Congestion Management in Deregulated Power by Rescheduling of Generators

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1690
Congestion Management in Deregulated Power by Rescheduling of
Generators
Durgesh Choudhary1, Dr. Niranjan Kumar2
1Dept. of EEE, NIT Jamshedpur, Jharkhand, India
2Associate Professor, Dept. of EEE, NIT Jamshedpur, Jharkhand, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – In the deregulated power system congestion
management is one of the most challenging tasks of System
Operator. . Due to congestion in the transmission lines, it is
not always possible to deliver all of the contracted power
transactions, where in both the buyers and sellers try to buy
and sell electric power so as to maximize their profit. System
Operators try to manage congestion, which otherwise
increases the cost of the electricity and also threatens the
system security and stability. To maintain the market
efficiency, it is very important that the congestion be
relieved in a fast, systematic and efficient manner.
One of the most practiced and an obvious technique of
congestion management is rescheduling the power outputs
of generators in the system. Generation sensitivity factor has
been used to identify the generators, which affects more on
the congested line. However, all generators in the system
need not take part in congestion management. Development
of sound formulation and appropriate solution technique for
this problem is aimed in this paper.
Key Words: Generator sensitivity factor, open power
flow, term Transmission Open-Access, Global best
position, Local best solution, Particle Position
1. INTRODUCTION
In deregulated environment, the term Transmission Open-
Access (TOA) indicates that the transmission network is
freely available to the other market participants such as
generators, customers, or other utilities that want to use
the transmission network for power transaction between
them and thus creates a situation in which transmission
network is not able to accommodate all the desired
transaction due to violations of some system constraint,
this is known as congestion. Congestion may be caused
due to various reasons, such as transmission line outages,
generator outages and change in energy demand. Increase
in power demand, unexpected outage of generation,
restriction on the construction of new lines, unscheduled
power flow in lines, tripping of transmission lines or
failures of other equipment are some of the potential
causes for congestion.
The literature survey reveals that various techniques have
been used to address the serious issues related to
Congestion management. The methods generally adopted
to manage congestion include rescheduling generator
outputs, supplying reactive power support or physically
curtail transactions. System operators generally use the
first option as much as possible and the last one as the last
resort. Several techniques of congestion management have
been reported in References [3]. The form of deregulated
electric power industry differs from country to country as
well as between different regions of a country. Different
models to deal the different transactions, interactions
between properties and limitations of the transmission
system and the economic efficiency of the energy market
have been mentioned in References [4]. Congestion
management techniques applied to various kinds of
electricity markets are presented in References [5].
Prioritization of electricity transactions and related
curtailment strategies in a system where pool and
bilateral/multilateral dispatches coexist is proposed in
References [6]. In References [7], congestion management
ensuring voltage stability is addressed. An optimal
topological configuration of a power system as a tool of
congestion management is presented in References [8]. A
corrective switching operation of transmission lines is
used instead of generation rescheduling to alleviate
congestion in this paper.
1.1 Impact of transmission congestion
Market efficiency, in the short term, refers to a market
outcome that maximizes the sum of the producer surplus
and consumer surplus, which is generally known as social
welfare. With respect to generation, market efficiency will
result when the most cost-effective generation resources
are used to serve the load. The difference in social welfare
between a perfect market and a real market is a measure of
the efficiency of the real market. The effect of transmission
congestion is to create market inefficiency.
Congestion affects virtually each market players either
in a positive or in a negative way. At the import side buyer
may suffer a decrease in consumer surplus because due to
transmission constraints it has limited access of energy
from other resources, therefore he has to buy energy from
the higher-price seller located at the import side. This lack
of choice causes either the higher payment prices or
reduced purchases of energy. Therefore, the buyer’s
consumer surplus decreases. However, a seller located at
the import side may offer his output at higher prices, and if

the demand is fixed and buyer can afford the price them
seller may sell more energy, resulting in an increased
producer surplus.
2. PARTICAL SWARM OPTIMIZATION
The Particle Swarm Optimization algorithm (abbreviated
as PSO) is a novel population-based stochastic search
algorithm and an alternative solution to the complex non-
linear optimization problem. The PSO algorithm was first
introduced by Dr. Kennedy and Dr. Eberhart in 1995 and
its basic idea was originally inspired by simulation of the
social behavior of animals such as bird flocking, fish
schooling and so on. It is based on the natural process of
group communication to share individual knowledge when
a group of birds or insects search food or migrate and so
forth in a searching space, although all birds or insects do
not know where the best position is. But from the nature of
the social behavior, if any member can find out a desirable
path to go, the rest of the members will follow quickly.
2.1 Basics of the PSO
The PSO algorithm basically learned from animal’s activity
or behavior to solve optimization problems. In PSO, each
member of the population is called a particle and the
population is called a swarm. Starting with a randomly
initialized population and moving in randomly chosen
directions, each particle goes through the searching space
and remembers the best previous positions of itself and its
neighbors. Particles of a swarm communicate good
positions to each other as well as dynamically adjust their
own position and velocity derived from the best position of
all particles. The next step begins when all particles have
been moved. Finally, all particles tend to fly towards better
and better positions over the searching process until the
swarm move to close to an optimum of the fitness function
The PSO method is becoming very popular because of its
simplicity of implementation as well as ability to swiftly
converge to a good solution. It does not require any
gradient information of the function to be optimized and
uses only primitive mathematical operators.
As compared with other optimization methods, it is faster,
cheaper and more efficient. In addition, there are few
parameters to adjust in PSO. That’s why PSO is an ideal
optimization problem solver in optimization problems. PSO
is well suited to solve the non-linear, non-convex,
continuous, discrete, integer variable type problems.
2.2 The PSO algorithm
The term particle refers to a member of population which
is mass less and volume less m dimensional quantity. It can
fly from one position to other in m dimensional search
space with a velocity. The fitness function in PSO is same as
the objective function for an optimization problem.
In real number space, each individual possible solution can
be represented as a particle that moves through the
problem space. The position of each particle is determined
by the vector and its movement by the velocity of the
particle given by
The information available for each individual is based on
I) its own experience (The decisions it has made so far
,stored in memory)
II) The knowledge of performance of other individuals in
its neighborhood.
The relative importance of these two information can vary
from one decision to other. A random weight is applied to
each part of the information and the velocity is determined
as
( )
Where,
=position of particle for iteration
= positive acceleration coefficients more than 1.0.
Normally its value is taken
Generally or .
= random numbers between 0.0 & 1.0.
= local best position for iteration
= global best position for iteration
Steps in PSO
The PSO method is explained as above. The
implementation of the algorithm is indicated below:
1. Initialize the swarm by assigning a random
position to each particle in the problem space as
evenly as possible.
2. Evaluate the fitness function of each particle.
3. For each individual particle, compare the particle’s
fitness value with it’s . If the current value is
better than the (previous) value, then set this
value as the and the current particle’s
position as .
4. Identify the particle that has the best fitness value
among all particles and corresponding position of
the particle as .
5. Update the velocity and positions of all the
particles using equations.
6. Repeat steps 1 to 5 until a stopping criterion is met
(e.g. maximum number of iterations or a sufficient
good fitness value).
7. Global best position gives the solution of the
problem.

3. CONGESTION MANAGEMENT PROBLEM
FORMULATION
Congestion Management by generator rescheduling
problem can be divided into two parts. Part I of the
problem is to identify the sensitivities of the generators
which contribute to the congestion of the line. Here a
branch is made out to create the congestion in other lines.
Part II of the problem is to reschedule the generators with
minimum congestion cost. The OPF base case solution is
the preferred solution as it is solved for lowest cost while
considering voltage and flow limit constraints
The generators in the system under consideration
have different sensitivities to the power flow on the
congested line. A change in real power flow in a
transmission line k connected between bus i and bus j due
to change in power generation by generator g can be
termed as generator sensitivity to congested line (GS).
Mathematically, GS for line k can be written as
….(1)
Where
= Real power flow on congested line-k
= Real power generated by the gth generator
The basic power flow equation on congested line can be
written as
+
….(2)
Where
= Voltage magnitude
= Phase angle at the ith bus
= Conductance
= Susceptance of the line connected between buses i and
j
Neglecting P-V coupling, (1) can be expressed as
= + ….(3)
The first terms of the two products in (3) are obtained by
differentiating (2) as follows:
= - + ( ) …(4)
= - ( ) ….(5)
= …(6)
The active power injected at a bus-s can be represented as
…(7)
Where is the active load at bus-s. can be expressed as
∑
∑
Where n is the number of buses in the system.
Differentiating w.r.t. and , the following relations can
be obtained:
∑
…(9)
Neglecting P-V coupling, the relation between incremental
change in active power at system buses and the phase
angles of voltages can be written in matrix form as
[ ]= [H][ ]
[H]=
[ ]
[ ]= [H]-1 [ P] …(10)
= [M][ P]
[M]= [H]-1
For finding the values of and [ ] need to be
find out. However, is a singular matrix of rank one
deficiency, So it is not directly invertible. The slack bus has
been considered as the reference node and assigned as bus
number 1. The elements of first row and first column of
[H]can be eliminated to obtain a matrix [ ] which can be
inverted to obtain matrix [ ], where [ ] represents a
matrix with its first row and column deleted or a vector
with the first element deleted.
Using these relations the following equation can be
obtained
[ ]=[ ][[ ] ….(11)
The actual vector [ ] can be found by simply adding the
element to as shown by the following relation
[ ]= [
[ ]
][ P]+ [ ] …(12)
The second term of the sum in (12) vanishes as , being
the change in phase angle of slack bus is zero. Accordingly,
(12) reduces to
[ ]= [
[ ]
][ P] ….(13)

Thus required elements of and are found out
from (13).
It is to be noted that the generator sensitivity values
thus obtained are with respect to the slack bus as the
reference. So the sensitivity of the slack bus generator to
any congested line in the system is always zero.
denotes how much active power flow over a
transmission line connecting bus-i and bus-j would change
due to active power injection by generator g. The system
operator selects the generators having non uniform and
large magnitudes of sensitivity values as the ones most
sensitive to the power flow on the congested line and to
participate in congestion management by rescheduling
their power outputs.
4. CASE STUDY
The PSO algorithm for congestion management, delineated
in the previous sections, has been implemented using
Visual C++ programming language. The performance of the
algorithm has been studied IEEE 30-bus systems. The
performance of the proposed method on IEEE 30-bus has
been compared with [15]. While using PSO to solve the
congestion problem, algorithm validity and influence of
different PSO parameters have also been studied.
4.1 IEEE 30Bus System
The IEEE 30-bus system consists of six generator buses and
24 load buses. Slack node has been assigned bus number 1.
Here line 1-3 is removed to create congestion in the
system. Two lines have been found to be congested, that
are between buses 2 and 1 and that between buses 6 and 2.
Power flow details of congested lines are given in the table
The values of generator sensitivities computed for the
congested line 2-1 are presented in Table 2.
Table -1: Power flow in Congested line in IEEE 30 Bus
System
Sl. No. From
bus
To bus Power flow
(MW)
Line
limit
(MW)
1. 1 2 170.43 130
2. 2 6 66.24 65
Table -2: Generator data of IEEE 30 Bus System
Sl.
No.
Bus
(MW) (MW)
GSF Status of Gen.
1. 1 184 360.2 0 Slack bus Gen
2. 2 56 140 -
0.8623
Participating
3. 5 25 100 -
0.9171
Participating
4. 8 15 100 -
0.9027
Participating
5. 11 20 100 -
0.9171
Participating
6. 13 10 100 -
0.9041
Participating
Table -3: Generator cost bids
Gen No. 1 2 5 8 11 13
Cost Bid
($/Mwh)
11 17 19 20 15 10
Table -4: Comparison of results for IEEE 30-bus system
Parameters Techniques
PSO Method used in
[15]
Total congestion cost
($/h)
1483.5 1560
Power flow (MW) on
line 1–2 after
Congestion
Management
111.36 115.42
Power flow (MW) on
line 2-6 after
Congestion
Management
53.89 54.79
(MW) -54 -58
(MW) 20.5 20.5
(MW) 11.5 14.5
(MW) 6 8
(MW) 8.5 9.2
(MW) 7.5 Not
participating
Total Power Generation
Rescheduled (MW)
108 110.2
Close values of sensitivities point out that the 30-bus
system is practically a very small system compared to a
realistic power network. All the generators show strong
influence on the congested line flow. This is because a small
system is generally very tightly connected electrically.
Thus, all the generators are chosen to participate in
congestion management.
The generator cost curves have been assumed to be
quadratic such that cost of rescheduling is proportional to
the square of the change in active power output as
represented in Table 5.8. Generators selected for
participation in congestion management are asked to
reschedule their outputs optimally on the basis of their
bids so that the cost of rescheduling gets minimized.
However the algorithm does not take into account the

change of nodal prices or generator bids at pre or post
congestion management situation.
Fig -1: Convergence profile of fitness function of PSO
For comparison purpose, the methodology for generator
selection proposed in [15] has been made use of using two
different values of MF, which are design variables that
regulate the number of generators taking part to be chosen
by the operator (as defined in [15]). Generators having
positive values of sensitivities are multiplied by their
respective bidding values and arranged in a decreasing
order according to the resultant product.
Generators having negative sensitivities are divided by
their bidding values and arranged in increasing order
according to the result. The top entry in each category
(positive and negative) multiplied by MF determines the
cut-off criteria for generators to be selected. MF varies
between 0 to 1, the higher its value, lesser is the number of
selected generators.
5. CONCLUSIONS
The present work focuses on demonstrating a technique
for optimum selection of generators for congestion
management. Generators from the system are selected for
congestion management based on their sensitivities to the
power flow of the congested line followed by corrective
rescheduling. The problem of congestion is modeled as an
optimization problem and solved using Particle Swarm
Optimization.
The method has been tested on IEEE 30-bus systems
successfully. The result obtained PSO is compared. Both
the results are quite satisfactory but Cost of rescheduling
is less in case of PSO. Thus it can be said that results
obtained from PSO is better . Rescheduling of generators
for congestion management is fruitful process as it
maintained the supplied quality, security of grid and also
taking care of the interest of the consumers without
shedding any load.
REFERENCES
[1] Sudipta 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, Nov. 2008.
[2] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J.
Thomas, "MATPOWER: Steady-State Operations,
Planning and Analysis Tools for Power Systems
Research and Education," Power Systems, IEEE
Transactions on, vol. 26, no. 1, pp. 12–19, Feb. 2011.
[3] A. Kumar, S. C. Srivastava, and S. N. Singh, “Congestion
management in competitive power market: A
bibliographical survey,” Elect. Power Syst. Res., vol.
76, pp. 153–164, 2005.
[4] Y. H. Song and I.-F. Wang, “Operation of Market
Oriented Power Systems”, New York: Springer, 2003,
ch. 6.
[5] K. L. Lo, Y. S. Yuen, and L. A. Snider, “Congestion
management in deregulated electricity markets,” in
Proc. Int. Conf. Electric Utility Deregulation and
Restructuring and Power Technologies, London, U.K.,
2000, pp. 47–52.
[6] R. S. Fang and A. K. David, “Transmission congestion
management in an electricity market,,” IEEE Trans.
Power Syst., vol. 14, no. 3, pp. 877–883, Aug. 1999.
[7] A. J. Conejo, F. Milano, and R. G. Bertrand, “Congestion
management ensuring voltage stability,” IEEE Trans.
Power Syst., vol. 21, no. 1, pp. 357–364, Feb. 2006.
[8] G. Granelli, M. Montagna, F. Zanellini, P. Bresesti, R.
Vailati, and M. Innorta, “Optimal network
reconfiguration for congestion management by
deterministic and genetic algorithms,” Elect. Power
Syst. Res., vol. 76, pp. 549–556, 2006.
[9] A.K. Singh , S.K. Parida, ”Congestion management with
distributed generation and its impact on electricity
market”, Electrical Power and Energy Systems, 48
(2013) 39–47.
[10] Adhip, D Thukaram,” Congestion Management Based
on Virtual Real Power Flows”, 2016 Biennial
International Conference on Power and Energy
Systems:Towards Sustainable Energy (PESTSE).
[11] S.Sivakumar, D.Devaraj, “Congestion Management in
Deregulated Power system by Rescheduling of
Generators Using Genetic Algorithm”, International
Conference on Power, Signals, Controls and
Computation (EPSCICON), 8 – 10 January 2014.
[12] Subhasish Deb, Arup Kumar Goswami, “Rescheduling
of Real Power for Congestion Management using
Cuckoo Search Algorithm”, 2014 Annual IEEE India
Conference (INDICON).
[13] Shaojun Huang, Qiuwei Wu, Lin Cheng, Zhaoxi Liu, and
Haoran Zhao, “Uncertainty Management of Dynamic
Tariff Method for Congestion Management in
Distribution Networks”, IEEE TRANSACTIONS ON
POWER SYSTEMS.
[14] Despina I. Koukoula and Nikos D. Hatziargyriou,
“Gossip Algorithms for Decentralized Congestion
Management of Distribution Grids”, IEEE
TRANSACTIONS ON SUSTAINABLE ENERGY.
[15] B. K. Talukdar, A. K. Sinha, S. Mukhopadhyay, and A.
Bose, “A computationally simple method for cost-

efficient generation rescheduling and load shedding
for congestion management,” Int. J. Elect. Power
Energy Syst., vol. 27, no. 5, pp. 379–388, Jun.–Jul.
2005.
[16] Ramesh Guguloth, T. K. Sunil Kumar, “LMP Calculation
and OPF Based Congestion Management in
Deregulated Power Systems”, 978-1-4673-8698-
2/16©2016 IEEE.
[17] F. Jian and J. W. Lamont, “A combined framework for
service identification and congestion management,,”
IEEE Trans. Power Syst., vol. 16, no. 1, pp. 56–61, Feb.
2001.
[18] H. Y. Yamina and S. M. Shahidehpour, “Congestion
management coordination in the deregulated power
market,” Elect. Power Syst. Res., vol. 65, no. 2, pp. 119–
127, May 2003.
[19] F. Capitanescu and T. V. Cutsem, “A unified
management of congestions due to voltage instability
and thermal overload,,” Elect. Power Syst. Res., vol. 77,
no. 10, pp. 1274–1283, Aug. 2007.
BIOGRAPHIES
Durgesh Choudhary
MTech, Power System student at
NIT Jamshedpur, Jharkhand
Dr. Niranjan Kumar
Associate Professor & Head
Department of Electrical &
Electronics Engineering
Ph.D IIT Roorkee(2010)
Photo

Congestion Management in Deregulated Power by Rescheduling of Generators

More Related Content

What's hot (17)

Similar to Congestion Management in Deregulated Power by Rescheduling of Generators (20)

More from IRJET Journal (20)

Recently uploaded (20)

Congestion Management in Deregulated Power by Rescheduling of Generators