Using UPFC and IPFC Devices Located by a Hybrid Meta-Heuristic Approach to Congestion Relief

doi:10.4236/epe.2013.57051

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Energy and Power Engineering, 2013, 5, 474-480

http://dx.doi.org/10.4236/epe.2013.57051 Published Online September 2013 (http://www.scirp.org/journal/epe)

Using UPFC and IPFC Devices Located by a Hybrid

Meta-Heuristic Approach to Congestion Relief

Hamid Iranmanesh*, Masoud Rashidi-Nejad

Islamic Azad University, Jiroft Branch, Jiroft, Iran

Email: *[email protected]

Received March 19, 2013; revised April 19, 2013; accepted April 26, 2013

mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work

is properly cited.

ABSTRACT

This paper proposes new methodology for the placement of FACTS devices in transmission systems to reduce conges-

tion. Congestion management comprises congestion relief and congestion cost. The traditional approach to remedying

congestion lies in reinforcing the system with additional transmission capacity. Although still feasible, this approach is

becoming more and more complex and it is often challenged by the public [1]. Congestion relief can be handled by us-

ing FACTS devices, where transmission capability may be improved. Congestion relief using FACTS devices requires a

two step approach: first, the optimal location of these devices in the network and then, the settings of their control pa-

rameters. UPFC and IPFC have full dynamic control on the transmission parameters, voltage, line impedance and phase

angle. Real Genetic Algorithm (RGA) optimization technique is used to solve this congestion relief problem while ana-

lytical hierarchy process (AHP) with fuzzy sets is implemented to evaluate RGA fitness function. The results are ob-

tained for modified IEEE 5 bus Test System.

Keywords: Congestion Relief; RGA; AHP; Fuzzy Sets; UPFC; IPFC

1. Introduction

Electricity industry restructuring and reregulation may

dictate maximum power transfer using the existing facili-

ties under transmission open access scheme. Procuring

electricity contracts associated with market participants’

requirements can cause more challenges considering en-

ergy management systems. Reregulation will impose new

necessities to power systems such as transmission open

access as well as non-discrimination access to the infor-

mation. Transmission congestion management is an im-

portant mechanism in order to solve power transfer bot-

tleneck both in the operation and planning horizons [2].

There are two issues with regards to applying transmis-

sion open access that should be considered: the so-called

transmission losses and transmission congestion. Con-

gestion is dependent on the network constraints that may

show the ultimate transmission capacity, while it can

restrict the concurrent electric power contracts [3].

It can be said that, under congestion conditions the

price of transferring electricity will be increased. In fact,

congestion management is an overall as well as in par-

ticular systematic way of improving electricity transfer in

which power systems planning and operating can be re-

garded.

Transmission congestion is dealing with some restric-

tions of electricity transfer via transmission network.

These restrictions are increased in the presence of open

access considering electricity restructuring environment

[4]. Under new conditions of power market, more con-

straints such as economical, environmental problems and

transmission rights as financial contracts will be added to

technical limitations of transmission capacity [2]. Con-

gestion relief is such a solution to release some blocked

capacity of transmission network. In literature, there are

some techniques suggested to increase the available

transfer capability (ATC). Among the proposed solutions

for ATC enhancement, the use of FACTS devices is re-

ported considerably [5]. It can be said that the application

of FACTS devices should be based upon the investiga-

tion of capital investment as well as operating costs and

the impacts of these devices of ATC improvement [6].

On the other hand, the optimum placement of FACTS

devices is an important issue in terms of planning hori-

zon [5], especially considering different types of these

devices. While from operating point of view, the coordi-

nation among these devices is much of interest both by

*Corresponding author.

H. IRANMANESH, M. RASHIDI-NEJAD 475

researchers and operation engineers.

2. Transmission Congestion Mathematical

Modelling

In order to study congestion problem, it is needed to de-

fine mathematical statements as a proposed model.

Mathematical modeling that is implemented in this paper

is based upon a multi-objective optimization problem in

which some new constraints are added to a conventional

optimization model that can be found in literature. In fact,

the model includes different terms for objective function

such as: improvement of voltage profile, reducing trans-

mission losses and minimizing capital investment for

FACTS devices incorporating ATC enhancement. The

optimum location as well as the capacity of UPFC can be

derived considering the role of these elements.

The study is carried out by implementing a perform-

ance index that can be defined as follow:

max

WPL

PI nPL









 (1)

where: m is real power transfer in line m, is

the maximum transfer capacity of line m, N is the number

of lines in the network. Wm is a non-negative real number

to show the importance of mth transmission line that can

be defined as weighting factor and n is defined as an op-

erating index that is usually less that one. When all

transmission lines work at their permissible conditions

(non-congestion situation) PI is very low, while if one or

more lines are congested it will be increased considera-

bly. To calculate the real power transfer in line m, DC

power flow is applied that is shown in the following rela-

tionship:

PL max

mn n

nns

mn nj

nns

SPm k

SPPm k





















(2)

The coefficients of Smn is the mnth component of ma-

trix S that is used in DC power flow and Pn is the real

power injected at bus n [7,8].

2.1. UPFC Model

The UPFC, shown in Figure 1, consists of two switching

converters operated from a common DC link. Converter

2 performs the main function of the UPFC by injecting

an AC voltage with controllable magnitude and phase

angle in series with the transmission line. The basic func-

tion of Converter 1 is to supply or absorb the active

power demanded by Converter 2 at the common DC link.

This is represented by the current. Converter 1 can also

generate or absorb controllable reactive power and pro-

vide independent shunt reactive compensation for the

line. This is represented by the current. A UPFC can

regulate active and reactive power simultaneously. In

principle, a UPFC can perform voltage support, power

flow control and dynamic stability improvement in one

and the same device [9].

2.2. IPFC Model

IPFC is mainly used to increase the active power in the

line and also to balance the power flow between the lines

in the transmission network. In its general form the IPFC

employs a number of dc-to-ac converters each providing

series compensation for a different line. IPFC is designed

as a power flow controller with n number of static syn-

chronous series compensator (SSSC) with a common dc

link. For maintaining the power flow stability in the line,

power is injected into each bus. The schematic diagram

of a simple IPFC with two SSSC is shown in Figure 2

[10,11].

A multi-objective optimization model is represented as

a compact form of Equations (3) [7,9].

Subject to the followings:

max

min ij

P Subject to the followings:

Figure 1. UPFC schematic diagram [9].

Figure 2. IPFC schematic diagram [10].

H. IRANMANESH, M. RASHIDI-NEJAD

476



cossin 0

giliijijFACTSijij FACTSij

PP VVGB







 







sincos 0

giliijijFACTSijij FACTSij

QQ VVGB







 





min max

iii

VVV

max

0.8

ij ij

PP

min maxgigi gi

PPP (3)

SVC

P

min maxgigi gi

QQQ

min maxshsh sh

QQQ

0.5 0.6

mn semn

XX

where: Pij is the real power flow through transmission

line ij; is the maximum capacity of line ij; is

the actual real load supply at bus i; N is bus number of

the system;

maxij

Pli

P is the real power generation at bus i;

Q is the reactive power generation at bus i; is the

actual reactive load supply at bus i;

Vi is the voltage

magnitude at bus i; are the real/reac-

tive part of the ijth element of the admittance matrix,

which may be a function of the reactance of FACTS De-

vice;

ij FACTSij FACTS





is the angle difference between the voltage at

bus i and that at bus j; max are the minimum/

maximum reactive power generation at generation bus i;

|Vi|min, |Vi|max are the minimum/maximum voltage mag-

nitude at bus I; Xse is the reactance of FACTS Device;

Xmn is the reactance of the line where FACTS Device has

been installed; Psh is the real power generation of FACTS

Device; minmax are the minimum/maximum reac-

tive power generation of FACTS Device [12].

mion

gi gi

,QQ

sh sh

3. Solution Algorithm

Heuristic methods may be used to solve complex opti-

mization problems. They are able to give a good solution

of a certain problem in a reasonable computation time,

but they do not assure to reach the global optimum. GA

is a global evolutionary search technique that can result a

feasible as well as optimal solutions. Based on the me-

chanics of natural selection and natural genetics, the GA

starts with a population of strings that represent the pos-

sible solutions and generates successive populations of

strings by combining survival of the fittest among string

structures [13,14].

3.1. Evaluation of Fitness Value via Fuzzy AHP

The proposed technique for multi-objective goal function

will include fuzzy sets theory (FST) [15] which charac-

terized variable O on X by its membership as μo(x): X→

And

[0,1]

analytic hierarchy process (AHP) procedures as

nform to mainly qualitative nature of deci-

etermine importance degree of each al-

3.2. Calculation of Exponential Weighting

Anas (AHP) is a method used to

3.3. Constraints with Unequal Importance

ance it

llowing

 FST to co

sion factors.

 AHP is for d

ternative.

Values Using AHP

lytical hierarchy proces

support complex decision-making process by converting

qualitative values to numerical values [16,17].

In case where constraints are of unequal import

should be ensured that alternatives with higher levels of

importance and consequently higher memberships are

more likely to be selected. The positive impact of the

levels of importance, wi, on fuzzy set memberships is

applied through the proposed criterion. It can be realized

by associating higher values of wi to constraints. For

example, the more important alternative the higher the

value associated with it. For example to evaluate fitness

function in RGA, it should have higher value for impor-

tant alternative for this case above process applied as

below:



 

itness N

cc c



 

 (4)

4. Case Study and Results Analysis

generators

4.1. Modified IEEE 5-Bus System with

As iin Figure 3, under normal condition,

stem is

A modified IEEE 5-bus system including 2

and 3 loads is selected to implement the proposed meth-

odology for congestion Relief. This system is simulated

using Power World software and MATLAB R2010b

software where the base MVA and base voltage are as-

sumed to be 100 MVA. In order to enhance power trans-

fer in the congested line, firstly the best location of

FACTS devices is derived and control parameters of the

allocated devices are then adjusted.

Congestion

t can be seen

maximum real power is transferred through line 2-1 by

amount of 88% of the permissible line capacity.

Voltage profile of modified IEEE 5-bus sy

own in Figure 4, where at bus 5 the magnitude of bus

voltage is 0.747 pu. Congestion can be taken into account

if real power transfer increases 80% of the line thermal

capacity [18]. By considering congestion condition, it

can be said that from congestion point of view there is no

security violation while from voltage profile point of

view this system needed to be compensated.

H. IRANMANESH, M. RASHIDI-NEJAD 477

Figure 3. Modified IEEE 5-Bus system with congestion.

Figure 4. Buses voltage profile.

4.2. Voltage Profile Improvement and FC

In th volt-

FC is assumed

any compensation, the electrical

ng mini-

Congestion Relief Using UPFCor IP

is case goal function includes three objectives:

age profile, Congestion value and loss. Optimization

process tries to relief congestion besides improving volt-

age profile considering less loss. Multi-objective optimi-

zation is handled via fuzzyfying objective function terms.

In this respect, membership functions of objective terms

are needed to be defined. Typical membership function

for voltage of each bus is depicted in Figure 5, while

Figure 6 shows congestion membership.

The membership of loss for UPFC or IP

shown in Figure 7.

First of all without

stem is studied in order to determine the power flow in

each of the transmission line and the bus voltages. The

power flow results and voltage profile without FACTS

Devices are given in Appendix 1 and Figure 4.

In fact, congestion should be relived satisfyi

um loss of UPFC or IPFC while reaching best voltage

profile. Moreover, considering AHP criterion, priority

factors of objective terms should be derived. It is as-

sumed that congestion is very strong important than

voltage profile, while it is absolutely important than the

costs of FACTS devices. It can be interpreted that: P12 = 7,

P = 1 , P = 9, P =

713 31

st prle is highly more impor-

Theatement “voltageofi

nt than costs of UPFC or IPFC “indicates:

0.2

0.4

0.6

0.8

hip

0.80.850.9 0.9511.05

Voltage(pu)

Degr e e of Membe r s

Figure 5. Membership of bus voltages.

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

00.511.522.5

Congestion

Degree of Membership

Figure 6. Membership of congestion value.

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0123

Loss Value

Degree of Membership

Figure 7. Membership of active power loss.

23 32

5, 15PP



The following weighting (W) ver is obtained via

cto

me matrix manipulations.





0.973370.218WE 670.068775

Fitness value of RGA can be evaluated using

Fuzzy-AHP as:





fitness of RGA for

Fitness( )( )( )

CxCxCx

where Ci is the i is membership of ith alternative and W

exponential weight of ith alternative.

This procedure can determine the

ch chromosome considering the importance of each

constraint as well as objective term. Implementing RGA

with the recombination rate of 76%, mutation rate of 3%

and regeneration of 21% considering ellipsis is applied as

it is depicted in Figures 8 and 9. These figures show the

best solution for UPFC is obtained after 68 generation. It

H. IRANMANESH, M. RASHIDI-NEJAD

478

Figure 8. Congestion variations versus number of RGA

generation (UPFC).

Figure 9. Voltage variations versus number of RGA gener

an be realized that line 3-4 is the candidate for UPFC

nsfer at

lin

r 97 genera-

tio

nalysis is carried out to study the effect of

solution for IPFC is

es 12 and 13 shows bus voltage profile and con-

tion (UPFC).

locations. In this regards the best capacity of those

FACTS device is 47.6% of line reactance which is equal

to 0.01428 pu and 15.61 MVAr for respectively.

By installing UPFC at their location, power tra

e 2-1 decreases to 74% of its maximum capacity and

the worst voltage is 0.972 which belongs to bus 5, while

it is improved significantly (Appendix 1).

The best solution for IPFC is obtained afte

n. But, the two converters of IPFC are embedded in

lines between buses 2-4 and 3-4 respectively close to bus

A detailed a

FC parameters on line flows and bus voltages but, only

few results are given for demonstration purpose. The

power flow results for IPFC parameters Vse = 0.1 pu and

θse = −150 are given in Appendix 1.

Figures 10 and 11 show the best

tained after 97 generation. By installing IPFC at their

location, power transfer at line 2-1 decreases to 73% of

its maximum capacity and the worst voltage is 0.995

which belongs to bus 4, while it is improved signifi-

cantly.

Figur

stion profile using the allocated UPFC or IPFC.

Figure 10. Congestion variations versus number of RGA

Generation (IPFC).

Figure 11. Voltage variations versus number of RGA gen-

eration (IPFC).

Figure 12. Buses voltage profile using proposed UPFC or

IPFC.

Figure 13. Congestion profile usingn normal condition, pro-

posed UPFC or IPFC.

H. IRANMANESH, M. RASHIDI-NEJAD

479

re connecting FACTS Dev

ment is an important issue in the re-

FC are the main commercially available

[1] R. Grunbaun,berg and B. Berg-

t-Oriented

nfluence of Price

The total loss befoice is

066301 MW and after connecting UPFC is reduce to

0.04471 MW and connecting IPFC between two lines the

loss is further reduced to 0.02421 MW.

5. Conclusions

Congestion manage

regulated environment of power systems. Congestion

should be relieved in order to use the maximum capacity

of transmission networks. It is well known that FACTS

technology can control voltage magnitude, phase angle

and circuit reactance clearly. Using these devices may

redistribute the load flow associated with regulating bus

voltages. Therefore, it is worthwhile to investigate the

effects of FACTS controllers on the congestion man-

agement.

UPFC and IP

CTS controllers. This paper presents an implementa-

tion of the RGA associated with Fuzzy-AHP to deter-

mine the location and capacity of these devices. The

proposed methodology is employed incorporating di-

mensional serialization valuing mechanism. Case studies

and the obtained results show the effectiveness of the

suggested criterion significantly.

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480

Appendix 1

Line From To Congestion without Facts Congestion with Congestion with

UPFC IPFC

1 1 2 88% 74% 73%

2 1 3 40% 35% 36%

3 2 3 22% 20% 22%

4 2 4 22% 23% 25%

5 2 5 69% 45% 40%

6 3 4 57% 16% 18%

7 4 5 18% 5% 10%