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TRANSMISSION
CONGESTION MANAGEMENT
IN DEREGULATED POWER
SYSTEM OPERATIONS

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ABSTRACT
The success of deregulation in other areas galvanized the deregulation of the electricity
business. In a time span of past few decades, the deregulation of the electricity industry has
been scrutinized all around the world. In the era of the competitive electricity market, the
transmission systems are not progressing at a rate anticipated to sustain growing demand.
For that reason, due to the scarcity of transmission capacity in order to coordinate all
appeal for transmission serviceability within a region simultaneously leads to a situation of
congestion. Adding up to the above cause, transmission congestion also breezes in by the
virtue of unpredictable demand change or disruption of the transmission line. The state of
a system for which transmission system is not able to accommodate all power transactions
because of the restrictions on physical or operational constraints is exemplified as
congestion. One of the crucial responsibility of system operator is to alleviate congestion as
it intimidates system security and may account for the rise in electricity price. As a
consequence of which results in market inefficiency. This research work intends to put
forward a solution to the issue of transmission congestion management based on optimal
generation rescheduling. In order to minimize the congestion cost, the most favorable
number of generators are carefully picked based on generator sensitivity factor to carry out
active power generation rescheduling. The elementary stage of research work constitutes
and discusses diverse schemes of PSO viz. CPSO, TVIW- PSO, TVAC TVIW-PSO and
RANDIW-PSO. Active power generation rescheduling has been brought out on IEEE-39 and
IEEE-118 bus test systems considering the above specified PSO variants one at a time. The
competences of different PSO schemes have been estimated to pact with the challenge of
transmission congestion management in the pool electricity market model. Comparative
analysis reveals that RANDIW-PSO emerged out to be the most remarkable scheme among
the four and provided finest results. At the same time, as congestion management is an
optimization problem, there is a probability that certain control variables may attain a
value close to their upper or lower limits which result in lowered value of network voltage
stability margin after congestion management. Henceforth to counterbalance this adverse
effect of congestion management, making a way further in the research work, reactive
power generation rescheduling and reactive support from capacitors have been
incorporated with active power generation viii rescheduling. The reactive power
generation rescheduling has been directed in such a manner that it leads towards increased
reactive reserve available at generating buses by employing RANDIW-PSO. A noteworthy
decline was perceived in congestion cost along with the enhancement of the voltage
stability margin of the system. As a supplement, it was also observed that system losses

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diminished simultaneously. The magnification of reactive reserve and rise in the system
line charging were also the noticeable observations made.
Chapter 1: Introduction
1.1 Introduction
In recent years, the electricity industry has been shifted from vertically integrated system to a
deregulated system. This process is intended to open the power sector with the ultimate target of
reducing consumer prices. The central idea of deregulation is that the power generation market needs
to be fully competitive while the operation of the transmission system is likely to continue as a
regulated monopoly. The transition of electric power industry from regulated to deregulated has led
to several operational and supervisory issues regarding transmission grid operation and planning.
The main reason for this is the existence of a limited transmission network. In deregulated
environment, the transmission system has not progressed at the rate desirable to sustain growing
demand. This limited transfer capability can be increased by constructing new lines, but this is not as
easy as it may seem; difficulty in getting right of way permissions due to property devaluation and
capital cost involved in construction and maintenance of new lines. Moreover, the induction of
transmission system as a common carrier in open access environment, lead to intensive usage of
transmission grids. This has caused unanticipated bottlenecks like congestion, lowered voltage
stability margin and sub-optimal operations of the transmission network.
Congestion is defined as a state of the transmission system when it is not able to support
all transactions as a result of the limitation of physical or operational constraints. In other words,
congestion is a consequence of network constraints. The transmission lines capability to transmit
the electric power is constrained by many factors such as MVA limit, stability limit, and voltage
limits [1]. The power system is said to be congested when any of these factors reach to its limit.

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Congestion in transmission system may arise due to the lack of coordination between generation
and transmission utilities. In the congested situations, it is difficult to accommodate all desired
transactions on congested transmission network because it leads to violation of transmission
system operating limits. Hence, congestion in the transmission system threatens the system
security. Also, there is a loss of revenue at the time of congestion as a cheaper generation cannot
be delivered through the transmission network to the desired load because of transmission
constraints. In other words, the consequence of congestion is higher total production costs, just
as the case of decreased social welfare. The presence of congestion in transmission network can
significantly limit competition by creating pockets of the system that can only be served by a
limited number of generating sources. Sometimes congestion may result into loss of load due to
an outage of congested line. Therefore, it is the prime need for the system operator to manage
congestion and to operate the system securely.
1

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1.2 Transmission Congestion management
Congestion management has been referred as a comprehensive set of actions, principally
the re-dispatch of the generation and load levels so as to establish a system state without
constraint violations. Congestion management problem deals with optimal coordination between
GENCOs and DISCOs such that transmission line flows remain within operating limits. In
deregulated environment, GENCO is an owner as well as operator of generators. DISCO is
basically a distribution company that delivers power to the end users. Transmission congestion
management is the sole responsibility of system operator. The system operator is an entity
assigned with the accountability of determining the necessary actions to ensure that no violations
of the various grid constraints occur.
In a deregulated environment, the GENCOs and DISCOs plan their transactions ahead of
time (called scheduling). But by the time of implementation of transactions, there may be
congestion in some of the transmission lines due to an unexpected outage or unpredicted demand
change. As a first priority, system operator would try to manage congestion using cost-free
solutions. These solutions include the use of tap changing and phase shifting transformers and
adjusting controlling parameters of flexible AC transmission devices. If congestion is still not
relieved, the system operator will adopt non-cost free solutions like re-dispatch from generators,
reschedule of demand contracts and as a last option load shedding is used to manage congestion
and to retain system security. These solutions play a vital role to solve a transmission congestion
management problem. Other congestion management techniques are based on finding new
contracts that redirect flows on congested routes. Some system operators have also adopted the
zonal based congestion management approach wherein the grid is partitioned into a number of
preferred zones.
Congestion can be entirely relieved by generation rescheduling [2]. Centralized optimal
power flow is an efficient technique used by the system operator to manage congestion. Active
power generation rescheduling has been widely accepted congestion management methods. In
deregulated environment, the presence of bilateral and multilateral contracts has increased the
complexity of congestion management problem [3]. Moreover, congestion management is done
using optimization technique may result in reduced voltage stability margin after optimization.
Therefore, it is prime needed to manage congestion in a way that system remains secure after

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congestion management. From the voltage stability viewpoint, only active power generation
rescheduling based methods may not be sufficient to manage congestion and to operate system
securely. Our approach is to use active and reactive generation rescheduling for the combined
objective of voltage secured congestion management. Also, reactive support from capacitors is to
be considered to reduce congestion cost with increased voltage stability margin.2

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1.3 Motivation and Problem Definition
In competitive electricity markets, all participants including generators and demands try to
increase their profit. While doing so, it imposes more stress on the transmission network which
operates as a medium between generations and demands. It leads to congestion in a transmission
system which is the thorniest challenge for the system operator. Congestion in transmission
system prevents new energy transactions and if allowed they result into violation of network
constraints. Transmission congestion threatens the system security. During the congestion period,
generators are not able to deliver the generations to the load even though it has the generation
capability; the customers have the willingness to purchase but there is no feasibility in a
transmission system which results into loss of revenue.
Here, the aim is to develop a methodology for transmission congestion management using
active and reactive power generation rescheduling (a) to manage congestion (b) to reduce
congestion cost and (c) to improve system security while managing congestion in deregulated
power system operations.
1.4 Objectives ofthe thesis
Congestion in transmission network has been one of the challenging issues in deregulated
operation of power systems. It has attracted the attention of many researchers in recent time. In
this research, this issue has been addressed by using active and reactive generation rescheduling.
In non-cost-free congestion management methods, generation rescheduling is the first choice of
the system operator to manage congestion because it provides cost effective solution but with
compromising system security. As transmission congestion management is purely an optimization
problem wherein some constraints are satisfied which result in some variables reaching near to
their limits. Which in turn results in reduced voltage stability margin of the network after
relieving congestion. The objective here is to manage congestion in transmission system along
with improving system security.
Summarizing the objectives of the work:

To relieve congestion in transmission network



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
To provide cost-effective solution to transmission congestion management problem



To prevent violation of network constraints



To retain or enhance systemvoltage security



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Expected outcomes:

A novel OPF formulation for voltage stability margin perspective congestion
management


Cost-effective solution to transmission congestion management problem



Decreased systemlosses



Significant increase in voltage stability margin



Increased systemreactive power reserve



Improved systemvoltage profile.

1.5 Thesis Organisation
The objective of the study is fulfilled with the development of an algorithm for voltage
stability margin perspective transmission congestion management which is capable of alleviating
congestion along with retaining voltage stability margin of the network. The research contribution
of this thesis has been organized into seven chapters briefly summarized below.
Chapter 2 presents the literature survey on existing approaches to transmission congestion
management. The transmission congestion management methods in vertically integrated structure
and deregulated market structure have been critically analyzed. Conventional congestion
management methods and associated congestion cost calculation procedures have been reported
in detail. A brief review on pricing signal based congestion management has also been presented.
From all the methods reported, the most appropriate method for real time operation has been
identified.
Chapter 3 emphases on the various issues related to transmission congestion management
procedure and its consequences in deregulated environment. The role of independent system
operator for congestion relief has been deliberated. The overall congestion management
procedure in deregulated market has been discussed. As congestion management problem is

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purely an optimization problem, it may result in lowered voltage stability margin after
optimization. Henceforth, the need for congestion management considering voltage stability
margin is discussed at the end of this chapter.
Chapter 4 deals with generation rescheduling based solution methodology for transmission
congestion management. A general formulation of optimal power flow for generation
rescheduling is discussed with an objective function, constraints and control variables. In order to
minimize congestion cost, optimum selection of generators for rescheduling is discussed using a
sensitivity factor called GS. The solution of congestion management problem using four different
variants of particle swarm optimization has been explained in the subsections of the chapter.
4

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Chapter 5 describes the comparative study of generation rescheduling based congestion
management methodology using four different variants of particle swarm optimization (PSO)
viz. CPSO, TVIW-PSO, TVAC-TVIW PSO, and RANDIW-PSO. These variants have been
implemented and tested on two test systems viz. IEEE 39 bus and modified IEEE 118 bus test
systems in pool market model. The outcome of the implementation is depicted and discussed in
the chapter.
Chapter 6 proposes the solution methodology for voltage stability margin perspective
transmission congestion management. The reactive reserve maximization has been suggested
using optimum rescheduling of reactive generations. The modal analysis for determination of
generator and bus participation factors is also discussed in subsections of the chapter. Voltage
stability margin (VSM) calculation using P-V curve is discussed at the end of this chapter.
Chapter 7 describes the implementation of proposed methodology, voltage stability margin
perspective congestion management, on IEEE 39 bus test system. The proposed methodology has
been tested for four different realistic scenarios. Results obtained using proposed methodology have
been compared with three comparable methods in the subsection of the chapter.
Chapter 8 describes the application of proposed methodology on large power network
viz. IEEE 118 bus test system. The four realistic scenarios discussed in Chapter 7 are also
implemented on 118 bus system in this chapter. The comparative results at the end this chapter
shows the effectiveness of the proposed method on modified IEEE 118 bus test system.
Chapter 9 provides the concluding remarks from work carried out and also lists the
suggestions for future scope of research in the domain of transmission congestion management.

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Conclusion and future work
Summary of the research contribution The functional prospects of a deregulated power
system stance a number of difficult problems in the restructuring of the electricity market.
One of such circumstances has been looked upon in this thesis work. This work aims to
contemplate a methodology for transmission congestion management by means of active
and reactive power generation rescheduling. The congestion management problem has
been solved by the well-known optimization method known as PSO. The different variants
of PSO have been implemented and the best algorithm has been identified for this specific
problem. The work highlights a cost effective congestion management problem studied
from the view point of system reactive support from generators and capacitors to improve
VSM of the system considered. This allows the market operator to bid and trade along with
the system operator who ensures the system operations to be held within the security
constraints. The proposed methodology using RANDIW-PSO has been implemented on an
IEEE 39 and IEEE 118 bus test systems. To validate the potency of the proposed method for
the realistic operation, four scenarios have been considered viz. (a) base case operation (b)
peak load operation (c) bilateral transaction and (d) bilateral and multilateral transactions.
Results authenticate that the proposed active and reactive power rescheduling algorithm
turned out to be an effective way of reducing congestion cost and by maximization of the
reactive reserve, the VSM has also been enhanced. For the peak load period, the congestion
cost experience a slide increment. It was found that the impact of the presence of bilateral
and multilateral transactions aid to increase the complexity of the algorithm for congestion
management. Implying the proposed algorithm, due to its capability, it was able to resolve
the complexity with optimised congestion cost. Henceforth, to sum up, it can be concluded
that the generation rescheduling with reactive support from the generators and capacitors
can certainly contribute to manage congestion with a higher level of security. Also, one of
the chief advantages of the proposed algorithm being workability, that may be help full to
the system operator to manage transmission congestion in the pool as well as hybrid
electricity markets.
9.2 Future scope of work The proposed method for transmission congestion management.

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 network contains FACTS devices. The proposed method can assist to better utilization
and operation of FACTS devices. The active power demand can be integrated as a control
variable in rescheduling process to
 manage congestion. By adding automation features, the proposed algorithm can be
extended for online.

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