Bat Inspired Algorithm Based Optimal Power Flow Technique ...Due to environmental, financial impacts and political reasons, the commissioning of new ... MATLAB/SIMULINK model results

Bat Inspired Algorithm Based Optimal Power Flow Technique with UPFC

Dr.M.Karthikeyan1, Sirak Gebrehiyot2, Degu Menna Eligo3 and Wondimu Dawit4 1Assistant Professor, 2-4Lecturer, 1-4Department of Electrical and Computer Engineering,

College of Engineering, Wolaita Sodo University, Ethiopia [email protected]

Abstract-In this paper an optimization algorithm is used to provide a solution for optimal power flow problem

in a power system with UPFC. Here the bat inspired algorithm is used as an optimization algorithm. Based on

power balance condition, bat inspired algorithm develops the different types of generator candidate solutions. By

using the generator combinations, fuel cost and emission dispatch has been evaluated. From the evaluation results,

the most suitable generator candidate solutions are identified. So the fuel cost, emission dispatch and power loss

are maintained economically. With the real power limits of the generators UPFC injected voltage magnitude and

voltage angle have been found. The optimal placement of UPFC depends on the power flow deviation and the

combination of the power system buses. Finally this proposed method is tested with standard IEEE-14 bus system

with MATLAB/simulink platform. The performance of the proposed method is evaluated and also compared

with ABC algorithm and NR method. The result shows that the superiority of the proposed method.

Key words: Optimal Power Flow, UPFC, Bat inspired algorithm, ABC, NR method

I. INTRODUCTION

Now-a-days the demand for electrical power has increased tremendously due to enhanced technical

advancements and also due to the increased population growth in both developed and developing countries. This

has created heavy stress in the existing power system network thereby, it requires network expansion and network

operation nearer to its saturation limits. Due to environmental reasons and high capital cost, many countries have

restricted its operation to build new transmission lines and retrofitting of existing transmission equipment. In

recent days power system restructuring is providing a potential solution to the violation of system operating limits.

Efficient power system operation and planning of transmission and distribution grid is considered to be the

major challenge for management of inherent issue of power system in a complex situation. For the factors

described above, it becomes evident that the operation of power system strategies has become a challenging

engineering goal which requires efficient use of all the power system components without disturbing the technical

operation limits in the real time employment to supply the quality and reliable electric power to the consumers.

It is evident that the generating stations are not close to the load centres thereby power has to be transmitted

over the long distances. Due to environmental, financial impacts and political reasons, the commissioning of new

transmission lines are often restricted. As a result, the power utilities are enforced to rely on the existing

transmission networks instead of commissioning new transmission lines. In order to enhance the efficiency of

transmission and distribution of electrical power, the transmission networks are allowed to operate closer to the

technical limits, where severe stress is subjected to the entire power system and it results in cascading of outages.

In order to ease the stress in the existing transmission networks, Flexible Alternating Current Transmission System

(FACTS) devices play a leading role and also offer a potential solution to the existing problems. Network security,

stability and reliability are considered to be the important issues in today’s power system operation and control.

JASC: Journal of Applied Science and Computations

Volume 5, Issue 10, October/2018

ISSN NO: 1076-5131

Page No:1071

This paper deals with the application of the proposed bat inspired algorithm technique for satisfactory

delivery of electric power with the effective utilization of system capacity by operating the FACTS device such as

Unified Power Flow Controller (UPFC) for optimal power flow analysis.

It includes minimization of fuel cost, emission dispatch, real power loss and installation cost. The proposed

technique using MATLAB in standard IEEE-14 bus system have been carried out to validate the performance of

the proposed method.

II. RECENT RESEARCH WORKS

The objective of a optimal power flow problem is to attain the entire voltage angle and magnitude

information for every bus in a power system.[1]. Optimal power flow (OPF) problem contracts with finding an

optimal operating point of a power system that minimizes the cost function such as generation cost or transmission

loss on power and voltage variables [2-3]. Several optimization methods [4-6] have been implemented and applied

to work out the OPF problem, through fuzzy emissions constraints, particle swarm optimization, evolutionary

algorithm, iterative approach, genetic algorithm and computational intelligence methods [7].

Rajendra B Sadaphale et al.,[8] explained an application of power world simulator for high quality electrical

energy to the consumer in a secure & economic manner. They also elaborated some of the important constraints

such as voltage security, environmental constraints with the consideration of these constraints the quality power

were supplied to the consumer with minimum pollution level. They considered SVC as a FACTS controller and

tested with and without controller. The comparisons are on based of normal, optimal power flow results obtained

for the IEEE 14 bus system. The voltages of highly loaded buses are increased with SVC and the transmission

losses decreased.

Vishnu et al.,[9] have considered constantly growing electricity demands and transactions, existing power

networks required to be enhanced in order to increment its loadability such as to accomplish more power transfers

with less network expansion cost. Existing power system loading margin can be upgraded by optimal allocation

and setting of FACTS devices. They suggested a Particle Swarm Optimization (PSO) based algorithm to determine

the optimal location and setting of FACTS devices to improve the loading margin as well as voltage stability and

small signal stability. The objective function is formulated as maximizing the loadability of the power system with

load generation balance as equality constraint as well as voltage stability, generation limit and line limit constraints

as inequality constraint. IEEE 14 - Bus standard test system is taken into account to test the potency of the

proposed approach using MATLAB/PSAT.

Pavan kumar et al., [10] have aimed to present a reliable method to meet the requirements by developing

a N-R based load flow calculation program through which control settings of the UPFC can be determined

directly. The proposed method keeps the N-R load flow algorithm intact and requires only a little modification to

the Jacobian matrix in the iterative procedure. A Mat lab program has been developed to calculate the control

settings parameters of the UPFC after the load flow is converged. The proposed method is tested on standard

IEEE-14 bus system.

Nandha Kumar et al., [11] have presented a new approach for optimal location of FACTS controllers in a

multi machine power system using MATLAB coding. Using the proposed method, the location of FACTS

controller, their type and rated values are optimized simultaneously. Among the various FACTS controllers,

Thyristor Controlled Series Compensator (TCSC) and Unified power Flow Controller (UPFC) are considered.



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Adit Pandita et al., [12] have focused on three techniques for inclusion of the steady state models of the

UPFC in power flow programs. They also presented a review of various benefits and applications of UPFC in

power flow studies such as minimization of loss, enhancement of loadability, voltage stability etc. using various

optimization techniques. A case study is also shown to analysis effect of UPFC using comprehensive NR method

based power flow.

Sharaddha kondane et al., [13] have studied Unified Power Flow Controller to improve the power flow

over a transmission line in a standard IEEE 14 bus system by using MATLAB / SIMULINK in a power system

block set. For the selected standard system, real and reactive power flows are compared with and without UPFC

to prove the performance. Active and reactive power through the transmission line cannot be controlled without

UPFC but with the circuit model for UPFC using rectifier and inverter circuits, this performance gets improved.

In this paper implementation and digital simulation using UPFC to improve the power quality is presented. The

MATLAB/SIMULINK model results are presented to verify the results.

Dipli V Patil et al., [14] have investigated to minimize the power loss in the transmission line using FACTS

device as UPFC. Improving the system’s reactive power handling capacity via FACTS device is ready for

preventing of voltage instability and hence voltage collapse. It has been performed on IEEE 14 bus system to

minimize the transmission losses and improve the voltage profile. They have shown the result in MATLAB/

Simulink when UPFC implement in transmission system and also compared with STATCOM.

Deepa et al., [15] have focused on optimally locating the Unified Power Flow Controller (UPFC) device

in power system based on Static Voltage Stability Index (SVSI) technique using Imperialist Competitive Algorithm

(ICA). The main objective is to employ ICA optimization technique which is applied to solve the optimal power

flow problem in presence of UPFC device. The ICA optimization technique is also used to minimize the power

losses and installation cost of UPFC device. Due to this, the voltage profile is improved thereby enhances the

stability in power system. This technique is tested in standard IEEE 6-bus system, IEEE 14-bus system, IEEE

30-bus system. The performance of ICA is compared with other optimization techniques like Particle Swarm

Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Gravitational Search

Algorithm (GSA) to show the effectiveness of the algorithm.

S.N.Singh et al.,[16] have suggested the suitable locations to enhance the system loadability with Unified

Power Flow Controller (UPFC), a very versatile and powerful FACTS controller. The effectiveness of the

proposed algorithm is tested and illustrated on 5-bus and IEEE 14-bus systems. Karthikeyan et al.,[17] have

proposed hybrid technique as the combination of artificial bee colony (ABC) algorithm and artificial intelligence

(AI) technique. The purpose of the ABC algorithm is used to optimize the optimal operating range of generation

limits. With AI technique, they determined the optimal injected voltage magnitude and voltage angle of UPFC.

The proposed hybrid technique is implemented in MATLAB working platform and the power flow parameters

are evaluated.

Balasubramaniyan et al., [18] have proposed an optimal location of FACTS devices in power system using

Evolutionary algorithms. Using the proposed method, the location of FACTS controllers, their type and rated

values are optimized simultaneously. From the FACTS family, series device Thyristor Controlled Series

Compensator (TCSC), Shunt device Static Compensator (STATCOM) and series and shunt device Unified Power

Flow Controller



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(UPFC) are considered. The proposed algorithms are very effective methods for the optimal choice and placement

of FACTS devices to improve the power quality of power systems. The proposed algorithm has been applied to

IEEE -30 bus system.

Aadil Latif et al., [19] have presented an over view of three optimization algorithms namely real coded

genetic algorithm, particle swarm optimization and a relatively new optimization technique called bat algorithm.

Simulations are carried out for two test cases. First is a six-generator power system with a simplified convex

objective function. The second test case is a five-generator system with a non-convex objective function. Finally

the results of the modified algorithm are compared with the results of genetic algorithm, particle swarm and the

original bat algorithm. The results demonstrate the improvement in the Bat Algorithm.

Lenin et al., [20] have proposed a new Improved version of Bat Algorithm (IBA) to solve optimal reactive

power dispatch problem. The proposed algorithm utilizes chaotic behaviour to produce a candidate solution in

behaviours analogous to acoustic monophony. The proposed IBA has been tested on standard IEEE 30, IEEE

57 bus test systems and simulation results show clearly the better performance of the proposed algorithm in

reducing the real power loss.

This paper mainly focus on optimal power flow problem with FACTS controller such as UPFC have been

incorporated to achieve the minimization of losses, economical generation allocation and emission dispatch. An

algorithm based on Bat Inspired technique is proposed for solving OPF in power system operation. The results

obtained with the proposed method were compared with the existing method.

III. MODELING OF UPFC

The UPFC is one of the FACTs devices which are able to control the active and the reactive power flow in

transmission networks. Also, the UPFC may provide the reactive power compensation between the two nodes.

In general, the UPFC consists of two voltage source converters by a DC link [21]. The coupling transformer is

providing the connections for these converters to the power system. The shunt side is connected to the sending

end node of the system and the series side is connected to the receiving end node of the system. The UPFC is not

able to generate the active power since, the converters active power is balanced while the active power is neglected

by the DC link. The UPFC initialization in power system and the equivalent circuit model [22] are illustrated in

Figure 1 and Figure 2 respectively.

The injected voltage and voltage angle of UPFC are represented as ��and ��respectively. The injected

voltage of the UPFC depends on the shunt injected voltage (��(��)) and series injected voltage(��(��)).

Figure 1 Simple model of UPFC



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Figure 2 Equivalent circuit of UPFC

The injected voltage of UPFC is determined as follows:

�� = ��(��) + ��(��) (1)

�� = ��(��)��(��) + ��(��)�� + ��(��)��(��) + ��(��)�� (2)

where,

V��ϵV�� ≤ V�� ≤ V��

�� and

θ��ϵθ�� ≤ θ�� ≤ θ��

�� are the controllable injected voltage and voltage angle of the converter.

The control voltage depends on the shunt and series injected voltage of the system. From the above

equivalent circuit model, the power injection equations are derived from the power flow studies. By using load

flow analysis in Figure 2, the real and reactive power of bus � and � are determined. Similarly, the real and reactive

powers injected by the coupling transformer are calculated. The importance of the power injection representation

is that the symmetric characteristics of admittance matrix will not be destroyed [23]. The equations are described

as follows[24]:

Real and reactive power at bus k:

�� = �� + ��(�� (�� − ��) + �� (�� − ��)) + ��(��)�� − ��(��)� +

�� − ��(��)�� + ��(��)�� − ��(��)� + ��(��) �� −

��(��)�� (3)

�� = −�� + ��(�� (�� − ��) − �� (�� − ��)) + ��(��)�� −

��(��)� − �� − ��(��)�� + ��(��)��(��) �� − ��(��)� − ��(��) �� − ��(��)��

(4)

Real and reactive power at bus m:

�� = �� + ��(�� (�� − ��) + �� (�� − ��)) + ��(��)�� −

��(��)� + �� − ��(��)�� (5)

�� = −�� + ��(�� (�� − ��) − �� (�� − ��)) + ��(��)�� −

��(��)� − �� − ��(��)�� (6)

Series converter injected real and reactive power:

��(��) = ��(��)� �� + ��(��)�� (��) − �� + �� (��) − �� +

��(��)�� (��) − �� + �� (��) − �� (7)

��(��) = −��(��)� �� + ��(��)�� (��) − �� − �� (��) − �� +

��(��)�� (��) − ��)� − �� (��) − �� (8)



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Shunt converter injected real and reactive power:

��(��) = −��(��)� ��(��) + ��(��)��(��)�� (��) − �� + ��(��) ��(��) − �� (9)

��(��) = ��(��)� ��(��) + ��(��)��(��) ��(��) − �� − ��(��) ��(��) − ��(10)

where,

�� = �� + ��

�� = �� + �� (11)

�� = �� = �� + ��

��(��) = ��(��) + ��(��)

��(��) = ��(��) + ��(��)

The above described equation is the admittance values of the bus, between the buses and power converters.

The real power loss of the converter is assumed as lossless then, the converter equation is changed as follows [24]:

��(��) + ��(��) = 0 (12)

The above real power equations are used as the guiding principle for conducting limit revisions. The control

parameters of the UPFC are determined accurately from the real power equation. The real power adjustment of

the UPFC is based on the real power converter equation.

A. Problem Formulation:

Objective Function:

The OPF problem is solved with the variable parameters of the UPFC device. The objective function is

minimization of total fuel cost, the total emission and the UPFC installation cost. Here, the multi-objective

problem is mathematically formulated as a constrained nonlinear multi-objective optimization problem as follows:

Minimize, ),(,),(),,( xtcxtextf UPFC (13)

In minimizing the generation cost, the equality constraints and inequality constraints should be satisfied

as shown in the following equations:

The equality constraints are:

∑ �� − ∑ ��

�� − �� = 0 (14)

∑ �� − ∑ ��

�� − �� = 0

where,

�� and �� – Real and reactive power of ith generator

�� and �� – Real and reactive power of jth generator

�� and �� - Real and reactive power losses

The inequality constraints are :

�� ≤ �� ≤ ��

��

�� ≤ �� ≤ ��

�� (15)

�� ≤ �� ≤ ��

��

�� ≤ �� ≤ ��

��

where,

�� and ��

�� - minimum and maximum real power generation of ith bus

�� and ��

�� - minimum and maximum reactive power generation of ith bus

�� and ��

�� - minimum and maximum value of voltage at ith bus

�� and ��

�� - minimum and maximum value of voltage angle at ith bus

The objective function of fuel cost, emission and UPFC installation cost are described as follows:



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Fuel cost ),( xtf :

The total fuel cost ($/hr) of the system can be represented in quadratic function which is described as

follows,

Fuel cost

)(),( 2

1GiiGii

NG

i

PcPbaxtfi

(16)

where, �� , �� and �� are the fuel cost coefficient of �� generator. �� is the real power of the �� generator.

Emission ),( xte :

The total environmental emission ),( xte is expressed as follows,

Emission )(),( 2

1iGiGi

NG

iii

PPxte

(17)

where, �� , �� and �� are the emission coefficient of �� generator.

UPFC installation cost CUPFC (t,x)

The installation cost of UPFC )/($ KVAr is described as follows:

UPFC installation cost

CUPFC (t,x) = 0.0003S2 - 0.2691S + 188.22 (18)

where, S is the real power operating range of UPFC.

B. Proposed BIA based approach:

Implementation of BIA for OPF Solution

In this paper, an algorithm based optimal power flow technique with FACTS controller is proposed. Bat

Inspired Algorithm is used in the proposed method. The bat- inspired algorithm is an optimization algorithm,

which is derived from the echolocation behaviour of micro-bats with varying pulse rates of emission and loudness

[25-26].The optimal generation limits are determined based on the minimum value of the fuel cost and emission.

The UPFC is used as a FACTS controller for reducing the real power deviation of system by injecting the voltage

magnitude and voltage angle. The injected voltage magnitude and voltage angle are decided in order to reduce the

power loss of the system and installation cost of the UPFC.

Step by Step Algorithm of Proposed Method

This section describes the determination of the generator real power limits based on the power balance

condition. Here, the different types of generator candidate solutions are developed according to the power balance

condition. In each generator candidate solutions [27] fuel cost and emission dispatch has been identified. From

that, the minimized fuel cost, emission dispatch and power loss generator candidate solution are attained. The

generators real power limits are once assigned to the generators, the OPF of the system can be maintained by the

UPFC.

Steps to find the generator generation limits

Step 1: Input micro-bats ( iB ) population is randomly generated, i.e., IEEE standard benchmark system generators

possible candidate solutions, which should satisfy the power balance condition. The each micro-bat has the

velocity vector )( iv and position vector )( ix , which is described in the following equation (19).

bnmnmn

bmm

bmm

bnnn

bb

bnnn

bb

i

xvxvxv

xvxvxv

xvxvxv

B

),(),(),(

),(),(),(

),(),(),(

222

111

222

22221

2221

112

12121

1111

(19)



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Step 2: To assign the echolocation parameters, the micro-bat populations are incorporated with the echolocation

parameters like frequency )( if , pulse rate )( ir and the loudness parameters )( il . These parameters are non-negative

real values with the following ranges.

maxmin fff i (20)

maxmin rrr i (21)

maxmin lll i (22)

Here, we assign the frequency range 0min f and 1max f , the pulse rate minimum value 5.0min r is and the

loudness maximum value is 1max l . The remaining values are determined by the following equation (23).

sec

min

1

nl and 1

11max

dnr (23)

where, secn is the number of sections in the discrete set used for sizing the design variable and nd is the

number of discrete design variables.

Step 3: Evaluate the objective function of the initial populations; the required objective function is described in

the following equation (24).

Ф = Minimize, ),(,),(),,( xtcxtextf UPFC (24)

Step 4: Store the current population and increase the iteration count as t+1, i.e., iteration t = t+1.

Step 5: The current population of generators candidate solutions are randomly updated based on the frequency

and the velocity. Initially the frequency can be evaluated, which is described in the following equation (25).

iti uffff )( minmaxmin (25)

where, uithe random number of values, which is selected from 0 to1 , then the frequency is applied into

the velocity equation, which can be described in the following equation (26).

])([ 11i

ti

ti

ti uXXvroundv

(26)

where, tiv and 1t

iv are the velocity vectors of the micro-bats at the time steps t and 1t tiX and 1t

iX are the position vectors of the micro-bats at time steps t and 1t

X is the current global best solution.

Hereafter the local search is performed in the randomly selected population, which is described in the

following equation (27). tavgji

ti

ti lxx ,

1 (27)

where, ji , is a random number between -1 and 1, tavgl is the average value of loudness at time step t .

Step 6: Find the fitness of the new micro-bats population using the equation (4.15). After evaluation, the micro-

bats echolocation parameters are updated for better moving of the micro-bats, which can be described in the

following equation (28).

)]*exp(1[. max1' trrandlal t

iii (28)

where, 'il and il are the previous and updated values of the loudness

1tr is the pulse rate of the micro-bats in time step t+1

a and are the adaptation parameters of the loudness and pulse rate.

Step 7: Find the best micro-bats, which satisfies the objective function (24).



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Step 8: The steps 4 to 7 is continued until the termination criteria is attained.

Once the process is finished, the algorithm is ready to give the accurate generator candidate solutions based

on the minimization of the fuel cost, emission dispatch and power loss. The selected real power settings are applied

to the generator; so the OPF of the system is maintained by the UPFC. The power flow parameter of the UPFC

depends on the injected voltage magnitude and angle. Based on the output of the network, the UPFC voltage and

voltage angle is injected. After connecting the UPFC, the load flow analysis is applied. Here, the Newton Raphson

load flow algorithm is used for analyzing the power flow solution. The flow chart of BIA based method is

illustrated in Fig.3.

Here, the generator optimum combinations are identified by the bat inspired algorithm based technique in

order to minimize the fuel cost and emission dispatch. Also, depending on the power flow deviation and the

combination of the power system buses, the optimal location of the UPFC has been evaluated. Then the UPFC

power flow parameters are obtained in order to minimize the power loss and UPFC installation cost.

Fig.3 Flow Chart of the proposed BIA method

IV. RESULTS and DISCUSSION

The proposed method is tested on IEEE-14 bus test system which consists of 5 generators and 20 lines

which is shown in Fig.4. Bus 1 is considered as a slack bus, buses 2, 3, 6 and 8 are generator buses and the

remaining buses are load buses. The system data is taken from references [29]. The power loss of IEEE-14 bus

system is 13.809 MW which is determined by applying the power flow solution. Then, based on the described data

set, the optimal power flow concept of proposed integrated technique is discussed. The optimal generation limits

of the generator bus are selected by the Bat Inspired algorithm. The optimal generation limits are determined,

based on the objective function of fuel cost and emission.



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The injected voltage, voltage angle of all the buses of the system were obtained. The voltage magnitude and

summary of the results of the proposed method and existing methods from NR and ABC are tabulated in Table

1.

Fig.4 One line diagram of IEEE-14 bus system

Table 1. UPFC injected voltage magnitude and voltage angle

Bus No. NR Method ABC

Proposed Bat

Inspired Algorithm

Voltage

in p.u

Voltage

Angle

in degree

Voltage

Angle

in degree

Voltage

Angle

in degree

Voltage

in p.u

Voltage

Angle

in degree

1 1.06 0.00 1.06 0.00 1.06 0

2 1.045 -4.9891 1.045 -2.8745 1.034 -2.6529

3 1.01 -12.7492 1.01 -7.799 1.01 -7.2654

4 1.0132 -10.242 1.0262 -5.594 1.0241 -5.3241

5 1.0166 -8.760 1.0289 -4.5991 1.0294 -4.4216

6 1.07 -14.4469 1.07 -5.8627 1.059 -5.2142

7 1.0457 -13.2368 1.0574 -5.3733 1.047 -4.1845

8 1.08 -13.2368 1.09 -4.410 1.076 -4.3152

9 1.0305 -14.8207 1.0414 -5.586 1.041 -5.2148

10 1.0299 -15.036 1.0391 -6.1652 1.0263 -6.0021

11 1.0461 -14.8581 1.0509 -6.1341 1.043 -5.8136

12 1.0533 -15.2973 1.0540 -6.6808 1.043 -6.7012

13 1.0466 15.331 1.0483 -6.6991 1.043 -6.5947

14 1.0193 -16.0717 1.0263 -7.2567 1.0158 -6.8263

Fig.5 and Fig.6 show the UPFC injected voltage magnitude and voltage angle. From these results it is

observed that the voltage profile of the tested system has been improved in the proposed approach when

compared to existing methods.



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Fig.5 UPFC injected voltage magnitude profile of IEEE-14 bus system

Fig.6 UPFC injected voltage angle profile of IEEE-14 bus system

UPFC is connected at different buses and the power loss, UPFC installation cost, fuel cost and emission are

determined. The UPFC connected buses and the corresponding power loss, cost base parameters and emission

of the tested system are tabulated in Table 2 and 3. It is observed from the Table 2 the total power loss is less in

the proposed BIA technique when compared to existing method.

Table 2. Power loss in UPFC connected buses

UPFC connected

Bus

Power Loss in MW

ABC Method Proposed Bat Inspired

Algorithm

2-3 8.630 6.23

2-4 6.217 5.542

3-4 4.036 3.9476

4-5 5.951 5.8683

6-13 5.052 4.958

6-11 6.749 6.254

6-12 5.788 5.547

10-11 4.255 4.65

12-13 5.539 5.487

13-14 5.773 5.253

0.95

1

1.05

1.1

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Vo

ltag

e M

agn

itu

de

in

p.u

.

Bus Number

NR method ABC algorithm Proposed BI algorithm

-20

-15

-10

-5

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Vo

ltag

e A

ngl

e in

p.u

.

Bus Number

NR method ABC algorithm Proposed BI algorithm



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Table 3. Fuel cost, emission and installation cost of UPFC connected Buses

UPFC connected Bus Fuel Cost

in $/hr

Emission

In Kg/hr

Installation Cost

In $/KVAr

2-3 852.6241 271.2356 192.35

2-4 825.4525 259.0041 190.1254

3-4 780.5629 251.2369 188.22

4-5 815.2358 259.5241 189.225

6-13 790.2584 250.2589 188.22

6-11 805.2369 279.3612 188.22

6-12 826.8756 262.3541 189.658

10-11 726.4528 244.2235 188.22

12-13 764.2389 256.3241 188.22

13-14 771.2543 257.1523 188.22

The fuel cost for the UPFC connected buses and the UPFC installation cost are shown in Fig.7 and 8.

Fig.7 Fuel Cost for the UPFC connected Bus

Fig.8 UPFC Installation Cost

The convergence characteristics of the proposed method are shown in Fig.9. From these result it is observed

that the power loss of the proposed method is less when compared with existing methods.

Fig 9 Comparison of power loss

650

700

750

800

850

900

2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14

Fuel

Co

st in

$/h

r

UPFC Connected Bus

186

188

190

192

194

2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14Inst

alla

tio

n C

ost

in

$/K

VA

r

UPFC Connected Bus

0

2

4

6

8

10

2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14

Po

wer

Lo

ss in

MW

UPFC connected Bus

ABC algorithm Bat Inspired algorithm



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The real power generation of the proposed method is 265.02 MW in order to minimize the power loss

which is 5.374 MW. In ABC algorithm the real power generation is 264.799 MW for the power loss of 5.799MW.

The results are tabulated in Table 4 and the comparison between these two methods is given in Fig.10.

Table 4. Real power generation of the IEEE-14 bus system

UPFC connected

Bus

Real Power Generation

in MW

ABC[28] Proposed Bat

Inspired Algorithm

2-3 267.630 265.234

2-4 265.217 266.005

3-4 263.036 262.962

4-5 264.951 265.321

6-13 264.052 267.215

6-11 265.749 264.1897

6-12 264.788 265.875

10-11 263.255 263.012

12-13 264.539 265.089

13-14 264.773 265.251

Fig.10 Comparison of real power generation

Table 5. Comparison of fuel cost, emission and installation cost

Method Fuel Cost

in $/hr

Emission in

Kg/hr

Installation

Cost in $/KVAr

ABC[28] 803.2546 262.745 192.965

Proposed Bat

Inspired Algorithm

795.82 259.07

189.07

It is observed from the Table 5, fuel cost, emission and installation cost is less in the proposed method when

compared to existing method and also it is very clear that the installation cost of UPFC is optimum in the proposed

method.

260

262

264

266

268

2-3 2-4 3-4 4-5 6-13 6-11 6-12 10-11 12-13 13-14

Rea

l Po

wer

Gen

erat

ion

in

MW

UPFC Connected Bus

ABC algorithm Proposed Bat Inspired algorithm



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V. CONCLUSION

In this paper, the bat inspired algorithm based OPF technique with FACTS controller is proposed. Then,

the proposed technique is implemented and the OPF performance is tested with standard IEEE-14 bus system.

The optimal generation limits are determined by BI algorithm and the fuel cost, emission and power loss are

analyzed. Then, the analyzed results are compared with existing techniques. From the comparative analysis, it is

found that the proposed technique has less fuel cost, emission and power loss. Also, the UPFC injected voltage

magnitude and voltage angle are selected as the best possible values when compared with existing techniques.

Thus, the power variations of the buses are controlled and the UPFC installation cost is maintained economically,

since, the proposed technique gives considerably better convergence solution compared to existing method.

REFERENCES

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[2] JavadLavaei and Steven H. Low,“Convexification of Optimal Power Flow Problem”, In Proceedings of conference on OPF,

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[4] Sourav Mallick, D.V.Rajan, S.S.Thakur, P.Acharjee and S.P.Ghoshal, “Development of a new algorithm for power flow

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[5] Keyan Liu, Yunhua Li and Wanxing Sheng, ”The decomposition and computation method for distributed optimal power

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[6] Mahdad,K.Srairi and T. Bouktir, ”Optimal power flow for large-scale power system with shunt FACTS using efficient

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[7] Priyanka Roy and Chakarabarti, ”Genetic algorithm based optimal power flow solution for determination of spot pricing of

generators in deregulated electricity environment of a developing country”, Journal of Acta Electrotehnica, Vol.53, No.1, 2012.

[8] Rajendra B Sadaphale and Vikram s Patil, “Optimal power flow of IEEE 14 bus system using FACTS device with

voltage constraints”, International Journal of Advances in electrical and electronics engineering, V2N1:113-118, 2015

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Loadability Assessment of IEEE-14 Bus System Using Facts Devices Incorporating Stability Constraints”, International Journal

of Industrial Electronics and Electrical Engineering, Volume-2, Issue-10, 2014.

[10] P.Pavan Kumar and N.Poornachandrarao, “ Improvement of power flow in the power system network by using UPFC

device”, International Journal of Engineering Research and Applications, Vol. 2, Issue 3, 2012.

[11] E. Nanda Kumar and Dr. R. Dhanasekaran, “Optimal Solution of IEEE Standard 14 Bus System using MATLAB

Simulation incorporating with FACTS Devices”, International Journal of Electronic and Electrical Engineering, Volume 5, Number

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[12] Adit Pandita and Dr.Sanjay K Jain, “ A Review on Power Flow Analysis with UPFC and its Applicability”,

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[14] Dipali V. Patil and Parag Chourey, “ Application of UPFC used IEEE Bus Standard System for Minimizing the

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Improved Bat Algorithm”, International Journal of Research in Electrical and Electronics Technology, Volume 1 - Issue 2, 2014.

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Biographical Details

Dr.M.Karthikeyan received the B.E degree from Madurai Kamaraj University in 1997, M.E. degree

from Vinayaka Missions University in 2007 and Ph.D degree from PRIST University in 2015. Currently

he is working as an Assistant Professor, Department of Electrical and Computer Engineering, College

of Engineering, Wolaita Sodo University, Ethiopia. He has more than 16 years of experience in various engineering

colleges affiliated to Anna University. He is a life member of ISTE and member in Institution of Engineers (India).

He has published more than 5 text books for Anna university affiliated college students and also reviewed chapters

for Tata McGraw Hill published books of Electromagnetic Field, Modern Power System Analysis and etc. He has

published more than 11 international journals and attended more than 15 international / national conferences.



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Mr.Sirak Gebrehiyot received the B.Sc degree from Haramaya University, Ethiopia in 2011 and M.Sc.

degree from Hawassa University, Ethiopia in 2016. Currently he is working as a Lecturer, Department

of Electrical and Computer Engineering, College of Engineering, Wolaita Sodo University, Sodo,

Ethiopia. He has more than 6 years of teaching experience in Wolaita Sodo University. He has published 3

international journals.

Mr.Degu Menna Eligo received the B.Ed degree in Electrical/Electronics from Adama University

in 2008 and M.Sc degree in Industrial Automation and Control Management from Adama Science and

Technology University in 2014. Currently he is working as a Head of Department and Lecturer,

Department of Electrical and Computer Engineering, College of Engineering, Wolaita Sodo University, Sodo,

Ethiopia. He has more than 8 years of experience in various engineering colleges in Ethiopia. He has published

more than 3 text books for TVET college students. He got two national awards from minster of Science and

Technology of Ethiopia. The major books are Electrical Installations, Electrical Power level I, and etc. He also

reviewed Building Electrical Installation and Industrial Motor Control curriculums. He has published more two

international journals and attended 3 international / national conferences.

Mr.Wondimu Dawit received M.Sc. Degree in Industrial Automation and Control Application

Technology, from Adama Science and Technology Univ., on July 03, 2014 G.C., B.Ed. in Electrical

and Electronics Technology Engineering, from BAHIR DAR University Faculty of Engineering on 23

SEP 2008 and DIPLOMA in Electrical and Electronics Technology from Adama Science and

Technology Univ., on 8, July 2000. Currently he is working as an Associate dean for academic affairs in the College

of Engineering and as a Lecturer, in the Department of Electrical and Computer Engineering, College of

Engineering, Wolaita Sodo University, Sodo, Ethiopia. He has more than 14+ years of work experience in different

Technology collages and Institutes with best performance and 3+ years of work experience in wolaita sodo

university( from 2007-2009 E.C till now).



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