Optimal Allocation of TCSC and DG Unit for Congestion ... · IEEE 30 bus power systems network. The congestion was relieved; minimize losses and power production cost, the test system

International Journal on Electrical Engineering and Informatics - Volume 8, Number 2, June 2016

Optimal Allocation of TCSC and DG Unit for Congestion Management in

Deregulated Power Systems

Guguloth Ramesh and T. K. Sunil Kumar

Department of Electrical Engineering, National Institute of Technology,

Calicut, Kerala -671601, India

Abstract: Transmission network congestion is an important issue in deregulated power

systems, whenever the physical or operational constraints in a transmission network become

active, the system is said to be in a state of congestion. Congestion management (CM) is a

mechanism to prioritize the transactions and commit to such a schedule that will not overload

the network. This paper presents relieving transmission network congestion in two ways, one is

without disturbing the existing transactions by optimal placement of thyristor controlled series

capacitor (TCSC) and, another is rescheduling generators including optimal placement of

distribution generation (DG) unit. Congestion in a transmission system can result in very high

locational prices for electricity determined by marginal costs from optimal power flow (OPF)

based CM problems. The problem is solved by particle swarm optimization (PSO) and genetic

algorithm (GA) for fast convergence ratio and minimizes production cost; compare its results

with each other for finding a good economical solution method among them. The considerable

cost of TCSC and better performance of DG unit, it is necessary to find their optimal location,

where sensitivity methods are suggested to determine the optimal placement of these devices.

The proposed approach is tested on a standard IEEE 30 bus test system.

Keywords: CM, OPF, GA, PSO, TCSC, DG Unit, and Sensitivity Index Methods

1. Introduction

The electricity supply industries all over the world are shifting their electricity business in a

competitive environment with technical innovations expecting the reduction in the electricity

price and better customer focus [1]. In competitive electricity market congestion occurs when

the transmission network is unable to accommodate all of the desired transactions due to a

violation of system operating limits. The congestion problem frequently occurs in a

deregulated environment, when compared to its counterpart regulated environment [2].

The congestion management is a mechanism to prioritize the transactions and commit to such a

schedule, which would not overload the network. The congestion management schemes are

strongly coupled with the overall market design [3]. Efficient allocation of scarce transmission

capacity of the desired participants of the market is one of the main objectives of congestion

management schemes. Market-based solutions to congestion are deemed fairer as they

contribute better to economic efficiency than that of generation load curtailment, type of

contract and first come first serve [4]. A vast literature has been found, the transmission

congestion management in deregulated power systems, it involves defining a set of rules to

ensure control over generators and loads in order to maintain an acceptable level of system

security and reliability [5].

In an interconnected power system, the objective is to find the real and reactive power

scheduling of each generating unit in such way that it minimizes the operating cost [6]. It is

possible through optimal power flow (OPF) solution and the OPF method has been proposed

by Dommel and Tinny [7]-[8]. The OPF solution is obtained by both classical methods and

intelligent methods [9]. The basic principle for the transmission congestion management could

be illustrated with the help of the traditional spot pricing theory [10].

The OPF based congestion management problem, the objective function and constraints are

nonlinear and non-convex [11]-[12]. To solve such OPF problem classical techniques are

Received: July 10th

, 2015. Accepted: June 11st

, 2016

DOI: 10.15676/ijeei.2016.8.2.5

303

suffering with a slow convergence ratio not always seeking to the optimal solution, new

numerical methods are then needed to cope with these difficulties [13]. Intelligent methods

have high-speed convergence ratio and not being trapped with local minima, which can give

global OPF solution and good economical solution. The PSO and GA methods are intelligence

methods used for solving OPF based CM problem [14]-[17].

The thyristor controlled series capacitor (TCSC) is one of versatile FACTS devices; it is

possible to increase power transfer capability of the existing power transmission system at a

lower investment cost and shorter installation time compared to building the new additional

line [18]. The optimal location of TCSC can be placed based on the most negative real power

flow sensitivity index [19]. The Distributed Generation (DG) is power generation part of micro

grid; it generates power by utilizing renewable energy sources and located at the consumer site

[20]. The location of the DG unit can be placed based on transmission line relief (TLR)

sensitivity index, which is calculated through power transmission distribution factor (PTDF)

[21].

This paper presents an OPF based CM solution by intelligence method (PSO and GA) of

IEEE 30 bus power systems network. The congestion was relieved; minimize losses and power

production cost, the test system data's has been taken from [22]. The congestion management is

essential in deregulated power markets, where congestion management is tackled by the

independent system operator (ISO). The ISO is responsible for both market settlements

(including scheduling and dispatch) and transmission system management (including

transmission pricing and security aspects), where relieving congestion is one of the security

aspects. To relieve the congestion ISO can use mainly two types of techniques which are i)

Operation of FACTs devices, particularly series devices ii) Re-dispatch of generation and

curtailment of loads. In this paper the TCSC and DG units are used for congestion

management, the cost of TCSC and DG units are considered as a transmission price, where the

DGs are particularly used instead of load curtailment (the load curtailment equal to DGs power

generation), which is also one of the technique to relieve congestion. This DGs are belong to

IPPs even though its power will be bought and utilized for congestion management, where the

power producing cost of DGs will be included in transmission price.

2. OPF based congestion management problem formulation

The basic principle for the transmission congestion management could be illustrated with

the help of the traditional spot pricing theory. In this framework, the central dispatcher

optimally dispatches the generators such that the social welfare is maximized while satisfying

the operation and security related constraints. The OPF problem is to optimize the steady state

performance of a power system in terms of an objective function while satisfying several

equality and inequality constraints. The general OPF problem is formulated as follows

min 𝑓(𝑥) (1)

Subjected to 𝑔(𝑥) = 0, ℎ(𝑥) ≤ 0 (2)

where 𝑓(𝑥) is objective function, 𝑔(𝑥) & ℎ(𝑥) are equality and inequality constraints. The

objective function for the OPF reflects costs associated with generating power in the system.

The fuel cost of generator 𝑖 can be represented as a quadratic function of real power

generation.

𝐶𝑃𝐺𝑖= 𝑎𝑖 + 𝑏𝑖𝑃𝐺𝑖 + 𝑐𝑖𝑃𝐺𝑖

2 (3)

where 𝑃𝐺𝑖 is the amount of generation in MW at each generator 𝑖. The objective function of

the entire power system is written as the sum of the quadratic cost model at each generator.

𝑓(𝑥) = 𝐶𝑡 = ∑ (𝑎𝑖 + 𝑏𝑖𝑃𝐺𝑖 + 𝑐𝑖𝑃𝐺𝑖2)

𝑁𝐺𝑖=1 (4)

Guguloth Ramesh, et al.

304

where 𝐶𝑡 & 𝑁𝐺 are the total cost of power generation and no. of generating unit, 𝑎𝑖, 𝑏𝑖&𝑐𝑖 are

cost coefficient of 𝑖𝑡ℎ unit in the system. The equality constrains 𝑔(𝑥) is power generation

equal to demand plus losses in the system.

𝑃𝐺𝑖 − 𝑃𝐷𝑖 = ∑ |𝑉𝑖||𝑉𝑗||𝑌𝑖𝑗| 𝑐𝑜𝑠(𝛿𝑖 − 𝛿𝑗 − 𝜃𝑖𝑗)𝑁𝐵𝑗=0 (5)

𝑄𝐺𝑖 − 𝑄𝐷𝑖 = ∑ |𝑉𝑖||𝑉𝑗||𝑌𝑖𝑗| 𝑠𝑖𝑛 (𝛿𝑖 − 𝛿𝑗 − 𝜃𝑖𝑗)𝑁𝐵𝑗=0 (6)

where 𝑁𝐵 is no. of buses, the power flow equations between 𝑖𝑡ℎ to 𝑗𝑡ℎ bus are given (5)&(6).

The inequality constraints ℎ(𝑥) is power generation, voltage and transmission limits.

𝑃𝐺𝑖,𝑚𝑖𝑛 ≤ 𝑃𝐺𝑖 ≤ 𝑃𝐺𝑖,𝑚𝑎𝑥 (7)

𝑄𝐺𝑖,𝑚𝑖𝑛 ≤ 𝑄𝐺𝑖 ≤ 𝑄𝐺𝑖,𝑚𝑎𝑥 (8)

𝑉𝑚𝑖𝑛 ≤ 𝑉𝑖 ≤ 𝑉𝑚𝑎𝑥 (9)

𝑇𝑖𝑗 ≤ 0 (10)

where 𝑃𝐺𝑖 & 𝑄𝐺𝑖 are real and reactive power generation limits, 𝑉𝑖 is voltage magnitude at 𝑖𝑡ℎ

bus. 𝑇𝑖𝑗 Is the bilateral transaction between 𝑖𝑡ℎ to 𝑗𝑡ℎ bus? The OPF based CM problem has

been solved by intelligent methods (PSO and GA).

3. Particle swarm optimization (PSO) and genetic algorithms (GA) methods

Particle Swarm Optimization (PSO) is a heuristic search method inspired by the social

model of bird swarms and fish schooling. PSO is designed for the solution of nonlinear

problems with continuous variables, which is developed by Kennedy and Eberhart in 1995.

Each individual, which corresponds to a candidate solution, is referred as a particle in a

multidimensional search space. The particles in the search space adjust their location and

velocity according to their own experience and the experience of neighbors, the theoretical

equations and algorithms of this method is given [14]-[15]. The PSO based approach has been

implemented using MATLAB 2010 program coding. Initially, several runs have been done

with different values of the PSO key parameters such as initial inertia weight and maximum

allowable velocity. In our implementation, the initial inertia weight 𝒘 = 𝟎 and the number of

intervals in each space dimension 𝑵 are selected as 1.0 and 10 respectively. Other parameters

are selected as: number of particles 𝒏 = 20, decrement constant ∝= 0.98, 𝒄𝟏 = 𝒄𝟐 = 2, and

the search will be terminated if (a) the number of iterations since the last change of the best

solution is greater than 20; or (b) the number of Iteration reaches 50.

Genetic algorithm (GA) is a general purpose optimization algorithm based on the

mechanics of natural selection and genetics. They operate on string structures (chromosomes),

typically a concatenated list of binary digits representing a coding of the control parameters

(phenotype) of a given problem. Chromosomes themselves are composed of genes. The real

value of a control parameter, encoded in a gene, is called an allele. GA is an attractive

alternative to other optimization methods because of its robustness and all derivations are given

in [16]-[17]. GA is a stochastic search method; it is difficult to formally specify convergence

criteria. As the fitness of a population may remain static for a number of generations before a

superior individual is found, the application of convergence termination criteria becomes

problematic, a common practice is to terminate GA after a pre-specified number of generations

or iterations (in our case the number of generations or iteration is 50) and then test the fitness

of the best members in the last population. If no acceptable solutions are found, the GA may be

restarted or fresh search initiated.

Optimal Allocation of TCSC and DG Unit for Congestion

305

4. Optimal Placement of TCSC and DG unit

A. Modelling and optimal placement of TCSC

The effects of FACTS devices like TCSC on the network can be seen as a controllable

series reactance inserted in the related transmission line. Fig.1 shows modeling of TCSC in a

transmission line, which is connected between 𝑖𝑡ℎ to 𝑗𝑡ℎ bus.

Figure 1. Modeling of transmission line with TCSC

During steady state the TCSC can be considered as a static capacitor/ reactor offering

impedance 𝑋𝑇𝐶𝑆𝐶 . The capacitive reactance of TCSC is obtained from the equation (11) is

given by

𝑋𝑇𝐶𝑆𝐶 = 𝑥𝑐

(1−𝑥𝑐

𝑥𝑡𝑐𝑟) (11)

where 𝑥𝑐 & 𝑥𝑡𝑐𝑟 are the capacitive and inductive reactance of the TCSC. The working range of

TCSC is considered to be ± 0.5𝑋𝐿, where 𝑋𝐿 is total line inductive reactance before connecting

TCSC. The controllable reactance 𝑋𝑇𝐶𝑆𝐶 is directly implemented in the power flow equations.

The real and reactive power flow equations are given below, when TCSC inserted in branch 𝑘

which is connected between 𝑖𝑡ℎ to 𝑗𝑡ℎ bus.

𝑃𝑖𝑗𝑐 = 𝑉𝑗

2𝐺𝑖𝑗𝑐 − 𝑉𝑖𝑉𝑗 [𝐺𝑖𝑗

𝑐 cos 𝛿𝑖𝑗 + 𝐵𝑖𝑗𝑐 sin 𝛿𝑖𝑗 (12)

𝑄𝑖𝑗𝑐 = − 𝑉𝑗

2(𝐵𝑖𝑗𝑐 + 𝐵𝑠ℎ) − 𝑉𝑖𝑉𝑗 [𝐺𝑖𝑗

𝑐 sin 𝛿𝑖𝑗 − 𝐵𝑖𝑗𝑐 cos 𝛿𝑖𝑗) (13)

where 𝐺𝑖𝑗𝑐 =

𝑟𝑖𝑗

𝑟𝑖𝑗2 +(𝑋𝐿−𝑋𝑇𝐶𝑆𝐶 )

2 & 𝐵𝑖𝑗𝑐 =

−(𝑋𝐿−𝑋𝑇𝐶𝑆𝐶 )

𝑟𝑖𝑗2 +(𝑋𝐿−𝑋𝑇𝐶𝑆𝐶 )

2 are conductance and suceptance.

Due to high cost of FACTS devices, it is necessary to use cost benefit analysis to analyze

whether the new FACTS device is cost effective among several candidate locations where they

actually installed. The TCSC cost in line- 𝑘 is given by

𝐶𝑇𝐶𝑆𝐶(𝑘) = 𝑐 . 𝑥𝑐 (𝑘). 𝑃𝑘2 . 𝑏𝑎𝑠𝑒 𝑝𝑜𝑤𝑒𝑟 (14)

where 𝑐 is the unit investment cost of FACTS, 𝑥𝑐 (𝑘) is the series capacitive reactance and 𝑃𝑘

is the actual power flow in line- 𝑘. The objective function for placement of TCSC will be

𝑓(𝑥) = 𝐶𝑡 + 𝐶𝑇𝐶𝑆𝐶(𝑘) (15)

where 𝐶𝑡 is the system power production cost without TCSC. The optimal placement of TCSC

based on real power flow sensitivity index (PI) approach. The severity of the system loading

under normal and congested cases can be described by a real power line flow performance

index, as given below

𝑃𝐼 = ∑𝑤𝑘

2𝑛

𝑁𝐿𝑘=1 (

𝑃𝐿𝑘

𝑃𝐿𝑘𝑚𝑎𝑥)

2𝑛

(16)


306

where 𝑃𝐿𝑘 is real power flow and 𝑃𝐿𝑘𝑚𝑎𝑥 is the rated capacity of line 𝑘, 𝑛 is the exponent, 𝑤𝑘

real non-negative coefficient which may be used to reflect the importance of the lines and 𝑁𝐿

is the no. of lines in the system. The exponent has been taken 2 and 𝑤𝑘 =0. The location of

TCSC device can be based on static or dynamic performance of the system. The sensitivity

factor methods are generally used to find the best location to enhance the static performance of

the system. PI will be small when all the lines are within their limits and reach a high value

when there are overloads. Thus, it provides a good measure of severity of the line overloads for

a given state of the power system, where TCSC should be placed in the line having highest

negative PI sensitivity value.

B. Optimal placement of DG unit

Optimal placement of DG unit is determined by Transmission line relief (TLR) sensitivities

approach. TLR sensitivities can be considered as the inverse of power transfer distribution

factors (PTDFs) [23]. Both TLR and PTDFs determine the sensitivity of the flow on a line of

power generating with the DG unit at respect bus. TLR sensitivity values at all the load buses

for the congested lines are considered, and used for calculating the necessary power generating

with the DG unit by rescheduling generation for the alleviation of transmission line congestion.

The TLR sensitivity at load bus 𝑚 with respect to congested line 𝑘 (between 𝑖𝑡ℎ to 𝑗𝑡ℎ bus) is

𝑆𝑖𝑗𝑚 given by

𝑆𝑖𝑗𝑚 =

∆𝑃𝑖𝑗

∆𝑃𝑚 (17)

∆𝑃𝑖𝑗 = 𝑃𝑖𝑗 − 𝑃𝑖𝑗,𝑚𝑎𝑥 (18)

where ∆𝑃𝑖𝑗 is excess power flow in line 𝑘 , and 𝑃𝑖𝑗 and 𝑃𝑖𝑗,𝑚𝑎𝑥 are actual power flow and

maximum power flow limit of transmission line 𝑘. The new real power injection with DG at

load bus 𝑚 is 𝑃𝑚𝑛𝑒𝑤 can be calculated by

𝑃𝑚𝑛𝑒𝑤 = 𝑃𝑚 −

𝑆𝑖𝑗𝑚

∑ 𝑆𝑖𝑗𝑙𝑁𝐿

𝑙=0

∆𝑃𝑖𝑗 (19)

where 𝑃𝑚𝑛𝑒𝑤 is the actual real power requirement at load bus 𝑚, it can be generated with DG

unit. 𝑃𝑚 is available real power before DG connect, 𝑆𝑖𝑗𝑙 sensitivity of power flow on line 𝑘 due

to power generation at load bus 𝑚, 𝑁𝐿 the total number of load buses having with DG units in

the system. The lower the TLR sensitivity is more the effect of a single MW power transfer at

any load bus, where optimal placement of the DG unit has been considered the load bus has

highest negative TLR sensitivity value.

5. Simulation Results

The proposed methods have been demonstrated on IEEE 30 bus test system. The test

system analyzed under normal and congested operating condition, where congestion occurs due

to overload and line outages. Fig.2 shows IEEE 30 bus test system, bus-1 has been taken as a

reference bus with its adjacent voltage 1.06 (p.u). It has six generators, 41 transmission lines

and 21 loads and the total demand of the system is 283.40 MW. The system data are taken

from [22] and generation data is given in Table 1.


307

Figure 2. IEEE 30 bus test systems

Table 1. IEEE 30 Bus System Operating Cost

Gen. No.

𝑎𝑖 ($/ℎ)

𝑏𝑖( $ 𝑀𝑊ℎ)⁄

𝑐𝑖 ($ 𝑀𝑊ℎ⁄ )2

Gen. 1 100 2.00 0.00375

Gen.2 60 1.75 0.01750

Gen. 3 40 1.00 0.06250

Gen. 4 30 3.25 0.00834

Gen. 5 25 3.00 0.02500

Gen. 6 20 3.00 0.02500

DG Unit 10 0.95 0.00100

Figure 3. IEEE 30 bus test systems OPF solution by PSO


308

The test system’s OPF problem is formulated and its solutions are obtained by PSO and

GA under normal operating condition. The OPF solutions with PSO and GA are shown in

fig.3 and fig.4, where demand and line constraints of test systems is same in both cases. The

comparison is made between two methods for obtaining good economical solutions between

them, where 50 iteration has been taken in both cases.

Figure 4. IEEE 30 bus test systems OPF solution by GA

Figure 5. IEEE 30 bus test system voltage profile

Figure 5 shows the voltage profile of the system under normal operating condition, where

the voltage at each bus is within specified limits (0.9 to 1.1.p.u). Both PSO and GA methods

are given a good voltage profile, there is no voltage stability problem. The detailed OPF

solutions with PSO and GA are tabulated in Table 2.

0.99

1

1.01

1.02

1.03

1.04

1.05

1.06

1.07

1.08

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

PSO GA

Vo

ltag

e M

agn

itu

de

(p.u

)

Buses


309

Table 2. IEEE 30 Bus System OPF solution by PSO and GA

OPF

Method

Gen.1

(MW)

Gen.2

(MW)

Gen.3

(MW)

Gen.4

(MW)

Gen.5

(MW)

Gen.6

(MW)

Total

Gen.

(MW)

Total

Load

(MW)

Total

Loss

(MW)

Total

Cost ($/hr)

PSO 159.94 52.76 22.15 26.60 15.01 15.14 291.60 283.40 8.20 1078.94

GA 159.83 54.40 22.93 25.93 12.58 13.24 288.91 283.40 5.53 1068.96

Table 2 shows, the generator dispatched are all most same in both the OPF solutions,

when compared the total loss and generation cost it’s been a little bit different. Compare

solutions between PSO and GA, the total cost of the system with PSO is 1078.94 and with GA

is 1068.96($/hr). There is savings 9.98 ($/hr) in solution with GA method, so that it has been

chosen among them for further calculations, this is one of the advantages to minimize cost and

losses of the system.

A. IEEE 30 bus test system congested due to overload

In restructured power system one of the major problems is network congestion, this

problem occurs in two ways, one is system overload and another is lined outages. This section

deals with transmission congestion is due to overload, where the congestion has been created in

the system by increase demand of 25 % at each load bus (.i.e. increase in demand is from

283.40 to 354.25 MW). Fig.6 shows system congested due to overload (which was obtained

by the PLP method in the power world simulator, where this figure was taken for visible

purpose only, but OPF based CM solution has been obtained by GA) in line-5 (buses 2-6).

From the OPF based CM solution, it has been found that the MVA limit of the line-5 was

1.177(p .u), which is more than that of its MVA line loading limit 1.00 (p .u). It will be

relieved by optimal placement of TCSC or DG unit.

Figure 6. IEEE 30 bus test systems congested due to overload

The table 3 the first column shows line numbers which have been found negative PI

sensitivity index values ( column 3) w.r.to TCSC out of 41 lines in the system, where line-

4(bold type) has highest negative value than that of other lines. The TCSC should be

incorporating line-4, and OPF based CM solution obtained with TCSC.

From OPF based CM solution, it is observed that, congestion has been relieved in line-5

and the reactance of TCSC was found (𝑥𝑇𝐶𝑆𝐶 =0.0671 p.u ). Where the MVA rating on line-5


310

decreased from 1.177 to 0.9577 (p.u), transfer power capacity of the line-4 is increased from 55

to 60 % and total power production cost is decreased from 1377.07 to 1374.23 ($/h). The cost

difference between system without and with TCSC is 2.84 ($/hr), which is the cost of the

TCSC of relieving congestion and maintain the system in stable.

Table 3. IEEE 30 Bus PI and TLR Sensitivity under Overload Condition

Line No.

Buses

PI w.r.to TCSC

Bus No.

TLR w.r.to DG Unit

3 2-4 -1.8923 4 -0.6512

4 2-5 -2.0561 6 -0.1231

7 4-6 -1.3974 7 -1.0948

8 4-12 -0.9987 28 -0.9432

9 5-7 -0.7213 30 -1.3089

The table 3, the fourth column shows bus numbers which have been found negative TLR

sensitivity index values ( column 5) w.r.to DG unit out of available DG units in the system,

where bus-30 (bold type) has highest negative TLR index value than other buses. The DG unit

should be placed at bus-30, and OPF based CM solution obtained by rescheduling including

with DG unit. The OPF based CM solution has been obtained considering 6 MW DG unit, it

observed that congestion is relieved in line-5, where the MVA rating on line-5 is decreased

from 1.177 to 0.9866 (p.u) and total power production cost is decreased from 1377.07 to

1363.88 ($/h). The cost difference between system without and with the DG unit is 13.19

($/hr), which is the cost of minimization with the DG unit as well as congestion has been

relieved.

B. IEEE 30 bus test system congested due to line outages

In restructured power system one of the major problems is network congestion, this

problem occurs in two ways, one is system overload and another is lined outages. This section

deals with transmission congestion is due to line outages, where the line-13 (buses 6-10) is

taken out of the system due to a fault. The OPF based CM solution has been obtained without

line-13 at same demand 283.4 MW.

Figure 7. IEEE 30 bus test systems congested due to line outages


311

Figure 7 shows system congested due to line outage (which was obtained by the PLP

method in the power world simulator, where this figure was taken for visible purpose only but

OPF based CM solution has been obtained by GA). It was found that line-17 (buses 9-10) has

been congested, where the MVA limit of the line-17 was 1.051(p.u) which is more than that of

its line loading limit 1.00 (p.u). It will be relieved by optimal placement of TCSC or DG unit.

Table 4. IEEE 30 Bus PI and TLR sensitivity under Line Outages Condition

Line No.

Buses between

PI w.r.to TCSC

Bus No.

TLR w.r.to DG Unit

8 4-12 -1.401 10 -0.2047

10 6-7 -1.775 17 -0.4138

25 12-16 -3.546 20 -0.9801

29 16-17 -2.872 21 -0.8612

40 28-27 -2.008 22 -0.6745

The table 4 the first column shows line numbers which have been found negative PI

sensitivity index values ( column 3) w.r.to TCSC out of 41 lines in the system, where line-

25(bold type) has highest negative value than that of other lines. The TCSC should be

incorporating line-25, and OPF based CM solution obtained with TCSC. From OPF based CM

solution, it is observed that, congestion has been relieved in line-13 and the reactance of TCSC

was found (𝑥𝑇𝐶𝑆𝐶 = 0.092 p.u ). Where the MVA rating on line-17 decreased from 1.051 to

0.9577 (p.u), transfer power capacity of line-25 is increased from 63 to 72 %, and total power

production cost is decreased from 1068.96 to 1067.23 ($/h). The cost difference between

system without and with TCSC is 1.73 ($/hr), which is the cost of the TCSC of relieving

congestion and maintain the system in stable.

The table 4 the fourth column shows bus numbers which have been found negative TLR

sensitivity index values (column 5) w.r.to DG unit out of available DG units in the system,

where bus-20 (bold type) has highest negative TLR index value than other buses. The DG unit

should be placed at bus-20, and OPF based CM solution obtained by rescheduling including

with DG unit. The OPF based CM solution has been obtained considering 4 MW DG unit, it

observed from the results that congestion was relieved in line-17, where the MVA rating on

line-17 is decreased from 1.051 to 0.9762 (p.u) and total power production cost is decreased

from 1068.96 to 1062.81 ($/h). The cost difference between system without and with the DG

unit is 6.15 ($/hr), which is the cost of minimization with the DG unit as well as congestion has

been relieved.

From section 5 A&B, where system analyzed congestion in one line and it relieved by

placing TCSC and DG unit. It’s also possible multi line congestions and it can be relieved by

more than one TCSC or DG unit and combined TCSC and DG unit if required, all the results

are obtained from the MATLAB GA toolbox and its evaluated with Power World Simulator.

6. Conclusion

Congestion management is an important issue in deregulated power systems. The standard

IEEE 30 bus has been taken as a test system in a deregulated environment. The OPF based CM

problem has been formulated and it is solved by both PSO and GA methods under normal

operating conditions. Compared its results in selecting the best economical solution method for

minimization losses and cost of the system, where the solution with GA gave best economical

results for minimizing system operating cost. Optimal placement of TCSC and DG unit has

been used for relieving congestion and minimize cost of the system, optimal placement was

suggested by PI and TLR sensitivity methods. The system has been analyzed in congestion due

to overload and line outage conditions, where congestion has been relived and system

maintained secure state.


312

7. Acknowledgement

This work has been supported by National Institute of Technology Calicut for progress

review of PhD work in year June 2015.

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Ramesh Guguloth is currently a Research Scholar in Electrical Engineering

Department, National Institute of Technology, Calicut, Kerala-673601,

India. He completed a B. Tech (Electrical Engineering) at Christu Jyothi

Institute of Technology and Science under JNTU Hyderabad in the year

2008, M. Tech (Power Systems) at National Institute of Technology Calicut

(India) in the year 2011. His interested areas in Research are Power Systems,

Restructuring Power Systems, Flexible AC Transmission System, and Micro

grid.

T. K. Sunil Kumar, Assistant Professor, Electrical Engineering Department,

NIT Calicut, Kerala -673601, India. He completed a B. Tech in Electrical

Engineering at N.S.S College of Engineering, Palakkad, M. Tech in

Electrical Engineering at NIT Jamshedpur, and PhD. From IIT Kharagpur.

His interested areas are Model Matching Controller Design Methods with

Applications in Electric Power Systems, Restructuring Power Systems,

Flexible AC Transmission System, and Micro grid.


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Optimal Allocation of TCSC and DG Unit for Congestion ... · IEEE 30 bus power systems network. The congestion was relieved; minimize losses and power production cost, the test system

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