TELKOM
NIKA
, Vol.12, No
.3, Septembe
r 2014, pp. 5
41~548
ISSN: 1693-6
930,
accredited
A
by DIKTI, De
cree No: 58/DIK
T
I/Kep/2013
DOI
:
10.12928/TELKOMNIKA.v12i3.77
541
Re
cei
v
ed Fe
brua
ry 25, 20
14; Re
vised
May 13, 20
14
; Accepte
d
Ju
ne 12, 201
4
Multi Population
Ev
olutionary Pr
ogramming
Appr
oach
for Distributed
Gene
rati
on Ins
t
al
l
a
ti
on
M.F. Baharo
m
1a
, M.H. Jal
i
2a
, M
.
Fani
3a
, W.
M.
Bukha
r
i
4a
, Z.H. Bohari
5a
, M.N M.
Nasir
6a
,
N. Adna
n
1b
a
Departeme
nt of Electrical, U
n
iversit
y
T
e
chn
i
cal
Mal
a
ysi
a
Melak
a
,UT
e
M, Hang T
uah Ja
ya,
761
09 D
u
ria
n
T
unggal, Mela
ka, Mala
ysi
a
b
Jurutera Peru
ndi
ng Z
a
a
ba , JPZ
,
No. 4 Jalan Punc
ak Seti
a
w
a
ngsa
4,
T
a
man Setia
w
a
n
g
sa, 542
00, Ku
ala
Lumpur Malaysia
Email: moh
a
m
ad.faiza
l
@ute
m.edu.m
y
1a
, mohd.h
a
fiz@ut
e
m
.edu.m
y
2a
, fani@ut
e
m.edu.
m
y
3a
,
bukh
a
ri@
u
tem.edu.m
y
4a
, zul
h
asrizal
@
utem.
edu.m
y
5a
, moh
a
mad.n
a
im@
u
tem.edu.m
y
6a
,
nazmimh
d@
g
m
ail.com
1b
A
b
st
r
a
ct
T
h
i
s
paper d
e
scribes
the i
m
pact of de
velopment
distributio
n in ord
e
r to iden
ti
fy
optimu
m
loca
tion an
d si
z
e
for di
stribu
tion
genera
t
io
n (D
G) in po
w
e
r system ne
tw
o
r
k.
H
i
g
h
d
e
m
a
n
d
o
n
t
h
e
l
oad
w
ill caus
e unst
abl
e contro
l p
o
w
er di
stribute
d
throug
h p
o
w
e
r loss via
pow
er trans
miti
on.
Therefore s
m
all-
scale e
l
ectricity
gen
eratio
n is r
equ
ired to
ens
ure lar
ge
pow
e
r
gen
erated c
a
n be us
ed for
p
a
rticul
ar loc
a
tio
n
to mini
mi
z
e
po
w
e
r losses. In
add
ition, th
e i
m
p
l
e
m
e
n
ta
tio
n
of distrib
u
tio
n
gen
eratio
n w
ill
hel
p to re
duc
e
the
capita
l cost co
mp
are
d
to the
existing
pow
e
r
plant
d
ue to
space, sp
eed
and
pow
er re
quir
e
ment. T
h
us
prop
er DG loc
a
tion w
ill s
i
gn
i
f
icantly i
m
pr
ov
e the i
m
p
a
ct of the pow
er flow
ana
lysis b
y
consid
eri
ng
the
source
of e
ner
gy w
h
ich
is e
a
s
ily o
b
tai
ned. T
h
is stu
d
y w
ill
b
e
co
nducte
d
b
y
usin
g Matl
ab
an
d the
pro
p
o
s
ed
alg
o
rith
m
(MP
EP) w
ill be a
p
p
lied
on IEEE 3
0
bus
es radi
al
distrib
u
tion sys
tem
n
e
tw
ork. As a result, the
DG
can
be
locat
e
d at
opti
m
a
l
l
o
cation
an
d si
ze d
epe
nd
ing
o
n
the
loss
es c
onsu
m
e i
n
var
i
ous ty
pe
of D
G
techno
lo
gy sys
tems
used
in
the netw
o
rk.
On the ot
h
e
r
han
d, the c
o
n
d
itio
n a
nd l
o
c
a
tion
DG itself
w
ill
gen
erate o
p
ti
mal pow
er contri
butio
n de
pen
di
ng on d
e
si
gn strategi
es that h
a
ve be
en i
m
ple
m
e
n
ted.
Ke
y
w
ords
: mu
l
t
i
p
opu
l
a
tion e
v
ol
u
t
i
o
n
a
ry p
r
ogra
m
m
i
n
g
,
m
i
grati
on,
op
tim
a
l
l
o
cation
an
d
si
z
i
ng, IE
E
E
d
i
st
ribut
io
n s
y
stem
1. Introduc
tion
Nowdays, di
stribution network
system
plays an im
portant role to enhan
ce e
l
ectrical
power sou
r
ce
s delivered from generatio
n to end u
s
e
r
. The syste
m
will distrib
u
te electricity via
power line
by con
s
idering t
he realibility and eco
nomi
c
wi
se ba
sed
on need
or
demand re
qui
red
by consumer.
Since the dis
t
ribution requires some
of source
s to distribute energy, DG source is
an alternative
l
y element
that can be co
nsidered
to cater the rapidly energy need directly from
consum
er. However, the influence of transformer
wil
l
affect the load requi
rem
ent and energy
transformation capa
city based o
n
de
sign stra
tegie
s
that have been u
s
ed.
There
are
two
distribution system categories that have been impl
e
m
ented in Malaysia; ut
ility subsystem
and
facility subsystem.
As mentioned earlier, DG
will inject the energy
or ele
c
tricity through distribution system
that might require input source to gen
erate pow
er.
Therefore a lot o
f
sources that might b
e
conside
r
ed to
install the DG based on t
y
pe and location needs.
Ge
ne
r
a
lly,
D
G
is
r
e
la
ted
to
th
e
s
m
a
l
l
p
o
w
e
r
s
o
u
r
c
e
s
which
mean
a
s
e
l
e
c
t
r
i
c
a
l
p
o
w
e
r
g
e
n
e
r
a
t
i
o
n
t
h
a
t
d
i
r
e
c
t
l
y
c
o
n
n
e
c
t
e
d
t
o
t
h
e
elec
t
r
ic
g
r
id
o
f
d
i
s
t
r
i
b
u
tion
netw
o
r
k
[1
]
,[2
]
.
T
h
e use
o
f
DG
can
b
e
c
l
ass
i
f
i
ed
into tw
o m
a
j
o
r
f
a
cto
r
s
t
h
a
t
f
o
cu
s on
th
e
r
e
n
e
w
ed in
ter
e
st in
d
i
strib
u
ted
g
e
n
e
ratio
n
i.e.
ele
c
tr
i
c
ity
m
a
r
k
et
lib
eralizatio
n
an
d
en
v
i
r
o
n
m
en
tal co
n
c
e
r
n
s
[1
].
B
a
se
d
o
n
th
e ra
ting
o
f
d
i
str
i
b
u
ted
g
e
n
e
ratio
n
sou
r
ces,
it d
e
pen
ds
o
n
capacity
o
f
d
i
str
i
b
u
tion
sy
stem
co
rr
el
ated to
t
h
e
v
o
ltag
e
lev
e
l
w
i
th
in
d
i
st
r
i
b
u
tio
n
sy
stem
.
T
h
er
e i
s
an
i
ssu
e
r
e
late
d
with
distr
i
bu
ted g
e
n
e
r
a
tion
w
h
i
c
h
ca
n
b
e
va
r
i
fied
s
i
gnific
a
ntly
w
i
th
the
r
a
ting. T
her
e
f
o
r
e,
it is
ap
pr
op
r
i
a
t
e
to in
tr
o
d
u
ce
categ
o
r
i
e
s
o
f
d
i
str
i
b
u
ted
g
e
n
e
r
atio
n
[2
].
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 12, No. 3, September 20
14: 54
1 – 548
542
Table 1. DG types si
zin
g
capa
city
M
i
cr
o d
i
strib
u
ted
g
e
n
e
r
a
tio
n
:
1
W
a
tt~5
kW
S
m
a
ll dist
ribu
t
e
d
g
e
n
e
ra
t
i
on
:
5
k
W
~
5
MW
M
e
d
i
u
m
d
i
str
i
b
u
t
ed
gen
e
r
atio
n
:
5
M
W
~5
0
M
W
L
a
rg
e
dist
ribu
t
e
d
g
e
n
e
ra
t
i
on
:
50
MW
~
300MW
In
th
e
last d
e
cade
y
ear, th
e
in
cr
e
a
s
in
g
l
y
w
i
d
e
sp
r
e
ad
u
s
a
g
e
s
o
n
te
ch
n
o
l
o
g
y
an
d
inno
va
tio
n
s
ha
ve
c
h
a
n
g
e
d
the
ty
p
e
o
f
d
i
s
t
r
i
b
u
ted
ge
ner
a
t
io
n
to
b
e
c
o
m
e
va
lua
b
le
.
Re
cently
o
t
he
rs
co
untr
y
s
t
a
r
t
to
use
r
enew
ab
le
e
n
e
r
gy
,
th
u
s
i
t
i
s
r
e
q
u
ir
e
d
to
ins
t
a
ll
gene
r
a
tio
n
s
y
s
t
e
m
th
at
m
a
y
b
e
ap
p
l
i
ed eith
e
r
a
t
h
o
m
e
, b
u
sin
e
ss,
or
o
t
h
e
r
pr
iv
ately
-
ow
n
e
d
pr
op
e
r
ty
.
T
h
u
s
,
T
a
b
l
e 1
s
h
ow
s
the
ty
p
e
o
f
tec
hno
logy
that us
es
f
o
r
DG
ins
t
allatio
n
b
a
s
e
d
o
n
the
s
i
z
e
r
a
tin
g
.
T
her
e
a
r
e
s
e
ve
r
a
l
r
e
a
s
o
n
s
d
u
e
to
the
inc
r
e
a
s
i
ng
o
f
te
c
h
no
lo
gy
o
n
DG
s
u
c
h
as
highly
p
o
t
e
n
t
i
a
l
s
a
n
d
a
d
v
a
n
t
a
g
e
s
,
i
n
c
r
e
a
s
i
n
g
o
f
ele
c
trical d
e
m
a
n
d
s
,
t
e
c
h
n
i
c
a
l
a
n
d
e
c
onomi
c
al
co
n
s
train
t
s
in
co
n
s
tru
c
tion
o
f
n
e
w
p
o
w
e
r
p
l
an
ts
an
d
n
e
w
tr
an
sm
ission lin
e
s
.
T
h
er
ef
or
e
th
e
ad
v
a
n
t
ag
e
s
of
d
i
str
i
b
u
te
d g
e
n
e
r
atio
n
ca
n
b
e
categ
o
r
i
zed
in
to
th
r
e
e
key
s
;
tech
n
i
cal,
eco
n
o
m
i
c
al
an
d en
v
i
r
o
n
m
en
tal
b
e
n
e
f
i
ts
[3
].
T
e
c
hni
cal
:
th
e
pr
od
u
c
in
g
o
n
g
o
o
d
ef
f
i
cien
cy
, g
r
id rein
f
o
rcem
en
t,
p
o
w
e
r
losse
ss
r
e
d
u
ctio
n
,
r
e
lia
b
ility
,
elim
in
atin
g o
r
def
er
r
i
n
g
th
e
u
p
g
r
ad
e
s
o
f
po
w
e
r
sy
stem
,
im
p
r
o
v
in
g
load
f
a
ctor
s
an
d
v
o
ltag
e pr
o
f
il
e an
d
th
u
s
in
crea
sed
pow
er
q
u
ality
.
Eco
nom
i
c
:
t
h
e
ope
ra
t
i
n
g
cost
re
du
ci
n
g
on
t
r
a
n
s
m
i
ssi
o
n
a
n
d
di
st
ri
bu
t
i
on
(T
&
D
)
,
s
h
o
r
t
e
r
co
n
s
tr
u
c
tion
tim
e
s,
to
sav
e
th
e
f
o
ssil f
u
el
an
d
d
e
crea
si
n
g
in elect
r
icit
y
p
r
ice.
Envi
ronm
ent
a
l
: th
e r
e
d
u
c
t
i
o
n
s
i
n
e
m
i
s
s
i
o
n
o
f
g
r
een
h
o
u
s
e
g
a
se
s
a
n
d
to
o
v
er
co
m
e
th
e
glo
b
a
l w
a
r
m
in
g.
Sin
c
e
th
e
u
s
ag
e
o
f
DG
t
e
ch
n
o
lo
g
y
i
s
w
i
dely
u
s
e
d
,
it
r
e
q
u
i
r
e
s
th
e pla
nning
of
ele
c
tr
i
c
sy
stem
w
h
i
c
h
i
s
su
i
t
ab
le
to
th
e
d
i
st
r
i
b
u
tio
n
n
e
t
w
or
k.
It
co
u
l
d b
e
co
n
s
ider
a
s
o
n
e o
f
th
e
m
o
st
v
i
ab
le
o
p
t
io
n
s
to
e
a
se som
e
o
f
p
r
ob
le
m
s
f
a
ced in po
w
e
r
sy
stem
s,
f
o
r
ex
am
p
l
e
h
i
g
h
losses, lo
w
r
e
liab
ility
and
p
o
o
r
pow
er
q
u
ality [4
].
T
h
er
ef
ore
b
y
id
entif
y
ing
sev
e
r
a
l
f
a
ct
or
s such
as
ty
p
e
o
f
tech
n
o
l
o
g
y
,
good
lo
catio
n
an
d
ca
p
a
city
o
f
th
e
u
n
i
ts a
r
e
the im
p
o
r
t
a
n
t
p
a
r
t
f
o
r
DG
in
stallatio
n
s
.
Stud
y about
D
G
in
s
t
a
lla
tio
n
,
it
is
e
s
sen
t
ial to
d
e
te
r
m
in
e
th
e si
ze an
d
l
o
cation
o
f
th
e
lo
cal g
e
n
e
r
ati
o
n
b
e
i
n
g
p
l
a
c
ed t
o
r
e
d
u
ce
th
e
lin
e
losse
s
[5
].
T
h
e
r
e
a
r
e
sev
e
r
a
l o
f
stu
d
ie
s o
n
th
e
opt
i
m
u
m
DG
pl
a
c
e
m
e
n
t
f
o
r
m
i
n
i
m
u
m
po
w
e
r
l
o
s
s
e
s
su
ch
a
s
2/
3
ru
l
e
[6],
o
p
t
i
m
a
l
po
w
e
r
f
l
ow
ap
pr
oaches [8
]
,
analy
t
ical
ap
pr
oac
hes [7
] and the ar
tif
i
cial inte
lligent m
e
tho
d
s
such as Genet
i
c
A
l
g
o
r
i
th
m [9
],
T
a
b
u
Sea
r
ch
[1
1
]
an
d P
a
r
t
icle Sw
ar
m
O
p
t
im
izatio
n [10
]
.
In
a
d
d
i
tion
,
o
t
h
e
r
ar
tif
i
cial
intelligent u
s
ed
is Ev
o
l
utio
nar
y
P
r
o
g
r
a
m
m
i
ng
w
h
ic
h an
algor
i
th
m
m
e
tho
d
that
w
a
s
or
igi
n
ally
in
tr
o
d
u
c
ed
by
Dr
.
L
a
w
ren
c
e
J
.
Fo
g
e
l
i
n
1
9
6
0
. Evolutionary Pro
g
r
ammin
g
(EP
)
is defin
ed a
s
a
mutation-ba
sed evol
utiona
ry algo
rithm
applie
d to
di
screte
search spa
c
e. T
h
u
s
b
a
si
c
step
s to
apply EP techniqu
e are ini
t
ialization, mu
tation, combi
nation an
d se
lection [12]
On
t
h
i
s
st
u
d
y
,
a
n
op
t
i
m
i
ze
d
m
u
l
t
i
po
pu
l
a
t
i
on
ev
ol
u
t
i
o
n
a
ry
p
r
o
g
ra
m
m
i
n
g
(M
PEP)
i
s
d
e
sc
r
i
b
e
d
to
analy
z
e th
e
o
p
t
im
al
loca
tio
n
an
d
size
o
f
d
i
str
i
b
u
te
d
g
e
n
e
r
a
tion
b
y
r
e
g
e
n
e
r
ati
ng
n
e
w
p
opu
l
a
t
i
on
o
n
t
h
e
sy
st
e
m
o
r
ca
l
l
e
d
m
i
g
r
a
t
i
o
n
o
r
m
u
l
t
i
po
pu
l
a
t
i
on
.
In
orde
r
t
o
co
nd
uct the
s
t
u
d
y
,
a
n
a
l
l
e
l
e
m
i
g
r
a
t
i
o
n
t
e
c
h
n
i
q
u
e
h
a
s
b
e
e
n
p
r
o
p
o
s
e
d
to incorporate with
MPEP algorith
m
as mentione
d above
.
2. Rese
arch
Metho
d
Pro
p
o
s
e
d
m
e
t
h
odol
og
y
i
n
t
h
i
s
pa
pe
r
i
s
p
r
e
s
e
n
t
e
d
t
o
i
d
e
n
t
i
f
y
t
h
e
opt
i
m
a
l
si
t
t
i
n
g
a
n
d
si
z
e
o
f
d
i
s
t
r
i
b
u
ted ge
ner
a
t
io
n ins
t
a
lla
tio
n
b
y
m
o
nito
r
i
n
g
p
o
w
e
r
lo
s
s
e
s
a
t
m
i
nim
u
m
p
o
w
er
an
d
to
identify
the
best
si
ze
d
u
e
to
th
e
lo
catio
n
s
o
f
DG i
n
stalled
.
T
h
u
s
,
th
e
o
b
j
e
ctiv
e
f
u
n
c
tio
n
an
d
im
p
o
s
ed
con
s
tr
ain
t
s o
f
th
e
lo
cation
an
d
size
m
e
th
o
d
ar
e def
in
ed
a
s
f
o
llo
w
:
i. Ob
jective
functio
n
T
h
e
r
e
a
l
p
o
w
e
r
lo
ss
es
is
s
e
lec
t
ed
to
b
e
an
o
b
j
e
c
tive
func
tio
n
o
n
th
is
s
t
ud
y,
w
h
e
r
e
a
s it is r
e
q
u
i
r
e
d
to
su
m
th
e
en
ti
re p
o
w
e
r
l
o
a
d
at
all
n
o
d
e
in
th
e
3
0
B
u
s
r
adial
d
i
str
i
b
u
tion
sy
stem
s.
T
h
e pr
e op
tim
i
zation
is r
e
q
u
i
r
ed
to
id
en
tif
y
th
e
in
itial
lo
sse
s
o
n
th
e
sy
ste
m
.
T
h
u
s
,
b
y
stud
y
i
ng activ
e
p
o
w
e
r inj
e
cted through
l
o
a
d
bus
es
,
the s
y
s
t
e
m
w
ill
b
e
af
f
e
cted
b
a
sed
o
n
th
e
ty
p
e
o
f
DG
u
s
e
d
.
Hen
c
e,
an
alg
o
r
i
th
m
tech
n
i
q
u
e
o
f
m
i
g
r
a
t
i
o
n
h
a
s
b
e
e
n
u
s
e
d
i
n
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Multi Populati
on Evolutionary
Program
m
i
ng Approach
for Di
st
ributed .... (M.F. Baharom
)
543
th
is sy
stem to o
p
t
im
ize
th
e
ob
j
e
ctiv
e
f
u
n
c
tio
n
.
F
o
r t
h
e
losses
m
i
n
i
m
i
zatio
n
ca
se an
d
load
f
l
ow
an
aly
s
is, th
e
o
b
j
ectiv
e
f
u
n
c
tio
n
f
is g
i
v
en
b
y
:
∑
(1
)
(2
)
Whe
r
e:
Pi
is the real
power lo
sses
of
n bus di
stri
bution sy
ste
m
.
Ii
is the curre
n
t magnitude
Ri
is the re
si
stance
i
i
.
Im
pose
d
co
n
s
t
r
ai
nt
s
T
h
e g
e
n
e
r
a
t
o
r
vo
ltag
e
w
i
ll
b
e
th
e lo
a
d
/b
u
s
v
o
ltag
e p
l
u
s
so
m
e
v
a
lu
es
r
e
lat
ed
to
t
h
e
im
p
e
d
a
n
c
e
o
f
li
n
e
a
n
d
p
o
w
e
r f
l
ow
s
alo
n
g
th
e lin
e. It
proves
t
h
e
larg
er
th
e
im
p
e
d
a
n
c
e
an
d
po
w
e
r
f
l
o
w
,
th
e la
r
g
er
th
e
v
o
ltag
e
r
i
se.
T
h
e
in
crea
sed
a
c
tiv
e
po
w
e
r
f
l
o
w
s
o
n
th
e
d
i
st
r
i
b
u
tion
n
e
t
w
o
r
k
h
a
v
e
a
lar
g
e im
p
a
ct
o
n
th
e
v
o
ltag
e
lev
e
l
b
e
cau
s
e
th
e
r
e
sistiv
e
ele
m
e
n
ts o
f
lin
es
o
n
d
i
str
i
bu
tio
n
n
e
tw
o
r
ks
a
r
e
h
i
g
h
e
r th
a
n
oth
e
rs
lin
e. T
h
i
s
causes
to
X/R
r
a
tio
o
f
a
p
pr
o
x
im
ately
1
rath
er
th
an
a
m
o
r
e
ty
p
i
cal
v
a
lue
o
f
5
on
tr
an
sm
ission
netw
o
r
ks
[1
3
]
.
Gener
all
y
,
the
stab
ilit
y assum
p
tio
n
f
o
r
all
n
o
d
e
v
o
ltag
es
i
s
m
a
in
tain
e
d
b
e
t
w
e
en 0
.
9
5
an
d
1
.
0
5
pu
.
T
h
e
v
o
ltag
e m
u
st
b
e
kep
t
w
i
th
in
stan
dard
lim
its
on ea
ch
b
u
s;
it can
b
e
ex
p
r
e
s
sed
a
s
:
Vi,
m
i
n
≤
Vi
≥
Vi,
max
(3
)
A
s
DG
i
s
h
a
v
e
a specif
ic
lim
itatio
n
b
y
d
e
p
e
n
d
in
g
on
th
e ty
p
e
o
f
en
e
r
g
y
resou
r
ces
at
a
n
y
give
n
lo
c
a
ti
o
n
, the
ac
tive
p
o
w
e
r
f
o
r
D
G
is nece
s
s
a
r
y
to
b
e
s
e
t
up b
y
its
lo
w
e
r
and
u
p
p
e
r
lim
its
as:
Pdg, mi
n
≤
Pdg
≥
Pdg
,
ma
x
(4
)
2.1
Evol
uti
o
n
a
ry
Progr
am
mi
ng (
E
p)
Ev
o
l
u
t
io
n
a
r
y
P
r
o
g
r
a
m
m
i
n
g
(EP)
i
s
on
e
o
f
th
e
ar
tif
i
cial
in
telli
g
e
n
c
e
co
m
p
u
t
atio
n
a
l
en
g
i
n
e
s
f
o
r
do
in
g
o
p
t
im
iza
t
io
n
pr
oce
s
s i
n
t
h
e
pow
er
sy
stem w
h
i
c
h
can be
repr
e
s
en
ted b
y
u
s
i
n
g
m
a
th
em
atical
eq
u
a
tion
.
T
h
is
alg
o
r
i
th
m
tech
n
i
q
u
e
co
n
s
i
s
ts
o
f
sev
e
ral
pr
og
r
a
m
m
i
n
g
co
des
d
e
v
e
lop
m
ent p
r
ocesses
w
h
i
c
h
are
in
itializatio
n,
f
i
tness
calc
ulatio
n,
m
u
tation,
c
o
m
b
inatio
n,
s
e
lec
t
io
n
a
n
d
fina
lly
c
o
nve
r
ge
n
c
e
tes
t
to
produce r
e
sults.
i. In
itia
liza
t
io
n
E
v
o
l
utio
na
r
y
p
r
o
g
r
a
m
m
i
ng
is
o
n
e
o
f
the
c
o
m
p
e
n
sa
tio
n
te
chniq
u
es
w
her
e
the
a
l
go
r
i
thm
u
s
ed
in
th
e
p
a
r
t
icu
l
a
r
l
oad h
a
s
a
po
ten
t
ial
to
cau
s
e
th
e
sy
stem to o
p
t
i
m
ize
.
Hen
c
e,
th
e
i
n
i
t
i
a
l
i
z
a
t
i
o
n
proc
e
s
s
i
s
re
qu
i
r
e
d
t
o
g
e
n
e
ra
t
e
20
pop
u
l
a
t
i
o
n
s
by
ra
n
d
om
i
z
i
n
g
t
h
e
v
a
ri
o
u
s
num
b
e
rs
i
n
p
u
t
v
a
r
i
ab
les
w
h
e
r
e
s
o
m
e
o
f
t
h
e
m
a
r
e
v
a
r
i
ab
les
f
o
r
lo
catio
n
while
o
t
hers
are
DG
si
ze.
Therefore thi
s
process is t
h
e
m
a
in
ob
je
ct
i
v
e
f
o
r
t
h
i
s
proje
c
t
.
In
a
ddi
t
i
on
,
throu
g
h
t
h
e
re
qu
i
r
e
m
e
n
t
o
f
RP
P
,
the i
n
j
e
cted
Var
va
l
u
e
will
be
rand
o
m
i
z
ed based
o
n
the
m
a
r
k
et
app
licatio
ns.
T
h
eref
o
r
e
,
t
h
e
r
a
n
d
o
m
num
ber g
e
n
e
r
a
t
i
o
n
will
be
used
b
a
s
e
d
o
n
t
h
e
M
A
T
L
A
B
s
y
n
t
a
x
b
e
l
o
w
:
X
loc
a
tion
= r
oun
d(
ra
nd
(x,
y
)
*
A+B
)
)
(5
)
X
si
zi
n
g
= rand
(x,y)
*
A+B
(6
)
ii.
F
i
tn
ess
C
a
lcu
l
a
t
io
n
Fitn
ess
cal
c
u
l
atio
n is
th
e
p
r
ocess
w
h
er
e
th
e e
q
u
a
tio
n
/f
u
n
c
tion
/su
b
r
o
u
t
in
e is to b
e
o
p
t
im
ize
d
. It can
b
e
a
sin
g
le
m
a
th
em
a
t
ical e
q
u
a
tion
o
r
a
lo
n
g
su
b
r
o
u
tin
e
. T
h
e
o
b
j
ectiv
e
is
to
m
a
x
i
m
i
ze
th
e
m
i
n
i
m
u
m
lo
sse
s
b
y
in
j
e
ctin
g
th
e
r
e
a
c
tiv
e
p
o
w
e
r
in
to
th
e
sy
stem
.
bu
sd
at
a(
x
location
(b
il_0
1
),6)=x
si
zin
g
(
b
i
l
_01
) (
7
)
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 12, No. 3, September 20
14: 54
1 – 548
544
busdat
a
(
b
us_n
o1
,1
1
)
=x
Q_inj
e
c
t
ed
(b
il_
01
) (
8
)
iii.
Mu
ta
tio
n
Mu
tatio
n
i
s
th
e
p
r
o
c
e
s
s
to b
r
e
e
d
th
e offsprin
g
f
r
om
t
h
e g
e
n
e
rated
r
a
n
d
o
m
n
u
m
b
e
r
ba
s
e
d
on
t
h
e
G
a
u
ssi
a
n
M
u
t
a
t
i
on
e
q
u
a
t
i
on
a
s
be
l
o
w
:
,
,
0,
(9
)
iv.
Co
m
b
ina
t
io
n
T
h
is
pro
c
ess
is a co
m
b
i
n
at
io
n
b
e
t
w
een
th
e p
a
r
e
n
t
a
n
d
o
f
f
s
pr
in
g
in
se
r
i
e
s
b
y
row
s
.
T
h
e
num
b
e
r
o
f
r
o
w
s
w
ill b
e
doub
l
ed
.
v.
Selection
A
f
ter the
o
f
f
s
p
r
in
g
w
a
s g
ener
a
ted
b
y
the m
u
tatio
n
p
r
o
c
ess,
it
w
ill
co
m
b
i
n
e
w
i
th
the
p
a
ren
t
s to
p
r
o
c
eed
w
i
th th
e
n
e
x
t
step n
a
m
ed as
sele
ctio
n
.
Sin
c
e
t
h
is
pr
o
j
ect
i
s
to
m
a
x
i
m
i
ze th
e
m
i
n
i
m
u
m
lo
sses,
th
e
r
ef
o
r
e th
e
m
a
in
ob
jectiv
e o
f
sel
e
ctio
n
pr
oce
ss
is
to
sele
ct t
h
e
b
e
st su
r
v
i
v
al
r
epr
e
s
e
n
t
ed
as
m
i
n
i
m
u
m
p
o
w
e
r
loss w
h
ich
h
a
s a
po
ten
t
ial
to
b
e
ado
p
t
ed
in n
e
w
g
e
n
e
ration
de
f
i
n
i
t
i
on
pro
c
e
ss.
vi.
Co
nve
r
gence
Test
T
h
e
co
n
v
e
r
g
e
n
c
e
te
st
is
t
o
d
e
te
r
m
in
e
t
h
e
stop
p
i
n
g
cr
iter
i
o
n b
y
d
e
f
i
n
i
n
g
t
h
e
m
i
n
i
m
u
m
f
i
t
n
e
s
s
and m
a
x
i
m
u
m
f
i
t
n
e
s
s
≤
0.
000
1.
MA
T
L
A
B
s
y
n
t
a
x
of c
o
n
v
e
r
g
e
n
c
e
t
e
st
c
a
n
b
e
ex
p
r
e
s
se
d a
s
sh
ow
n
b
e
l
o
w
:
ma
x
fitness
– m
i
n
fitnes
s
=
≤
0.0
001
(
10)
The process
will be contin
uously repeated until it achi
eves ta
rget tolerance as required.
Figure 1. Flowchart for MPEP Algorithm
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Multi Populati
on Evolutionary
Program
m
i
ng Approach
for Di
st
ributed .... (M.F. Baharom
)
545
2
.
2
El
l
e
l
e
M
i
g
r
a
t
i
o
n
On
E
v
ol
u
t
i
ona
r
y
Pr
og
r
a
m
m
i
ng
T
e
ch
n
i
qu
e o
f
EP
is
pr
ef
erab
le
to
so
lv
e
an
y p
o
w
e
r
sy
stem m
a
tters.
If
EP
is
m
o
dif
i
ed
b
y
ad
d
i
n
g
so
m
e
o
t
her
s techn
i
q
u
e,
it
w
ill
beco
m
e
m
o
re
p
o
w
e
r
f
ul and
ef
f
i
cient. A
s
to
o
p
t
im
ize
the
sy
st
e
m
,
m
u
l
t
i
popu
l
a
t
i
on
o
r
m
i
g
r
a
t
i
o
n
i
s
t
h
e
t
e
ch
n
i
qu
e
w
h
i
c
h
c
a
n
m
a
k
e
a
n
i
m
p
r
ov
e
m
e
n
t
on
t
h
e
s
y
s
t
e
m
s
.
T
h
is
m
e
c
hanis
m
na
m
e
d
as
Alle
le
s
m
i
g
r
a
t
i
o
n
te
c
h
niq
u
e is
to
se
le
c
t
the
b
e
s
t
lo
c
a
tio
n
an
d
si
ze
b
y
g
i
v
i
n
g
th
e
m
a
x
i
m
u
m
v
a
lu
e
o
f
m
i
n
i
m
u
m
v
o
ltage. Generally
,
A
llele’
s
m
i
g
r
atio
n
is
a
m
e
tho
d
to
gene
r
a
te
a
new
p
o
p
u
la
tio
n
b
y
m
o
d
i
fy
ing
a
m
u
ta
tio
n
va
l
u
e
[
1
4
]
.
T
h
e
m
i
gr
a
t
io
n
m
o
d
e
ls
can
b
e
ex
pr
e
ss i
n
th
e
eq
u
a
tio
n
b
e
low
:
P
1
(t
+ 1)
= (
(
1
−
m)
*
P
1
(t)
)
+ (
m
*P
2
(t))
(
11)
m
=
rand(
1
,1)*1
(
12)
W
her
e:
m
i
s
ra
n
dom
n
u
m
be
r
of
t
h
e
f
r
a
c
t
i
on
of
po
pu
l
a
t
i
o
n
.
P
1
is the v
a
r
i
ab
le
o
f
initial
lo
ad
.
P
2
is
the v
a
r
i
ab
le
o
f
m
u
tatio
n
v
a
lue.
3. Results a
nd Analy
s
is
T
h
e
pu
rp
ose
d
o
f
th
is m
e
th
o
d
is
to
sh
o
w
th
e
ef
f
e
ctiv
en
ess
o
f
th
e
Mu
lti p
o
p
u
latio
n
t
r
a
d
i
t
i
o
n
a
l
E
P
a
l
g
o
ri
t
h
m
o
n
I
E
E
E
3
0
b
u
s
ra
d
i
a
l
d
i
s
t
ri
b
u
t
i
o
n
s
y
s
t
e
m
.
All
the
resu
lts
h
a
s
been
de
ter
m
in
ed
b
y
us
ing
se
vera
l
type
o
f
DGs
sys
t
e
m
such
as
wind
turb
ine
,
ph
otovolta
ic PV,
micro
h
y
dro
an
d others
DG
techno
logy to genera
t
e
the
tes
t
ed
s
y
ste
m
.
The
an
alysis
will be
d
i
vide
d
in
to
two
cond
itons
which
ar
e w
i
th
and
w
i
thou
t DG
con
t
ribu
tions
.
3.1. Analy
s
is
w
i
thou
t DG implementation
T
h
e
b
a
se ca
se pow
er
f
l
ow
is ca
r
r
i
ed
ou
t f
o
r
th
e
IEEE
3
0
b
u
s
sy
stem w
i
th
o
u
t
i
n
clu
d
i
n
g
D
G
s
.
I
t
is
inf
e
r
r
e
d
the
ac
tive
p
o
w
e
r
lo
ss
is 1
4
.2
7
MW while reactiv
e
p
o
w
e
r
loss
is
4
.
7
4
MVar
.
T
h
e
v
o
ltag
e
m
a
g
n
i
tu
d
e
s a
t
all the buse
s
are sh
ow
n in
F
i
g
u
r
e
2
.
B
a
s
e
d
o
n
t
h
e
r
e
s
u
l
t
s
,
i
t
c
l
e
a
r
l
y
s
t
a
t
e
s
th
e
m
i
n
i
m
u
m
vo
ltage
va
lu
e
s
c
a
n
b
e
e
a
s
i
l
y
id
e
n
tifie
d
thro
ugh
the
c
har
t
w
h
i
c
h
l
o
c
a
t
e
d
f
r
o
m
bu
s
n
o
2
2
to
2
7
.
In
o
r
d
e
r
to d
e
t
e
r
m
i
n
e
the lo
s
s
set o
n
th
e s
y
s
t
em
, the i
n
j
e
c
t
e
d
reac
tiv
e
p
o
w
e
r o
n
th
e
b
u
s
n
o
1
0
is r
e
q
u
i
r
ed
to
i
d
en
tif
y
th
e
p
r
e
-
op
tim
i
ze
v
a
lu
e
f
o
r
th
e
r
e
al
an
d
rea
c
tive
p
o
w
e
r
.
T
h
e set
u
p
v
a
lu
e f
o
r
r
eal
an
d
r
e
a
c
tiv
e
pow
e
r
f
o
r th
e
p
r
e
o
p
t
im
izatio
n
is
7
MVar
.
T
h
e
r
ef
or
e,
th
e p
o
w
e
r loss
f
o
r th
e
sy
stem
is
3
.
1
0
6
5
MW
.
Table 2. Base value witho
u
t DG install
a
tion
P lo
ad
(M
W
)
14.27
Q lo
ad
(M
Va
r
)
4.74
V
o
lt
a
g
e
m
a
g
n
i
t
u
de
(
V
m
)
0.9417
<
V
m
<
1
P
o
w
e
r
L
o
sses,
M
W
3.1065
Rea
c
tive ele
m
ents will aff
e
ct voltage in t
he system
on each b
u
s by con
s
ide
r
ing loa
d
deman
d. The
flactuation in
voltage profil
es de
pen
ds
o
n
the co
nfigu
r
ation of the
netwo
rk it
self
to
prod
uce the
voltage level
whi
c
h d
epe
n
d
s
on the
de
sign
strategie
s
. Based
on t
he
configu
r
ati
on
above, it extremely sho
w
s the fa
r di
stance betwee
n
the locatio
n
of buse
s
a
nd so
urce
s will
cre
a
te the lo
w voltage po
ssi
bility comp
ared to oth
e
r
buses n
e
a
r
est from sou
r
ce
s. In worst ca
se
con
d
ition,
wh
en the
r
e i
s
he
avy load
req
u
i
red f
r
om
bu
s 22 to
27, it
can redu
ce
th
e voltage
ra
pi
dly
while
the l
o
a
d
current
will
tend
to in
crease d
ue to
load
ca
pa
city dem
and.
In
fact, the
he
at
influen
ced
by
tran
smitting t
he e
nergy fro
m
gen
eratio
n
,
tran
smissio
n
an
d di
strib
u
t
ion will
lead
to
gene
rate
harmonic for th
e
system.
Mea
n
whil
e, pa
ssi
ve com
pon
en
t su
ch a
s
ca
p
a
sitor ba
nk will
not totally compen
sate the
loss d
ue to certain lim
it that has bee
n set. Thus, th
e DG in
stallation
has b
een u
s
e
d
to improve
voltage profil
e in the syste
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 12, No. 3, September 20
14: 54
1 – 548
546
Figure 2. Voltage profile wi
thout DG
for I
EEE 30 bus
Distri
bution
systems.
3.2. Analy
s
is
w
i
th DG
Th
e
prin
cip
a
l
of t
h
e
DG
i
n
sta
l
l
a
ti
on
can
b
e
si
mp
lif
ie
d a
s
a
PQ
bu
s m
o
d
e
l
e
d i
n
t
h
e
n
e
t
work
. Gen
e
ra
lly,
loa
d
flow
s
t
ud
y
e
x
plains
th
e DG
s
y
s
t
ems
act as
s
ourc
e
s
b
y
in
jecting
re
a
c
tiv
e
p
o
w
er
to
th
e sy
st
em d
e
p
e
n
d
i
n
g
on
t
h
e
ty
p
e
an
d si
ze of
DG
i
n
st
all
a
ti
on.
Co
mm
on
ly,
inj
e
cti
n
g i
n
P
Q
b
u
s
e
s
wil
l
im
pa
ct t
h
e l
o
sse
s
a
nd v
o
lt
ag
e
profi
l
e
th
ro
ug
h
all
t
he
sy
st
e
m
.
As
me
nti
o
n
e
d
e
a
rli
e
r,
th
e
v
o
l
t
ag
e p
r
ofi
l
e can b
e
i
m
p
r
ov
ed by
i
n
du
cin
g
re
ativ
e po
wer cal
l
e
d
a
s
Va
r
sou
r
ce
s in
pa
rti
c
ul
a
r
lo
cati
on f
o
r
re
du
cin
g
th
e l
o
sse
s
o
c
cu
rre
d
i
n
th
e net
wo
rk sy
st
em
.
Th
e
r
ef
o
r
e, it
ca
n b
e
cl
ea
rly p
r
ov
ed t
h
at th
e in
crea
si
n
g
of vo
lta
ge
profi
l
e
on
ea
ch b
u
s
wil
l
sl
ig
htly
red
u
c
e th
e l
o
sse
s
by i
n
sta
lli
ng
DG a
s
st
at
ed
in
Fig
u
r
e 3
b
e
l
o
w.
Figure 3. Voltage profile be
fore and afte
r compe
n
sent
ed
Based
on th
e
re
sults, th
e
comp
ari
s
o
n
h
a
ve bee
n ma
de bet
wee
n
both cases where
b
y
with and
wi
thout DG in
stallation in
the net
work syste
m
. The blue
colour
rep
r
e
s
ents
uncompe
nse
n
ted
voltage
profile while
red col
our
re
pre
s
ent
s
co
mpen
sente
d
voltage p
r
ofil
e by
injectin
g rea
c
tive power i
n
the certai
n area at
the t
e
st network
system. Synchron
ou
s voltage
comp
en
sato
r (SVC) h
a
s b
e
en used to inj
e
ct the re
acti
ve and re
al p
o
we
r in load
bus a
s
requi
red.
0.
91
0.
92
0.
93
0.
94
0.
95
0.
96
0.
97
0.
98
0.
99
1
1.
01
1
2
3
4
5
6
7
8
9
1
01
1
1
21
3
1
41
51
6
1
71
8
1
92
02
1
2
22
3
2
42
52
6
2
72
8
2
93
0
Voltage
(p.
u
)
Bu
s
no
0.
91
0.
92
0.
93
0.
94
0.
95
0.
96
0.
97
0.
98
0.
99
1
1.
01
123456789
1
0
1
1
1
2
1
3
1
4
1
5
1
6
1
7
1
8
1
9
2
0
2
1
2
2
2
3
2
4
2
5
2
6
2
7
2
8
2
9
3
0
Voltage
in
p.
u
No
of
Bu
s
before
compensat
e
d
after
compensat
e
d
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Multi Populati
on Evolutionary
Program
m
i
ng Approach
for Di
st
ributed .... (M.F. Baharom
)
547
wi
t
h
2
D
G
Pl
o
a
d
(
M
W)
14.27
Qlo
a
d (M
V
a
r
)
13.513
P
o
wer lo
sses
(M
W
)
2.9507
mi
n
(
V
m
)
0.9501
Op
ti
m
a
l Lo
catio
n
29
16
D
G
O
p
t
i
m
a
l
S
i
zin
g
(
M
W
)
2.7879
7.1979
Q in
j
e
c
t
ed
Bu
s
2
-
15.6787
B
u
s
11
2.4854
In addition,
capa
citor h
a
s been i
n
je
cte
d
into two
g
enerator
nod
es
whi
c
h a
r
e
bus
2 an
d
13
locate
d o
n
th
e loa
d
b
u
s.
T
hus, it
obviou
s
ly sh
o
w
s the
voltage i
s
sli
ghtly improve
d
. The
maxim
u
m
voltage i
s
1
p
u
while mi
nim
u
m voltage
is 0.95
pu. Th
e
low voltage l
e
vels
starte
d
from b
u
s 21
to
30 have been improved by
installing the DG.
Table 3. Re
al
powe
r
losse
s
and voltage
profile with 1
type of DG installation
wi
t
h
1
D
G
Pl
o
a
d
(
M
W)
14
.27
Qlo
a
d (M
V
a
r
)
8.6937
P
o
wer lo
sses
(M
W
)
2.8071
mi
n
(
V
m
)
0.9502
Op
ti
m
a
l Lo
catio
n
23
D
G
O
p
t
i
m
a
l
S
i
zin
g
(
M
W
)
4.55511
Q in
j
e
c
t
ed
B
u
s
2
-
42.9363
B
u
s
11
-
7
.1166
In
o
r
d
e
r
to
id
en
tif
y
th
e
op
tim
a
l
size
a
n
d
lo
catio
n
of
DG
s th
us there
a
r
e t
w
o
d
i
f
f
e
r
e
n
t
lo
cati
o
n
s
ha
ve bee
n
co
n
s
id
e
r
e
d
f
o
r
th
ese
stu
d
i
e
s
i
n
ord
e
r to
in
stall
o
f
DGs
te
ch
n
o
log
y
.
T
h
e
r
ef
or
e th
rough
T
a
b
l
e
3
an
d
T
a
b
l
e
4,
th
e
lo
sse
s
h
a
v
e
b
e
e
n
in
d
i
ca
te
d
based o
n
co
n
s
train
t
o
f
r
eal
losses
respect
to
th
e
set
u
p
losse
s
w
h
ile
v
o
ltage
pr
o
f
ile i
s
i
m
p
r
o
v
ed
d
u
e to
Var inj
e
ction
into th
e
lo
ad
b
u
s
sy
stem
.
Based
on
T
a
b
l
e
3
,
it
sh
ow
s
th
e
lo
catio
n
f
o
r
in
stalli
n
g
1
ty
p
e
DG tech
n
o
l
o
g
i
es
th
ro
u
g
h
th
e
sy
ste
m
is
b
u
s
n
o
d
e
2
3
.
T
h
e ty
p
e
o
f
DG u
s
ed i
s
w
i
n
d
tu
r
b
in
e
w
i
th
th
e stan
dard
r
a
n
g
e
o
f
si
ze
from 25
0W to
3
M
W
.
Sin
c
e
th
e si
ze
that
p
r
o
d
u
ce
d
o
n
th
e
sy
stem
is
4
.
55
51
1
M
W
t
h
a
t
cl
ose
to
5
M
W
th
u
s
it
is su
itab
l
e
t
o
b
e
ap
p
lie
d
o
n
th
e sy
stem
.
Table 4. Re
al
powe
r
losse
s
and voltage
profile with 1
type of DG installation
T
a
bl
e
4
i
n
di
c
a
t
e
s t
h
e
u
s
e
of
t
w
o
t
y
pe
s of
D
G
s
w
h
i
c
h
a
r
e
m
i
cro
h
y
dro
a
n
d
ph
ot
ov
ol
t
a
i
c
PV.
T
h
e
o
p
t
im
al
lo
catio
n
an
d si
ze
f
o
r
P
V
sy
stem
i
s
bu
s n
o
de
16
w
i
th
7
.
19
79
MW cl
ose
to
8
M
W
.
T
h
en
,
o
t
h
e
rs
ty
p
e
o
f
DG
u
s
ed
are
m
i
cro
h
y
d
r
o
whe
r
e
th
e
sta
n
d
a
r
d si
ze o
f
w
i
n
d
tu
rb
in
e
i
s
fr
o
m
2
0
0
W
to
3
MW
.
T
h
e
o
p
t
im
a
l
lo
ca
tio
n
and
s
i
ze
fo
r
this
D
G
tec
hno
lo
gy
is
b
u
s
no
d
e
2
9
w
i
th
2
.
7879
MW
w
h
i
c
h
i
s
su
it
able
f
o
r
t
h
is
sy
st
em
.
I
n
o
r
d
e
r
t
o
m
a
x
i
m
i
ze
t
h
e
m
i
n
i
m
u
m loss
es
w
h
i
l
e
i
m
prov
i
s
e
t
h
e
v
o
l
t
a
g
e
p
r
of
i
l
e
, t
h
e
po
w
e
r
l
o
ss
e
s
prod
u
c
e
d
i
s
2.9
5
07
M
W
a
n
d
0.95
01
p
u
whi
c
h
les
s
t
h
an ba
s
e
case
s
con
d
i
t
ion
s
.
4.
Conclu
sion
As
a
resu
lt,
the
main
ob
jec
t
ive
o
f
th
is
s
t
ud
y h
a
s
be
en
ach
i
e
v
ed
. Th
e
losses
ar
e
r
educ
in
g fr
om 3
.
10
65
MW
to
2
.
8
071MW
fo
r 1
D
G
ins
t
a
l
la
tion
.
Wh
ile
for
2
DG
ins
t
a
l
la
tion, the
po
we
r
l
o
sse
s
red
u
c
e
s
t
o
2.9
5
0
7
M
W
t
h
ro
ug
h o
p
tim
a
l DG si
ze;
2.7
8
7
9
M
W
an
d 7.
19
79
M
W
.
T
her
e
f
or
e
,
it
h
a
s bee
n prove
d
tha
t
the
minimu
m powe
r
loss
es
ca
n be
pr
oduce
d
b
y
impro
v
ing
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 12, No. 3, September 20
14: 54
1 – 548
548
volt
ag
e profi
l
e
d
u
e
to DG
i
n
sta
l
l
a
ti
on.
B
a
se
d on
t
h
e pre opt
im
i
z
at
i
on re
su
lt
i
n
F
i
g
u
re 3, wit
h
o
u
t
co
nsider
in
g
DG
in
the
s
y
s
t
e
m
will
c
aus
e uns
ta
ble vo
ltag
e
c
ond
itions
; 0.
94
17
< V
m
<
1
p.u
.
Thus,
rea
c
tive po
wer ha
s been
used
to inject Va
r sou
r
ce th
ro
ugh the
net
work
system
to
enha
nce po
wer lo
se
s a
s
well as volta
g
e
profile. Indu
cing Var
will re
quire SV
C an
d ca
pa
sitor. I
n
addition, cha
nge PQ b
u
ses
will affect the vo
ltage and lo
sse
s
in the
system requi
re
d.
Furthe
rmo
r
e,
multi popula
t
ion of traditional (EP)
ca
n be u
s
ed a
s
a tool to ide
n
tify the optimal
locatio
n
an
d
size ba
se
d o
n
the sy
stem
applie
d.
As
a co
ntributio
n
,
this study
can be
applie
d to
the variou
s type of appli
c
at
ion espe
cia
lly toward g
r
e
e
n
technol
ogy studies.
Ackn
o
w
l
e
dg
ements
This
re
se
a
r
c
h
wa
s
su
pp
orted
b
y
gra
n
t
from
U
n
iv
ersiti
Te
knikal
Mala
y
s
i
a
M
e
la
ka
a
nd a
l
so te
ch
nica
ll
y
suppo
rt f
r
o
m
Resea
r
che
r
s.
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anc
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f
thum
b
f
o
r m
o
de
l
l
i
ng
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G
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i
butio
n
inte
r
a
ctio
n
,
pr
e
s
e
n
te
d
at
Pow
e
r
Eng
i
n
e
e
r
ing
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ci
e
t
y
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ica
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a
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al
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v
al
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