This document summarizes research on approaches to managing congestion in deregulated electricity markets. It reviews various congestion management methods that have been proposed, including nodal pricing, price area congestion management, available transfer capability based approaches, using thyristor controlled phase shifting transformers, and flexible AC transmission systems devices. It also discusses optimization techniques that have been applied to congestion management problems, such as genetic algorithms and particle swarm optimization. The document provides examples of research on applying these different congestion management methods and optimization techniques to address transmission network congestion issues in deregulated power systems.

1. 2018 1st IEEE International Conference on Power Energy, Environment & Intelligent Control (PEEIC2018)
978-1-5386-2341-1/18/$31.00 ©2018 IEEE
Congestion Management Approaches in
Deregulated Electricity Market: A
Comprehensive Review of Outcomes,
Challenges and Opportunities
Divyanshi Srivastava
Department of Electrical Engineering
Madan Mohan Malaviya University of Technology
Gorakhpur (UP), India
srivas0511@gmail.com
Sudhir Kumar Srivastava
Department of Electrical Engineering
Madan Mohan Malaviya University of Technology
Gorakhpur (UP), India
sudhirksri05@gmail.com
Abstract—The electricity sector across the world is
experiencing a radical change in its business as well as in the
operational model. Restructuring of electric supply industry is
taking place at a faster rate and it has brought considerable
changes .Various transformations have been done in power
sector by which monopolistic electricity market is converted
into deregulated power market. Congestion is principal
problem that an independent system operator faces in the
deregulated environment. Moreover, open access transmission
network is one of the major causes for power congestion in
transmission lines. Tackling transmission network congestion is
a major challenge in post deregulated era. Congestion
management is a central issue in electricity supply industry.
This review work unites the various publications on congestion
management in past few years.
Keywords—Congestion, Market power, Conventional
Methods, Optimization
I. INTRODUCTION
Electricity supply industry has been undergoing a rapid
and irreversible change. In the restructuring process the
vertically integrated utilities are being unbundled and they
are opened up for the competition with private players. The
introduction of competition in the electric industry helps in
improving the overall efficiency. This has resulted into an
end to the era of monopoly in the power sector. The
deregulation process has taken a variety of formats in
different parts of the world and the reasons for adopting the
reforms by power industry vary from country to country.
Electric energy is not, a simple commodity unlike other
commodities traded in the market. This is because it has
some of the distinguishing characteristics such as it cannot be
stored in large quantities. Electricity can be transported on a
real-time basis, and in a manner heavily constrained by
myriad physical laws that are complicated in their
interactions but nearly instantaneous in their impact.
Restructuring of the electric power industry includes
several challenges such as choosing an appropriate auction
strategy for electricity, congestion management on
transmission lines, market efficiency and equilibrium [1],
mitigating market power [2]. There are fundamentally two
distinct sorts of congestion administration techniques
selected to handle clog in the transmission lines. The first is
cost- free means and the other is non- cost free means [3].
The cost- free means involves installment of phase shifting
transformer on transmission lines, redesigning the basic
topology of existing power system, out- aging of the
congested transmission line, and use of compensational
conventional FACTS devices. They are coined as cost- free
means because marginal costs (and not capital costs) usage
are minimal. In non-cost free methods we mainly consider
economy as one of the important objective function.
Various problems occurring due to congestion involves
reduction in market efficiency, consumers are forced to limit
the consumption of electricity (as electricity prices
increases), security concern may be affected, the stability of
the system reduces and system may collapse due to cascade
tripping .Congestion may occur due to generator outages,
uneven energy demand requirements and cumbersome
transactions. Different conventional methods and
optimization techniques has been presented for congestion
management.
II. CONGESTION MANAGEMENT METHODOLOGIES
A. Nodal Pricing Method
The nodal pricing method is basically used to model an
electricity market by keeping in mind the technical and
economical considerations such as generation limits of power
plants, cost functions of generators, demand elasticity and
optimize the whole electricity system. The basic principle of
nodal pricing method is that the producers basically focus on
the maximization of their respective payoffs by bidding
marginal costs. This method can be depicted as a completely
coordinated implicit auction. The nodal prices differ
according to various geographical locations and hence they
are named as Locational Marginal Prices (LMP).
In [4], M.B. Nappu performed an effectual nodal price
modeling in order to manage transmission issues due to
congestion in power network. The approach was basically
implemented using locational marginal pricing and optimal
flows of power using shift factor method. It was concluded
that an effective modeling helps in determining transmission

2. 2018 1st IEEE International Conference on Power Energy, Environment & Intelligent Control (PEEIC2018)
pricing which could produce economical signal whenever
congestion occurs in the transmission lines.
In [5], Rajesh Retnamony decided Locational Marginal
Pricing (LMP) of every generator at each transport by nodal
estimating strategy. The fundamental target work is the
assurance of Locational Marginal Price and amplification of
social welfare. LMP assumes a pivotal part in deregulated
power market. Our fundamental concentration is too kept up
LMP esteems as most reduced as could reasonably be
expected and for that important advances have been taken.
In [6], the main focus is the determination of Nodal
Congestion Prices (NCPs) using ANN soft computing based
technique. Under varying load condition, ANN is basically
trained to provide NCPs at respective node/bus. Multi layer
sustain forward system has been prepared utilizing
Levenberg-Marquardt (LM) calculation. It has been observed
that ANN training utilizing LM calculation is very quick and
the results can be directly accessed by market participants
before trading.
B. Price Area Congestion Management (PACM)
PACM is widely practised in the Nordic countries and in
India also. This type of congestion management technique is
basically supported in the bilateral and day ahead type of
markets [7]. When the congestion occurs on the transmission
line than it is predicted by system operator and the system is
split into predicted congestion areas. Generation or load each
has specific price area for which spot market bidders must
submit a separate bid. During the market settlements if there
is no congestion than price areas will not exist and the market
will settle at one price. If congestion occurs, than the prices
areas are separately settled at prices that should satisfy the
transmission constraints of that particular area of the power
system.
The prices are decided according to the generation costs.
The areas having cheaper generation costs will be having
lower prices and the areas having excess load will have
higher prices. An advantage of the market splitting method is
that new gencos may decide to add capacity in deficit zones
and thus more competition can be introduced and would help
in overall prices to decrease. A limitation associated with this
type of system is that it can be used only when physical
zones are connected in a radial fashion.
C. ATC Based Congestion Management
“ATC (Available Transfer Capability) of a transmission
network in power sector is unutilized transfer capacity that
is available for further transactions to the various market
participants”. Power transaction can be done between buyer
and seller when sufficient ATC is available. The market
members can be refreshed persistently about ATC through
the web based framework, for example, Open Access Same
Time Information System (OASIS).
ATC is defined as,
ATC = TTC –TRM – {ETC + CBM} (1)
TTC (Total Transfer Capability): It is characterized as the
greatest measure of energy which can be exchanged over the
system while fulfilling all security requirements.
TRM (Transmission Reliability Margin): It is characterized
as the measure of exchange capacity important to guarantee
that the interconnected transmission arrange is under a safe
scope of vulnerabilities.
ETC (Existing Transfer Commitments): It alludes to the
exchange ability of the framework that must be held for the
exchanges which are as of now dedicated.
CBM (Capacity Benefit Margin): It is the measure of
exchange ability by the heap serving substances to guarantee
access to generation from interconnected frameworks to
meet generation unwavering quality necessities.
With a specific end goal to fulfill the expanding interest
of energy exchanges and to keep up ordinary power
advertise activity adequate, power transmission capacity
ought to be kept up. A few techniques have been created to
upgrade ATC on existing transmission control networks.
In [8], the main focus is on ATC enhancement using
FACTS devices. Maximum power flow transfer can be
achieved by optimal power flow based ATC enhancement
Model using FACTS Control. From the results it has been
obtained that UPFC can enhanced the ATC to a large extent
by simultaneously maintaining the balance of line flow on
the transmission network and by regulating the node
voltage. Finally, it could be observed that the FACTS
devices help in boosting the transfer capability and their role
on ATC improvement is basically system dependent.
In [9], ATC is estimated for both base case normal
operating conditions, contingency cases involving single
line outage and generator outage with bilateral transactions
using Power Transfer Distribution Factor (PTDF), Outage
Transfer Distribution Factor (OTDF) and Generator Outage
Distribution Factor (GODF) methods respectively. ATC
applications help system participants in locating appropriate
locations for generation and trading various transactions.
The determination of ATC increases economic benefits in
competitive power markets. Solutions obtained after
estimation are quite encouraging and it is observed that the
ATC is estimated with very less computation time using
distribution factor method.
D. Congestion Management By Thyristor Controlled Phase
Shifting Transformer
The Thyristor controlled phase shifting transformer
device is a combination of thyristor and phase shifting
transformer. These transformers have a complex
transformation ratio. The phase shifting transformers
reduces the transmission losses of lines by controlling the
power flow. The mechanical tap changers are replaced by
power electronic device such as thyristor in order to increase
the speed of phase shifting transformers. The phase
difference across the terminals of phase shifting transformer
is absorbed by the series transformer (also known as
boosting transformer). Series transformers take the active as
well as reactive power to the transmission line which is
absorbed by a shunt transformer.
In [10], Amit Sharma and Ram Avtar Jaswal proposed
the implementation of TCPST on IEEE 9 Bus System to
solve congestion problem on transmission lines. PID
controller was used in order to control the opening and
closing time of thyristors in TCPST. It was observed that
total power flow is controlled up to a great extent.

3. 2018 1st IEEE International Conference on Power Energy, Environment & Intelligent Control (PEEIC2018)
In [11], Hossein Nasir Aghdam presented a scheme to
solve the congestion problem on transmission network and
improvement of voltage using phase shifting transformer. It
was observed that PST can be used for power flow
regulation by which the transfer capacity of the transmission
network can be increased.
E. Flexible AC Transmission System (FACTS) Device
FACTS Devices are used to enhance the maximum load
ability of transmission system. These devices improve the
active power level. ATC can also be improved by using
FACTS devices. ATC is an essential term as far as the
deregulated market is concerned, as it helps in the planning
and controlling the overall transmission system structure.
Main constraints for ATC are network voltage limits,
steady-state stability limits and the thermal limits. FACTS
devices provide new type of control strategies which are
relevant and appropriate both for steady and dynamic state
of the system.
FACTS devices when optimally placed mitigates the
problem of congestion on the network [12]. There are three
methods namely Line loss Sensitivity Indices (Method-I),
Total System Loss Sensitivity Indices (Method-II), Real
Power Flow PI Sensitivity Indices (Method –III) which can
be utilized for the optimal placement of FACTS Devices. In
[13], by solving Economic Dispatch Problem FACTS
devices can be optimally placed.
Unified Power Flow Controller (UPFC) is one of the
most versatile devices in FACTS family. In [14], UPFC was
optimally placed based on some existing method and the
effect of the UPFC has been shown on the formation of
congestion clusters/zones and reduction in the requirement
of the real power rescheduling for the congestion
management or energy management. The bids of the
generators, in the most sensitive clusters, have been utilized
to re-scheduling their real power outputs along with the
optimal setting of the UPFC parameters for the congestion
management.
Congestion management can be implemented by
optimization of loads and by the inclusion of FACTS
devices for optimal power flow. In [15], Nodal price based
sensitivity index is implemented for economic load dispatch
using optimal power flow method. By evaluating Nodal
price sensitivity factors congestion management is done on
the given system.
Thyristor Controlled Series Capacitor is a series FACTS
device. The TCSC idea is that capacitor is associated in
series with the transmission line and the thyristor controlled
inductor is specifically associated in parallel with the
capacitor. TCSC upgrades power transmission capacity,
improves the system stability, reduce losses present in the
system and enhance voltage profile of the lines. In [16], a
comparison of three different methods, namely minimization
of total real power loss based sensitivity index method,
minimization of real power flows based sensitivity index
method and the minimization of total reactive power loss
minimization based sensitivity method has been done and
finally the best optimal place of TCSC is found on the
transmission network. In [17], Naresh Acharya and N.
Mithulananthan presented two new techniques for the
position of FACTS devices to diminish congestion. The
general goal of FACTS device position can either be to limit
clog or to amplify social welfare. Ch. Rambabu et al. [18]
proposed a precise technique for multi type of FACTS
devices. This model is consolidated into a Newton Raphson
calculation in order to carry out the load flow analysis.
Constant load variation and voltage level changes occur
frequently in power system. System voltage deviation
reduction has been performed by allocating STATCOM
device and Fitness value comprising of real power loss and
total voltage deviation has been reduced by placing UPFC
device [19].
III. OPTIMIZATION TECHNIQUES
Congestion Management is basically a non linear
problem which could be solved by optimization techniques.
The commonly used optimization methods are Genetic
Algorithm, Particle Swarm Optimization, Artificial Bee
Colony optimization and Novel Flower Pollination
Algorithm.
A. Genetic Algorithm
Genetic Algorithm is computerized search and an
optimization technique derived from the concept of natural
selection and heredity. GA is based on Darwin’s Theory of
evolution that is “survival of the fittest”. Individuals having
the best genotype in the population participate for the next
generation. GA basically works on the population of
solution. The basic principle involved is to find fitness value
derived from the objective function of problem which is
assigned to each individual of the population. Analysis is
done on the population and the individuals having better
results are given top fitness values. In every iterative process
a new group of individuals with significantly improved
quality is generated. GA uses randomized operators.
In congestion management sometimes the target work is
non linear hence Genetic Algorithm method is used to obtain
the best global optimum solution [20]. In [21], generator
rescheduling approach is used to tackle congestion on
transmission lines and in order to identify minimal value of
rescheduling Genetic Algorithm approach is used. In order to
find out the generator which influences further the congested
transmission line in the system, generation sensitivity factor
should be computed. From the results it has been observed
that the proposed approach is efficient in solving the problem
of congestion on transmission network.
In [22], a Reconfiguration Algorithm which is based on
genetic algorithm will be find out the most congested area on
the network and focuses on least lost situation as well as
ensuring efficient condition of the system. It has been
observed that congestion management can be efficiently done
by Genetic Algorithm.
Pouria Maghouli et al. [23], proposed a static
transmission development technique utilizing a multi-target
advancement structure. To overcome the challenges in
settling the nonconvex and blended whole number nature of
the advancement issues, the hereditary based NSGA II

4. 2018 1st IEEE International Conference on Power Energy, Environment & Intelligent Control (PEEIC2018)
calculation is utilized followed by the fuzzy decision making
process.
B. Particle Swarm Optimization
Particle Swarm optimization (PSO) is a problem solving
technique based on the concept of social interaction. This
technique has been effectively applied to a wide variety of
optimization and search problems. In this technique, a
swarm of n individuals either directly or indirectly
communicate with each other in order to search directions
(gradients). This optimization is a powerful search
technique.
Sandeep Kumar and Dr.C.P. Gupta proposed a technique
for congestion in power network which is based on the
rescheduling of generators in the pool electricity model
without bilateral contracts and with one bilateral contract
and also comparison is done on the amount of rescheduling
in both the cases [24]. Generator sensitivities of the
participating generators present on the congested line has
been calculated. They proposed an optimization method
based on PSO which can minimize deviation of the
rescheduled values from the scheduled values of the
generator outputs. Inertia weight and particle size should be
properly selected in order to help in easy convergence on the
other hand improper selection of these parameters provide
inferior results. It has been observed that the proposed
algorithm helps in alleviating congestion on the power
network.
In [25], for optimal placement and sizing of DG Units
particle swarm optimization algorithm has been proposed in
order to tackle network congestion and minimize locational
marginal price (LMP) of various buses. The fundamental
target is to enhance the voltage and speculation costs. From
the results it has been observed that LMP differences
between the buses have been reduced and the voltage profile
got improved. Results demonstrate that DG units help in
diminishing the transmission line stacking.
In [26], congestion management has been done by using
PSO for optimal placement and sizing of unified power flow
controller (UPFC) FACTS device on the transmission
network. It has been found out that PSO algorithm helps in
allocating the optimal GENCOS and UPFC location on the
power network. The solution provided by the Newton
Raphson minimizes the mismatch of power flow equations.
From the results it can be concluded that it is a cost effective
method and reduces the total annual cost and helps in
improving the system voltage profile.
In the electrical power economy, profit maximization
and risk minimization are the two factors which govern the
competitiveness of the utilities in the market. This requires
an exact estimation of the Value at Risk. Hence a suitable
bidding strategy must be obtained by properly optimizing
the expected profit and the Value at Risk (VaR). In order to
determine optimal strategy Particle Swarm Optimization
(PSO) method can be considered to be a strategic optimal
bidding algorithm [27]. It is obtained PSO technique is the
most effective method for profit maximization and
congestion management
C. Artificial Bee Colony Optimization
The Artificial Bee Colony Optimization technique has
been derived by the by the wise rummaging conduct of
bumble bee swarm. Heavy current can flow in power
network due to unexpected contingency occurrence which
leads to a stressed condition causing congestion in the
transmission system. When heavy current flows in the system
it imposes high generation fuel cost. Therefore congestion in
transmission network should be properly managed in order to
minimize heavy flow of current.
In [28], Bee Colony optimization is applied for
contingency based congestion management on the power
system and also for cost minimization. In this research work,
this technique is applied to preserve the system security by
optimizing the flow of current in the system variables. N-1
contingency is utilized in order to forecast the event and it
has been executed offline so the framework execution can be
dissected.
In [29], a technique has been proposed in order to solve
the problem of congestion by bee colony optimization based
on generator rescheduling. Generators have been chosen on
the basis of Generator Sensitivity Factor (GSF) and then
optimization technique is utilized for rescheduling of those
selected generators. It has been observed that the solutions
based on ABC algorithm give better results and shows
system stability with less dispatch costs.
In [30], Dervis Karaboga and Celal Ozturk explained
Bunching investigation, utilized as a part of numerous
controls and applications, is an imperative device and an
enlightening errand trying to distinguish homogeneous
gatherings of items in light of the estimations of their
properties. In this work, ABC is utilized for information
grouping on benchmark issues and the execution of ABC
calculation is contrasted and Particle Swarm Optimization
(PSO) calculation and other nine methods from the writing.
D. Novel Flower Pollination Algorithm
Flower pollination occurs due to transfer of pollens
which are carried out by the pollinators such as insects,
birds, honeybees. About 90% pollination in blooming plants
is Biotic Pollination in which pollens are transferred by the
pollinators and the rest 10% pollination is of Abiotic
Pollination in which pollinators are not required [31]. The
main objective of flower pollination is to produce the
optimal reproduction of plants by surviving the fittest flower
among the flowering plants. Flower pollination algorithm is
basically inspired by the nature.
In [32], Sumit Verma and V. Mukherjee proposed
Flower Pollination Algorithm to tackle the problem of
congestion under deregulated environment. The main
objective of this algorithm is to relieve congestion problem
by means of rescheduling the real power output of
generators. From the results it has been observed that the

5. 2018 1st IEEE International Conference on Power Energy, Environment & Intelligent Control (PEEIC2018)
problem of congestion is solved up to a great extent and the
rescheduling costs are much reduced.
IV. CONCLUSION
An exhaustive and comprehensive review of congestion
management in deregulated electricity market has been
exhibited in this paper. Initially the analysis is done on the
different types of conventional methods generally used to
tackle congestion on the system. Relevant and essential
study is done on every topic. It has been observed that
optimization techniques play a major role in solving the non
linear and multivariable problem. Optimization algorithms
help in mitigating congestion in transmission lines. Latest
developments in the field of power sector and challenges
faced during congestion management are discussed. This
paper provides new techniques that can be further extended
for future research work.
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