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nte
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l J
o
urna
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nfo
rm
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t
ics a
nd
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m
m
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ica
t
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n T
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hn
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g
y
(
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J
-
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CT
)
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l.
9
,
No
.
2
,
Au
g
u
s
t
2020
,
p
p
.
100
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N:
2252
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7
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2
.
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eduction by
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K
a
na
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n
De
p
a
rtme
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id
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In
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ev
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an
17
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2
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ted
Feb
11
,
2
0
20
In
th
is
wo
r
k
Tu
n
d
ra
wo
lf
a
lg
o
rit
h
m
(TW
A)
is
p
ro
p
o
se
d
to
s
o
lv
e
t
h
e
o
p
ti
m
a
l
re
a
c
ti
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e
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o
we
r
p
ro
b
lem
.
In
t
h
e
p
ro
jec
ted
T
u
n
d
ra
wo
lf
a
lg
o
rit
h
m
(TW
A)
in
o
rd
e
r
t
o
a
v
o
i
d
t
h
e
se
a
rc
h
in
g
a
g
e
n
ts
fro
m
trap
p
i
n
g
i
n
to
th
e
l
o
c
a
l
o
p
ti
m
a
l
t
h
e
c
o
n
v
e
rg
in
g
to
wa
rd
s
g
l
o
b
a
l
o
p
t
ima
l
is
d
iv
i
d
e
d
b
a
se
d
o
n
two
d
iffere
n
t
c
o
n
d
i
ti
o
n
s.
In
th
e
p
ro
p
o
se
d
T
u
n
d
ra
wo
lf
a
lg
o
rit
h
m
(TW
A)
o
m
e
g
a
tu
n
d
ra
wo
lf
h
a
s b
e
e
n
tak
e
n
a
s se
a
rc
h
in
g
a
g
e
n
t
a
s a
n
a
lt
e
rn
a
ti
v
e
o
f
in
d
e
b
te
d
to
p
u
rsu
e
th
e
first
th
re
e
m
o
st
e
x
c
e
ll
e
n
t
c
a
n
d
i
d
a
tes
.
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a
la
ti
n
g
t
h
e
se
a
rc
h
i
n
g
a
g
e
n
ts’
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u
m
b
e
rs
will
p
e
r
k
u
p
th
e
e
x
p
lo
ra
t
io
n
c
a
p
a
b
i
li
ty
o
f
th
e
T
u
n
d
ra
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lf
wo
lv
e
s
in
a
n
e
x
ten
siv
e
ra
n
g
e
.
P
ro
p
o
se
d
Tu
n
d
ra
wo
lf
a
l
g
o
rit
h
m
(TW
A)
h
a
s
b
e
e
n
tes
ted
in
sta
n
d
a
r
d
IEE
E
1
4
,
3
0
b
u
s
tes
t
sy
ste
m
s
a
n
d
sim
u
latio
n
re
su
lt
s
sh
o
w
th
e
p
ro
p
o
s
e
d
a
l
g
o
rit
h
m
re
d
u
c
e
d
th
e
r
e
a
l
p
o
we
r
lo
ss
e
ffe
c
ti
v
e
l
y
.
K
ey
w
o
r
d
s
:
Op
tim
al
r
ea
ctiv
e
p
o
wer
T
r
an
s
m
is
s
io
n
lo
s
s
T
u
n
d
r
a
wo
lf
alg
o
r
ith
m
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
:
Kan
ag
asab
ai
L
en
in
,
Dep
ar
tm
en
t o
f
E
E
E
,
Pra
s
ad
V.
Po
tlu
r
i Sid
d
h
ar
th
a
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
,
Kan
u
r
u
,
Vijay
awa
d
a
,
An
d
h
r
a
Pra
d
esh
,
5
2
0
0
0
7
,
I
n
d
ia.
E
m
ail:
g
k
len
in
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
R
ea
ctiv
e
p
o
wer
p
r
o
b
lem
p
lay
s
an
im
p
o
r
tan
t
r
o
le
in
s
ec
u
r
e
an
d
ec
o
n
o
m
ic
o
p
e
r
atio
n
s
o
f
p
o
wer
s
y
s
tem
.
Nu
m
er
o
u
s
ty
p
es
o
f
m
eth
o
d
s
[
1
-
6
]
h
a
v
e
b
ee
n
u
tili
ze
d
to
s
o
lv
e
th
e
o
p
tim
al
r
ea
ct
iv
e
p
o
wer
p
r
o
b
lem
.
Ho
wev
er
m
an
y
s
cien
tific
d
if
f
icu
lties
ar
e
f
o
u
n
d
wh
ile
s
o
lv
in
g
p
r
o
b
lem
d
u
e
to
an
ass
o
r
tm
en
t
o
f
co
n
s
tr
ain
ts
.
E
v
o
lu
tio
n
a
r
y
tech
n
iq
u
es
[
7
-
1
7
]
ar
e
ap
p
lied
to
s
o
lv
e
th
e
r
ea
ct
iv
e
p
o
wer
p
r
o
b
lem
.
T
h
is
p
ap
e
r
p
r
o
p
o
s
es
T
u
n
d
r
a
wo
lf
alg
o
r
ith
m
(
T
W
A)
to
s
o
lv
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
At
f
ir
s
t,
s
ea
r
ch
in
g
ag
e
n
ts
h
as
b
ee
n
ag
g
r
av
ated
t
o
s
ca
tter
all
o
v
er
th
e
ex
ten
s
iv
e
r
an
g
e
o
f
p
r
o
b
in
g
s
p
ac
e
to
d
is
co
v
er
th
e
p
r
o
b
ab
le
p
r
ey
as
an
alter
n
ativ
e
o
f
cr
o
w
d
in
g
in
th
e
r
eg
io
n
o
f
th
e
r
eg
u
lar
lo
ca
l
o
p
tim
al.
T
h
is
p
h
ase
is
also
ter
m
ed
as
ex
p
lo
r
atio
n
p
er
io
d
.
I
n
th
e
s
u
b
s
eq
u
e
n
t
ex
p
lo
itatio
n
p
h
ase,
s
ea
r
ch
in
g
a
g
en
ts
s
h
o
u
ld
h
av
e
t
h
e
ab
ilit
y
to
in
f
lu
en
ce
th
e
in
f
o
r
m
atio
n
o
f
th
e
p
r
o
b
ab
le
p
r
ey
to
c
o
n
v
e
r
g
e
in
th
e
d
ir
ec
tio
n
o
f
th
e
g
lo
b
al
o
p
tim
al
v
al
u
e.
I
n
g
e
n
er
al
tr
ac
k
in
g
o
r
h
u
n
tin
g
ac
tio
n
is
s
o
litar
y
p
o
s
s
ess
ed
alp
h
a,
b
eta
an
d
d
elta
T
u
n
d
r
a
wo
lf
wh
ile
th
e
r
e
m
ain
in
g
T
u
n
d
r
a
w
o
lv
es
ar
e
in
d
eb
ted
to
g
o
b
e
h
in
d
th
em
th
at
in
clu
d
e
o
m
eg
a
T
u
n
d
r
a
wo
lf
.
I
n
s
eq
u
en
ce
to
au
g
m
en
t
th
e
ex
p
lo
r
ati
o
n
ca
p
ab
ilit
y
o
f
th
e
s
ea
r
ch
a
g
e
n
ts
,
s
ev
er
al
alter
atio
n
s
h
av
e
b
ee
n
s
u
g
g
ested
.
I
n
th
e
p
r
o
p
o
s
ed
T
u
n
d
r
a
w
o
lf
alg
o
r
ith
m
(
T
W
A)
o
m
e
g
a
t
u
n
d
r
a
wo
lf
h
as
b
ee
n
tak
en
as
s
ea
r
ch
in
g
ag
en
t
as
an
alter
n
at
iv
e
o
f
in
d
eb
ted
to
p
u
r
s
u
e
th
e
f
ir
s
t
th
r
ee
m
o
s
t
ex
ce
llen
t
ca
n
d
id
ates.
Pro
p
o
s
ed
T
u
n
d
r
a
wo
lf
alg
o
r
ith
m
(
T
W
A)
h
as
b
ee
n
test
ed
in
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
,
b
u
s
tes
t
s
y
s
tem
s
an
d
s
im
u
latio
n
r
esu
lts
s
h
o
w
th
e
p
r
o
jecte
d
alg
o
r
ith
m
r
ed
u
ce
d
th
e
r
ea
l
p
o
wer
lo
s
s
ef
f
ec
tiv
ely
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
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8
7
7
6
R
ea
l p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
t
u
n
d
r
a
w
o
lf a
lg
o
r
ith
m
(
K
a
n
a
g
a
s
a
b
a
i Len
in
)
101
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
Ob
jectiv
e
o
f
th
e
p
r
o
b
lem
is
to
r
ed
u
ce
th
e
t
r
u
e
p
o
wer
lo
s
s
:
F
=
P
L
=
∑
g
k
k
∈
Nbr
(
V
i
2
+
V
j
2
−
2
V
i
V
j
c
os
θ
ij
)
(
1)
Vo
ltag
e
d
ev
iatio
n
g
iv
en
as f
o
l
lo
ws:
F
=
P
L
+
ω
v
×
Vol
ta
ge
De
via
tion
(
2
)
Vo
ltag
e
d
ev
iatio
n
g
iv
en
b
y
:
Vol
ta
ge
De
via
tion
=
∑
|
V
i
−
1
|
N
p
q
i
=
1
(
3
)
C
o
n
s
tr
ain
t (
E
q
u
ality
)
,
P
G
=
P
D
+
P
L
(
4
)
C
o
n
s
tr
ain
ts
(
I
n
eq
u
ality
)
,
P
g
s
l
ack
m
i
n
≤
P
g
s
l
ac
k
≤
P
g
s
l
ack
m
ax
(
5
)
Q
gi
m
i
n
≤
Q
gi
≤
Q
gi
m
ax
,
i
∈
N
g
(
6
)
V
i
m
i
n
≤
V
i
≤
V
i
m
ax
,
i
∈
N
(
7
)
T
i
m
i
n
≤
T
i
≤
T
i
m
ax
,
i
∈
N
T
(
8
)
Q
c
m
i
n
≤
Q
c
≤
Q
C
m
ax
,
i
∈
N
C
(
9
)
3.
T
UNDR
A
WO
L
F
A
L
G
O
RI
T
H
M
I
n
th
e
p
r
o
p
o
s
ed
T
u
n
d
r
a
w
o
lf
alg
o
r
ith
m
(
T
W
A)
h
u
n
tin
g
b
eh
av
io
r
o
f
t
h
e
T
u
n
d
r
a
w
o
lf
h
as
b
ee
n
im
itated
to
d
esig
n
th
e
alg
o
r
it
h
m
f
o
r
s
o
lv
in
g
th
e
o
p
tim
al
r
e
ac
tiv
e
p
o
wer
p
r
o
b
lem
.
I
n
T
u
n
d
r
a
wo
lf
alg
o
r
ith
m
,
th
e
m
o
v
em
e
n
t o
f
w
o
lf
is
d
escr
ib
ed
b
y
,
̅
=
|
̅
̅
(
)
−
̅
(
)
|
(
10
)
̅
(
+
1
)
=
̅
(
)
−
⃗
∙
⃗
⃗
⃗
(
1
1
)
⃗
=
2
.
1
−
(
1
2
)
⃗
=
2
.
2
(
13
)
=
2
−
2
⁄
(
14
)
T
h
e
s
tate
o
f
wo
lv
es a
r
e
a
d
ju
s
ted
b
y
,
⃗
⃗
⃗
⃗
⃗
⃗
=
|
1
⃗
⃗
⃗
⃗
⃗
,
⃗
⃗
⃗
⃗
⃗
−
⃗
|
(
1
5
)
⃗
⃗
⃗
⃗
⃗
=
|
2
⃗
⃗
⃗
⃗
⃗
,
⃗
⃗
⃗
⃗
⃗
−
⃗
|
(
1
6
)
⃗
⃗
⃗
⃗
⃗
=
|
3
⃗
⃗
⃗
⃗
⃗
,
⃗
⃗
⃗
⃗
⃗
−
⃗
|
(
1
7
)
W
h
en
th
e
v
al
u
e
o
f
“A”
a
r
e
l
o
ca
ted
in
[
-
1
,
1
]
ca
p
r
icio
u
s
ly
,
wh
ich
i
n
d
icate
th
e
p
r
o
ce
d
u
r
e
o
f
lo
ca
l
s
ea
r
ch
p
er
ce
p
tib
ly
in
th
is
p
h
ase
th
e
T
u
n
d
r
a
wo
lv
es
attac
k
to
war
d
s
th
e
p
r
ey
.
T
u
n
d
r
a
w
o
lv
es
ar
e
f
o
r
ce
d
to
m
ak
e
a
g
lo
b
al
s
ea
r
ch
W
h
en
|
A
|
>
1
.
T
h
r
o
u
g
h
t
h
e
p
ar
a
m
eter
“a
”
f
lu
ctu
atio
n
r
an
g
e
o
f
“A”
ca
n
b
e
d
ec
r
ea
s
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
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I
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u
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104
102
I
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th
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p
r
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ith
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ed
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e
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⃗
⃗
⃗
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1
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,
⃗
⃗
⃗
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−
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|
(
19
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⃗
⃗
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21
)
⃗
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⃗
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=
|
4
⃗
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⃗
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,
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⃗
⃗
⃗
⃗
⃗
−
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|
(
22
)
1
⃗
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⃗
⃗
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−
1
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⃗
⃗
⃗
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.
(
⃗
⃗
⃗
⃗
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(
2
3
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2
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3
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(
2
5
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4
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3
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2
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̅
(
+
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3
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+
4
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⃗
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4
(
2
7
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C
o
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m
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Sear
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itiated
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in
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⃗
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est s
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⃗
,
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t=t+1
en
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wh
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R
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⃗
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I
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I
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2
2
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I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
9
,
No
.
2
,
Au
g
u
s
t
20
20
:
1
0
0
–
104
104
5.
CO
NCLU
SI
O
N
I
n
th
is
p
ap
er
T
u
n
d
r
a
wo
lf
alg
o
r
ith
m
(
T
W
A)
s
u
cc
ess
f
u
lly
s
o
lv
ed
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
we
r
p
r
o
b
lem
.
Pro
p
o
s
ed
alg
o
r
ith
m
p
er
k
u
p
t
h
e
ex
p
lo
r
atio
n
ca
p
a
b
ilit
y
o
f
t
h
e
T
u
n
d
r
a
wo
lf
wo
l
v
es
in
an
ex
t
en
s
iv
e
m
o
d
e.
Als
o
it
m
ak
es
th
e
s
ea
r
ch
ag
e
n
ts
to
s
p
r
ea
d
wid
ely
d
u
r
i
n
g
e
x
p
lo
r
ati
o
n
p
h
ase.
I
n
t
h
e
p
r
o
p
o
s
ed
T
u
n
d
r
a
wo
lf
al
g
o
r
ith
m
(
T
W
A)
o
m
eg
a
tu
n
d
r
a
wo
lf
h
a
s
b
ee
n
tak
en
as
s
ea
r
ch
in
g
ag
e
n
t
as
an
alter
n
ativ
e
o
f
in
d
eb
te
d
to
p
u
r
s
u
e
t
h
e
f
ir
s
t
th
r
ee
m
o
s
t
ex
ce
llen
t
ca
n
d
id
ate
s
.
T
h
is
m
o
d
e
o
f
h
u
n
ti
n
g
ac
tio
n
in
cr
ea
s
es
th
e
ef
f
icien
cy
.
Pro
p
o
s
ed
T
u
n
d
r
a
wo
lf
alg
o
r
ith
m
(
T
W
A)
h
as
b
ee
n
te
s
ted
in
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
b
u
s
test
s
y
s
tem
s
an
d
s
im
u
latio
n
r
esu
lts
s
h
o
w
t
h
e
p
r
o
jecte
d
alg
o
r
ith
m
r
ed
u
ce
d
th
e
r
ea
l
p
o
wer
lo
s
s
.
Per
ce
n
tag
e
o
f
r
ea
l
p
o
wer
lo
s
s
r
ed
u
ctio
n
h
as
b
ee
n
im
p
r
o
v
e
d
wh
en
co
m
p
ar
ed
to
o
th
er
s
tan
d
ar
d
alg
o
r
ith
m
s
.
RE
F
E
R
E
NC
E
S
[1
]
K.
Y.
Lee
,
“
F
u
e
l
-
c
o
st
m
i
n
imis
a
ti
o
n
fo
r
b
o
t
h
re
a
l
a
n
d
re
a
c
ti
v
e
-
p
o
we
r
d
isp
a
tch
e
s,”
Pr
o
c
e
e
d
in
g
s
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Distrib
u
ti
o
n
C
o
n
fer
e
n
c
e
,
v
o
l
.
1
3
1
,
n
o
.
3
,
p
p
.
8
5
-
9
3
.
1
9
8
4
.
[2
]
N.
I.
De
e
b
.
“
An
e
fficie
n
t
tec
h
n
i
q
u
e
f
o
r
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
u
sin
g
a
re
v
ise
d
li
n
e
a
r
p
ro
g
ra
m
m
in
g
a
p
p
r
o
a
c
h
,
”
El
e
c
tric P
o
we
r S
y
ste
m R
e
se
a
rc
h
,
v
o
l
.
15
,
n
o
.
2
,
p
p
.
1
2
1
-
1
3
4
,
1
9
9
8
.
[3
]
M
.
R.
Bjelo
g
rli
c
,
M
.
S
.
Ca
lo
v
ic,
B.
S
.
Ba
b
ic
,
“
Ap
p
li
c
a
ti
o
n
o
f
Ne
wto
n
’s
o
p
ti
m
a
l
p
o
we
r
fl
o
w
i
n
v
o
lt
a
g
e
/rea
c
ti
v
e
p
o
we
r
c
o
n
tro
l
,
”
IEE
E
T
ra
n
s
Po
w
e
r S
y
ste
m
,
v
o
l.
5
,
n
o
.
4
,
p
p
.
1
4
4
7
-
1
4
5
4
,
1
9
9
0
.
[4
]
S
.
G
ra
n
v
il
le
,
“
Op
ti
m
a
l
re
a
c
ti
v
e
d
isp
a
tch
t
h
ro
u
g
h
i
n
terio
r
p
o
i
n
t
m
e
th
o
d
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
m
,
vol
.
9
,
n
o
.
1
,
p
p
.
1
3
6
-
1
4
6
,
1
9
9
4
.
[5
]
N.
G
ru
d
in
in
.
,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
u
sin
g
su
c
c
e
ss
iv
e
q
u
a
d
ra
ti
c
p
ro
g
ra
m
m
in
g
m
e
th
o
d
,
”
IE
EE
T
ra
n
s
a
c
ti
o
n
s
o
n
P
o
we
r S
y
ste
m
,
v
o
l
.
13
,
n
o
.
4
,
p
p
.
1
2
1
9
-
1
2
2
5
,
1
9
9
8
.
[6
]
Ng
S
h
i
n
M
e
i,
R.
;
S
u
laim
a
n
,
M
.
H.;
M
u
sta
ffa
,
Z.
;
Da
n
i
y
a
l,
H.
,
“
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
so
lu
ti
o
n
b
y
lo
s
s
m
in
imiz
a
ti
o
n
u
sin
g
m
o
t
h
-
flam
e
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
,
”
A
p
p
l.
S
o
ft
Co
mp
u
t
.,
v
o
l.
5
9
,
p
p
.
2
1
0
-
2
2
2
,
2
0
1
7
.
[7
]
Ch
e
n
,
G
.
;
Li
u
,
L.
;
Zh
a
n
g
,
Z
.
;
H
u
a
n
g
,
S
.
,
“
O
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
b
y
imp
r
o
v
e
d
G
S
A
-
b
a
se
d
a
lg
o
rit
h
m
wit
h
th
e
n
o
v
e
l
stra
teg
ies
t
o
h
a
n
d
le c
o
n
st
ra
in
ts”
Ap
p
l.
S
o
ft
Co
mp
u
t
.
,
v
o
l.
5
0
,
p
p
.
5
8
-
7
0
,
2
0
1
7
.
[8
]
Na
d
e
ri,
E.
;
Na
rima
n
i,
H.;
F
a
th
i,
M
.
;
Na
rima
n
i,
M
.
R
,
“
A
n
o
v
e
l
fu
z
z
y
a
d
a
p
ti
v
e
c
o
n
fi
g
u
ra
ti
o
n
o
f
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
t
o
so
l
v
e
larg
e
-
sc
a
le o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
tch
,
”
Ap
p
l.
S
o
ft
Co
mp
u
t
.
,
v
o
l.
5
3
,
4
4
1
-
4
5
6
,
2
0
1
7
.
[9
]
He
id
a
ri,
A.A.;
Ali
Ab
b
a
sp
o
u
r,
R.
;
Re
z
a
e
e
Jo
rd
e
h
i,
A.
,
“
G
a
u
ss
i
a
n
b
a
re
-
b
o
n
e
s
wa
ter
c
y
c
le
a
lg
o
ri
th
m
fo
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
tch
i
n
e
lec
tri
c
a
l
p
o
we
r
sy
ste
m
s
,
”
Ap
p
l.
S
o
ft
Co
mp
u
t
.,
v
o
l.
5
7
,
p
p
.
6
5
7
-
6
7
1
,
2
0
1
7
.
[1
0
]
M
a
h
a
letc
h
u
m
i
M
o
rg
a
n
,
No
r
Ru
l
Ha
sm
a
Ab
d
u
ll
a
h
,
M
o
h
d
He
rwa
n
S
u
laim
a
n
,
M
a
h
fu
z
a
h
M
u
sta
fa
,
R
o
sd
iy
a
n
a
S
a
m
a
d
.
“
Be
n
c
h
m
a
rk
S
tu
d
ies
o
n
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
(ORPD)
Ba
se
d
M
u
lt
i
-
o
b
jec
ti
v
e
Ev
o
l
u
ti
o
n
a
r
y
P
ro
g
ra
m
m
in
g
(M
OE
P
)
Us
in
g
M
u
tatio
n
Ba
se
d
o
n
Ad
a
p
ti
v
e
M
u
ta
ti
o
n
Ad
a
p
ter
(AMO)
a
n
d
P
o
l
y
n
o
m
ial
M
u
tati
o
n
Op
e
ra
to
r
(P
M
O)
,
”
J
o
u
rn
a
l
o
f
E
lec
trica
l
S
y
ste
ms
,
2
0
1
6
.
[1
1
]
Re
b
e
c
c
a
Ng
S
h
in
M
e
i,
M
o
h
d
H
e
rwa
n
S
u
laim
a
n
,
Z
u
rian
i
M
u
sta
f
fa
,
“
An
t
Li
o
n
Op
ti
m
ize
r
fo
r
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
S
o
l
u
ti
o
n
,
”
J
o
u
rn
a
l
o
f
El
e
c
trica
l
S
y
ste
ms
,
S
p
e
c
ia
l
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