Rahul Dutta
Feng Wang2
Bradley F. Bohlmann
Kim A. Stelson
Center for Compact and Efficient Fluid Power,
Department of Mechanical Engineering,
University of Minnesota,
Minneapolis, MN 55414
Analysis of Short-Term Energy
Storage for Midsize Hydrostatic
Wind Turbine1
This paper presents a novel method of capturing more energy from the wind using shortterm energy storage in a hydrostatic wind turbine. A hydrostatic transmission (HST) not
only provides reliable operation but also enables energy management features like
energy regeneration using hydraulic accumulators. In this study, turbulence-induced
wind transients occurring near the rated power are exploited to extract more energy
from the wind. Wind characteristics are analyzed to develop models to quantify the
energy losses due to the wind turbulence and the potential energy gains from the shortterm energy storage. A dynamic simulation model of the hydrostatic wind turbine and
the proposed energy storage system is developed. A rule-based control strategy for the
energy storage is proposed. Results show that in a 50 kW hydrostatic wind turbine,
the annual energy production (AEP) can be increased by 4.1% with a 60 liter hydraulic
accumulator. [DOI: 10.1115/1.4025249]
Keywords: midsize wind turbine, hydrostatic wind turbine, hydrostatic transmission,
short-term energy storage, hydraulic accumulator
1
Introduction
Due to rising fuel costs, the need to reduce carbon dioxide
emissions and national priorities such as energy self-sufficiency,
the installation of wind turbines for electrical generation has
expanded rapidly worldwide over the last decade. The U.S.
Department of Energy has a goal of having 20% of nation’s
energy come from wind by 2030 [1]. However, they report that
gearbox reliability is a major issue and gearbox replacement is
quite expensive. In a recent study, it was reported that the major
components contributing to low reliability and increased
downtime of wind turbines are found to be the gearbox, power
electronics and the pitch systems [2].
A HST functions as a continuously variable transmission and
eliminates the need for a gearbox. The variable ratio of the hydrostatic transmission decouples the generator speed from rotor
speed, allowing the generator to run at synchronous speed in the
presence of time-varying rotor and wind speeds. This eliminates
the use of bulky and expensive power converters. Moreover, the
hydrostatic transmission provides more damping to the load shaft
and the transmission in the case of wind gusts, which improves
system reliability. Although a hydrostatic transmission has lower
transmission efficiency than a gearbox, the overall system efficiency is still competitive compared to the gearbox turbine since
there is no need for a power converter. With a hydrostatic transmission, it is also easier to develop an energy storage system by
simply adding a hydraulic accumulator, since the power is transferred through the fluid. Emerging technologies in HST components, such as digital displacement pumps and motors which
could provide efficiencies approaching those of a gearbox, have
fueled increased interest in using HSTs in wind turbines [3].
This study focuses on the application of hydrostatic transmissions to midsize wind turbines. A midsize wind turbine, typically
1
This paper was submitted to 2012 ASME Dynamic Systems and Control
Conference (DSCC’12) and has been selected as the top 20 outstanding finalist
paper.
2
Corresponding author.
Contributed by the Dynamic Systems Division of ASME for publication in the
JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received
September 21, 2012; final manuscript received August 8, 2013; published online
September 23, 2013. Assoc. Editor: Luis Alvarez.
defined as 100 kW to 1 MW, can be constructed from readily
available commercial hydraulic components. To make the hydrostatic drives more attractive in the wind application, a short-term
energy storage using hydraulic accumulators is proposed in this
paper to increase the energy capture of the turbine.
Most of the prior work related to short-term energy storage has
been on power smoothing and power quality improving. Shortterm energy storage in the wind/diesel system was investigated
and it was found to decrease the yearly fuel consumption of
diesel generators [4]. Flywheels and supercapacitors were
studied to smooth power fluctuations, improve power quality and
provide low voltage ride through capability by storing short-term
energy [5,6].
The situation where energy storage is used to adjust the
mismatch between a power source and sink also occurs in others
contexts. For example, hybrid vehicles use energy storage to
increase fuel economy. By not requiring the engine power to precisely match the instantaneous driveline power requirement, an
additional degree of freedom is created, allowing the engine to
operate more efficiently. By operating the engine near its most
efficient points and storing the excess energy for later use, fuel
economy can be increased. Hydraulic hybrid vehicles are an
example that is particularly relevant to this study since the energy
is stored in an accumulator [7].
The power curve shown in Fig. 1 illustrates the different operation regions of a wind turbine. There are, in general, four different
Fig. 1 Power curve of a typical wind turbine
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C 2014 by ASME
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turbine operation regions. In region 1, the power in the wind is not
sufficient to overcome the turbine losses and the turbine is
stopped. In region 2 where the wind speed is between cut-in and
rated wind speed, the turbine is operated to capture maximum
power. In region 3, the wind speed is above rated wind speed and
the generator output is maintained at its rated power level. The
power above the rated is spilled by pitching the blade to limit the
power to the rated level. In region 4, the wind speed is too high
and the turbine is shut down to prevent damage.
This paper studies the short-term energy storage during turbulent wind conditions between regions 2 and 3 to increase energy
capture. No previous work on the use of short-term energy storage
during the transition between regions 2 and 3 to increase the
annual energy production of a turbine was found in the literature.
A striking feature of the wind spectrum studied by some
researchers is the spectral gap that separates the macro and micrometeorological range [15]. This helps us to make an important
assumption about the statistical nature of wind. Given a turbulent
wind profile defined by the aforementioned spectrum, the mean
wind speed can be considered constant if the time interval chosen
lies within the spectral gap. Random process with the mean varying very slowly can still be considered as a stationary process
[16]. For instance, a 1-h wind profile can be considered as a stationary process with the mean defined by the annual, seasonal,
and diurnal variations and the short-term turbulent fluctuations
superimposed on this mean. The turbulent wind speed, u, is represented as
u ¼ u þ u~
2
Wind Characteristics
2.1 Wind Spectrum. Wind speed varies both spatially and
temporally. Global winds are caused by solar radiation which
causes pressure differential across the earth surface. These large
scale wind flows affect the local wind variation. Spatial variation
(close to the earth surface) is also affected by surfaces topography
like mountain slopes, valleys and other structures. At any particular location, the temporal variation is due to different atmospheric
conditions, local features, and boundary layer effects [8,9]. The
wind speed spectrum at a given location provides an indication of
types of variations and time scales. Figure 2 shows a typical wind
speed spectrum.
The wind spectrum in Fig. 2 shows four distinct peaks. The first
peak occurs at a frequency of 1 year, the second peak occurs every
4 days (due to depressions and anticyclones), the third peak with
its peak at 1 day is the diurnal variation mainly due to temperature
variations between day and night, and the last peak has a time
period of 1 min. Atmospheric wind turbulence represented by the
micrometrological range (around the 1 min peak) is the short-term
variation with a time period ranging from 30 s to a few minutes.
These short-term turbulent fluctuations are used for the energy
storage analysis in this study.
2.2 Turbulent Wind Model. Turbulence is a complex
physical phenomenon and it is best understood or described by its
statistical properties. The distribution of mean wind speeds over
long period can be approximated by a Weibull distribution. This
Weibull distribution is used in the IEC 61400-12 standard to calculate the annual energy production for a site based on the annual
average wind speed [11]. When evaluating the distribution of
wind speeds over a short time period such as 10 min, the motion is
dominated by turbulence, and a Gaussian distribution can be used
to approximate turbulent wind [12,13]. Kaimal measured the
probability distributions of wind turbulence and observed Gaussian distributions with filtered short-term time series data [14].
(1)
where u is the mean wind speed and u~ is a random variable having
a Gaussian distribution with a zero mean. A standard deviation, r,
is defined by
r ¼ uI
(2)
where I is the turbulence intensity (in %) at a particular location.
The above description is useful only if the spectral gap in the
spectrum is distinct. Recent studies show that this spectral gap is
not always distinct. In some cases, this spectral gap is not
observed at all [17]. To make some engineering conclusions, it is
assumed in this study that there is a distinct spectral gap in the
wind spectrum and a turbulent wind of 20 min to 1 h duration can
be considered a stationary random process.
Wind input profiles are needed to evaluate the influence of
turbulence on the turbine output power and to investigate the performance of the energy storage system. There are two ways
of generating wind input: using real world time series data or generating a synthetic wind profile. Although using real world time
series data looks attractive, it is difficult to find these data. Moreover, these data are mostly site-dependent and may not reflect the
general situations. Therefore, a synthetically generated wind profile using statistical method is used in this study.
Turbulent wind speed can be represented as the sum of a mean
and a turbulence variation as shown in Eq. (1). In this study, the
NREL code—TurbSim, is used to generate the turbulent wind
profile. TurbSim is a stochastic 3D wind simulator. The spectra of
wind velocity component in TurbSim is defined in frequency
domain and an inverse Fourier transform is used to generate time
series data [18,19]. Although TurbSim can generate full-field
wind data, only the stream wise (u) component is used in this
study. Normal turbulence model and IEC Kaimal spectrum are
selected to represent a wind condition for a high turbulence site.
The wind spectrum assumed in the Turbsim (IEC Kaimal spectrum) represents the variation in the micrometeorological range as
shown in Fig. 2. The spectrum is given by
Fig. 2 Illustrative wind speed spectrum (after van der hoven [10])
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Fig. 8 Generator output powers with and without storage
Each time when the wind speed goes above rated (11 m/s in
Fig. 7), the excess energy is stored in the accumulator. Assuming
the accumulator has a large capacity and all stored energy is
released during the time range, the theoretical excess energy captured is 3.7 106 J according to Eq. (20).
The 10-min wind profile was used as an input to the hydrostatic
turbine model. Figure 8 shows a comparison of generator output
powers with and without energy storage. Figure 9 shows the accumulator state of charge throughout the cycle simulation.
The total energy captured with energy storage is
7
7
7
Estore ¼ Egen þ Eacc ¼ 2:61 10 J þ 0:025 10 J ¼ 2:64 10 J
The total energy produced without energy storage is
Eno
store
¼ Egen ¼ 2:35 107 J
Thus, the excess energy produced is
Eexcess ¼ Estore Eno
store
¼ 2:83 106 J
yearly energy production of a turbine for a given site. To calculate
the AEP at a site, the turbine output power curve and the wind
speed distribution at the site must be known. The hourly mean
wind speed has a Weibull distribution, which is to give the probability density at each wind speed
k uk1
u k
(21)
exp f ðuÞ ¼
c c
c
where u is the hourly mean wind speed, c is the scale factor and k
is the shape factor.
To calculate AEP, 1-h averaged wind speeds are collected over
a year. This distribution is represented by a Weibull distribution.
Although the long-term wind speed distribution is Weibull, the
short-term, micrometeorological distribution is Gaussian with the
constant mean from the Weibull distribution and a standard deviation proportional to the turbulence intensity.
The hourly expected average power, Pave, is given by
ð uf
ð ur
PðuÞnet f ðuÞdu þ Prated
f ðuÞdu
(22)
Pave ¼
uc
In this case, the turbine with energy storage produces about
12% more energy than without storage. The excess energy of
2.83 106 J is less than the theoretical value of 3.7 106 J found
earlier. This could be caused by the large blades inertia, slow controller response and the losses in the drivetrain.
ur
where uc is the cut-in wind speed, ur is the rated wind speed, and
uf is the cut-out wind speed.
5.3 Influence of Energy Storage on AEP. The simulation
results show the advantage of using energy storage to capture
more energy from the given turbulent wind profile. Installing such
a system will provide a higher AEP for a hydrostatic turbine. The
annual energy production is a statistical estimate of the expected
Fig. 9 Accumulator SOC
Journal of Dynamic Systems, Measurement, and Control
Fig. 10 Turbine output power curves with and without energy
storage
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Fig. 3 Loss in average rotor power due to wind turbulence in AOC 15/50
Fig. 4 Short-term wind energy storage and reuse
Fig. 5
Schematic of short-term energy storage for a hydrostatic wind turbine
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Table 1
Rule-based control strategy for energy storage system
Accumulator status
Rotor speed
Control action
SOC ¼ 0 (accumulator empty)
x < xrated
Maintain fine pitch angle
Control Dm1 to operate turbine in region 2 (Kx2 law)
Isolate pump/motor M2 (Dm2 ¼ 0)
x xrated
Maintain fine pitch angle
Control line pressure using Dm1 to maintain rotor speed at
xstore(xstore ¼ xrated þ dx).
Engage pump/motor 2 to control power output of the generator at
rated value and store excess power in the accumulator
Maintain fine pitch angle
Control Dm1 to operate turbine in region 2 (Kx2 law)
Engage pump/motor 2 to release stored energy while limiting the
generator power output to rated value
p < pmax
Maintain fine pitch angle
Control line pressure using Dm1 to maintain rotor speed
at xstore(xstore ¼ xrated þ dx).
Engage pump/motor 2 to control power output of the
generator at rated value and store excess power in the
accumulator
x < xrated
0 < SOC < 1 (accumulator partially charged)
x xrated
p pmax
SOC ¼ 1 (accumulator full)
x < xrated
Maintain fine pitch angle
Control Dm1 to operate turbine in region 2 (Kx2 law)
Engage pump/motor 2 to release stored energy while limiting
the generator power output to rated value
x xrated
Control pitch to rated rotor speed xrated
Control Dm1 to operate turbine in region 2 (Kx2 law)
Engage pump/motor 2 to maintain rated generator output
the power of po Dml xgen but the output shaft power of motor
M1 is ðpo þ DpÞ Dm1 xgen . Thus, the excess power is stored
in the accumulator through the pump/motor M2. Therefore,
assuming no losses
Dp Dm1 ¼ paccumulator Dm2
po Vo
Vgas
(12)
where po, Vo and Vgas are the initial gas pressure, initial gas
volume, and the gas volume. The oil volume in the accumulator,
Voil, is
ðt
ðt
(13)
Voil ¼ Qacc dt ¼ Dm2 xgen dt
0
0
where Qacc is the oil flow rate of the accumulator. The gas volume
can also be expressed as
Vgas ¼ Vo Voil
(14)
SOC ¼
Voil
Vo
(16)
A drawback of this approach is that the maximum line pressure
is limited by the components pressure rating. The line pressure is
designed as high as possible to minimize mechanical losses in the
pump and motor. Accommodating the increased line pressure during storage implies selecting a larger pump to reduce the line pressure or selecting components with higher pressure rating.
4.3 Control Strategy. A control strategy for the proposed
energy storage system is developed. The control strategy is a rulebased one in which the control action is made based on the SOC
of the accumulator and the rotor speed. The detailed control
actions at different accumulator status and the rotor speeds are
shown in Table 1.
The Kx2 law shown in Table 1 is a control law widely used in
the wind industry. It is used in region 2 to achieve the maximum
rotor power coefficient. It makes the rotor run at the optimum tipspeed ratio point by adjusting the rotor speeds at different wind
speeds. Unlike a direct rotor speed feedback control, the Kx2 law
controls the rotor speed indirectly by adjusting the rotor reaction
torque. The details of this control law can be found in the literature [21].
5
The energy stored in the accumulator, Eacc, is given by
where SOC is the state of charge of the accumulator. The SOC is
defined as
(11)
where Dp is the pressure increase in the line, Dml is the displacement of the variable motor M1, paccumulator is the accumulator
pressure, and Dm2 is the displacement of the variable pump/motor
M2.
Assuming the gas in the accumulator undergoes an isothermal
change, the accumulator pressure is expressed as
paccumulator ¼
Control pitch to control rotor speed at xstore or slightly
higher than xstore
Control Dm1 to maintain prated or keep Dm1 constant
Engage pump/motor 2 to control power output of the
generator at rated value and store excess power
in the accumulator
Simulation Study
(15)
5.1 Simulation Model. The target application of this study is
midsize wind turbines. The hydrostatic wind turbine model
consists of turbine blades, hydrostatic transmission and the
Journal of Dynamic Systems, Measurement, and Control
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Eacc ¼ po Vo ln
Vo
Vgas
¼ po Vo ln
1
1 SOC
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Table 2 Main simulation parameters of HST wind turbine
Component
Parameter
Rotor
Fixed displacement pump
rotor diameter
rated rotor speed
rated rotor power
displacement
efficiency map (pressure and speed dependent)
Pipeline
Value
Unit
15
88
65
m
rpm
kW
2512
—
pipe length
internal diameter
2
1
cc/rev
—
m
inch
Variable displacement motor (M1)
displacement
efficiency map (pressure, speed, and displacement fraction dependent)
125
—
cc/rev
—
Variable displacement pump/motor (M2)
displacement
efficiency map (pressure, speed, and displacement fraction dependent)
size
synchronous speed
55
—
40
1800
cc/rev
—
L
rpm
Accumulator
Synchronous generator
generator. Characteristics of AOC 15/50 turbine blades are used
for the blades model. AOC 15/50 is a 50 kW turbine manufactured
by Atlantic Orient Corporation [22]. The aerodynamic model of
AOC 15/50 blades was built using FAST code. FAST is a multibody wind turbine dynamics code developed by NREL. This code
was interfaced with the hydrostatic turbine model as an SFunction [23].
The pump and motor efficiencies used in the model are provided by some hydraulic component manufacturer. For the fixed
displacement pump, the efficiencies are pressure and speed
dependent. For the variable displacement motors, the efficiencies
are pressure, speed, and displacement fraction dependent. The
dynamic simulation model is developed using the best available
engineering practice after consulting with the experienced experts
from major wind turbine and hydraulic component manufacturers.
In a conventional gearbox turbine, the Kx2 law controls the
rotor speed to achieve optimum tip-speed ratio by adjusting the
rotor reaction torque in real-time. In a hydrostatic wind turbine,
the control strategy is to first convert this rotor reaction torque
command defined by the Kx2 law to the line pressure command
and then use a PI controller to track this pressure command.
Pressure tracking is achieved by adjusting the displacement of the
hydraulic motor in the HST. Detailed hydrostatic wind turbine
modeling and controller design can be found in our previous work
[24]. The main simulation parameters of the HST wind turbine are
shown in Table 2.
When the wind speed goes above rated (11 m/s for AOC 15/50
turbine), the wind induced torque will increase if the blade pitch
angle is maintained at a high aerodynamic efficiency status. To
keep the rotor at its rated speed, the rotor reaction torque must
Fig. 6 Wind torque as a function of wind speed at the rated
rotor speed
also increase when the wind induced torque increases. The wind
induced torque as a function of wind speed at the rated rotor speed
of 88 rpm for AOC 15/50 turbine is shown in Fig. 6.
A cubic spline was fit to the data and also shown in Fig. 6. The
wind torque as a function of wind speed at the rated rotor speed of
88 rpm is given by
sðvÞ ¼ 0:0061v3 þ 0:2257v2 1:2459v þ 1:3839 kNm
(17)
For the wind speed above rated, it is expressed as
v ¼ vo þ Dv
(18)
where vo ¼ 11 m=s. Let the rotor power at rated condition be
sðvo Þxo and above rated condition (store excess energy) be
sðvÞxo . Thus, the stored power is expressed as ½sðvÞ sðvo Þxo .
Let sstore be defined as
(
½sðvÞ sðvo Þ; sgnðsðvÞ sðvo ÞÞ ¼ 1
sstore ¼
(19)
0;
sgnðsðvÞ sðvo ÞÞ ¼ 1
Therefore, the energy stored for duration of T seconds, Estore, is
given by
Estore ¼
ðT
sstore ðvÞxo dt
(20)
0
5.2 Simulation Results. A 10-min turbulent wind profile
with a mean speed of 11 m/s was generated using the TurbSim
and is shown in Fig. 7. A hub height wind series was extracted
and used in the model.
Fig. 7
10-min turbulent wind profile
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Fig. 8 Generator output powers with and without storage
Each time when the wind speed goes above rated (11 m/s in
Fig. 7), the excess energy is stored in the accumulator. Assuming
the accumulator has a large capacity and all stored energy is
released during the time range, the theoretical excess energy captured is 3.7 106 J according to Eq. (20).
The 10-min wind profile was used as an input to the hydrostatic
turbine model. Figure 8 shows a comparison of generator output
powers with and without energy storage. Figure 9 shows the accumulator state of charge throughout the cycle simulation.
The total energy captured with energy storage is
7
7
7
Estore ¼ Egen þ Eacc ¼ 2:61 10 J þ 0:025 10 J ¼ 2:64 10 J
The total energy produced without energy storage is
Eno
store
¼ Egen ¼ 2:35 107 J
Thus, the excess energy produced is
Eexcess ¼ Estore Eno
store
¼ 2:83 106 J
yearly energy production of a turbine for a given site. To calculate
the AEP at a site, the turbine output power curve and the wind
speed distribution at the site must be known. The hourly mean
wind speed has a Weibull distribution, which is to give the probability density at each wind speed
k uk1
u k
(21)
exp f ðuÞ ¼
c c
c
where u is the hourly mean wind speed, c is the scale factor and k
is the shape factor.
To calculate AEP, 1-h averaged wind speeds are collected over
a year. This distribution is represented by a Weibull distribution.
Although the long-term wind speed distribution is Weibull, the
short-term, micrometeorological distribution is Gaussian with the
constant mean from the Weibull distribution and a standard deviation proportional to the turbulence intensity.
The hourly expected average power, Pave, is given by
ð uf
ð ur
PðuÞnet f ðuÞdu þ Prated
f ðuÞdu
(22)
Pave ¼
uc
In this case, the turbine with energy storage produces about
12% more energy than without storage. The excess energy of
2.83 106 J is less than the theoretical value of 3.7 106 J found
earlier. This could be caused by the large blades inertia, slow controller response and the losses in the drivetrain.
ur
where uc is the cut-in wind speed, ur is the rated wind speed, and
uf is the cut-out wind speed.
5.3 Influence of Energy Storage on AEP. The simulation
results show the advantage of using energy storage to capture
more energy from the given turbulent wind profile. Installing such
a system will provide a higher AEP for a hydrostatic turbine. The
annual energy production is a statistical estimate of the expected
Fig. 9 Accumulator SOC
Journal of Dynamic Systems, Measurement, and Control
Fig. 10 Turbine output power curves with and without energy
storage
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Table 3 AEP comparison results with and without energy
storage
AEP (MW h)
Change in AEP
Steady
wind
Turbulent
wind
Turbulent wind with storage
(40 liter accumulator)
224
Base
223.5
0.23%
231.7
þ3.4%
the accumulator size. For a 50 kW wind turbine, A 40 liter accumulator increases AEP by 3.4% and a 60 liter accumulator
increases AEP by 4.1%.
Due to the limitation on the maximum torque, the system can
handle or the cost associated to design a system to handle higher
loads, the energy storage was reanalyzed by putting a constraint
on the maximum rotor power. It is found that even if the maximum rotor shaft torque is restricted to 20% above rated, the AEP
still increases by 2.8%.
5
Fig. 11 Sensitivity study of accumulator size on AEP
The annual energy production is given by
AEP ¼ Pave 8760 kWh
(23)
More details about how to calculate the annual energy production of a turbine can be found in the literature [25].
Figure 10 compares the turbine output power curves for three
cases: steady wind, turbulent wind without storage and turbulent
wind with storage (assume a 40 liter accumulator). Note that the
rated generator power of 55 kW is lower than the rated rotor
power of 65 kW, which is due to the transmission losses. This
power curve is then used to calculate AEP.
For each mean wind speed in the turbine output power curve,
ten simulations (ten wind profiles with the same mean, turbulence,
and spectrum) are run and the mean generator power is evaluated
for each case. The mean generator power for each wind speed,
Pmean, is calculated as
ðT
Pgenerator dt
Pmean ¼
0
T
(24)
where T is the time period of the wind profile.
Ten mean generator powers are then averaged to calculate the
ensemble average for each mean wind speed.
Assuming a Weibull distribution (k ¼ 2, c ¼ 9.59) for wind, the
AEPs are calculated for the steady wind, turbulent wind and the
turbulent wind with storage. Table 3 shows AEP comparison
results for three cases. The loss in AEP due to turbulent wind is
mere 0.23% from the baseline AEP (steady wind), whereas with
storage the AEP increases 3.4% with a 40 liter accumulator.
5.4 Sensitivity Study on Accumulator Size. The AEP
changes with different accumulator sizes. To determine the
appropriate accumulator size, multiple simulations are conducted
to understand the sensitivity of accumulator size to the AEP.
Figure 11 shows the AEP increase as to the accumulator size. The
sensitivity study was conducted with limited numbers of accumulator sizes, which are shown with discrete date points in Fig. 11. A
trend line is added to show how the AEP increase changes with
the accumulator size. Results show that the AEP increases with
Conclusions
This paper presents a novel approach of capturing more energy
from the wind using short-term energy storage in a hydrostatic
wind turbine. A hydrostatic transmission not only provides
reliable operation but also enables easy energy storage using
hydraulic accumulators. In this study, turbulence-induced wind
transients occurring near the rated power are exploited to extract
more energy from the wind. Turbulent wind oscillations with time
scale ranging from 30 s to a few minutes is used for the energy
storage analysis in this study. A Gaussian distribution is used to
approximate the turbulent wind conditions.
A dynamic simulation model of the hydrostatic wind turbine
and the proposed energy storage system is developed. A rulebased control strategy for the energy storage is investigated. The
simulation model is used to quantify the potential energy gains in
AEP from energy storage. A sensitivity study of accumulator size
on the AEP is also presented. Results show that the AEP increases
with the accumulator size. In a 50 kW hydrostatic wind turbine, a
40 liter accumulator increases AEP by 3.4% and a 60 liter accumulator increases AEP by 4.1%.
A detailed cost analysis is required to determine whether the
increase in the AEP will offset the increased cost of implementing
the energy storage in a turbine. A 60 liter accumulator increases
AEP by 4.1% and costs a few thousand dollars, a small additional
cost for a 50 kW wind turbine. Many questions remain to determine the technical and economic feasibility of the proposed
energy storage system. Lab and field testing of a prototype will
help to assess the real world performance. It may also be desirable
to develop new control strategies and energy storage configurations in the future. For example, model predictive control could
further increase the system energy capture by using future wind
speed information.
Acknowledgment
This work is funded by the Initiative for Renewable Energy &
the Environment (IREE), a signature program of the Institute on
the Environment at the University of Minnesota. This material is
based upon work supported by the Department of Energy Technology Laboratory under Award No. DE-EE0005190.
References
[1] U.S. Department of Energy, Energy Efficiency and Renewable Energy, 2008,
“20% Wind Energy by 2030: Increasing Wind Energy’s Contribution to
the U.S. Electricity Supply,” U.S. Department of Energy, Paper No. DOE/
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