A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

The Wind Farm Aggregation 

Impact on Power Quality 

Preprint 

J.T. Bialasiewicz 

University of Colorado at Denver 

E. Muljadi 

National Renewable Energy Laboratory

To be presented at the 32nd Annual Conference of the IEEE 

Industrial Electronics Society (IECON '06) 

Paris, France 

November 7–10, 2006 

Conference Paper 

NREL/CP-500-39870 

November 2006 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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1

The Wind Farm Aggregation Impact on Power Quality 

Jan T. Bialasiewicz Eduard Muljadi

Department of Electrical Engineering National Wind Technology Center 

Univ. of Colorado at Denver and Health Sciences Center National Renewable Energy Laboratory 

CB 110, P.O. Box 173364 1617 Cole Boulevard 

Denver, Colorado 80217-3364 Golden, Colorado 80401 

jan.bialasiewicz@cudenver.edu eduard_muljadi@nrel.gov

Abstract – This paper explores the effects of wind farm power 

fluctuations on the power network. A dynamic simulation of a 

wind farm is performed and the spatial distribution of the wind 

turbines is considered. In a wind farm, many wind turbines feed 

power into the power grid at the point of common coupling. The 

power fluctuation from one turbine may cancel that of another, 

which effectively rectifies the power fluctuation of the overall 

wind farm. The effect of power fluctuations is quantified by 

measuring the flicker and the voltage variation for different 

case studies. 

We took a conservative approach to explore a wind farm that 

consists mainly of stall-controlled wind turbines with fixed 

frequency induction generators and a specified grid with a 

known short circuit capacity.

I. INTRODUCTION 

The power fluctuation at each wind turbine is affected by the 

type of turbine, the control algorithm, the wind speed 

fluctuation, and the tower shadow effect. The power 

measurement from a single wind turbine usually shows a 

large fluctuation of output power. Because many turbines are 

connected in a wind farm, the power fluctuation from one 

turbine may cancel that of another, which effectively rectifies 

the power fluctuation of the overall wind farm. 

As wind energy technologies progress, wind turbines 

become larger. Manufacturers are currently producing 

multimegawatt wind turbines. Thus, fewer turbines are 

needed to deliver the same power and the power fluctuation 

of an individual wind turbine will have a greater impact on 

the power network. The impact on a weak grid will be even 

greater. 

We used a simulation program to investigate the impact of 

the turbine distribution on a large wind farm. Many 

researchers have investigated various aspects of electrical 

power systems on a wind farm. Wind farms with variable 

speed [1−2] or fixed speed wind turbines [3−4] were 

investigated under varying conditions. The voltage 

fluctuations as a function of X/R ratio, the reactive power 

fluctuations on voltage variation, the harmonics components 

at the point of common coupling (PCC), and the flicker 

emission out of a wind farm were presented in reference [5] 

and reference [6]. The flicker emission from a wind farm is 

reduced as the grid stiffness (Sk/Sn ratio) increases [7]. Also, 

the flicker emission is affected dramatically by the 

turbulence intensity. The flicker emission at 16% turbulence 

intensity is twice as high as a turbulence intensity of 8% [8]. 

In this paper, we focus on the aggregation impact on the 

wind farm output at the PCC. We used the same wind 

turbulence intensity and impedance of the transmission line 

and measured the real and reactive power fluctuations, the 

voltage fluctuations, and the flicker emission (using design 

specification in IEC 61000-4-15[9]) at the PCC of a wind 

farm. By quantifying the difference in power and voltage 

fluctuations and flicker level, we were able to treat a wind 

farm as a single turbine or as multiple groups of turbines. 

 

Ideally, we would like to model every wind turbine on the 

wind farm. Unfortunately, a large wind farm can have more 

than 100 turbines on site. Therefore, all the turbines cannot 

be represented simultaneously, because the computing time 

will be excessive. This study introduces a properly defined 

aggregation model that closely represents a real wind farm 

without simulating each turbine. Section II presents the 

aggregation model structure and assumptions for a large 

wind farm; Section III reports the results of the aggregation 

impact analysis; Section IV provides a comparison of real 

power, reactive power, and voltage at the PCC for two wind 

farm models; and Section V summarizes the paper. 

II. AGGREGATION CHARACTERISTICS OF A 

LARGE WIND FARM 

 

Each wind turbine is represented by an induction machine 

and a stall-controlled wind turbine with a rated power of 225 

kilowatts (kW). Reactive power compensation in each 

turbine is provided by a 99 kilovar (kvar) capacitor. The 

wind turbine is operated at a fixed frequency and its tower 

shadow is set to reduce the wind speed by 20% every time a 

blade passes in front of the tower. The duration of time a 

blade passes the tower is represented by an arc of 10% out of 

120 degrees for a three-bladed turbine. Three pulsations are 

created by tower shadow, commonly known as the 3P effect. 

We made, the following assumptions for the aggregation 

model investigated in this research: 

• For the total number of 200 wind turbines in the wind 

farm, we investigate different groupings. 

• The wind speed is uniform for each group of wind 

turbines. The wind speed applied to one group is timeshifted with respect to the other, according to the wind 

speed and the distance between centers of the wind 

turbine groups. 

• The groups are arranged in sequence: because of the 

difference in wind speed at each turbine location, all the 

wind turbines are not started at the same time. 

• Our interest is in the long-term simulation. 

• All the turbines in the wind farm are exposed to the 

same time series wind speed with an average speed of 
18.7 m/s and turbulence level of 19.7%. The time series 

wind speed shifts by 1 minute for each group. 

• Power contributions at PCC of each group are chosen 

randomly. 

• The impact of wind turbine distribution is evaluated by 

comparing the flicker and the voltage fluctuations based 

on groupings (1 group only and 16 groups). 

The electrical output power of the wind turbine is connected 

to a PCC and then transmitted to an infinite bus. The short 

circuit capacity of the wind farm is 212 MVA. 

In Figure 1, we present an example of wind farm 

aggregation. The distance between the center of group1 and 

group 2 is d2, and the distance between the center of group 2 

and group 3 is d1. The wind, blowing from the right to the 

left, will arrive first in group 1, then in group 2, and last in 

group 3. 

The worst case assumption is to consider a wind farm as 

represented by one group of 200 wind turbines because all 

the wind turbines in this group will be synchronized and the 

same wind fluctuations and tower shadow effects of each will 

affect the output power of the wind farm and the power 

quality at the PCC. This one-group non-aggregated simulated 

wind farm is called WF1G. Its power quality is compared to 

that of the same wind farm that is aggregated into 16 groups 

of wind turbines (called WF16G). The same wind time series 

is used in both simulations, but in the latter case we have the 

15-second time shift of the wind speed between groups (to 

simulate the spatial distribution of turbulence and gust 

fronts). The electrical output power of the groups is fed into 

the same PCC on the power grid. 

j XS RS

Infinite 

Bus

VS

ES

PCC

IS

Group 1

Group 2

Group 3

d1 d2

w

i

n

d

Fig. 1. Three groups of wind turbines are feeding the same 

transmission line at the PCC. 

III. RESULTS OF AGGREGATION IMPACT ANALYSIS 

Our simulation results show that there is no major difference 

in the generated real power by each wind turbine in the 

WF1G model and in the WF16G model. As shown in Figure 

2, whether in a WF1G or in a WF16G, a wind turbine driven 

by the same time series wind speed and affected by its tower 

shadow will produce a fluctuating power with a maximum 

average value of about 225 kW. 

Wind Turbine P & Q

Time (sec)

0 100 200 300 400 500 600

P and Q at WTG

0

50

100

150

200

250

300 Real Power (kW)

Wind Turbine Real Power (16 groups)

T ime (sec)

0

WF1G 

100 200 300 400 500 600

P (kW) at WTG

0

50

100

150

200

250

300 Real Power (kW)

WF16G 

Fig. 2. Real power output of a turbine in a WF1G and in a WF16G. 

Wind Farm P&Q

Time (sec)

10 11 12 13 14 15 16 17 18 19 20

P (MW) at PCC

0

10

20

30

40

50

60 Real Power

Wind Farm Real Power (16 groups)

Time (sec)

10 11 12 13 14 15 16 17 18 19 20

P at PCC

0

10

20

30

40

50

60 Real Power (MW) WF1G WF16G 

Fig. 3. Real power output of a wind farm in a WF1G and in a WF16G. 

2
Fig.4. Real power output of a wind farm in a WF1G and a WF16G. 

Fig. 5. Comparison of voltage variation at PCC for the wind farm models WF1G and WF116G. 

The wind farm output consists of the sum of the output 

of each individual group. The real power output of the 

wind farm is shown in Figure 3 between t = 10 seconds 

and t = 20 seconds to capture the fluctuation caused by the 

tower shadow and the wind turbulence. The output of a 

WF1G shows a very strong 3P effect; in the WF16G the 

tower shadow effects seem to cancel out among the 

groups. The overall power fluctuation, which is the sum of 

turbulence-induced fluctuation and tower shadow-induced 

fluctuations, is obviously smoother in the WF16G than in 

the WF1G. 

Figure 4 compares the time series output power 

variations at the PCC for WF1G and WF16G. The output 

of the wind farm WF1G is an amplification of the output 

of one turbine because, in a WF1G, each wind turbine is 

synchronized. In the trace for the WF16G the amplitude of 

fluctuations is significantly reduced. The wind turbine 

aggregation definitely makes the collective power 

fluctuations at the PCC smoother because of the 

cancellation effect among wind turbines. 

In Figure 5, the voltage variations of WF1G and WF16G 

are compared. The voltage fluctuations apparently 

correspond to the real and reactive power fluctuations. The 

voltage fluctuation for WF1G is much larger than that for 

WF16G. Although the voltage fluctuations are large in the 

WF1G system, the voltage variation never reaches +5%, a 

generally acceptable limit in the utility industry. 

IV. REAL POWER, REACTIVE POWER, AND 

VOLTAGE AT PCC 

Figure 6 demonstrates that the nature of the output power 

pulsation in a stall-controlled, fixed-speed wind turbine 

generator is influenced by three main factors: 

• The nature of wind speed variation (average wind 

speed variation, which is slow, and the turbulence of 

the wind, which is relatively faster than the change of 

average wind speed). 

• The characteristic of the power curve. (Operation 

below rated wind speed gives a positive, large slope 

∆P/∆v > 0. Operation around rated wind speed ∆P/∆v

~ 0 and higher gives a negative, small slope ∆P/∆v < 

0.)

• The 3P power pulsation is due to the tower shadow 

effect. 

Wind Farm P&Q

Time (sec)

0 100 200 300 400 500 600

P (MW) and Q (MVAR) at PCC

-20

0

20

40

60 Real Power 

Reactive Power

Wind Farm P&Q

Time (sec)

0 100 200 300 400 500 600

P and Q at PCC

-20

0

20

40

60 Real Power (MW)

Reactive Power (MVAR)

WF1G WF16G 

Vph at PCC (in p.u.) - 16 groups

Time (sec)

0 100 200 300 400 500 600

Per phase voltage (in p.u.)

.90

.95

1.00

1.05

1.10 Per phase voltage at PCC

Vph at Infinite Bus and at PCC (in p.u.)

Time (sec)

0 100 200 300 400 500 600

Per phase voltage (in p.u.)

.90

.95

1.00

1.05

1.10 Per phase voltage at PCC

 G1FW G61FW

3
In addition, as shown in Figure 7, the variation of real 

power (P) is accompanied by the variation of reactive 

power (Q) demand. In an induction generator, reactive 

power demand increases nonlinearly with real power. The 

slope ΔQ/ΔP in a low power region is low; in a high power 

region it is high. 

For both WF1G and WF16G wind farm configurations, 

the maximum values for both turbines, with and without 

tower shadow, are almost the same because in the 

maximum swing, the wind turbine is operated in stall 

mode (a high wind speed region where an almost flat line 

can be found on a power curve). The minimum values can 

be found when the wind turbine operates in the lower wind 

speed region (linear portion of the power curve). We can 

expect that the minimum values will be lower for WF1G 

than for WF16G because of the synchronization of the 

entire wind farm in the former and of the aggregation 

effect in the latter. 

The variation of the reactive power is affected by the 

wind speed variation and the tower shadow as in the case 

of the real power output. We know from the real/reactive 

power relationship shown earlier, that the reactive power 

varies nonlinearly with respect to the real power. The P-Q 

characteristic of an induction machine is probably close to 

a quadratic relationship as the output real power increases, 

especially in the higher power region. The maximum 

values of the reactive power have a wider spread than 

those of the real power because of the P-Q characteristic of 

induction machines. The minimum value of the reactive 

power for WF1G is too small to show, indicating the 

reactive power swing reaches down to zero. The standard 

deviation and overall characteristic of the reactive power 

show a general trend in which the WF1G has a greater 

variation than the WF16G. 

The voltage variation at the PCC is a result of real and 

reactive power output variations of the wind farm. The 

variation of reactive power is more dominant in causing 

the voltage fluctuation at the PCC, as will be shown later. 

Fig.6. A typical power curve of a wind turbine 

generator 

0 4 8 12 16 20 0

60

120

180

240

300

Wind Speed (m/s)

Aerodynamic Power (kW)

ΔP/Δv > 0 

ΔP/Δv = 0 

ΔP/Δv < 0 

0 100 200 300

50

70

90

110

130

150

Fig.7. Reactive power demand of a typical 

induction generator 

Real Power (kW)

Reactive Power (kVAR)

ΔQ/ΔP 

low 

ΔQ/ΔP 

high 

3P - Votage at PCC

0.00025

0

0.00005

0.0001

0.00015

0.0002

0 5 10 15 20

Number of Groups

V at PCC (% Vrated)

3P - REAL AND REACTIVE POWER

6

0

1

2

3

4

5

0 5 10 15 20

Number of Groups

P AND Q ( % rated )

3P Real Pow er 3 P Reactive Pow er

Fig. 8. The 3P components of a wind farm output: (a) Real and reactive power output, 

(b) Voltage at the PCC. 

4
As mentioned in the previous section, each wind turbine is 

compensated by ac capacitors to improve the reactive 

power. Although the ac capacitor generates an almost 

constant reactive power, the variable wind speed causes a 

variable reactive power to be absorbed by the induction 

generator. 

Our simulations show that the maximum deviation of the 

PCC voltage above average value occurs when the reactive 

power absorbed by the wind farm decreases to low values 

(corresponding to low generation). Thus, this maximum 

voltage deviation can be correlated to the deviation of 

reactive and real power below average value. The 

minimum of the voltage value at PCC occurs (mostly) 

when the reactive power absorbed by the wind farm 

increases to high values. Thus this minimum voltage can 

be correlated to the deviation of the reactive power above 

average value. The standard deviation and overall 

characteristic of the voltage at the PCC show a general 

trend in which the voltage fluctuation for WF1G has a 

larger voltage variation than that for WF16G. 

To appreciate the impact of tower shadow, the 

amplitudes of the 3Ps component found on the real power, 

reactive power, and voltage at the PCC are plotted as a 

function of the number of groups in a wind farm and 

shown in Figure 8. The 3P component of the real and 

reactive power outputs of the wind farm is reduced 

dramatically as the number of the groups in a wind farm 

increases. The change of the 3P components on the real 

power, reactive power, and voltage at the PCC is 

significant as the number of group increases. For example, 

from the WF1G (one group) to the WF2G (two groups), 

the 3P drops by about 70%. As the number of groups 

increases, the 3P components decrease and practically 

disappear at WF16G. 

The flicker level at the PCC can be computed based on the 

voltage waveform at the PCC. The voltage at the PCC is 

computed by feeding the voltage time series into the 

flicker meter described in the IEC 61000-4-15. The flicker 

level measures the annoyance level a human eye perceives 

when a specific light is powered by fluctuating voltage 

source. Although a high flicker level may not affect 

sensitive equipment, it may affect the lighting system and 

cause a flicker that affects eye perception. This annoys 

human sight and eventually creates fatigue that may lead to 

a serious accident on the factory floor. 

In this paper, the flicker level is measured by applying a 

time series of voltage at the PCC to the flicker meter. The 

wind farm groupings were subjected to this measurement 

and the wind turbines were subjected to a specific average 

wind speed and turbulence intensity. Since the voltage is 

affected by the voltage fluctuation, the flicker level at the 

PCC depends on the fluctuating voltage caused by the 

tower shadow and the wind turbulence. As the number 

groupings of the wind turbine increases, the flicker level 

Pst decreases dramatically (see Figure 9). Although the 

flicker level is affected by the number of groups in the 

wind farm and the level of turbulence, it is not directly 

proportional to the 3Ps. For example, Figure 8 shows that 

the 3P component of the voltage at the PCC is dramatically 

reduced as the number of groups in the wind farms 

increases from one to two by a factor of about 700%; the 

Pst level decreases by only a factor of about 25%. As the 

number of groups in a wind farm increases, the Pst level 

will finally reach very small values. 

PST - Flicker Level - Votage at PCC

0

0.5

1

1.5

2

2.5

0 5 10 15 20

Number of Groups

Pst (in per unit)

Fig. 9. Pst level of the voltage at the PCC as a function of 

 the number of the groups in the wind farm. 

V. CONCLUSIONS 

This paper investigates the effects of wind turbine 

aggregation on a large wind farm. The method of analysis 

used to measure the characteristics of a wind farm plays an 

important role in determining the final conclusion. 

Therefore, a wind farm must be represented fairly so it 

reflects a real wind farm as closely as possible. 

• From the power fluctuation perspective, the larger the 

area of the wind farm the more diverse the wind 

profile that drives each turbine. Thus, there is a greater 

chance that the fluctuation in one turbine will be out 

of phase with another on the other side of the wind 

farm. 

• This paper shows that the more groups used to 

represent a wind farm, the smaller the fluctuation. The 

same conclusion can be drawn that a wind farm with 

more small turbines creates fewer power/voltage 

fluctuations on the power grid than a few large 

turbines. 

• As the number of turbines in a wind farm increases 

over a large area, the characteristics of the wind farm 

are masked by the collective impact. Thus the impact 

of tower shadow and wind turbulence on the wind 

farm will be leveled out. 

• The Pst level does not decrease in the same fashion as 

the 3P components as the number of groups in the 

wind farm increases. Thus, it shows that the 3P 

component is not the main source of flicker. In the 

real farm, the Pst level is expected to be lower than 

that represented by the WF16G. 

• The collective behavior of the wind farm follows the 

same pattern of a wind turbine (∆Q/∆P is large at high 

5
6

power) and wind turbine characteristics (∆P/∆vwind is 

low in the high power region). 

V. REFERENCES 

[1] J.W. Smith and D.L. Brooks, “Voltage Impacts of 

Distributed Wind Generation on Rural Distribution 

Feeders,” in Proceedings of the IEEE Power 

Engineering Society Transmission and Distribution 

Conference, v.1, 2001 Transmission and Distribution 

Conference and Exposition IEEE/PES, 2001, October 

28−November 2, 2001, Atlanta, GA, pp. 492-497. 

[2] T. Thiringer, T. Petru and C. Liljegren, “Power 

Quality Impact of a Sea Located Hybrid Wind Park,” 

IEEE Transactions on Energy Conversion, vol. 16, no. 

2, June 2001, pp. 123−127. 

[3] E. Muljadi, Y. Wan, C. Butterfield and B. Parsons, “A 

Study of a Wind Farm Power System,” A Collection of 

the 2002 ASME Wind Energy Symposium Technical 

Papers Presented at the 39th AIAA Aerospace 

Sciences Meeting and Exhibit, January 14−17, 2002, 

Reno, NV, pp. 361−370. 

[4] A.D. Hansen, P. Sorensen, L. Janosi and J. Bech, 

“Wind Farm Modeling for Power Quality,” IEEE 

Industrial Electronics Society Conference, November 

29−December 2, 2001, Denver, Colorado, pp. 

1959−1964. 

[5] J. Courault, “Energy Collection on Offshore Wind 

Farm DC Applications,” Second International 

Workshop on Transmission Networks for Offshore 

Wind Farms, March 30−31, 2001, Royal Institute of 

Technology, Electric Power Systems, Stockholm, 

Sweden. 

[6] G. Ronsten, S. Thor H. Ganander, H. Johansson, T. 

Thiringer, T. Petru and H. Bergstrom, “Evaluation of 

Loads, Power Quality, Grid Interaction, 

Meteorological Conditions and Power Performance of 

the First Swedish Offshore Wind Farm at 

Bockstigen,” Second International Workshop on 

Transmission Networks for Offshore Wind Farms, 

March 30−31, 2001, Royal Institute of Technology, 

Electric Power Systems, Stockholm, Sweden. 

[7] M.P. Papadopoulos, S.A. Papthanassiou, N.G. 

Boulaxis and S.T. Tentzerakis, “Voltage Quality 

Change by Grid-connected Wind Turbines,” European 

Wind Energy Conference, March 1–5, 1999, Nice, 

France, pp. 782−785. 

[8] H. Amaris, J.Usaola and C. Vilar, “A More Realistic 

Flicker Coefficient for Wind Turbines Evaluation,” 

European Wind Energy Conference, March 1–5, 1999, 

Nice, France, pp. 762−765. 

[9] Electromagnetic Compatibility (EMC) Part 4: Testing 

and Measurement Techniques - Section 15: Flicker 

Meter―Functional and Design Specifications, 

International Standard IEC 61000-4-15 First edition 

1997-11. 
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This paper explores the effects of wind farm power fluctuations on the power network. A dynamic simulation of a wind 

farm is performed and the spatial distribution of the wind turbines is considered. 

15. SUBJECT TERMS

wind power; wind farm; utility grid integration 

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