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STABILIZATION OF GRID CONNECTED WIND AND PV SYSTEM … › aej › issue › 2012-v1-1 ›...
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STABILIZATION OF GRID CONNECTED WIND
AND PV SYSTEM BY COORDINATED PSS AND
BATTERY CONTROLLER
Cuk Supriyadi Ali Nandar1, Takuhei Hashiguchi
1, Tadahiro Goda
1
1School of Information Science and Electrical Engineering, Kyushu University.
744 Motooka, Nishi-ku, Fukuoka, Japan 819-0395
Received Date: August 30, 2011
Abstract
An interconnecting renewable energy system to operate in parallel with the grid has become a new
trend. However, the intermittent of power output of renewable energy system may cause a serious
problem of frequency and voltage fluctuation in the electricity grid. Moreover, the large
penetration of renewable energy also may cause lack of damping of the electromechanical
oscillation modes in main grid, and it usually causes severe problems of low frequency oscillations
in power systems. This paper proposes a coordinated design of Power System Stabilizer (PSS) and
battery controller for stability improvement in grid connected large wind generation (WG) and
photo voltaic (PV) system. The PSS is used for stabilizing low frequency oscillation while the
battery is used to alleviate power fluctuation from wind power and PV system. The structures of
both controllers are the first-order lead-lag compensator. The control parameter optimization
problem based on an enhancement of system damping is formulated. The genetic algorithm is used
to solve optimization problem. The effectiveness of the proposed controllers is confirmed by
nonlinear simulation results using ObjectStab Package and Matlab Software.
Keywords: Battery Controller, Genetic Algorithm, Grid Connected, Power System Stabilizer, PV System, Wind Power
Introduction
Today, negative effects of global warming have become one of the leading issues in the
world. The extreme bad weather in the most of area on the world may significantly reduce
productivity. Fossil fuels become a dominant source of fuels used in the generation of
electricity, and contributing nearly three quarters of CO2 emissions [1]. They cause
negative impact in the climate change. Therefore, reducing emission in the air and oil fuel
used by increasing implementation of renewable energy to prevent global warming is
highly needed. One of the most interesting solutions is increasing the diversification of
energy sources.
In power system, stand alone renewable energy is experiencing dramatic growth such
as wind and PV system. Nevertheless, wind power and PV system are intermittent due to
worst case weather conditions such as an extended period of overcast skies or when there
is no wind for several weeks. As a result, PV and wind generation systems are variable and
unpredictable. To overcome this problem, the hybrid wind power with diesel generation
has been suggested by many works [2-7]. A hybrid wind-diesel system is very reliable
because the diesel acts as a cushion to take care of variation in wind speed and would
always maintain an average power equal to the set point. Another problem faced by stand
alone power system is the energy storages such as battery, fuel cell, SMES etc requires
additional investment, building space and routine maintenance [8]. There will also be
energy losses in the charging-discharging process. To reduce the energy storages capacity
and cost of investment, interconnecting PV and wind power generation system to operate
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in parallel with the grid is a very new trend [9]. However, the intermittent of output power
of PV and wind power generation system may cause power fluctuation of the grid, and it
also cause a serious problem of frequency and voltage fluctuation of the grid. Moreover,
the large penetration of renewable energy also may cause lack of damping of the
electromechanical oscillation modes in main grid, and it usually causes severe problems of
low frequency oscillations in power systems. To enhance frequency stability, an effective
controller for reducing power fluctuation, improving damping of the electromechanical
oscillation modes, and maintaining the system frequency within the acceptable range is
significantly required.
To overcome this problem, Power System Stabilizer (PSS) and aqua electrolyzer (AE)
controller design using H∞ Decentralized controller has been successfully applied to
control frequency in a microgrid system [10,11]. However, the order of H∞ controller
depends on that of the plant. This leads to the complex structure controller which is
different from the conventional lead/lag compensator. Despite the significant potential of
control techniques mentioned above, power system utilities still prefer the conventional
lead/lag compensator structure. This is due to the ease of implementation, the long-term
reliability, etc. This paper proposes a coordinated design of PSS and battery based
controller for stability improvement. The performance conditions in the damping ratio and
the real part of the dominant mode is applied to formulate the optimization problem. In this
work, the structures of the proposed controllers are the first-order lead/lag compensator.
To achieve the controller parameters, the genetic algorithm (GA) is used to solve the
optimization problem. Various simulation studies are carried out to confirm the
effectiveness of the proposed controller.
Figure 1. System configuration of SMIB with WG , PV system and Battery
System Modeling and Control Design
A. Power System Modeling
System configuration of wind generation (WG) and Photo voltaic (PV) system be
connected to single machine infinite bus (SMIB) in Figure 1 is used in this study. This
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system consists of single machine infinite bus (SMIB), wind power generation and PV
system, and battery. SMIB system is obtained from reference [12,13]. The wind generation
and PV are modeled by the random active power source. The maximum generating
capacity of each G1, WG and PV are 100 MVA, 20 MVA and 40 MW, respectively.
Battery is installed in bus B4 to absorb and release electric power fluctuation from both
WG and PV system. The generator is represented by a 3rd
-state transient model using
ObjectStab software package. It is equipped with an automatic voltage regulator (AVR) as
depicted in Fig. 2. Power system stabilizer (PSS) is installed in the generator system to
enhance dynamic stability.
Figure 2. automatic voltage regulator (AVR) system
System data of SMIB are shown in Table 1 [13] as follows;
Table 1 : System Data
Sbase (p.u) xd x'd xq T'do M Ka Ta
1000 MW 1.8 p.u 0.3 p.u 1.7 p.u 8 sec 6.5 sec 50 0.05
where
xd : d-axis synchronous reactance
x'd : d-axis transient reactance
xq : q-axis synchronous reactance
T'do : d-axis transient open circuit time constant
M : inertia constant
Ka : AVR gain
Ta : AVR time constant
The linearized state equation of system in Fig. 1 can be expressed as
uBXAX
(1)
uDXCY (2)
where the state vector Tfdq EeX ' ; Y is the output signal of system; u is the
control output signal of both PSS and battery controller. In this study, PSS uses only the
angular velocity deviation ( ) as a feedback input signal. Moreover, the input signals of
active and reactive power controllers of battery are active power deviation and reactive
power deviation in a line from bus B4 to bus B5.Note that the system in (1) is a Multi-
Input Multi-Output (MIMO) system. The proposed control method is applied to design
both PSS and battery controller simultaneously.
B. Battery Modeling
The block diagram of battery is shown in Fig. 3. In this study, the battery is modeled by
the first-order transfer function with time constant BATTT = 0.3 s [11]. For the controller, the
simple and practical controller is represented by the 1st order lead/lag controller.
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Figure 3. Block diagram of battery with P and Q controller
Where BATTP and BATTQ are the battery active and reactive power outputs ,PBATTu and
QBATTu are the control output signals of the battery P controller and Q controller,
respectively. lineP and
lineQ are the input signals of the battery P controller and Q controller
in a line from bus B4 to B5, PK ,
1PT and 2PT are gain and time constants of battery P-
controller, QK ,
1QT and 2QT are gain and time constants of the battery Q-controller and
BATTT is the time constant of battery signal.
C. PSS Modeling
Figure 4. Block diagram of PSS
As shown in Fig. 4, the PSS controller is represented by a simple 1st order lead/lag
controller. For washout, it is modeled by the first-order transfer function with time
constant Tw = 2 sec. Δupss is the control output signal of the PSS, is the angular
velocity deviation as a feedback input signal, K is the gain of PSS, Ti are time constants of
PSS, Tw is the time constant of signal washout. The gain and time constants of PSS are
optimized by proposed control design.
D. Control Design
The design procedure of a proposed controller design is explained as follows,
Step 1 Generate the objective function for GA optimization.
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In this paper, the controllers are designed to yield the damping ratio and the real part of the
dominant mode. Based on [14], the optimization problem of controller is formulated as
Minimize
specspec
specspec
(3)
Subject to ,min ,maxi i iK K K
,min ,maxij ij ijT T T , i=3, j=2 (4)
where and spec are actual and desired damping ratio, respectively, and
spec are actual
and desired real part of the electromechanical mode, minK and
maxK are minimum and
maximum gains of controllers, minT and
maxT are minimum and maximum time constants of
controllers.
Imaginary
axis
Real axis
: Dominant modes
before control
: Dominant modes
after control
ζspec
σspec
σspec≼σ ζspec≽ζ
Figure 5. D-stability region
Note that the objective of the optimization (3) is to move the dominant inter-area
oscillation modes to the D-stability region as shown in Fig.5. The optimization problem is
solved by GA.
Step 2 Initialize the search parameters for GA. Define genetic parameters such as
population size, crossover, mutation rate, and maximum generation.
Step 3 Randomly generate the initial solution.
Step 4 Evaluate objective function of each individual in (3) and (4).
Step 5 Select the best individual in the current generation. Check the maximum
generation.
Step 6 Increase the generation.
Step 7 While the current generation is less than the maximum generation, create new
population using genetic operators and go to step 4. If the current generation is the
maximum generation, then stop.
Simulation Results
In the optimization, the ranges of search parameters and GA parameters are set as follows:
spec and spec are desired damping ratio and desired real part of the dominant mode are set
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as 0.1 and -0.1, respectively. min,PSSK and
max,PSSK are minimum and maximum gains of
PSS are set as 1 and 50, respectively. min,BATTK and
max,BATTK are minimum and maximum
gains of BATT are set as 1 and 10, respectively. minT and
maxT are minimum and
maximum time constants of controllers are set as 0.01 and 2, crossover probability is 0.9,
mutation probability is 0.1, population size is 80 and maximum generation is 80.
As a result, the final setting of the optimized parameters of the proposed stabilizers are
given in table 2
Table 2 : Optimized parameters of the proposed stabilizers
Individual BATT Coordinated PSS and BATT
PSS BATT
KP KQ KP KQ
K 5.1667 7.99 36.46 7.500 8.2519
T1 0.9431 0.9549 1.8786 0.8365 0.9089
T2 0.0533 0.099 1.0771 0.6023 0.4011
Table 3 shows the eigenvalue and damping ratio of the dominant oscillation mode.
Clearly, penetration of PV and WG system into SMIB may cause the lack of damping ratio
of the dominant mode. Table 3 also shows that the damping ratio of the dominant mode of
both the individual BATT and the coordinated BATT and PSS are improved in comparison
with No controller case. The damping ratio and real part specification can be achieved by
both controllers.
Table 3 : Dominant mode
Cases Eigenvalues (damping ratio)
Without Controller, without PV & WG -0.0099 ± j2.50, ξ = 0.0039
Without Controller with PV & WG -0.0056 ± j2.49, ξ = 0.0025
With Battery Controller -0.338 ± j3.24, ξ = 0.104
With PSS and Battery Controller -0.695 ± j3.29, ξ = 0.207
Next, non linear simulations are carried out under four operating conditions to evaluate
performance of the proposed stabilizers as shown in Table 4.
Table 4 : Operating conditions
Case Disturbance
1 No Faults
2 Random load fluctuation
3 One circuit of line between B2 and B3 is opened at 5 s for 0.50 s and re-closed
4 One circuit of line between B2 and B3 is opened at 5 s and not re-closed
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Figure 6. PV power input.
Figure 7. Random WG power input.
Figure 8. Generator G1 speed deviation of case 1
In the first case, the system is subjected to the PV system power input and random WG
power input as shown in Fig.6 and Fig. 7, respectively. The system response of generator
G1 speed deviation is shown in Fig.8. By the individual BATT and Coordinated PSS and
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BATT, the power fluctuation is significantly reduced in comparison to those of No
controller and individual PSS. It is shown that the battery is effective to absorb power
fluctuation from both random WG and PV system.
Figure 9. Random load change
Figure 10. Generator G1 speed deviation of case 2
In case 2, the random load change as shown in Fig.9 is injected to the system. Figure
10 shows that both BATT and PSS&BATT are able to damp power fluctuations. The
speed deviation in case of both BATT and PSS&BATT are much lower than those of No
controller and individual PSS.
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0 5 10 15 20 25 30-5
-4
-3
-2
-1
0
1
2
3
4x 10
-3
Time (sec)
Spee
d de
viat
ion
(rad
/sec
)
without Controller
PSS
BATT
PSS & BATT
Figure 11. Generator G1 speed deviation of case 3
In case 3, it is assumed that the line power transfers from bus B2 to B3 via two lines,
then one line is suddenly opened at 5 s and re-closed after 0.50 s. Simulation result is
depicted in Fig. 11. In case of No controller, speed deviation of generator G1 is unstable.
On other hand, the power fluctuations are effectively stabilized by both BATT, PSS and
PSS&BATT. However, the overshoot and setting time of speed deviation in case of the
coordinated PSS and BATT, and individual PSS are lower than individual BATT
controller. It is clear that the coordinated PSS and BATT can stabilize not only power
fluctuation but also system with fault disturbance. On other hand, individual PSS is
effective to improve oscillation mode only, and individual BATT is can suppress the
power fluctuation from random wind and PV system effectively, but it has a small effect
for system with fault disturbance.
0 5 10 15 20 25 30-2
-1.5
-1
-0.5
0
0.5
1
1.5
2x 10
-3
Time (sec)
Spee
d de
viat
ion
(rad
/sec
)
Without Controller
PSS
BATT
PSS & BATT
Figure 12. Generator G1 speed deviation of case 4
Finally, it is assumed that two parallel lines between bus B2 and B3 are operated from
the beginning of simulation time at 0 s. At 5 sec. one line of two parallel lines between bus
B1 and B2 is suddenly opened and not re-closed. As shown in Fig.12, system without
controller loses stabilizing effect. It is not able to damp out speed deviation of G1. Beside
that, the damping effect of individual BATT is deteriorated. The speed deviation of
generator G1 takes long time to damp out. In contrast, proposed coordinated PSS and
BATT controller is capable of stabilizing speed deviation. It still retains system stability
successfully.
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Conclusion
The coordinated design of PSS and Battery-based controller for stability improvement in
the grid connected wind power and PV system has been presented. The proposed
controllers are designed to yield the damping ratio and the real part of the dominant mode.
To obtain the controller parameters, the optimization problem can be automatically solved
by GA. Since the structure of controller is the first-order lead/lag compensator, it is easy to
implement in practical systems. Non linear simulation results using ObjactStab package
and Matlab software clearly confirm that the proposed controller is much superior to damp
both power fluctuation from renewable energy and system under various disturbances in
comparison with individual Battery and No controller.
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