is becoming a more significant contributor to 

the global electrical energy generation portfolio as more wind 

turbines are installed. In some regions of the world the wind 

resource, electricity transmission infrastructure, and many 

other factors present favorable conditions for installing large 

amounts of wind energy. Many of these regions are reaching 

high penetrations of wind energy, reaching or exceeding 20% 

of their annual electricity demand from wind energy [1]. A 

trend can be observed in the United States (US) in which 

the system operators in regions with higher wind penetrations 

have accommodated the increase of wind generation variability 

by procuring additional frequency regulating reserves [1]. 

Frequency regulation, sometimes referred to as secondary 

frequency control, is a required ancillary service for which 

participating generation capacity is dedicated to following the 

power commands from the system operator. The regulation 

power command is referred to here as the automatic generation 

control (AGC) signal which is used to regulate grid frequency 

and maintain scheduled power exchanges between areas [2]. 

Golden, Colorado, 80401 

Economic ancillary service markets exist to compensate generators that participate in frequency regulation, and the US 

ancillary service markets are now required to implement performance based compensation for regulation services, meaning 

that participating resources that follow the AGC signal more 

accurately can receive higher economic compensation [3], 

increasing the motivation for fast responding generation to 

provide frequency regulation services. 

Historically, wind energy has not participated in marketbased frequency regulation, even though wind turbines made 

by many of the leading manufacturers have the capability of 

controlling their active power output [4], and recent studies 

have shown that as long as there is adequate wind resource, 

wind turbines can track power commands rapidly and accurately [5]. This increases the motivation to study the AGC 

tracking performance of wind turbines with the new performance metrics used for frequency regulation compensation. 

In this paper we analyze the simulated performance of a wind 

turbine when providing frequency regulation services. The 

wind turbine control system is capable of providing active 

power control (APC) services, as described in [6] and [7], 

and is used to track AGC power commands. The performance 

of the wind turbine and control system are evaluated using 

performance metrics that are used to determine economic 

compensation in several US markets. The fatigue loading 

induced on the structural components of the turbine are also 

evaluated when providing regulation services. It is important 

to note that the results shown in this paper are for a single 

turbine, and it is expected that using a wind power plant 

consisting of multiple wind turbines will improve the AGC 

tracking performance scores due to the spatial filtering of the 

wind provided by aggregating multiple turbines. 

This paper is organized as follows: 

•	 Section II provides an overview of the wind turbine APC 

controller. 

•	 Section III explains the regulation performance metrics 

that are used by several US system operators that determine economic compensation for these services. 

•	 Section IV highlights selected simulation results including the AGC tracking performance scores and the fatigue 

loads induced on turbine components. 

•	 Section V provides concluding remarks and areas of 

future research. 

1 
II. WIND TURBINE FREQUENCY REGULATION 

The primary goal of a traditional wind turbine control 

system is to maximize energy production while protecting 

the turbine components from damaging loads. When the wind 

turbine is generating power below the nameplate capacity of 

the turbine, or rated power Prated, traditional control systems 

use the power electronics to actuate the load torque on the 

generator shaft τg to control the rotational speed of the rotor 

for maximum power capture from the wind stream. When 

the turbine is producing power at Prated, the control system 

actuates the blade pitch motors to change the collective blade 

pitch angle β and shed the excess aerodynamic power to 

regulate the power production. An example of a traditional, 

or baseline control system can be found in [8]. 

Figure 1. A schematic depicting the interconnection between the wind power 

plant control system, individual turbine control system, utility grid, and system 

operator. 

The participation of wind energy in frequency regulation 

requires that each wind power plant establish communication 

with the system operator to receive the power dispatch schedule, the regulation capacity, and the AGC power command. 

The wind power plant control system sends power commands 

to the individual turbines, which need not be uniform. A 

schematic of the interconnection between the wind power plant 

control system, individual turbine control system, utility grid, 

and system operator can be seen in Figure 1. 

The desire for wind energy to participate in active power 

control ancillary services has motivated industry and academia 

to research and develop control systems that are capable of 

providing these services. Much of the industry research that 

has been performed in this area remains largely proprietary, but 

several of the major wind turbine manufacturers have demonstrated the capability of providing APC ancillary services. An 

overview of prior work in wind turbine APC control systems 

can be found in [4]. 

A. Wind Turbine APC Control System

The research presented in this paper uses an individual 

wind turbine control system based on prior research presented 

in [7]. This controller was designed to be practical, directly 

implementable on utility scale turbines, and be capable of 

providing all of the APC ancillary services. The control system 

is designed to be implemented as shown in Figure 1, where 

the wind power plant controller passes the power commands 

and grid frequency directly to the individual turbines. This 

APC wind turbine control system has been extensively tested 

in simulation and has been validated through field tests on the 

3-bladed controls advanced research turbine (CART3) at the 

National Renewable Energy Laboratory (NREL). 

The APC control system uses a standard baseline pitch 

control system as described in [8], so all APC capability is 

implemented through modifications of the generator torque 

control loop, though there are other methods of implementing 

wind turbine APC through the blade pitch control system 

or through combined torque and pitch control, as described 

in [4] and [7]. It should be noted that in this paper we do 

not consider the internal controls of the power electronics, 

as the power electronic controller timescales are an order of 

magnitude smaller than those used in this study. 

The APC control system is capable of operating in three 

power de-rating modes, each using a different power reserve 

strategy as follows: 

•	 Mode 1- The control system tracks an absolute power 

command. If there is insufficient wind to generate the 

commanded power the control system will aim to maximize power capture as the baseline controller would. 

•	 Mode 2- The control system aims to maintain a specified 

power reserve. 

•	 Mode 3- The control system aims to maintain a power 

reserve that is proportional to the available power in the 

wind. 

Providing frequency regulation requires that the control 

system receive and track AGC power commands Pagc which 

are added to the power dispatch schedule Ps. The results 

presented in this paper are limited to operation in de-rating 

mode 1, as the power commands in this mode use an absolute 

reference, whereas in modes 2 and 3 the power reference 

is determined by the estimated power available in the wind, 

which is not standard practice and would produce performance 

scores that are dependent on various parameters, such as the 

bandwidth of the low-pass filters used to estimate the power 

available in the wind. 

III. FREQUENCY REGULATION AND CONTROL 

There are several active power control regimes that span 

various time-scales, each having a different purpose and implementation. The focus of the research in this paper is on 

the capability for wind turbines to follow a power schedule 

dispatch or set-point Ps and provide frequency regulation 

services by following an AGC power command Pagc as 

directed by the system operator. An overview of the other 

frequency control regimes may be found in [9]. 

In the US, system operators will typically schedule power 

generation resources to meet the load forecast and procure sufficient ancillary services using a least-cost cooptimization [10]. The compensation for resources has historically included an energy payment and a capacity payment 

for the amount of generation capacity that is dedicated to 

following the AGC power commands. In 2011 the US Federal 

Energy Regulatory Commission (FERC) issued Order 755 

which requires system operators in regions with organized 

2 
Kd

M − |E(k)|

k=1 

SA = max 0, (6) M

K

Kd

|E(k)| ⎪⎪⎪⎨ ⎪⎪⎪⎬ Kd k=1 SP = max 0, 1 − (7) K

Kd

k=1

⎪⎪ n ⎪⎩ |Pagc(k)|

⎪⎪⎪⎭

fC (X(kx), Y (ky)) =

X(kx :kx+kW )T Y (ky :ky+kW ) (10) X(kx :kx+kW )T X(kx :kx+kW )

 

  

fD(d) = kW − d

kW

(11)

wholesale power markets to implement performance-based 

compensation for resources providing frequency regulation Ma = M − U (4) services [3]. The system operators have adopted various per- Kd

M = |Pagc(k) − Pagc(k − 1)| (5) 

k=2

formance metrics to qualify how well resources track the K

AGC power command signal and each factor the performance 

scores into their compensation model differently [10]. This 

paper will use the performance metrics adopted by CAISO, K

the California independent system operator, and PJM, a system 

operator spanning 13 states including Pennsylvania, Ohio, and 

Virginia. The performance scores from these regions were 

chosen because they use different performance scores that 

are thoroughly documented and were among the earliest to 

implement performance based compensation. The CAISO and 

PJM performance metrics are described in Sections III-A 

where M is the mileage of the AGC command, U is the 

under-response of the resource, and Kd is the number of time 

samples in the performance evaluation period. It should be 

noted that the performance score has a lower saturation limit 

of 0 and is only calculated during periods in which 

K

M is non- and III-B, respectively. Many US frequency regulation markets zero. The under response U is calculated as U = |E(ku)|

(k) changes direction are divided into regulation-up and regulation-down services, where ku are time samples when Pagc provided by resources that have capability and capacity to 

increase or decrease their power when the AGC command 

is positive or negative, respectively. In the US the AGC signal 

is generated by the system operator every 2 to 6 seconds [2]. 

Each resource participating in frequency regulation is assigned a power dispatch schedule Ps, regulation capacity up 

and sign (Pagc(k+1)−Pagc(k))= sign(E(k)). 

The CAISO region is one of the US regulation markets 

which divide the regulation service into regulation up and 

regulation down services, denoted with superscripts U and D, 

respectively. 

RU B. PJM Performance Metrics , and regulation capacity down RD. For simplicity, this 

paper will refer to the AGC power commands Pagc as they are 

received by the resource, and are assumed to be bounded by 

[−RD, RU ]. All values are expressed as a percentage of the 

rated power of the resource Prated so that the performance 

scores can be analyzed without obfuscating the results with 

unnecessary parameters. The net power command Pcmd for 

The PJM region also uses an AGC update interval of 

4 seconds, but the measured resource response is sampled 

every 10 seconds and uses 1 hour performance evaluation 

period. The PJM performance scores are calculated as the 

hourly average of a weighted sum of three different metrics, 

the precision score SP , the correlation score SC , and the delay 

the resource to follow at each time step k is score SD as described in [11]. The precision score is 

⎧ ⎫

Pcmd(k) = Ps(k) + Pagc(k) (1) 

E(k) = PR(k) − Pagc(k) (3) 

PR(k) = Pgen(k) − Ps(k) (2) 

where n is the number of samples in which Pagc is non-zero. 

The delay score SD and correlation score SC are 

where P SC (k) =fC (Pagc(k), PR(k+δk)) (8) gen is the power generated by the resource, PR is the 

AGC response of the resource, and E is the AGC tracking SD(k) =fD(δk) (9) 

 

  

error. 

A. CAISO Performance Metrics

CAISO uses an AGC update interval of 4 seconds and 

a performance evaluation period of 15 minutes. CAISO has 

implemented performance based compensation through the 

use of a form of “mileage payment” which is added to the 

energy and regulation capacity payments. The term mileage 

refers to the absolute amount of power injected and withdrawn, 

or amount of actuation a resource provides. This mileage 

payment is adjusted based on the performance of the resource 

and is calculated as the product of the mileage price, the actual 

mileage Ma, and the accuracy performance score SA, 

δk = argmax(fD(d)+fC (Pagc(k), PR(k+d))) (12) 

d∈[0:kW ]

where fC is referred to as the “normalized correlation coefficient” between two signals and kW is the number of measured 

samples in a 5-minute window [11]. It should be noted that 

the PJM documentation refers to SC as the “accuracy score” 

but this paper will refer to it as the correlation score so it is 

not confused with the CAISO accuracy score. 

Resources in the PJM region must pass a qualification test 

before being used as a regulation reserve by scoring above 

3 
0.75 when responding to AGC test signals. A resource will 

become disqualified if the performance score over the past 

100 hours drops below 0.4 [11]. 

IV. RESULTS 

Simulations were used to analyze the performance of a 

wind turbine with the APC control system when participating 

in frequency regulation. The metrics that were analyzed are 

the power production, the damage equivalent loads (DELs) 

that are induced on the turbine components, and the AGC 

power command tracking performance metrics described in 

Sections III-A and III-B. 

A. Simulation Environment

The results are generated when following absolute power 

commands in de-rating mode 1. The power schedule is fixed at 

a constant value for each simulation to provide insight into the 

power set-point and level of participation that can be achieved 

for each mean wind speed. 

The AGC commands used during simulations are the 40­

minute test signals used to qualify facilities in the PJM area, 

and consist of the traditional low-frequency AGC signal, 

RegA, and a high-frequency AGC signal, RegD, described 

in [11]. These AGC signals are normalized to ±1 and the 

positive and negative commands are scaled by RU and RD, 

respectively. An example of the received power commands for 

both the RegA and RegD signals can be seen in Fig 2. 

Figure 2. An example of the power commands when Ps = 90% and 

RU =RD = 10%. This example illustrates the low-frequency and highfrequency AGC signals used for qualification in the PJM area, denoted as 

RegA and RegD, respectively. 

The simulations were run with the FAST wind turbine 

simulation code developed at NREL, which calculates the 

response of aeroelastic wind turbine models to turbulent wind 

inflow using blade element momentum theory [12]. The turbine model used in this study is the CART3 FAST turbine 

model. The turbulent wind fields were generated with the 

NREL TurbSim code for a range of mean wind speeds using 

the IEC von Karman and Kaimal spectral models and IEC 

turbulence characteristics A, B, and C, as defined in [13] 

and [14]. Results presented in Sections IV-B and IV-C were 

calculated from the aggregate results of six sets of 40 minute 

simulations, one for each combination of spectral model and 

turbulence characteristic. 

The standard deviations of the rotor averaged wind speeds 

for wind fields with different mean wind speeds can be seen 

in Table I. The normalized mean power generated from the 

baseline control system is also shown in Table I. It should 

be noted that the rated wind speed of the CART3 turbine is 

approximately 12.3 m/s, which means a uniform wind field 

at this speed will allow the turbine to reach Prated in steadystate. 

v

[m/s] σ[m/s] σA

[m/s] σB[m/s] σC[m/s] 

P Baseline

[%] 

10 1.35 1.52 1.33 1.19 53 

12 1.52 1.72 1.49 1.33 88 

14 1.68 1.90 1.65 1.48 99 

16 1.85 2.09 1.81 1.62 100 

Table I
 

THE STANDARD DEVIATION σ OF THE ROTOR AVERAGED WIND SPEED FOR
 

THE TOTAL ENSEMBLE AND TURBULENCE CHARACTERISTIC A, B, AND C,
 

AS WELL AS THE MEAN POWER GENERATED BY THE BASELINE CONTROL
 

SYSTEM NORMALIZED TO Prated FOR WIND FIELDS WITH MEAN WIND
 

SPEEDS FROM 10 TO 16 M/S.
 

B. AGC Power Command Tracking

Figure 3 shows the performance scores as wind speeds vary 

from 11 to 15 m/s with Ps = 80% and Ru = RD = 20%. 

The wind fields with 11 m/s mean do not have enough power 

available to allow adequate tracking at this set-point and participation level. As expected, the performance improves as the 

mean wind speed of the simulations increase. The performance 

scores when using the RegA and RegD signals are very similar 

to each other, indicating the amount of actuation induced by 

the RegD signal is not a limiting factor. The metric PLoss

represents difference in normalized mean power generated 

between each scenario and the baseline control system which 

aims to maximize power production. The normalized actual 

mileage Ma/M is used to show the relative effect that the 

under response U has on the mileage payments. 

Figure 4 shows the performance metrics at various power 

set-points when RU =RD = 20% and using the RegD AGC 

signal for mean wind speeds of 12 m/s. The results are 

shown for each IEC turbulence characteristic with A being the 

most turbulent, C being the least turbulent. As expected, the 

overall performance degrades as the power set-point increases 

due to the limitations of the wind resource. It can also be 

seen that higher performance scores can be achieved as the 

turbulence decreases, particularly when operating at higher 

power set-points. Figure 4 also shows the accuracy score for 

“up” and “down” regulation separately, denoted by SU

A and

SD

A , respectively. 

One interesting trend that can be observed in the results 

shown in Figures 3 and 4 is that the PJM delay score SD

remains very high, even when the other performance scores 

decrease, because even when the wind turbine cannot closely 

track the AGC command, the rapid actuation capability of the 

4 
Figure 3. The performance scores when Ps = 80% and RU =RD = 20%

when tracking the RegA and RegD power commands. 

Figure 4. The performance scores when tracking the RegD AGC command at 

various set-points with RU =RD = 20% for each turbulence intensity class 

with mean wind speeds of 12 m/s. 

wind turbine allows for the maximum normalized coherence 

to occur with very little time shift. 

Another useful analysis is determining the performance 

scores under higher wind speeds when the power schedule 

is equal to the rated power of the turbine Ps = Prated

so that the wind turbine is not persistently shedding power 

that could have otherwise been captured. Figure 5 shows the 

accuracy performance score when RD is 10%, 20%, and 30% 

with mean wind speeds of 13, 14, and 15 m/s. This figure 

shows that performance improves as RD increases because 

there is a decreased probability of experiencing wind resource 

deficiencies as RD increases, as the power available from 

the wind is the factor limiting performance, not the increased 

magnitude of actuation. 

C. Damage Equivalent Loads (DELs)

It is important to assess the impact of providing AGC 

services on the fatigue loads of turbine structural components. The fatigue loads are represented as DELs, which are 

Figure 5. The performance scores when the power schedule is equal to Prated

and tracking the RegD AGC command at various levels of participation down. 

calculated from the bending moment load cycles with an 

alpha version of MLife, a damaging loads assessment code 

developed at NREL [15]. The DELs were calculated using a 

Wohler exponent of 10 for the composite blades and 3 for  ̈

the low-speed shaft and steel tower. The RegD signal is used 

as the AGC command in the DEL analysis since this signal 

induces more actuation and has a higher frequency content 

than the RegA signal. 

The DELs, mean power, and RMS pitch rates were compared for wind fields with a mean of 14 m/s for various power 

set-points and participation amounts, as seen in Figure 6. All 

metrics are presented as the percentage change from baseline 

operation, or maximum power capture scenario. The RMS 

blade pitch metric is compared to represent the wear-and-tear 

on the pitch motors. It should be noted that larger negative 

numbers represent a more significant decrease compared to 

the baseline, which is an improvement for the DELs and the 

RMS pitch rates. 

Figure 6. The DELs for 14 m/s mean wind speeds normalized by the DELs 

when operating in baseline operation (maximum power capture). 

It can be seen in Figures 6 that de-rating the turbine 

generally reduces the DELs, the RMS pitch rates, and naturally 

the power capture. Participating in regulation services appears 

5 
to have very little effect on the damage equivalent loads. This 

analysis did not consider the effects on the power electronics 

because the timescales required to model the power electronics 

are an order of magnitude smaller than those used in this 

research. 

V. CONCLUSIONS AND FUTURE WORK 

This paper presents the results of simulating an individual wind turbine with a control system that is capable of 

providing frequency regulation services by tracking an AGC 

command scaled to various participation factors added to a 

constant power schedule. The control system used in this study 

utilizes industrial standard sensors and actuators and many 

manufacturers currently have wind turbine control systems 

with similar capabilities. The US regulation markets now 

compensate resources partially based on their performance 

scores, which provides increased motivation for resources with 

rapid actuation capability to participate in AGC. 

The simulation results presented in this paper show that 

when significant wind resource is available, a wind turbine 

can control its power output to track the AGC signal very 

rapidly and accurately, allowing for high performance scores. 

The simulation results show that the wind turbine can achieve 

similar performance scores when tracking both rapidly and 

slowly varying AGC test signals. The results also show that 

the turbine and control system used in this study experience a 

performance score decrease for wind fields with a mean wind 

speed of 12 m/s, which is close to the rated wind speed of 

the turbine. The amount of performance degradation depends 

on the turbulence of the wind fields, with less turbulent 

winds producing higher performance scores. Decreasing the 

power set-point also allows for increased performance scores, 

particularly at wind fields with mean wind speeds of 12 m/s. 

Reducing the power of the scheduled set-point also has a 

beneficial effect on the damage equivalent loads (DELs) and 

the participation in regulation services has a negligible impact 

on these loads. 

Providing regulation services does require that a wind 

turbine or wind power plant to capture less than the maximum 

available power from the wind. This means that the expected 

economic revenue for participating in regulation services must 

be greater than the expected loss in revenue for the energy 

sales in order for this service to be viable, particularly because 

wind turbines have no fuel costs. In the US, production tax 

credits are given to wind power plant owners based on the 

energy production, and adds an economic bias toward capturing maximum power rather than participating in regulation 

services. 

It is important to note that the results shown in this paper 

represent the performance of a single 600 kW wind turbine. 

It is recommended that future work analyze the performance 

scores of larger scale wind turbines, wind power plants, and 

non-collocated wind power plants. It is expected that the 

performance scores of wind power plants would be higher than 

those of an individual wind turbine, as the spatial filtering 

of aggregating the power of multiple turbines over a larger 

geographical area would reduce the effective variability in the 

wind resource. 

It is clear that accurate wind forecasting is a very important 

component to the viability of wind power plants participating in the current frequency regulation markets, as the 

performance scores are largely dictated by the available wind 

resource. wind power plant owners would require an expected 

increase in revenue before opting to provide regulation services, which is highly dependent upon their confidence in these 

probabilistic wind forecasts. 

ACKNOWLEDGMENTS 

This work was supported by the U.S. Department of Energy 

under Contract No. DE-AC36-08GO28308 with the National 

Renewable Energy Laboratory. 

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[14] International Electrotechnical Committee	 et al., “IEC 61400-1: Wind 

turbines part 1: Design requirements,” International Electrotechnical

Commission, 2005. 

[15] G. Hayman and M. Buhl Jr, “MLife user’s guide for version 1.00,” 2012. 

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