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a
c
ce
s
s
i
b
i
l
i
t
y
an
d
g
r
i
d
l
i
m
i
ta
t
i
o
n
s
[
4
]
,
[
5
]
.
R
o
b
u
s
t
o
p
t
i
m
iz
a
t
i
o
n
a
p
p
r
o
a
ch
e
s
h
a
v
e
a
l
s
o
b
e
en
i
n
t
r
o
d
u
c
e
d
to
ex
p
l
ic
i
t
l
y
m
a
n
ag
e
d
e
m
an
d
u
n
ce
r
t
a
in
t
y
in
ch
ar
g
i
n
g
n
e
t
w
o
r
k
s
[
6
]
.
P
a
r
t
ic
l
e
s
w
a
r
m
o
p
t
i
m
iz
a
t
i
o
n
(
P
S
O
)
h
a
s
d
e
m
o
n
s
tr
a
t
e
d
ef
f
e
c
t
i
v
en
e
s
s
in
d
e
t
er
m
i
n
in
g
o
p
t
i
m
a
l
s
t
a
t
i
o
n
p
l
a
ce
m
en
t
w
it
h
i
n
a
d
i
s
t
r
ib
u
t
i
o
n
s
y
s
t
e
m
s
[
7
]
,
w
h
i
le
an
t
c
o
lo
n
y
o
p
t
i
m
iz
a
t
i
o
n
(
A
C
O
)
h
a
s
b
e
e
n
a
p
p
l
i
e
d
t
o
s
o
lv
e
c
o
m
p
l
ex
lo
c
a
t
i
o
n
p
l
an
n
in
g
p
r
o
b
l
e
m
s
[
8
]
.
Hy
b
r
i
d
m
et
a
h
e
u
r
i
s
t
i
c
m
e
th
o
d
s
f
u
r
th
er
en
h
a
n
c
e
s
o
l
u
t
io
n
q
u
a
l
i
ty
b
y
co
m
b
i
n
in
g
c
o
m
p
l
e
m
en
t
a
r
y
o
p
t
i
m
i
z
a
t
i
o
n
s
t
r
e
n
g
th
s
[
9
]
-
[
1
4
]
.
T
h
e
in
t
e
g
r
a
t
i
o
n
o
f
ar
t
i
f
i
c
ia
l
in
t
e
l
l
i
g
en
c
e
(
A
I
)
h
a
s
s
i
g
n
if
i
ca
n
t
l
y
im
p
r
o
v
e
d
t
h
e
a
d
ap
t
a
b
i
li
t
y
o
f
E
V
c
h
a
r
g
i
n
g
n
e
t
wo
r
k
s
.
D
e
ep
l
ea
r
n
in
g
m
o
d
e
l
s
s
u
c
h
a
s
r
ec
u
r
r
e
n
t
n
e
u
r
a
l
n
e
t
wo
r
k
s
h
av
e
b
e
e
n
e
m
p
l
o
y
e
d
f
o
r
a
c
c
u
r
a
t
e
f
o
r
e
ca
s
t
i
n
g
o
f
E
V
ch
a
r
g
i
n
g
d
em
a
n
d
i
n
s
m
ar
t
g
r
id
s
[
1
5
]
.
AI
-
d
r
i
v
e
n
lo
a
d
f
o
r
ec
a
s
t
i
n
g
t
e
ch
n
iq
u
e
s
e
n
h
an
c
e
p
r
e
d
i
c
t
iv
e
ac
c
u
r
a
cy
a
n
d
s
u
p
p
o
r
t
b
e
t
t
er
i
n
f
r
a
s
t
r
u
c
t
u
r
e
p
l
a
n
n
i
n
g
[
1
6
]
.
R
e
i
n
f
o
r
c
em
e
n
t
l
e
a
r
n
i
n
g
s
t
r
a
t
e
g
i
e
s
h
av
e
a
l
s
o
b
ee
n
ap
p
li
e
d
to
o
p
t
i
m
i
z
e
ch
a
r
g
i
n
g
o
p
e
r
a
t
i
o
n
s
d
y
n
am
i
c
a
l
ly
in
r
e
s
p
o
n
s
e
t
o
r
e
a
l
-
t
im
e
g
r
id
c
o
n
d
i
t
i
o
n
s
[
9
]
.
M
a
ch
i
n
e
l
ea
r
n
i
n
g
–
b
a
s
ed
s
c
h
ed
u
l
in
g
f
r
a
m
e
wo
r
k
s
f
u
r
th
e
r
im
p
r
o
v
e
o
p
e
r
a
t
io
n
a
l
e
f
f
i
c
i
e
n
c
y
an
d
l
o
a
d
b
a
l
an
c
in
g
in
s
m
ar
t
g
r
id
en
v
i
r
o
n
m
e
n
t
s
[
1
7
]
.
I
n
a
d
d
i
t
i
o
n
to
o
p
t
i
m
i
z
a
t
io
n
an
d
f
o
r
ec
a
s
t
i
n
g
,
r
e
c
en
t
s
t
u
d
ie
s
h
a
v
e
e
m
p
h
a
s
i
z
e
d
r
e
s
i
l
i
en
c
e
e
n
h
an
c
em
e
n
t
t
h
r
o
u
g
h
AI
-
b
a
s
e
d
p
r
ed
i
c
t
iv
e
a
n
a
ly
t
i
c
s
a
n
d
d
e
m
a
n
d
r
e
s
p
o
n
s
e
s
t
r
a
t
eg
i
e
s
[
1
8
]
.
T
h
e
i
n
t
eg
r
a
t
io
n
o
f
r
e
n
e
wa
b
l
e
e
n
er
g
y
s
o
u
r
c
es
i
n
t
o
E
V
c
h
ar
g
in
g
s
t
a
t
io
n
s
h
a
s
b
ee
n
r
e
co
g
n
i
z
e
d
a
s
a
c
r
i
t
i
c
a
l
f
a
c
to
r
in
i
m
p
r
o
v
in
g
s
u
s
t
a
i
n
a
b
i
l
i
ty
an
d
r
e
d
u
c
i
n
g
g
r
id
s
t
r
e
s
s
[
1
9
]
.
AI
-
b
a
s
e
d
o
p
t
i
m
i
z
a
t
io
n
f
r
a
m
e
wo
r
k
s
h
av
e
a
l
s
o
b
e
en
d
e
v
e
lo
p
e
d
to
en
h
an
c
e
o
v
e
r
a
ll
c
h
a
r
g
i
n
g
n
e
t
w
o
r
k
p
er
f
o
r
m
an
c
e
u
n
d
e
r
d
y
n
a
m
ic
o
p
e
r
a
t
i
n
g
c
o
n
d
i
t
io
n
s
[
2
0
]
.
A
d
v
an
c
e
d
s
m
a
r
t
c
h
a
r
g
in
g
s
t
r
at
e
g
i
e
s
u
s
in
g
d
e
ep
l
e
ar
n
in
g
c
o
n
t
r
i
b
u
t
e
to
im
p
r
o
v
e
d
f
l
e
x
ib
i
l
i
ty
a
n
d
r
e
l
i
a
b
il
i
t
y
in
E
V
e
c
o
s
y
s
t
e
m
s
[
2
1
]
.
D
e
s
p
i
t
e
t
h
e
s
e
ad
v
a
n
c
em
e
n
t
s
,
s
i
g
n
i
f
i
ca
n
t
ch
a
l
l
en
g
e
s
r
e
m
a
in
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n
d
e
s
i
g
n
i
n
g
a
d
ap
t
i
v
e
,
co
s
t
-
e
f
f
e
c
t
i
v
e,
a
n
d
r
e
s
i
l
i
en
t
E
V
c
h
a
r
g
i
n
g
n
e
t
w
o
r
k
s
c
ap
a
b
l
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o
f
w
i
th
s
t
a
n
d
i
n
g
r
e
a
l
-
wo
r
ld
v
a
r
i
ab
i
l
i
t
y
an
d
u
n
f
o
r
e
s
e
en
g
r
i
d
e
v
e
n
t
s
.
R
e
ce
n
t
wo
r
k
s
o
n
o
p
ti
m
a
l
s
c
h
ed
u
l
in
g
u
s
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n
g
e
v
o
l
u
ti
o
n
ar
y
m
e
t
h
o
d
s
d
e
m
o
n
s
t
r
a
t
e
t
h
e
i
m
p
o
r
ta
n
c
e
o
f
c
o
o
r
d
in
a
t
e
d
ch
a
r
g
i
n
g
c
o
n
t
r
o
l
i
n
m
in
i
m
i
z
in
g
s
y
s
t
em
lo
s
s
es
a
n
d
im
p
r
o
v
i
n
g
s
er
v
i
ce
q
u
al
i
t
y
[
2
2
]
.
AI
-
b
a
s
e
d
d
e
m
an
d
r
e
s
p
o
n
s
e
m
e
ch
an
i
s
m
s
f
u
r
t
h
e
r
s
t
r
en
g
th
e
n
g
r
id
r
e
s
i
li
e
n
c
e
an
d
o
p
er
a
t
i
o
n
a
l
s
t
a
b
i
l
i
ty
i
n
l
a
r
g
e
-
s
c
a
l
e
E
V
i
n
t
e
g
r
a
t
i
o
n
s
c
en
a
r
io
s
[
2
3
]
-
[
2
5
]
.
T
h
e
r
ef
o
r
e
,
in
t
eg
r
a
t
in
g
A
I
-
d
r
iv
en
f
o
r
ec
a
s
t
i
n
g
w
i
t
h
i
n
te
ll
i
g
e
n
t
o
p
t
i
m
i
z
a
t
io
n
f
r
a
m
e
w
o
r
k
s
p
r
e
s
en
t
s
a
p
r
o
m
is
i
n
g
d
i
r
ec
t
i
o
n
f
o
r
d
e
v
e
l
o
p
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n
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s
u
s
t
a
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n
ab
l
e
,
f
le
x
ib
l
e
,
a
n
d
r
e
s
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l
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t
E
V
ch
a
r
g
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n
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n
f
r
a
s
t
r
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c
tu
r
e
s
s
u
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t
a
b
le
f
o
r
m
o
d
er
n
s
m
a
r
t
c
i
t
i
e
s
.
T
h
e
m
a
in
co
n
tr
i
b
u
t
i
o
n
s
o
f
t
h
i
s
s
t
u
d
y
a
r
e
s
u
m
m
a
r
iz
ed
a
s
f
o
l
l
o
w
s
:
i
)
A
u
n
if
i
e
d
A
I
-
d
r
iv
en
f
r
a
m
e
w
o
r
k
c
o
m
b
i
n
in
g
d
em
an
d
f
o
r
e
c
a
s
t
in
g
,
o
p
t
i
m
i
za
t
i
o
n
,
a
n
d
r
e
s
i
l
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n
c
e
ev
a
lu
a
t
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f
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E
V
c
h
ar
g
in
g
n
e
t
w
o
r
k
p
l
an
n
in
g
;
i
i)
I
n
t
eg
r
a
t
i
o
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f
l
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g
s
h
o
r
t
-
t
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m
m
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y
(
L
S
T
M
)
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b
a
s
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s
p
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t
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p
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ed
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c
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w
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h
h
y
b
r
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d
G
A
–
P
S
O
o
p
t
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m
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t
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f
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ch
a
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g
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s
t
a
t
io
n
p
l
a
c
em
e
n
t
;
i
i
i
)
I
n
c
o
r
p
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r
a
t
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f
d
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p
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to
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t
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r
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d
d
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r
b
a
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d
d
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d
f
l
u
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t
u
a
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s
;
i
v
)
S
c
e
n
a
r
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b
a
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l
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,
r
en
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ab
l
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m
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t
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c
y
,
a
n
d
g
r
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c
a
p
ac
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t
y
r
e
d
u
c
t
i
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;
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n
d
v
)
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o
m
p
a
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p
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v
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d
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b
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ed
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r
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a
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.
T
h
i
s
w
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d
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f
f
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m
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x
i
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in
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ly
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a
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h
in
a
s
i
n
g
l
e
AI
-
d
r
i
v
e
n
f
r
am
e
w
o
r
k
.
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
i
s
s
t
u
d
y
p
r
o
p
o
s
e
s
a
r
ep
r
o
d
u
c
ib
l
e
AI
-
d
r
iv
e
n
f
r
a
m
e
w
o
r
k
f
o
r
d
e
s
i
g
n
in
g
a
r
e
s
i
l
i
e
n
t
E
V
c
h
ar
g
in
g
s
t
a
t
i
o
n
n
e
t
w
o
r
k
u
n
d
er
s
t
o
ch
a
s
t
i
c
d
e
m
a
n
d
an
d
g
r
id
co
n
s
tr
a
i
n
t
s
.
T
h
e
m
e
th
o
d
o
lo
g
y
co
n
s
i
s
t
s
o
f
f
i
v
e
m
a
in
s
t
a
g
e
s
:
i
)
d
a
t
a
c
o
l
l
e
c
t
io
n
an
d
p
r
ep
r
o
c
e
s
s
i
n
g
,
i
i)
L
S
T
M
-
b
a
s
e
d
s
p
a
t
io
t
e
m
p
o
r
a
l
d
e
m
a
n
d
f
o
r
e
c
a
s
t
i
n
g
,
i
i
i
)
h
y
b
r
i
d
G
A
–
PSO
-
b
a
s
ed
c
h
a
r
g
i
n
g
s
t
a
t
i
o
n
p
l
ac
e
m
en
t
o
p
t
i
m
iz
a
t
i
o
n
,
i
v
)
r
e
s
i
l
i
e
n
c
e
e
v
a
lu
a
t
io
n
u
n
d
e
r
m
u
l
t
i
p
l
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g
r
i
d
s
t
r
e
s
s
s
c
e
n
a
r
i
o
s
,
an
d
v
)
co
m
p
a
r
a
t
iv
e
p
er
f
o
r
m
an
c
e
an
a
ly
s
i
s
.
F
i
g
u
r
e
1
i
l
l
u
s
t
r
a
te
s
th
e
o
v
e
r
a
l
l
wo
r
k
f
l
o
w
o
f
t
h
e
p
r
o
p
o
s
ed
f
r
a
m
e
w
o
r
k
.
2
.
1
.
D
a
t
a
s
et
d
e
s
c
r
i
p
t
io
n
a
n
d
p
r
e
p
r
o
c
e
s
s
i
n
g
2
.
1
.
1
.
D
a
t
a
s
o
u
r
c
e
s
T
h
e
d
a
t
a
s
e
t
s
u
s
e
d
i
n
th
i
s
s
t
u
d
y
c
o
n
s
i
s
t
o
f
:
i)
E
V
ch
a
r
g
i
n
g
d
e
m
an
d
d
a
t
a
:
Ho
u
r
l
y
ch
a
r
g
i
n
g
d
em
a
n
d
(
k
W
)
co
l
l
e
c
t
ed
f
r
o
m
p
u
b
l
ic
a
n
d
s
e
m
i
-
p
u
b
l
i
c
E
V
c
h
ar
g
in
g
s
t
a
t
io
n
s
in
a
n
u
r
b
an
I
n
d
i
a
n
c
i
ty
(
C
h
en
n
a
i
m
e
t
r
o
p
o
l
i
ta
n
r
eg
i
o
n
)
o
v
e
r
o
n
e
y
e
a
r
;
i
i
)
T
r
a
f
f
ic
an
d
m
o
b
i
l
i
t
y
d
a
t
a
:
H
o
u
r
ly
v
e
h
i
cl
e
f
l
o
w
in
t
en
s
i
t
y
an
d
p
a
r
k
i
n
g
a
v
a
i
la
b
i
l
i
ty
o
b
t
a
i
n
ed
f
r
o
m
m
u
n
i
c
ip
a
l
t
r
an
s
p
o
r
t
r
e
co
r
d
s
;
i
i
i)
G
r
i
d
d
a
t
a
:
T
r
a
n
s
f
o
r
m
e
r
c
ap
a
c
i
ty
l
i
m
i
t
s
,
f
e
e
d
er
lo
a
d
in
g
,
an
d
v
o
l
ta
g
e
co
n
s
t
r
a
in
t
s
p
r
o
v
i
d
ed
b
y
t
h
e
lo
ca
l
d
i
s
t
r
ib
u
t
io
n
u
t
i
l
i
t
y
;
a
n
d
iv
)
R
e
n
e
w
a
b
l
e
e
n
er
g
y
d
a
t
a
:
H
i
s
t
o
r
i
ca
l
s
o
la
r
P
V
g
en
e
r
a
t
io
n
p
r
o
f
i
l
e
s
(
k
W
)
f
r
o
m
r
o
o
f
t
o
p
in
s
t
a
l
l
a
t
i
o
n
s
co
-
lo
ca
t
e
d
w
i
t
h
s
e
l
e
c
t
ed
c
h
a
r
g
i
n
g
s
t
a
t
io
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
R
esil
ien
t E
V
ch
a
r
g
in
g
s
ta
tio
n
n
etw
o
r
k
d
esig
n
u
s
in
g
A
I
a
lg
o
r
ith
ms
(
Dee
p
a
S
o
ma
s
u
n
d
a
r
a
m
)
1545
2
.
1
.
2
.
Da
t
a
s
t
ruct
ure
T
h
e
f
in
al
d
ataset
is
s
tr
u
ctu
r
ed
as a
m
u
ltiv
ar
iate
tim
e
s
er
ies
,
as in
(
1
)
.
=
[
,
,
,
]
(
1
)
W
h
er
e
Dt:
h
is
to
r
ical
E
V
c
h
ar
g
in
g
d
e
m
an
d
(
k
W
)
,
T
t:
tr
af
f
ic
d
en
s
ity
in
d
ex
,
Vt:
g
r
id
v
o
ltag
e/l
o
ad
in
d
icato
r
,
an
d
R
t: r
en
ewa
b
le
en
er
g
y
g
en
er
ati
o
n
(
k
W
)
.
Fig
u
r
e
1
. T
h
e
wo
r
k
f
lo
w
d
iag
r
am
o
f
th
e
A
I
-
d
r
i
v
en
r
esil
ien
t n
etwo
r
k
d
esig
n
f
r
am
ewo
r
k
f
o
r
o
p
tim
al
E
V
ch
ar
g
in
g
s
tatio
n
p
lace
m
en
t
2
.
1
.
3
.
P
re
pro
ce
s
s
ing
s
t
eps
Pre
p
r
o
ce
s
s
in
g
s
tep
s
wer
e
p
e
r
f
o
r
m
e
d
as
f
o
llo
ws
:
i)
Miss
in
g
v
alu
es
wer
e
h
an
d
led
u
s
in
g
lin
ea
r
in
ter
p
o
latio
n
;
ii)
All
f
ea
tu
r
es
wer
e
n
o
r
m
alize
d
to
t
h
e
r
a
n
g
e
[
0
,
1
]
u
s
in
g
m
in
–
m
ax
n
o
r
m
aliza
tio
n
;
iii)
T
h
e
d
ata
wer
e
s
p
lit
in
to
7
0
%
tr
ain
in
g
,
1
5
%
v
alid
atio
n
,
an
d
1
5
%
test
in
g
s
ets
;
an
d
iv
)
Sli
d
in
g
tim
e
wi
n
d
o
ws
o
f
2
4
h
o
u
r
s
wer
e
u
s
ed
to
ca
p
t
u
r
e
d
aily
d
e
m
an
d
cy
cles.
2
.
2
.
L
ST
M
-
ba
s
ed
dem
a
nd
f
o
re
ca
s
t
ing
m
o
del
An
L
STM
n
etwo
r
k
was
em
p
lo
y
ed
to
f
o
r
ec
ast
h
o
u
r
ly
E
V
ch
a
r
g
in
g
d
e
m
an
d
d
u
e
to
its
ef
f
ec
t
iv
en
ess
in
m
o
d
elin
g
tem
p
o
r
al
d
e
p
en
d
e
n
c
ies.
T
h
e
ad
o
p
ted
ar
c
h
itectu
r
e
co
n
s
is
ts
o
f
an
in
p
u
t
lay
er
with
2
4
-
tim
e
s
tep
s
an
d
f
o
u
r
f
ea
tu
r
es,
f
o
llo
wed
b
y
t
h
e
f
ir
s
t
L
STM
lay
er
co
n
tain
in
g
6
4
u
n
its
with
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U
)
ac
tiv
atio
n
.
A
d
r
o
p
o
u
t
lay
er
w
ith
a
d
r
o
p
o
u
t
r
ate
o
f
0
.
2
is
a
p
p
lied
to
r
ed
u
ce
o
v
er
f
itti
n
g
.
T
h
is
is
f
o
llo
wed
b
y
a
s
ec
o
n
d
L
STM
lay
er
with
3
2
u
n
its
,
an
d
a
f
u
lly
co
n
n
ec
ted
l
ay
er
co
m
p
r
is
in
g
1
6
n
eu
r
o
n
s
.
Fin
ally
,
th
e
o
u
tp
u
t
lay
er
co
n
tain
s
a
s
in
g
le
n
eu
r
o
n
th
at
p
r
ed
icts
th
e
E
V
ch
ar
g
in
g
d
em
an
d
in
k
W
.
T
h
e
m
o
d
el
w
as
tr
ain
ed
u
s
in
g
th
e
m
ea
n
s
q
u
ar
ed
e
r
r
o
r
(
MSE
)
lo
s
s
f
u
n
ctio
n
an
d
o
p
tim
ized
u
s
i
n
g
th
e
Ad
am
o
p
tim
izer
.
A
lea
r
n
in
g
r
ate
o
f
0
.
0
0
1
was
s
elec
ted
,
with
a
b
atch
s
iz
e
o
f
3
2
a
n
d
a
to
tal
o
f
1
0
0
tr
ai
n
in
g
e
p
o
ch
s
.
E
a
r
ly
s
to
p
p
in
g
w
ith
a
p
atien
ce
v
alu
e
o
f
1
0
was
em
p
lo
y
ed
to
p
r
ev
e
n
t
o
v
er
f
itti
n
g
a
n
d
en
s
u
r
e
o
p
ti
m
al
m
o
d
el
co
n
v
er
g
e
n
ce
.
T
h
e
MSE
o
b
jectiv
e
is
d
ef
in
ed
as
(
2
)
.
=
1
n
∑
(
Di
−
Di
̂
k
i
=
1
)
2
(
2
)
W
h
er
e
Di
an
d
Di
̂
d
en
o
te
ac
tu
al
a
n
d
p
r
ed
icted
d
em
an
d
,
r
esp
ec
tiv
ely
.
T
h
e
tr
ain
e
d
L
STM
m
o
d
el
g
en
e
r
ates
s
p
atio
tem
p
o
r
al
d
em
a
n
d
f
o
r
ec
a
s
ts
th
at
ar
e
d
ir
ec
tly
f
ed
in
t
o
th
e
o
p
tim
izatio
n
s
tag
e.
2
.
3
.
H
y
brid G
A
–
P
SO
o
pti
m
iza
t
io
n f
o
r
cha
rg
ing
s
t
a
t
i
o
n pla
ce
m
ent
2
.
3
.
1
.
P
ro
blem
f
o
rm
ula
t
io
n
C
h
ar
g
in
g
s
tatio
n
p
lace
m
en
t
is
m
o
d
eled
as
a
m
u
lti
-
o
b
jecti
v
e
o
p
tim
izatio
n
p
r
o
b
lem
th
at
m
in
im
izes
in
s
tallatio
n
co
s
t
an
d
g
r
id
v
i
o
latio
n
s
wh
ile
m
ax
im
izin
g
u
s
er
a
cc
ess
ib
ilit
y
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
is
d
ef
in
e
d
as
m
in
im
izin
g
.
=
1
+
2
+
3
(
3
)
W
h
er
e
C
in
s
tall
r
ep
r
esen
ts
in
s
t
allatio
n
an
d
o
p
e
r
atio
n
al
co
s
t,
L
u
s
er
d
en
o
tes
th
e
av
er
a
g
e
u
s
e
r
tr
av
el
d
is
tan
ce
to
ch
ar
g
in
g
s
tatio
n
s
,
an
d
L
g
r
id
i
n
d
icate
s
th
e
g
r
id
l
o
ad
v
io
latio
n
p
e
n
alty
.
T
h
e
weig
h
tin
g
c
o
ef
f
icien
ts
W
1
,
W
2
,
W
3
b
alan
ce
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
co
s
t,
ac
ce
s
s
ib
ilit
y
,
an
d
g
r
id
c
o
n
s
tr
ain
ts
in
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
2
,
J
u
n
e
20
2
6
:
1
5
4
3
-
1552
1546
Fo
r
th
e
GA,
a
p
o
p
u
latio
n
s
ize
o
f
5
0
is
u
s
ed
with
to
u
r
n
am
e
n
t
s
elec
tio
n
f
o
r
p
ar
e
n
t
s
elec
tio
n
.
Sin
g
le
-
p
o
in
t
cr
o
s
s
o
v
er
is
ap
p
lied
with
a
cr
o
s
s
o
v
er
r
ate
o
f
0
.
8
,
wh
i
le
m
u
tatio
n
is
in
tr
o
d
u
ce
d
wit
h
a
r
ate
o
f
0
.
0
5
to
m
ain
tain
s
o
lu
tio
n
d
i
v
er
s
ity
.
T
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
r
u
n
s
f
o
r
1
5
0
g
en
er
atio
n
s
to
ac
h
iev
e
co
n
v
er
g
en
ce
.
Fo
r
th
e
PS
O
co
m
p
o
n
en
t,
a
s
war
m
s
ize
o
f
5
0
p
a
r
ticles
is
co
n
s
id
er
ed
.
T
h
e
i
n
er
tia
weig
h
t
is
s
et
to
0
.
7
to
b
alan
ce
ex
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
,
wh
ile
th
e
co
g
n
itiv
e
an
d
s
o
cial
co
ef
f
icien
ts
c
1
an
d
c
2
ar
e
b
o
th
s
et
to
1
.
5
to
g
u
i
d
e
p
ar
ticle
m
o
v
em
e
n
t
b
ased
o
n
in
d
iv
id
u
al
an
d
g
lo
b
al
b
est
s
o
lu
tio
n
s
.
Velo
city
an
d
p
o
s
itio
n
u
p
d
ates
ar
e
g
iv
e
n
by
(
4
)
an
d
(
5
)
.
V
i
t+
1
=
ω
v
i
t
+c
1
r
1
(p
i
–
x
i
t
)
+
c
2
r
2
(
g
–
x
i
t
)
(
4
)
x
i
t+
1
= x
i
t
+ V
i
t+
1
(
5
)
2
.
3
.
4
.
H
y
bridi
za
t
io
n
s
t
ra
t
eg
y
GA
is
f
ir
s
t
u
s
ed
to
g
en
er
ate
d
iv
er
s
e
ca
n
d
id
ate
s
o
lu
tio
n
s
.
T
h
e
b
est
-
p
er
f
o
r
m
in
g
in
d
iv
i
d
u
al
s
ar
e
th
en
s
elec
ted
f
o
r
f
u
r
t
h
er
o
p
tim
izat
io
n
.
T
h
ese
in
d
iv
i
d
u
als
ar
e
r
e
f
in
ed
u
s
in
g
PS
O
to
ac
ce
ler
at
e
co
n
v
er
g
en
ce
an
d
av
o
id
lo
ca
l
o
p
tim
a.
2
.
4
.
Reinf
o
rc
e
m
ent
lea
rning
-
ba
s
ed
a
da
ptiv
e
re
s
ilience
en
ha
ncem
ent
A
d
ee
p
r
ein
f
o
r
ce
m
en
t
lear
n
i
n
g
(
DR
L
)
ag
en
t
is
in
co
r
p
o
r
ated
to
d
y
n
am
ically
ad
j
u
s
t
s
tatio
n
u
tili
za
tio
n
u
n
d
er
r
ea
l
-
tim
e
d
em
a
n
d
an
d
g
r
id
d
is
tu
r
b
an
ce
s
,
wh
er
e
th
e
s
tate
co
n
s
is
ts
o
f
cu
r
r
en
t
d
em
an
d
,
g
r
id
lo
ad
,
an
d
r
en
ewa
b
le
av
aila
b
ilit
y
,
th
e
ac
tio
n
in
v
o
lv
es
lo
ad
r
e
d
is
tr
ib
u
tio
n
am
o
n
g
s
tatio
n
s
,
an
d
th
e
r
e
war
d
is
d
ef
i
n
ed
as
d
em
an
d
s
atis
f
ac
tio
n
m
in
u
s
a
p
en
alty
f
o
r
g
r
id
v
io
latio
n
s
.
A
d
ee
p
Q
-
n
etwo
r
k
(
DQN)
with
two
h
id
d
en
lay
er
s
(
6
4
an
d
3
2
n
eu
r
o
n
s
)
is
em
p
l
o
y
ed
,
tr
ain
ed
u
s
in
g
ex
p
er
ien
ce
r
e
p
lay
an
d
a
d
is
co
u
n
t
f
ac
to
r
γ
=
0
.
9
5
.
2
.
5
.
Resili
ence
a
s
s
ess
m
ent
u
nd
er
g
rid c
o
ns
t
ra
ints
T
o
ev
al
u
ate
r
esil
ien
ce
,
we
s
i
m
u
late
m
u
ltip
le
d
em
a
n
d
s
ce
n
ar
io
s
,
in
clu
d
in
g
p
ea
k
/
o
f
f
-
p
ea
k
v
ar
iatio
n
s
an
d
s
tatio
n
o
u
ta
g
es.
T
h
e
r
esil
ien
ce
in
d
ex
(
R
)
m
ea
s
u
r
es
th
e
n
etwo
r
k
’
s
a
b
ilit
y
to
s
er
v
e
E
V
u
s
er
s
u
n
d
er
s
tr
ess
,
as c
alcu
lated
in
(
6
)
.
=
∑
∑
(
6
)
W
h
er
e
Si
s
u
p
p
lied
d
em
an
d
at
s
tatio
n
iii,
an
d
Di
=
p
r
ed
icted
d
em
an
d
at
s
tatio
n
iii.
A
h
ig
h
er
R
in
d
icate
s
b
etter
r
esil
ien
ce
.
Gr
id
co
n
s
tr
ain
ts
s
u
ch
as
tr
an
s
f
o
r
m
e
r
ca
p
ac
ity
an
d
v
o
ltag
e
lim
its
ar
e
in
co
r
p
o
r
ated
v
ia
p
e
n
alty
f
u
n
ctio
n
s
in
t
h
e
o
p
tim
izatio
n
s
tag
e
to
en
s
u
r
e
f
ea
s
ib
ilit
y
.
2
.
6
.
Renew
a
ble
ener
g
y
inte
g
ra
t
io
n a
nd
g
rid lo
a
d mo
delin
g
C
h
ar
g
in
g
s
tatio
n
s
ar
e
in
te
g
r
at
ed
with
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
(
s
o
lar
,
win
d
)
,
an
d
th
eir
a
v
ailab
ilit
y
is
p
r
ed
icted
u
s
in
g
h
is
to
r
ical
g
e
n
er
atio
n
d
ata.
Gr
id
lo
a
d
at
tim
e
t is ca
lcu
lated
as
(
7
)
.
=
∑
=
1
−
∑
=
1
(
7
)
W
h
er
e
P
i
t
=
E
V
ch
a
r
g
in
g
p
o
wer
d
em
a
n
d
,
R
j
t
=
r
en
ewa
b
l
e
g
en
e
r
atio
n
at
s
tatio
n
N,
an
d
M
=
n
u
m
b
e
r
o
f
r
en
ewa
b
le
u
n
its
.
T
h
is
en
s
u
r
es th
e
o
p
tim
izatio
n
m
i
n
im
izes g
r
id
s
tr
ess
wh
ile
m
ax
im
izin
g
r
en
ewa
b
le
u
tili
za
tio
n
.
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
wa
s
im
p
lem
en
ted
u
s
in
g
Py
th
o
n
an
d
T
en
s
o
r
Flo
w
lib
r
ar
ies.
T
h
e
L
STM
m
o
d
el
was
tr
ain
ed
u
s
in
g
o
n
e
-
y
ea
r
h
o
u
r
ly
E
V
ch
ar
g
in
g
d
em
an
d
d
ata.
T
en
ca
n
d
id
ate
c
h
ar
g
in
g
lo
ca
tio
n
s
wer
e
s
elec
ted
with
in
an
u
r
b
an
d
is
tr
ib
u
tio
n
n
etwo
r
k
.
T
h
e
h
y
b
r
id
GA
–
PS
O
alg
o
r
ith
m
o
p
tim
ized
s
tatio
n
p
lace
m
en
t
co
n
s
id
er
in
g
tr
a
n
s
f
o
r
m
er
ca
p
a
city
,
f
ee
d
er
lo
a
d
in
g
,
a
n
d
r
e
n
ewa
b
le
en
er
g
y
a
v
ailab
ilit
y
.
Mu
ltip
le
o
p
er
atio
n
al
s
ce
n
ar
io
s
wer
e
s
im
u
lated
,
in
c
lu
d
in
g
p
ea
k
d
em
a
n
d
,
s
tatio
n
o
u
tag
e,
g
r
id
ca
p
ac
ity
r
e
d
u
cti
o
n
,
a
n
d
r
en
ewa
b
le
in
ter
m
itten
cy
.
E
ac
h
o
p
tim
izatio
n
ex
p
e
r
im
en
t
was
ex
ec
u
ted
f
i
v
e
tim
es
to
en
s
u
r
e
co
n
v
er
g
en
c
e
co
n
s
is
ten
cy
,
an
d
th
e
b
est s
o
lu
tio
n
b
ased
o
n
th
e
m
in
im
u
m
o
b
jectiv
e
f
u
n
ctio
n
v
alu
e
was selec
ted
f
o
r
an
aly
s
is
.
3.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
T
h
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
s
ec
tio
n
p
r
esen
t
a
co
m
p
r
eh
en
s
iv
e
ev
alu
atio
n
o
f
t
h
e
p
r
o
p
o
s
ed
AI
-
d
r
iv
en
E
V
ch
ar
g
i
n
g
s
tatio
n
n
etwo
r
k
d
esig
n
f
r
am
ewo
r
k
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
L
STM
-
b
ased
d
em
an
d
f
o
r
ec
asti
n
g
,
h
y
b
r
id
GA
–
PS
O
o
p
tim
izatio
n
,
r
esil
ien
ce
ass
ess
m
en
t,
an
d
g
r
id
–
r
en
ewa
b
le
in
teg
r
atio
n
is
a
n
aly
ze
d
u
s
in
g
r
ea
l
-
wo
r
ld
d
e
m
an
d
s
ce
n
ar
io
s
.
C
o
m
p
ar
ativ
e
r
esu
lts
b
etwe
en
AI
al
g
o
r
ith
m
s
an
d
c
o
n
v
e
n
tio
n
al
o
p
t
im
izatio
n
m
eth
o
d
s
h
ig
h
lig
h
t
im
p
r
o
v
em
e
n
ts
in
ac
cu
r
ac
y
,
co
s
t
ef
f
icien
c
y
,
an
d
n
etwo
r
k
r
o
b
u
s
tn
ess
.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
e
ef
f
ec
tiv
en
ess
o
f
AI
-
b
ased
ap
p
r
o
ac
h
es
in
h
an
d
lin
g
u
n
ce
r
tai
n
d
em
a
n
d
a
n
d
g
r
id
c
o
n
s
tr
ain
t
s
.
E
ac
h
s
u
b
s
ec
tio
n
p
r
o
v
id
es d
etailed
in
s
ig
h
ts
s
u
p
p
o
r
ted
b
y
n
u
m
er
ical
r
esu
lts
,
f
i
g
u
r
es,
an
d
p
er
f
o
r
m
a
n
ce
tab
les.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
R
esil
ien
t E
V
ch
a
r
g
in
g
s
ta
tio
n
n
etw
o
r
k
d
esig
n
u
s
in
g
A
I
a
lg
o
r
ith
ms
(
Dee
p
a
S
o
ma
s
u
n
d
a
r
a
m
)
1547
3
.
1
.
De
m
a
nd
f
o
re
ca
s
t
ing
re
s
u
lt
s
T
h
e
L
STM
-
b
ased
f
o
r
ec
asti
n
g
m
o
d
el
p
r
e
d
icted
h
o
u
r
l
y
E
V
ch
ar
g
in
g
d
e
m
an
d
f
o
r
ten
ca
n
d
id
ate
s
tatio
n
s
o
v
er
a
2
4
-
h
o
u
r
h
o
r
iz
o
n
.
T
h
e
m
o
d
el
id
en
tifie
d
a
p
ea
k
d
em
an
d
o
f
1
2
0
k
W
at
Statio
n
4
d
u
r
i
n
g
th
e
ev
en
in
g
p
e
r
io
d
(
6
–
8
PM)
,
co
r
r
esp
o
n
d
in
g
to
h
ig
h
co
m
m
u
ter
r
etu
r
n
tr
af
f
ic,
wh
ile
o
f
f
-
p
ea
k
d
em
an
d
av
er
a
g
ed
3
0
k
W
ac
r
o
s
s
th
e
r
em
ain
i
n
g
s
tatio
n
s
.
T
h
e
m
o
d
el
ac
h
iev
ed
a
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
MSE
)
o
f
4
.
2
k
W
²
o
n
th
e
test
d
ataset,
in
d
icatin
g
s
tr
o
n
g
p
r
e
d
ictiv
e
ac
cu
r
ac
y
.
T
h
e
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
d
ir
e
ctly
in
f
lu
en
ce
d
th
e
q
u
ality
o
f
o
p
tim
izatio
n
o
u
tco
m
es.
L
o
wer
p
r
ed
ictio
n
er
r
o
r
r
ed
u
ce
d
o
v
er
esti
m
atio
n
o
f
in
f
r
astru
ctu
r
e
ca
p
ac
ity
an
d
m
in
im
ized
u
n
d
er
-
p
r
o
v
is
io
n
in
g
r
is
k
s
.
C
o
m
p
ar
ed
to
tr
ad
itio
n
al
AR
I
MA
an
d
r
eg
r
ess
io
n
-
b
ased
m
o
d
els
(
ev
alu
ated
s
ep
ar
ately
)
,
th
e
L
STM
r
ed
u
ce
d
f
o
r
ec
asti
n
g
er
r
o
r
b
y
ap
p
r
o
x
im
ately
1
8
%,
p
r
im
a
r
ily
d
u
e
to
its
ab
ilit
y
to
ca
p
tu
r
e
n
o
n
lin
ea
r
tem
p
o
r
al
d
e
p
en
d
e
n
cies
an
d
s
ea
s
o
n
al
ch
ar
g
in
g
b
eh
a
v
io
r
s
.
Fig
u
r
e
2
i
llu
s
tr
ates
th
e
h
o
u
r
ly
v
ar
iatio
n
o
f
to
tal
g
r
i
d
lo
ad
i
n
co
m
p
ar
is
o
n
with
r
en
ewa
b
le
en
er
g
y
co
n
tr
i
b
u
tio
n
o
v
e
r
2
4
h
o
u
r
s
.
I
t
ca
n
b
e
o
b
s
er
v
ed
t
h
at
r
en
ewa
b
le
g
en
e
r
atio
n
p
a
r
tially
o
f
f
s
ets
p
ea
k
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u
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.
O
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h
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ith
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r
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u
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3
,
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ased
o
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izatio
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r
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ce
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th
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er
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to
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m
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ap
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e
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im
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as
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ain
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atial
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le
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atio
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o
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ates
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ile
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ent
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ased
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e
c
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ris
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u
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n
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y
An
a
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d
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rk
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g
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ro
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n
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c
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h
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fro
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h
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s
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d
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se
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ter
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DCS,
a
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d
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ial
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tec
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c
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m
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Priy
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r
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a
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d
e
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u
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h
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r
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sy
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m
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v
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a
n
d
sm
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.
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e
c
a
n
b
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c
o
n
tac
ted
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m
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:
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a
n
k
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c
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a
c
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in
.
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a
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th
a
n
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m
M
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m
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ra
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1
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9
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m
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.
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d
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.
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s AC
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h
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s
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h
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sin
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c
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n
b
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c
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tac
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t
e
m
a
il
:
k
iru
b
a
d
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.