I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
,
p
p
.
1
3
7
9
~
1
388
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
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1
/ijee
cs
.v
3
6
.
i
3
.
pp
1
3
7
9
-
1
3
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8
1379
J
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m
ep
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:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
Im
pa
cts of
ele
ctri
c vehicle
cha
rg
ing
statio
ns a
nd
D
G
s
on RDS
for impro
v
ing
vo
ltag
e stability
usin
g
honey
badg
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o
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M
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ba
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ula
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n
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y:
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ed
May
24
,
2
0
2
4
R
ev
is
ed
Sep
3
,
202
4
Acc
ep
ted
Sep
7
,
2
0
2
4
Th
e
i
n
telli
g
e
n
t
c
o
m
p
u
tatio
n
a
l
te
c
h
n
iq
u
e
u
se
d
in
th
is
re
se
a
rc
h
h
a
n
d
les
t
h
e
m
u
lt
i
-
o
b
jec
ti
v
e
v
o
lt
a
g
e
sta
b
i
li
ty
o
p
ti
m
iza
ti
o
n
(M
OV
S
O)
p
ro
b
lem
in
ra
d
ia
l
d
istri
b
u
ti
o
n
s
y
ste
m
s
(RDS).
Th
e
o
b
jec
ti
v
e
s
o
f
th
e
p
ro
p
o
se
d
re
se
a
rc
h
a
re
to
m
in
imiz
e
n
e
two
rk
l
o
ss
,
lo
we
r
t
h
e
a
v
e
ra
g
e
v
o
lt
a
g
e
d
e
v
iatio
n
i
n
d
e
x
(AV
DI),
a
n
d
im
p
ro
v
e
th
e
v
o
lt
a
g
e
sta
b
il
it
y
in
d
e
x
(VSI)
o
f
RDS
b
y
tak
i
n
g
in
t
o
a
c
c
o
u
n
t
th
e
re
c
e
n
tl
y
c
re
a
ted
d
istri
b
u
ted
g
e
n
e
ra
to
rs
(DG
s)
a
n
d
e
lec
tri
c
v
e
h
icle
c
h
a
rg
in
g
sta
ti
o
n
s
(EVCS
s).
To
a
d
d
re
ss
th
e
M
OV
S
O
p
r
o
b
lem
,
a
n
o
v
e
l
a
n
d
in
n
o
v
a
ti
v
e
h
o
n
e
y
b
a
d
g
e
r
a
lg
o
rit
h
m
(HBA
)
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
is
p
u
t
fo
rth
.
T
h
e
tw
o
sta
g
e
s
o
f
HBA
,
k
n
o
wn
a
s
t
h
e
"
d
i
g
g
in
g
"
a
n
d
"
h
o
n
e
y
"
p
h
a
se
s,
a
re
re
sp
o
n
sib
le
fo
r
e
ffe
c
ti
v
e
ly
i
d
e
n
ti
fy
i
n
g
t
h
e
id
e
a
l
p
o
si
ti
o
n
a
n
d
a
p
p
r
o
p
riate
q
u
a
n
t
it
y
o
f
EVCS
s
a
n
d
DG
s.
Th
e
sta
n
d
a
rd
IEE
E
3
3
n
o
d
e
tes
t
sy
ste
m
with
d
iffere
n
t
c
a
se
st
u
d
ies
is
c
o
n
sid
e
re
d
t
o
v
a
li
d
a
te
th
e
p
e
rf
o
rm
a
n
c
e
o
f
HBA
.
Th
e
sim
u
latio
n
re
su
lt
s
o
f
imp
r
o
v
e
d
v
o
lt
a
g
e
p
r
o
fil
e
,
m
in
imiz
e
d
p
o
we
r
lo
ss
,
AV
DI
a
n
d
imp
r
o
v
e
d
VSI
a
re
tab
u
late
d
.
Th
e
p
ro
p
o
se
d
HBA
f
in
e
-
tu
n
e
s
th
e
i
d
e
a
l
p
o
siti
o
n
a
n
d
siz
e
o
f
th
e
EVCS
s
to
sig
n
ifi
c
a
n
tl
y
e
n
h
a
n
c
e
RDS
p
e
rfo
rm
a
n
c
e
u
n
d
e
r
h
ig
h
e
r
l
o
a
d
i
n
g
c
ircu
m
st
a
n
c
e
s.
To
d
e
m
o
n
stra
te
t
h
e
e
ff
ica
c
y
a
n
d
o
rig
i
n
a
li
t
y
o
f
th
e
s
u
g
g
e
ste
d
HBA
,
th
e
n
u
m
e
rica
l
re
su
lt
s
a
re
c
o
n
tra
ste
d
with
th
o
se
o
f
e
a
rli
e
r
so
ft
c
o
m
p
u
ti
n
g
tec
h
n
iq
u
e
s.
K
ey
w
o
r
d
s
:
DG
u
n
its
Ho
n
ey
b
a
d
g
er
al
g
o
r
ith
m
Po
wer
lo
s
s
m
in
im
izatio
n
R
ad
ial
d
is
tr
ib
u
tio
n
s
y
s
tem
Vo
ltag
e
s
tab
ilit
y
en
h
an
ce
m
e
n
t
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ma
d
h
u
b
a
b
u
T
h
i
r
u
v
ee
d
u
la
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
i
n
g
,
An
n
am
alai
Un
iv
er
s
ity
An
n
am
alai
Nag
ar
,
T
a
m
iln
ad
u
,
I
n
d
ia
E
m
ail: m
ad
h
u
au
2
0
1
9
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Mo
r
e
elec
tr
icity
is
n
ee
d
ed
to
r
u
n
I
n
d
ia'
s
ex
p
an
d
in
g
f
leet
o
f
elec
tr
ic
v
e
h
icles
(
E
Vs).
Ot
h
er
f
ac
to
r
s
co
n
tr
ib
u
tin
g
to
its
r
is
in
g
a
p
p
ea
l
ar
e
its
b
etter
p
e
r
f
o
r
m
an
c
e,
r
ed
u
ce
d
m
ain
ten
an
ce
n
ee
d
s
,
an
d
lo
w
ca
r
b
o
n
f
o
o
tp
r
i
n
t.
T
h
e
r
is
e
in
EV
u
s
a
g
e
h
as
af
f
ec
ted
th
e
d
is
tr
ib
u
tio
n
s
y
s
tem
's
ef
f
icien
cy
[
1
]
.
T
h
e
r
ad
ial
d
is
tr
ib
u
tio
n
s
y
s
tem
'
s
(
R
D
S)
d
ep
en
d
a
b
ilit
y
an
d
p
er
f
o
r
m
an
ce
a
r
e
af
f
ec
te
d
b
y
th
e
p
lace
m
e
n
t
o
f
th
e
EV
ch
ar
g
in
g
s
tatio
n
s
(
E
VC
Ss
)
.
T
h
e
f
u
n
d
a
m
en
tal
is
s
u
e
is
th
at
th
e
R
DS
is
les
s
ef
f
icien
t
d
u
e
to
th
e
E
VC
S's
in
co
r
r
ec
t
p
lace
m
en
t
[
2
]
.
I
n
g
en
e
r
al,
E
VC
Ss
ar
e
u
tili
ze
d
as
lo
ad
s
.
R
ea
ctiv
e
an
d
ac
tu
a
l
p
o
wer
lo
s
s
es,
v
o
ltag
e
d
ev
iati
o
n
,
v
o
ltag
e
p
r
o
f
ile,
an
d
v
o
ltag
e
s
tab
ilit
y
in
d
ex
(
VSI
)
m
in
im
izatio
n
all
r
is
e
p
r
o
p
o
r
tio
n
ately
in
th
e
d
is
tr
ib
u
tio
n
s
y
s
tem
with
in
cr
ea
s
in
g
lo
ad
.
T
h
e
allo
ca
tio
n
p
r
o
b
lem
f
o
r
E
VC
S
s
in
R
DS
h
as
b
ee
n
s
tu
d
i
ed
r
ec
en
tly
,
a
n
d
a
n
u
m
b
er
o
f
tr
ad
itio
n
al
an
d
s
o
f
t
c
o
m
p
u
tin
g
alg
o
r
ith
m
s
h
av
e
b
ee
n
r
e
p
o
r
ted
.
An
im
p
r
o
v
ed
ch
ick
en
s
war
m
o
p
tim
izatio
n
[
3
]
h
as
b
ee
n
ap
p
lied
to
o
p
tim
ize
th
e
lo
ca
ti
o
n
an
d
s
ize
o
f
s
o
lar
p
o
wer
ed
E
VC
S
s
an
d
lo
wer
th
e
p
o
wer
lo
s
s
an
d
im
p
r
o
v
e
v
o
ltag
e
at
all
b
u
s
s
es
an
d
VSI
.
A
h
y
b
r
id
g
r
ay
wo
lf
o
p
tim
iz
atio
n
an
d
p
ar
ticle
s
war
m
o
p
ti
m
izatio
n
(
PSO
)
[
4
]
was
u
s
ed
to
allo
ca
te
th
e
E
V
C
Ss
an
d
ca
p
ac
ito
r
s
f
o
r
lo
wer
th
e
p
o
wer
l
o
s
s
in
R
DS.
T
h
e
s
am
e
E
VC
Ss
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
7
9
-
1
3
8
8
1380
ca
p
ac
ito
r
s
h
as
b
ee
n
o
p
tim
ized
u
s
in
g
q
u
an
tu
m
-
b
eh
av
e
d
an
d
g
au
s
s
ian
m
u
tatio
n
al
d
r
ag
o
n
f
ly
alg
o
r
ith
m
[
5
]
an
d
s
im
u
lated
r
esu
lts
ar
e
co
m
p
ar
ed
with
PS
O
an
d
b
io
g
eo
g
r
ap
h
y
-
b
ased
o
p
tim
izatio
n
(
BBO
)
m
eth
o
d
s
.
T
h
e
E
VC
Ss
an
d
d
is
tr
ib
u
te
d
g
e
n
er
ato
r
s
(
DGs
)
h
as
b
ee
n
u
s
ed
to
im
p
r
o
v
e
th
e
v
o
ltag
e
s
tab
ilit
y
o
f
R
DS
b
y
co
m
b
in
ed
Ha
r
r
ies
Haw
k
o
p
tim
izatio
n
an
d
teac
h
i
n
g
-
lear
n
i
n
g
b
ased
o
p
tim
izatio
n
(
T
L
B
O
)
alg
o
r
ith
m
s
[
6
]
.
Desig
n
an
d
m
o
d
ellin
g
th
e
E
VC
Ss
u
s
in
g
Mo
n
te
C
ar
l
o
s
im
u
latio
n
m
eth
o
d
[
7
]
a
n
d
l
o
ca
tio
n
an
d
s
ize
o
f
E
VC
Ss
h
a
s
b
ee
n
id
en
tifie
d
b
y
s
am
e
ap
p
r
o
ac
h
.
A
tr
ad
itio
n
al
E
VC
Ss
'
ch
ar
g
in
g
f
r
eq
u
en
cy
l
o
ca
tio
n
was
an
aly
ze
d
with
a
n
y
lo
g
ic
tech
n
iq
u
e
[
8
]
.
T
h
e
p
u
b
lic
EV
'
s
o
p
tim
ized
ch
ar
g
in
g
s
tatio
n
s
en
h
a
n
ce
th
e
le
v
el
o
f
s
h
ar
in
g
ch
a
r
g
in
g
.
T
h
e
ap
p
r
o
ac
h
p
r
ac
tically
id
e
n
tifie
s
th
e
lo
ca
tio
n
a
n
d
s
ize
o
f
E
VC
Ss
,
im
p
r
o
v
es
th
e
s
tate
o
f
ch
ar
g
e
(
SOC
)
o
f
E
Vs.
A
h
y
b
r
id
ch
i
ck
en
s
war
m
o
p
tim
izatio
n
an
d
T
L
B
O
[
9
]
h
as
b
ee
n
ap
p
lied
to
g
et
th
e
Par
eto
o
p
tim
al
s
o
lu
tio
n
o
f
lo
ca
tio
n
s
an
d
v
alu
es
o
f
E
VC
Ss
in
R
DS.
T
h
e
E
VC
Ss
an
d
DGs
ar
e
o
p
tim
ally
allo
ca
ted
u
s
in
g
(
AI
)
a
p
p
r
o
ac
h
[
1
0
]
a
n
d
an
aly
ze
th
e
r
eliab
ilit
y
o
f
R
DS.
T
h
e
AI
a
p
p
r
o
ac
h
h
as
b
ee
n
b
ased
o
n
h
y
b
r
id
g
r
ey
wo
lf
o
p
tim
izatio
n
an
d
PS
O
an
d
o
u
tco
m
es
of
p
o
wer
lo
s
s
,
v
o
ltag
e
an
d
VSI
was
d
is
p
lay
ed
.
B
y
em
p
lo
y
in
g
DG
an
d
E
VC
Ss
b
ased
o
n
th
e
V2
G
m
o
d
e,
an
im
p
r
o
v
e
d
h
ar
m
o
n
y
PS
O
[
1
1
]
was
p
u
t
in
to
p
lace
to
im
p
r
o
v
e
th
e
lev
el
o
f
v
o
ltag
e
an
d
th
e
n
et
s
av
in
g
o
f
R
DS.
T
h
r
o
u
g
h
th
e
b
est
p
o
s
s
ib
le
E
VC
S
al
lo
ca
tio
n
,
th
e
PS
O
[
1
2
]
was
u
s
ed
t
o
i
m
p
r
o
v
e
v
o
l
t
a
g
e
s
t
a
b
i
l
it
y
i
n
t
h
e
u
n
b
a
l
a
n
c
e
d
r
a
d
i
a
l
d
i
s
t
r
i
b
u
ti
o
n
s
y
s
t
e
m
.
U
ti
l
i
zi
n
g
a
h
y
b
r
i
d
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
(
GA
)
-
P
SO
[
1
3
]
,
p
l
u
g
-
i
n
E
V
C
Ss
a
n
d
P
V
-
c
o
n
s
t
r
a
i
n
e
d
D
G
s
h
a
v
e
b
e
e
n
a
l
l
o
c
a
te
d
t
o
m
a
x
i
m
i
z
e
th
e
v
o
l
t
a
g
e
p
r
o
f
i
l
e
.
A
s
im
ilar
p
r
o
b
lem
h
as
b
ee
n
s
o
lv
ed
u
s
in
g
th
e
cu
c
k
o
o
s
ea
r
c
h
alg
o
r
ith
m
with
GA
[
1
4
]
a
n
d
th
e
L
ev
y
-
en
h
an
ce
d
o
p
p
o
s
itio
n
-
b
ased
g
r
ad
ien
t
-
b
ased
o
p
tim
izer
[
1
5
]
.
T
o
s
u
cc
ess
f
u
lly
a
d
d
r
ess
t
h
e
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
is
s
u
e
o
f
f
in
d
in
g
th
e
id
ea
l
p
o
s
itio
n
an
d
n
ec
e
s
s
ar
y
v
alu
e
o
f
E
VC
Ss
,
a
u
n
i
q
u
e
an
d
en
h
an
ce
d
v
er
s
io
n
o
f
th
e
h
o
n
ey
b
ad
g
e
r
alg
o
r
ith
m
(
HB
A)
[
1
6
]
is
r
ec
o
m
m
en
d
ed
.
T
h
r
ee
d
is
tin
ct
E
VC
Ss
,
s
u
ch
as
lev
el
-
1
,
lev
el
-
2
,
an
d
lev
el
-
3
,
ar
e
allo
c
ated
o
p
tim
ally
u
s
in
g
th
e
PS
O
[
1
7
]
m
eth
o
d
th
at
was
u
s
ed
.
T
h
e
m
o
d
el
h
as
b
ee
n
s
im
u
lated
u
s
in
g
o
p
en
DSS
an
d
MA
T
L
AB
.
T
h
e
m
o
s
t
ef
f
ec
ti
v
e
way
t
o
allo
ca
te
DSTA
T
C
OM
an
d
E
VC
S
in
th
e
I
n
d
ian
R
DS
is
th
r
o
u
g
h
th
e
u
s
e
o
f
th
e
b
ald
ea
g
le
s
ea
r
c
h
alg
o
r
ith
m
[
1
8
]
.
Fo
r
s
o
lar
-
in
teg
r
ated
E
VC
Ss
,
a
wo
r
k
ab
le
s
tr
ateg
y
[
1
9
]
h
as
b
ee
n
estab
lis
h
ed
to
d
ec
r
ea
s
e
R
DS
p
o
wer
lo
s
s
in
u
r
b
an
ar
ea
s
.
T
h
e
m
eth
o
d
en
h
an
ce
s
th
e
SOC
o
f
E
Vs
an
d
p
r
ac
tically
lo
ca
tes
an
d
s
izes
E
VC
S
s
.
T
h
e
id
ea
l
lo
ca
tio
n
s
o
f
E
V
-
C
Ss
ar
e
id
en
tifie
d
b
y
ap
p
ly
in
g
th
e
T
L
B
O
alg
o
r
ith
m
[
2
0
]
,
wh
ich
tak
es
in
to
ac
co
u
n
t
th
e
g
o
als
o
f
m
ax
im
izin
g
th
e
VSI
an
d
lo
wer
in
g
ac
tu
al
p
o
wer
l
o
s
s
an
d
AVDI
.
T
o
ev
o
lv
e
th
e
v
o
ltag
e
s
tab
ilit
y
s
o
lu
tio
n
in
R
DS,
a
f
r
esh
an
d
ef
f
ec
tiv
e
m
eta
-
h
e
u
r
is
tic
m
eth
o
d
o
f
g
r
o
u
p
teac
h
in
g
o
p
tim
iza
tio
n
(
GT
O)
alg
o
r
ith
m
[
2
1
]
is
r
ec
o
m
m
en
d
ed
.
So
f
t
o
p
en
p
o
in
ts
[
2
2
]
p
lay
an
im
p
o
r
tan
t
p
ar
t
in
en
h
an
cin
g
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
'
v
o
ltag
e
s
tab
ili
ty
f
o
r
b
o
th
n
o
r
m
al
an
d
f
au
lt
s
ce
n
ar
io
s
.
T
h
e
p
o
s
itio
n
an
d
s
ize
o
f
m
u
ltip
le
d
is
tr
ib
u
ted
g
en
er
atio
n
u
n
its
ca
n
b
e
o
p
tim
ally
m
an
ag
ed
wh
ile
m
in
im
izin
g
th
e
o
v
er
all
ac
tiv
e
p
o
wer
lo
s
s
u
s
in
g
th
e
PS
O
with
t
im
e
-
v
ar
y
in
g
ac
ce
l
er
atio
n
co
ef
f
icien
ts
(
PS
O
-
T
VAC)
tech
n
iq
u
e
[
2
3
]
.
Fin
d
in
g
a
s
o
lu
tio
n
to
t
h
e
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
v
o
ltag
e
s
tab
ilit
y
p
r
o
b
lem
w
h
ile
tak
i
n
g
E
VC
Ss
an
d
DG
u
n
its
in
to
co
n
s
id
er
atio
n
is
th
e
m
ain
o
b
jectiv
e
o
f
t
h
e
cu
r
r
e
n
t
in
q
u
ir
y
.
T
h
e
n
ewly
cr
ea
ted
HB
A
f
in
d
s
th
e
id
ea
l
p
lace
m
en
ts
an
d
n
ec
e
s
s
ar
y
v
alu
es
f
o
r
DGs
an
d
E
VC
Ss
.
U
s
in
g
a
ty
p
ical
I
E
E
E
3
3
-
n
o
d
e
test
s
y
s
tem
,
th
e
m
eth
o
d
'
s
ef
f
ec
tiv
en
ess
is
ev
alu
ated
.
T
h
e
s
u
g
g
ested
alg
o
r
ith
m
ex
am
i
n
es
th
e
E
VC
Ss
an
d
DG
u
n
its
u
s
in
g
f
iv
e
d
is
tin
ct
ca
s
es,
an
d
th
e
o
u
t
co
m
es a
r
e
co
n
tr
asted
with
t
h
o
s
e
o
f
ex
is
tin
g
s
o
f
t c
o
m
p
u
tin
g
tech
n
iq
u
es.
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
T
h
e
p
r
o
p
o
s
ed
p
r
o
b
lem
s
o
lv
es
a
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
is
s
u
e
in
R
DS
b
y
tak
in
g
in
t
o
ac
co
u
n
t
b
o
th
DGs
an
d
E
VC
Ss
.
T
h
e
p
r
o
p
o
s
ed
p
r
o
b
lem
'
s
k
ey
o
b
jectiv
es
ar
e
to
d
ec
r
ea
s
e
p
o
wer
lo
s
s
,
im
p
r
o
v
e
v
o
ltag
e
at
ea
ch
b
u
s
,
an
d
r
e
d
u
ce
t
h
e
AVDI
an
d
VSI
o
f
R
DS.
T
o
o
v
er
co
m
e
th
e
ch
allen
g
e,
an
in
n
o
v
ativ
e
HB
A
m
eth
o
d
o
l
o
g
y
is
u
s
ed
:
1
(
)
=
∑
∗
2
=
1
(
1
)
2
(
)
=
1
∑
|
1
−
|
2
=
1
(
2
)
3
(
)
=
[
|
|
4
−
4
(
+
)
2
−
4
(
+
)
|
|
2
]
(
3
)
(
)
=
min
{
1
1
(
)
+
2
2
(
)
+
3
(
1
3
(
)
)
}
(
4
)
C
o
n
s
tr
ain
ts
:
∑
−
=
=
1
(
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
I
mp
a
cts o
f e
lectric v
eh
icle
ch
a
r
g
in
g
s
ta
tio
n
s
a
n
d
DGs o
n
R
DS
fo
r
…
(
Ma
d
h
u
b
a
b
u
Th
ir
u
v
ee
d
u
la
)
1381
∑
−
=
=
1
(
6
)
≤
≤
(
7
)
0
.
95
≤
≤
1
.
05
,
=
1
,
2
,
3
,
.
.
.
.
.
,
(
8
)
≤
≤
(
9
)
≤
≤
(
1
0
)
I
n
o
r
d
er
t
o
r
ed
u
ce
p
o
wer
lo
s
s
es,
av
er
ag
e
v
o
ltag
e
d
e
v
iatio
n
,
an
d
m
ax
im
ize
th
e
VSI
,
th
e
u
ltima
te
o
b
jectiv
e
f
u
n
ctio
n
is
g
en
er
ate
d
.
Ma
th
em
atica
lly
,
th
is
is
r
ep
r
esen
ted
in
(
4
)
.
T
h
e
p
o
wer
b
al
an
ce
co
n
s
tr
ain
ts
o
f
a
c
t
u
a
l
a
n
d
r
e
a
c
t
i
v
e
p
o
w
e
r
a
r
e
r
e
p
r
e
s
e
n
t
e
d
i
n
(
5
)
a
n
d
(
6
)
.
T
h
e
g
e
n
e
r
a
t
o
r
l
i
m
i
t
s
o
f
D
G
u
n
i
t
s
a
r
e
r
e
p
r
e
s
e
n
t
e
d
i
n
(
7
)
.
T
h
e
v
o
ltag
e
lim
its
o
f
ea
c
h
b
u
s
ar
e
r
e
p
r
esen
ted
in
(
8
)
.
T
h
e
c
h
ar
g
in
g
p
o
in
t
lim
it
an
d
E
V
c
h
ar
g
in
g
s
tatio
n
lim
it
co
n
s
tr
ain
ts
ar
e
r
ep
r
esen
ted
in
(
9
)
an
d
(
1
0
)
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
I
n
o
r
d
e
r
to
h
elp
with
th
e
m
o
s
t
ef
f
icien
t
allo
ca
tio
n
o
f
E
VC
Ss
an
d
DG
s
in
R
D
S,
th
e
c
u
r
r
en
t
wo
r
k
p
r
esen
ted
th
e
HB
A,
a
n
o
v
el
a
n
d
ef
f
ec
tiv
e
m
etah
e
u
r
is
tic
o
p
t
im
izatio
n
ap
p
r
o
ac
h
.
Hash
im
e
t
a
l
.
[
2
4
]
g
en
er
ated
a
p
r
o
p
o
s
ed
alg
o
r
ith
m
th
at
tak
es
in
s
p
ir
atio
n
f
r
o
m
th
e
clev
er
f
o
r
ag
in
g
b
e
h
av
io
u
r
o
f
h
o
n
ey
b
ad
g
er
s
.
D
u
e
to
two
d
is
tin
ct
p
h
ases
,
k
n
o
wn
as
th
e
h
o
n
ey
p
h
ase
an
d
th
e
d
ig
g
in
g
p
h
ase
,
wh
ich
ar
e
o
f
ten
r
ef
er
r
ed
to
as
th
e
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
p
h
ases
,
HB
A
h
as
g
r
ea
ter
s
ee
k
i
n
g
ab
ilit
y
.
Fo
r
th
e
p
u
r
p
o
s
e
o
f
s
o
lv
in
g
n
o
n
-
li
n
ea
r
,
m
ix
ed
-
in
teg
e
r
,
an
d
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
,
it is
an
ef
f
icien
t o
p
tim
izatio
n
t
o
o
l
[
2
5
]
,
[
2
6
]
.
3
.
1
.
M
a
t
hema
t
ica
l r
epre
s
ent
a
t
io
n o
f
H
B
A
Usu
ally
,
HB
A
i
s
d
iv
id
ed
in
to
two
d
is
tin
ct
s
tag
es:
d
ig
g
in
g
p
h
ase
an
d
h
o
n
ey
p
h
ase.
I
n
HB
A,
th
e
p
o
p
u
latio
n
o
f
p
o
ten
tial so
lu
tio
n
s
ca
n
b
e
ex
p
r
ess
ed
m
ath
em
at
ically
b
y
,
=
[
11
12
13
…
1
21
22
23
…
2
…
…
…
.
.
1
2
3
…
]
(
1
1
)
i
th
p
o
s
itio
n
o
f
h
o
n
e
y
b
ad
g
er
,
=
[
1
,
2
…
…
.
.
]
(
1
2
)
T
h
e
f
o
llo
win
g
s
tep
s
ar
e
co
n
s
id
er
ed
wh
en
a
d
d
r
ess
in
g
n
o
n
-
lin
e
ar
o
p
tim
izatio
n
p
r
o
b
lem
s
:
Step
1
:
in
itial
s
etu
p
:
b
eg
in
b
y
in
itializin
g
th
e
HB
A
with
a
p
o
p
u
latio
n
co
u
n
t
(
N)
a
n
d
th
eir
r
e
s
p
ec
tiv
e
p
o
s
itio
n
s
,
c
a
l
c
u
l
a
te
d
u
s
i
n
g
t
h
e
:
=
+
1
×
(
−
)
w
h
e
r
e
t
h
e
r
a
n
d
o
m
i
n
t
e
g
e
r
1
i
s
i
n
a
r
a
n
g
e
o
f
0
t
o
1
.
Step
2
:
in
ten
s
ity
ca
lcu
latio
n
:
d
ef
in
e
th
e
in
ten
s
ity
,
wh
ich
r
el
ates
th
e
p
r
ey
'
s
co
n
ce
n
tr
atio
n
lev
el
to
its
d
is
tan
ce
f
r
o
m
th
e
h
o
n
e
y
b
ad
g
er
.
T
h
e
s
tr
en
g
th
o
f
t
h
e
p
r
e
y
’
s
s
m
ell
is
ca
lcu
lated
u
s
in
g
th
e
in
v
e
r
s
e
s
q
u
ar
e
law
,
as
illu
s
tr
ated
:
=
2
×
4
2
wh
er
e
th
e
r
a
n
d
o
m
in
teg
er
r
2
is
in
a
r
an
g
e
o
f
0
to
1
,
=
(
−
+
1
)
2
an
d
=
−
.
Step
3
:
d
en
s
ity
f
ac
to
r
u
p
d
ate:
th
e
d
e
n
s
ity
f
ac
to
r
α
e
n
s
u
r
es
a
b
alan
ce
d
tr
an
s
itio
n
f
r
o
m
ex
p
lo
r
atio
n
to
ex
p
lo
itatio
n
b
y
r
e
g
u
latin
g
t
im
e
-
v
ar
y
in
g
r
an
d
o
m
n
ess
.
T
h
e
d
ec
r
ea
s
in
g
f
ac
to
r
α
\
alp
h
aα
,
wh
ich
r
ed
u
ce
s
r
an
d
o
m
n
ess
o
v
er
tim
e,
is
u
p
d
a
ted
u
s
in
g
th
e:
=
×
e
xp
(
−
)
w
h
e
r
e
i
s
t
h
e
m
a
x
i
m
u
m
q
u
a
n
t
i
ty
o
f
i
t
e
r
a
t
i
o
n
s
a
n
d
C
is
a
c
o
n
s
t
an
t
(
t
y
p
i
c
a
l
l
y
C
≥
1
,
w
i
t
h
a
d
e
f
a
u
l
t
v
a
l
u
e
o
f
2
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
7
9
-
1
3
8
8
1382
Step
4
:
lo
ca
l
o
p
tim
a
escap
e:
t
o
av
o
id
g
ettin
g
tr
ap
p
ed
in
lo
c
al
o
p
tim
a,
th
is
s
tep
,
alo
n
g
wit
h
th
e
f
o
llo
win
g
two
,
is
cr
u
cial.
Step
5
:
u
p
d
atin
g
a
g
en
t
p
o
s
itio
n
s
:
in
th
is
s
tag
e,
th
e
p
o
s
itio
n
s
o
f
th
e
a
g
en
ts
ar
e
u
p
d
ated
.
T
h
e
r
e
ar
e
two
s
tag
es
t
o
u
p
d
atin
g
an
HB
A
p
o
s
itio
n
: th
e
h
o
n
ey
p
h
ase
an
d
th
e
d
ig
g
in
g
p
h
ase.
Dig
g
in
g
p
h
ase:
in
th
is
p
h
ase
,
th
e
m
o
v
em
en
ts
o
f
th
e
h
o
n
ey
b
ad
g
er
m
im
ic
th
o
s
e
o
f
a
ca
r
d
i
o
id
.
T
h
ese
m
o
v
em
en
ts
ca
n
b
e
s
im
u
lated
u
s
in
g
th
e
f
o
ll
o
win
g
f
o
r
m
u
las:
=
+F
×
×
×
+F
×
3
×
∝
×
×
|
c
os
(
2
4
)
×
[
1
−
c
os
(
2
5
)
]
|
(
1
3
)
F
=
(
)
=
{
1
,
6
≤
0
.
5
−
1
,
6
(
14)
is
a
r
an
d
o
m
i
n
teg
er
in
t
h
e
r
an
g
e
o
f
0
a
n
d
1
.
Ho
n
ey
p
h
ase:
t
he
s
ce
n
ar
io
th
at
ar
is
es wh
en
a
h
o
n
e
y
b
a
d
g
er
f
o
llo
ws a
h
o
n
ey
g
u
id
e
to
g
et
to
a
b
ee
h
iv
e
m
ay
b
e
ex
p
r
ess
ed
m
ath
em
atica
lly
as
,
=
+
F
×
1
×
α
×
1
is
a
r
an
d
o
m
in
teg
er
in
th
e
r
a
n
g
e
o
f
0
an
d
1
.
3
.
2
.
I
m
ple
m
ent
a
t
io
n o
f
H
B
A
m
et
ho
do
lo
g
y
f
o
r
a
llo
ca
t
i
o
n o
f
DG
s
a
nd
E
VCS
s
pro
ble
m
T
h
e
f
o
ll
o
win
g
s
tep
s
ar
e
ap
p
lied
f
o
r
s
o
lu
tio
n
o
f
MO
VSO
p
r
o
b
lem
b
y
o
p
tim
al
allo
ca
ti
o
n
o
f
E
VC
Ss
an
d
DGs
u
s
in
g
HB
A
ap
p
r
o
ac
h
:
i)
R
ea
d
th
e
lin
e,
b
u
s
,
lo
a
d
d
ata
a
n
d
f
ea
tu
r
es o
f
E
Vs an
d
C
Ss
o
f
p
r
o
p
o
s
ed
3
3
an
d
6
9
n
o
d
e
test
s
y
s
tem
s
.
ii)
R
u
n
th
e
d
is
tr
ib
u
tio
n
p
o
wer
f
lo
w
an
d
ca
lcu
late
th
e
lo
s
s
u
s
in
g
th
e
ex
ac
t lo
s
s
f
o
r
m
u
la
f
o
r
th
e
b
ase
ca
s
e.
iii)
Fix
a
n
u
m
b
er
o
f
E
VC
Ss
an
d
DGs n
u
m
b
er
o
f
ch
ar
g
in
g
p
o
in
ts
(
C
Ps
)
an
d
r
atin
g
ch
ar
g
in
g
p
o
in
ts
th
at
ar
e
to
b
e
u
s
ed
in
th
e
R
DS.
iv
)
Set th
e
p
o
p
u
latio
n
,
d
im
e
n
s
io
n
,
to
p
an
d
lo
wer
b
o
u
n
d
s
,
an
d
m
ax
im
u
m
q
u
an
tity
o
f
iter
atio
n
s
f
o
r
th
e
HB
A.
v)
Set th
e
iter
atio
n
to
1
.
v
i)
Dete
r
m
in
e
th
e
f
itn
ess
o
f
ea
ch
d
ig
g
in
g
p
h
ase,
wh
ich
in
clu
d
es
p
o
wer
lo
s
s
in
a
n
etwo
r
k
an
d
th
e
m
o
s
t
s
u
itab
le
lo
ca
tio
n
s
f
o
r
E
VC
Ss
an
d
DGs.
v
ii)
Fo
r
ea
ch
d
ig
g
in
g
a
n
d
h
o
n
e
y
p
h
ase,
ass
ess
th
e
m
u
lti
-
o
b
jectiv
e
f
u
n
ctio
n
s
.
v
iii)
Af
ter
u
p
d
atin
g
th
e
lo
ca
tio
n
s
o
f
th
e
d
ig
g
i
n
g
an
d
h
o
n
ey
p
h
ases
,
s
to
r
e
th
e
ar
r
a
y
'
s
to
p
f
itn
ess
v
alu
es.
ix
)
C
o
m
p
u
te
th
e
p
r
esen
t p
o
s
itio
n
o
f
d
ig
g
in
g
p
h
ase
an
d
h
o
n
e
y
p
h
ase.
x)
Ver
if
y
th
at
all
co
n
s
tr
ain
ts
h
av
e
b
ee
n
m
et;
if
n
o
t,
p
r
o
ce
ed
to
s
tep
6
; o
th
er
wis
e,
p
r
o
g
r
ess
to
t
h
e
n
ex
t step
.
x
i)
Ver
if
y
if
th
e
q
u
an
tity
o
f
iter
ati
o
n
p
r
o
ce
s
s
es
m
atch
es
th
e
m
ax
im
u
m
q
u
an
tity
o
f
iter
atio
n
s
;
if
s
o
,
m
o
v
e
o
n
to
th
e
n
ex
t
p
h
ase.
I
f
n
o
t,
m
o
v
e
o
n
to
s
tep
5
.
x
ii)
W
h
en
th
e
v
o
ltag
e
p
r
o
f
ile,
p
o
wer
lo
s
s
,
AVDI
,
an
d
VSI
g
lo
b
al
b
est
s
o
lu
tio
n
ar
e
d
is
p
lay
ed
,
th
e
p
r
o
g
r
am
is
s
to
p
p
ed
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Utilizin
g
th
e
s
tan
d
ar
d
I
E
E
E
te
s
t
s
y
s
tem
3
3
n
o
d
e,
th
e
a
p
p
lica
b
ilit
y
an
d
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
C
HB
A
ar
e
ev
alu
ated
.
T
h
e
test
s
y
s
te
m
'
s
l
in
e
an
d
b
u
s
d
ata
ar
e
o
b
t
ain
ed
f
r
o
m
r
ef
er
e
n
ce
[
1
1
]
.
A
PC
with
an
i5
I
n
tel
C
o
r
e
4
2
1
0
U
p
r
o
ce
s
s
o
r
r
u
n
n
in
g
at
u
p
to
2
.
5
GHz
a
n
d
8
GB
o
f
R
AM
m
em
o
r
y
is
u
s
ed
t
o
d
o
th
e
s
im
u
latio
n
s
u
tili
zin
g
th
e
MA
T
L
AB
1
4
.
0
p
latf
o
r
m
.
T
h
e
cu
r
r
e
n
t r
esear
ch
co
n
s
id
er
s
b
atter
y
elec
tr
ic
ca
r
s
(
B
E
Vs)
an
d
p
lu
g
-
i
n
h
y
b
r
id
EV
s
(
PHEV
s
)
wh
en
in
s
tallin
g
s
u
itab
le
ch
ar
g
in
g
p
l
ac
es
(
C
P
s
)
.
T
ab
le
1
p
r
esen
ts
th
e
E
V
-
C
Ss
'
d
e
s
ig
n
f
ea
tu
r
es.
T
h
e
v
ar
ieties
o
f
E
Vs,
th
eir
p
o
wer
r
atin
g
s
in
k
W
,
th
e
m
in
im
u
m
an
d
m
a
x
im
u
m
q
u
an
tity
o
f
ch
ar
g
in
g
p
lace
s
,
an
d
th
e
ch
ar
g
in
g
s
tatio
n
r
atin
g
s
in
k
W
ar
e
all
p
r
o
v
id
ed
.
T
h
e
c
h
ar
g
in
g
s
tatio
n
s
h
av
e
a
m
i
n
im
u
m
p
o
we
r
r
atin
g
o
f
9
7
5
k
W
an
d
a
m
ax
im
u
m
p
o
wer
r
atin
g
o
f
1
,
6
7
4
.
5
k
W
,
as in
d
icate
d
in
T
ab
le
1
.
T
ab
le
1
.
Simu
latio
n
-
s
p
ec
if
ic
a
ttrib
u
tes o
f
E
V
an
d
C
Ss
Ty
p
e
o
f
EV
P
o
w
e
r
r
a
t
i
n
g
o
f
EV
(
k
W
)
Q
u
a
n
t
i
t
y
o
f
C
P
s
C
S
’
s rat
i
n
g
i
n
k
W
M
i
n
i
m
u
m
M
a
x
i
m
u
m
M
i
n
i
m
u
m
M
a
x
i
m
u
m
C
h
e
v
r
o
l
e
t
v
o
l
t
2
.
2
0
0
2
5
.
0
0
3
5
.
0
0
5
5
.
0
0
7
7
.
0
0
C
H
A
N
G
A
N
Y
I
D
O
N
G
3
.
7
5
0
2
0
.
0
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3
0
.
0
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7
5
.
0
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1
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5
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sl
a
m
o
d
e
l
X
1
3
.
0
0
1
5
.
0
0
2
5
.
0
0
1
9
5
.
0
0
3
2
5
.
0
0
B
M
W
i
3
4
4
.
0
0
1
0
.
0
0
2
0
.
0
0
4
4
0
.
0
0
8
8
0
.
0
0
S
A
E
J1
7
7
2
s
t
a
n
d
a
r
d
7
.
0
0
3
0
.
0
0
4
0
.
0
0
2
1
0
.
0
0
2
8
0
.
0
0
T
h
e
C
S
’
s
o
v
e
r
a
l
l
p
o
w
e
r
r
a
t
i
n
g
(
k
W
)
9
7
5
1
,
6
7
4
.
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
I
mp
a
cts o
f e
lectric v
eh
icle
ch
a
r
g
in
g
s
ta
tio
n
s
a
n
d
DGs o
n
R
DS
fo
r
…
(
Ma
d
h
u
b
a
b
u
Th
ir
u
v
ee
d
u
la
)
1383
4
.
1
.
T
est
s
y
s
t
em
1
:
33
-
n
o
de
wit
h E
VC
Ss
o
nly
First,
C
H
B
A
'
s
ef
f
ec
tiv
en
ess
a
n
d
p
er
f
o
r
m
an
ce
ar
e
ass
ess
ed
u
s
in
g
a
ty
p
ical
I
E
E
E
-
3
3
n
o
d
e
t
est
s
y
s
tem
.
T
h
e
3
3
-
n
o
d
e
n
etwo
r
k
'
s
s
y
s
tem
an
d
lo
ad
d
ata
ar
e
ta
k
en
f
r
o
m
[
9
]
.
T
h
is
test
s
y
s
tem
co
n
s
id
er
s
a
v
o
ltag
e
r
atin
g
o
f
1
2
.
6
6
KV
an
d
an
a
b
s
o
lu
te
ac
t
u
al
an
d
r
ea
ctiv
e
p
o
wer
lo
a
d
o
f
3
,
7
1
5
k
W
an
d
2
,
3
0
0
KVAr
.
T
h
e
p
o
p
u
latio
n
s
ize
is
4
0
,
th
e
m
ax
im
u
m
q
u
an
tity
o
f
iter
atio
n
s
is
1
0
0
,
an
d
t
h
er
e
ar
e
s
ev
en
to
tal
v
ar
iab
les
in
th
e
HB
A
m
eth
o
d
.
T
h
e
f
iv
e
d
is
tin
ct
test
ca
s
es li
s
ted
b
elo
w
h
av
e
b
ee
n
u
s
ed
to
ex
am
i
n
e
th
e
p
r
o
p
o
s
ed
s
y
s
tem
:
−
B
ase
ca
s
e
d
is
tr
ib
u
tio
n
lo
ad
f
lo
w
an
aly
s
is
.
−
I
n
cr
ea
s
in
g
lo
a
d
d
em
a
n
d
with
m
in
im
u
m
n
u
m
b
er
o
f
C
Ps
at
m
in
im
u
m
p
o
wer
r
atin
g
all
C
Ss
.
−
I
n
cr
ea
s
in
g
lo
a
d
d
em
a
n
d
with
m
ax
im
u
m
n
u
m
b
e
r
o
f
C
Ps
at
m
ax
im
u
m
p
o
wer
r
atin
g
all
C
Ss
.
−
Op
tim
al
allo
ca
tio
n
o
f
E
V
-
C
Ss
u
s
in
g
C
HB
A
with
m
in
im
u
m
n
u
m
b
er
o
f
C
Ps
at
m
in
im
u
m
p
o
wer
r
atin
g
all
C
Ss
.
−
Op
tim
al
allo
ca
tio
n
o
f
E
VC
Ss
u
s
in
g
C
HB
A
m
ax
im
u
m
q
u
an
ti
ty
o
f
C
Ps
at
m
ax
im
u
m
p
o
wer
r
atin
g
all
C
Ss
.
I
n
itially
,
d
is
tr
ib
u
tio
n
lo
a
d
f
l
o
w
an
aly
s
is
ap
p
r
o
ac
h
is
ap
p
lie
d
an
d
d
ete
r
m
in
es
th
e
b
ase
ca
s
e
v
o
ltag
e
o
f
ea
ch
b
u
s
,
VSI
,
m
in
im
u
m
VSI
,
v
o
ltag
e
d
ev
iatio
n
in
d
ex
,
m
in
im
u
m
v
o
ltag
e
an
d
p
o
wer
l
o
s
s
o
f
th
e
n
etwo
r
k
,
wh
ich
is
co
n
s
id
er
ed
as
c
ase
1
.
I
n
ca
s
e
2
,
o
p
tim
ally
in
teg
r
ate
th
r
ee
ch
ar
g
i
n
g
s
tatio
n
s
co
n
n
e
cted
to
s
u
b
f
ee
d
er
s
with
a
d
is
tan
ce
o
f
1
m
eter
ea
c
h
.
I
n
th
is
ca
s
e,
co
n
s
id
er
th
e
m
i
n
im
u
m
n
u
m
b
er
o
f
C
Ps
with
th
e
m
in
im
u
m
p
o
wer
r
atin
g
o
f
all
C
Ss
.
Fo
r
C
Ss
,
at
least
a
p
o
wer
r
atin
g
o
f
9
7
5
k
W
is
r
eq
u
ir
ed
.
T
h
e
r
ef
o
r
e
,
t
h
e
lo
ad
d
em
an
d
is
in
cr
ea
s
ed
to
6
,
6
4
0
k
W
(
3
,
7
1
5
+9
7
5
×
3
=
6
,
6
4
0
)
b
y
in
s
tallin
g
t
h
e
3
C
Ss
to
th
e
s
u
b
f
ee
d
e
r
(
lo
a
d
d
em
an
d
is
1
.
7
8
7
3
tim
es
th
e
b
ase
ca
s
e
d
em
an
d
)
.
No
w
d
is
tr
ib
u
tio
n
lo
ad
f
lo
w
is
ap
p
lied
,
an
d
th
e
o
u
tco
m
e
s
o
f
th
e
s
tu
d
y
ar
e
d
is
p
lay
ed
in
T
ab
le
2
.
I
t
in
clu
d
es
a
p
o
wer
lo
s
s
o
f
5
7
6
.
1
7
0
5
k
W
,
AVDI
o
f
0
.
0
1
0
8
,
a
VSI
o
f
0
.
4
9
8
4
(
p
.
u
)
an
d
a
Vm
in
o
f
0
.
8
4
0
8
(
p
.
u
)
r
esp
ec
tiv
ely
.
T
ab
le
2
.
C
o
m
p
a
r
in
g
HB
A,
T
L
B
O,
an
d
HHO
r
esu
lts
f
o
r
I
E
E
E
-
3
3
n
o
d
e
test
s
y
s
tem
with
E
VC
S
s
o
n
ly
C
a
se
M
e
t
h
o
d
s
Lo
c
a
t
i
o
n
s
o
f
EV
c
h
a
r
g
i
n
g
s
t
a
t
i
o
n
s
p
o
w
e
r
l
o
ss
e
s (
k
W
)
A
V
D
I
(
p
.
u
)
V
S
I
(
p
.
u
)
V
mi
n
C
a
se
1
(
b
a
se
c
a
se)
lo
a
d
3
,
7
1
5
k
W
-
-
2
1
0
.
9
8
9
7
0
.
0
0
4
0
5
4
1
0
.
6
6
7
1
7
4
0
.
9
0
3
8
C
a
se
2
l
o
a
d
6
,
6
4
0
k
W
-
-
5
7
6
.
1
7
0
5
0
.
0
1
0
8
0
.
4
9
8
4
0
.
8
4
0
8
C
a
se
3
l
o
a
d
8
7
3
8
-
-
1
0
2
4
.
3
9
0
8
0
.
0
1
8
7
0
.
3
8
5
4
0
.
7
8
8
8
C
a
se
4
H
B
A
(
p
r
o
p
o
s
e
d
)
2
,
2
0
,
2
3
2
8
1
.
2
9
0
.
0
0
4
6
8
9
8
0
.
6
5
1
4
4
5
0
.
9
0
1
0
TLB
O
[
2
0
]
2
,
1
9
,
2
5
2
9
5
.
6
4
7
4
0
.
0
0
4
7
0
.
6
4
9
9
0
.
8
9
8
2
A
LO
[
2
7
]
2
,
1
9
,
2
5
2
9
5
.
6
4
7
4
0
.
0
0
4
7
0
.
6
4
9
9
0
.
8
9
8
2
F
P
A
[
2
8
]
2
,
1
9
,
2
5
2
9
5
.
6
4
7
4
0
.
0
0
4
7
0
.
6
4
9
9
0
.
8
9
8
2
C
S
A
[
2
9
]
2
,
1
9
,
2
5
2
9
5
.
6
4
7
4
0
.
0
0
4
7
0
.
6
4
9
9
0
.
8
9
8
2
P
S
O
[
3
0
]
2
,
1
9
,
2
5
2
9
5
.
6
4
7
4
0
.
0
0
4
7
0
.
6
4
9
9
0
.
8
9
8
2
C
a
se
5
H
B
A
(
p
r
o
p
o
s
e
d
)
2
,
2
0
,
2
3
3
8
4
.
5
8
4
2
0
.
0
0
5
1
2
1
0
.
6
4
1
1
0
.
9
0
0
3
TLB
O
[
2
0
]
2
,
1
9
,
2
5
3
9
0
.
6
2
6
6
0
.
0
0
5
3
0
.
6
3
8
1
0
.
8
9
4
1
A
LO
[
2
7
]
2
,
1
9
,
2
5
3
9
0
.
6
2
6
6
0
.
0
0
5
3
0
.
6
3
8
1
0
.
8
9
4
1
F
P
A
[
2
8
]
2
,
1
9
,
2
5
3
9
0
.
6
2
6
6
0
.
0
0
5
3
0
.
6
3
8
1
0
.
8
9
4
1
C
S
A
[
2
9
]
2
,
1
9
,
2
5
3
9
0
.
6
2
6
6
0
.
0
0
5
3
0
.
6
3
8
1
0
.
8
9
4
1
P
S
O
[
3
0
]
2
,
1
9
,
2
5
3
9
0
.
6
2
6
6
0
.
0
0
5
3
0
.
6
3
8
1
0
.
8
9
4
1
Ob
j
e
c
t
i
v
e
f
u
n
c
t
i
o
n
u
si
n
g
H
B
A
(
p
r
o
p
o
sed)
0
.
8
2
1
9
8
Similar
ly
,
in
ca
s
e
3
,
co
n
s
id
er
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
C
Ps
with
th
e
m
ax
im
u
m
p
o
wer
r
at
in
g
o
f
all
C
Ss
.
T
h
e
m
ax
im
u
m
lo
ad
o
f
ch
ar
g
in
g
s
tatio
n
s
is
1
,
6
7
4
.
5
k
W
,
an
d
th
e
to
tal
lo
ad
is
1
,
6
7
4
.
5
×3
+3
,
7
1
5
=
8
,
7
3
8
.
5
k
W
(
2
.
3
5
2
2
tim
es th
e
b
ase
ca
s
e
v
alu
e)
.
T
h
e
p
er
f
o
r
m
e
d
lo
ad
f
lo
w
an
aly
s
is
an
d
s
im
u
latio
n
r
esu
lts
ar
e
p
r
o
jecte
d
in
th
e
s
am
e
tab
le.
T
h
e
o
b
tain
e
d
p
o
wer
lo
s
s
is
1
,
0
2
4
.
3
9
0
8
k
W
,
AVDI
is
0
.
0
1
8
7
,
VSI
is
0
.
3
8
5
4
(
p
.
u
)
a
n
d
Vm
in
is
0
.
7
8
8
8
(
p
.
u
)
r
esp
ec
tiv
ely
.
I
n
th
is
ca
s
e,
p
o
wer
lo
s
s
an
d
A
VDI
ar
e
in
cr
ea
s
ed
d
u
e
to
m
a
x
im
u
m
lo
ad
d
em
an
d
.
T
h
e
p
r
o
p
o
s
ed
HB
A
is
ap
p
lie
d
in
ca
s
es
2
an
d
3
an
d
o
p
ti
m
ally
allo
ca
tes
th
e
b
est
lo
ca
t
io
n
o
f
th
e
ch
a
r
g
in
g
s
tatio
n
s
,
wh
ich
ar
e
co
n
s
id
er
e
d
in
ca
s
es
4
an
d
5
.
I
n
ca
s
e
4
,
c
o
n
s
id
er
in
g
th
e
t
o
tal
lo
ad
d
em
an
d
o
f
6
,
6
4
0
k
W
an
d
th
e
p
r
o
p
o
s
ed
HB
A,
o
p
tim
ize
th
e
b
est
lo
ca
tio
n
o
f
C
S
with
th
e
m
i
n
im
u
m
q
u
a
n
tity
o
f
C
Ps
.
T
h
e
o
b
tain
ed
o
p
tim
al
p
lace
s
ar
e
2
,
2
0
a
n
d
2
3
,
r
esp
ec
tiv
ely
.
Ou
tco
m
es
o
f
th
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
,
s
u
ch
as
p
o
wer
l
o
s
s
,
AVDI
,
VSI
an
d
Vm
in
ar
e
2
8
1
.
2
9
k
W
,
0
.
0
0
4
6
8
9
8
(
p
.
u
)
,
0
.
6
5
1
4
4
5
(
p
.
u
)
an
d
0
.
9
0
1
0
(
p
.
u
)
r
esp
ec
tiv
e
ly
.
Her
e,
p
o
wer
lo
s
s
is
4
8
.
8
2
% r
ed
u
ce
d
co
m
p
a
r
ed
with
ca
s
e
2
(
with
o
u
t
o
p
tim
iza
tio
n
)
.
T
a
b
le
2
p
r
esen
ts
th
e
s
im
u
latio
n
r
esu
lts
th
at
d
em
o
n
s
tr
at
e
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
s
u
g
g
ested
HB
A
ap
p
r
o
a
ch
wh
en
co
m
p
ar
ed
to
T
L
B
O,
AL
O,
PS
O,
FP
A,
an
d
C
SA
tech
n
iq
u
es.
Similar
ly
,
in
ca
s
e
5
,
m
ax
im
u
m
ch
a
r
g
i
n
g
p
o
i
n
ts
with
a
m
ax
im
u
m
lo
ad
o
f
8
,
7
3
8
.
5
k
W
ar
e
co
n
s
id
er
ed
to
r
u
n
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
7
9
-
1
3
8
8
1384
d
is
tr
ib
u
tio
n
lo
ad
f
lo
w
with
H
B
A.
T
h
e
HB
A
ef
f
ec
tiv
ely
tu
n
ed
th
e
b
est
p
lace
s
o
f
C
Ss
an
d
t
h
e
b
est
p
lace
s
ar
e
2
,
2
0
,
2
3
r
esp
ec
tiv
ely
.
E
x
p
er
im
en
tal
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
s
u
ch
as
p
o
wer
lo
s
s
,
AVDI
,
VSI
an
d
Vm
in
ar
e
3
8
4
.
5
8
4
2
,
0
.
0
0
5
1
2
1
(
p
.
u
)
,
0
.
6
4
1
1
(
p
.
u
)
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d
0
.
9
0
0
3
(
p
.
u
)
r
esp
ec
tiv
ely
.
Her
e
,
p
o
wer
lo
s
s
is
6
2
.
4
4
%
r
ed
u
ce
d
c
o
m
p
ar
e
d
with
ca
s
e
2
(
with
o
u
t
o
p
tim
izatio
n
)
.
T
h
e
co
n
v
e
r
g
en
ce
c
u
r
v
e
f
o
r
th
e
3
3
-
n
o
d
e
s
y
s
tem
is
s
h
o
wn
in
Fig
u
r
e
1
.
T
h
e
lo
we
s
t,
av
er
ag
e
an
d
h
ig
h
est
v
alu
es
o
f
ev
e
r
y
o
b
jectiv
e
f
u
n
ctio
n
f
o
r
th
e
3
3
-
n
o
d
e
test
s
y
s
tem
ar
e
s
h
o
wn
in
T
a
b
le
3
.
C
o
m
p
ar
ativ
e
s
tu
d
ies
with
o
t
h
er
o
p
tim
izatio
n
alg
o
r
ith
m
s
(
T
L
B
O,
AL
O,
PS
O,
FP
A
an
d
C
SA)
ar
e
co
n
s
id
er
ed
to
v
er
if
y
th
e
s
u
p
e
r
io
r
ity
o
f
th
e
p
r
o
p
o
s
ed
HB
A.
Fig
u
r
e
1
.
C
o
n
v
er
g
e
n
ce
cu
r
v
e
f
o
r
3
3
n
o
d
e
test
s
y
s
tem
T
ab
le
3
.
C
o
m
p
a
r
is
o
n
o
f
ea
ch
o
b
jectiv
e
f
u
n
ctio
n
s
f
o
r
3
3
n
o
d
e
test
s
y
s
tem
V
a
l
u
e
s
o
f
o
b
j
e
c
t
i
v
e
f
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n
c
t
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p
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e
r
l
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sses
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k
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I
(
p
.
u
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S
I
(
p
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mi
n
C
a
se
4
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o
p
t
i
ma
l
a
l
l
o
c
a
t
i
o
n
o
f
EV
C
S
s
u
si
n
g
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B
A
w
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t
h
m
i
n
i
m
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m
n
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mb
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m
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m
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r
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t
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g
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l
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1
0
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P
s w
i
t
h
9
7
5
k
W
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i
n
i
m
u
m
v
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l
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e
2
8
1
.
2
9
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0
0
4
6
8
9
8
0
.
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5
1
4
4
5
0
.
9
0
1
0
A
v
e
r
a
g
e
v
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l
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e
2
8
5
.
3
2
5
6
0
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0
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1
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7
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5
6
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a
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m
v
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e
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6
0
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0
0
4
9
7
5
0
.
9
4
9
9
0
.
9
9
8
2
C
a
se
5
:
o
p
t
i
ma
l
a
l
l
o
c
a
t
i
o
n
o
f
EV
C
S
s
u
si
n
g
H
B
A
w
i
t
h
m
a
x
i
m
u
m
n
u
mb
e
r
o
f
C
P
s
a
t
ma
x
i
m
u
m
p
o
w
e
r
r
a
t
i
n
g
a
l
l
C
S
s (
1
5
0
C
P
s w
i
t
h
1
,
6
7
5
k
W
)
M
i
n
i
m
u
m
v
a
l
u
e
3
8
4
.
5
8
4
2
0
.
0
0
5
1
2
1
0
.
6
4
1
1
0
.
9
0
0
3
A
v
e
r
a
g
e
v
a
l
u
e
3
8
7
.
2
3
1
4
0
.
0
0
5
2
3
4
7
0
.
7
8
1
9
0
.
9
1
3
9
M
a
x
i
m
u
m
v
a
l
u
e
3
8
9
.
9
5
4
6
0
.
0
0
5
3
0
0
0
.
9
8
8
1
0
.
9
9
5
4
4
.
2
.
T
est
s
y
s
t
em
2
:
3
3
-
n
o
de
wit
h E
VC
Ss
a
nd
DG
u
nits
T
h
is
ca
s
e
co
n
s
id
er
s
E
VC
Ss
a
n
d
DGs
f
o
r
s
o
lu
ti
o
n
o
f
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
p
r
o
b
le
m
.
T
h
e
d
ata
o
f
th
e
E
VC
Ss
ar
e
tak
en
f
o
r
m
r
ef
er
en
ce
[
9
]
a
n
d
g
iv
en
in
T
a
b
le
1
.
T
h
e
HB
A
alg
o
r
ith
m
ic
s
p
e
cif
icatio
n
in
clu
d
es
p
o
p
u
latio
n
s
ize=
5
0
,
m
ax
i
m
u
m
iter
atio
n
s
=2
0
0
,
t
o
tal
v
ar
iab
les=9
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
h
as
b
ee
n
an
aly
ze
d
o
n
th
e
f
o
llo
win
g
d
if
f
er
e
n
t te
s
t c
ases
:
−
B
ase
ca
s
e
d
is
tr
ib
u
tio
n
lo
ad
f
lo
w
an
aly
s
is
.
−
Op
tim
al
allo
ca
tio
n
o
f
DGs
an
d
E
V
-
C
Ss
u
s
in
g
C
H
B
A
w
ith
m
in
im
u
m
n
u
m
b
er
o
f
C
Ps
at
m
in
im
u
m
p
o
we
r
r
atin
g
all
C
Ss
.
I
n
itially
,
th
e
d
is
tr
ib
u
tio
n
lo
ad
f
lo
w
an
aly
s
is
ap
p
r
o
ac
h
is
ap
p
l
ied
an
d
d
eter
m
in
es th
e
b
ase
ca
s
e
v
o
ltag
e
o
f
ea
ch
b
u
s
,
VSI
,
m
in
im
u
m
VSI
,
VDI
,
m
in
im
u
m
v
o
ltag
e
an
d
p
o
wer
l
o
s
s
o
f
th
e
p
r
o
p
o
s
ed
n
etwo
r
k
.
I
t
co
n
s
id
er
ed
C
ase
1
an
d
o
b
tain
e
d
s
im
u
latio
n
r
esu
lts
,
wh
ich
a
r
e
g
iv
en
in
T
ab
le
4
.
I
n
ca
s
e
2
,
co
n
s
id
er
in
g
E
VC
Ss
an
d
DGs
with
a
to
tal
lo
ad
d
em
an
d
o
f
6
,
6
4
0
k
W
an
d
th
e
s
u
g
g
ested
HB
A
o
p
tim
izes
th
e
b
est
p
lace
m
en
t
o
f
C
Ss
an
d
DGs
with
th
e
m
in
im
u
m
q
u
an
tity
o
f
C
Ps
.
T
h
e
o
b
tain
ed
o
p
tim
al
lo
ca
tio
n
s
o
f
DGs
an
d
E
VC
Ss
ar
e
2
8
,
2
9
,
1
4
an
d
2
,
1
9
,
2
5
r
esp
ec
tiv
ely
.
T
h
e
o
b
tain
ed
o
p
tim
al
s
izes
o
f
DGs
an
d
E
VC
Ss
ar
e
1
,
500
k
W
,
7
9
3
k
W
,
1
,
3
1
6
k
W
an
d
9
7
5
k
W
,
9
7
5
k
W
,
9
7
5
k
W
r
esp
ec
tiv
ely
.
T
h
e
v
o
ltag
e
an
d
VSI
at
ea
ch
b
u
s
f
o
r
th
e
b
ase
ca
s
e
ar
e
o
n
ly
E
VC
Ss
an
d
b
o
th
DGs
an
d
E
VC
Ss
ar
e
co
m
p
ar
ed
an
d
n
u
m
er
ically
r
e
p
o
r
te
d
in
T
ab
le
4
.
Ad
d
itio
n
ally
,
a
p
icto
r
ial
co
m
p
a
r
is
o
n
is
g
iv
en
,
as seen
in
Fig
u
r
es 2
an
d
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
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2
5
0
2
-
4
7
52
I
mp
a
cts o
f e
lectric v
eh
icle
ch
a
r
g
in
g
s
ta
tio
n
s
a
n
d
DGs o
n
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fo
r
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(
Ma
d
h
u
b
a
b
u
Th
ir
u
v
ee
d
u
la
)
1385
T
ab
le
4
.
C
o
m
p
a
r
in
g
HB
A,
T
L
B
O,
an
d
HHO
r
esu
lts
f
o
r
I
E
E
E
-
3
3
n
o
d
e
test
s
y
s
tem
with
E
VC
S
s
an
d
DG
u
n
its
C
a
se
M
e
t
h
o
d
s
D
G
s
a
n
d
EV
c
h
a
r
g
i
n
g
st
a
t
i
o
n
l
o
c
a
t
i
o
n
s
S
i
z
e
o
f
EV
C
S
s
a
n
d
D
G
s
i
n
kW
P
o
w
e
r
l
o
ss
e
s
(
k
W
)
A
V
D
I
(
p
.
u
)
V
S
I
(
p
.
u
)
V
mi
n
C
a
se
1
(
b
a
se
c
a
se)
-
-
-
2
1
0
.
9
8
9
7
0
.
0
0
4
0
5
4
1
0
.
6
6
7
1
7
4
0
.
9
0
3
8
C
a
se
2
H
B
A
(
p
r
o
p
o
s
e
d
)
2
8
,
2
9
,
1
4
a
n
d
2
,
1
9
,
2
5
1
5
0
0
,
7
9
3
,
1
3
1
6
a
n
d
9
7
5
,
9
7
5
,
9
7
5
8
3
.
3
6
1
0
.
0
0
0
4
3
6
0
.
8
8
2
7
7
0
.
9
0
1
0
TLB
O
[
6
]
1
3
,
2
4
,
3
0
a
n
d
2
,
1
9
,
2
5
8
3
4
.
6
1
,
1
5
0
0
,
1
1
3
9
.
3
9
a
n
d
9
7
5
,
9
7
5
,
9
7
5
9
4
.
3
8
4
7
0
.
0
0
4
7
0
.
8
7
9
9
0
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9
6
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5
HHO
[
6
]
1
3
,
2
4
,
3
0
a
n
d
2
,
1
9
,
2
5
8
3
7
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0
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1
5
0
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1
1
3
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a
n
d
9
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9
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4
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8
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9
9
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9
6
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5
Ob
j
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t
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n
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si
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H
B
A
(
p
r
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p
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3
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9
Fig
u
r
e
2
.
C
o
m
p
a
r
in
g
t
h
e
v
o
lta
g
e
p
r
o
f
ile
o
f
th
e
3
3
-
n
o
d
e
test
s
etu
p
in
th
e
b
ase
s
ce
n
ar
io
with
o
n
ly
E
VC
Ss
an
d
DGs
in
ad
d
itio
n
to
E
VC
Ss
Fig
u
r
e
3
.
C
o
m
p
a
r
is
o
n
o
f
th
e
3
3
-
n
o
d
e
test
s
y
s
tem
's VSI
f
o
r
th
e
b
ase
s
ce
n
ar
io
,
with
o
n
ly
E
VC
S
s
an
d
DGs in
alo
n
g
with
E
VC
Ss
T
h
e
o
u
tco
m
es o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
s
u
ch
as p
o
wer
lo
s
s
,
AVDI
,
VSI
,
an
d
Vm
in
,
ar
e
n
u
m
er
ically
tab
u
lated
in
T
ab
le
4
.
Acc
o
r
d
i
n
g
to
th
e
c
o
m
p
a
r
is
o
n
,
th
e
p
o
wer
lo
s
s
r
ed
u
ctio
n
o
f
th
e
s
u
g
g
ested
test
s
y
s
tem
s
h
o
ws
a
6
0
.
4
9
%
im
p
r
o
v
e
m
e
n
t
co
m
p
ar
ed
to
t
h
e
b
ase
ca
s
e
m
eth
o
d
an
d
a
5
.
2
2
%
im
p
r
o
v
em
en
t
o
v
er
o
th
er
ex
is
tin
g
m
eth
o
d
s
lik
e
HHO
an
d
T
L
B
O.
Fig
u
r
e
4
illu
s
tr
ates
th
e
co
n
v
er
g
e
n
ce
ch
a
r
ac
ter
is
tics
o
f
DGs
an
d
E
VC
Ss
p
lace
m
en
t
f
o
r
th
e
I
E
E
E
-
3
3
n
o
d
e
s
y
s
tem
.
Simu
latio
n
r
esu
lts
,
co
m
p
ar
e
d
with
T
L
B
O
an
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e
P
h
.
D
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
fro
m
De
p
a
rtme
n
t
o
f
E
EE
,
An
n
a
m
a
lai
Un
iv
e
rsit
y
,
An
n
a
m
a
lain
a
g
a
r,
In
d
ia
i
n
th
e
y
e
a
r
2
0
0
8
a
n
d
2
0
1
5
re
sp
e
c
ti
v
e
l
y
.
He
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
s
a
As
so
c
iate
P
ro
fe
ss
o
r
i
n
t
h
e
sa
m
e
d
e
p
a
rtme
n
t
(O
n
De
p
u
tatio
n
t
o
G
o
v
e
rn
m
e
n
t
C
o
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
,
Ba
rg
u
r
,
In
d
ia).
Hi
s
re
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
p
o
we
r
sy
ste
m
o
p
e
ra
ti
o
n
a
n
d
c
o
n
tro
l,
h
y
d
r
o
th
e
rm
a
l
sc
h
e
d
u
li
n
g
,
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
s,
h
y
b
rid
e
lec
tri
c
v
e
h
icle
,
d
e
re
g
u
late
d
p
o
we
r
sy
ste
m
s,
a
n
d
c
o
m
p
u
tati
o
n
a
l
i
n
telli
g
e
n
c
e
a
p
p
li
c
a
ti
o
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
so
k
a
n
e
e
e
@g
m
a
il
.
c
o
m
.
Dr
.
J
BV
S
u
b
r
a
h
m
a
n
y
a
m
is
a
P
h
.
D
.
,
M
E
a
n
d
B
.
Tec
h
.
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
E
n
g
i
n
e
e
rin
g
(EE
E)
f
ro
m
JN
TUH
–
Hy
d
e
ra
b
a
d
,
In
d
ia,
with
tw
o
d
e
c
a
d
e
s
o
f
rich
e
x
p
e
rien
c
e
in
Tea
c
h
i
n
g
,
Train
i
n
g
,
Re
se
a
rc
h
,
In
d
u
str
y
,
P
r
o
jec
ts
a
n
d
Co
n
su
l
tan
c
y
.
He
p
u
b
li
sh
e
d
1
1
p
a
ten
ts
a
n
d
1
2
5
P
a
p
e
rs
in
re
p
u
ted
in
tern
a
ti
o
n
a
l
a
n
d
n
a
ti
o
n
a
l
jo
u
r
n
a
ls
&
c
o
n
fe
re
n
c
e
s
.
His
re
se
a
rc
h
in
tere
st
is
in
a
u
t
o
m
a
ti
o
n
o
f
p
o
we
r
sy
ste
m
s.
He
is
a
n
e
x
p
e
rt
in
c
o
n
d
i
ti
o
n
m
o
n
it
o
rin
g
o
f
in
d
u
strial
e
q
u
ip
m
e
n
t
t
h
ro
u
g
h
m
o
d
e
rn
d
iag
n
o
stic
tec
h
n
i
q
u
e
s.
He
i
m
p
lem
e
n
ted
th
e
late
st
G
P
S
a
n
d
G
IS
tec
h
n
o
lo
g
ies
in
m
a
n
y
p
o
we
r
u
ti
li
t
ies
in
I
n
d
ia
su
c
c
e
ss
fu
ll
y
.
He
e
x
e
c
u
ted
m
a
n
y
in
tern
a
ti
o
n
a
l
a
n
d
n
a
ti
o
n
a
l
le
v
e
l
tec
h
n
ica
l
p
r
o
jec
ts
e
ffe
c
ti
v
e
ly
f
u
n
d
e
d
b
y
DST
,
Niti
Aa
y
o
g
,
DBT,
AICTE,
P
o
we
r
F
i
n
a
n
c
e
Co
rp
o
ra
ti
o
n
,
M
in
istr
y
o
f
P
o
we
r,
G
o
v
e
rn
m
e
n
t
o
f
I
n
d
ia,
APDRP
,
DRU
M
,
USAID
a
n
d
DFID
-
U
n
it
e
d
Ki
n
g
d
o
m
(UK
).
He
e
n
su
re
s
stro
n
g
En
g
in
e
e
rin
g
p
ro
fe
ss
io
n
a
l
fo
u
n
d
a
ti
o
n
is
lai
d
t
o
th
e
fa
c
u
l
ty
a
n
d
st
u
d
e
n
ts
in
p
e
rfo
rm
in
g
t
h
e
ir
d
u
ti
e
s
a
n
d
re
sp
o
n
si
b
il
it
ies
.
He
o
b
tai
n
e
d
su
c
c
e
ss
fu
ll
y
NBA
Ac
c
re
d
it
a
ti
o
n
,
NA
AC
Ac
c
re
d
it
a
ti
o
n
,
Au
to
n
o
m
o
u
s
sta
tu
s,
2
(f)
a
n
d
1
2
(
B)
sta
tu
s,
Un
i
v
e
rsity
sta
tu
s
a
n
d
J
NTUH
P
e
rm
a
n
e
n
t
Affili
a
ti
o
n
to
En
g
in
e
e
ri
n
g
c
o
ll
e
g
e
s
in
In
d
i
a
a
n
d
ABET
Ac
c
re
d
it
a
ti
o
n
t
o
f
o
re
ig
n
U
n
iv
e
rsiti
e
s
a
b
r
o
a
d
.
Re
c
ip
ien
t
o
f
‘Be
st
P
ro
fe
ss
o
r
Aw
a
rd
’
b
y
S
a
lma
n
Bin
Ab
d
u
l
Az
iz
Un
iv
e
rsity
,
S
a
u
d
i
Ara
b
ia
f
o
r
th
e
a
c
a
d
e
m
ic
y
e
a
r
2
0
1
2
-
2
0
1
3
.
Org
a
n
ize
d
m
a
n
y
i
n
tern
a
ti
o
n
a
l
a
n
d
n
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s,
se
m
in
a
rs
a
n
d
wo
rk
s
h
o
p
s
o
n
late
st
tec
h
n
o
lo
g
ica
l
d
e
v
e
lo
p
m
e
n
ts
t
o
stu
d
e
n
ts,
fa
c
u
lt
y
,
p
ra
c
ti
c
in
g
e
n
g
in
e
e
rs i
n
in
d
u
str
y
a
n
d
re
se
a
rc
h
e
rs.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jb
v
sjn
m
@g
m
a
il
.
c
o
m
.
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