International Journal of Computer Applications (0975 – 8887)
International Conference on Advances in Emerging Technology (ICAET 2016)
18
Congestion Management in Deregulated Power Market –
a Review
Shivam Sharma
GIMT, Kurukshetra
Haryana, India
Mohan Kashyap
IKGPTU, Jalandhar
Punjab, India
SatishKansal
BHSBIET, Lehragaga, Sangrur
Punjab, India
ABSTRACT
In a deregulated power market, the optimum power flow
(OPF) for an interconnected grid system is an important
concern as related to transmission loss and operating
constraints of power network. The increased power
transaction as related to increased demand and satisfaction of
those demand to the competition of generation companies
(GENCOs) are resulting the stress on power network which
further causes the danger to voltage security, violation of
limits of line flow, increase in the line losses, large
requirement of reactive power, danger to power system
stability and over load of the lines i.e. congestion of power in
system. It can be managed by rescheduling of generators or
optimal location of distributed generation (DG) at minimum
cost with minimum loss without disturbing the operating
constraints. This paper reviews some of congestion
management (CM) methods including the nodal pricing
method, differential evolution (DE), addition of renewable
energy sources, extended quadratic interior point (EQIP)
based OPF, mixed integer nonlinear programming, particle
swarm optimization (PSO), cost free methods and Genetic
Algorithm (GA). Each technique has its own significance and
potential for promotion of rescheduling of generators in a
deregulated power system.
Keywords
EQIP, FACTS, OPF, MINLP
1. INTRODUCTION
The restructuring of power system contains the paradigm shift
in power grids control activities. The deregulation of power
network has caused a large usage of the transmission grids.
Here mostly, power network operates at the rated capacity as
market players maximize the profit by using as much as of
existing transmission resources. The increased trend in the
number of contracts signed for the electricity market trades
and availability of less no. of transmission resources leads to
power network congestion [36]. Real-time transmission
congestion is considered as the operating condition for which
there is not sufficient transmission capability to employ all
traded transactions at same time due to few unexpected
contingencies. The congestion problem is increasing day by
day due to certain reasons:
Unexpected power flows in the large-scale transmission
due to reasons of higher service quality and lower
electricity prices.
Due to very small investment in the power networks to
meet the demand in deregulated power market.
• Fast change in power flows of power networks due to
integration of wind power into power transmission
networks.
Efficient congestion management is necessary for effective
operation of power market while congestion occurs. The
objective of managing the congestion is to take control actions
for relieving congestion of power transmission networks. As
per principle, congestion management can be viewed at the
different timescales, for example:
• Long-term transmission capacity planning that can be
done yearly, monthly, weekly or daily;
• Short-term scheduling of the transmission constraints in
day-ahead market; or
• Re-dispatching of the generation in real time balancing
market.
• Various methods for congestion management may be
broadly described under two domains [39]:
• Technical methods which take into consideration out-
aging of the congested lines, operation of Flexible
Alternating Current Transmission Systems (FACTS)
devices and operation of the transformer taps or phase
shifter.
• Non-Technical methods which take into consideration
nodal and zonal pricing [18, 22], counter trading [21], re-
dispatching [18], market splitting [21], auctioning and
load curtailment [27].
This paper reviews some of important methods and techniques
used for congestion management by optimal location of
rescheduled generators and their sizing in power system
networks.
2. CONGESTION MANAGEMENT
METHODOLOGIES
There are two basic paradigms that can be applied for
congestion management. These are cost-free means and not-
cost-free means. The cost-free means take into consideration
the actions like outages of the congested lines or operation of
the transformer taps, phase shifters, or FACTS devices. These
means are named as cost-free only due to reason that marginal
costs taken in their usage are nominal. The not-cost-free
means take into consideration the security-constrained
generations re-dispatch, network sensitivity factors methods,
congestion pricing and market-based methods, and application
of the FACTS devices [35].
This paper reviews some of important methods and techniques
used for congestion management by optimal location of
rescheduled generators and their sizing in power system
networks.
International Journal of Computer Applications (0975 – 8887)
International Conference on Advances in Emerging Technology (ICAET 2016)
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3. CONVENTIONAL OPTIMIZATION
TECHNIQUES
This section reviews conventional methods for managing the
congestion which include extended quadratic interior point
(EQIP) based OPF [4], promotion of renewable energy
sources [3], using Static Synchronous Compensator
(STATCOM) [5,6], Unified Power Flow Controller (UPFC)
[29] and mixed integer nonlinear programming (MINLP) [7].
Literature [4,17] has presented an improved method for
transmission line over the load alleviation in deregulated
power network using load shedding and FACTS devices.
Load shedding and FACTS devices are employed for
relieving the congestion by extended quadratic interior point
based OPF. According to reference [5], FACTS devices may
be an alternative to minimize the flows in the heavily loaded
lines causing an improved power capability, low system loss,
increased stability of network by controlling power flows in
the network. Modelling, simulation and analysis of a 5-bus
system using MATLAB is demonstrated in literature [5,6].
Simulation methods needed for both steady state and dynamic
operation of systems with FACTS devices UPFC [14, 26, 29]
and STATCOM [5, 6] are analyzed.
Another approach has proposed congestion management
methodology in a deregulated environment by using optimal
placement of thyristor controlled series compensators
(TCSCs) in the transmission network [7, 23, 26]. The location
of TCSCs in power network is decided by using the integer
variables; therefore formulation of the proposed problem
takes form of mixed integer nonlinear programming (MINLP)
problem. The optimization problem also achieves the
minimization of reactive power procurement cost paid to the
GENCOs for reactive power supplied in deregulated power
network. The comparison of proposed approach with existing
approaches concludes that by placing TCSCs on locations as
decided by proposed approach, congestion is managed
efficiently [17, 20]. An optimal model for managing the
congestion has been proposed with more concern to
promotion of renewable energy sources (RES) in deregulated
electricity market [3]. It developed an optimal model of
congestion management for the deregulated power network
that dispatches pool in combination with the privately
negotiated bilateral and multilateral contracts while increasing
social benefit. This model measures the locational marginal
pricing (LMP) based on the marginal cost theory [24].
4. ARTIFICIAL INTELLIGENT
TECHNIQUES
This section reviews artificial intelligent techniques for
example differential evolution, firefly algorithm, particle
swarm optimization, genetic algorithm, fuzzy system and
hybrid approaches.
SujathaBalaraman [2] has presented an algorithm for
managing the congestion in pool based power market with the
use of differential evolution. The aim of proposed work is to
avoid the deviations from transaction schedules resulting low
cost of congestion. Numerical results on IEEE 30 bus test
system are illustrated and compared with PSO for observing
the solution quality [34]. Different case studies results have
proved DE to be an efficient tool for managing the
transmission congestion in deregulated power market. S.M.H
Nabavi [1,26] has proposed genetic algorithm to obtain the
optimal generation levels in the deregulated environment. The
main concern is on congestion in the lines, which limits the
transfer capability of network with the available generation
capacity. Nodal pricing method is employed to calculate the
locational marginal price of each generator at each bus.
Simulation results on the basis of proposed GA and power
world simulator software are demonstrated and compared for
IEEE 30-bus system [29].
Literature [8, 13, 15, 34] has presented an algorithm for
managing the congestion in pool based power market based
on Particle Swarm Optimization. The proposed approach
efficiently relieves the line overloads with lower deviations in
generations from the initial market settlement. Security
constraints for example load bus voltages and lines loading
are efficiently handled in optimization problem [31].
Numerical results on two systems say modified IEEE 30 bus
and IEEE 57 bus test systems are demonstrated and compared
with random search method (RSM) and simulated annealing
(SA) method in terms of solution quality. The experimental
results show that PSO is one among challenging optimization
methods, which is indeed capable of providing the higher
quality solutions for proposed congestion management
problem.
The deregulated power market suffers from the problems
occurring in congestion management. FACTS devices may be
used to minimize the flows in loaded lines, causing increased
stability and low power loss in system [17]. Ushasurendra and
S.S Parathasarthy [10] have presented a fuzzy technique to
select optimal location of thyristor controlled series capacitor
to control the active power flows and for the reduction of
congestion in transmission line. Line utilization factors (LUF)
and real power performance index (RPPI) factor are utilized
to decide the level of congestion in transmission line [25].
Transmission congestion management is one of important and
critical tasks of system operator. Literature [11, 15, 16]
proposed a transmission congestion management algorithm by
optimal rescheduling of active powers of generators using
Firefly algorithm. The developed method has been tested on
IEEE 30 bus test system and results of many case studies have
been compared with that of SA and real coded genetic
algorithm (RCGA) methods [19]. Results conclude that firefly
algorithm is most capable of providing high quality solutions
for CM problem. N. Chidambararaj and K. Chitra [12] have
presented a combined technique to solve the problem of
congestion in transmission lines. For improving CM of
cuckoo search (CS) algorithm, artificial neural network
(ANN) is combined with CS algorithm. CS optimizes active
power changes of generator when transmission congestion
exists. Then, ANN is employed to predict generator
rescheduling according to transmission congestion [34]. Thus,
performance of CS algorithm is improved.
S. Thangalakshmi and P. Valsalal [9] have developed a hybrid
fish bee swarm optimization based algorithm to minimize the
congestion. Fish bee swarm optimization is developed by two
algorithms i.e. artificial bee colony (ABC) and fish school
search (FSS) methods. The proposed algorithm is tested on
IEEE 30 bus test system. Results show best performance of
proposed optimization to decrease the congestion. Reference
[33] solves CM model cost control problem in real-time
power systems. Various technical areas in power system need
simultaneous optimization of multiple and conflicting
objectives with complex non-linear constraints. Recent
research on multi-objective evolutionary methods has proved
that population-based stochastic algorithms are most
beneficial approaches for these types of problems [40].
Transmission congestion can largely limit less costly
generation units from being dispatched in the power system
operation. Optimal transmission switching acting as a
International Journal of Computer Applications (0975 – 8887)
International Conference on Advances in Emerging Technology (ICAET 2016)
20
congestion management tool is employed to change the
network topology which further would lead to more power
system market efficiency. The transmission switching (TS) is
formulated as an optimization problem to obtain most
influential lines as the candidates for disconnection [37, 41].
Locational marginal prices are utilized in the transmission
engineering mainly as near real-time pricing signals in
deregulated power system. Literature extends this concept to
the distribution engineering using formulation of distribution
LMP signal on the basis of power flow sensitivities in
distribution system. A Jacobean-based sensitivity analysis has
been proposed to apply in distribution pricing method [42]. In
a deregulated power market, when congestion exists in a
transmission line, it violates the security and increase the cost
of system. Generator rescheduling is one of ways adopted by
ISO to minimize the transmission congestion in deregulated
power market. Reference [43] developed an artificial bee
colony (ABC) based generator rescheduling for managing the
congestion. ABC algorithm is a new metaheuristic method
inspired by the intelligent foraging behaviour of the honeybee
swarm.
In literature [44], an evolutionary optimization technique
based methodology has been presented to sustain total
generation cost even in the contingent states of power network
for the consumer welfare. Congestion management is one of
technical challenges in the power system deregulation.
Reference [45] proposes the single objective and multi-
objective optimization approaches for the optimal choice,
location and size of TCSC and Static Var Compensators
(SVC) in deregulated power system to enhance the branch
loading (minimize congestion), reduce the line losses and
improve the voltage stability. Reference [46] presents an
approach for selecting the optimal locations and capacities of
multiple FACTS devices for relieving the congestion and
considering the voltage stability in deregulated power market.
Reference [47] develops the model of stochastic behaviour of
nodal prices of electrical power in the deregulated power
markets in USA. Congestion management is one of most
important issues for reliable and secure system operations in a
deregulated power market. Reference [48] proposes a
cost/worth analysis approach for the optimal location and
sizing of the distributed resources (DRs) to remove congestion
and improve security of system. As one of large operating
challenges in power market is to minimize the transmission
system congestion for its secure operation. Reference [49] has
addressed mainly the issue of congestion management
employing TCSC.
In power system, transmission lines congestion and usage of
FACTS devices are closely linked and it is significant due to
their role in the power delivery system improvement.
Reference [50] shows how GENCOS market power gets
advancements due to FACTS devices. Reference [51] presents
a Swarm intelligence based Optimization to minimize the
congestion in power system with transmission line overload.
The proposed algorithm utilizes a standard congestion
sensitivity Index to choose the congested lines in a large
power system network and optimizes congestion management
charge without any installation of FACTS devices and the
load curtailment.
5. CONCLUSION
In a fast changing deregulated power market, congestion
management has become critical issue. New challenges and
factors are focusing the evolution of newer methodologies. In
this paper, a review on congestion management methods and
techniques available in literature for recent years is presented.
An attempt has been made to give importance to all emerging
trends in Congestion Management. Table A1 (Appendix)
compares the different algorithms for congestion management
based on type of contingency using IEEE 30 bus test system.
Table A2 (Appendix) compares conventional congestion
management methods with different characteristics.
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7. APPENDIX
Table A1. Comparison of Different Algorithms used for Congestion Management Tested on IEEE 30-Bus System
S.
No.
Congestion Management
Methods
Type of
Contingency
Net Power
Violation
Real Power
after CM
(in MW)
CM Cost
($/MWH)
Change in
Active Power
(in MW)
References
1
Particle Swarm
Optimization (PSO)
Outage of line
1 – 2
23.393
-
538.95
23.906
[8]
2
Random Search Method
(RSM)
-
716.25
23.339
3
Simulated Annealing
(SA)
-
719.86
23.809
4
Real Coded Genetic
Algorithm (RCGA)
281.637
265.009
2737.2
110.957
[28]
5
Artificial Bee Colony
Algorithm (ABC)
129.95
2867.3
107.711
6
Simulated Annealing
(SA)
264.214
3672.7
112.737
[11]
7
Firefly Algorithm (FFA)
-
2350.24
22.009
8
Differential Evolution
(DE)
23.393
252.83
457.694
23.014
[2]
9
Neural Network –
Cuckoo Search (ANN –
CS)
Outage of line
10 – 17
-
248.79
149.745
124.21
[12]
10
Cuckoo Search
Algorithm (CS)
-
215.59
151.146
163.41
11
Particle Swarm
Optimization (PSO)
-
283.44
161.498
182.68