FINAL VERSION sss.pptx

by
S.K.B.PradeepKumar Ch
1834110033
Supervisor
Dr. G.BALAMURUGAN
Professor
DEPARTMENT OF ELECTRICAL ENGINEERING
ANNAMALAI UNIVERSITY
ANNAMALAI NAGAR
November 2022

 OBJECTIVE OF THE Research
 Literature review & Gaps
 Problem (Objective Function) Formulation
 Solution Methodology
 Proposed Optimization Algorithm
 Results And Discussions
 Conclusions And References
 Publications supported to Research
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.
 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 are the more important
optimization problems for 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 and Capacitors, Network
reconfiguration to enhance the voltage profile,
minimize the power loss and improve the net saving
cost of RDS.
Gaps Identified from Literature

8
 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.

9
 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

10
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

11
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

12
•These all have been successfully employed to solve
the voltage stability problem, However each
algorithm has its own merits & demerits
•This thesis simple and parameter less algorithm of
MFO is proposed to solve the various voltage
stability problem under regulated and deregulated
power system

14
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.

15
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 figure 2, where the light is the
prime source and convergence of moths be exercised by
preserving a fixed angle.

16
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
 
 
 

 
 
 

17
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,
1,1 1,2 1,
2,1 2,2 2,
,1 ,2 ,
d
d
n n n d
F F F
F F F
M
F F F

 
 

 

 

 

 
 
1
2
n
OF
OF
OF
OF
 
 
 

 
 
 

18
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
 

19
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

 
  
 
 

21
In this article, an uncomplicated 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.

22
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.

23
The mathematical equation for computing the VSI is
formulated as The mathematical equation for computing
the VSI is formulated as
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
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
 
 

24
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.

25
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. 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

    

26
4. Voltage profile
Bus voltage of each bus must lies between minimum and
maximum limits of the tolerable limits.
5. Line current
The line current in each branch must lie within the
thermal limit.
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
 
   
 

27
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
 
 
 

28
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 areto 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.
STEP 6: Calculate fitness (i.e. loss in network) for each
moth by placing DG and Capacitor at their respective
buses using eqn. (15).

29
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

31
CASE STUDY AND RESULTS
In the present investigation, two standard test systems
such as 12 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: 12- node RDS
32
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

33
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 12 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.

34
Table 1. Voltage profile for 12-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

35
Fig. 6. Voltage profile for 12 bus system with
different cases

36
Table 2. VSI for 12- 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

37
Fig. 7. VSI for 12-node system with different
cases

38
Table 3. Simulation results of 12-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
39

40
Fig. 10. Convergence curve of DGs with Capacitor
placement

41
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 12.6028 36.6375
DG at UPF 109.2 45.09 10.7744 45.8301
DG and
Capacitor
71.93 63.8 3.1693 84.0659

42
Fig. 11. Power loss for 12-node system

43
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: 33 Bus RDS
44
In second case, the large scale system of 33 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.

45
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

46
Table 6. Voltage profile for 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

47
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..

48
Table 7. VSI for 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

49
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..

50
Fig. 12. Voltage profile
for 33 node RDS (Case1,
2 and 3)
Fig. 13. Voltage profile for 33
node RDS (Case 4 and 5)

51
Fig. 14. VSI for 33 node
RDS (Case 1, 2 and 3)
Fig. 15. VSI for 33 node RDS (Case
4 and 5)

52
Table 8. Numerical results for 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

53
Fig.17. Convergence curve for
Single DG placement
Fig. 18. Convergence curve for
combined DG (UPF) with Capacitor
placement

54
Table 9. Numerical results of 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

55
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

56
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
57
The 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
58
This paper presents 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 33 and 69-node RDS.

59
The best simulation results of loss reduction, voltage
enhancement, and cost-saving are numerically and
graphically reported.
The dominance of the obtained results is compared with
other soft computing methods available in the literature.

PROBLEM FORMULATION
60
2.1 Voltage stability Indices (VSI)
The VSI for the node can be mathematically represented
using the following equation.
2.2 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 

61
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 


62
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 

63
3.2 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...

64
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
65
In this study two standard test systems of 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 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.

66
Fig 1. Network Reconfiguration with capacitors for 33-node RDS

67
Fig.2. Comparison of Voltage
profile of base case and
proposed MFO
Fig.3. Comparison of VSI of
base case and proposed MFO

68
Table 1. Simulation results of 33- node with network
reconfiguration

69
Table 2: Comparisons of numerical results in various methods for 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

70
Fig. 4: Convergence characteristics of
33-node test system

71
Test case 2 : 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.

72
Fig 5. Network Reconfiguration with capacitors for 69-node RDS

73
Fig. 6 Voltage profile for base
case and Reconfiguration with
placement of capacitor in 69-
bus test system
Fig. 7 VSI for base case and
Reconfiguration with placement
of capacitor in
69-bus test system

74
Table 3: Comparisons of numerical results in various methods for 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 69-node
test system

SUMMARY
75
In this study 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.

76
•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 anyhow to improve the performance.

77
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 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.

78
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
,

79
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).
The obtained loss reduction with DG is converted into cost value
using Eq. (5)
The present cost value of CNLR is calculated using Eq. (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
)
( 

80
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).
The present cost value of CDG,Gen is estimated using Eq. (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
,
, )
( 

81
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
The present cost value of CDG,O&M is calculated using Eq. (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).
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
(
)
(
)
,
( 


82
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..

83
an array corresponding to eqn. (16)
eqn. (17) based on their fitness values
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
84
Table1 Voltage profile for base case placement of DG in 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

85
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..

86
Fig. 1 Voltage profile for
base case placement of
DG in 33-bus test system
Fig. 2 VSI for base case
placement of DG in 33-bus
test system

87
Table 2 VSI for base case placement of DG in 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

88
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…

89
Various cost of DISCOs MFO
(Proposed algorithm)
Fuzzy with DE
Algorithm
Cost of Energy Loss
Reduction (K$/year)
36.43
39.89
(K$/year)
407.77
341.16
Cost of DG
(K$/year)
21.748
18.19
Investment Cost of DG
(K$/year)
2752.46
2302.86
Table 3 simulation results of 33 bus system after 1 year of planning
period

90
Various cost of
DISCOs
MFO
Fuzzy with DE
Algorithm
Cost of Energy Loss
Reduction (M$/year)
1.77 1.94
Cost of DG Power
Generation (M$/year)
19.82 16.58
Cost of DG
(M$/year)
1.06 0.88
(M$/year)
2.75 2.30
Benefit of DISCOs (M$/year) 21.59 18.53
Expenses of DISCOs
(M$/year)
3.81 3.19
Total profit of DISCOs
(M$/year)
17.78 15.34
Table 4 simulation results of 33 bus system after planning
period

91
Fig. 3 Comparison of
cost, benefit and profit
of DISCOs for 33-bus
test system
Fig. 4 Convergence curve
of 33-bus test system

92
Results for 69 node test system
Table 5 Voltage profile for base case and placement of DG in 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

93
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…

94
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

95
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

96
Table 6 VSI for base case and placement of DG in 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

97
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…

98
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

99
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

100
Fig. 5 Voltage profile for
base case and placement
of DG in 69-bus test
system
Fig. 6 VSI for base case
and placement of DG in
69-bus test system

101
Table 5 simulation results of 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

102
Table 6 simulation results of 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

103
Fig. 7 Convergence curve of 69-bus test system

summary
104
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 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
105
In this paper, 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.

106
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
Operating Costs
Present worth Factor
For an operating cost in a planning year, present worth value is
given by the equation (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

107
Maintenance Costs
The annual cost present worth value during the period of planning is
estimated as in equation (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),
The present worth value of power generated by distribution company is given
by the equation (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

108
b. Revenue from loss reduction
The present worth value for loss minimization revenue in a period of
planning is given by equation (10),
Objective function for DISCOs profit
Maximum profit of DISCOs is given by the equation (11) and (12),
 
 





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

 
 
 
 
       
 
 
   
     
   
 
 

109
System Constraints
a. Constraints for Power balance
Constraints for active and reactive power are given in equation (13)
and (14),
b. Voltage constraints
Each bus must satisfy the following voltage constraint as in equation
(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
  

110
d. Constraints for size of capacitors and DGs
al
T
DG S
S Re
30
.
0

.
Re
30
.
0
. act
T
Cap S
S 

IMPLEMENTATION
111
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

112
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.
an array corresponding to eqn. (16)
eqn. (17) based on their fitness values
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
113
With a view of assessing the superiority of the proposed MFO, a test
has been carried out on 33 bus and 69 node redial distribution
network under deregulated environment.
The 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.
The optimization process has been performed in MATLAB version
R2014a Intel core i3 PC with 2.10 GHz speed and 4GB RAM.

114
Fig. 1. Network Reconfiguration by
installation of DGs and Capacitor in 33-
node test system

115
Test system 1: 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.

116
Table 1. 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 33
node test system

117
Fig. 3. VSI for base case and
Reconfiguration with
installation of DG and
capacitor in 33node test
system
Table 2. Cost-Benefit analysis of DISCOs
for 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

118
Table 3. Comparison of Various Cost and
Benefits of DISCOs for 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

119
Fig. 4. Convergence curve of 33-node test system

120
Fig. 5. Network Reconfiguration with placement of DG and Capacitor in
69 node test system

121
Test case 2: 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.

122
Fig. 6.Voltage profile for base
case and Reconfiguration with
placement of DG and
capacitor in 69-bus test
system
Fig. 7. VSI for base case and
Reconfiguration with
placement of DG and capacitor
in 69-bus test system

123
Table 4. Optimal location and sizing of DG and capacitor
for 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

124
Table 5. Cost-Benefit analysis of DISCOs for 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

125
Table 6. Comparison of Various Cost and Benefits of DISCOs for
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

126
Fig. 8. Convergence curve of in 69-node
test system

SUMMARY
127
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
128
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.

129
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.

130
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1. S.K.B.Pradeep Kumar Ch1*, G. Balamurugan1, Y. Butchi raju2,
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Environment, Communicated to Advanced Engineering Science

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