STABILIZATION OF GRID CONNECTED WIND AND PV SYSTEM … › aej › issue › 2012-v1-1 › 25-34... · 2019-11-25 · STABILIZATION OF GRID CONNECTED WIND AND PV SYSTEM BY COORDINATED

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

ASEAN Engineering Journal, Vol 1 No 1, ISSN 2229-127X, e-ISSN 2586-9159 p.25

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


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.


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.


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


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


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


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


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.


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


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


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.


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.

References

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Documents

STABILIZATION OF GRID CONNECTED WIND AND PV SYSTEM … › aej › issue › 2012-v1-1 › 25-34... · 2019-11-25 · STABILIZATION OF GRID CONNECTED WIND AND PV SYSTEM BY COORDINATED