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1243
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h
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//
ija
p
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esco
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co
m/
M
ulti
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bje
ctive pl
a
nning
of distri
bu
ted
reso
ur
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PV
a
nd
SVC
)
with
NSG
A
-
II
for
radia
l net
wo
r
ks:
a
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tion to t
he
IEE
E
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m
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a
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u
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m
p
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(S
VC)
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ra
d
i
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two
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k
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h
e
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-
d
o
m
in
a
ted
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rt
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g
g
e
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e
ti
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G
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p
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e
d
a
s
t
h
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ro
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u
st
m
e
th
o
d
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ra
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l
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o
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ti
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ly
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lo
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in
g
t
h
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twe
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two
c
o
n
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li
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ti
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g
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s:
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c
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m
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n
d
v
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iza
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t
o
p
ti
m
iza
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o
n
fra
m
e
wo
rk
is
th
e
k
e
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o
v
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lt
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l
e
v
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ra
g
in
g
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ifi
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p
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tera
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ti
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m
a
x
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o
v
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ll
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e
two
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k
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y
.
S
imu
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s
a
re
p
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rf
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rm
e
d
o
n
th
e
sta
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d
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rd
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9
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K
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Dis
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T
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CC B
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li
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C
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p
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A
uth
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r
:
Hass
an
e
Ou
s
s
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n
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b
r
ah
im
E
lectr
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E
n
g
in
ee
r
in
g
Dep
ar
t
m
en
t,
Po
ly
tech
Ma
r
a
d
i,
Un
iv
e
r
s
ity
Dan
Dick
o
Dan
k
o
u
lo
d
o
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f
Ma
r
ad
i
Ma
r
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i,
Nig
er
E
m
ail: h
ass
an
e2
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2
5
@
y
ah
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o
.
f
r
1.
I
NT
RO
D
UCT
I
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E
f
f
ec
tiv
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m
an
a
g
em
en
t
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f
ac
tiv
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lo
s
s
es
an
d
v
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ltag
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p
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in
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ad
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tio
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etwo
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k
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a
m
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ch
allen
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o
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p
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r
s
,
p
a
r
ticu
lar
ly
in
e
n
v
ir
o
n
m
en
ts
with
h
ig
h
lo
ad
v
ar
iab
ilit
y
.
T
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ese
n
etwo
r
k
s
ar
e
ch
ar
ac
ter
ized
b
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a
tr
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-
lik
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s
tr
u
ctu
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d
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g
lin
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s
.
As
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t
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ar
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itiv
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v
o
ltag
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im
b
alan
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an
d
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wh
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ca
n
co
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p
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m
is
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s
u
p
p
ly
q
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ality
a
n
d
s
y
s
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s
tab
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Dr
iv
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ity
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f
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o
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ce
s
in
to
d
is
tr
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u
tio
n
n
etwo
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k
s
is
ac
ce
ler
atin
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.
Ph
o
to
v
o
ltaics
(
PV)
,
as
an
ef
f
icien
t
an
d
d
ec
en
tr
alize
d
s
o
u
r
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d
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n
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p
ar
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ly
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u
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r
ap
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d
g
r
o
wth
an
d
in
f
r
astru
ctu
r
e
co
n
s
tr
ain
ts
[
1
]
,
[
2
]
.
I
n
ad
d
itio
n
to
p
r
o
v
i
d
in
g
ac
tiv
e
en
er
g
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,
PV
s
y
s
tem
s
ca
n
co
n
tr
ib
u
te
t
o
v
o
ltag
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r
eg
u
latio
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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5
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3
,
Sep
tem
b
er
20
2
6
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24
3
-
1
25
2
1244
wh
en
p
r
o
p
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ly
s
ized
an
d
p
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s
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T
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im
p
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g
r
i
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tab
ilit
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[
3
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-
[
5
]
.
Ho
wev
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e
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ac
t
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PV
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th
e
v
o
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ati
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o
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-
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iv
ial.
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o
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ca
n
e
x
ac
er
b
ate
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o
lta
g
e
im
b
ala
n
ce
s
o
r
ca
u
s
e
o
v
er
v
o
ltag
e.
T
h
is
is
wh
y
o
p
tim
izin
g
PV
p
lace
m
e
n
t
a
n
d
s
izin
g
is
ess
en
tial
to
en
s
u
r
e
a
r
ea
l
im
p
r
o
v
em
en
t
i
n
g
r
id
p
er
f
o
r
m
an
ce
[
6
]
,
[
7
]
.
At
th
e
s
am
e
tim
e,
th
e
lack
o
f
r
ea
ctiv
e
p
o
wer
c
o
n
tr
o
l
is
a
m
ajo
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ca
u
s
e
o
f
v
o
ltag
e
in
s
tab
ili
ty
in
d
is
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ib
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tio
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n
etwo
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k
s
[
8
]
.
T
o
o
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er
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m
e
th
e
ch
allen
g
e
o
f
d
y
n
am
ic
lo
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s
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tem
o
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ato
r
s
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c
r
ea
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ly
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r
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en
t
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AC
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tr
an
s
m
is
s
io
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s
y
s
tem
s
(
FA
C
T
S)
d
ev
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s
u
ch
as
th
e
s
tatic
v
ar
co
m
p
en
s
ato
r
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SVC
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,
wh
ich
p
r
o
v
id
e
au
to
m
atica
lly
ad
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s
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le
an
d
h
ig
h
ly
ef
f
ec
tiv
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r
ea
ctiv
e
p
o
w
er
m
an
ag
em
en
t
[
9
]
-
[
1
1
]
.
Dr
iv
en
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y
ad
v
a
n
ce
m
en
ts
in
p
o
wer
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tr
o
n
ics,
SVC
s
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e
in
cr
ea
s
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g
ly
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r
ef
er
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ed
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v
er
tr
ad
itio
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al
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ix
e
d
ca
p
ac
ito
r
s
.
S
VC
s
o
f
f
er
d
y
n
a
m
ic
an
d
p
r
ec
is
e
r
ea
ctiv
e
p
o
wer
c
o
n
tr
o
l,
en
a
b
lin
g
th
em
to
ef
f
ec
ti
v
ely
m
an
ag
e
f
ast
lo
ad
v
a
r
iatio
n
s
,
s
u
p
p
o
r
t
v
o
ltag
e
s
tab
ilit
y
,
an
d
en
s
u
r
e
h
ig
h
p
o
w
er
q
u
ality
[
1
2
]
-
[
1
4
]
.
Ho
wev
er
,
th
is
in
teg
r
atio
n
ca
n
o
n
ly
b
e
ef
f
ec
tiv
e
i
f
th
e
p
lac
em
en
t
an
d
s
izin
g
o
f
th
is
eq
u
i
p
m
en
t
ar
e
o
p
tim
ized
u
s
in
g
a
r
ig
o
r
o
u
s
a
p
p
r
o
ac
h
ca
p
ab
le
o
f
ad
d
r
ess
in
g
s
ev
er
al
co
n
f
lictin
g
o
b
jectiv
es
[
1
5
]
.
T
r
a
d
itio
n
al
s
in
g
le
-
o
b
jectiv
e
m
et
h
o
d
s
a
r
e
o
f
ten
in
ad
e
q
u
ate
as
th
ey
f
a
il
to
ca
p
tu
r
e
th
e
n
ec
ess
ar
y
tr
ad
e
-
o
f
f
s
b
etwe
en
co
n
f
lictin
g
g
o
als
lik
e
e
n
er
g
y
ef
f
icien
cy
a
n
d
v
o
ltag
e
s
tab
ilit
y
.
Sin
ce
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
p
r
esen
t
a
co
m
p
le
x
co
m
b
in
ato
r
ial
p
r
o
b
lem
in
v
o
lv
in
g
n
u
m
er
o
u
s
n
o
d
es,
th
e
o
p
tim
al
s
itin
g
an
d
s
izin
g
o
f
eq
u
i
p
m
en
t
(
PV
an
d
SVC
)
b
ec
o
m
es
cr
itical
to
e
n
s
u
r
in
g
t
ec
h
n
ical
f
ea
s
ib
ilit
y
an
d
m
ax
i
m
u
m
n
etwo
r
k
b
en
ef
it
[
1
6
]
.
M
etah
eu
r
is
tics
,
an
d
in
p
ar
ticu
lar
m
u
lti
-
o
b
jectiv
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
s
u
ch
as
NSGA
-
I
I
[
1
7
]
,
[
1
8
]
,
o
f
f
er
a
r
o
b
u
s
t
an
d
f
le
x
ib
le
s
o
lu
tio
n
to
th
is
ty
p
e
o
f
p
r
o
b
lem
.
NSGA
-
I
I
ca
n
g
en
e
r
ate
a
s
et
o
f
n
o
n
-
d
o
m
i
n
ated
s
o
lu
tio
n
s
,
o
f
f
e
r
in
g
th
e
n
etwo
r
k
o
p
er
ato
r
s
ev
er
al
b
ala
n
ce
d
alter
n
ativ
es.
Un
lik
e
p
r
e
v
io
u
s
wo
r
k
s
s
u
ch
as
[
1
2
]
,
wh
ich
f
o
cu
s
ed
s
o
l
ely
o
n
SVC
p
lace
m
en
t,
o
r
[
1
9
]
w
h
ich
o
p
tim
ized
PV
allo
c
atio
n
with
o
u
t
r
ea
ctiv
e
co
m
p
en
s
atio
n
,
th
is
p
ap
er
p
r
o
p
o
s
es
a
jo
in
t
o
p
ti
m
izatio
n
ap
p
r
o
ac
h
.
W
h
ile
[
1
5
]
d
is
cu
s
s
es
PV
-
in
v
er
ter
r
e
g
u
latio
n
,
o
u
r
m
eth
o
d
o
lo
g
y
s
p
ec
if
ically
u
tili
ze
s
th
e
NSGA
-
I
I
alg
o
r
ith
m
to
s
im
u
ltan
eo
u
s
ly
s
o
lv
e
th
e
tr
a
d
e
-
o
f
f
b
etwe
en
PV
ac
tiv
e
p
o
wer
in
jectio
n
an
d
SVC
-
b
ased
r
ea
ctiv
e
p
o
we
r
s
u
p
p
o
r
t.
T
h
is
s
im
u
ltan
eo
u
s
a
p
p
r
o
ac
h
allo
ws
f
o
r
ca
p
tu
r
in
g
th
e
co
u
p
lin
g
ef
f
ec
ts
b
etwe
en
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
f
lo
ws,
lead
in
g
to
a
s
u
p
er
io
r
r
ed
u
cti
o
n
in
lo
s
s
es
co
m
p
ar
ed
to
s
eq
u
en
tial
o
r
is
o
lated
p
lan
n
in
g
s
tr
ateg
ies.
T
h
is
s
tu
d
y
aim
s
to
a
p
p
ly
NSGA
-
I
I
to
s
im
u
ltan
eo
u
s
ly
o
p
tim
ize
th
e
l
o
ca
tio
n
an
d
s
izin
g
o
f
a
PV
s
y
s
tem
an
d
a
n
SVC
in
th
e
s
tan
d
ar
d
I
E
E
E
3
3
-
b
u
s
n
etwo
r
k
.
T
h
e
o
b
jectiv
e
s
ar
e
to
m
in
im
ize
ac
tiv
e
lo
s
s
es
an
d
im
p
r
o
v
e
th
e
v
o
ltag
e
p
r
o
f
ile.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
is
v
alid
ated
b
y
s
im
u
latio
n
in
th
e
MA
T
L
AB
en
v
ir
o
n
m
e
n
t,
an
d
th
e
r
esu
lts
ar
e
an
aly
ze
d
ac
co
r
d
in
g
to
p
u
r
el
y
tech
n
ical
cr
iter
ia.
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
m
at
er
ial
u
s
ed
an
d
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
f
th
is
s
tu
d
y
.
T
h
e
m
ater
ial
u
s
ed
len
d
s
cr
ed
ib
ilit
y
to
th
e
r
esu
lts
o
b
tain
ed
u
s
in
g
th
is
m
eth
o
d
.
2
.
1
.
T
est
net
wo
rk
T
h
e
n
etwo
r
k
u
s
ed
f
o
r
th
is
s
tu
d
y
is
th
e
s
tan
d
ar
d
I
E
E
E
3
3
-
b
u
s
test
m
o
d
el
[
2
0
]
,
co
m
p
o
s
ed
o
f
3
3
n
o
d
es
an
d
3
2
b
r
a
n
ch
es,
with
a
n
o
m
i
n
al
v
o
ltag
e
o
f
1
2
.
6
6
k
V
an
d
a
n
o
m
in
al
b
ase
p
o
wer
o
f
1
0
0
MV
A.
T
h
is
n
etwo
r
k
is
wid
ely
u
s
ed
as a
test
b
ed
f
o
r
d
is
tr
ib
u
tio
n
n
etwo
r
k
o
p
tim
izatio
n
s
tu
d
ies.
Fig
u
r
e
1
s
h
o
ws its
to
p
o
lo
g
y
.
2
.
2
.
P
ho
t
o
v
o
lt
a
ic
m
o
del
T
h
e
PV
is
m
o
d
eled
as
an
ac
tiv
e
p
o
wer
s
o
u
r
ce
.
W
h
en
p
la
ce
d
at
n
o
d
e
i,
th
e
p
o
wer
at
th
at
n
o
d
e
b
ec
o
m
es
(
1
)
.
,
=
+
(
1
)
W
h
er
e
P
i
,
n
ew
is
th
e
n
ew
ac
tiv
e
p
o
w
er
at
n
o
d
e
i,
P
i
is
ac
tiv
e
p
o
wer
at
n
o
d
e
i,
P
PV
is
th
e
ac
tiv
e
p
o
wer
in
jecte
d
b
y
th
e
PV.
T
h
e
r
ea
ctiv
e
p
o
wer
o
f
th
e
n
o
d
e
r
em
ain
s
u
n
c
h
an
g
e
d
b
ec
au
s
e
Q
PV
=
0
.
Fig
u
r
e
1
.
I
E
E
E
3
3
-
b
u
s
s
tan
d
a
r
d
n
etwo
r
k
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I
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n
g
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8
7
9
2
Mu
lti
-
o
b
jective
p
la
n
n
in
g
o
f
d
is
tr
ib
u
ted
r
eso
u
r
ce
s
(
P
V
and
S
V
C
)
w
ith
…
(
Ha
s
s
a
n
e
Ou
s
s
ey
n
i I
b
r
a
h
im
)
1245
2
.
3
.
SVC
mo
del
T
h
e
SVC
is
r
ep
r
esen
ted
as
a
s
o
u
r
ce
o
f
ad
ju
s
tab
le
r
ea
ctiv
e
p
o
wer
.
As
s
h
o
wn
in
Fig
u
r
e
2
,
th
e
s
h
u
n
t
r
ea
ctan
ce
is
ad
ju
s
ted
a
u
to
m
ati
ca
lly
,
d
ep
e
n
d
in
g
o
n
th
e
s
tate
o
f
th
e
n
etwo
r
k
.
I
t
ca
n
th
e
n
in
je
ct
o
r
a
b
s
o
r
b
cu
r
r
e
n
t
at
th
e
n
o
d
e
wh
er
e
it is
co
n
n
ec
ted
.
C
o
n
n
ec
ted
at
n
o
d
e
k
,
th
e
c
u
r
r
en
t a
b
s
o
r
b
ed
b
y
th
e
SVC
is
g
iv
en
b
y
(
2
)
.
=
∗
(
2
)
W
h
er
e
S
V
C
is
cu
r
r
en
tly
ab
s
o
r
b
ed
b
y
SVC
,
B
S
V
C
s
u
s
ce
p
tan
ce
o
f
SVC
,
V
k
is
th
e
v
o
ltag
e
am
p
litu
d
e
at
b
u
s
k
.
B
ased
o
n
th
is
cu
r
r
en
t,
t
h
e
p
o
w
er
o
f
th
e
in
s
talled
SVC
is
d
ete
r
m
in
ed
b
y
(
3
)
.
=
∗
=
−
∗
2
(
3
)
T
h
u
s
,
th
e
p
o
wer
in
jecte
d
in
t
o
n
o
d
e
i is m
o
d
if
ied
in
t
h
e
p
o
we
r
f
lo
w
u
s
in
g
A
lg
o
r
ith
m
1
as
(
4
)
.
,
=
−
(
4
)
W
h
er
e
Q
i
,
n
ew
is
th
e
n
ew
r
ea
ctiv
e
p
o
wer
at
n
o
d
e
i,
Q
i
is
r
ea
ctiv
e
p
o
wer
at
n
o
d
e
i,
Q
S
V
C
is
r
ea
ctiv
e
p
o
wer
in
jecte
d
/ab
s
o
r
b
ed
b
y
th
e
SVC
.
Fig
u
r
e
2
.
SVC
m
o
d
el
as v
ar
ia
b
le
s
h
u
n
t
s
u
s
ce
p
tan
ce
2
.
4
.
P
o
wer
f
lo
w
N
e
t
w
o
r
k
a
n
al
y
s
is
i
s
p
e
r
f
o
r
m
e
d
u
s
i
n
g
t
h
e
b
a
c
k
wa
r
d
/
f
o
r
w
a
r
d
s
w
e
e
p
a
l
g
o
r
it
h
m
[
2
1
]
,
[
2
2
]
(
A
l
g
o
r
i
t
h
m
1
)
.
T
h
is
alg
o
r
ith
m
ca
lcu
lates n
o
d
e
v
o
ltag
es,
lin
e
cu
r
r
en
ts
,
an
d
a
ctiv
e
an
d
r
ea
ctiv
e
lo
s
s
es f
o
r
ea
ch
co
n
f
i
g
u
r
atio
n
.
Alg
o
r
ith
m
1
.
B
FS
alg
o
r
ith
m
Step
1
:
−
R
ea
d
n
etwo
r
k
d
ata
(
n
u
m
b
er
o
f
n
o
d
es,
n
u
m
b
er
o
f
b
r
an
c
h
es,
d
ata
lin
e,
d
ata
b
u
s
)
−
R
ea
d
v
o
ltag
e
an
d
b
ase
p
o
wer
.
−
R
ea
d
to
ler
an
ce
(
0
,
0
0
0
0
1
)
−
Slack
b
u
s
(
1
p
.
u
.
)
Step
2
:
−
Fo
r
m
cu
r
r
e
n
t a
n
d
v
o
ltag
e
m
atr
ices.
−
I
n
itialize
iter
atio
n
s
,
k
=
1
.
Step
3
: Per
f
o
r
m
b
ac
k
war
d
s
wee
p
.
−
C
alcu
late
cu
r
r
en
t in
jectio
n
s
at
d
if
f
er
en
t n
o
d
es.
−
C
alcu
late
b
r
an
ch
cu
r
r
en
ts
.
Step
4
: Per
f
o
r
m
a
f
o
r
war
d
s
wee
p
.
−
C
alcu
late
th
e
v
o
ltag
e
d
r
o
p
.
−
C
alcu
late
th
e
n
ew
n
o
d
e
v
o
ltag
es,
[
V
(
k
+1
)
]
.
Step
5
: E
v
alu
ate
th
e
ter
m
in
atio
n
cr
iter
io
n
.
−
C
alcu
late
th
e
m
ax
im
u
m
d
if
f
er
en
ce
b
etwe
en
th
e
n
o
d
al
v
o
ltag
e
v
alu
es o
f
two
c
o
n
s
ec
u
tiv
e
it
er
atio
n
s
.
−
C
h
ec
k
if
th
e
m
ax
im
u
m
d
if
f
e
r
e
n
ce
is
less
th
an
th
e
to
ler
an
ce
.
−
I
f
y
es,
g
o
to
Step
6
.
−
I
f
n
o
:
p
r
o
ce
e
d
to
th
e
n
ex
t iter
a
tio
n
,
k
=
k
+
1
,
an
d
r
etu
r
n
to
S
tep
3
.
Step
6
: Per
f
o
r
m
t
h
e
p
o
wer
b
al
an
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
1
24
3
-
1
25
2
1246
3.
O
P
T
I
M
I
Z
AT
I
O
N
P
RO
B
L
E
M
F
O
RM
U
L
A
T
I
O
N
3
.
1
.
Dec
is
io
n v
ec
t
o
r
T
h
e
d
ec
is
io
n
v
ec
to
r
x
i
n
teg
r
at
es
b
o
th
to
p
o
lo
g
ical
an
d
s
izin
g
p
ar
am
eter
s
.
Def
in
e
d
b
y
(
5
)
,
it
co
m
p
r
is
es
f
o
u
r
c
o
n
tr
o
l
v
ar
iab
les:
PV
lo
c
atio
n
(
)
,
an
d
ac
tiv
e
p
o
wer
(
)
,
alo
n
g
s
id
e
SVC
lo
ca
tio
n
(
)
,
an
d
r
ea
ctiv
e
p
o
wer
(
)
.
T
h
is
f
o
r
m
u
latio
n
en
ab
les
th
e
alg
o
r
ith
m
to
d
eter
m
in
e
th
e
o
p
tim
al
jo
in
t
co
n
f
ig
u
r
atio
n
,
as sh
o
wn
in
(
5
)
.
=
[
,
,
,
]
(
5
)
W
h
er
e
is
th
e
PV in
jectio
n
n
o
d
e
an
d
is
th
e
SVC
in
jectio
n
n
o
d
e.
3
.
2
.
O
bje
c
t
iv
e
f
un
ct
io
n
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
s
ar
e
te
ch
n
ical
in
n
atu
r
e,
m
in
im
izin
g
b
o
th
ac
tiv
e
lo
s
s
es a
n
d
v
o
ltag
e
d
ev
iatio
n
:
−
1
(
)
: to
tal
ac
tiv
e
lo
s
s
es (
k
W
)
−
2
(
)
: q
u
ad
r
atic
d
ev
iatio
n
o
f
v
o
ltag
es f
r
o
m
1
p
.
u
.
3
.
3
.
Co
ns
t
ra
ints
C
o
n
s
tr
ain
ts
in
clu
d
e:
T
h
e
co
n
s
tr
ain
t
aim
ed
at
m
ain
tain
in
g
v
o
ltag
e
with
in
th
e
n
o
r
m
ativ
e
r
an
g
e
is
f
o
r
m
u
lated
b
y
t
h
e
r
elatio
n
s
h
ip
th
at
d
ef
in
es
t
h
e
ac
ce
p
tab
le
li
m
its
f
o
r
g
r
id
v
o
ltag
e.
I
t
s
tip
u
l
ates
th
at
th
e
v
o
ltag
e
at
ea
ch
n
o
d
e
m
u
s
t r
em
ain
,
as sh
o
wn
in
(
6
)
.
0
.
95
.
≤
≤
1
.
05
.
(
6
)
T
h
e
s
izin
g
co
n
s
tr
ain
ts
ar
e
b
o
u
n
d
ed
b
y
th
e
h
o
s
tin
g
ca
p
ac
ity
lim
its
o
f
th
e
n
o
d
es
a
n
d
th
e
e
q
u
ip
m
en
t
r
atin
g
s
.
T
o
en
s
u
r
e
p
r
o
p
er
in
te
g
r
atio
n
o
f
P
V
an
d
SVC
,
th
e
in
jecte
d
p
o
we
r
m
u
s
t b
e
ex
p
r
ess
ed
as (
7
)
a
n
d
(
8
)
.
≤
≤
(
7
)
≤
≤
(
8
)
I
n
th
is
s
tu
d
y
,
th
e
n
u
m
e
r
ical
b
o
u
n
d
s
ar
e
s
et
to
:
=
100
,
=
1000
=
−
2000
,
=
2000
3
.
4
.
Sim
ula
t
i
o
n pa
ra
m
e
t
er
s
T
h
e
p
o
p
u
latio
n
s
ize
o
f
1
0
0
w
as
s
elec
ted
to
en
s
u
r
e
s
u
f
f
icien
t
d
iv
er
s
ity
i
n
th
e
d
ec
is
io
n
s
p
a
ce
with
o
u
t
h
ig
h
c
o
m
p
u
tatio
n
al
c
o
s
t.
A
m
ax
im
u
m
o
f
5
0
g
e
n
er
atio
n
s
wa
s
ch
o
s
en
as
t
h
e
s
to
p
p
in
g
cr
iter
io
n
,
as
p
r
elim
in
ar
y
test
s
s
h
o
wed
th
at
th
e
Par
eto
f
r
o
n
t
s
tab
ilizes
an
d
co
n
v
er
g
es
ef
f
ec
tiv
ely
with
in
th
is
r
an
g
e.
T
h
e
cr
o
s
s
o
v
er
p
r
o
b
a
b
ilit
y
is
s
et
to
0
.
9
to
en
c
o
u
r
ag
e
th
e
co
m
b
in
atio
n
o
f
g
o
o
d
g
e
n
es,
wh
ile
th
e
m
u
tatio
n
p
r
o
b
a
b
ilit
y
is
s
et
to
1
/n
(
wh
er
e
n
is
th
e
n
u
m
b
er
o
f
d
ec
is
io
n
v
a
r
iab
les)
to
p
r
e
v
en
t
p
r
em
at
u
r
e
co
n
v
er
g
e
n
ce
.
T
ab
le
1
s
h
o
ws
th
e
co
n
f
ig
u
r
atio
n
o
f
th
e
NSGA
-
I
I
alg
o
r
ith
m
f
o
r
th
e
s
im
u
latio
n
o
f
A
lg
o
r
ith
m
2
,
wh
er
e
N:
p
o
p
u
latio
n
s
ize,
P
:
p
o
p
u
latio
n
,
G:
n
u
m
b
er
o
f
g
e
n
er
atio
n
s
,
an
d
x
: d
ec
is
io
n
v
ec
t
o
r
.
Alg
o
r
ith
m
2.
NSGA
I
I
o
p
tim
i
za
tio
n
alg
o
r
ith
m
f
o
r
(
PV+SVC
)
lo
ca
tio
n
Step
1
: I
n
itialize
a
p
o
p
u
latio
n
P₀ o
f
N
r
an
d
o
m
in
d
iv
id
u
als x
ᵢ
.
Step
2
: E
v
alu
ate
th
e
o
b
jectiv
e
f
u
n
ctio
n
s
f
₁
(
x
ᵢ
)
,
f
₂(
x
ᵢ
)
f
o
r
ea
c
h
in
d
iv
id
u
al.
−
f
₁:
to
tal
ac
tiv
e
lo
s
s
es (
v
ia
B
SF
)
an
d
f
₂:
q
u
a
d
r
atic
d
ev
iatio
n
o
f
v
o
ltag
es.
Step
3
: A
p
p
ly
n
o
n
-
d
o
m
in
ate
d
s
o
r
tin
g
to
r
a
n
k
in
d
iv
id
u
als in
f
r
o
n
ts
Step
4
: Calcu
late
th
e
cr
o
wd
d
i
s
tan
ce
f
o
r
ea
ch
i
n
d
iv
id
u
al
in
it
s
f
r
o
n
t
Step
5
: Rep
ea
t f
o
r
g
=
1
to
G:
−
Select
p
ar
en
ts
b
y
to
u
r
n
am
en
t
b
ased
o
n
r
a
n
k
+
c
r
o
wd
d
is
tan
ce
−
Ap
p
ly
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
to
g
en
er
ate
a
p
o
p
u
latio
n
Qg
−
E
v
alu
ate
f
₁
an
d
f
₂
f
o
r
ea
c
h
in
d
i
v
id
u
al
in
Qg
−
C
o
m
b
in
e
Pg
an
d
Qg
→
R
g
−
Ap
p
ly
n
o
n
-
d
o
m
in
ated
s
o
r
tin
g
to
R
g
an
d
s
elec
t th
e
N
b
est in
d
iv
id
u
als to
f
o
r
m
Pg
+1
Step
6
: A
t th
e
en
d
o
f
G
g
e
n
er
a
tio
n
s
:
−
E
x
tr
ac
t th
e
f
in
al
Par
eto
f
r
o
n
t F₁
T
h
is
is
a
s
ec
o
n
d
p
u
ll f
r
o
m
s
tep
6
,
s
o
th
e
p
u
ll n
ee
d
s
to
b
e
ad
d
ed
at
th
e
b
e
g
in
n
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Mu
lti
-
o
b
jective
p
la
n
n
in
g
o
f
d
is
tr
ib
u
ted
r
eso
u
r
ce
s
(
P
V
and
S
V
C
)
w
ith
…
(
Ha
s
s
a
n
e
Ou
s
s
ey
n
i I
b
r
a
h
im
)
1247
T
ab
le
1
.
Simu
latio
n
c
o
n
f
i
g
u
r
at
io
n
V
a
r
i
a
b
l
e
s
V
a
l
u
e
s
P
o
p
u
l
a
t
i
o
n
s
i
z
e
1
0
0
N
u
mb
e
r
o
f
g
e
n
e
r
a
t
i
o
n
s
50
O
p
e
r
a
t
o
r
s
U
n
i
f
o
r
m
c
r
o
sso
v
e
r
,
G
a
u
ssi
a
n
m
u
t
a
t
i
o
n
S
e
l
e
c
t
i
o
n
N
o
n
-
d
o
m
i
n
a
t
e
d
s
o
r
t
i
n
g
+
c
r
o
w
d
d
i
st
a
n
c
e
4.
ANALY
SI
S O
F
R
E
SU
L
T
S
AND
DIS
CUSS
I
O
N
4
.
1
.
Sepa
ra
t
e
inje
ct
io
n o
f
P
V
a
nd
SVC
B
ef
o
r
e
o
p
tim
izatio
n
,
th
e
b
ac
k
war
d
/f
o
r
war
d
s
wee
p
alg
o
r
ith
m
was
v
alid
ated
o
n
th
e
b
ase
ca
s
e
I
E
E
E
33
-
b
u
s
s
y
s
tem
.
T
h
e
ca
lcu
lated
ac
tiv
e
lo
s
s
es
wer
e
2
0
2
.
6
8
k
W
,
wh
ich
m
atch
es
th
e
s
tan
d
ar
d
b
e
n
ch
m
ar
k
v
alu
es
f
o
u
n
d
in
th
e
liter
atu
r
e
[
2
3
]
,
w
ith
a
d
if
f
er
en
ce
o
f
0
.
4
8
%
,
co
n
f
ir
m
in
g
th
e
ac
c
u
r
ac
y
o
f
th
e
p
o
wer
f
lo
w
m
eth
o
d
.
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
r
e
s
u
lts
o
b
tain
ed
wh
en
th
e
PV
s
y
s
te
m
an
d
th
e
SVC
ar
e
in
teg
r
ated
in
d
ep
en
d
en
tly
in
to
th
e
g
r
i
d
.
T
h
e
o
b
jectiv
e
is
t
o
e
v
alu
ate
th
e
in
d
iv
i
d
u
al
im
p
ac
t
o
f
ea
c
h
tech
n
o
lo
g
y
o
n
ac
tiv
e
lo
s
s
es
an
d
v
o
ltag
e
p
r
o
f
ile.
4
.
1
.
1
.
P
V
s
ce
na
rio
o
nly
I
n
th
is
s
ce
n
ar
io
,
a
PV
s
o
u
r
c
e
is
in
jecte
d
in
to
an
o
p
tim
iz
ed
b
u
s
with
v
ar
ia
b
le
ac
tiv
e
p
o
wer
.
No
r
ea
ctiv
e
co
m
p
en
s
atio
n
d
ev
ice
s
ar
e
u
s
ed
.
T
h
e
NSGA
-
I
I
alg
o
r
ith
m
was
u
s
ed
to
d
eter
m
in
e
th
e
lo
ca
tio
n
(
n
o
d
e
1
4
)
a
n
d
o
p
tim
al
PV
p
o
wer
(
9
7
3
.
7
k
W
)
b
y
m
in
im
izin
g
ac
tiv
e
lo
s
s
es
an
d
im
p
r
o
v
i
n
g
v
o
ltag
e.
T
h
e
r
esu
lts
s
h
o
w
a
r
ed
u
ctio
n
in
lo
s
s
es
f
r
o
m
2
0
2
.
6
8
k
W
to
1
2
8
.
7
1
k
W
,
r
ep
r
e
s
en
tin
g
an
im
p
r
o
v
em
en
t
o
f
3
6
.
5
0
%
co
m
p
ar
e
d
to
th
e
b
aselin
e
n
etwo
r
k
.
T
h
e
v
o
ltag
e
p
r
o
f
ile
in
Fig
u
r
e
3
(
g
r
ee
n
cu
r
v
e)
is
im
p
r
o
v
ed
ar
o
u
n
d
th
e
in
jectio
n
p
o
in
t a
n
d
in
cr
ea
s
es
th
e
m
in
im
u
m
v
o
ltag
e
f
r
o
m
0
.
9
1
3
1
p
.
u
.
to
0
.
9
3
1
9
p
.
u
.
,
b
u
t
r
e
m
ain
s
in
s
u
f
f
icien
t
o
n
d
is
tan
t
b
r
a
n
ch
es,
wh
er
e
v
o
ltag
es
b
elo
w
0
.
9
5
p
.
u
.
p
er
s
is
t.
T
h
e
q
u
ad
r
atic
d
ev
i
atio
n
is
r
ed
u
ce
d
to
0
.
0
5
1
p
.
u
.
,
co
m
p
ar
ed
to
0
.
1
1
7
p
.
u
.
in
th
e
b
ase
ca
s
e.
T
h
ese
r
esu
lts
co
n
f
ir
m
t
h
at
ac
tiv
e
lo
c
al
in
jectio
n
v
ia
th
e
PV
r
ed
u
ce
s
cu
r
r
en
t
f
lo
ws
an
d
lo
s
s
es,
b
u
t is n
o
t su
f
f
icien
t to
s
tab
ilize
th
e
en
tire
g
r
id
with
o
u
t r
ea
ctiv
e
co
m
p
e
n
s
atio
n
.
4
.
1
.
2
.
SVC
s
ce
na
rio
o
nly
T
h
is
s
ce
n
ar
io
in
teg
r
ates
an
SVC
in
to
an
o
p
tim
ized
b
u
s
,
with
v
ar
iab
le
r
ea
ctiv
e
p
o
wer
s
h
o
wn
in
Fig
u
r
e
3
(
r
ed
cu
r
v
e)
.
No
PV
is
u
s
ed
.
Op
tim
izatio
n
u
s
in
g
NSGA
-
I
I
d
eter
m
i
n
ed
t
h
e
b
est
lo
c
atio
n
(
n
o
d
e
3
0
)
an
d
co
m
p
en
s
atio
n
le
v
el
o
f
1
3
2
3
.
9
k
VAR
o
f
r
ea
ctiv
e
p
o
wer
.
Activ
e
lo
s
s
es
ar
e
r
ed
u
ce
d
to
1
4
3
.
6
k
W
,
an
im
p
r
o
v
em
e
n
t o
f
2
9
.
1
5
% c
o
m
p
ar
ed
to
th
e
b
ase
ca
s
e.
T
h
e
v
o
ltag
e
p
r
o
f
ile
is
im
p
r
o
v
e
d
ac
r
o
s
s
th
e
en
tire
n
etwo
r
k
with
a
m
in
im
u
m
v
o
ltag
e
o
f
0
.
9
2
5
6
p
.
u
.
T
h
ese
r
esu
lts
s
h
o
w
th
at
PV
im
p
r
o
v
es
th
e
v
o
ltag
e
p
r
o
f
ile
an
d
r
ed
u
ce
s
lo
s
s
es
m
u
ch
m
o
r
e
ef
f
ec
tiv
ely
.
T
h
e
q
u
a
d
r
atic
d
ev
iatio
n
is
r
ed
u
ce
d
to
0
.
0
7
0
p
.
u
.
,
c
o
m
p
ar
e
d
to
0
.
1
1
7
p
.
u
.
in
th
e
b
ase
ca
s
e.
T
ab
le
2
s
h
o
ws
th
e
r
esu
lts
f
r
o
m
th
e
PV
an
d
SVC
s
ce
n
ar
io
s
im
u
latio
n
al
o
n
e.
T
h
e
SVC
ac
ts
as
a
d
y
n
am
ic
r
e
g
u
lato
r
,
ca
p
ab
le
o
f
ab
s
o
r
b
in
g
o
r
g
e
n
er
atin
g
r
ea
ctiv
e
p
o
wer
ac
co
r
d
in
g
to
th
e
n
ee
d
s
o
f
th
e
g
r
id
,
th
er
eb
y
im
p
r
o
v
in
g
o
v
er
all
s
tab
ilit
y
.
Fig
u
r
e
3
.
V
o
ltag
e
p
r
o
f
ile
o
f
tw
o
s
ce
n
ar
io
s
T
ab
le
2.
C
o
m
p
a
r
is
o
n
o
f
s
ep
ar
ate
in
jectio
n
S
c
e
n
a
r
i
o
A
c
t
i
v
e
l
o
sses
(
k
W
)
A
v
e
r
a
g
e
v
o
l
t
a
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d
e
v
i
a
t
i
o
n
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p
.
u
.
)
Lo
ss
i
m
p
r
o
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e
m
e
n
t
(
%)
B
a
se
n
e
t
w
o
r
k
2
0
2
.
6
8
0
.
1
1
7
—
P
V
o
n
l
y
1
2
9
.
3
2
0
.
0
5
1
3
6
.
5
0
S
V
C
o
n
l
y
1
4
3
.
6
0
0
.
0
7
0
2
9
.
1
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
1
24
3
-
1
25
2
1248
T
h
e
co
m
p
ar
ativ
e
an
aly
s
is
s
h
o
ws
th
at
PV
alo
n
e
o
f
f
er
s
b
etter
p
er
f
o
r
m
an
ce
th
a
n
SVC
alo
n
e,
b
o
th
in
ter
m
s
o
f
lo
s
s
r
ed
u
ctio
n
an
d
v
o
ltag
e
r
eg
u
latio
n
.
Ho
wev
er
,
ea
ch
tech
n
o
lo
g
y
ac
ts
o
n
d
i
f
f
er
en
t
lev
er
s
:
PV
r
ed
u
ce
s
ac
tiv
e
f
lo
ws,
wh
ile
SVC
r
eg
u
lates
r
ea
ctiv
e
f
lo
ws.
T
h
ese
r
esu
lts
s
u
g
g
est
th
at
jo
i
n
t
in
jectio
n
co
u
ld
co
m
b
in
e
th
e
ad
v
a
n
tag
es o
f
b
o
t
h
ap
p
r
o
ac
h
es.
4
.
2
.
J
o
int
inje
ct
io
n (
P
V
+
S
VC)
T
h
e
Par
eto
f
r
o
n
t
o
b
tain
e
d
at
t
h
e
en
d
o
f
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
u
s
in
g
NSGA
-
I
I
illu
s
tr
ates
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
two
ess
en
tial
tech
n
ical
cr
iter
ia:
m
in
i
m
izin
g
ac
tiv
e
lo
s
s
es
an
d
r
e
g
u
latin
g
th
e
v
o
ltag
e
p
r
o
f
ile.
E
ac
h
p
o
in
t
o
n
th
e
f
r
o
n
t
r
e
p
r
esen
ts
a
n
o
n
-
d
o
m
in
ated
s
o
lu
tio
n
,
i.e
.
,
n
o
o
th
er
s
o
lu
tio
n
ca
n
s
im
u
ltan
eo
u
s
ly
im
p
r
o
v
e
b
o
t
h
o
b
jectiv
es
with
o
u
t
d
e
g
r
ad
i
n
g
o
n
e
o
f
th
e
m
.
T
h
ese
r
esu
lt
s
ar
e
p
r
esen
ted
in
Fig
u
r
e
4
.
T
h
e
d
o
w
n
war
d
s
lo
p
e
o
f
th
e
f
r
o
n
t
co
n
f
ir
m
s
th
e
an
tag
o
n
is
tic
n
atu
r
e
o
f
th
e
two
o
b
jectiv
es:
s
o
lu
tio
n
s
th
at
s
ig
n
if
ican
tly
m
in
im
ize
lo
s
s
es
ten
d
to
ex
h
ib
it
a
s
lig
h
t
v
o
ltag
e
im
b
alan
ce
,
wh
ile
s
o
lu
t
io
n
s
with
p
er
f
ec
tly
r
eg
u
lated
v
o
ltag
e
s
h
o
w
s
lig
h
tl
y
h
ig
h
e
r
lo
s
s
es.
T
h
is
d
iv
er
s
ity
o
f
s
o
lu
tio
n
s
o
f
f
er
s
n
etwo
r
k
o
p
e
r
ato
r
s
s
tr
ateg
ic
f
lex
ib
ilit
y
,
allo
win
g
th
em
to
ch
o
o
s
e
a
co
n
f
ig
u
r
atio
n
tailo
r
ed
to
lo
ca
l
p
r
io
r
ities
—
wh
eth
er
m
ax
im
izin
g
en
er
g
y
ef
f
icien
cy
o
r
en
s
u
r
i
n
g
v
o
ltag
e
q
u
ality
.
T
h
e
o
p
tim
ized
p
o
i
n
t,
lo
ca
ted
at
7
5
.
9
4
k
W
o
f
lo
s
s
es
an
d
0
.
0
1
6
9
p
.
u
.
o
f
v
o
ltag
e
d
ev
i
atio
n
,
r
ep
r
esen
ts
a
p
ar
ticu
lar
ly
in
te
r
esti
n
g
tech
n
i
ca
l
b
alan
ce
f
o
r
r
ad
ial
n
etwo
r
k
s
with
lo
w
r
eg
u
latio
n
m
a
r
g
i
n
s
,
as
th
e
v
alu
es
i
n
T
ab
le
3
a
n
d
Fig
u
r
es
5
an
d
6
s
h
o
w.
T
h
e
r
esu
lts
wer
e
o
b
tain
ed
wh
en
t
h
e
PV
s
y
s
tem
an
d
th
e
SVC
wer
e
s
im
u
ltan
eo
u
s
ly
in
teg
r
ated
in
t
o
th
e
g
r
id
.
Un
lik
e
th
e
p
r
e
v
io
u
s
s
ce
n
ar
io
s
,
th
is
c
o
n
f
ig
u
r
ati
o
n
is
b
ased
o
n
jo
i
n
t
o
p
tim
izatio
n
o
f
th
e
p
lace
m
e
n
t
an
d
s
izin
g
o
f
b
o
t
h
p
iece
s
o
f
e
q
u
ip
m
en
t u
s
in
g
th
e
NSGA
-
I
I
al
g
o
r
ith
m
.
T
h
e
o
p
tim
al
p
lace
m
e
n
t
o
f
th
e
PV
u
n
it
at
b
u
s
1
4
is
ju
s
tifie
d
b
y
its
to
p
o
lo
g
ical
lo
ca
tio
n
;
it
i
s
s
itu
ated
at
th
e
en
d
o
f
a
h
e
av
ily
lo
ad
e
d
f
ee
d
er
,
wh
er
e
v
o
ltag
e
d
r
o
p
s
ar
e
m
o
s
t
s
ig
n
if
ican
t.
I
n
jectin
g
ac
tiv
e
p
o
wer
h
er
e
lo
ca
lly
s
u
p
p
lies
th
e
lo
ad
a
n
d
d
r
asti
ca
lly
r
ed
u
ce
s
cu
r
r
e
n
t
f
lo
w
f
r
o
m
th
e
s
u
b
s
tatio
n
.
C
o
n
v
er
s
ely
,
th
e
SVC
is
p
lace
d
at
b
u
s
3
0
,
lo
ca
ted
in
a
d
if
f
er
en
t
later
al
b
r
an
c
h
.
T
h
is
lo
ca
tio
n
allo
ws
th
e
SVC
t
o
p
r
o
v
i
d
e
r
ea
ctiv
e
s
u
p
p
o
r
t
th
at
u
p
lifts
th
e
v
o
ltag
e
p
r
o
f
ile
i
n
th
e
s
u
r
r
o
u
n
d
in
g
ar
ea
,
co
m
p
lem
e
n
tin
g
t
h
e
PV'
s
a
ctio
n
.
T
h
e
PV
u
n
it
co
n
tr
ib
u
tes
m
o
r
e
to
lo
s
s
r
ed
u
c
tio
n
b
ec
au
s
e
t
h
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
'
s
R
/X
r
atio
is
h
ig
h
,
m
a
k
in
g
ac
tiv
e
cu
r
r
e
n
t
r
ed
u
ctio
n
m
o
r
e
e
f
f
ec
tiv
e
f
o
r
m
in
im
izin
g
lo
s
s
es c
o
m
p
ar
ed
t
o
r
ea
ctiv
e
co
m
p
en
s
atio
n
.
J
o
in
t
o
p
tim
izatio
n
ac
h
iev
es
th
e
b
est
p
er
f
o
r
m
an
ce
am
o
n
g
all
s
im
u
lated
s
ce
n
ar
io
s
.
Activ
e
lo
s
s
es
ar
e
7
5
.
9
4
k
W
,
o
r
6
2
.
5
3
%
co
m
p
a
r
ed
to
th
e
b
ase
n
etwo
r
k
,
v
er
s
u
s
3
6
.
5
0
%
an
d
2
9
.
1
5
%
f
o
r
PV
alo
n
e
an
d
SV
C
alo
n
e,
r
esp
ec
tiv
ely
.
T
h
e
v
o
lta
g
e
p
r
o
f
ile
is
s
tab
ilized
ac
r
o
s
s
t
h
e
en
tire
g
r
id
,
with
a
v
o
ltag
e
d
ev
iatio
n
r
ed
u
ce
d
to
0
.
0
1
6
9
p
.
u
.
,
an
d
n
o
o
u
t
-
of
-
lim
it
v
o
ltag
es
o
b
s
er
v
ed
.
T
h
ese
r
esu
lts
d
em
o
n
s
tr
ate
th
e
PV
s
y
n
er
g
y
b
etwe
en
ac
tiv
e
p
o
wer
g
e
n
er
atio
n
an
d
SVC
-
b
ased
r
ea
ctiv
e
c
o
m
p
en
s
atio
n
,
wh
ich
en
a
b
les
m
o
r
e
b
alan
c
ed
p
o
wer
f
lo
w
a
n
d
ef
f
ec
tiv
e
g
r
id
r
eg
u
latio
n
.
Fig
u
r
e
4
.
Par
eto
f
r
o
n
t
T
ab
le
3
.
S
im
u
latio
n
r
esu
lt
P
a
r
a
me
t
e
r
s
R
e
s
u
l
t
s
o
b
t
a
i
n
e
d
O
p
t
i
mal
n
o
d
e
1
4
(
P
V
)
3
0
(
S
V
C
)
I
n
j
e
c
t
e
d
p
o
w
e
r
9
7
3
.
7
k
W
(
P
V
)
1
3
2
3
.
9
k
V
A
R
(
S
V
C
)
A
c
t
i
v
e
l
o
sses
(
k
W
)
7
5
.
9
4
M
i
n
i
m
u
m
v
o
l
t
a
g
e
(
p
.
u
.
)
0
.
9
6
3
1
A
v
e
r
a
g
e
v
o
l
t
a
g
e
d
e
v
i
a
t
i
o
n
(
p
.
u
.
)
0
.
0
1
6
9
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Mu
lti
-
o
b
jective
p
la
n
n
in
g
o
f
d
is
tr
ib
u
ted
r
eso
u
r
ce
s
(
P
V
and
S
V
C
)
w
ith
…
(
Ha
s
s
a
n
e
Ou
s
s
ey
n
i I
b
r
a
h
im
)
1249
Fig
u
r
e
5
.
Op
tim
ized
ac
tiv
e
p
o
wer
(
PV+SVC
)
Fig
u
r
e
6
.
Vo
ltag
e
p
r
o
f
ile
b
ef
o
r
e
an
d
a
f
ter
o
p
tim
izatio
n
4
.
3
.
Co
m
pa
ra
t
iv
e
a
na
ly
s
is
a
nd
benc
hm
a
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o
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n
f
ir
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th
e
tech
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e
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it
o
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r
o
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izatio
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ased
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n
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ies
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o
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ith
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lin
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ateg
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o
n
th
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E
test
n
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r
k
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h
e
d
ata
in
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ab
le
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r
ly
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s
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ate
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at
t
h
e
p
r
o
p
o
s
ed
jo
in
t
o
p
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izatio
n
o
f
PV
an
d
SVC
u
s
in
g
NSGA
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I
I
y
ield
s
a
s
u
b
s
tan
tially
h
ig
h
er
lo
s
s
r
ed
u
ctio
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6
2
.
5
3
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co
m
p
a
r
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to
s
in
g
le
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u
n
it
p
lace
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s
tr
a
teg
ies
u
s
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g
co
n
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en
tio
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al
al
g
o
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ith
m
s
.
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h
is
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en
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m
a
r
k
c
o
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ir
m
s
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o
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u
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t
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d
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jectio
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t f
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ab
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is
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p
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i
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(
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41
[
2
5
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S
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.
42
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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2
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o
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a
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ar
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m
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en
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ato
r
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SVC
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is
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ib
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n
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k
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n
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NSGA
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o
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ith
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n
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o
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te
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ate
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ad
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s
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in
im
izin
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tiv
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s
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n
d
i
m
p
r
o
v
i
n
g
th
e
v
o
ltag
e
p
r
o
f
ile.
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latio
n
s
p
er
f
o
r
m
ed
o
n
th
e
s
tan
d
ar
d
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E
E
E
3
3
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b
u
s
n
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r
k
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av
e
d
em
o
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s
tr
ated
th
at
th
e
jo
in
t
an
d
o
p
tim
ized
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te
g
r
atio
n
o
f
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(
0
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9
7
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at
n
o
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e
1
4
)
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d
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1
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3
2
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at
n
o
d
e
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ig
n
if
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im
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r
o
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tr
ical
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o
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m
a
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ce
o
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h
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n
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k
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o
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ated
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n
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ig
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r
atio
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s
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th
e
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er
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a
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ce
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th
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r
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n
t
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g
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m
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en
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o
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r
ch
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f
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ce
th
e
s
tu
d
y
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m
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p
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ate
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lo
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d
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to
t
h
e
o
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to
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tem
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s
;
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d
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u
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d
o
p
tim
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:
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ten
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th
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f
r
am
ewo
r
k
to
a
m
u
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ti
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o
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y
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r
ly
o
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tim
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n
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o
r
izo
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n
s
id
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s
o
n
al
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ar
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n
s
in
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en
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m
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Au
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C
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DATA AV
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Der
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d
ata
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av
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th
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d
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g
au
th
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,
[
HOI
]
,
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r
eq
u
est.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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l Po
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E
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g
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N:
2252
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8
7
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2
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(
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1251
RE
F
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R
E
NC
E
S
[
1
]
W
.
Ji
e
a
n
d
K
.
R
a
b
n
a
w
a
z
,
“
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e
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Fr
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e
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n
E
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3
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c
u
rre
n
tl
y
a
l
ec
tu
r
er
-
re
se
a
rc
h
e
r
in
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
a
n
d
I
n
d
u
strial
C
o
m
p
u
ti
n
g
a
t
th
e
P
o
ly
tec
h
n
ic
S
c
h
o
o
l
o
f
Un
iv
e
rsity
Da
n
Dic
k
o
Da
n
k
o
u
l
o
d
o
o
f
M
a
ra
d
i
,
Nig
e
r.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
h
a
ss
a
n
e
2
0
2
5
@y
a
h
o
o
.
fr
.
Abd
o
u
l
M
a
li
k
Ma
m
a
n
Is
sa
k
a
o
b
tain
e
d
h
is
Un
i
v
e
rsity
Tec
h
n
o
l
o
g
y
Di
p
lo
m
a
(DU
T)
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
in
2
0
2
2
a
t
th
e
Un
i
v
e
rsity
In
st
it
u
te
o
f
Tec
h
n
o
lo
g
y
(IUT).
Wi
th
th
is
tec
h
n
ica
l
f
o
u
n
d
a
ti
o
n
,
he
c
o
n
ti
n
u
e
d
h
is
st
u
d
ies
to
sp
e
c
ialize
,
e
a
rn
in
g
a
b
a
c
h
e
l
o
r'
s
d
e
g
re
e
in
a
u
to
m
a
ti
o
n
a
n
d
in
d
u
stri
a
l
c
o
m
p
u
ti
n
g
(El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
D
e
p
a
rtme
n
t)
i
n
2
0
2
3
.
Th
e
se
d
e
g
re
e
s
we
re
o
b
tain
e
d
a
t
t
h
e
Un
iv
e
rsity
Da
n
Dic
k
o
Da
n
k
o
u
lo
d
o
o
f
M
a
ra
d
i
(UD
DM).
He
is
c
u
rre
n
tl
y
a
stu
d
e
n
t
i
n
t
h
e
M
a
ste
r
2
S
u
sta
in
a
b
le
En
e
rg
y
S
y
ste
m
s
P
ro
g
ra
m
a
t
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
(F
S
T)
o
f
UDDM
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
a
m
a
n
issa
k
a
9
0
@g
m
a
il
.
c
o
m
.
Mo
u
ss
a
G
o
n
d
a
o
b
tain
e
d
a
d
e
g
re
e
in
e
lec
tro
n
ic
e
n
g
in
e
e
rin
g
sp
e
c
ializin
g
in
c
o
m
m
u
n
ica
ti
o
n
s
fro
m
t
h
e
Ho
u
a
ri
Bo
u
m
e
d
ie
n
e
Un
i
v
e
rsity
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
(UST
HB)
in
Al
g
iers
,
Al
g
e
ria
,
in
2
0
0
8
.
In
2
0
1
6
,
h
e
o
b
tain
e
d
a
m
a
ste
r'
s
d
e
g
re
e
in
a
p
p
li
e
d
sc
ien
c
e
s
,
sp
e
c
ializin
g
in
tele
c
o
m
m
u
n
ica
ti
o
n
s
n
e
two
r
k
s,
fro
m
t
h
e
Éco
le
d
e
T
e
c
h
n
o
lo
g
ie
S
u
p
é
rieu
re
(ET
S
)
i
n
M
o
n
trea
l,
Ca
n
a
d
a
.
He
is
c
u
rre
n
t
ly
a
P
h
.
D
.
stu
d
e
n
t
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
a
t
th
e
Do
c
to
ra
l
S
c
h
o
o
l
o
f
En
g
i
n
e
e
rin
g
S
c
ien
c
e
s
(ED
-
S
DI)
a
t
th
e
Un
iv
e
rsity
o
f
Ab
o
m
e
y
-
Ca
lav
i
(UA
C)
in
Be
n
in
.
Th
e
fo
c
u
s
o
f
h
is
t
h
e
sis
re
se
a
rc
h
is
th
e
sta
ti
c
a
n
d
d
y
n
a
m
i
c
sta
b
il
it
y
o
f
e
lec
tri
c
it
y
tran
sm
issio
n
n
e
two
r
k
s
i
n
th
e
c
o
n
tex
t
o
f
re
n
e
wa
b
le
e
n
e
rg
y
i
n
teg
ra
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
o
u
ss
a
.
g
o
n
d
a
@u
d
d
m
.
e
d
u
.
n
e
o
r
a
h
m
a
d
.
n
a
i2
0
1
1
@g
m
a
il
.
c
o
m
.
Ar
o
u
n
a
O
lo
u
l
a
d
e
o
b
tain
e
d
a
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fro
m
th
e
Ab
o
m
e
y
-
Ca
lav
i
P
o
l
y
tec
h
n
ic
S
c
h
o
o
l
in
2
0
1
1
.
I
n
2
0
1
3
,
h
e
o
b
tain
e
d
a
p
o
stg
ra
d
u
a
te
d
e
g
re
e
in
En
g
i
n
e
e
rin
g
S
c
ien
c
e
s.
He
h
a
s
h
e
l
d
a
d
o
c
to
ra
te
fr
o
m
t
h
e
Un
i
v
e
rsity
o
f
A
b
o
m
e
y
-
Ca
lav
i
sin
c
e
2
0
1
9
.
He
sp
e
c
ialize
s
in
e
lec
tri
c
a
l
n
e
two
rk
o
p
ti
m
iza
ti
o
n
a
n
d
th
e
re
li
a
b
il
it
y
o
f
e
n
e
rg
y
tran
sp
o
r
t
a
n
d
d
istri
b
u
ti
o
n
s
y
ste
m
s.
As
a
n
e
x
p
e
rt
i
n
e
lec
tri
c
a
l
n
e
two
r
k
s
a
n
d
m
a
c
h
in
e
s,
h
e
is
c
u
rre
n
tl
y
c
o
n
su
lt
i
n
g
o
n
o
n
e
o
f
S
BEE
'
s
p
r
o
jec
ts
to
d
e
n
sify
a
n
d
e
x
ten
d
e
lec
tri
c
a
l
n
e
two
rk
s.
He
is
th
e
a
u
th
o
r
o
f
se
v
e
ra
l
a
rti
c
les
.
He
wa
s
d
irec
to
r
o
f
e
lec
tri
c
it
y
d
ist
rib
u
ti
o
n
a
t
t
h
e
Be
n
in
e
se
El
e
c
tri
c
it
y
Co
m
p
a
n
y
.
He
h
o
l
d
s
c
e
rti
fica
ti
o
n
s
in
se
v
e
ra
l
field
s,
i
n
c
lu
d
in
g
c
o
rp
o
ra
te
fin
a
n
c
e
a
n
d
p
r
o
jec
t
m
a
n
a
g
e
m
e
n
t.
His
m
o
st
re
c
e
n
t
c
e
rti
fica
ti
o
n
is
th
e
P
ro
jec
t
M
a
n
a
g
e
m
e
n
t
P
ro
fe
ss
io
n
a
l
(P
M
P
)
c
e
rti
fica
ti
o
n
fro
m
th
e
P
ro
jec
t
M
a
n
a
g
e
m
e
n
t
In
stit
u
te
(P
M
I).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
o
l
o
u
a
r
o
u
@
y
a
h
o
o
.
fr
.
Fra
n
ç
o
is
-
Xa
v
ier
Fi
fa
tin
is
a
fu
ll
p
ro
fe
ss
o
r
a
t
CAME
S
U
n
iv
e
rsit
y
.
He
is
a
ffil
iate
d
wit
h
t
h
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
a
t
t
h
e
A
b
o
m
e
y
-
Ca
lav
i
P
o
l
y
tec
h
n
ic
S
c
h
o
o
l
(E
P
AC)
o
f
th
e
U
n
iv
e
rsi
ty
o
f
Ab
o
m
e
y
-
Ca
lav
i
(UA
C),
Re
p
u
b
li
c
o
f
Be
n
in
.
He
is
c
u
rre
n
tl
y
t
h
e
d
i
re
c
to
r
o
f
t
h
e
La
b
o
ra
to
ry
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Tele
c
o
m
m
u
n
ica
ti
o
n
s
a
n
d
Ap
p
li
e
d
C
o
m
p
u
ter
S
c
ien
c
e
(LE
TIA),
a
s
we
ll
a
s
th
e
d
e
p
u
ty
c
o
o
rd
in
a
t
o
r
o
f
t
h
e
Co
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
-
En
e
rg
y
I
n
fra
stru
c
tu
r
e
En
v
ir
o
n
m
e
n
t
(Co
E
-
EIE
).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
fifatin
f@g
m
a
il
.
c
o
m
.
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