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Optimal control of battery for grid-connected wind-storage
system
Liang, L; Zhong, J
The IEEE 15th International Conference on Harmonics and
Quality of Power (ICHQP 2012), Hong Kong, 17-20 June 2012. In
Proceedings of the 15th ICHQP, 2012, p. 701-706
2012
http://hdl.handle.net/10722/153081
International Conference on Harmonics and Quality of Power
Proceedings. Copyright © IEEE.
Optimal Control of Battery for
Grid-Connected Wind-Storage System
Liang Liang, Student Member, IEEE, and Jin Zhong, Senior Member, IEEE
Abstract- The penetration level of large-scale wind farms is
restricted by the output uncertainties of wind power generations.
Energy
storage
systems
with
fast
response
time
and
high
operation efficiencies, such as, flywheel and battery could be used
as one of the solutions for large-scale wind power integration to
power grid. To mitigate the power fluctuation of wind farm, an
optimal control method of battery energy storage system is
proposed for grid-connected wind system in this paper. Based on
one-day
ahead
forecasted
data
of
wind
farm
output,
an
optimization model is proposed to minimize the output power
fluctuation as well as energy storage subj ect to battery capacity
and power constraints. The solution algorithm is presented to
solve the nonlinear optimization model. Finally, case studies with
different output power curves of wind farm are presented to
analyze the inter-relationship of energy storage capacity, energy
storage output power and wind power fluctuation and typical
configuration of battery energy storage system for wind-storage
hybrid system is proposed.
Index Terms-Energy storage, optimal sizing, wind farm,
renewable energy integration, power fluctuation.
I. INTRODUCTION
ithin past two decades, the penetration level of wind
power generation in power system has increased
significantly due to the mature of wind power
generation technologies and the requirement of energy and
environment. Consequently, wind power could take greater
effect to power grid than ever before [1]. It is a great challenge
to integrate wind power generation to power systems due to
the fluctuation of wind. Connection of large-scale wind power
generations to the grid may cause frequency stability problem
to the power system, as wind power fluctuations occur in the
time scale of minutes and even in seconds [2-5]. The related
research is mainly on two aspects, power system operation
level and wind farm level. The first aspect concerns how to
provide abundant reserves and manage them effectively [6].
The other aspect is to reduce the effect of power fluctuations
of wind farms using various methods. Compared to the first
aspect, reducing power fluctuations of wind farm could
facilitate wind power integration to the grid and help maintain
current operation modes of power system operations even with
high wind penetration in the system.
Nowadays, battery energy storage system has been regarded
W
This work was partly supported by grants from the Research Grant
Council of Hong Kong (Project No. HKU10 /CRF/10 ) and the State Key
Laboratory of Power Systems of Tsinghua University, Beijing, China.
Liang Liang and Jin Zhong are with the Department of Electrical and
Electronic Engineering, The University of Hong Kong, Hong Kong (e­
mail: lliang@eee. hku. hk;jzhong@eee. hku. hk).
978-1-4673-1943-0 /12/$31.00 ©20 12 IEEE
701
as an effective solution to reduce power fluctuations of
renewable energy [7-10]. Battery energy storage systems
could compensate the output power fluctuation of renewable
energy in seconds due to its ability of rapid change of power
output. In this way, battery energy storage system could
greatly mitigate the intermittent characteristics of renewable
energy generations, hence, lower the output fluctuation to an
operation-acceptable level. In this case, the compensated
renewable energy generation could be integrated into power
grid directly and easily.
Control methods have been proposed to reduce output
power fluctuations of wind farm [11-14]. A real-time control
method for the energy storage system is presented in [11]. The
energy storage system works as a low-pass filter and the
output power fluctuation of the whole system is greatly
reduced in most cases. Some improved control methods are
represented in [12-13]. The state of charging (SOC) has been
introduced into the control loop. The control method could
keep the battery system away from deep charging area so as to
obtain a longer life for the system. The wind forecasting
system has been introduced in [14]. Based on the forecasting,
the system operator could obtain the hourly output of the wind
farm in one day ahead. In this case, the battery system could
select the daily average output of the wind farm as a set point,
then compensate the difference between the actual output and
the forecasted output of the wind farm. Furthermore, the
development of wind forecast technology provides a
possibility of predicating the wind farm output in a certain
time [15-19].
In this paper, a new method is proposed for selecting
suitable operating set points for various battery systems, such
as Lithium-ion battery energy storage system, redox flow
battery energy storage system and NaS battery energy storage
system. The battery system could rapidly compensate the
power fluctuation of wind farm based on these set points. It is
assumed that the day-ahead wind farm output is available for
the control system of the hybrid wind-battery system. The set
points of the battery system could be calculated by carrying
out the proposed optimization process. The topology structure
of the studied wind-storage hybrid system is described in
Section II. The objective function considering wind farm
outputs and the power fluctuation of the wind-storage hybrid
system is proposed in Section III. By minimizing the objective
function, the battery set point of next day can be obtained. The
solution algorithm is represented in Section IV. Case studies
are presented in Section V, the relationship of the capacity of
energy storage system, the output power of energy storage
2
system and the value of proposed objective function are
studied and analyzed. Section VI concludes.
II.
A. Objective Function
HYBRID WIND-BATTERY SYSTEM TOPOLOGY
�
�
+ n
0
Wind Turbi ne
Output
Total Output
BJ
I
C=
P,
-
,
BD"'''d
Jl/=1t PB, - E�,ep
J2
,
(1)
n
=
�
Energy StGrage
�
Battery
+ 0,aflery
hybrid system at time i, and PT, PB, + Pw; . PB, and Pw, indicate
the output power of the battery energy storage system and
wind turbines at time i, respectively EBc"""d indicates the
desired exchanging energy of battery energy storage system in
one day. T"ep indicates the time interval between two data
points. F;,allery indicates the factor of energy exchanging of the
battery energy storage system. .
In the objective function, Pw, could be obtained from the
Wind Turbine
Output
2
where, � indicates the output power of the wind-storage
35kV/llOkV
Output
Wind Turbine
I1=1 (PT,+, - PT, )
'-.--'
I
,
'
,
,
'
I\ II
690V/35kV
Wind T.urbine
The objective function is to minimize the power fluctuation
and the energy charged and discharged in the wind-storage
hybrid system.
day-ahead wind forecasting results. T"ep is decided by the
wind forecasting system. Nonnally a smaller time interval
leads to a better compensation result. Different F;,allelY and
380V/35kV
EB
. d
frSlre
System
Fig.1 The topology diagram of the hybrid wind-storage system
Fig. l shows the topology diagram of the wind-storage
hybrid system. The wind-storage hybrid system includes
several wind turbines, which are controlled by power
converters and connected with power grid through individual
transfonners. The total output power of the wind farm is the
sum of output from all wind turbines. The battery bank is
consisted of a large number of serial and parallel connected
batteries. The battery bank is connected to the system through
the power converter system (PCS) and a transfonner. As a
result, the capacity of the battery energy storage is decided by
the total capacity of all batteries, and the rated output power is
equal to the rated power of PCS. The wind-storage hybrid
system connects to the power grid through a 35kVIllOkV
transformer.
The battery energy storage system is a controllable source
in the wind-storage hybrid system. The infonnation of actual
output of the total wind turbines could be transferred to the
battery system. This feather makes it possible to reduce the
output fluctuation of the wind-storage hybrid system by
selecting a series of suitable operation set points for the
controllable battery energy storage system.
III. MATHEMATIC MODEL
The purpose of the proposed model is to pre-determine set
points of battery systems according to day-ahead forecasted
wind power, in order to minimize the output fluctuation of
wind farm. The objective function is optimized subject to the
battery capacity and power constraints.
702
can be used to represent different operation status of
the wind-storage hybrid system. The operation set points of
battery system, PB, ' are obtained by solving the proposed
optimization model.
The first item of the objective function represents the power
fluctuation of the wind-storage hybrid system, and the second
item represents the total energy of wind system exchanged
with the battery system. The first item minimizes the
fluctuation of hybrid wind-storage system.
The second item minimizes the difference between the
actual energy and the desired energy exchanged between the
battery system and the power grid.
The control method is proposed to calculate the optimal
operation set points for the next day, as a result, the SOC of
the battery is expected to be kept around 50% at the end of
each day. In other word, the energy exchanged between the
battery system and the power grid is supposed to be the
difference between the 50% SOC and the initial energy of the
battery system. When the second item is minimized, the
difference between the actual exchanged energy and the
expected exchange energy is minimized. For example, as
shown in Fig. 2, the curve represents the wind farm output of
one day. When the capacity is large enough, says Eness' the
output of the wind-storage hybrid system could be controlled
as the average output of the wind farm. If the capacity of the
battery energy storage system is larger than Eness ' the output
power could even be controlled as a constant value within a
region between the upper and lower boundary as shown in
Fig. 2. In this case, by minimizing the second item in the
objective function, the set points of the battery energy storage
system will be set to help to control the SOC of the battery
system close to 50%.
[
n-I
n
2
EBD"i"d
" ( P.71 1 -p.)
minC= �
� B;
71 +F.baltelY x "(p.)+
1=1
Tstep
1=1
Pr, =PBi+Prv,
n
E
E
S.t. -�::;L(PBi)::;�
i=1
J',tep
J',tep
]
3
2
(5)
(6)
As Pw, could be obtained from one-day ahead wind farm
,
"�,, --;"�,,--;"'�"--c;,,
O"o�--7
, ,-c;--7
, , -c;-, , --;
--;- -,
--:,
--;- :--!;
,,--;,;; , -c;,,--c,C:-, -;;
,,-,C:-, --o"�,, --;;
Time (Hour)
Fig.2 Possible output power area of the hybrid wind-storage system
Based on the initial value of the SOC, the value of EB� can
be obtained to make sure that the final SOC at the end of the
day is close to 50%.
The two items are optimized simultaneously, and in certain
cases, both items could be minimized to zero at the same time.
B.
forecast system, the PBi will decide the value of the objective
function. The battery energy storage system needs to select
suitable value to minimize the value of objective function.
Consequently, the control system of battery system is required
to complete the quadratic optimization problem effectively.
The problem could be described as below:
. 1
mm-x T Hx+fT x
(7)
x
s.t.
Constrains
Battery energy storage system is a new type of power
component in the power system. Battery energy storage
system could change the real-time balancing characteristic of
power generation and demand. Different from conversional
generation components in the power system, the battery
system has capacity and power constrains need to be
considered:
n
E
E
-�::;"(p.Bi)::;�
L..
Tstep
Tstep
f=l
EBal/elY =EBlteg +EB
>
('linE N)
(2)
Ax::;b
(8)
lb::; x::; ub
where H,f,A,b,lb and ub are defined as below:
(9)
H =C +2xF"allety xD
2
-2
-2
4 -2
-2 4
C=
(10)
-2
(3)
pos
2
{
4 -2
-2 4 -2
-2
2
( 4)
where, EB,reg indicates the maximum amount of energy that
can be absorbed from the power grid. EB indicates the
maximum energy of the battery system could be injected to
the power grid. EBallery indicates the capacity of battery energy
storage system. N indicates the total data points in the forecast
results of wind farm. PB"., indicates the maximum output
power of battery energy storage system.
Equation (2) shows that the energy exchanged with battery
system should not exceed the capacity limit of the battery
system. Normally, it is assumed that the initial SOC of the
1''''
EBallery
battery IS· 50%, and EB/leg =EB =--2
D=
(11)
J,1 =2x(p.
Wi -P.W, )- 2xF.ballery
EBD"i"d
TSlep
(12 )
J;=2x(Pw- Pw1+1 )- 2X(Pw -pw)
I
I-I
I
(13)
pos
Equation (4) shows that the output power constraint of the
battery system. The maxim output power is decided by the
PCS system of the battery system.
IV. SOLUTION ALGORITHM
The main idea of this control method of hybrid wind­
storage system is to minimize the objective function:
703
J;n =2X(P.Wit -P.W/I_1 )- 2xF.bauery
EBD"'i"d
Tstep
(14)
4
0
0
0
0
0
0
0
1.2
0
A=
1
1
1
-\
0
0
0
-\
-\
-\
-\
0
0
0
0
-\
-\
-1
-1
s­
S
Q5 0.8
(15)
B- 0.6
::0
o
E
co
LL
"0
c:
-1
-1
\
�
:;
0..
-1
OA
3:
,,_ ''''.
30% R,",
60% Rated Power Battery
E
Bpo,
Y'"ep
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Time (Hour)
EBpo,
b=
Fig.3 The output of wind fann with small fluctuation
Y',teP
Fig.3 shows one typical power curve of wind farm
compensated with battery system which has different rated
output power. The average output power of the wind farm is
more than 0.6 p.u. of the wind farm, while the power
fluctuation is not large. The output power fluctuation of wind
farm could be greatly reduced with a lO% rated battery energy
storage system. The output power of the wind-storage hybrid
system could be kept at a constant value when the rated power
of battery system increasing to 60% in this case.
(16)
E
�
Y',teP
E
�
Y',teP
-PBm"
lb=
l j lp,_. j
:
ub=
-PBma:,
:
(17)
PBma:,
= FB,
(18)
This problem is a standard quadratic optimization problem.
If there are 24 data points in the day-ahead forecasting data of
the wind farm, the problem could be solved in less one second.
Even if the output power data of wind farm includes 1440
points which means the data acquiring system could sample
the wind farm every one minute. The output set points for
battery energy storage system of next day could be calculated
in 1 minute.
x,
/
1.0
V. C ASE STUDY
The daily output power curve of wind farm includes 1440
points, the time interval is one minute. The maximum output
power of battery system is set to 60% rated power of the wind
farm. The maximum capacity is set to 4 hours according to the
rated power of battery system. There are 4 typical daily curves
of wind farm in this case study. The value of 0,attery is set to
le-04. The value of EBL""'''d is set to zero. Based on the
proposed optimal control method, Fig.3 to Fig.6 shows the
compensation result for the wind-storage hybrid system.
704
10% Rated Power Battery
0.9
s­
ci.
-0.8
� ����-+���-----r--���r----------!��
�O.7
:;
�
30%
,g. 0.6
(5
ated Power Battery
60% Rated Power Battery
E 0.5
�
'U 0.4
c:
3:
0.3
0.2
0.
\
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Time (Hour)
Fig.4 The output of wind farm with strong wind
FigA is the second typical output power curve of wind farm
compensated with battery system. The total wind output power
in FigA is larger than in Fig. 3. The average output power is
more than 0.7 p.u. of the wind farm. The output power of wind
farm has reduced more than 0.6 p.u. in one hour. Even in this
case, a battery system with 60% rated output power could
totally compensate the output power fluctuation of wind farm.
24
5
1.0
�
Wind Farm OutPut
10% Rated Power BatteI)'
0.8
s
S
Q; 0.6
30% Rated Power BatteI)'
�
CL
:;
%
0.4
o
E
'"
lJ..
"0
c:
�-----mr
0.2
�
Rated Battery Capacity (Hour)
Rated Battery Power (p.u)
Fig.7 the r elationship among power , capacity and
the value of objective function
7---;
;- , --:,
;;---;c
-<l .2�
3 ---:
;- -4 -5�6----!;
;- 00- --!:11;12c-!;:'3-'
--!;
;;- 4 --:,50- --!:16�17c-!;:1 8-'�' --;;20
21c-;O;�---;!
8 ---;
0 ----!;---;
Fig.7 shows the relationship among the rated power of
battery system, the rated capacity of battery system and the
Fig.5 The output of wind fann with large fluctuation
value of objective function. The relationship is calculated
Fig.5 shows the situation of output power curve of wind
based
on case 1 which is described in Fig.3.
farm with large fluctuation. In this case, even if the battery
In
order
to have an acceptable value of objective function,
with a 60% rated output power, the large power fluctuation
the
rated
power
of battery system should not less than 10% of
could not be eliminated totally. And more, in order to store
the
rated
output
power of wind farm and the capacity of
more output power from wind farm, the battery with a 10%
battery
system
should
larger than 0.8 hour. When the capacity
rated power need to send power into grid at initial state to
of
battery
larger
than
1.6
hour and the rated power of battery
obtain a larger energy storage capacity.
larger than 15%, the effect of increasing the rated power and
0.9
capacity of battery system for reducing the value of objective
function is not so obvious as the original system.
0.8
Time (Hour)
_0.7
'"
VI. C ONCLUSION
An optimal control of battery system for grid-connected
�
CL
_ 0.5
wind-storage system is presented in this paper. Based on the
5.
:;
results of wind forecasting, an optimization model is proposed
00.4
to plan the set points of the battery system for the next day to
E
'"
LL 0.3
reduce the power fluctuation of the wind-storage hybrid
"0
c:
system.
The model could be described as a quadratic
� 0.2
optimization problem. Such problem could be solved in less
than one minute with modem computer.
Four typical output power curves of wind farm are tested in
section
V. The results show that battery energy storage system
Time (Hour)
with
a
10%
rated power and 0.8 hours capacity could greatly
Fig.6 the output of wind fann with large fluctuation and
small average output power
reduce the output power fluctuation and facilitate the wind
Fig.6 shows the output power curve of wind farm has a low power system integrate into power grid. If the rated power of
average value but a large fluctuation between 10: 00am and battery system is larger than 60% rated power, it is possible to
11: OOam. The battery with 60% rated power could totally eliminate the power fluctuation in the output power of wind
compensate the power fluctuation in this case, while the farm in one day.
output of the wind-storage hybrid system with a 30% rated
Although the precision of wind forecast system may take
power battery system still has a great power fluctuation.
great effect to the result, some researchers are focusing on this
In all four output power curves of wind farm studied above, area and better forecast methods with higher accuracy could
a battery system with 10% rated power could compensate the be expected.
small transient power fluctuation part in the output power of
wind farm. While a battery with 30% rated power could
VII. R EFERENCES
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s
10% Rated Power Battel)'_____...
Q) 0.6
705
6
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Liang Liang was born in Beijing, China, in 1983 . He received his B.Sc.
degree from Tsinghua University, Beijing, China, in 2005, and the M. Sc.
degree from Institute of Electrical Engineering, Chinese Academy of Sciences,
Beijing, China, in 2008 . He is currently working toward the Ph. D. Degree in
Electrical Engineering at the University of Hong Kong, Hong Kong.
His current research interests include areas of energy storage applications
in power system, wind energy and renewable energy.
Jin Zhong (M'05 , SM'IO) received her B.Sc. degree from Tsinghua
University, Beijing, China, the M. Sc. degree from China Electric Power
Research Institute and the Ph. D. degree from Chalmers University of
Technology, Gothenburg, Sweden, in 2003. At present, she is an Associate
706
Professor in the Department of Electrical and Electronic Engineering, the
University of Hong Kong.
Her areas of interest are power system operation, electricity sector
d eregulation, ancillary service pricing, and smart grid.