Post on 26-Jul-2016
description
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 1
Congestion Management in Deregulated Power
Systems using Series FACTS Controller
K.Ganesan1, K.Pandiarajan
2, R.Sathishkumar
3
PG Scholar, Electrical and Electronics Engineering, Regional Office, Anna University, Madurai, India1.
The HOD, Electronics and Communication Engineering, Sasurie College of Engineering., Vijayamangalam, India2.
Faculty, Electrical and Electronics Engineering, Regional Office, Anna University, Madurai, India3.
Abstract: The extensive restructuring process in the electric power industry has led to more demanding and different
usage of transmission grids irrespective of the designed limits. This has resulted in unanticipated congestion interfaces in
the regional transmission network. The system is however is not capable of withstanding the rapidly changing demands of
the competitive markets. Thus an efficient and sensible way of dealing with the congestion has become vital to maintain
the system reliability. This paper investigates the prediction of the optimal location and size of the Thyristor-Controlled
Series Capacitor (TCSC) so as to alleviate the congestion in a deregulated environment. The location of TCSC is identified
with the help of priority list based on the locational marginal price (LMP) difference between the buses. As LMP can be
easily calculated from the security constrained optimal power flow, this method reduces the computational difficulties.
Also the PSO algorithm fixes the optimal size of the TCSC which relieves the congestion thereby reduces the LMP and the
overall generation cost. The effectiveness of the proposed approach is tested in 6 bus and 30 bus systems under MATLAB
environment.
Keywords: Deregulated power systems, Thyristor-controlled series capacitor, Locational marginal price, Particle swarm
optimization, Congestion management.
I. INTRODUCTION
The restructuring process of the power industry
starts with the separation of the transmission activities
from the generation activities. The subsequent step was to
introduce competition in generation activities, either
through the creation of power pools, provision for direct
bilateral transactions or bidding in the spot markets. On
the other hand, the transmission system having significant
economies of scale consequently had a tendency to
become a monopoly. Thus it was felt necessary to
introduce regulation in transmission so as to prevent it
from overcharging for its services and to manage the
congestion [1]. The condition where overloads in
transmission lines or transformers occur is called
congestion. Congestion could prevent system operators
from dispatching additional power from a specific
generator. Congestion may occur due to various reasons,
such as transmission line outages, generator outages,
changes in energy demand and uncoordinated transactions.
Congestion may result in preventing new contracts,
infeasibility in existing and new contracts, additional
outages and monopoly of prices in some regions of power
systems and damages to system components. Congestion
may be prevented by means of rights, reservations and
congestion pricing. Also, it can be corrected by applying
controls such as phase shifters, tap transformers, TCSC,
redispatch of generation and load curtailment [2].
Several congestion management methods have
been presented in the literature. In [3] the authors‟
proposed a demand side based congestion management
approach to manage transmission line congestion for a
pool market model. FACTS devices like SVC and TCSC
were used to manage the function from single line
congestion case to three line congestion cases. The authors
have proved congestion cost was less after connecting
FACTS devices rather than without connecting. LMP
difference and congestion rent contribution methodologies
for locating series FACTS devices to manage congestion
in deregulated electricity markets have been depicted in
[4]. These methods were compared with sensitivity factor
method. The results proved that the best location of TCSC
was identified by proposed methods. The status of
congestion after placing the TCSC was not addressed. In
[5] an alleviation of overloads by redispatch of generators
with minimum rescheduling cost has been proposed. The
optimal rescheduling of active powers of generators was
selected based on the generator sensitivity to the congested
line, utilizing fuzzy adaptive bacterial foraging (FABF)
algorithm. In [6] particle swarm optimization based social
welfare maximization and congestion management has
been presented. The congestions were managed by using
rescheduling of generators. The participation of generators
was selected using generator sensitivities to the power
flow on overloaded lines. In [7] the authors‟ proposed a
type of security-constrained OPF for minimizing total
generation costs using FACTS devices. In [8] fuzzy
inference based generator active power rescheduling for
managing network congestion under normal and
contingency conditions has been discussed. The network
congestions were managed using counter flow information
obtained from the tracing of network virtual flows due to
various sources. In [9] the authors‟ proposed an optimal
congestion management approach in a deregulated
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 2
electricity market using particle swarm optimization with
time-varying acceleration coefficients. Minimum
redispatch cost was taken as objective function. The re-
dispatched generators were selected based on values of
generator sensitivity. PSO-TVAC is used to determine the
minimum redispatch cost. In [10] min cut algorithm has
been used to select proper location of TCSC for secured
optimal power flow under normal and contingencies
operating condition. They proposed two-step approach.
First, the optimal location of the TCSC in the network
must be ascertained by min cut algorithm and then, the
optimal power flow with TCSC under normal and
contingencies operating condition was solved. Reactive
power loss reduction and real power flow performance
index methods for the optimal location of TCSC have
been used in [11]. In reactive power loss reduction
method, TCSC were placed in a line having the most
positive loss sensitivity index. In real power flow
performance index method, TCSC were placed in a line
having most negative sensitivity index. In [12] the
authors‟ presented a multi-objective framework for
congestion management where three competing objective
functions such as total operating cost, voltage and
transient stability margins were simultaneously optimized.
Optimal location and sizing of series FACTS devices on
the most congested branches were determined by a priority
list based on locational marginal prices. In [13], TCSC has
been used for congestion management in a power system.
Minimization of severity index was taken as objective
function. The optimal location of TCSC was done by
sensitivity analysis and sizing of TCSC by using genetic
algorithm. In [14], the authors‟ used SQP techniques to
find the optimal number and placement of FACTS devices
to manage the transmission congestion. An adaptive
bacterial foraging algorithm for transmission congestion
management has been depicted in [15]. Congestions were
managed by rescheduling of generators. In this paper,
LMP difference based optimal location and PSO based
optimal sizing of TCSC are presented.
Section 2 presents the optimization problem
formulation for congestion management. Section 3 and 4
presents overview of Particle Swarm Optimization (PSO)
and Thyristor-Controlled Series Capacitor (TCSC)
respectively. Optimal location and sizing of TCSC is
given in section 5. The results achieved by applying the
proposed method on 6 bus and 30 bus systems are
presented in section 6. Finally, conclusion is given in
Section 7.
II. PROBLEM FORMULATION
A. Objective Function
The objective of the proposed method is to minimize the
total generation cost. The problem is stated
mathematically as
i j
DjjGiiPP
PBPCMinDjGi
)()(,
(1)
where, PGi and PDj are the active powers of pool generator
i with bid price Ci and pool load j with offer price Bj
respectively.
B. Problem constraints
Generation/load balance Equation
DG N
j
LDj
N
i
Gi PPP
11
0 (2)
Generator constraints
max,min, GiGiGi PPP (3)
max,min, GiGiGi QQQ (4)
Bus voltage constraints
max,min, iii VVV (5)
Transmission line flow limits
max
ijij SS
(6)
TCSC reactance limit
max,min, TCSCiTCSCiTCSCi XXX (7)
The working range of TCSC is considered as follows.
lTCSCl XXX 2.08.0 (8)
where
TCSCX =TCSC reactance and lX =Reactance of the line
where TCSC is located.
III. OVERVIEW OF PARTICLE SWARM
OPTIMIZATION
PSO is a simple and efficient population-based
optimization method [16]. PSO simulates the behaviors of
bird flocking. It uses a number of agents (particles) that
constitute a swarm moving around in the search space
looking for the best solution. Each particle is treated as a
point, N dimensional space which adjusts its “flying”
according to its own flying experience as well as the flying
experience of other particles. Each particle keeps track of
its coordinates in the solution space, which are associated
with the best solution (fitness) that has achieved so far by
that particle. This value is called particle best (pbest).
Another best value that is tracked by the PSO is the best
value obtained so far by any particle in the neighborhood
of that particle. This value is called global best (gbest).The
basic concept of modification of a searching point by PSO
is shown in figure1.
Figure 1: Concept of modification of a searching point
by PSO
where, pkix current searching point,
1pk
ix modified
searching point,
pkiv current velocity,
1pkiv modified velocity,
ip particle best position
and g
kp global best position.
Current
Motion
Influence
Particle
Memory
Influence
Swarm
Influence
pkix
1pkix
1pkiv
g
kp
ip
pkiv
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 3
PSO is initialized with a group of random
particles (solutions) and then searches for optima by
updating generations. In every iteration, each particle is
updated by two "best" values such as pbest and gbest.
After finding the two best values, the particle updates its
velocity and positions using the following Equations (9)
and (10).
)()(
)()(
)(2
)(1
)()1(
uii
uii
ui
ui
PgbestrandC
PpbestrandCVwV
(9)
)1()()1(
ui
ui
ui VPP (10)
where
The term )()()(u
ii Ppbestrand is called particle
memory influence.
The term )()()(u
ii Pgbestrand is called swarm
influence. )(u
iV is the velocity of thi particle at iteration u must lie
in the range.
max)(
min VVVu
i (11)
The parameter maxV determines the resolution, or
fitness, with which regions are to be searched between the
present position and the target position. If maxV is too
high, particles may fly past good solutions. If minV is too
small, particles may not explore sufficiently beyond local
solutions [17]. The constants 1C
and 2C pull each
particle towards pbest and gbest positions. The inertia
weight w is considered as an important parameter for the
convergence of the algorithm. A large inertia weight
facilitates exploration, i.e., searching newer areas, while a
small value tends to facilitate exploitation, i.e., a finer
searching of current search area. Thus, the choice of
inertia weight should be carefully made. Suitable selection
of inertia weight provides a balance between global and
local explorations, thus requiring less iteration on average
to find a sufficiently optimal solution. In general, the
inertia weight w is set according to the following
equation [18].
iteriter
wwww
max
minmaxmax (12)
where
maxw Maximum value of weighting factor,
minw Minimum value of weighting factor, w inertia
weighting factor, maxiter maximum number of
iterations and iter current number of iteration.
IV. THYRISTOR-CONTROLLED SERIES CAPACITOR
(TCSC)
Thyristor-controlled series capacitors (TCSC) are
connected in series with the lines. The effect of a TCSC on
the network can be seen as a controllable reactance
inserted in the related transmission line that compensates
for the inductive reactance of the line. This reduces the
transfer reactance between the buses to which the line is
connected. This leads to an increase in the maximum
power that can be transferred on that line in addition to a
reduction in the effective reactive power losses.
The series capacitors also contribute to an
improvement in the voltage profiles. Figure2 shows a
model of a transmission line with a TCSC connected
between buses i and j. The transmission line is represented
by its lumped π-equivalent parameters connected between
the two buses. During the steady state, the TCSC can be
considered as a static reactance -jXC. This controllable
reactance is directly used as the control variable to be
implemented in the power flow equation.
This controllable series line compensation can be
applied to achieve full utilization of transmission assets by
controlling the power flow in the lines, preventing loop
flows and with the use of fast controls, minimizing the
effect of system disturbances thereby reducing traditional
stability margin requirements.
The basic idea behind series capacitive
compensation is to decrease the overall effective series
transmission impedance (Xeff) from the sending end to the
receiving end. The power transmission over a single line is
characteristics by the equation
sinX
VVP RS (13)
The effective transmission impedance Xeff, with
the series capacitive compensation is given by
Ceff XXX (14)
or XkX eff )1( (15)
where k is degree of series compensation, i.e.
XXk C ; Its values range between 0 and 1
The series compensator is primarily applied to
solve power flow problems. These problems may be
related to the length of the line or the structure of the
transmission network. The electric length of the line can
be shortened to meet power transmission requirements by
fixed (percent) compensation of the line.
Figure 2: Model of a TCSC
Network structure related problems, which
typically result in power flow unbalance, as well as
parallel and loop power flows, may require controlled
series compensation, particularly if contingency or
planned network changes are anticipated.
Fixed or controlled series capacitive compensation
can also be used to minimize the end-voltage variation of
radial lines and prevent voltage collapse. Series
compensation, appropriately controlled to counteract
prevailing machine swings, can provide significant
transient stability improvement for post-fault systems and
can be highly effective in power oscillation damping.
R+jX
jBc jBc
-jXC Sij
Bus i Bus j
Sji
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 4
V. OPTIMAL LOCATION AND SIZING OF TCSC
A. Optimal location and sizing of TCSC using LMP
difference method
Locational Marginal Price (LMP) is the marginal
cost of supplying, at least cost, the next increment of
electric demand at a specific location (node) on the
electric power network, taking into account both supply
(generation/import) bids and demand (load/export) offers
and the physical aspects of the transmission system
including transmission and other operational constraints.
LMP (nodal price) at bus i can be determined using
the following equation
j
jijfirefi SFXL )()( Re
(16)
ii congestionlossrefi (17)
where
i Nodal price at bus i; ref Nodal price at the
reference bus; iloss Marginal cost of losses from ref.
bus to bus i; icogestion Marginal cost of transmission
congestion from reference bus to bus i; iL Marginal loss
factor at bus i (∂Ploss/∂Pi); j Shadow price of
constraint j and jiSF Shift factor for real load at bus i
on constraint j.
The step by step procedure of LMP difference method for
optimal location of TCSC is given below.
Step 1: Run the base case OPF to calculate the LMP at all
buses and the power flow across all line sections.
Step 2: Determine the absolute value of the LMP
difference for all buses and prioritize the values
in descending order of magnitude to form LMP
difference table.
Step 3: From the LMP difference table, place the TCSC
in the lines, which have high values of LMP
differences one by one.
Step 4: Using PSO algorithm, the size of TCSC are
optimized for the selected lines in step 3. The
detailed steps of PSO algorithm are given in
section 5.2.
Step 5: TCSC are placed one by one until the congestion
on the test systems is relieved.
Step 6: Determine the LMP values at all buses and total
generation cost.
B. Optimal sizing of TCSC based on particle swarm
optimization
The step by step procedure of PSO algorithm for
optimal sizing of TCSC is given below.
Step 1: Initialize the PSO parameters such as the particle
size (NP), number of generations or iterations (G),
and number of variables to be optimized, limits of
each variable in the particle, acceleration
constants and inertia weight (w). NP =20, G=100,
Cognitive Constant C1=2, Social Constant C2=2
and w = 0.9-0.4.
Step 2: An initial population is randomly generated
considering the variables to be optimized.
Step 3: For each particle in the population, run Newton
Raphson power flow under base case conditions
for the test systems and evaluate the objective
function using equation (1).
Step 4: Find particle best )( pbest and global
best )(gbest .
Step 5: Increase the iteration count.
Step 6: A new population is created by changing the
velocity, position of the particle.
Step 7: Evaluate the objective function values for each
new individual.
Step 8: Update pbest and gbest values by comparing
current fitness values with local best and global
best values.
Step 9: Update pbest and gbest .
Step 10: If stopping criteria is satisfied, then the best
individual is obtained, otherwise repeat the
procedure from step 5.
VI. SIMULATION RESULTS
The simulation studies are performed on computer
having 2.27 GHz Intel 5 processor with 2 GB of RAM in
MATLAB environment [19]. The following two cases are
considered for the study.
A. 6-Bus System under Base Case Condition
The objective function in this case is to
alleviate the congestion in the network under base case
conditions thereby minimizing the total generation cost.
Form the power flow results of 6 bus system under base
case before placing TCSC, it is observed that the line 2-4
gets congested and total generation cost of 3143.97 $/h.
The locational marginal price (LMP)
value of different buses is shown in Table I. The LMP
difference values between buses are calculated and the
priority list based on LMP differences is shown in Table
II.
TABLE I LMP VALUES OF THE BUSES
Bus Number LMP
($/MWhr) Bus Number
LMP ($/MWhr)
1 12.4922 4 15.6741
2 11.5646 5 12.9389
3 11.8766 6 12.2062
TABLE II PRIORITY LIST BASED ON LMP DIFFERENCES
From i-j LMP Difference
($/MWhr) From i-j
LMP Difference
($/MWhr)
2-4 4.1095 5-6 0.7327
1-4 3.1819 2-6 0.6416
4-5 2.7352 1-5 0.4467
2-5 1.3743 3-6 0.3295
3-5 1.0623 2-3 0.3120
1-2 0.9276
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 5
Line flow data and constraint status before and
after placing TCSC is shown in Table III. The generation
cost convergence characteristics for optimal sizing of
TCSC by PSO is shown in Figure3.
TABLE III LINE FLOW DATA AND CONSTRAINT STATUS
From
i-j
MVA limit
without TCSC with TCSC
P (MW)
Q (MVAR)
Status P
(MW) Q
(MVAR) Status
1-2 40 15.41 -9.58 -1.98 -1.21
1-4 60 33.95 22.50 34.69 32.63
1-5 40 27.86 12.80 17.29 14.41
2-3 40 0.29 -11.76 -0.93 -11.52
2-4 60 41.74 43.11 yes 44.12 32.33 relieved
2-5 30 17.35 14.93 19.43 13.71
2-6 90 25.03 12.67 24.76 12.51
3-5 70 23.18 21.57 26.29 19.53
3-6 80 47.50 59.90 49.89 58.99
4-5 20 3.21 -4.71 6.32 -2.91
5-6 40 -0.90 -9.03 -3.01 -7.91
The optimal values of reactance of TCSC using
PSO are shown in Table IV.
TABLE IV OPTIMAL REACTANCE VALUES OF TCSC
From i-j Reactance of TCSC
2-4 -0.0200
1-4 -0.1095
Form the power flow results of 6 bus system
under base case after placing TCSC, it is observed that the
congestion is relieved on line 2-4 also the total generation
cost is reduced to 3124.07 $/h.
0 10 20 30 40 50 60 70 80 90 1003225
3230
3235
3240
3245
Iteration Number
Gen
era
tio
n C
ost
($
/h)
Figure 3 Generation cost convergence characteristics for the optimal TCSC size (6 bus system)
B. 30-Bus System Under Base Case Condition
The objective function in this case is to alleviate the
congestion in the network under base case conditions thereby
minimizing the total generation cost. Form the power flow
results of 30 bus system under base case before placing TCSC,
it is observed that the lines 6-8 and 25-27 get congested and
total generation cost of 576.89 $/h.
The locational marginal price (LMP) value of different
buses is shown in Table V.
The LMP difference values between buses are
calculated and the priority list based on LMP differences is
shown in Table VI.
TABLE V LMP VALUES OF THE BUSES
Bus
Number
LMP
($/MWhr)
Bus
Number
LMP
($/MWhr)
Bus
Number
LMP
($/MWhr)
1 3.6617 11 3.8232 21 3.8540
2 3.6891 12 3.8100 22 3.8425
3 3.7542 13 3.8100 23 3.8133
4 3.7709 14 3.8677 24 3.8844
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 6
5 3.7444 15 3.8561 25 3.9320
6 3.7791 16 3.8488 26 3.9987
7 3.8008 17 3.8625 27 3.9157
8 5.3827 18 3.9112 28 4.1058
9 3.8232 19 3.9262 29 3.9664
10 3.8462 20 3.9100 30 4.0508
TABLE VI PRIORITY LIST BASED ON LMP DIFFERENCES
From i-j LMP Difference
($/MWhr) From i-j
LMP Difference ($/MWhr)
6-8 1.6037 15-23 0.0428
8-28 1.2769 22-24 0.0419
6-28 0.3267 4-12 0.0391
28-27 0.1901 12-16 0.0388
27-30 0.1351 1-2 0.0274
1-3 0.0925 9-10 0.0230
2-6 0.0900 6-7 0.0217
29-30 0.0844 3-4 0.0167
2-4 0.0818 25-27 0.0164
23-24 0.0711 10-17 0.0163
6-10 0.0671 19-20 0.0162
25-26 0.0666 18-19 0.0151
10-20 0.0638 16-17 0.0137
12-14 0.0577 14-15 0.0116
5-7 0.0565 21-22 0.0114
2-5 0.0553 4-6 0.0082
15-18 0.0551 10-21 0.0078
27-29 0.0507 10-22 0.0037
24-25 0.0476 12-13 0
12-15 0.0461 9-11 0
6-9 0.0441
The optimal values of reactance of TCSC using PSO
are shown in Table VII. Line flow data and constraint status
before and after placing TCSC is shown in Table VIII. The
generation cost convergence characteristics for optimal sizing
of TCSC by PSO is shown in Figure4.
TABLE VII OPTIMAL REACTANCE VALUES OF TCSC
From i-j Reactance of TCSC
6-8 -0.0080
8-28 -0.0903
Form the power flow results of 30 bus system under
base case after placing TCSC, it is observed that the
congestion is relieved on lines 6-8 and 25-27 also the total
generation cost is reduced to 574.51 $/h.
TABLE VIII LINE FLOW DATA AND CONSTRAINT STATUS
From
i-j
Line
limit
without TCSC with TCSC
P
(MW)
Q
(MVAR) Status
P
(MW)
Q
(MVAR) Status
1-2 130 21.04 -2.34 22.44 -3.82
1-3 130 20.50 -3.10 21.35 2.49
2-4 65 18.63 -5.85 20.01 1.89
3-4 130 17.88 -3.22 18.73 2.65
2-5 130 14.36 -0.69 15.19 3.01
2-6 65 21.66 -4.21 23.41 4.44
4-6 90 17.58 5.68 19.64 11.64
5-7 70 14.25 0.96 15.08 4.92
6-7 130 8.70 8.46 7.86 4.24
6-8 32 23.82 21.37 yes 20.96 22.22 relieved
6-9 65 7.27 -8.27 8.28 -3.20
6-10 32 4.15 -4.73 4.73 -1.83
9-11 65 0 0 0 0
9-10 65 7.27 -8.54 8.28 -3.35
4-12 65 11.06 -15.24 11.24 -7.31
12-13 65 -16.20 -34.01 -17.34 -25.36
12-14 32 4.68 2.08 4.89 2.14
12-15 32 6.07 3.18 6.84 3.55
12-16 32 5.31 5.04 5.65 4.42
14-15 16 -1.55 0.41 -1.34 0.48
16-17 16 1.76 3.14 2.10 2.53
15-18 16 7.20 3.75 7.26 3.19
18-19 16 3.93 2.70 4.0 2.16
19-20 32 -5.58 -0.73 -5.51 -1.26
10-20 32 7.85 1.58 7.78 2.11
10-17 32 7.27 2.73 6.92 3.34
10-21 32 -4.43 -11.56 -3.19 -7.10
10-22 32 -5.06 -8.39 -4.29 -5.75
21-22 32 -21.97 -22.87 -20.71 -18.34
15-23 16 -10.92 -2.72 -10.0 -1.74
22-24 16 -4.46 2.59 -2.03 4.60
23-24 16 2.03 2.39 3.51 3.54
24-25 16 -11.18 -1.75 -7.28 1.38
25-26 16 3.54 2.36 3.54 2.36
25-27 16 -14.96 -4.52 yes -10.92 -1.15 relieved
28-27 65 -11.45 -21.09 -8.37 -9,39
27-29 16 6.16 1.65 6.16 1.65
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 7
27-30 16 7.10 1.63 7.10 1.64
29-
30 16 3.68 0.60 3.68 0.60
8-28 32 -6.29 -9.07 -9.13 -8.06
6-28 32 -5.05 -14.50 0.85 -4.35
0 10 20 30 40 50 60 70 80 90 1000
0.5
1
1.5
2
2.5x 10
4
Iteration Number
Gen
era
tio
n C
ost
($
/h)
Figure 4 Generation cost convergence characteristics for the optimal TCSC size (30 bus system)
VII. CONCLUSION
In this paper we reviewed LMP difference based optimal
location and PSO based optimal sizing of series FACTS
controller, Thyristor-Controlled Series Capacitor (TCSC) for
congestion management in deregulated power systems. The
proposed methodologies have been tested and examined on 6
bus system and 30 bus system under base case conditions. In
case 1, the proposed method relieves the congestion on line
2-4 and total generation cost is reduced from 3143.97 $/h to
3124.07 $/h. In case 2, the proposed method relieves the
congestion occur on lines 6-8 and 25-27. The Total generation
cost is reduced from 576.89 $/h to 574.51 $/h. The result
shows that the proposed methodology is capable of managing
the transmission congestion with minimum generation cost.
REFERENCES
[1] Kankar Bhattacharya, Math H.J. Bollen and Jaap E. Daalder
“Operation of Restructured Power Systems”, Kluwer Academic
Publisers, London, 2001.
[2] Mohammad Shahidehpour and Muwaffaq Alomoush,
“Restructred Electrical Power Systems Operation, Trading and
Volatility”, Marcel Dekkar Inc., Newyork, 2001.
[3] Ashwani Kumar and Charan Sekhar “DSM based Congestion
Management in Pool Electricity Markets with FACTS Devices”,
Energy Procedia, Elsevier, vol. 14, pp.94-100, 2012.
[4] Naresh Acharya and N. Mithulananthan “Locating series FACTS
devices for congestion management in deregulated electricity
markets”, Electric Power Systems Research, vol. 77, pp. 352-360,
2007.
[5] C.H.Venkaiah and D.M. Vinod Kumar “Fuzzy adaptive bacterial
foraging congestion management using sensitivity based optimal
active power re-scheduling of generators”, Applied Soft Computing,
vol. 11, no. 8, pp.4921-4930, 2011.
[6] J. Hazra and A.K. Sinha “Congestion management using multi-
objective particle swarm optimization”, IEEE Transactions on power
systems, vol. 23, no. 4, pp.1560-1569, 2008.
[7] A. Berizzi, M. Delfanti, P. Marannino, M.S. Pasquadibisceglie
and A.Silvestri, “Enhanced Security Constrained OPF with FACTS
Devices”, IEEE Transactions on power systems, vol. 20, pp. 1597-
1604, 2005.
[8] S.S.D. Thukaram, “Network congestion management by fuzzy
inference using virtual flows”, International conference on power and
energy systems, pp. 1-6, 2011.
[9] Panida Boonyaritdachochai, Chanwit Boonchuay and Weerakorn
Ongsakul, “Optimal congestion management in an electricity market
using particle swarm optimization with time-varying acceleration
coefficients”, Computers and Mathematics with Applications, vol.
60, pp. 1068-1077, 2010.
[10] Thanhlong Duong, Yao JianGang and Vietanh Truong, “A new
method for secured optimal power flow under normal and network
contingencies via optimal location of TCSC”, Electrical Power and
Energy Systems, vol. 52, pp. 68-80, 2013.
[11] Hadi Besharat and Seyed Abbas Taher, “Congestion
management by determining optimal location of TCSC in deregulated
power systems”, Electrical Power and Energy Systems, vol. 30, pp.
563-568, 2008.
[12] Masoud Esmaili, Heidar Ali Shayanfar and Ramin Moslemi,
“Locating series FACTS devices for multi-objective congestion
management improving voltage and transient stability”, European
Journal of Operational Research, vol. 236, pp. 763-773, 2014.
[13] Abouzar Samimi and Peyman Naderi, A new method for optimal
placement of TCSC based on sensitivity analysis for congestion
management, Smart grid and renewable energy, vol. 3, no. 1, pp. 10-
16, 2012.
[14] S. Rahimzadeh and M. Tavakoli Bina, “Looking for optimal
number and placement of FACTS devices to manage the transmission
congestion”, Energy conversion and management, vol. 52, pp. 437-
46, 2011.
[15] B.K. Panigrahi and V. Ravikumar Pandi, Congestion
management using adaptive bacterial foraging algorithm, Energy
Conversion and Management, vol. 50, pp. 1202-1209, 2009.
[16] J. Kennedy and R. Eberhart, “Particle swarm optimization”,
IEEE International Conference on Neural Networks, Perth, Australia,
vol. 4, pp.1942–1948,1995.
[17] H.Y. Fan, and Y. Shi, „Study on Vmax of particle swarm
optimization‟, Proceedings on Workshop Particle Swarm
Optimization, Indianapolis, Purdue School of Engineering and
technology, India, 2001.
International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016
All Rights Reserved © 2016 IJORAT 8
[18] J. Hazra and A.K. Sinha, „Congestion management using multi
objective particle swarm optimization‟, IEEE Transactions on Power
Systems, vol. 22, no. 4, pp. 1726-1734, 2007.
[19] R.D. Zimmerman, C.E. Murillo-Sanchez and R.J. Thomas,
„MATPOWR: Steady-state operations, planning and analysis tools
for power systems research and education‟, IEEE Transactions on
Power Systems, vol. 26, no. 1, pp. 12-19,2011.