Congestion Management in a Deregulated Power

Congestion Management in a Deregulated Power System
by Rescheduling of Sensitive Generators and
Load Curtailment using PSO
S. R. Biswal & T. Kuanr
Department of Electrical Engineering, GITA, Bhubaneswar, India
Congestion exists in both new and traditional
systems but CM is more complex in case of
competitive power markets due to the unbundled
nature of the same, which calls for more
coordination. In the vertically integrated structure
,generation, transmission and distribution being
managed by one utility ,managing congestion
was relatively easier. Approaches used to manage
congestion vary with market structures. Several
techniques of congestion management have been
reported in [2].
Abstract – In a deregulated electricity market, CM is one of
the key functions of a System Operator (SO) as congestion
threatens system security and may cause rise in electricity
price resulting in market inefficiency.
Rescheduling generations and demands is an effective tool
to relieve congestion. This paper presents a congestion
management (CM) algorithm by optimal rescheduling of
active powers of generators and power consumption of
load. In addition to the rescheduling of
real
power
generation,
demand-side participation through load
curtailment has been considered to manage congestion.
Generator Sensitivity to the congested line and the costs of
generation and demand side adjustments are considered
while re-scheduling the generators and demands. The redispatch of transactions for congestion management in a
pool model is formulated as an Optimal Power Flow (OPF)
problem. Particle Swarm Optimization (PSO) is employed
to solve the OPF problem formulated. The proposed
method has been tested on IEEE-30 bus System and the
results show that the proposed technique is effectively
minimizing the cost of CM in alleviating congestion in the
transmission lines.
Out of the various approaches used for CM,
the most widely
used
is
generation
rescheduling.
Generators participate in the
congestion management market by bidding for
incrementing and
decrementing
their
production. Also, demands can bid as demand side
bidding (DSB) [3] for adjusting their loads.
However, it is crucial for SO to select the most
sensitive generators to re-schedule their optimal real
powers for congestion management. The CM
problem has two parts. The first part of the power
dispatch problem is to find out the preferred schedule
using Optimal Power Flow (OPF) and the
second part is rescheduling the generation and
demands for removing the congestion. The traditional
approach to OPF is to minimize the costs subject to
system security constraints. In the deregulated
market environment, the OPF problem aims at
maximizing social welfare based on generation
costs and the benefits to customers so as to achieve
optimal dispatch plan among operating units and
satisfy the system load demand in an economic
and reliable manner while respecting generator
operation constraints and line flow limits. The
objective of the second part is to minimize total
rescheduling cost.
Keywords – deregulation; congestion management; optimal
power flow; demand side bidding; sensitivity.
I.
INTRODUCTION
Increased volumes of power trade happening due
to the deregulation of electric power industry has led
to intensive usage of transmission network, which in
turn leads to more frequent congestion. Thus, one
of the most challenging problems in the operation
of restructured power systems is congestion
management. In a competitive power market, the
system is said to be congested when the producers
and consumers of electric energy desire to
produce and consume in amounts that would
cause the transmission system to operate at or
beyond one or more transfer limits [1].Congestion
may
result
in
preventing
new
contracts,
infeasibility in existing contracts, price spike in
some regions, market power abuse.
International Conference on Recent Innovations in Engineering & Technology, ISBN : 978-93-83060-46-7,19-20 April 2014, GITA, BBSR
200
Congestion Management in a Deregulated Power System by Rescheduling of Sensitive Generators and Load Curtailment using PSO
A literature survey on the application of
various evolutionary approaches to congestion
management reveals that researchers have not
attempted so far to consider Demand Side Bidding
(DSB) to relieve congestion in the overloaded
line(s).Z.X. Chen et al. [4] introduced PSO for
solving Optimal Power Flow (OPF)
which
deals with congestion management in pool market
and proved using IEEE 30 Bus system that
congestion relief using PSO is effective in
comparison with Interior Point Method and
Genetic Algorithm approach. J. Hazra and
Sinha
[5] proposed cost efficient generation
rescheduling and/or load sheddingapproach
for
congestion
management
in
transmission
grids using Multi Objective Particle Swarm
Optimization (MOPSO) method. S. Dutta and
Singh [6] proposed a technique for reducing
the
number
of participating generators and
optimum rescheduling of their outputs while
managing congestion in a pool at minimum
rescheduling cost and explored the ability of
PSO technique in solving congestion management
problem. Reference [7] proposes an optimal
congestion management approach in a deregulated
electricity
market
using
particle
swarm
optimization with
time-varying
acceleration
coefficients
(PSO-TVAC).
Venkaiah
and
D.M.Vinod Kumar [8] proposed fuzzy adaptive
bacterial
foraging (FABF) based congestion
management (CM) by optimal rescheduling of
active powers of generators selected based on the
generator sensitivity to the congested line. None of
the models have taken care of the role of demand
response in
congestion
management
in a
deregulated environment. So, the main objective of
this paper is to propose a model for congestion
management in deregulated power sector, with
demand side participation and solve the same using
PSO.
optimization problem to find the status of
transmission
congestion
under
the existing
transmission system condition. The second step is to
reschedule generation and load in a manner
that congestion is mitigated. The proposed method
has been tested on IEEE 30-bus system.
II. PROBLEM FORMULATION
The CM problem can be divided into two parts.
The first part of the problem is to find out the
preferred schedule using Optimal Power Flow
(OPF) and the second part is rescheduling the
generation and demands for removing the congestion.
A. Part 1
The objective for the first part is maximization of
social benefit which can be mathematically stated as
follows:
(1)
Where C g
and C d
are supply and demand
bids in $/MWhr, respectively; P and P are supply
d
and demand power levels ingMW, respectively.
Subject to
(2)
Where,
PD =Load Demand (MW)
P
L = Transmission loss (MW)
Pgi = Power output of ith generator (MW)
In this paper, a model for congestion
management that dispatches the pool, with
maximization of social benefit with all system
constraints, has been proposed. The bulk loads as
well as retailers are required to bid their maximum
demand and price. All generators are also required
to bid their supply price along with maximum
generation. The contribution of this paper is to
relieve congestion by rescheduling active power
outputs of generators based upon their sensitivity to
line flows in the overloaded line(s).In addition to
generators, demands are so participated into the
congestion market that the total rescheduling
cost is minimum. The proposed method has two
steps. The first step is to solve the social welfare
Where,
Pgimin =The lower bound on the active power output from
the ith generator.
Pgimax = The upper bound on the active power output
from the ith generator.
Qgimin =The lower bounds on the reactive power output
from the ith generator.
International Conference on Recent Innovations in Engineering & Technology, ISBN : 978-93-83060-46-7,19-20 April 2014, GITA, BBSR
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Congestion Management in a Deregulated Power System by Rescheduling of Sensitive Generators and Load Curtailment using PSO
Qgimax = The upper bound on the reactive power output
from the ith generator.
(5)
Where
.
Where
III. PARTICLE SWARM OPTIMIZATION
PSO(Particle Swarm Optimization) is a population
based stochastic optimization
technique
inspired
by social behavior of organisms such as bird flocking or
fish schooling, originally developed by Eberhat and
Kennedy in 1995[9].It optimizes a problem by trying to
improve a population of candidate solutions over several
generations.
International Conference on Recent Innovations in Engineering & Technology, ISBN : 978-93-83060-46-7,19-20 April 2014, GITA, BBSR
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Congestion Management in a Deregulated Power System by Rescheduling of Sensitive Generators and Load Curtailment using PSO
It provides a population based search procedure
in which individuals (or candidate solutions)
dubbed as particle change their position (state) with
time. The movement of the particles in the search
space is guided by its own experience and the
experience of a neighboring particle, making use of
the best position found by itself and the entire
swarm’s best known position. The PSO algorithm is
characterized by high speed of convergence and has
better exploration and exploitation provided by the
inherent combination of the local and global search
capabilities of the PSO algorithm.
(excluding the slack bus)to adjust their scheduled
power production are given in Table I . Increment
and decrement bids are assumed equal in this
work. Price bids of loads to adjust their power
consumption are given in Table II.
TABLE I.
PSO is initialized with a group of random particles
(candidate solutions) and then searches for optima by
updating the positions of these particles over generation.
In every generation, each particle is updated by
following two best values viz the best position(fitness) it
has achieved so far(pbest) and the best value obtained so
far by any particle in the population(called gbest or
global best).
PRICE BIDS OF GENERATORS FOR
POWER ADJUSTMENTS
Bus No.
C g ($/MWh)
2
13
5
12
8
11
11
11
13
12
Bus 1 is assigned as the reference bus. At
first market clearing happens based upon the
objective of maximizing social welfare. The
generation and demand schedule arrived at as a
result of market clearing causes congestion on
certain transmission lines. The details of which
are shown in Table III. The Generator
Sensitivities are computed for the congested lines
using (12).The GS values of 6 generation units in
the IEEE 30-bus system are shown in Table IV. In
the IEEE 30-bus system, it is observed that the GS
values of
all 6 generators are close
probably because of the system being very small. In
this case all the sensitivities are negative which
indicates increase in generation. The fact that all
generators show almost equal influence on the
congested lines necessitates that all the generator
buses are selected for the rescheduling to
alleviate the overload.
Based upon the knowledge of the two best values,
the particle updates its velocity and position, according
to the following equation:
represent the position and
velocity of the ith particle, respectively; d=1,2…,D
where D is the dimension of the search space;
i=1,2,…,N
where
N
is
the
size
of
the
population(swarm);w is the inertia weight used as
a parameter to control exploration and exploitation in
the search space;c1 and c2 are positive constant
parameters called acceleration coeffients;r1 and r2 are
uniform random numbers in [0,1].More details regarding
PSO can be found in [9].
TABLE II. PRICE BIDS OF LOADS FOR
POWER ADJUSTMENTS
Bus No.
3
4
5
7
8
12
15
IV. RESULTS
The IEEE 30-bus system has been used to
show the effectiveness of the proposed algorithm.
The IEEE 30-bus system consists of six generator
buses and 24 load buses.
To stress the system and create congestion, the
load at every bus is increased up to 1.3 times of
original load data. Price bids of generators
16
C d ($/MWh) Bus No.
23
19
21
23
23
24
22
30
24
23
21
C d ($/MWh)
24
24
23
23
21
International Conference on Recent Innovations in Engineering & Technology, ISBN : 978-93-83060-46-7,19-20 April 2014, GITA, BBSR
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Congestion Management in a Deregulated Power System by Rescheduling of Sensitive Generators and Load Curtailment using PSO
TABLE III. DETAILS OF CONGESTION
Congested Line
1-2
Power
Flow(MW)
357.32
Line
Limit(MW)
200
1-3
359.68
200
6-7
38.53
25
The following parameter setting is used for the
proposed PSO algorithm:
Population Size (N) = 50;
Initial inertia weight(wmax)=0.9; Final inertia
weight(wmin)=0.4; Maximum Iterations = 100;
Acceleration Constants c1=2,c2=2;
The methodology adapted provides the minimum
redispatch cost of $632.1346/h.
TABLE IV.GS VALUES FOR IEEE 30 BUS SYSTEM
GS Values
Congested
line
G2
G5
G8
G11
G13
1-2
-1.330
-0.754
-0.790
-0.881
-0.887
1-3
-0.307
-0.294
-0.308
-0.344
-0.346
6-7
-0.096
-0.165
-0.163
-0.139
-0.145
TABLE VI. RESCHEDULING FOR CM
Generator Rescheduling
It is observed that when the system is subjected to
this kind of stressed loading (1.3 times the base
load) re-scheduling of generator active power
outputs alone cannot not relieve congestion in the
lines completely.
However with simultaneous load adjustments
congestion is completely relieved. Thus demand
response plays a vital role to relieve the congestion in
such a case.
∆P1(MW)
0
∆P2(MW)
16.1181
∆P5(MW)
1.3145
∆P8(MW)
1.3770
∆P11(MW)
9.0461
∆P13(MW)
-0.0189
Load Adjustments
The PSO algorithm is employed to optimally
reschedule the active power of the generators and
power consumption of the load for relieving
congestion in the affected lines. For a larger system,
selected group of generators having the largest GS
values may be used to save the computational
effort. Table V depicts the cost of CM, power flows
on the congested line after rescheduling and the total
transmission loss.
The generators and loads
participating in congestion management and their
active power adjustments are presented in Table
VI.
∆P3(MW)
-0.0189
∆P4(MW)
-0.3120
∆P5(MW)
0
∆P7(MW)
-5.5010
∆P8(MW)
-2.5649
∆P12(MW)
-1.1504
∆P15(MW)
-0.0617
∆P16(MW)
-1.0305
∆P19(MW)
0
Approx. Cost Of CM($/hr)
632.1346
1107.7
∆P23(MW)
-0.8341
Power Flow on Previously
Congested Line(1-2) (MW)
Power Flow on Previously
Congested Line(1-3) (MW)
191.17
190.19
∆P24(MW)
-0.2895
194.10
195.22
∆P30(MW)
-1.1310
Power Flow on Previously
Congested Line(6-7) (MW)
24.14
135.95
Total Rescheduling(MW)
40.7686
Transmission Loss(MW)
14.8699
15.0328
TABLE V.RESULTS
International Conference on Recent Innovations in Engineering & Technology, ISBN : 978-93-83060-46-7,19-20 April 2014, GITA, BBSR
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Congestion Management in a Deregulated Power System by Rescheduling of Sensitive Generators and Load Curtailment using PSO
[4]
Z. X. Chen , L. Z. Zhang and J. Shu "Congestion
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pp.1019 ,2005.
[5]
J. Hazra, A.K. Sinha, ―Congestion management using
multiobjective particle
swarm
optimization,‖
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no.4, pp.1726–1734, 2007.
DSB is considered during both market clearing and
CM phases. The problem of congestion is
modeled as an optimization problem and solved
by particle swarm optimization technique. The
method has been tested on IEEE 30-bus system
successfully. Test results on the IEEE 30-bus system
prove the efficacy of the proposed approach in
managing transmission congestion in a deregulated
power system.
[6]
S. Dutta, S.P. Singh, ―Optimal rescheduling of
generators for congestion management based on
particle swarm optimization,‖ IEEE Transactions on
Power Systems,vol. 23,no.4, pp.1560–1569, 2008.
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market using particle swarm optimization with
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VI. REFERENCES
[8]
C. Venkaiah, D.M. Vinod Kumar, ―Fuzzy
adaptive bacterial foraging congestion management
using sensitivity based optimal active power rescheduling of generators,‖ Appl. Soft Comput. J.,
vol. 11, no.8,pp.4921-4930, 2011
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A. Kennedy and R. Eberhart, ―Particle Swarm
Optimization,‖ in Proc.IEEE Int. Conf. Neural
Networks, Nov. 29–Dec. 1 1995, vol. IV, pp.1942–
1948.
V. CONCLUSION
In this paper, the proposed congestion
management approach
based
on
PSO
is
efficiently minimizing the congestion management
cost. Redispatched generators are selected based on
GS. The present paper focuses on demonstrating
the role of demand side participation in CM.
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International Conference on Recent Innovations in Engineering & Technology, ISBN : 978-93-83060-46-7,19-20 April 2014, GITA, BBSR
205

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