Engineering, 2010, 2, 337-343
doi:10.4236/eng.2010.25044 Published Online May 2010 (http://www.SciRP.org/journal/eng)
337
Enhancement of Available Transfer Capability with Facts
Device in the Competitive Power Market
Bairavan Veerayan Manikandan1, Sathiasamuel Charles Raja2, Paramasivam Venkatesh2
1Mepco Schlenk Engineering College, Sivakasi, India
2Thiagarajar College of Engineering, Madurai, India
Received December 21, 2009; revised February 9, 2010; accepted February 12, 2010
Abstract
In order to facilitate the electricity market operation and trade in the restructured environment, ample trans-
mission capability should be provided to satisfy the demand of increasing power transactions. The conflict of
this requirement and the restrictions on the transmission expansion in the restructured electrical market has
motivated the development of methodologies to enhance the Available Transfer Capability (ATC) of the ex-
isting transmission grids. The insertion of FACTS devices in electrical systems seems to be a promising
strategy to enhance ATC. In this paper, the viability and technical merits of boosting ATC using Thyristor
Controlled Series Compensator (TCSC) is being analyzed. The work has been carried out on IEEE 30 bus
and IEEE 118 bus systems. Bilateral and multilateral transactions are considered. Particle Swarm Optimiza-
tion (PSO) algorithm and Genetic Algorithm (GA) are employed to obtain the optimal settings of TCSC.
Keywords: Available Transfer Capability, Flexible AC Transmission Systems, Particle Swarm Optimization,
Genetic Algorithm, Power Transfer Distribution Factors, Thyristor Controlled Series Compensator
1. Introduction
Deregulation of the electric industry throughout the
world aims at creating competitive markets to trade elec-
tricity, which generates a host of new technical chal-
lenges to market participants and power system re-
searchers. For transmission networks, one of the major
consequences of the non-discriminatory open-access
requirement is a substantial increase of power transfers,
which demand adequate available transfer capability [1]
to ensure all economic transactions. Researchers have
proposed various methods to evaluate ATC [2-4]. Suffi-
cient ATC should be guaranteed to support free market
trading and maintain an economical and secure operation
over a wide range of system conditions. However, tight
restrictions on the construction of new facilities due to
the increasingly difficult economic, environmental, and
social problems, have led to a much more intensive
shared use of the existing transmission facilities by utili-
ties and independent power producers (IPPs). These
concerns have motivated the development of strategies
and methodologies to boost the ATC of the existing
transmission networks.
Aimed at this problem, various ATC enhancement ap-
proaches have been proposed, where adjusting terminal
voltage of generators and taps changing of onload tap
changer (OLTC), particularly rescheduling generator
outputs, are considered as major control measures for
ATC boosting. On the other hand, it is highly recognized
that, with the capability of flexible power-flow control
and rapid action, flexible ac transmission systems
(FACTS) technology has a wide spectrum of impacts on
the way the transmission system operates, in particular
with respect to thermal, voltage, and stability constraints
[5].
ATC values are always limited ultimately by heavily
loaded circuits and/or nodes with relatively low voltage,
with the increase of system loading. FACTS concept
makes it possible to use circuit reactance, voltage mag-
nitude, and phase angle as controls to redistribute line
flow and regulate nodal voltage, thereby mitigating the
critical situation. In addition, partly due to the physical
constraints on circuit impedance and phase angle of
nodal voltage, most high-voltage transmission lines are
operating far below their thermal rating [6]. By the con-
trol of line reactance and voltage phase angle, FACTS
technology enables line loading to increase flexibly, in
some cases, all the way up to thermal limits. Therefore,
theoretically it can offer an effective and promising al-
ternative to conventional methods for ATC enhancement.

B. V. MANIKANDAN ET AL. 338
Undoubtedly, it is very important and imperative to carry
out studies on exploitation of FACTS technology to en-
hance the ATC [7].
In this paper, ATC is calculated using ACPTDF in
Combined Economic Emission Dispatch (CEED) envi-
ronment [8,9] and an attempt is made to enhance Avail-
able Transfer Capability using TCSC i.e. Thyristor con-
trolled series compensator [10,11]. TCSC is connected in
series with the line conductors to compensate for the
inductive reactance of the line. Both bilateral and multi-
lateral transactions are considered. Population based,
cooperative and competitive stochastic search algorithms
are very popular in the recent years in the research arena
of computational intelligence. Some well established
search algorithms such as PSO [12,13] and GA [14,15]
are successfully implemented to solve simple and com-
plex problems efficiently and effectively. Population
based search approach in GA is motivated by evolution
as seen in nature. PSO, on the other hand, is motivated
from the simulation of social behavior. The optimal set-
tings of TCSC are obtained using PSO and GA. The re-
sults are illustrated on both IEEE 30 bus and IEEE 118
bus systems.
2. Available Transfer Capability
Available Transfer Capability (ATC) is a measure of the
transfer capability remaining in the physical transmission
network for further commercial activity over and above
the already committed uses [1]. ATC can be expressed
as:
sCommitmentonTransmissiExistingTTCATC  (1)
where, Total Transfer Capability (TTC) is defined as the
amount of electric power that can be transferred over the
interconnected transmission network or particular path or
interface in a reliable manner while meeting all of a spe-
cific set of defined pre and post contingency conditions.
ATC at base case, between bus m and bus n using line
flow limit (thermal limit) criterion is mathematically
formulated using ACPTDF (or) PTDF as

,
min,
mnij mnL
A
TCTij N
(2)
where Tij,mn denotes the transfer limit values for each line
in the system. It is given by

max 0
,
,
,
max 0
,
,
;0
(infinite) ;0
()
;0
ij ij
ij mn
ij mn
ij mnij mn
ij ij
ij mn
ij mn
PP PTDF
PTDF
TPTD
PP PTDF
PTDF















,
F







(3)
where
max
ij
P is the MW power limit of a line between bus i
and j.
o
ij
P is the base case power flow in line between bus i
and j.
PTDFij,mn is the power transfer distribution factor for
the line between bus i and j when a transaction is taking
place between bus m and n.
NL is the total number of lines.
The optimal settings of generators under CEED envi-
ronment are considered as a base case power flow.
2.1. CEED Problem Formulation
Optimization of CEED problem has been mathematically
formulated and is given by the following equation:
1
(,)
Ng
i
i
minfFC EC 

 (4)
where φ is the optimal cost of generation (US$/h) and Ng
represents the number of generators connected in the
network.
The cost is optimized within the following constraints:
The power system constraint is given as follows


Ng
ildgi PPP 1
(5)
where Pd is the total load of the system and Pl is the
transmission losses of the system.
The power flow equation of the power network


,gv  0 (6)
The inequality constraint on real power generation of
each generator i
maxmin
gigigi PPP  (7)
where min
g
i
P and max
g
i
P are minimum and maximum
value of real power allowed at generator i respectively.
The inequality constraint on voltage of each PQ bus
maxmin
iii VVV  (8)
where and are minimum and maximum
voltage at bus i respectively.
min
i
Vmax
i
V
Power limit on transmission line
max
p
qpq
M
VAf MVAf (9)
where max
p
q
M
VAf is the maximum rating of transmission
line connected between buses p and q.
Total fuel cost of generation FC (US$/h) in terms of
control variables of generator powers can expressed as,

 
Ng
iigiigii cPbPaFC 1
2 (10)
where Pgi is the real power output of an ith generator and
ai , bi , ci are the fuel cost curve coefficients.

B. V. MANIKANDAN ET AL.339
Total emission of generation EC (lb/h) can be ex-
pressed as,

 
Ng
iigiigii PPEC 1
2
 (11)
where αi , βi and γi are emission coefficients.
The bi-objective optimization problem (4) is converted
into single optimization problem by introducing price
penalty factor, h , which blends fuel cost with emission,
()
M
inimizeFCh EC


 (12)
CEED optimization problem is solved using evolu-
tionary programming and more information is also
available in the papers [8,9]. In most of the developing
countries, the restructuring process of power industry is
in the infant stage. Still the structure is vertically inte-
grated but they purchase power from Independent Power
Producers (IPP) to meet the growing demand. Hence, the
regional transmission operator is responsible for the re-
dispatch of generator power by considering the physical
limits of the system and emissions standards. Also, there
are some power markets which support both bilateral
transactions based on ATC and centralized dispatch
based on bids. In these markets, assured firm transactions
are implemented first and then they will follow central-
ized dispatch mechanism with the remaining transfer
capacity. Therefore the proposed CEED based ATC cal-
culation and enhancement method can be well suited for
such cases described above.
2.2. ACPTDF Formulation
The AC power transfer distribution factors proposed for
calculation of ATC [4] were used to find various trans-
mission system quantities for a change in MW transac-
tion at different operating conditions.
Consider a bilateral transaction tk between a seller bus
m and buyer bus n. Line l carries the part of the trans-
acted power and is connected between buses i and j. For
a change in real power, transaction among the above
buyer and seller by ∆tk MW, if the change in a transmis-
sion line quantity q1 is ∆q1 , power transfer distribution
factors can be defined as,
k
l
mnij t
q
PTDF 


, (13)
The transmission quantity ql can be either real power
flow from bus i to j (Pij ) (or) real power flow from bus j
to bus i (Pji ). The above factors have been proposed to
compute at a base case load flow with results using sen-
sitivity properties of NRLF Jacobian. Consider full Jaco-
bian in polar coordinates [JT], defined to include all the
buses except slack (including ∆Q-∆V equations also for
PV buses).













































Q
P
J
Q
P
V
QQ
V
PP
VT
1
1



(14)
In a base case load flow, if only one of the kth
bilateral
transactions is changed by ∆tk MW, only the following
two entries in the mismatch vector on RHS of (14) will
be non zero.
k
itP



k jtP  (15)
With the above mismatch vector elements, the change
in voltage angle and magnitude at all buses can be com-
puted from (14) & (15) and, hence, the new voltage pro-
file can be calculated. These can be utilized to compute
all the transmission quantities ql and hence the corre-
sponding changes in these quantities ∆ql from the base
case. Once the ∆ql for all the lines corresponding to a
change in transaction ∆tk is known, PTDFs can be ob-
tained from (13).
3. Overview of PSO and GA
3.1 Particle Swarm Optimization
The PSO is a population-based optimization method first
proposed by Kennedy and Eberhart [12]. PSO technique
finds the optimal solution using a population of particles.
PSO is developed through simulation of bird flocking in
two-dimensional space. The position of each agent is
represented in X-Y plane with position(,)
x
y
SS
)
, Vx (ve-
locity along X-axis), and Vy (velocity along Y-axis).
Modification of the agent position is realized by the po-
sition and velocity information. Bird flocking optimizes
a certain objective function. Each agent knows its best
value so far, called ‘Pbest’, which contains the informa-
tion on position and velocities. This information is the
analogy of personal experience of each agent. Moreover,
each agent knows the best value so far in the group,
‘Gbest’ among all ‘Pbest’. This information is the analogy
of knowledge, how the other neighbouring agents have
performed. Each agent tries to modify its position by
considering current positions (,
x
y
SS, current velocities
(,)
x
Y
VV , the individual intelligence (Pbest), and the group
intelligence (Gbest).
The following equations are utilized, in computing the
position and velocities, in the X-Y plane:
1
11
22
()
()
i
kk
ii best
k
best i
VWVCrandPS
Crand GS
k
i


 



 

(16)
1kkk
iii
SSV
1


 (17)
where, 1k
i
V

is the velocity of ith individual at
(1)
th
K

B. V. MANIKANDAN ET AL. 340
iteration, is the velocity of ith individual at kth itera-
tion, W is the inertia weight, C1, C2 are the positive
constants having values (0,2.5), rand1, rand2 are the ran-
dom numbers selected between 0 and 1, is the best
position of the ith individual, Gbest is the best position
among the individuals (group best) and Si
k is the position
of ith individual at kth iteration. The acceleration coeffi-
cients C1, and C2 control how far a particle will move in
a single iteration. Typically, these are both set to a value
of 2.5.
k
i
V
i
best
P
The velocity of each particle is modified according to
(16) and the minimum and maximum velocity of each
variable in each particle is set within the limits of Vmin
and Vmax respectively. The position is modified according
to (17). The inertia weight factor ‘W’ is modified using
(18) to enable quick convergence.
iter
iter
WW
W


max
minmax
max
)(
)


) 

W
11
and



22
and  

(18)
where Wmax is the initial value of inertia weight equal to
0.9, Wmin is the final value of inertia weight equal to 0.4,
iter is the current iteration number and itermax is the
maximum iteration number. Small values of w result in
more rapid convergence usually on a suboptimal position,
while a too large value may prevent divergence.
The PSO system combines two models: A so-
cial-only model and the cognition-only model. These
models are represented by the velocity update, shown in
(16). The second term in the velocity update equation
is associated with cognition
since it only takes into account the particle’s own ex-
periences. The third term in the velocity update equation
represents the social interac-
tion between the particles. It suggests that individuals
ignore their own experience and adjust their behavior
according to the successful beliefs of individual in the
neighborhood.
(
i
k
best i
C rPS
(
k
best i
CrG S
3.2. Genetic Algorithm
GAs has been extensively used in power system optimi-
zation problems. GAs are search algorithms based on the
mechanics of natural selection and natural genetics. GAs
are different from other optimization methods. GAs
search from a population of points, not from a single
point. GAs can therefore discover a globally optimal
point. GAs can deal with non-smooth, non-continuous
and non-differentiable functions that are the real-life op-
timization problems. GAs use probabilistic transition
rules to select generations, not deterministic rules, so
they can search a complicated and uncertain area to find
the global optimum. However, to make GAs more prac-
ticable, the problems of memory and computing time
arising from the coding of large number of variables in
real life systems need to be solved. The steps involved in
simple GA are Initial population generation, Fitness
evaluation, Selection, Crossover and Mutation.
In this work, Real Coded Genetic Algorithm (RCGA)
with specialized crossover operator called Simulated
Binary crossover (SBX) and polynomial mutation is em-
ployed. Simulated Binary Crossover creates children
solutions in proportion to the difference in parent solu-
tions. The following steps are followed to create two
children solutions from two parents:
 Choose a random number ,[0,1]
i
u
 Calculate qi

as given in the below equation
1
1
1
1
(2 ),0.5
1,
2(1 )
c
ii
qi c
i
uu
otherwise
u















(19)
where, qi

is the spread factor and is defined as the
ratio of the absolute difference in offspring values to that
of the parents. 
c is the crossover index.
Then compute the offspring & as,
(1, 1)t
i
x(2,1)t
i
x
(1,1)(1, )(2,)
(2,1)(1, )(2, )
0.5 (1)(1)
0.5 (1)(1)
tt
iqiiqi
tt
iqiiq
xx
xx




t
i
t
ii
x
x


 



  


(20)
Newly generated offspring undergo polynomial muta-
tion operation. Like in the SBX operator, the probability
distribution can also be a polynomial function, instead of
a normal distribution. The new offspring (1, 1)t
i
y

is de-
termined as follows,
(1, 1)(1,1)() ttUL
ii ii
yx xx
i


 (21)
where and are the upper and lower limit values.
The parameter
U
i
xL
i
x
i

is calculated from the polynomial
probability distribution.

1/( 1)
1/( 1)
() 0.5(1)(1)
(2 )1,0.5
12(1),0.5
m
m
m
ii
im
ii
P
and
rif
rifr



 






 


r
(22)
where 
m is the mutation index. In this operator the
shape of the probability distribution is directly controlled
by the external parameter 
m and distribution is not dy-
namically changed with generations. Newly generated
individuals replace their parents and forms the parents
for the next generation. The iterative procedure can be
terminated when any one of the following criteria is met
i.e., an acceptable solution has been reached, a state with
no further improvement in solution is reached, control

B. V. MANIKANDAN ET AL.341
variables has converged to a stable state or a pre-defined
number of iterations have been completed.
4. Thyristor Controlled Series Compensator
A TCSC is a series-controlled capacitive reactance that
can provide continuous control of power on the AC line
over a wide range. It can be operated in both capacitive
and inductive modes. In capacitive mode, it reduces the
transfer reactance between the buses at which the line is
connected, thus increasing the maximum power that can
be transmitted and reducing the effective active and reac-
tive power losses. From the system viewpoint, the prin-
ciple of variable-series compensation is simply to in-
crease the fundamental-frequency voltage across a fixed
capacitor (FC) in a series-compensated line through ap-
propriate variation of the firing angle, . This enhanced
voltage changes the effective value of the series- capaci-
tive reactance
The basic conceptual TCSC module comprises a series
capacitor, C, in parallel with a thyristor-controlled reac-
tor, Ls as shown in Figure 1. However, a practical TCSC
module also includes protective equipment normally
installed with series capacitors.
The model of a transmission line with a TCSC connec
ted between bus–i and bus-j is shown in Figure 2.
In this work, TCSC is modelled as a variable reactance
whose value varies from –0.8 XL to +0.2 XL in order to
avoid over compensation of the line. XL is the reactance
of the line in which TCSC is connected.
5. Problem Formulation
The aim of the optimization is to perform the best
I
line
L
S
Figure 1. TCSC basic module.
jB
-
Z
ij
= r
ij
+ jx
ij
jB
-
Figure 2. Model of TCSC.
utilization of thes. The objec-e existing transmission lin
tive is to maximize the ATC i.e., uncommitted active
transfer capacity of the prescribed interface, when a
transaction is taking place between a seller bus (m) and
buyer bus (n). It is represented as
mn
M
aximizeATCp (23)
where ATCmn is given by Equation (2). The optimization
problem is solved subject to the following FACTS de-
vice constraint (24) and power flow constraints (5-9).
0.8 0.2
LTCSC L
X
XXp

 (24)
. Algorithm
he basic steps involved in enhancing ATC values with
ead the system input data.
in CEED environ-
m
3: Consider wheeling transactions (tk).
ution fac-
to
ansactions as variables, line flow, real
an
e limiting element in the system buses
i.e
nt.
ob-
ta
ncorporating TCSC
out,
th
he simulation studies are carried out on Intel Pentium
ork has been carried out on IEEE 30 bus and
IE
6
T
TCSC device using PSO and GA algorithm are given
below:
Step 1: R
Step 2: Run a base case load flow
ent and determine the optimal settings of generators
[8,9].
Step
Step 4: Compute AC power transfer distrib
rs as per (13).
Step 5: Take tr
d reactive power limits of generators as constraints and
compute the feasible wheeling transactions determine the
ATC as per (2).
Step 6: Find th
., that carry power close to thermal limit
Step 7: Place TCSC in the limiting eleme
Step 8: Run PSO algorithm and GA separately to
in the settings of TCSC.
Step 9: Calculate ATC after i
Step 10 : Is any other transaction has to be carried
en consider the next transaction and go to step 3, oth-
erwise stop the procedure.
7. Results and Discussion
T
Dual Core, 2.40 GHZ system in MATLAB 7.3 environ-
ment.
The w
EE 118 bus systems. For the ATC determination, gen-
erators setting are obtained from CEED environment as
explained by the authors in [8,9]. Thermal limit of each
line is considered as a constraint and reactive power de-
mand at load buses has been taken as constant. For run-
ning the PSO algorithm, the initial population of indi-
viduals are created by satisfying the constraints. For each
individual in the population, the fitness function, velocity

B. V. MANIKANDAN ET AL. 342
Table.1. ATC in MW – with and without TCSC using PSO.
System Method Transaction
between buses
ATC
Without
TCSC
(MW)
ATC With
TCSC
(MW)
Settings of
TCSC
(p.u.)
Position of
TCSC
Execution time
(sec)
2-28 22.970 26.553 0.0300 6-28 103.056707
5-23 23.901 23.935 0.1350 23-24 102.823764
IEEE 30
Bus ACPTDF
2-12 58.830 67.949 0.0895 22-24 103.232553
Table 2. ATC in MW – with and without TCSC using GA.
System Method Transaction
between buses
ATC With-
out TCSC
(MW)
ATC With
TCSC
(MW)
Settings of
TCSC
(p.u.)
Position of
TCSC
Execution time
(sec)
2-28 22.970 26.1473 0.0277 6-28 108.071454
5-23 23.901 23.8743 0.1332 23-24 107.427641
IEEE 30
Bus ACPTDF
2-12 58.830 67.2674 0.0843 22-24 107.136452
updation and new population creation are done as ex-
plained in Subsection 3.1.1.
With GA, the TCSC settings, placement and execution
time for the transaction (49-100) are 0.0148 p.u, line
81-80 and 146.347801 seconds respectively. Similarly,
for the transaction (1-118), the values are 0.0229 p.u, line
75-118 and 145.778420 seconds respectively.
The network and line datas for IEEE 30 bus system
IEEE 118 bus system is taken from [16]. There are 6
generators and 41 lines in the IEEE 30 bus system. In
IEEE 118 bus system, there are 13 generators and 99
lines. For each considered transaction, TCSC is placed in
the most limiting line i.e., line flow close to thermal limit.
Algorithms are run for 100 iterations.
7.2. Multilateral Transaction
The results for multilateral transaction using PSO are
shown in Figure 4. For the multilateral transaction be-
tween buses i.e., (2, 11) – (28, 26) considered in IEEE30
bus system, TCSC is placed in the line between buses
25-27 and its setting is 0.1044 p.u. The time taken for
completing the execution is 102.810250 seconds. For the
multilateral transaction between buses i.e., (25,59,46) –
(89,100,103,111) considered in IEEE 118 bus system,
TCSC is placed in the line between buses 100-103 and
its setting is 0.0262 p.u. The execution time is 136.
225383 seconds.
Three bilateral transactions and one multilateral trans-
action are considered for IEEE 30 bus system. Two bi-
lateral and one multilateral transaction are considered for
IEEE 118 bus system.
7.1. Bilateral Transaction
The position of TCSC, its settings from PSO algorithm
and the ATC values before and after incorporating TCSC
are shown in Table 1. The steps involved in simple GA
i.e., initial population generation, fitness evaluation, se-
lection, crossover and mutation are carried out for the
considered transactions and the results are given in Ta-
ble 2.
The results for multilateral transaction using GA are
shown in Figure 5. For the same multilateral transaction
considered in IEEE30 bus system, TCSC is placed in the
line between buses 25-27 and its setting is 0.1040 p.u.
For the IEEE 118 bus system, two bilateral transac-
tions between buses i.e., (49-100) and (1-118) are con-
sidered. The results obtained with TCSC using PSO and
GA are shown in Figure 3.
For the PSO algorithm, the settings of TCSC are
0.0185 p.u for the transaction (49-100) and 0.0240 p.u
for transaction (1-118). TCSC is placed in series with the
limiting line 81-80 for transaction (49-100) and for the
transaction (1-118), it is placed in the limiting line
75-118. The execution time is 139.047090 seconds for
transaction (49-100) and 137.654722 for transaction
(1-118). Figure 3. ATC results – using PSO and GA.

B. V. MANIKANDAN ET AL.343
10.5709
11.3668
47.899
60.0918
0
10
20
30
40
50
60
70
ATCinMW
IEEE30bus IEEE118bus
MultilateralTransaction
Withou t
TC SC
WithTCSC
Figure 4. ATC for Multilateral transaction – PSO.
10.571
11.348
47.899
59.877
0
10
20
30
40
50
60
ATCinMW
IE E E30Bus IEEE118bus
MultilateralTransaction
Without
TC SC
With
TC SC
Figure 5. ATC for Multilateral transaction – GA.
The time taken for completing the execution is 103.
120562 seconds. For the same multilateral transaction in
IEEE 118 bus system, TCSC is placed in the line be-
tween buses 100-103 and its setting is 0.0254 p.u. The
execution time is 134.927363 seconds.
8. Conclusions
From the view point of operational planning, this paper
evaluated the impact of FACTS device on ATC en-
hancement. The results demonstrated that the use of
FACTS devices, particularly the TCSC can boost the
ATC substantially. The considerable difference between
ATC values with and without TCSC justifies that the
FACTS technology can offer an effective and promising
solution to boost the usable power transfer capability,
thereby improving transmission services of the competi-
tive electricity market. On using PSO and GA for the
above problem, it is found that, PSO algorithm is pro-
viding very good enhanced result with minimum execu-
tion time compared to GA. But for the multilateral
transaction, the GA results are very much closer to PSO
results in all aspects including settings of TCSC and
execution time. Both algorithms predicted the same lim-
iting line for bilateral and multilateral transactions con-
sidered in IEEE test systems.
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