FINAL VERSION PPT.pptx

by
S.K.B.PradeepKumar Ch
1834110033
Under the Guidance of
Dr. G.BALAMURUGAN
Professor
DEPARTMENT OF ELECTRICAL ENGINEERING
ANNAMALAI UNIVERSITY
ANNAMALAI NAGAR
2022

 INTRODUCTION
 OBJECTIVE OF THE THESIS
 VOLTAGE STABILITY ENHANCEMENT IN RDS
 COST BENEFIT ANALYSIS OF DISCO’S AND DG OWNERS
 SOLUTION METHODOLOGY
 RESULTS AND DISCUSSIONS
 CONCLUSION AND REFERENCES
 LIST OF PUBLICATIONS
2
Overview of presentation

4
 In the regulated power industry, Distribution systems hold a
very significant position in the power system since it is the
main point of link between bulk power and consumers.
 Effective planning of radial distribution network is required to
meet the present growing domestic, industrial and commercial
load day by day.
 Distribution networks commonly employed in radial structure.
Due to inappropriate design and planning of distribution
networks, the power system would face various problems. It
includes decreasing reliability, increasing power loss,
reducing voltage stability and other safety issues.
 Among these problems, Voltage stability enhancement and
power loss minimization is the more important concern of
distribution system operators.

5
 Solutions addressed in literature to solve the above problems
are
 Capacitor placement
 Installation of DGs
 Using FACTS devices and
 Distribution Network Reconfiguration (DNR)
 In the present research, the following methods are adopted to
improvise the benefits for distribution system
 Distribution Generations (DGs)
 Capacitors placements and
 Network reconfiguration.

6
 Proper placement and optimal sizing of DGs and capacitors
to effectively regulate the voltage profile, reliability and
power quality, reduce the network losses, and maximize
operational benefits of DISCOs and DG owners.
 This research proposes optimization algorithm to address
the problems of distribution system by optimal placement
of DGs, Capacitors, and Network reconfiguration to
enhance the voltage profile, minimize the power loss and
improve the net saving cost of RDS.
Research Gaps from Literature

7
OVERVIEW OF DEREGULATED POWER
SYSTEM

8
 Deregulation of electric power systems have created a
competitive open market environment.
 A deregulated system allows the power supply to function
competitively, as well as allows the consumers to choose
suppliers of electric energy.
 Deregulation involves unbundling, represents
disaggregation into the basic parts of generation,
transmission and distribution
 Liberalization – Introduction of a less restrictive regulatory
framework for companies within a power sector.
 Modification of existing regulation.
 Privatization – sale of government assets to the private
sector

9
STRUCTURE OF REGULATED INDUSTRY
STRUCTURE OF DEREGULATED INDUSTRY

11
 To propose a simple and efficient optimization approach of
Moth Flame Optimization (MFO) algorithm for the solution
of voltage stability problem by optimal placement and sizing
of combined DGs and capacitors.
 To evolve a solution Methodology based on network
reconfiguration and optimal allocation and sizing of DGs and
Capacitors with a view of improving the voltage stability and
net saving cost of radial distribution system.
 To implement and investigate the performance of proposed
MFO approach for optimal allocation of DG units in DISCOs
so as to enhance the voltage profile, minimize the power loss,
and maximize benefit of the system.
 To suggest a practical approach of combined capacitor and
DG units with network reconfiguration to maximize the
Profit of DISCOs in a Competitive electricity market

12
EXISTING METH0DOLOGY IN LITERATURE
The classical methods such as
• Dynamic programming (DP)
• Lagrangian relaxation (LR)
• Mixed-integer programming (MIP)
• Benders decomposition
• Network flow with Newton’s method
• Linear programming and
• Nonlinear programming

13
Recently the researchers have proposed different
evolutionary techniques like
 Genetic Algorithm (GA)
 Evolutionary Programming (EP)
 Particle Swarm Optimization (PSO)
 Improved PSO(IPSO)
 Adaptive Particle Swarm Optimization(APSO)
 Differential Evolution (DE)
 Modified Differential Evolution(MDE)
 Modified Hybrid Differential Evolution(MHDE)
 Teaching Learning Based Optimization (TLBO)
 Improved Flower Pollination Algorithm (IFPA)
 ABC Algorithm
 Gravitational Search Algorithm and
 Symbiotic Organisms Search Algorithm

14
•These all have been successfully employed to solve the
voltage stability problem, However each algorithm has its
own merits & demerits
•This research presents a simple and parameter less algorithm
of MFO to solve the various voltage stability problem under
regulated and deregulated power system

16
Moth – Flame Optimization (MFO)
Technique
Moth fly optimization algorithm which was introduced by
Mirjalili during 2015.
It has received wider attention among the researchers and has
been applied to solve multi objective optimization algorithms.
It exhibits a competitive performance over other algorithms
because of its good convergence attitude.
This technique is formulated on the basis of biological
behaviour of moth fighting flames in field.
The MFO technique uses a community of moths in order to do
the optimization process and each and every moth is needs to
upgrade their position with reference to the flame.

17
It protects the moth to evade from the entrapment of local
optima and to regain its inspection process in the search space.
More specifically, its performance is on the virtue of the
transverse orientation process.
The navigating nature of the moth has inspired the researchers
to carry out this kind of optimization problem.
It is illustrated in the below figure, where the light is the prime
source and convergence of moths be exercised by preserving a
fixed angle.
Fig.: Artificial light
source for moths in
spiral flying path

18
MFO technique is basically swarm based optimization
module, the population of moth can be expressed as
While ‘n’ indicates amount of count of moths and ‘d’ refers
dimension size of the optimization problem in this solution
space.
Further, it is also pretended that there is a proportionate series
of fitness vectors, and it can be described as
1,1 1,2 1,
2,1 2,2 2,
,1 ,2 ,
d
d
n n n d
m m m
m m m
M
m m m

 
 

 

 

 

 
 
1
2
n
OM
OM
OM
OM
 
 
 

 
 
 

19
It is necessary for every moth to renew its place with respect
to the flame relevant over it, in order to avert the technique
slipping in the local optimal point.
This processes support the technique moving towards the
global searching mode.
Here the location of the moth and flame is the search area and
become variable matrices of the equal dimension.
The fitness value vectors are assumed as,
1
2
n
OF
OF
OF
OF
 
 
 

 
 
 

20
The suggested MFO implements three types of operations for
achieving the global best optimal values and the tasks are
outlined as,
The random distribution of moth is formulated as
Hence, the logarithmic spiral of the MFO algorithm may be
formulated as
The space among the i-th moth and the j-th flame represented
in the form of
( , , )
MFO I P T

     
   
, * ( )
M i j ub i lb j rand lb i
  
 
, coscos(2 )
bt
S Mi Fj Die t Fj

 
Di Fi Mi
 

21
Updating the count of Flames
During the course of iteration process, the number of flames
gets decreased so as to keep the equilibrium in exploration and
exploitation process.
This balanced decrement in the count of flames stabilises the
activities in the exploration space.
For the best moth position, the best flame has to be identified
from the previous iteration and the best objective function value
is obtained using equation.
N l
Flame no round N l
t

 
  
 
 

23
In this Research a simple methodology has been proposed for
optimally placing and sizing of the combined DG and capacitor
units in RDS.
The work is focused to improve the characteristic nature of
voltage profile and to minimise the network losses.
A metaheuristic algorithm namely Moth Flame Optimization
algorithm is employed to figure out this problem.

24
The algorithm works on the basis of natural behavior of moths
against lights and it has two essential components of moth and
flames.
The viability of the proposed method has been demonstrated
by the test case analysis on IEEE 12 and 33 node systems and
the observations are correlated with that of different methods
reported in literatures.

25
The mathematical equation for computing the VSI is
formulated as The mathematical equation for computing the
VSI is formulated as
(1)
Objective function
The prime intention of the proposed exercise is to minimize the
total active power loss at a point of full load condition of the
distribution system as suggested by the following equation
(2)
Where,
PL = Power Loss
Ipq= Current passing through the section connected across p and
q is specified as
 
2
4 2
4 4
F F F F
p q pq q pq q pq q pq p
VSI q V P X Q R P R Q X V
   
         
   
 
2
, | , B
L pq pq
p q p q S
MinP I R
 
 

26
2 2
2
pq pq
pq
p
P Q
I
V


Rpq = Series resistance
1. Equality and Inequality Constraints
The various equality and inequality constraints are presented
from equations (2) – (9).
While implementing the DGs, the voltage of different buses
and current through the lines are to be kept at safer limits for
the stable functioning of distribution system.

27
2. Real and reactive power flow limit
The mathematical representation of active and reactive power
flow over the line m is defined using equation (3) and (4)
(3)
(4)
3. Magnitude of the Voltage
The sending and receiving point voltage magnitude of the RDS
must satisfy equation (5)
 
2 2
2
pq
F L DG
pq q q q pq pq
p
R
P P P P P Q
V
    
 
2 2
2
pq
F L DG C
pq q q q q pq pq
p
X
Q Q Q Q Q P Q
V
     
   
2 2
2 2 2 2
2
2
pq pq
q p pq pq pq pq pq pq
p
R X
V V P R Q X P Q
V

     (5)

28
4. Voltage profile
Bus voltage of each bus must lies between minimum and
maximum limits of the tolerable limits.
(6)
5. Line current
The line current in each branch must lie within the thermal limit.
(7)
6. Capacity of DG unit
The capacity of DG unit should be less than or equal to some
percentage of total feeder load.
max
qq
min
q B
V q S

rated
pq pq B
I I pandq S
  
       
2 2 2 2
0.5
B B
DG DG L L
q q q q
q S q S
P Q P Q
 
   
  (8)

29
7. Size of the Capacitor
Size of the capacitor must be within the sum of reactive power
load of the system.
1.0
B B
c L
q q
q S q S
Q Q
 
 
  (9)

30
The MFO algorithm has been applied for the computation of best
solution by using the following steps
STEP 1: Read the system information
STEP 2: Execute distribution load flow for base case.
STEP 3: Fix number of DG and Capacitor are to be used to in RDS.
STEP 4: Initialize count of moths (Population), maximum no of
iterations, dimension, lower bound and upper bound (node and size of
DG and Capacitor respectively)
STEP 5: Set iteration=1.

31
STEP 6: Calculate fitness (i.e. loss in network) for each moth by
placing DG and Capacitor at their respective buses using eqn. (15).
STEP 7: Update the position of flame and save the best fitness values
in an array corresponding to eqn. (16)
STEP 8: Update the record of flames and the flames are arranged
using eqn. (17) based on their fitness values
STEP 9: Compute the present position of moths.
STEP 10: Check the all constrains are satisfied, if yes move to next
step, else go to step 6.
STEP 11: Check If the number of iteration process is equal to
maximum number of iterations, go to step 12. Otherwise go to step 5.
STEP 12: Display the optimal solution and STOP the program.

Fig. 5 Flow chart of the proposed MFO technique

33
CASE STUDY AND RESULTS
In the present investigation, two standard test systems such as
IEEE12 and 33 nodes are taken in to account to illustrate the
validity of devised algorithm.
The simulations are performed on MATLAB 14.0 platform.
The solution has been obtained with different test cases.
Test system 1: IEEE12 node RDS
In this test case, total capacity of the system is 11 KV, it contains
12 node and 11 lines with total real and reactive load of 435 kW and
395 KVAr.
The proposed MFO is a parameter less algorithm and it has only
common control parameters.
It includes agents or number of moth = 30, Most extreme number
of iterations = 100, Number of variables = 11. The following three
different test cases are analysed by MFO approach

34
Optimal allocation of capacitor alone with its best size and
placement
Optimal allocation of simply DGs operating at unity PF at
best location
Optimal allocation both capacitor and DGs operated at
unity PF with best size.
Distribution power flow method is proposed to do the base case
power flow.
The voltage profile of the IEEE12 bus system with dissimilar
cases are reported in Table 1 and graphically represented in fig.
6.
From the table, the voltage profiles are highly improved by
optimal placement of combined DG and capacitor compared
with base case, single capacitor and sing DG.
The VSI with various cases are presented in table 2 and also
graphically displayed in figure 7.

35
Table 1. Voltage profile for IEEE12-node RDS
Bus No. Base Case Capacitor DG
Capacitor with
DG
1 1.0000 1.0000 1.0000 1.0000
2 0.9943 0.9952 0.9966 0.9976
3 0.9890 0.9908 0.9937 0.9957
4 0.9806 0.9839 0.9895 0.9935
5 0.9698 0.9755 0.9852 0.9920
6 0.9665 0.9731 0.9841 0.9919
7 0.9637 0.9710 0.9833 0.9921
8 0.9552 0.9647 0.9837 0.9950
9 0.9471 0.9595 0.9869 0.9983
10 0.9442 0.9567 0.9841 0.9956
11 0.9433 0.9559 0.9832 0.9947
12 0.9431 0.9556 0.9830 0.9945

36
Fig. 6. Voltage profile for IEEE12 bus system
with different cases

37
Table 2. VSI for IEEE12- Bus radial distribution
system
Bus No. Base Case Capacitor DG
Capacitor
with DG
1 1.0000 1.0000 1.0000 1.0000
2 0.977305 0.980671 0.986215 0.990236
3 0.956650 0.963464 0.974889 0.983085
4 0.924073 0.936863 0.958529 0.974062
5 0.883900 0.905269 0.942180 0.968533
6 0.872648 0.896502 0.938503 0.968158
7 0.862645 0.889009 0.935716 0.968659
8 0.832376 0.866058 0.937471 0.980195
9 0.804778 0.847510 0.950408 0.992810
10 0.795627 0.838024 0.940342 0.982507
11 0.792651 0.834969 0.937102 0.979194
12 0.791954 0.834254 0.936344 0.978419

38
Fig. 7. VSI for IEEE12-node system with different
cases

39
Table 3. Simulation results of IEEE12-node RDS
Capacitor DG Capacitor with DG
Optimal
Location
9 9 9, 8
Optimal Size 0.2 0.2355 0.23296, 0.25
Power Loss
(KW)
12.6028 10.7744 3.1693

Fig. 8. Convergence curve of
Capacitor placement
Fig. 9. Convergence curve of DG
placement
40

41
Fig. 10. Convergence curve of DGs with Capacitor
placement

42
Table 4. Optimal location, size and minimum voltage for proposed
with existing method
Particulars
Conventional Method [4] MFO (Proposed)
Location and size
Min voltage
(p.u.)
Location and
size
Min voltage
(p.u.)
Base case -
0.94414 at bus
12
- -
Capacitor
0.16 MVAr
capacitor at bus
12
0.95596 at bus
11
0.2 MVAr
capacitor at bus
9
0.9556at bus
12
DG at UPF 0.2MW DG bus 12
0.98032 at bus
8
0.2355 MW DG
bus 9
0.9830 at bus
12
Both DG and Capacitor
0.12MW DG at bus
12 and 0.24 MVAr
capacitor at bus
12
0.9815 at bus
8
0.23296 MW DG
at bus 9 and
0.25 MVAr
capacitor at bus
8
0.9919at bus 6
Table 5. Network losses and loss reduction for proposed with
existing method
Particulars
Conventional Method MFO (Proposed)
Network
loss (kW)
% Loss
reduction
Network
loss (kW)
% Loss
reduction
Base case 198.9 - - -
Capacitor 134.3 32.47 126.028 36.6375
DG at UPF 109.2 45.09 107.744 45.8301
DG and
Capacitor
71.93 63.8 31.693 84.0659

43
Fig. 11. Power loss for IEEE12-node system

44
The numerical results are clearly reported in Table 3, it
includes optimal location, size and network losses of the RDS
with different cases.
The convergence characteristics of three different cases are
displayed in fig. 8, fig. 9 and fig. 10.
The power loss of the three different cases is graphically
displayed in fig. 11.
The comparative review has also been done to assess the
applicability and superiority of planned MFO.
Comparison of optimal location, size and minimum voltage for
proposed with prevailing practices are presented in Table 4.
The system losses and curtailment of losses are also compared
with accepted methods are recorded in Table 5.
From the table 1 and 5, it is established that the proposed MFO
enhances the voltage profile besides reducing the network losses
of the system.

Test system 2: IEEE33 Bus RDS
45
In second case, the large scale system of IEEE33 node system
is taken into account in order to demonstrate the efficacy of the
devised MFO methodology.
The voltage rating is 12.66 KV with a absolute load of 3.72
MW and 2.3 MVAR are considered in this test system.
The MFO algorithmic specification includes count of search
operators or count of moth = 40, Maximum iterations = 100,
total variables = 11.
The proposed system has been analysed on the following five
different test cases.

46
Optimal allocation of capacitor alone with its best size and
placement
Optimal allocation of simply DGs operating at unity PF at
best location
Optimal allocation of both capacitor and DGs operated at
unity PF with best size
Optimal allocation of both DGs and capacitor at 0.9 Pf lag
with economical size
Optimal allocation of both DGs and capacitor at 0.85 Pf lag
with economical size

47
Table 6. Voltage profile for IEEE 33-Bus RDS
Bus
No.
DG Capacitor
DGs with
Capacitor
0.9pf 0.85pf
1 1.0000 1.0000 1.0000 1.0000 1.0000
2 0.9977 0.9976 0.9981 0.9981 0.9980
3 0.9869 0.9863 0.9894 0.9894 0.9894
4 0.9820 0.9809 0.9860 0.9860 0.9859
5 0.9771 0.9757 0.9828 0.9828 0.9826
6 0.9644 0.9651 0.9758 0.9757 0.9754
7 0.9622 0.9616 0.9736 0.9723 0.9720
8 0.9601 0.9481 0.9716 0.9590 0.9587
9 0.9606 0.9419 0.9722 0.9528 0.9525
10 0.9617 0.9361 0.9733 0.9471 0.9468
11 0.9621 0.9352 0.9738 0.9463 0.9459
12 0.9631 0.9337 0.9747 0.9448 0.9445
13 0.9572 0.9276 0.9689 0.9388 0.9385
14 0.9551 0.9253 0.9668 0.9365 0.9362
15 0.9537 0.9239 0.9655 0.9351 0.9348
16 0.9524 0.9225 0.9642 0.9338 0.9335
17 0.9505 0.9205 0.9622 0.9318 0.9315

48
18 0.9499 0.9199 0.9617 0.9312 0.9309
19 0.9971 0.9970 0.9975 0.9975 0.9975
20 0.9936 0.9935 0.9940 0.9940 0.9939
21 0.9929 0.9928 0.9933 0.9932 0.9932
22 0.9922 0.9921 0.9926 0.9926 0.9926
23 0.9834 0.9827 0.9859 0.9859 0.9858
24 0.9767 0.9761 0.9793 0.9792 0.9792
25 0.9734 0.9728 0.9759 0.9759 0.9759
26 0.9625 0.9643 0.9747 0.9760 0.9756
27 0.9600 0.9634 0.9734 0.9765 0.9761
28 0.9487 0.9622 0.9695 0.9796 0.9791
29 0.9406 0.9617 0.9670 0.9824 0.9817
30 0.9371 0.9584 0.9656 0.9818 0.9812
31 0.9330 0.9543 0.9616 0.9779 0.9773
32 0.9321 0.9534 0.9607 0.9770 0.9765
33 0.9318 0.9531 0.9604 0.9768 0.9762
Cont..

49
Table 7. VSI for IEEE 33-Bus RDS
Bus
No.
DG
Ca
pac
ito
r
DG
wit
h
Ca
pac
ito
r
0.9
pf
0.8
5pf
1 1.000000 1.000000 1.000000 1.000000 1.000000
2 0.990658 0.990260 0.992234 0.992223 0.992179
3 0.948083 0.945563 0.957993 0.957918 0.957642
4 0.929628 0.925749 0.945122 0.945004 0.944569
5 0.911621 0.906312 0.932961 0.932798 0.932195
6 0.864250 0.867220 0.906301 0.906090 0.904886
7 0.857393 0.855922 0.898642 0.893643 0.892454
8 0.849914 0.808267 0.891004 0.844982 0.843824
9 0.852187 0.788058 0.893334 0.824305 0.823162
10 0.856275 0.768963 0.897526 0.804779 0.803649
11 0.857948 0.766341 0.899237 0.802093 0.800965
12 0.861449 0.761471 0.902823 0.797112 0.795987
13 0.840432 0.741699 0.881313 0.776887 0.775777
14 0.832976 0.734704 0.873676 0.769725 0.768619
15 0.828275 0.730290 0.868861 0.765206 0.764104
16 0.823721 0.726013 0.864197 0.760828 0.759730
17 0.816986 0.719689 0.857300 0.754355 0.753261

50
18 0.815009 0.717836 0.855274 0.752456 0.751364
19 0.988595 0.988202 0.990159 0.990147 0.990103
20 0.974411 0.974020 0.975963 0.975951 0.975907
21 0.971732 0.971341 0.973282 0.973270 0.973227
22 0.969241 0.968851 0.970789 0.970777 0.970734
23 0.935082 0.932685 0.944636 0.944563 0.944295
24 0.909875 0.907510 0.919301 0.919229 0.918964
25 0.897799 0.895451 0.907162 0.907090 0.906827
26 0.858406 0.865741 0.902720 0.907430 0.906143
27 0.849428 0.862451 0.897744 0.909307 0.907895
28 0.809630 0.858874 0.883649 0.921062 0.918845
29 0.782710 0.858405 0.874972 0.931568 0.928724
30 0.771404 0.846519 0.870080 0.929431 0.927186
31 0.757998 0.832475 0.855804 0.914726 0.912512
32 0.755169 0.829506 0.852793 0.911609 0.909399
33 0.754270 0.828563 0.851838 0.910620 0.908412
Cont..

51
Fig. 12. Voltage profile
for IEEE 33 node RDS
(Case1, 2 and 3)
Fig. 13. Voltage profile for IEEE
33 node RDS (Case 4 and 5)

52
Fig. 14. VSI for IEEE 33
node RDS (Case 1, 2 and
3)
Fig. 15. VSI for IEEE 33 node RDS
(Case 4 and 5)

53
Table 8. Numerical results for IEEE 33 node RDS
Components
Optimal
location
Optimal
Size
Power
Loss (kW)
Capacitor 29 1.7 157.6864
DG at UPF 12 1 129.9648
DG and Capacitor 12, 30 1,1. 2 75.0069
Fig. 16. Convergence curve for Single
Capacitor placement

54
Fig.17. Convergence curve for
Single DG placement
Fig. 18. Convergence curve for
combined DG (UPF) with Capacitor
placement

55
Table 9. Numerical results of IEEE 33
node RDS with low power factor
Cases
Optimal
location
Optimal
Size
Power Loss
(kW)
0.85pf
Capacitor 29 1
84.3753
DG 30 1.8
0.9pf
Capacitor 29 1
83.5144
DG 30 1.7

56
Table 10. Comparison of optimal location, size and minimum voltage for
proposed with existing method
Particulars
Conventional Method [1] MFO (proposed)
Location and
size
Min voltage
(p.u.)
Location
and size
Min voltage
(p.u.)
Base case - 0.9065 at 18 - -
Capacitor
1.0 MVAr at
33
0.91654 at 18
1.7 MVAr at
29
0.9499 at
18
DG at UPF 1.0 MW, at 18 0.9311 at 33 1MW at 12
0.9199 at
18
DG and
Capacitor
1.0 MW at 18
and 1.0 MVAr
at 33
0.96003 at 30
1.0 MW at
12 and
1.2 MVAr at
30
0.9617 at
18
0.9 PF lag
1.0 MW at 18
and 1.0 MVAr
at 33
0.9646 at 30
1.8 MW at
30 and 1.0
MVAr at 29
0.9312 at
18
0.85 PF lag
0.8 MW at 18
and 0.8 MVAr
at 33
0.9566 at 30
1.7 MW at
30 and 1.0
MVAr at 29
0.9309at 18

57
Table 11. Comparison of power loss and loss reduction
for proposed with existing method
Particulars
Conventional Method [1] MFO (proposed)
Network loss
(kW)
% Loss
reduction
Total real
power loss
(kW)
% Loss
reduction
Base case 213.3 - - -
Capacitor 164.6 22.83 157.6864 26.0729
DG at UPF 142.34 33.29 129.9648 39.0695
DG and
Capacitor
96.70 54.66 75.0069 64.8350
0.9 PF lag 90.9 57.38 83.5144 60.8465
0.85 PF lag 89.72 57.94 84.3753 60.4429

SUMMARY
58
In this work has been subjected to various case studies with
different configurations under two bench mark test systems to
substantiate the excellence of the projected algorithm.
The outcome of the problem has been compared with the other
conventional approach in order to validate the results.
The solution of the case studies demonstrates the strength of
this algorithm in distribution systems.

Optimal Network Reconfiguration and Capacitor
Placement for improving Voltage Stability and
Net Savings in Radial Distributed Systems
59
 In this case study the combined methodology of Capacitor
placement and Network reconfiguration is properly applied to
maximize the net saving cost, minimize the power loss and improve
the voltage profile.
The size and location of capacitors and tie-line switches of nodes are
optimally allocated by the effectual Moth-Flame Optimization (MFO)
algorithm.
The MFO is an effective nature-inspired algorithm based on the
chemical effect of light on moths as an animal with bilateral
symmetry.
This algorithm provides a better solution with less computational
time by two searching operators of Moth and Flame.
The Performance of the MOF is analyzed by a standard test system
of IEEE 33 and 69-node RDS.

60
The best simulation results of loss reduction, voltage
enhancement, and cost-saving are numerically and graphically
reported.
The experimental outcomes obtained from this method are
compared with that of other soft computing techniques available
in the literature.

PROBLEM FORMULATION
61
Voltage stability Indices (VSI)
The VSI for the node can be mathematically represented using
the following equation.
Objective function
The prime objective of the proposed work is to maximize the net
saving cost of the RDS. The savings of the RDS mainly depends
on the reconfiguration process, power loss, and optimal
allocation and value of capacitors.
The MFO algorithm-based net saving maximization is
mathematically formulated as,
           
 
       
   2
2
4
1
2
2
0
.
4
2
2
0
.
4
1
2
m
V
jj
x
m
Q
jj
r
m
P
jj
r
m
Q
jj
x
m
P
m
V
m
VSI





  B
o
N
i
ci
P
B
I
LA
LB
E N
C
Q
C
N
C
T
P
P
C
f
B












 
1
max 

62
Equality and inequality Constraints
Real and Reactive power limits
The active and reactive power of RDS are mathematically represented as
follows
Reactive Power Compensation Limits
The reactive power delivered by each switched capacitor is limited by its
lower and upper limits as,
The acceptable capacitor range 0 to 1500 KVAr with step of 50 KVAr.
)
(
)
(
1
2
j
P
i
P
P
NL
j
loss
NB
i
D
SS 
 



)
(
)
(
)
(
1
1
2
k
Q
j
Q
i
Q
Q
NC
k
C
NL
j
loss
NB
i
D
SS 

 





max
0 Ci
Ci Q
Q 


63
Voltage Profile Limits
The voltage magnitude of each node in the radial distribution system is
strictly maintained as,
Line thermal Limits
The current flows in the branches should not go beyond the thermal
capacity of the line.
max
min
i
i
i V
V
V 

 
max
)
,
(
, j
i
j
i I
I 

64
IMPLEMENTATION OF MFO ALGORITHM
The following steps are used for optimal allocation and sizing of capacitor
with network configuration to enhance the voltage profile using MFO
algorithm.
1. Read the line, bus, and load data of RDS,
2. Run the distribution power flow and calculate the loss using the exact
loss formula for the base case.
3. Fix a number of Capacitors that are to be used in the Radial Distribution
System.
4. Initialize the parameters of the MFO algorithm such as Population,
dimension, maximum no of iteration number, lower bound, and upper
bound (node and size of Capacitor respectively).
5. Set iteration=1
6. Calculate fitness (i.e. loss in a network) for each moth by placing DG and
Capacitor at their respective buses using Eqn. (15).
7. Evaluate the objective functions of each moth and determine the net
savings of RDS...

65
8. Update the position of flame and save the best fitness values in an array
corresponding to Eqn. (16)
9. Update the record of flames and the flames are arranged using Eqn. (17)
based on their fitness values
10. Compute the present position of moths.
11. Check that all constraints are satisfied, if yes move to the next step, else
go to step 6.
12. Check If the number of iteration processes is equal to a maximum
number of iterations, go to step 13. Otherwise, go to step 5.
13. Display the global best solution of net saving cost and voltage profile
and STOP the program.

RESULTS AND DISCUSSIONS
66
In this study two standard test systems of IEEE 33 and 69
node RDS are considered to determine the superior
performance of the proposed MFO algorithm.
The one-line diagram of Network Reconfiguration with
capacitors for IEEE 33-node RDS is shown in fig. 1.
The enhanced voltage for each node is compared to base case
voltage and graphically displayed in fig 2 and obtained VSI
also compared with fig. 3.

67
Fig 1. Network Reconfiguration with capacitors for IEEE33-node
RDS

68
Fig.2. Comparison of Voltage
profile of base case and
proposed MFO
Fig.3. Comparison of VSI of
base case and proposed MFO

69
Table 1. Simulation results of IEEE 33- node with network
reconfiguration

70
Table 2: Comparisons of numerical results in various methods for IEEE
33-node system
Method
Switches
opened
Ploss (KW) VSImin
Vmin
(p.u.)
Node no.
Capacito
r size
(KVAr)
Loss
reductio
n (%)
Base
case
33,34,35
,36,37
202.67 0.6951 0.9131 - - -
SA [12]
7,14,9,3
2,37
107.89 0.8235 0.9526
6
28
29
30
9
1050
450
300
300
150
46.77
HSA [12]
33,14,8,
32,28
108.45 0.8208 0.9519
6
28
29
30
9
900
300
600
300
300
46.49
MFO
(propose
d)
8, 28,
17,33
14
103.66 0.83083 0.95517
13
32
25
200
500
650
49.78

71
Fig. 4: Convergence characteristics of
IEEE 33-node test system

72
Test case 2 : IEEE 69 node RDS
In the second case, the large-scale system of 69 node is
considered to find the ability of the projected MFO algorithm.
The reconfiguration process and capacitor allocation are
implemented in this test system.

73
Fig 5. Network Reconfiguration with capacitors for IEEE 69-node RDS

74
Fig. 6 Voltage profile for base
case and Reconfiguration with
placement of capacitor in IEEE
69-bus test system
Fig. 7 VSI for base case and
Reconfiguration with placement
of capacitor in
IEEE 69-bus test system

75
Table 3: Comparisons of numerical results in various methods for IEEE 69-
node system
Method Switches
opened
Ploss (KW) VSImin Vmin (p.u.) Node no. Capacitor
size
(KVAr)
Loss
reduction
(%)
Base case 69,70,71,
72,73
224.97 0.6833 0.9090 - - -
MFPA[12] 10,68,60,
44,15
153.93 0.7494 0.9305 64
63
62
350
600
250
31.58
MFO
(Proposed
)
26, 45,
58, 16, 10
85.793 0.82688 0.95359 61
64
21
900
200
250
61.86
Fig. 8: Convergence
characteristics of IEEE 69-
node test system

SUMMARY
76
In this study IEEE 33 and 69 node test systems are taken into
account to test the performance of the MFO.
This algorithm effectively maximizes the net saving cost,
minimizes power loss, and improves the stability of the system.
From the results, it can be concluded that it is a most excellent
and robust algorithm for solving all engineering optimization
problems.

77
•In recent years, Distributed Generation (DG) has been utilized in
electric power networks increasingly. DG units can affect the
system operational conditions in different ways such as voltage
profile improvement, amending voltage stability, reliability
enhancement, securing power market, etc.
•The Distribution Companies (DISCOs) are continually trying to
supply reliable and economical electric power to consumers. The
design, operation and maintenance of the DISCOs are framed on
the lowest cost and for the highest benefit.
•The voltage profile enhancement and power loss reduction are
two important tasks in the DISCOs for achieving maximum profit.
Many technical ideas and creative programs are being developed
by the DISCOs to improve the performance.

78
An innovative parameter less algorithm of MFO is suggested
to optimize the best position and accurate value of DG units.
The uncertainty of load demand, power generation, electricity
price and reliability are considered in this work.
The validation of this method is tested on standard IEEE 33
and IEEE 69 node system to illustrate the superior performance
of MFO algorithm.
The simulation results of voltage profile, power loss, location
and size of DG, cost-benefit of DISCOs and DG owners are
numerically and graphically presented.
The comparative study also has been made to prove the
success of the devised method.

79
PROBLEM FORMULATION
The prime objective of this study is to maximize the profit of DG owners and
minimize the various cost of DISCOs. Profit of DG owners is mathematically
represented as follows.
where t = 1, 2,3,…,N; NPV = net present value, IF = inflation rate, and IR =
interest rate
The present cost value of CDG,Gen is estimated using Eq. (8).
Benefit Evaluation
This cost actually includes the sum of energy loss reduction cost, which is
taken as US$ 0.05 per kWh and the cost of DG generated power at US$ 300
per kW.
MPF
F 
)
max(
 

 Expenses
Benefit
MPF

 


N
t IR
IF
NPVFactor
1 1
1
,

80
Cost of Energy Loss Reduction
Initially, the load flow solution for the test system is solved without DG to
read real power losses, and again the process is repeated with the presence of
DG. The difference in losses represents the net loss reduction given through
Eq. (4).
(4)
The obtained loss reduction with DG is converted into cost value using Eq.
(5)
(5)
The present cost value of CNLR is calculated using Eq. (6).
(6)
where t = 1, 2,3,…,N; CNLR = cost of net loss reduction.
DG
loss
loss P
P
NLR ,


8760
)
/
(cos
)
( 

 KWh
aving
tofenergys
NLR
C NLR



N
t
t
NLR
NLR C
C
NPV
1
)
( 

81
Cost of DG Power Generation
The type of DG considered for this study is a solar PV system. The data for
this cost is taken from [6] and is calculated using Eq. (7).
(7)
The present cost value of CDG,Gen is estimated using Eq. (8).
(8)
where t = 1, 2,3,…,N; CDG,Gen = cost of DG power generation.
Expenses Cost
This cost includes the sum of DG investment cost and cumulative of the
operation and maintenance cost of DG over the planning period.
yr
KW
RCOST
DGGENERATO
DGSIZE
C GEN
DG 

 /
(
)
(
)
,
(



N
t
t
GEN
DG
GEN
DG C
C
NPV
1
,
, )
( 

82
Operation and Maintenance
Cost This cost includes the operation and maintenance O&M cost of DG
connected to the grid and is calculated using Eq. (9). The O&M cost details
of DG placement were taken from
(9)
The present cost value of CDG,O&M is calculated using Eq. (10).
(10)
where t = 1, 2,3,…,N; CDG,O&M = cost of DG operation and maintenance.
Investment Cost
The invested cost of optimally placed solar PV-type DG is calculated using
Eq. (11).
(11)
The economic validation of the above discussed objective function depends
on the optimal location and rating of DG.
yr
KW
M
DGO
DGSIZE
C M
O
DG 

 /
&
(
)
(
)
&
,
(



N
t
t
M
O
DG
M
O
DG C
C
NPV
1
&
,
&
, )
( 
KW
t
ent
DGinvestim
DGSIZE
C INV
DG /
cos
(
)
(
)
,
( 


83
1. Read the line, bus and load data of RDS, Technical and Commercial
Information of DISCOs and DGs owner, Market price and used market
parameters.
2. Initialize the parameters of MFO algorithm such as Population size, elite
size and maximum no of iteration number.
3. Randomly generate the populations (P) of MFO using a Heuristic
algorithm subjected to system constrains
4. Evaluate the objective functions of each population and determine the
DG owner’s profit and DISCO’s cost
5. Set iteration=1.
6. Calculate fitness (i.e. voltage profile, VSI, power loss) for each moth by
placing DG at their respective buses using eqn. (15).
7. Evaluate the objective functions of each moth and determine the profit of
DG owners and various cost of DISCOs..

84
corresponding to eqn. (16)
9. Update the record of flames and the flames are arranged using eqn. (17)
10. Compute the present position of moths.
11. Check the all constrains are satisfied, if yes move to next step, else go to
step 6.
12. Check If the number of iteration process is equal to maximum number of
iterations, go to step 13. Otherwise go to step 5.
13. Display the global best solution of various cost of DISCOs and profit of
DG owners STOP the program.

RESULTS AND DISCUSSION
85
Table1 Voltage profile for base case placement of DG in IEEE 33-bus test
system
Bus
No.
Base Case
voltage
MFO Voltage (Proposed)
1 1.0000 1.0000
2 0.9970 0.9978
3 0.9829 0.9888
4 0.9754 0.9845
5 0.9680 0.9803
6 0.9495 0.9688
7 0.9460 0.9649
8 0.9323 0.9496
9 0.9260 0.9423
10 0.9201 0.9366
11 0.9192 0.9357
12 0.9177 0.9342
13 0.9115 0.9282
14 0.9092 0.926
15 0.9078 0.9246
16 0.9064 0.9233

86
17 0.9043 0.9213
18 0.9037 0.9207
19 0.9965 0.9973
20 0.9929 0.9936
21 0.9922 0.9929
22 0.9916 0.9922
23 0.9793 0.9855
24 0.9726 0.9793
25 0.9693 0.976
26 0.9475 0.9688
27 0.9450 0.9691
28 0.9335 0.9695
29 0.9253 0.9722
30 0.9217 0.9695
31 0.9176 0.9637
32 0.9167 0.9627
33 0.9164 0.9622
CONT..

87
Fig. 1 Voltage profile for
base case placement of
DG in IEEE 33-bus test
system
Fig. 2 VSI for base case
placement of DG in IEEE
33-bus test system

88
Table 2 VSI for base case placement of DG in IEEE 33-bus test system
Bus
No.
Base case VSI MFO VSI (Proposed)
1 1.0000 1.0000
2 0.9978 0.9978
3 0.9888 0.9888
4 0.9845 0.9845
5 0.9803 0.9803
6 0.9688 0.9688
7 0.9649 0.9649
8 0.9496 0.9496
9 0.9423 0.9423
10 0.9366 0.9366
11 0.9357 0.9357
12 0.9342 0.9342
13 0.9282 0.9282
14 0.926 0.926
15 0.9246 0.9246
16 0.9233 0.9233
17 0.9213 0.9213
18 0.9207 0.9207
19 0.9973 0.9973
20 0.9936 0.9936

89
21 0.9929 0.9929
22 0.9922 0.9922
23 0.9855 0.9855
24 0.9793 0.9793
25 0.976 0.976
26 0.9688 0.9688
27 0.9691 0.9691
28 0.9695 0.9695
29 0.9722 0.9722
30 0.9695 0.9695
31 0.9637 0.9637
32 0.9627 0.9627
33 0.9622 0.9622
Cont…

90
Various cost of DISCOs Fuzzy with DE Algorithm MFO
(Proposed algorithm)
Cost of Energy Loss
Reduction (K$/year)
39.89
36.43
(K$/year)
341.16
407.77
Cost of DG
(K$/year)
18.19
21.748
Investment Cost of DG
(K$/year)
2302.86
2752.46
Table 3 simulation results of IEEE 33 bus system after 1 year of planning
period

91
Various cost of
DISCOs
Fuzzy with DE Algorithm
MFO
Cost of Energy Loss
Reduction (M$/year)
1.94 1.77
Cost of DG Power
Generation (M$/year)
16.58 19.82
Cost of DG
(M$/year)
0.88 1.06
(M$/year)
2.30 2.75
Benefit of DISCOs (M$/year) 18.53 21.59
Expenses of DISCOs
(M$/year)
3.19 3.81
Total profit of DISCOs
(M$/year)
15.34 17.78
Table 4 simulation results of IEEE 33 bus system after
planning period

92
Fig. 3 Comparison of
cost, benefit and profit
of DISCOs for IEEE 33-
bus test system
Fig. 4 Convergence curve
of IEEE 33-bus test
system

93
Results for IEEE69 node test system
Table 5 Voltage profile for base case and placement of DG in IEEE 69-bus test
system
Bus
No.
Base Case voltage MFO voltage (Proposed)
1 1.0000 1.0000
2 1.0000 1.0000
3 0.9999 1.0000
4 0.9998 0.9999
5 0.9990 0.9994
6 0.9901 0.9954
7 0.9808 0.9913
8 0.9786 0.9903
9 0.9774 0.9899
10 0.9724 0.9849
11 0.9713 0.9838
12 0.9682 0.9807
13 0.9652 0.9778
14 0.9623 0.9749
15 0.9595 0.9721
16 0.9589 0.9716

94
Bus No. Base Case voltage MFO voltage (Proposed)
17 0.9580 0.9707
18 0.9580 0.9707
19 0.9576 0.9702
20 0.9573 0.9699
21 0.9568 0.9694
22 0.9568 0.9694
23 0.9567 0.9694
24 0.9565 0.9692
25 0.9564 0.9690
26 0.9563 0.9690
27 0.9563 0.9690
28 0.9999 0.9999
29 0.9999 0.9999
30 0.9997 0.9998
31 0.9997 0.9997
32 0.9996 0.9996
33 0.9994 0.9994
34 0.9990 0.9990
Cont…

95
Cont…
35 0.9989 0.9990
36 0.9999 0.9999
37 0.9998 0.9998
38 0.9996 0.9996
39 0.9995 0.9996
40 0.9995 0.9996
41 0.9988 0.9989
42 0.9986 0.9986
43 0.9985 0.9985
44 0.9985 0.9985
45 0.9984 0.9984
46 0.9984 0.9984
47 0.9998 0.9998
48 0.9985 0.9986
49 0.9947 0.9947
50 0.9942 0.9942
51 0.9785 0.9903
52 0.9785 0.9903

96
Cont…
53 0.9747 0.9894
54 0.9714 0.9889
55 0.9669 0.9883
56 0.9626 0.9877
57 0.9400 0.9864
58 0.9289 0.9858
59 0.9246 0.9856
60 0.9196 0.9857
61 0.9122 0.9851
62 0.9119 0.9848
63 0.9115 0.9845
64 0.9096 0.9827
65 0.9090 0.9822
66 0.9713 0.9838
67 0.9713 0.9838
68 0.9678 0.9804
69 0.9678 0.9804

97
Table 6 VSI for base case and placement of DG in IEEE 69-bus test system
Bus
No.
Base Case voltage MFO voltage (Proposed)
1 0.999873 0.999901
2 0.999747 0.999802
3 0.999383 0.999520
4 0.996107 0.997632
5 0.960462 0.981715
6 0.924856 0.965590
7 0.917012 0.962027
8 0.912785 0.960235
9 0.894102 0.941076
10 0.890205 0.937074
11 0.878620 0.925191
12 0.868057 0.914353
13 0.857676 0.903700
14 0.847491 0.893246
15 0.845655 0.891359
16 0.842551 0.888172

98
17 0.842524 0.888145
18 0.840888 0.886465
19 0.839839 0.885388
20 0.838148 0.883651
21 0.838125 0.883628
22 0.837873 0.883370
23 0.837326 0.882807
24 0.836734 0.882199
25 0.836490 0.881949
26 0.836421 0.881879
27 0.999719 0.999774
28 0.999433 0.999488
29 0.998949 0.999004
30 0.998864 0.998919
31 0.998437 0.998491
32 0.997413 0.997468
33 0.996074 0.996129
34 0.995806 0.995861
Cont…

99
Cont…
35 0.999692 0.999747
36 0.999005 0.999060
37 0.998372 0.998427
38 0.998190 0.998244
39 0.998181 0.998236
40 0.995394 0.995449
41 0.994232 0.994287
42 0.994079 0.994134
43 0.994046 0.994101
44 0.993654 0.993709
45 0.993652 0.993707
46 0.999184 0.999322
47 0.994199 0.994336
48 0.978866 0.979002
49 0.976842 0.976978
50 0.916908 0.961901
51 0.916872 0.961864
52 0.902380 0.958548

100
Cont…
53 0.890413 0.956633
54 0.874070 0.954215
55 0.858387 0.952091
56 0.778088 0.947590
57 0.744278 0.946861
58 0.731249 0.946575
59 0.715445 0.947414
60 0.692565 0.945709
61 0.691969 0.944790
62 0.690793 0.943416
63 0.685034 0.936688
64 0.683323 0.934684
65 0.890005 0.936868
66 0.890003 0.936866
67 0.877483 0.924022
68 0.877480 0.924019
69 0.999873 0.999901

101
Fig. 5 Voltage profile for
base case and placement
of DG in IEEE 69-bus test
system
Fig. 6 VSI for base case
and placement of DG in
IEEE 69-bus test system

102
Table 5 simulation results of IEEE 69 bus system after 1 year of planning
period
Various cost of DISCOs
MFO
(K$/year)
61.84
(K$/year)
600
Cost of DG
(K$/year)
32
(K$/year)
4050

103
Table 6 simulation results of IEEE 69 bus system after planning period
Various cost of
DISCOs
MFO
(M$/year)
3.01
(M$/year)
29.17
Operation and Maintenance Cost
of DG
(M$/year)
1.56
(M$/year)
4.05
Benefit of DISCOs (M$/year) 32.18
Expenses of DISCOs (M$/year) 5.61
Total profit of DISCOs (M$/year) 26.57

104
Fig. 7 Convergence curve of IEEE 69-bus test system

SUMMARY
105
In this work solves the voltage stability problem under
deregulated environment using MFO.
The proposed methodology effectively maximize the
profit of DG owners and minimize the various costs of
DISCO’s. it also improve the voltage profile and reduce the
network losses in IEEE 33 bus and 69 bus node test system.
Moreover the comparison study made with fuzzy and
Differential Evolution (DE) algorithm. To evaluate the
performance of proposed (MFO) algorithm.

Network Reconfiguration with Optimal allocation
of Capacitors and DG units for Maximizing DISCOs
Profit in a Restructured Power Market
106
In this research, recently developed and comprehensive moth flame
optimization algorithm is presented for maximizing the profit of the DISCOs
under competitive environment.
The execution process of network reconfiguration and proper placement and
sizing of capacitors and DG units are taken care of by MFO algorithm.
Reconfiguration of network is a mechanism of shuffling existing pattern of
feeders duly changing ON and OFF status of tie-line switches to improve the
performance of the DISCOs.
The algorithm also minimizes the various types of cost such as investment,
maintenance and operational cost.
The proposed MFO algorithm is implemented on IEEE 33 node and 69
node systems to evaluate its performance.
Evaluation of solution in MATLAB software demonstrates the skills of
MFO in DISCOs.

107
PROBLEM FORMULATION
The cost evaluation and benefit evaluation of allocation of Capacitors and
DG units in a network is represented as below:
Cost evaluation of DG units and Capacitors allocation
Investment cost
(1)
Operating Costs
(2)
Present worth Factor (3)
For an operating cost in a planning year, present worth value is given by
the equation (4)
(4)

 





NCap
i
j
capj
NDG
i
i
DGi IC
K
IC
K
C
1
1
1
 






NDG
i
i
DGi T
OC
K
C
1
2
t
n
t
t
IR
IF











1 1
1

    t
NDG
i
i
DGi T
OC
K
C
PWV 




 
1
2

108
Maintenance Costs
(5)
The annual cost present worth value during the period of planning is
estimated as in equation (6)
(6)
Benefit evaluation of DG units and Capacitors allocation
a. Distribution line active power demand reduction
Energy delivered to Grid in a segment over time is given in equation (7),
(7)
The present worth value of power generated by distribution company is
given by the equation (8)
(8)
    










 
 

NCap
i
capj
j
capj
NDG
i
DGi
i
DGi MC
IC
K
MC
IC
K
C
1
1
3
      t
NCap
i
capj
j
capj
NDG
i
DGi
i
DGi MC
IC
K
MC
IC
K
C
PWV 












 
 
 1
1
3






NDG
i
G
DGi T
EP
K
B
1
1
  t
NDG
i
G
DGi T
EP
K
B
PWV 




 
1
1

109
b. Revenue from loss reduction
(9)
The present worth value for loss minimization revenue in a period of planning
is given by equation (10),
(10)
Objective function for DISCOs profit
Maximum profit of DISCOs is given by the equation (11) and (12),
(11)
 
 





NDG
i
NCap
j
G
ij T
EP
LOSS
B
1 1
2
  t
NDG
i
NCap
j
G
ij T
EP
LOSS
B
PWV 





  
 
1 1
2
s
Investment
Benefits
Z 

Max
   
1 1
1 1
.
1 . 1 .
NCap
NDG
t
DGi G ij G DGi i
i j
NCap
NDG
t t
DGi i DGi Cap j capj
i j
MaxZ K EP LOSS EP K OC T
K IC MC K IC MC

 
 
 
 
       
 
 
   
     
   
 
 
(12)

110
System Constraints
a. Constraints for Power balance
Constraints for active and reactive power are given in equation (13) and (14),
(13)
(14)
b. Voltage constraints
Each bus must satisfy the following voltage constraint as in equation (15),
(15)
c. Current limit
Current flowing in the distribution segment should not exceed from their
maximum ratings as given in equation (16),
   
1
cos sin , 1,2,3,......
N
i i j ij i j ij i j
j
P VV G B i N
   

 
     
 

   
1
sin cos , 1,2,3,......
N
i i j ij i j ij i j
j
Q VV G B i N
   

 
     
 

min max
i i i
V V V
  i N
 
,
,
i iRated Br
I I i N
  
(16)

111
d. Constraints for size of capacitors and DGs
al
T
DG S
S Re
30
.
0

.
Re
30
.
0
. act
T
Cap S
S 
(17)

IMPLEMENTATION
112
The following procedural steps are followed for optimal allocation
and sizing of combined DG and capacitor with network configuration
to evaluate the profit of DISCOs in a competitive electricity market
using MFO algorithm.
1. Read the line data, bus data and load data of RDS, cost of
installation, running cost and maintenance cost of the capacitor
and DG, Interest rate, Inflation rate, Market price and Planning
period.
2. Run the distribution power flow and calculate the loss using exact
loss formula for base case.
3. Choose the number of Capacitors and DG to be used to in Radial
Distribution System.
4. Set the parameters of MFO algorithm such as Population,
dimension, maximum number of iterations, lower and upper bound
(size and node Capacitor and DG respectively).
5. Fix iteration=1

113
6. Compute fitness (i.e. loss in network) for each and every moth by
installing DG and Capacitor at their respective buses using eqn. (15).
7. Evaluate the objective functions of each moth and determine the profit
of DISCOs.
corresponding to eqn. (16)
9. Update the record of flames and the flames are arranged using eqn. (17)
10.Estimate the present position of moths.
11.If all constraints are satisfied move to next step, if all constraints not
satisfied go to step 6.
12.Go to step 13, if maximum number of iterations is attained. else go to
step 5.
13.Display the global best solution of various cost and DISCOs profit and
STOP the program.

RESULT AND DISCUSSION
114
With a view of assessing the superiority of the
proposed MFO, a test has been carried out on IEEE 33
bus and 69 node redial distribution network under
deregulated environment.
Obtaining global solution of deregulated system is
more complex and competitive than conventional
RDS.
The proposed methodology has more ability to
achieve the best numerical solutions.

115
Fig. 1. Network Reconfiguration by
installation of DGs and Capacitor in IEEE
33-node test system

116
Test system 1: IEEE 33-node RDS
In this test case, capacitors and DG placement are carried out in the
33-node RDS with planning period of 10 years.
Network reconfiguration is also made to improve the voltage profile
and profit of DISCOs.
The projected MFO is properly optimized the location and size of the
DG and capacitor.
The best placement and tuned value of DG and capacitor are 30, 18
and 1.5 MW, 0.9 MVAr respectively.
The tie-line switches 33, 34, 35, 36 are used for network
reconfiguration.
The switches 7, 28, 12, 15, 21 are opened for enhancing the voltage
stability by proposed MFO method.
The one line diagram of network Reconfiguration with positioning of
DG and Capacitor in 33-bus test system by proposed method is
displayed in figure1.

117
Table 1. IEEE 33-node test system
Parameters Optimal Value
Open switches 7, 28, 12, 15, 21
Tie-line switches 33, 34, 35, 36
DG Optimal placement 30
Capacitor Optimal placement 18
Capacitor Optimal sizing 0.9 MVAr
DG Optimal sizing 1.5 MW
Voltage stability index (p.u) 0.91256
Minimum Voltage (p.u) 0.97740
Fig. 2. Voltage profile
for base case and
Reconfiguration by the
installation of DG units
and capacitors for IEEE
33 node test system

118
Fig. 3. VSI for base case and
Reconfiguration with
installation of DG and
capacitor in IEEE 33node test
system
Table 2. Cost-Benefit analysis of DISCOs for IEEE
33 node test system
Costs, Benefits and Profits of DISCOs Values (Rs)
Capacitor Installation cost 9 x 104
DG Installation cost 375 x 105
Reduction in purchased energy benefits 4.99 x 108
Loss reduction benefits 5.4979x 107
DG Maintenance cost 6.342 x 107
DG Operational costs 2.495 x 108
Capacitor Maintenance cost 1.8988 x 105
Planning period 10 year
Total profit of DISCOs 2032.8 x 105

119
Table 3. Comparison of Various Cost and
Benefits of DISCOs for IEEE 33-nodetest
system
Parameters
Value of Various costs,
Benefits and Profit of
DISCOs
Value of Various
costs, Benefits and
Profit of DISCOs
PSO [21]
MFO
(Proposed)
Location DG and Capacitor
8
30
30
18
Size DG and Capacitor
1.5 (MW)
0.9 (MVAr)
1.5 MW
0.9 MVAr
Network Reconfiguration No Yes
Open switches - 7,28,12,15,21
Tie-line switches - 33, 34, 35,36
Planning period 10 year 10 year
Installation cost of DG (Rs) 375 x 105 375 x 105
Installation cost of Capacitor (Rs) 9 x 104 9 x 104
Benefits of loss reduction (Rs) 4.35 x 107 5.4979x 107
Benefits of reduction in purchased
energy (Rs)
4.99 x 108 4.99 x 108
Operational costs of DG (Rs) 2.49 x 108 2.495 x 108
Maintenance cost of DG (Rs) 6.34 x 107 6.342 x 107
Maintenance cost of Capacitor (Rs) 1.94 x 105 1.8988 x 105
Total profit of DISCOs (Rs) 1937.94 x 105 2032.8 x 105

120
Fig. 4. Convergence curve of 33-node test system

121
Fig. 5. Network Reconfiguration with placement of DG and Capacitor in
IEEE 69 node test system

122
Test case 2: IEEE 69-node test system
In order to validate the applicability of the MFO algorithm, it is tested on a of 69 bus
test system to obtain maximum profit of DISCOs.
Planning period of Ten years is considered for this process.
The MFO algorithm computes the required configuration with best location and size
of the DG and capacitor.
The one line diagram of network reconfiguration with placement of DG and
Capacitor in 69 bus test system is shown in figure 5.

123
Fig. 6.Voltage profile for base
case and Reconfiguration with
placement of DG and
capacitor in IEEE 69-bus test
system
Fig. 7. VSI for base case and
Reconfiguration with
placement of DG and capacitor
in IEEE 69-bus test system

124
Table 4. Optimal location and sizing of DG and capacitor
for IEEE 69- node DISCOs considering network
reconfiguration
Parameters Optimal Value
Open switches 14, 17, 55, 12, 41
Tie-line switches 69, 70, 71, 72, 73
DG Optimal location 61
Capacitor Optimal location 27
DG Optimal sizing 1.5 MW
Capacitor Optimal sizing 0.8 MVAr
Minimum Voltage (p.u) 0.97985
Voltage stability index (p.u) 0.91896
Power loss (KW) 24.05

125
Table 5. Cost-Benefit analysis of DISCOs for IEEE 69-nodetest
system
Costs, Benefits and Profits of
DISCOs
Values (Rs)
DG Installation cost 375 x 105
Capacitor Installation cost 10.5 x 104
Loss reduction benefits 6.684x 107
Reduction in purchased
energy benefits
4.99x 108
Operational charges of DG 2.495 x 108
Maintenance charges of DG 6.342 x 107
Maintenance charges of
Capacitor
1.8988x 105
Planning period 10 year
Total profit of DISCOs 2151.4x 105

126
Table 6. Comparison of Various Cost and Benefits of DISCOs for
IEEE 69-nodetest system
Parameters Value of Various costs, Benefits and
Profit of DISCOs
PSO [56]
MFO
(Proposed)
61 61
Capacitor Location 61 27
DG Size 1.5 MW 1.5 MW
Capacitor Size 1.2 MVAr 1.05 MVAr
Network Reconfiguration No Yes
Open switches - 14, 17, 55, 12, 41
Tie-line switches - 69, 70, 71, 72, 73
Planning period 10 year 10 year
Installation cost of DG (Rs) 375 x 105 375 x 105
Installation cost of Capacitor
(Rs)
1.2 x 105 10.5 x 104
Benefits of loss reduction (Rs) 6.52 x 107 6.684x 107
Benefits of reduction in
purchased energy (Rs)
4.99 x 108 4.99x 108
Operational costs of DG (Rs) 2.49 x 108 2.495 x 108
Maintenance cost of DG (Rs) 6.34 x 107 6.342 x 107
Maintenance cost of Capacitor
(Rs)
2.45 x 105 1.8988x 105
Total profit of DISCOs (Rs) 2137.19 x 105
2151.40 x 105

127
Fig. 8. Convergence curve of in IEEE 69-
node test system

SUMMARY
128
The proposed methodologies effectively reduce the line losses
and thereby boost the benefits of the DGs owners.
Moreover, it also saves various types of cost such as
investment and operational cost by selling the power recovered
from the minimized line losses.
The test studies were carried out on standard IEEE 33 and 69-
node systems.
The experimental results show that the proposed MFO
algorithm is a promising approach for improving the profit
margin of distribution companies.

CONCLUSION
129
In this research work, an intelligent computational optimization
algorithm of moth–flame optimization (MFO) is proposed to solve
the various voltage stability problems in regulated and deregulated
power system.
MFO is considered one of the promising meta-heuristic
algorithms and successfully applied and improve the voltage
stability of radial distribution systems.
It has two essential components of moths and flames. Moreover,
both the moths and the flames have been considered as a solution.
 The two searching operators of MFO has effectively improve
the voltage profile, maximize the net saving cost and reduce the
network losses of RDS by proper allocation and sizing of various
capacitors, DG units and network reconfiguration process.

130
Also, a devised MFO algorithm has been applied to improve the
benefits of DISCOs and DG owners under deregulated environment.
Here, the various cost and revenue of DISCOs and DG owners are
calculated.
It includes investment cost of capacitors and DGs, operating and
maintenance cost of capacitors and DGs, customer interruption cost,
substation cost, Revenue and profit of DG owners..Numerical
example with IEEE standard RDS test systems has been considered
to prove the performance of MFO.
The result shows that the proposed algorithm offers an increase in
profit with less computational time compared to other competitive
algorithms. Therefore, it can be concluded that the proposed MFO
approach paves the best way for solving the power system
optimization problems under deregulated environment.

131
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