Australian Journal of Basic and Applied Sciencesajbasweb.com/old/ajbas/2014/August/326-338.pdf ·...

Australian Journal of Basic and Applied Sciences, 8(13) August 2014, Pages: 326-338

AENSI Journals

Australian Journal of Basic and Applied Sciences

ISSN:1991-8178

Journal home page: www.ajbasweb.com

Corresponding Author: Ramesh Kumar Selvaraju, Assistant Professor, Department of Electrical Engineering, Annamalai

University, Annamalainagar - 608002, Tamil Nadu, India.

Tel: 91-9842597594; E-mail: [email protected].

Design of Artificial Cooperative Search algorithm based Load Frequency Controller for

Interconnected Deregulated Power Systems with AC-DC Parallel Tie-lines 1Ramesh Kumar Selvaraju and 2Ganapathy Somaskandan

1 Assistant professor, Department of Electrical Engineering, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India. 2 Professor, Department of Electrical Engineering, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India.

A R T I C L E I N F O A B S T R A C T

Article history:

Received 25 June 2014

Received in revised form

8 July 2014

Accepted 25 July 2014 Available online 20 August 2014

Keywords: Artificial Cooperative Search

algorithm, AC-DC parallel tie- lines,

Deregulated Power System, Load Frequency Control.

This paper addresses the design of load frequency controller for an electric power

system in deregulated environment using Artificial Cooperative Search (ACS)

algorithm. Artificial cooperative search algorithm is a new two-population based global

search optimization algorithm, formulated to solve complex numerical optimization

problems. The Load Frequency Control (LFC) has been a major aspect in electric power system operation and control throughout the years, to provide reliable and

efficient power supply. Because of ever-changing load demand and wide range of

operating scenarios existing in multi-area deregulated environment, conventional methods for designing load frequency controllers are incapable of performing good

dynamic performance. In this paper, artificial cooperative search optimization

algorithm has been proposed for the optimal design of load frequency controllers in deregulated environment. To validate the effectiveness of the proposed ACS algorithm,

it is tested on a two area thermal deregulated power system with AC-DC parallel tie lines. The HVDC link is an interconnection between the systems, in parallel with the

AC tie-line to damp the system frequency deviation effectively. Results indicate that

the controllers designed using proposed ACS algorithm improves the system performances during varying load demands and different operating scenarios of

deregulated power system.

© 2014 AENSI Publisher All rights reserved.

To Cite This Article: Ramesh Kumar Selvaraju and Ganapathy Somaskandan., Design of Artificial Cooperative Search algorithm based

Load Frequency Controller for Interconnected Deregulated Power Systems with AC-DC Parallel Tie-lines. Aust. J. Basic & Appl. Sci.,

8(13): 326-338, 2014

INTRODUCTION

In the last decade, the existing electrical power systems are in a shifting phase from conventional integrated

utility environment to deregulated environment. Power system operation and control in a deregulated scenario,

such as Load Frequency Control (LFC) is an ancillary service that came into a possession of a principal role to

keep the reliability of electrical power system at a satisfactory level, and is going to be more useful nowadays in

view of complexity of the interconnected power systems (Singh et al., 2013). The main goal of the LFC is to

maintain zero steady state errors for frequency deviation and minimize unscheduled tie-line power flow

oscillations between the neighbouring interconnected control areas (Rezvantalab et al., 2009). A brief review on

different control methodologies on design of LFC controllers in deregulated environment has been done by Anil

Kumar and Venkata Ramana (2013). The modern interconnected power systems are large, complex and exhibit

different kinds of uncertainties and disturbances due to system parameter changes. The conventional control

method for the design of LFC controllers provides zero steady state deviation, but it exhibits poor dynamic

performance. To improve this, there have been continuing efforts in the design of LFC controller with better

dynamic response using artificial intelligence algorithms.

In this paper, a new two-population based global search optimization algorithm, Artificial Cooperative

Search Algorithm (ACS) has been used for optimal tuning of controller parameters in deregulated power

systems. The proposed ACS algorithm is implemented on a two area interconnected deregulated power system

with AC-DC parallel tie-lines. The main interest in HVDC transmission system is because it has economic,

environmental and performance advantages over other alternatives. Because of the unique advantages of HVDC

transmission system, it has been applied widely in operating a DC link in parallel with an AC link

interconnecting control areas to get an improved system dynamic performance (Ganapathy and Velusami, 2010,

Kumar and Ibraheem, 2004). Simulation results show that the proposed ACS algorithm tuned controller

327 Ramesh Kumar Selvaraju and Ganapathy Somaskandan, 2014


improves the dynamic response of the deregulated power system and provides a control system that satisfies the

LFC requirements.

Multi-Area Lfc Model In Deregulated Environment:

The structure of electrical utilities has changed dramatically after the induction of deregulation principles.

Before deregulation the ancillary services such as scheduling, system control and dispatch, reactive supply and

voltage control from generation sources, regulation and frequency response, energy imbalance and operating

reserve – spinning were provided by a single utility called as control area. Each control area has its own

generating resources, transmission and distribution capacities located within its boundary. After deregulation,

the new deregulation framework permits new companies to emerge namely, generation companies (GENCO’s),

transmission companies (TRANSCO’s), distribution companies (DISCO’s), and independent contract

administrator (ICA). To resolve any difference between different companies, an independent contract

administrator is preferred (Bekhouche, 2002).

To improve the efficiency of operation of the existing power system scenario, deregulation principle has

been introduced into the power system structure. In deregulated environment, the GENCOs and DISCOs can

have various combinations of bilateral contracts in-between themselves. The DISCO Participation Matrix

(DPM) can be used to realize the bilateral contracts among various GENCOs and DISCOs. The details of the

contracts between the GENCO and DISCO are provided by DPM. The number of rows in DPM is equal to the

number of GENCOs and the number of columns in DPM is equal to the number of DISCOs in the deregulated

scenario. Each entry of DPM is a fraction of a total load power contract between a DISCO and GENCO in the

system. The total sum of all the entries of DPM column is unity (Donde et al., 2001).

1cpfiji (1)

The proposed deregulated power system is a two-area thermal power system with AC-DC parallel ties-lines

in deregulated scenario as shown in Fig.1. The HVDC link is used as system interconnection in parallel with AC

tie-line to effectively damp the frequency oscillation of AC system. The corresponding DPM matrix is as given

below, where cpf represents the contract participation factor.

DPM=

44cpf43cpf

34cpf33cpf

42cpf41cpf

32cpf31cpf24cpf23cpf

14cpf13cpf

22cpf21cpf

12cpf11cpf

The off diagonal entries in the DPM represent the demand of DISCO in one area with the GENCO in

another area.

Statement of the Problem:

The state space representation of the LFC model in deregulated environment under consideration is given

by;

ΓDBUAX.X (2)

Where X represents system state vector matrix, U represents system control vector matrix, D represents

system disturbance vectors, A represents system state matrix, B represents system control input matrix and Ґ

represents disturbance input matrix of appropriate dimensions.

XT

22e22g22g21e21g

2dc21tie12e12g12g

11e11g121

XPPXP

FPPXPP

XPFACEACE

(3)

U T

2C1C PP

(4)

DT

2uC1uC4L3L2L1L PPPPPP

(5)

The corresponding values of co-efficient matrices A, B and Ґ are determined using the values of the system

parameters given by Donde et al., (2001) and Ganapathy and Velusami (2010). For reducing the frequency

deviations and area inter tie-line power deviations to be zero during steady state, the Area Control Error (ACE)

should be reduced to zero. To obtain the desired value of ACE, the ACEs are expressed as

ACE1= β1∆F1 + ∆Ptie1-2,error (6)

ACE2= β2∆F2 - ∆Ptie1-2,error (7)



g11sT1

1

r12

r12r12

sT1

TsK1

t11sT1

1

t12sT1

1

g12sT1

1

ps1

ps1

sT1

K

)s(1F

g21sT1

1

r22

r22r22

sT1

TsK1

t21sT1

1

t22sT1

1

g22sT1

1

ps2

ps2

sT1

K

)s(2F

s

12T2

)s(1dP

)s(2dP

DC

C

sT1K

D

)s(11eX )s(11gP

)s(12eX )s(12g'P

)s(12gP

)s(21eX

)s(P 1c

)s(P 2c )s(X 22e

)s(P 21g

)s(P 22g)s('P 22g

Fig. 1: Two-area LFC block diagram in Deregulated scenario with AC-DC tie-line.

AC-DC Transmission System:

Fig. 2: Block diagram of AC-DC transmission system.

The block diagram of a two area interconnected power system with AC-DC transmission line is shown in

Fig.2. The HVDC link is connected in parallel with the AC tie-line as shown in the Fig.2. The HVDC link

consists of mainly of rectifier circuit at one end of the HVDC link and an inverter circuit at the other end of the

link. Originally it is assumed that, area-2 initially supplied AC power through AC transmission line to area-1. It

is assumed that in area-2 there are sudden large load changes, which causes sudden demand for electric power

and serious problem of frequency deviations in area-2. To stabilizing the frequencies deviations in area-2 the

required bulk power for effectively damping out the frequency deviations in area-2 is supplied through the

HVDC link (Kumar and Ibraheem, 2004). The proposed method has large capability of frequency stabilization

to other interconnected areas having less capability. The proposed control can serve as a new auxiliary service

for stabilizing future deregulated power systems. The transfer function block diagram of HVDC link is given in

Fig.3.

ΔF1 ΔPdc

Fig. 3: Transfer function block diagram of incremental DC link power flow.

Artificial Cooperative Search Algorithm:

Artificial Cooperative Search algorithm (ACS) (Civicioglu, 2013) is a swarm intelligence algorithm, which

has been developed for solving complex optimization problems. A mutualism based biological interaction exists

between different living species in nature. The living species involved in a mutualism based biological

interaction try to derive mutual benefits from the mentioned interaction. Cooperation is the interaction of

homogenous living species that adopt mutualism. Mutualism and cooperation based biological interaction of



two eusocial superorganisms living in the same environment inspired the ACS algorithm. The habitat concept in

ACS algorithm matches the search space concept that belongs to the related problem.

In ACS algorithm, a superorganism consisting of random solutions of the related problem corresponds to an

artificial superorganism migrating to more productive feeding areas. ACS algorithm contains two

superorganisms; α and β that have artificial sub-superorganisms equal to the dimension of the population (N).

The dimension of the problem (D) is equal to the number of individuals within the related sub-superorganisms.

In ACS algorithm, α and β superorganisms are used for the detection of artificial Predator and Prey sub-

superorganisms. The Predator sub-superorganisms in ACS algorithm can pursue the Prey sub-superorganisms

for a period of time while they migrate towards global minimum of the problem. When the iterative calculation

process of ACS algorithm that is named as co evolution process is considered, it can be seen that the two

superorganisms looking for the global minimum of the related problem, establish cooperation based biological

interaction between each other. In ACS algorithm, the initial values of the individuals of ith sub-superorganism

of α (i.e., α_ (i,j)) and β (i.e., β_(i,j)) are defined by using (8) and (9)

jlow)jlowjup.(rand0g,j,i (8)

jlow)jlowjup.(rand0g,j,i (9)

where i = 1, 2, 3, ... , N, j = 1, 2, 3, ... , D and g = 0, 1, 2, 3, ... , max cycle. The ‘g’ value here denotes the

generation number expressing the co evolution level containing the related superorganisms. The rand shows a

random number chosen from the uniform distribution with U[0 1]. The upj and lowj are the upper and lower

limits of search space for jth dimension of the related problem. The fitness values are obtained by the related

sub-super organisms computed by using (10) and (11);

i;i fy (10)

if;iy (11)

The biological interaction location, X, between Predator and Prey sub-superorganisms is modeled using the

equation (12);

)edatorPrey(PrRedatorPrX (12)

Where, R is the Scale factor that controls the speed of biological interaction. The probabilistic nature of

ACS algorithm causes the super-organism that is determined as the predator to be changed in each generation.

Therefore, ACS algorithm provides a cooperative/co-evolution process for both of the superorganisms. The

pseudo code of ACS algorithm is provided in (Civicioglu P, 2013). The proposed algorithm can be implemented

with convergence, iteration or tolerance as the stopping criteria. In this proposed study, iteration count has been

taken as the stopping criteria

Optimal gain tuning:

The proposed deregulated system is a two-area thermal power system with AC-DC tie-lines in deregulated

scenario as shown in Fig.1. ACS algorithm is applied for optimizing the proportional plus integral controller

gains for a two area interconnected thermal power system in deregulated environment. The LFC model is a two

area interconnected power system with each area having one reheat unit and one non-reheat unit. The objective

is to obtain the optimum values of the controller parameters which will minimize the performance index, J

(Ganapathy and Velusami, 2010).

dt2dc

P221tie

P21

FJ

(13)

For LFC in deregulated environment using ACS algorithm, initially the random generated biological

interaction location X is used to calculate fitness value. For each iteration the sub-superorganism (α and β)

values are obtained using (10) and (11). The predator and prey sub-superorganisms are determined in each

generation by using α and β superorganisms. The biological interaction location, X, between predator and prey

is updated using (12). The objective function J is calculated for each set of X using (13). ACS algorithm

provides a cooperative / co evolution process for both the superorganisms. The process is repeated until

optimum gains corresponding to global minimum objective function value are obtained.

Simulation results and discussion:

An interconnected two-area system thermal power system having one reheat turbine and one non-reheat

turbine in each area with AC-DC parallel tie-lines in the deregulated environment is used to demonstrate the

effectiveness of the proposed artificial cooperative search algorithm. Table 1 shows the optimal controller gain

values obtained using ACS algorithm and the corresponding cost functions. The simulations are performed for

different contract scenarios of the deregulated environment. The data required for the simulations are taken from

(Donde et al., 2001and Ganapathy and Velusami, 2010).



Table I: Optimal Proportional plus Integral Gain Values.

Type of PI controller Feedback gains Cost function value

Controller designed using ISE criteria KP = 0.5250 Ki = 0.4200

132.4831

Controller designed using ACS Algorithm KP = 0.0027

Ki = 1.0925

79.8201

Poolco Scenario:

In the poolco scenario of case study, all the GENCOs present in each area participate equally in LFC

operation. The area participation factors are assumed equally as given apf1 = apf2 = apf3 = apf4 = 0.5. In this

scenario the load change is assumed to occur only in area-1, so the load is demanded by DISCO-1 and DISCO-

2. The PU load of DISCO-1 and DISCO-2 are assumed as 0.1pu MW. Therefore the entries in Disco

Participation Matrix becomes modified as given below

DPM=

00

00

00

00

00

00

5.05.0

5.05.0

The generation of GENCO (PMi) is expressed in terms of Contract Participation Factor (cpf) and load

demand of DISCOs (PLj) as given below

PMi = j

ijcpf PLj (14)

PMi = cpfi1PL1+ cpfi2PL2+ cpfi3PL3+ cpfi4PL4

For the Poolco scenario,

PM1 = (0.5x 0.1) + (0.5x 0.1) + 0 + 0 = 0.1p.u MW

PM2 = (0.5x 0.1) + (0.5x 0.1) + 0 + 0 = 0.1p.u MW

PM3 = 0 + 0 + 0 + 0 = 0 p.u MW

PM4 = 0 + 0 + 0 + 0 = 0 p.u MW

Fig.4 and Fig.5 show the simulation results of area frequency deviation, Fig.6 shows the actual power flow

from area-1 to area-2, Fig.7 shows the dc power flow and Fig.8, Fig.9, Fig10, Fig.11 show the actual generated

powers of the GENCOs

0 10 20 30 40 50 60 70 80-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Time in sec

F

1 i

n H

z

Conventional PI

ACSA PI

Fig. 4: Frequency deviation of area 1.

0 10 20 30 40 50 60 70 80-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Time in sec

F

2 i

n H

z

Conventional PI

ACSA PI




0 10 20 30 40 50 60 70 80-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

Time in sec

P

tie i

n p

.u.

MW

Conventional PI

ACSA PI

Fig. 6: Change in tie line power.

0 10 20 30 40 50 60 70 80-0.2

-0.15

-0.1

-0.05

0

0.05

Time in sec

P

dc i

n p

.u.

MW

Conventional PI

ACSA PI

Fig. 7: Change in DC tie line power.

0 10 20 30 40 50 60 70 800

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Time in sec

P

g1 i

n p

.u.

MW

Conventional PI

ACSA PI

Fig. 8: Generated power of GENCO 1.

0 10 20 30 40 50 60 70 800

0.02

0.04

0.06

0.08

0.1

0.12

Time in sec

P

g2 i

n p

.u.

MW

Conventional PI

ACSA PI




0 10 20 30 40 50 60 70 80-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Time in sec

Pg

3 i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 80-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

Time in sec

P

g4 i

n p

.u.

MW

Conventional PI

ACSA PI


It has been observed that the frequency deviation in each area goes to zero in the steady state and also the

power flow during the steady state over the tie-line is zero. The generation of GENCOs reaches the desired

value of generation during the steady state.

Bilateral contract:

In this scenario of case study, all the DISCOs have a contract with any GENCO in any other area as per the

DPM. Each DISCO is assumed to demand 0.1 p.u MW power from the GENCOs. The area participation factors

are apf1 = 0.75, apf2 = 0.25, apf3 = 0.5, apf4 = 0.5. Therefore the entries in DPM becomes as given below

DPM=

00

7.01

25.03.0

25.00

00

3.00

25.02.0

25.05.0

The scheduled tie-line power from area 1 to area 2 is calculated from the values of the off diagonal

elements of the DPM using the following expression,

Ptie scheduled = LjP

4

3i

2

1jijcpfLjP

2

1i

4

3jijcpf

(15)

= (cpf13+cpf23)PL3 + (cpf14+cpf24) PL4 – (cpf31+cpf41) PL1 – (cpf32+cpf42) PL2

= (0+0)0.1 + (0.3+0)0.1–(0+0.3)0.1–(0.25+0.25)0.1 = - 0.05 p.u MW

The desired generation of GENCO (PMi) during steady state is calculated using (14) as follows,

PM1 = (0.5x 0.1) + (0.25x 0.1) +0+ (0.3x0.1) = 0.105 p.u MW

PM2 = (0.2x 0.1) + (0.25x 0.1) +0+0 = 0.045 p.u MW

PM3 = (0x0.1) + (0.25x0.1)+(1x0.1)+(0.7x0.1) = 0.195 p.u MW

PM4 = (0.3x0.1) + (0.25x0.1) + 0 + 0 = 0.055 p.u MW

Fig.12 and Fig.13 show the area frequency deviation during the bilateral scenario, Fig.14 shows the actual

power flow from area-1 to area-2 during the bilateral scenario , Fig.15 shows the scheduled tie-line power flow

from area-1 to area-2, Fig.16 shows the dc power flow and Fig.17, Fig.18, Fig19, Fig.20 show the actual

generated powers of the GENCOs.



0 10 20 30 40 50 60 70 80-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

Time in sec

F

1 i

n H

z

Conventional PI

ACSA PI

Fig.12: Frequency deviation of area 1.

0 10 20 30 40 50 60 70 80-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time in sec

F

2 i

n H

z

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 80-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Time in sec

P

tie i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 80-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Time in sec

P

tic s

ch

ed

ule

i

n p

.u M

W

Conventional PI

ACSA PI

Fig. 15: Scheduled tie line power.



0 10 20 30 40 50 60 70 80-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

Time in sec

Pd

c in

p.u

. M

W

Conventional PI

ACSA PI

Fig. 16: Change in DC tie line power .

0 10 20 30 40 50 60 70 800

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Time in sec

P

g1 i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 800

0.02

0.04

0.06

0.08

0.1

0.12

Time in sec

P

g2 i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 800

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Time in sec

P

g3 i

n p

.u.

MW

Conventional PI

ACSA PI




0 10 20 30 40 50 60 70 80-0.02

0

0.02

0.04

0.06

0.08

0.1

Time in sec

Pg

4 i

n p

.u.

MW

Conventional PI

ACSA PI


From the simulation, it has been noted that by implementing the proposed ACS algorithm designed

controller, the frequency deviations and tie-line power deviations are quickly driven back to zero and have better

response. It is also observed that the generations of all the GENCOs and the scheduled tie-line power flows are

at their desired levels during the steady state.

Contract violation:

In this case, the DISCO 1 violates the contract by demanding 0.1 pu MW excess power than its contracted

power. The uncontracted power will be supplied by the GENCOs present in the same area of the DISCO which

violates the contract that is GENCO1 and GENCO2. The total load in area 1 is equal to the load of DISCO 1,

load of DISCO 2 and the uncontracted load, which is equal to 0.3 pu MW. Similarly the load in area 2 is equal

to the sum of the loads of DISCO 3 and DISCO 4 which is 0.2 pu MW. The DPM is same as in scenario 2. The

distribution of uncontracted load among the GENCOs is decided by the area participation factor.

0 10 20 30 40 50 60 70 80-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Time in sec

F

1 i

n H

z

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 80-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Time in sec

F

2 i

n H

z

Conventional PI

ACSA PI




0 10 20 30 40 50 60 70 80-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Time in sec

Pti

e i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 80-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Time in sec

P

tic s

chedule

i

n p

.u M

W

Conventional PI

ACSA PI

Fig. 24: Scheduled tie line power.

0 10 20 30 40 50 60 70 80-0.2

-0.15

-0.1

-0.05

0

0.05

Time in sec

P

dc i

n p

.u.

MW

Conventional PI

ACSA PI

Fig.25: Change in DC tie line power.

0 10 20 30 40 50 60 70 800

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Time in sec

P

g1 i

n p

.u.

MW

Conventional PI

ACSA PI




0 10 20 30 40 50 60 70 800

0.02

0.04

0.06

0.08

0.1

0.12

Time in sec

Pg

2 i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 800

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Time in sec

P

g3 i

n p

.u.

MW

Conventional PI

ACSA PI


0 10 20 30 40 50 60 70 80-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Time in sec

P

g4 i

n p

.u.

MW

Conventional PI

ACSA PI


Fig. 26 and Fig.27 show that Generation of GENCO 1 and GENCO 2 are affected by the uncontracted load

of DISCO 1, whereas Fig.28 and Fig.29 show that the generation of GENCO 3 and GENCO 4 remain unaltered

due to the uncontracted load. The uncontracted load of DISCO 1 is met by the GENCO 1 and GENCO 2 in area

1. The simulation results reveal that frequency deviations and tie line power deviations are reduced effectively

during the presence of DC tie –line in parallel with AC tie-line.

Conclusion:

In this work, a new a new global search optimization algorithm, the Artificial Cooperative Search

Algorithm (ACS) has been introduced for the solution of LFC problem in deregulated power environment. The

proposed algorithm has been applied to tune the controller gains for a two area deregulated power system with

AC-DC parallel tie-lines and has been tested under various possible bilateral contracts. The simulation results

reveal that the controller designed using ACS algorithm suppresses the frequency deviations and tie-line power

deviations effectively. The area frequency responses, tie-line power flow response and generation of GENCOs

exhibit less overshoot, less undershoot and minimum settling time in the presence of a DC tie-line in parallel

with an AC tie-line.



ACKNOWLEDGEMENT

The authors wish to thank the authorities of Annamalai University, Annamalainagar, Tamilnadu, INDIA for

the facilities provided to prepare this paper.

REFERENCES

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