abc algorithm based comparative analysis of optimal svc and tcsc placement to maximize load ability

8/10/2019 ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability

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ABC ALGORITHM BASED COMPARATIVE ANALYSIS OF OPTIMAL SVC AND TCSC

PLACEMENT TO MAXIMIZE LOADABILITY

Y. V. BALARAMA KRISHNA RAO1, R. SRINIVASA RAO2 & V. V. K. REDDY3

1Research Scholar, JNTU College of Engineering, Kakinada, Andhra Pradesh, India

2Associate Professor, JNTU College of Engineering, Kakinada, Andhra Pradesh, India

3Director, NBKR Institute of Science and Technology, Nellore, Andhra Pradesh, India

ABSTRACT

This paper presents a comparative analysis on optimal placement of SVC and TCSC devices to maximize the

transfer capacity of the power system. Ant Bee Colony (ABC) algorithm is applied to determine the optimal location and

sizing of these FACTS devices that maximizes system load ability, reduces real power loss, reduces voltage deviation,

with minimum cost of installation. Three case studies namely base case, only SVC, Only TCSC are considered. The

proposed method has been tested on IEEE 6 and 30 bus systems. The results indicate that the proposed method gives better

solution and will be useful in power system planning.

KEYWORDS: FACTS, TCSC, SVC, ABC, System Load Ability, Optimal Location

INTRODUCTION

The demand on the power system keep on increasing everyday in this globalized competitive world, so electric

power system utilities have to operate close to their operating limits to meet the uncertain maximum demand. With the use

of FACTS devices, the electric utilities are able to control the magnitude and direction of real and reactive power flow,

increase transmission line transfer capability, minimizing real power loss and hence enhance the better utilization of

existing power systems [1]. Available transfer capability (ATC) is a measure of the transfer capability remaining in a

physical transmission network for further commercial activity of committed uses. It is observed form the literature [2-20]

this is often modelled as a optimization problem with non-linear objectives and constraints and state of art traditional and

non-traditional optimization techniques are applied to tackle this problem. In [2] an evolutionary approach is presented to

solve the optimal placement of Multi type FACTS devices. In [3] Bifurcation analysis and Eigenvector analysis is applied

to determine the maximum loading point. FACTS devices are solid-state converters that have the capability of control of

various electrical parameters in transmission circuits. Modelling of FACTS devices for power flow studies and the

integration of those devices into power flow studies were reported [4, 5]. The application of FACTS technology offers an

effective and promising solution for enhancing the usable power transfer capability, and hence improves transmission

services of the competitive electricity market [6]. Population based, cooperative and competitive stochastic search

algorithm such Genetic Algorithm (GA) [7-10] is implemented to solve complex problems efficiently. Non-traditional

optimization technique, genetic algorithm is used to optimize the various process parameters involved in introduction of

FACTS devices such as the location of the device, their type, and their rated value of the devices in a power system.

Application of PSO [11, 12] for solving various optimization problems in electrical engineering is explained.

The optimal location of FACTS devices for reducing the production cost along with the device’s cost using real

International Journal of Electrical and

Electronics Engineering Research (IJEEER)

ISSN(P): 2250-155X; ISSN(E): 2278-943X

Vol. 4, Issue 6, Dec 2014, 53-64

© TJPRC Pvt. Ltd.



54 Y. V. Balarama Krishna Rao, R. Srinivasa Rao & V. V. K. Reddy

Impact Factor (JCC): 5.9638 Index Copernicus Value (ICV): 3.0

power flow performance index was explained in [13]. A hybrid tabu search and simulated annealing was proposed to

minimize the generator fuel cost in optimal power flow control with multi-type FACTS devices [14].In some approaches

Power flow algorithm with the presence of TCSC and UPFC has been formulated and solved[15]. A hybrid GA approach

to solve optimal power flow in a power system incorporating FACTS devices has been reported [16, 17]. A Non-dominated Sorting Particle Swarm Optimization (NSPSO) is used to solve a mixed continuous discreet Multi-objective

optimization problem which consist of optimal location and size of Static VAR Compensators (SVC) and Thyristor

Controlled Series Capacitors (TCSC) in order to maximize Static Voltage Stability Margin (SVSM), reduce power losses

(PL) and minimize load Voltage Deviations (VD)[23]. In [24] Optimal placement using the sensitivity of transmission loss

with respect to the control parameters of devices using the interior point method for the minimizing real power loss and

multiply with new equation of SVC and TCSC. The new equation of SVC is sum of reactive power flow at the bus and the

new equation of TCSC is sum of real power loss in transmission line.

In this paper, application of ABC algorithm for optimal placement of FACTS devices which has minimum cost of

installation, to maximize the system loadability (MSL), while satisfying the power system constraints, for SVC and TCSC

is presented.. The variables for the optimization for each device are its location in the network, its setting and the

installation cost, in the case of single-type devices. TCSC has been modelled as a variable line reactance inserted in the line

and SVC is modelled as a reactive source injected both ends of the line.

Computer simulations were done for IEEE 6 and 30 bus systems for the base case without FACTS device and

with the insertion of SVC and TCSC. In the test cases with SVC and TCSC it is observed that SL cannot be increased

beyond a limit after placing. The maximum value of SL that can be achieved without violating the constraints is known as

maximum system loadability (MSL). The MSL, minimum number of FACTS devices required to attain the MSL and the

optimal installation cost of FACTS devices are obtained for single type of FACTS devices SVC and TCSC using ABC

algorithm.

MODELLING OF FACTS DEVICES

The power system as well as the FACTS devices is modelled using power flow equations. The FACTS devices

considered in this paper are Static Var Compensators (SVC) and Thyristor Controlled Series Compensators (TCSC), since

these FACTS devices, besides Phase Angle Regulators, are used most frequently in power systems [21].

SVC Variable Susceptance Model

An SVC is shunt-connected device with the line at both ends and influences the voltage VSVC at the bus to which

it is connected by injecting or absorbing reactive power QSVC [1]. This characteristic is modelled by a shunt-connected

variable susceptance BSVC at both ends of the transmission line as shown in figure 1.This model represents the fundamental

frequency equivalent of all shunt modules making up the SVC. This model is an improved version of SVC models. In this

paper only this model is considered for case studies



ABC Algorithm Based Comparative Analysis of Optimal SVC and TCSC Placement to Maximize Load Ability 55


Figure 1: Model of SVC

QSVC = −V2SVC * BSVC (1)

As the size of the SVC is limited, a lower (BSVCmin) and an upper (BSVC

max,) bound exist for the effective

susceptance BSVC, thus,

BSVCmin

≤ BSVC ≤ BSVCmax.

TCSC Variable Reactance Model

Figure 2: Model of TCSC

A TCSC is connected in series with a transmission line and is able to influence the active power flow through the

line by adapting the reactance of the line [22]. Hence, the device is modelled as a variable reactance X TCSC connected in

series with the line, as shown in Figure 2. The total reactance Xline of the line including the TCSC is therefore given by

Xline new= Xold + XTCSC (2)

Where Xold is the reactance of the line itself. The effective reactance X TCSC of the TCSC is limited to a range

between XTCSCmin and XTCSC

max determined by the size of the device.

XTCSCmin ≤ XTCSC ≤ XTCSC

max

SVC Load Flow Models

The circuit shown in Figure 1 is used to derive the SVC's nonlinear power equations required by Newton's load

flow method. The voltages and angles at the buses i and j are Vi, δi and V j, δ j respectively. The real and reactive power

flow between the buses i to bus j can be written as follows.

gi

1

P cos( ) sin( ) 0n

di i i j ij ij ij ij

j

P P VV G Bδ δ

=

− − − + = ∑ (3)

gi1

Q sin( ) cos( ) 0n

di i SVC i j ij ij ij ij

j

Q Q Q VV G Bδ δ

=

− − − − − = ∑ (4)





min max gi gi gi gi

V Const if Q Q Q= ≤ ≤

min max

SVC SVCi SVC Q Q Q≤ ≤

The effective reactive bus injection by SVC is determined and that buses are treated like voltage controlled bus[1].

TCSC Load Flow Models

'gi

1

P 'cos( ) sin( ) 0n


j

P P VV G Bδ δ =

− − − + = ∑ (5)

gi1

Q sin( ) 'cos( ) 0n


j

Q Q VV G Bδ δ

=

− − − − = ∑ (6)

m in m ax

gi gi g i g iV C o nst if Q Q Q= ≤ ≤

Gij1,Bij

1 is the modified line conductance and susceptance due to TCSC reactance which is treated as a variable.

Facts Device’s Constraints

The injected reactive power of SVC at the bus,QSVC is considered within the limits is given by

•

−100MVAR ≤ QSVC ≤ 100MVAR

The reactance of TCSC inserted at in the transmission line XTCSC is considered within the limits is given by

•

−0.8 X L≤ X TCSC ≤ 0.8 X L p.u

PROBLEM FORMULATION

Installation Cost

SVC Installation Cost

The implementation of the variable shunt susceptance models in a Newton-Raphson load flow algorithm requires

the incorporation of a non standard type of bus, namely PVB. This is a controlled bus where the nodal voltage magnitude

and active and reactive powers are specified while the SVC’s total susceptance BSVC is handled as state variable. If BSVC

is within limits, the specified voltage magnitude is attained and the controlled bus remains PVB-type. However, if BSVC

goes out of limits, so the bus becomes PQ-type. In this situation, the SVC will act as an unregulated voltage compensator

whose production or absorption of reactive power capabilities will be a function of the nodal voltage at the SVC point of

connection to get the voltage 1.0 p. u. Cost of SVC. The cost of installation of SVC as taken from Siemens database and

reported in [19, 20] given by

C SVC = 0.0003S 2 − 0.3051S + 127.38 in $/KVAR (7)

IC=CSVC*S*1000 in $ (8)

where S is the operating range of the FACTS devices in MVAR given by S = |Q2| − |Q1| where Q is the reactive

power flow in the line after installing FACTS device in MVAR and Q1 is the reactive power flow in the line before

installing FACTS device in MVAR





TCSC Installation Cost

The cost of installation of TCSC taken from Siemens database and reported in [19, 20]. The costs of installation

of FACTS device TCSC is given by

C TCSC = 0.0015S 2 − 0.7130S + 153.75 (9)

IC=CTCSC*S*1000 in $ (10)

Transmission Losses

The proposed algorithm also considers the transmission loss minimization for selecting optimal location of SVC.

Transmission loss minimization is responsible for the redistribution of the reactive power throughout the network, which in

turn induces changes in the active power generated by the slack bus. It has been observed that if the network losses were

reduced in only 0.15%, a more uniform voltage profile was observed at all the buses of a power system. The real power

losses can be calculated using (12)

1

i gen n

gi di

i i

TL P P∈

=

= −∑ ∑ (11)

Where n is the number of buses.

Voltage Deviations

In a power system, it is desirable to maintain the voltage deviations within ±5%. In this paper, the optimal

location and size of SVC is determined by observing minimum value of VD. Voltage deviation is calculated as follows

2

1

( )n

ref

i i

i

VD V V =

= −∑ (12)

Vi –Voltage at ‘ith b

Vi ref –Reference Voltage at ‘ith bus

Line Flow Limits Violation

It is important to maintain the line flow limits of the transmission lines within the limits. The line flow deviation

(LFD) is calculated by

2( )ref

ij iji

ij lines

LFD LF LF ∈

= −∑ (13)

LFij –Line flow of line ‘ij’

LFij ref – Line flow limit of line ‘ij’

System Loadability

The system loadability (SL) is calculated by using the equation given by





0i

d d P Pλ =

(14)

where λ is the loading parameter, Pd 0 and Pd

i are the

Intial system load and system load after installation of ‘i’th set of FACTS devices. Uniform loading of the load is

assumed in this paper. The optimal placement of FACT devices can be expressed mathematically as follows

min max

min max

min max

min max

min max

( , , , , , , ) 0

0 1

i i i

i i i

svc TCSC

gi gi gi

gi gi gi

i i i

SVC SVC SVC

TCSC TCSC TCSC

Max

Subject to

f V P Q X X

P P P

Q Q Q

V V V

X X X

X X X

λ

λ δ

λ

=

≤ ≤

≤ ≤

≤ ≤

≤ ≤

≤ ≤

≤ ≤ (15)

Where F=λ +W1*TL+W2*VD+W3*LFD+W4*SVC (cost) + W5*TCSC (cost).

W1-W5 are weightings for the multi objective functions.

f (V, δ, P, Q) is the power flow equations described by equations(3-6).

ARTIFICIAL BEE COLONY ALGORITHM

In the ABC model, the colony consists of three groups of bees: employed bees, onlookers and scouts. It is

assumed that there is only one artificial employed bee for each food source. In other words, the number of employed bees

in the colony is equal to the number of food sources around the hive. Employed bees go to their food source and come back

to hive and dance on this area.

The employed bee whose food source has been abandoned becomes a scout and starts to search for finding a new

food source. Onlookers watch the dances of employed bees and choose food sources depending on dances.

The main steps of the algorithm are given below: Initial food sources are produced for all employed bees Each

employed bee goes to a food source in her memory and determines a neighbour source, then evaluates its nectar amountand dances in the hive Each onlooker watches the dance of employed bees and chooses one of their sources depending on

the dances, and then goes to that source. After choosing a neighbour around that, she evaluates its nectar amount.

Abandoned food sources are determined and are replaced with the new food sources discovered by scouts. The best food

source found so far is registered until requirements are met.

In ABC, a population based algorithm, the position of a food source represents a possible solution to the

optimization problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution.

The number of the employed bees is equal to the number of solutions in the population. At the first step, a randomly

distributed initial population (food source positions) is generated. After initialization, the population is subjected to repeat

the cycles of the search processes of the employed, onlooker, and scout bees, respectively. An employed bee produces a





modification on the source position in her memory and discovers a new food source position. Provided that the nectar

amount of the new one is higher than that of the previous source, the bee memorizes the new source position and forgets

the old one. Otherwise she keeps the position of the one in her memory. After all employed bees complete the search

process; they share the position information of the sources with the onlookers on the dance area.

Each onlooker evaluates the nectar information taken from all employed bees and then chooses a food source

depending on the nectar amounts of sources. As in the case of the employed bee, she produces a modification on the source

position in her memory and checks its nectar amount. Providing that its nectar is higher than that of the previous one,

the bee memorizes the new position and forgets the old one. The sources abandoned are determined and new sources are

randomly produced to be replaced with the abandoned ones by artificial scouts.

Procedures of ABC

• Initialize (Move the scouts)

• Move the onlookers

• Move the scouts only if the counters of the employed bees hit the limit

•

Update the memory.

•

Check the termination condition

Probability of Selecting a Nectar Source

(16)

Where Pi is the probability of selecting the ith employed bee S: The number of employed, θi is The position of

the ith employed bee and F(θ i ) is The fitness value.

Movement of the Onlookers: Calculation of the new position is given by

(17)

where χ i is The position of the onlooker bee, t is The iteration number, θk is The randomly chosen employed bee, j is The dimension of the solution and ϕ is A series of random variable in the range[0, 1].

The Movement of the Scout Bees Follows Equation

(18)

where r is a random number in the range[0.1].

CASE STUDIES

The proposed algorithm for optimal placement of SVC and TCSC has been tested on IEEE 6 and 30 bus systems.





Pijb and Qijb are the real and reactive power flow in the line i–j before placing FACTS device, respectively. P ija and Qija are

the real and reactive power flow in the line i–j after placing FACTS device, respectively. TL b and TLa are the transmission

loss occurring in the system before and after installation of FACTS devices SVC and TCSC.

IEEE 6 Bus System

The bus data and line data of the six bus sample system are taken from [17] and it contains three generator and

three load buses with 11 transmission lines. The location, settings of FACTS devices and optimal installation cost are

obtained using the ABC algorithm for optimal placement of SVC and TCSC devices and it is given in Table 1. To prove

the effectiveness of optimally placing SVC and TCSC in a power network three case studies are considered. They are

• Base case without any FACTS device

• with SVC

•

With TCSC

Table 1: IEEE 6 Bus System

In the SVC placement it is placed in 4 lines where as it is placed in 3 lines in [12].The MSL as well as IC

determined in this paper is better that reported in [12].The transmission loss also minimized for increased demand is an

added advantage.

In the TCSC placement it placed only in two lines where as it placed in 5 lines in [12].The MSL is as good while

cost slightly higher as in the ref[12].It has been observed that the results in [12 for TCSC placement violate the line flow

limits of lines (1-2),(1-4),(1-5)and (2-6)

IEEE 30 Bus System

The bus data and line data of the 30 bus system are taken from Matpower3.0 and this system comprises of one

slack bus, 5 PV buses, 24 PQ buses and 41 lines. The location, settings of FACTS devices and optimal installation cost areobtained using the ABC algorithm for optimal placement of SVC and TCSC devices and it is given in Table 2. To prove

the effectiveness of optimally placing SVC and TCSC in a power network three case studies are considered. They are

• Base case without any FACTS device

• with SVC

• With TCSC





Table 2: IEEE 30 Bus System

In the SVC placement it is placed in 3 lines where as it is placed in 8 lines in [12].The MSL is as good while IC

determined in this case higher than reported in [12].The transmission loss is slightly increased for increased demand. In [12]

the line flow limit is ignored so the result offered by this ABC is more applicable in real time.In the TCSC placement it

placed only in 4 lines where as it placed in 8 lines in [12]. The MSL as well as IC determined in this paper is better that

reported in [12].

CONCLUSIONS

In this paper, a method for optimal placement and sizing of SVC & TCSC is found to minimize the cost of

installation of SVC and TCSC and for improving the system load ability. Simulations are performed using ABC algorithm

on IEEE6 and 30 bus systems. The location of the devices, settings of FACTS devices and optimal installation cost are

obtained for optimal placement of FACTS devices SVC and TCSC. In the IEEE 6and 30 test systems TCSC gives

maximum system load ability at minimum cost of installation. It is also observed from test results that in IEEE 6 bus

system the transmission loss is decreased for increased system load ability and in IEEE 30 bus system the transmission loss

is slightly increased for increased system load ability.

ACKNOWLEDGMENTS

The authors sincerely thank the management and authorities of Krishna Chaitanya Institute of Technology and

sciences for providing the facilities to carry out this research

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abc algorithm based comparative analysis of optimal svc and tcsc placement to maximize load ability

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