Energy and Power Engineering, 2013, 5, 474-480
http://dx.doi.org/10.4236/epe.2013.57051 Published Online September 2013 (http://www.scirp.org/journal/epe)
Using UPFC and IPFC Devices Located by a Hybrid
Meta-Heuristic Approach to Congestion Relief
Hamid Iranmanesh*, Masoud Rashidi-Nejad
Islamic Azad University, Jiroft Branch, Jiroft, Iran
Received March 19, 2013; revised April 19, 2013; accepted April 26, 2013
Copyright © 2013 Hamid Iranmanesh, Masoud Rashidi-Nejad. This is an open access article distributed under the Creative Com-
mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work
is properly cited.
ABSTRACT
This paper proposes new methodology for the placement of FACTS devices in transmission systems to reduce conges-
tion. Congestion management comprises congestion relief and congestion cost. The traditional approach to remedying
congestion lies in reinforcing the system with additional transmission capacity. Although still feasible, this approach is
becoming more and more complex and it is often challenged by the public [1]. Congestion relief can be handled by us-
ing FACTS devices, where transmission capability may be improved. Congestion relief using FACTS devices requires a
two step approach: first, the optimal location of these devices in the network and then, the settings of their control pa-
rameters. UPFC and IPFC have full dynamic control on the transmission parameters, voltage, line impedance and phase
angle. Real Genetic Algorithm (RGA) optimization technique is used to solve this congestion relief problem while ana-
lytical hierarchy process (AHP) with fuzzy sets is implemented to evaluate RGA fitness function. The results are ob-
tained for modified IEEE 5 bus Test System.
Keywords: Congestion Relief; RGA; AHP; Fuzzy Sets; UPFC; IPFC
1. Introduction
Electricity industry restructuring and reregulation may
dictate maximum power transfer using the existing facili-
ties under transmission open access scheme. Procuring
electricity contracts associated with market participants’
requirements can cause more challenges considering en-
ergy management systems. Reregulation will impose new
necessities to power systems such as transmission open
access as well as non-discrimination access to the infor-
mation. Transmission congestion management is an im-
portant mechanism in order to solve power transfer bot-
tleneck both in the operation and planning horizons [2].
There are two issues with regards to applying transmis-
sion open access that should be considered: the so-called
transmission losses and transmission congestion. Con-
gestion is dependent on the network constraints that may
show the ultimate transmission capacity, while it can
restrict the concurrent electric power contracts [3].
It can be said that, under congestion conditions the
price of transferring electricity will be increased. In fact,
congestion management is an overall as well as in par-
ticular systematic way of improving electricity transfer in
which power systems planning and operating can be re-
garded.
Transmission congestion is dealing with some restric-
tions of electricity transfer via transmission network.
These restrictions are increased in the presence of open
access considering electricity restructuring environment
[4]. Under new conditions of power market, more con-
straints such as economical, environmental problems and
transmission rights as financial contracts will be added to
technical limitations of transmission capacity [2]. Con-
gestion relief is such a solution to release some blocked
capacity of transmission network. In literature, there are
some techniques suggested to increase the available
transfer capability (ATC). Among the proposed solutions
for ATC enhancement, the use of FACTS devices is re-
ported considerably [5]. It can be said that the application
of FACTS devices should be based upon the investiga-
tion of capital investment as well as operating costs and
the impacts of these devices of ATC improvement [6].
On the other hand, the optimum placement of FACTS
devices is an important issue in terms of planning hori-
zon [5], especially considering different types of these
devices. While from operating point of view, the coordi-
nation among these devices is much of interest both by
*Corresponding author.
C
opyright © 2013 SciRes. EPE

H. IRANMANESH, M. RASHIDI-NEJAD 475
researchers and operation engineers.
2. Transmission Congestion Mathematical
Modelling
In order to study congestion problem, it is needed to de-
fine mathematical statements as a proposed model.
Mathematical modeling that is implemented in this paper
is based upon a multi-objective optimization problem in
which some new constraints are added to a conventional
optimization model that can be found in literature. In fact,
the model includes different terms for objective function
such as: improvement of voltage profile, reducing trans-
mission losses and minimizing capital investment for
FACTS devices incorporating ATC enhancement. The
optimum location as well as the capacity of UPFC can be
derived considering the role of these elements.
The study is carried out by implementing a perform-
ance index that can be defined as follow:
2
max
12
n
N
mm
mm
WPL
PI nPL




 (1)
where: m is real power transfer in line m, is
the maximum transfer capacity of line m, N is the number
of lines in the network. Wm is a non-negative real number
to show the importance of mth transmission line that can
be defined as weighting factor and n is defined as an op-
erating index that is usually less that one. When all
transmission lines work at their permissible conditions
(non-congestion situation) PI is very low, while if one or
more lines are congested it will be increased considera-
bly. To calculate the real power transfer in line m, DC
power flow is applied that is shown in the following rela-
tionship:
PL max
m
PL
1,
1,
:
:
N
mn n
nns
mN
mn nj
nns
SPm k
Pl
SPPm k













(2)
The coefficients of Smn is the mnth component of ma-
trix S that is used in DC power flow and Pn is the real
power injected at bus n [7,8].
2.1. UPFC Model
The UPFC, shown in Figure 1, consists of two switching
converters operated from a common DC link. Converter
2 performs the main function of the UPFC by injecting
an AC voltage with controllable magnitude and phase
angle in series with the transmission line. The basic func-
tion of Converter 1 is to supply or absorb the active
power demanded by Converter 2 at the common DC link.
This is represented by the current. Converter 1 can also
generate or absorb controllable reactive power and pro-
vide independent shunt reactive compensation for the
line. This is represented by the current. A UPFC can
regulate active and reactive power simultaneously. In
principle, a UPFC can perform voltage support, power
flow control and dynamic stability improvement in one
and the same device [9].
2.2. IPFC Model
IPFC is mainly used to increase the active power in the
line and also to balance the power flow between the lines
in the transmission network. In its general form the IPFC
employs a number of dc-to-ac converters each providing
series compensation for a different line. IPFC is designed
as a power flow controller with n number of static syn-
chronous series compensator (SSSC) with a common dc
link. For maintaining the power flow stability in the line,
power is injected into each bus. The schematic diagram
of a simple IPFC with two SSSC is shown in Figure 2
[10,11].
A multi-objective optimization model is represented as
a compact form of Equations (3) [7,9].
Subject to the followings:
max
min ij
ij
P
P Subject to the followings:
Figure 1. UPFC schematic diagram [9].
Figure 2. IPFC schematic diagram [10].

H. IRANMANESH, M. RASHIDI-NEJAD 476

1
cossin 0
n
giliijijFACTSijij FACTSij
j
PP VVGB 


 



1
sincos 0
n
giliijijFACTSijij FACTSij
j
QQ VVGB 


 


min max
iii
VVV
max
0.8
ij ij
PP
min maxgigi gi
PPP (3)
0
SVC
P
min maxgigi gi
QQQ
min maxshsh sh
QQQ
0.5 0.6
mn semn
X
XX
where: Pij is the real power flow through transmission
line ij; is the maximum capacity of line ij; is
the actual real load supply at bus i; N is bus number of
the system;
maxij
Pli P
g
i
P is the real power generation at bus i;
g
i
Q is the reactive power generation at bus i; is the
actual reactive load supply at bus i;
li
Q
Vi is the voltage
magnitude at bus i; are the real/reac-
tive part of the ijth element of the admittance matrix,
which may be a function of the reactance of FACTS De-
vice;
,
ij FACTSij FACTS
GB

ij

is the angle difference between the voltage at
bus i and that at bus j; max are the minimum/
maximum reactive power generation at generation bus i;
|Vi|min, |Vi|max are the minimum/maximum voltage mag-
nitude at bus I; Xse is the reactance of FACTS Device;
Xmn is the reactance of the line where FACTS Device has
been installed; Psh is the real power generation of FACTS
Device; minmax are the minimum/maximum reac-
tive power generation of FACTS Device [12].
,
mion
gi gi
QQ
,QQ
sh sh
3. Solution Algorithm
Heuristic methods may be used to solve complex opti-
mization problems. They are able to give a good solution
of a certain problem in a reasonable computation time,
but they do not assure to reach the global optimum. GA
is a global evolutionary search technique that can result a
feasible as well as optimal solutions. Based on the me-
chanics of natural selection and natural genetics, the GA
starts with a population of strings that represent the pos-
sible solutions and generates successive populations of
strings by combining survival of the fittest among string
structures [13,14].
3.1. Evaluation of Fitness Value via Fuzzy AHP
The proposed technique for multi-objective goal function
will include fuzzy sets theory (FST) [15] which charac-
terized variable O on X by its membership as μo(x): X→
And
[0,1]
analytic hierarchy process (AHP) procedures as
fo
nform to mainly qualitative nature of deci-
etermine importance degree of each al-
3.2. Calculation of Exponential Weighting
Anas (AHP) is a method used to
3.3. Constraints with Unequal Importance
ance it
llowing
 FST to co
sion factors.
 AHP is for d
ternative.
Values Using AHP
lytical hierarchy proces
support complex decision-making process by converting
qualitative values to numerical values [16,17].
In case where constraints are of unequal import
should be ensured that alternatives with higher levels of
importance and consequently higher memberships are
more likely to be selected. The positive impact of the
levels of importance, wi, on fuzzy set memberships is
applied through the proposed criterion. It can be realized
by associating higher values of wi to constraints. For
example, the more important alternative the higher the
value associated with it. For example to evaluate fitness
function in RGA, it should have higher value for impor-
tant alternative for this case above process applied as
below:

 
12
12
itness N
N
w ww
cc c

F
x
xx    (4)
4. Case Study and Results Analysis
generators
4.1. Modified IEEE 5-Bus System with
As iin Figure 3, under normal condition,
stem is
sh
A modified IEEE 5-bus system including 2
and 3 loads is selected to implement the proposed meth-
odology for congestion Relief. This system is simulated
using Power World software and MATLAB R2010b
software where the base MVA and base voltage are as-
sumed to be 100 MVA. In order to enhance power trans-
fer in the congested line, firstly the best location of
FACTS devices is derived and control parameters of the
allocated devices are then adjusted.
Congestion
t can be seen
maximum real power is transferred through line 2-1 by
amount of 88% of the permissible line capacity.
Voltage profile of modified IEEE 5-bus sy
own in Figure 4, where at bus 5 the magnitude of bus
voltage is 0.747 pu. Congestion can be taken into account
if real power transfer increases 80% of the line thermal
capacity [18]. By considering congestion condition, it
can be said that from congestion point of view there is no
security violation while from voltage profile point of
view this system needed to be compensated.

H. IRANMANESH, M. RASHIDI-NEJAD 477
Figure 3. Modified IEEE 5-Bus system with congestion.
Figure 4. Buses voltage profile.
4.2. Voltage Profile Improvement and FC
In th volt-
FC is assumed
as
any compensation, the electrical
sy
ng mini-
m
21
Congestion Relief Using UPFCor IP
is case goal function includes three objectives:
age profile, Congestion value and loss. Optimization
process tries to relief congestion besides improving volt-
age profile considering less loss. Multi-objective optimi-
zation is handled via fuzzyfying objective function terms.
In this respect, membership functions of objective terms
are needed to be defined. Typical membership function
for voltage of each bus is depicted in Figure 5, while
Figure 6 shows congestion membership.
The membership of loss for UPFC or IP
shown in Figure 7.
First of all without
stem is studied in order to determine the power flow in
each of the transmission line and the bus voltages. The
power flow results and voltage profile without FACTS
Devices are given in Appendix 1 and Figure 4.
In fact, congestion should be relived satisfyi
um loss of UPFC or IPFC while reaching best voltage
profile. Moreover, considering AHP criterion, priority
factors of objective terms should be derived. It is as-
sumed that congestion is very strong important than
voltage profile, while it is absolutely important than the
costs of FACTS devices. It can be interpreted that: P12 = 7,
P = 1 , P = 9, P =
713 31
1
9
st prle is highly more impor-
ta
Theatement “voltageofi
nt than costs of UPFC or IPFC “indicates:
0
0.2
0.4
0.6
0.8
1
hip
0.80.850.9 0.9511.05
Voltage(pu)
Degr e e of Membe r s
Figure 5. Membership of bus voltages.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
00.511.522.5
Congestion
Degree of Membership
3
Figure 6. Membership of congestion value.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0123
Loss Value
Degree of Membership
4
Figure 7. Membership of active power loss.
23 32
5, 15PP


The following weighting (W) ver is obtained via
so
cto
me matrix manipulations.


0.973370.218WE 670.068775
nT
Fitness value of RGA can be evaluated using
Fuzzy-AHP as:


3 12
12
fitness of RGA for
ea
3
Fitness( )( )( )
w ww
CxCxCx
where Ci is the i is membership of ith alternative and W
exponential weight of ith alternative.
This procedure can determine the
ch chromosome considering the importance of each
constraint as well as objective term. Implementing RGA
with the recombination rate of 76%, mutation rate of 3%
and regeneration of 21% considering ellipsis is applied as
it is depicted in Figures 8 and 9. These figures show the
best solution for UPFC is obtained after 68 generation. It

H. IRANMANESH, M. RASHIDI-NEJAD 478
Figure 8. Congestion variations versus number of RGA
generation (UPFC).
Figure 9. Voltage variations versus number of RGA gener
an be realized that line 3-4 is the candidate for UPFC
nsfer at
lin
r 97 genera-
tio
nalysis is carried out to study the effect of
IP
solution for IPFC is
ob
es 12 and 13 shows bus voltage profile and con-
ge
a-
tion (UPFC).
c
locations. In this regards the best capacity of those
FACTS device is 47.6% of line reactance which is equal
to 0.01428 pu and 15.61 MVAr for respectively.
By installing UPFC at their location, power tra
e 2-1 decreases to 74% of its maximum capacity and
the worst voltage is 0.972 which belongs to bus 5, while
it is improved significantly (Appendix 1).
The best solution for IPFC is obtained afte
n. But, the two converters of IPFC are embedded in
lines between buses 2-4 and 3-4 respectively close to bus
4.
A detailed a
FC parameters on line flows and bus voltages but, only
few results are given for demonstration purpose. The
power flow results for IPFC parameters Vse = 0.1 pu and
θse = −150 are given in Appendix 1.
Figures 10 and 11 show the best
tained after 97 generation. By installing IPFC at their
location, power transfer at line 2-1 decreases to 73% of
its maximum capacity and the worst voltage is 0.995
which belongs to bus 4, while it is improved signifi-
cantly.
Figur
stion profile using the allocated UPFC or IPFC.
Figure 10. Congestion variations versus number of RGA
Generation (IPFC).
Figure 11. Voltage variations versus number of RGA gen-
eration (IPFC).
Figure 12. Buses voltage profile using proposed UPFC or
IPFC.
Figure 13. Congestion profile usingn normal condition, pro-
posed UPFC or IPFC.

H. IRANMANESH, M. RASHIDI-NEJAD 479
re connecting FACTS Dev
0.
ment is an important issue in the re-
FC are the main commercially available
FA
[1] R. Grunbaun,berg and B. Berg-
t-Oriented
nfluence of Price
The total loss befoice is
066301 MW and after connecting UPFC is reduce to
0.04471 MW and connecting IPFC between two lines the
loss is further reduced to 0.02421 MW.
5. Conclusions
Congestion manage
regulated environment of power systems. Congestion
should be relieved in order to use the maximum capacity
of transmission networks. It is well known that FACTS
technology can control voltage magnitude, phase angle
and circuit reactance clearly. Using these devices may
redistribute the load flow associated with regulating bus
voltages. Therefore, it is worthwhile to investigate the
effects of FACTS controllers on the congestion man-
agement.
UPFC and IP
CTS controllers. This paper presents an implementa-
tion of the RGA associated with Fuzzy-AHP to deter-
mine the location and capacity of these devices. The
proposed methodology is employed incorporating di-
mensional serialization valuing mechanism. Case studies
and the obtained results show the effectiveness of the
suggested criterion significantly.
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H. IRANMANESH, M. RASHIDI-NEJAD 480
Appendix 1
Line From To Congestion without Facts Congestion with Congestion with
UPFC IPFC
1 1 2 88% 74% 73%
2 1 3 40% 35% 36%
3 2 3 22% 20% 22%
4 2 4 22% 23% 25%
5 2 5 69% 45% 40%
6 3 4 57% 16% 18%
7 4 5 18% 5% 10%