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ltip
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FF
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a
s
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[
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]
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4
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5
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izin
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d
ac
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p
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[
6
-
8
]
.
So
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[
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1
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.
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1
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i
n
cl
u
d
es
P
SO
[
1
6
-
1
7
]
,
g
en
et
ic
al
g
o
r
ith
m
(
G
A
)
[
1
8
-
2
0
]
,
d
if
f
er
en
tia
l
e
v
o
lu
t
io
n
(
DE
)
[
2
1
]
,
f
r
ac
tal
s
ea
r
c
h
al
g
o
r
ith
m
(
FS
A
)
[
2
2
]
,
teac
h
i
n
g
lear
n
in
g
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
T
L
O
A
)
[
2
3
]
,
b
ee
co
lo
n
y
al
g
o
r
ith
m
(
B
C
A
)
[
2
4
]
,
cr
o
w
o
p
ti
m
izat
io
n
alg
o
r
ith
m
(
C
O
A
)
[
2
5
]
an
d
h
y
b
r
id
alg
o
r
ith
m
(
H
A
)
[
2
6
]
.
Dete
r
m
i
n
is
tic
m
eth
o
d
s
h
ad
a
n
a
d
v
an
ta
g
e
o
f
u
s
i
n
g
a
lo
w
iter
atio
n
n
u
m
b
er
a
n
d
r
ea
ch
in
g
t
h
e
s
a
m
e
r
es
u
lt
s
f
o
r
d
if
f
er
en
t
i
m
p
le
m
en
tatio
n
s
.
Ho
wev
er
,
th
e
y
s
u
f
f
er
i
n
g
f
r
o
m
ta
k
i
n
g
p
ar
tial d
er
iv
ati
v
e
w
it
h
r
esp
ec
t to
co
n
tr
o
l v
ar
iab
l
es b
ef
o
r
e
ex
ec
u
tin
g
a
n
iter
ati
v
e
alg
o
r
ith
m
.
He
n
ce
,
th
e
y
co
u
ld
n
o
t
b
e
ap
p
lied
f
o
r
s
o
lv
in
g
E
L
D
p
r
o
b
lem
s
co
n
tai
n
i
n
g
n
o
n
-
d
if
f
er
en
tiab
l
e
f
u
n
ctio
n
s
.
On
th
e
co
n
tr
ar
y
,
m
eta
h
eu
r
i
s
tic
a
lg
o
r
it
h
m
s
d
id
n
o
t
s
u
f
f
er
f
r
o
m
t
h
e
s
h
o
r
tco
m
i
n
g
o
f
t
h
e
d
eter
m
in
i
s
tic
alg
o
r
it
h
m
s
a
n
d
t
h
e
y
co
u
ld
d
ea
l
w
i
th
n
o
n
d
i
f
f
er
en
t
iab
le
f
u
n
ctio
n
an
d
co
m
p
l
icate
d
p
r
o
b
lem
.
B
u
t
th
e
m
e
tah
e
u
r
is
t
ic
al
g
o
r
ith
m
s
h
ad
t
h
e
s
a
m
e
d
is
ad
v
an
ta
g
es
o
f
f
a
lli
n
g
in
to
l
o
ca
l
s
ea
r
ch
zo
n
es
w
it
h
lo
ca
l
o
p
tim
a
l
s
o
l
u
tio
n
o
r
a
n
ea
r
b
y
g
lo
b
al
s
o
l
u
tio
n
w
it
h
w
o
r
s
e
q
u
al
it
y
t
h
a
n
th
e
b
est
s
o
lu
tio
n
s
.
As
E
L
D
p
r
o
b
le
m
i
s
m
o
r
e
c
o
m
p
l
i
c
a
t
e
d
b
y
c
o
n
s
i
d
e
r
i
n
g
m
u
c
h
c
o
m
p
l
i
c
a
t
e
d
f
o
s
s
i
l
f
u
e
l
c
o
s
t
f
u
n
c
t
i
o
n
(
F
F
C
F
)
a
n
d
c
o
n
s
t
r
a
i
n
t
s
.
V
L
E
s
w
e
r
e
c
o
n
s
i
d
e
r
e
d
d
u
r
i
n
g
p
o
w
e
r
i
n
c
r
e
a
s
i
n
g
o
r
d
e
c
r
e
a
s
i
n
g
p
r
o
c
e
s
s
o
f
t
h
e
r
m
a
l
p
o
w
e
r
p
lan
ts
[
2
6
]
.
A
co
m
p
licated
F
FC
F
co
n
s
id
er
ed
MFSs
f
o
r
b
u
r
n
in
g
f
u
el
a
n
d
d
r
iv
i
n
g
s
tea
m
o
r
g
as
tu
r
b
in
e
s
[
2
7
]
.
T
h
e
co
m
b
in
atio
n
o
f
V
L
E
s
an
d
MFSs
w
a
s
i
m
p
le
m
e
n
ted
an
d
th
e
s
o
lu
tio
n
o
f
t
h
e
p
r
o
b
lem
w
as
f
o
u
n
d
in
th
e
s
tu
d
y
[
2
8
]
.
T
h
e
m
o
d
el
w
it
h
b
o
th
V
L
E
s
an
d
MFS
s
is
th
e
m
o
s
t
co
m
p
licated
FF
C
F
i
n
E
L
D
p
r
o
b
lem
s
.
Ho
w
e
v
er
,
all
th
e
co
n
s
id
er
ed
p
r
o
b
lem
s
d
id
n
o
t
co
n
s
id
er
r
en
e
w
ab
le
e
n
er
g
ie
s
li
k
e
w
i
n
d
p
o
w
er
p
la
n
ts
o
r
p
h
o
to
v
o
ltaic
s
y
s
te
m
s
.
No
w
ad
a
y
s
,
w
i
n
d
p
o
w
er
p
lan
t
s
(
W
PP
s
)
ca
n
p
r
o
d
u
ce
a
v
er
y
h
ig
h
p
o
w
er
an
d
s
u
p
p
l
y
elec
tr
icit
y
t
o
lo
ad
s
v
ia
tr
an
s
m
is
s
io
n
p
o
w
er
n
et
w
o
r
k
.
T
h
er
e
w
er
e
s
t
u
d
ie
s
co
m
b
in
i
n
g
t
h
e
w
in
d
p
o
w
er
p
lan
t
an
d
th
er
m
al
p
o
w
er
p
lan
t
s
[
2
9
-
3
0
]
f
o
r
d
eter
m
i
n
in
g
T
FF
C
m
i
n
i
m
izatio
n
.
I
n
t
h
e
s
tu
d
y
[
2
9
]
,
w
i
n
d
v
elo
cit
y
w
a
s
m
o
d
eled
b
y
a
p
r
o
b
a
b
ilit
y
f
u
n
c
tio
n
an
d
its
p
o
w
er
w
as
d
ep
en
d
en
t
o
n
t
h
e
f
u
n
ctio
n
.
T
h
er
m
al
p
o
w
er
p
lan
ts
w
er
e
in
c
h
ar
g
i
n
g
o
f
p
r
o
d
u
cin
g
a
r
e
m
ai
n
in
g
p
o
w
e
r
af
ter
w
i
n
d
p
o
w
er
p
lan
t
ca
n
s
u
p
p
l
y
i
ts
p
o
w
er
to
lo
ad
s
.
T
h
e
s
tu
d
y
m
ai
n
l
y
in
tr
o
d
u
ce
d
th
e
c
h
an
g
e
o
f
w
in
d
v
elo
cit
y
o
r
co
n
s
id
er
ed
th
e
u
n
ce
r
tain
t
y
o
f
w
i
n
d
v
elo
cit
y
r
at
h
er
th
an
m
i
n
i
m
izi
n
g
to
tal
f
u
el
co
s
t
o
f
T
P
P
s
.
I
n
th
e
s
tu
d
y
[
3
0
]
,
P
SO
an
d
b
at
al
g
o
r
ith
m
(
B
A
)
w
er
e
ap
p
lied
t
o
o
p
tim
ize
p
o
w
er
g
en
er
atio
n
f
o
r
T
P
P
s
an
d
W
PP
s
.
T
w
o
s
y
s
te
m
s
w
it
h
7
p
lan
t
s
an
d
1
6
p
la
n
t
s
u
s
i
n
g
SF
S
w
e
r
e
e
m
p
lo
y
ed
i
n
t
h
e
s
tu
d
y
.
T
h
e
d
e
m
o
n
s
tr
atio
n
w
as
th
at
p
o
w
er
g
e
n
er
atio
n
f
r
o
m
W
PP
co
u
ld
r
ed
u
ce
T
FF
C
o
f
all
T
P
P
s
an
d
B
A
w
a
s
s
u
p
er
io
r
to
P
SO in
ter
m
s
o
f
r
e
d
u
cin
g
T
FF
C
.
I
n
th
i
s
p
ap
er
,
W
P
P
s
an
d
T
PP
s
ar
e
co
m
b
in
ed
to
p
r
o
d
u
ce
elec
tr
icit
y
to
lo
ad
s
in
w
h
ic
h
MFS
s
is
co
n
s
id
er
ed
in
T
PP
s
to
p
r
o
d
u
ce
elec
tr
icit
y
.
I
n
ad
d
itio
n
,
t
h
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
b
ec
o
m
e
s
m
o
r
e
co
m
p
licated
s
in
ce
V
L
E
s
is
co
n
s
id
er
ed
d
u
r
in
g
th
e
g
en
er
atio
n
p
r
o
ce
s
s
o
f
th
e
T
P
P
s
.
T
w
o
s
y
s
te
m
s
w
it
h
elev
e
n
p
lan
t
s
in
cl
u
d
in
g
o
n
e
W
P
P
an
d
ten
T
P
P
s
ar
e
tak
e
n
i
n
to
ac
co
u
n
t.
F
o
r
r
ea
ch
in
g
t
h
e
o
p
ti
m
a
l
g
e
n
er
atio
n
o
f
th
e
p
lan
t
s
,
th
r
ee
m
eth
o
d
s
in
c
lu
d
i
n
g
an
t
li
o
n
alg
o
r
it
h
m
(
AL
A
)
[
3
1
]
,
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
[
3
2
]
an
d
cu
ck
o
o
s
ea
r
ch
alg
o
r
it
h
m
(
C
S
A
)
[
3
3
]
ar
e
i
m
p
le
m
e
n
ted
.
T
h
e
n
o
v
elt
y
a
n
d
co
n
tr
ib
u
tio
n
o
f
th
e
p
ap
e
r
is
as f
o
llo
w
s
:
C
o
m
b
i
n
e
W
P
P
s
an
d
T
P
P
s
w
h
er
e
T
PP
s
co
n
s
id
er
MFSs
an
d
VL
E
s
C
o
m
p
ar
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
P
SO,
AL
A
an
d
C
S
A
P
r
o
v
id
e
o
p
ti
m
al
s
o
lu
tio
n
s
f
o
r
r
ed
u
cin
g
T
FF
C
f
o
r
t
h
e
co
m
b
in
ed
s
y
s
te
m
Oth
er
p
ar
ts
o
f
t
h
e
p
ap
er
ar
e
as
f
o
llo
w
s
:
Sectio
n
2
d
escr
ib
es
o
b
j
ec
tiv
e
f
u
n
ctio
n
an
d
co
n
s
tr
a
in
ts
o
f
t
h
e
co
m
b
i
n
ed
s
y
s
te
m
.
Sectio
n
3
p
r
esen
t
C
u
c
k
o
o
s
ea
r
c
h
al
g
o
r
ith
m
.
Sectio
n
4
s
h
o
w
s
th
e
r
esu
lt
co
m
p
ar
is
o
n
s
b
et
w
ee
n
C
S
A
a
n
d
t
w
o
o
th
er
m
et
h
o
d
s
.
Sectio
n
5
s
h
o
w
s
th
e
co
n
clu
s
io
n
s
.
2.
T
H
E
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
2
.
1
.
O
bje
ct
iv
e
f
un
ct
io
n o
f
t
he
E
L
D
pro
ble
m
w
it
h si
ng
le
f
uel o
ptio
n
I
n
o
p
ti
m
al
o
p
er
atio
n
o
f
t
h
e
c
o
m
b
i
n
ed
s
y
s
te
m
,
p
o
w
er
g
e
n
e
r
ated
b
y
W
P
P
s
an
d
T
PP
s
is
s
u
p
p
lied
to
lo
ad
s
in
w
h
ic
h
t
h
e
co
s
t
o
f
p
o
w
er
f
r
o
m
W
P
P
s
is
s
u
p
p
o
s
ed
to
b
e
a
co
n
s
tan
t
an
d
it
is
m
u
c
h
less
t
h
a
n
t
h
e
co
s
t
o
f
p
o
w
er
f
r
o
m
T
P
P
s
.
So
,
th
e
o
b
jectiv
e
is
to
u
s
e
al
l p
o
w
er
f
r
o
m
W
PP
s
w
h
ile
t
h
e
co
s
t
f
r
o
m
T
PP
s
.
T
h
e
o
b
j
ec
tiv
e
is
as f
o
llo
w
s
:
Min
i
m
ize
1
()
Nt
tt
t
T
FF
C
FF
C
P
(
1
)
w
h
e
r
e
F
F
C
t
i
s
t
h
e
f
o
s
s
i
l
f
u
e
l
c
o
s
t
o
f
t
h
e
t
t
h
T
P
P
;
P
t
i
s
p
o
w
e
r
g
e
n
e
r
a
t
i
o
n
o
f
t
h
e
tth
T
P
P
;
Nt
i
s
t
h
e
n
u
m
b
e
r
o
f
T
P
P
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
0
2
2
-
1028
1024
As M
FS
s
is
co
n
s
id
er
ed
,
FFC
t
o
f
th
e
tt
h
T
PP
is
r
ep
r
esen
ted
a
s
f
o
llo
w
s
:
2
1
1
1
,
1
,
min
,
1
,
ma
x
2
2
2
2
,
2
,
min
,
2
,
ma
x
2
,
,
min
,
,
ma
x
()
...
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
tt
t
M
t
t
M
t
t
M
t
M
t
t
M
a
P
b
P
c
f
o
r
P
P
P
a
P
b
P
c
f
o
r
P
P
P
F
F
C
P
a
P
b
P
c
f
o
r
P
P
P
(
2
)
w
h
er
e
M
is
th
e
n
u
m
b
er
o
f
f
u
el
s
;
P
t,
M,
min
an
d
P
t,
M,
max
ar
e
t
h
e
lo
w
er
b
o
u
n
d
a
n
d
u
p
p
er
b
o
u
n
d
o
f
p
o
w
er
g
e
n
er
atio
n
o
f
th
e
tth
T
PP
f
o
r
th
e
Mth
f
u
el
.
As co
n
s
id
er
i
n
g
t
h
e
V
L
E
s
i
n
t
h
e
g
en
er
atio
n
p
r
o
ce
s
s
,
FF
C
t o
f
th
e
tt
h
T
PP
is
r
ep
r
esen
ted
as f
o
llo
w
s
:
2
1
1
1
1
1
,
m
in
,1
,
m
in
,1
,
m
a
x
2
2
2
2
2
2
,
m
in
,
2
,
m
in
,
2
,
m
a
x
2
,
m
in
,
,
m
in
,
s
in
(
(
)
)
s
in
(
(
)
)
()
...
s
in
(
(
)
)
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
tt
t
M
t
t
M
t
t
M
t
M
t
M
t
t
t
M
t
t
M
a
P
b
P
c
P
P
fo
r
P
P
P
a
P
b
P
c
P
P
fo
r
P
P
P
FFC
P
a
P
b
P
c
P
P
fo
r
P
P
P
,
m
a
x
(
3
)
T
h
e
FFC
t
w
it
h
o
u
t a
n
d
w
it
h
V
L
E
s
i
s
p
lo
tted
in
Fi
g
u
r
e
1
.
Fig
u
r
e
1
.
FF
C
w
it
h
a
n
d
w
i
th
o
u
t V
L
E
s
2
.
2
.
P
o
w
er
g
ener
a
t
io
n o
f
w
ind
t
urbin
es
W
in
d
tu
r
b
in
e
s
ca
n
p
r
o
d
u
ce
a
n
d
s
u
p
p
l
y
elec
tr
ici
t
y
to
lo
ad
s
b
u
t
t
h
eir
s
tab
ili
t
y
i
s
n
o
t
ce
r
tain
d
u
e
to
th
e
in
f
lu
e
n
ce
o
f
w
i
n
d
v
elo
cit
y
.
I
f
w
i
n
d
v
elo
cit
y
is
h
ig
h
,
p
o
w
er
g
en
er
atio
n
o
f
th
e
w
i
n
d
tu
r
b
i
n
es
is
h
i
g
h
an
d
v
ice
v
er
s
a.
T
h
e
g
en
er
atio
n
o
f
t
h
e
win
d
tu
r
b
in
e
s
is
d
eter
m
in
ed
as
f
o
llo
w
s
: [
2
9
]
:
,
,
0
0
w
r
a
t
e
w
w
r
a
t
e
i
f
V
Vi
V
Vi
W
P
i
f
Vi
V
Vr
WP
Vr
Vi
W
P
i
f
Vr
V
Vo
el
se
(
4
)
w
h
er
e
W
P
w
an
d
W
P
w
,
r
ate
ar
e
P
o
w
er
g
en
er
atio
n
a
n
d
r
ated
p
o
w
er
g
e
n
er
atio
n
o
f
t
h
e
w
t
h
W
PP
;
V
an
d
Vr
,
ar
e
s
p
ee
d
o
f
w
i
n
d
,
r
ated
s
p
ee
d
o
f
w
i
n
d
at
th
e
w
t
h
W
P
P
;
an
d
Vi
an
d
Vo
ar
e
t
h
e
m
i
n
i
m
u
m
a
n
d
m
ax
i
m
u
m
s
p
ee
d
o
f
w
i
n
d
f
o
r
p
o
w
er
g
e
n
er
atio
n
.
2
.
3
.
Co
ns
t
ra
ints o
f
t
he
pro
ble
m
2
.
3
.
1
.
Co
ns
t
ra
ints o
f
WP
P
s
W
in
d
p
o
w
er
i
s
d
ep
en
d
en
t
o
n
v
alu
e
s
o
f
v
elo
cit
y
.
So
,
as
t
h
e
v
elo
cit
y
h
a
s
t
h
e
lo
w
e
s
t
v
al
u
e,
th
e
p
o
w
e
r
is
m
i
n
i
m
u
m
a
n
d
as
t
h
e
v
e
lo
cit
y
h
a
s
t
h
e
h
i
g
h
est
v
alu
e,
t
h
e
p
o
w
er
is
m
a
x
i
m
u
m
.
As
a
r
es
u
lts
,
li
m
i
ts
o
f
w
i
n
d
p
o
w
er
ar
e
as f
o
llo
w
s
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Op
tima
l g
en
era
tio
n
fo
r
w
in
d
-
t
h
erma
l p
o
w
er p
la
n
t sys
tems w
ith
mu
ltip
le
fu
el
s
o
u
r
ce
s
(
P
h
a
n
N
g
u
ye
n
V
in
h
)
1025
,
m
i
n
,
m
a
x
;
1
,
.
.
.
,
w
w
w
W
P
W
P
W
P
w
N
w
(
5
)
w
h
er
e
WP
w
is
p
o
w
er
g
e
n
er
ati
o
n
o
f
th
e
w
th
W
P
P
;
WP
w
,
min
a
n
d
WP
w
,
min
ar
e
th
e
lo
w
er
b
o
u
n
d
an
d
u
p
p
er
b
o
u
n
d
o
f
p
o
w
er
g
e
n
er
atio
n
o
f
t
h
e
w
t
h
W
PP
; a
n
d
Nw
is
th
e
n
u
m
b
er
o
f
W
PP
s
.
2
.
3
.
2
.
Co
ns
t
ra
ints o
f
T
P
P
s
A
cti
v
e
p
o
w
er
g
e
n
er
atio
n
li
m
i
ts
:
I
n
t
h
e
p
r
o
b
le
m
,
th
e
f
o
s
s
il
f
u
e
l
s
o
u
r
ce
s
at
T
PP
s
ar
e
s
u
p
p
o
s
ed
to
b
e
p
len
ti
f
u
l
an
d
u
n
li
m
ited
.
Ho
wev
er
,
th
e
p
o
w
er
g
en
er
atio
n
o
f
ea
c
h
T
PP
is
co
n
s
tr
ai
n
ed
d
u
e
to
t
h
e
li
m
it
s
o
f
g
as/
s
tea
m
tu
r
b
i
n
es a
n
d
g
en
er
a
to
r
s
.
T
h
e
g
en
er
atio
n
r
estrictio
n
is
d
escr
ib
ed
as f
o
llo
w
s
:
,
m
i
n
,
m
a
x
t
t
t
P
P
P
(
6
)
I
n
th
e
co
n
s
tr
ai
n
t,
P
t,
min
an
d
P
t
,
max
ar
e
th
e
m
in
i
m
u
m
a
n
d
m
a
x
i
m
u
m
ac
t
iv
e
p
o
w
er
g
e
n
er
ati
o
n
o
f
t
h
e
tth
T
P
P
.
B
ec
au
s
e
th
e
T
PP
s
u
s
e
MFSs
,
P
t,
min
an
d
P
t,
max
ar
e,
r
esp
ec
tiv
ely
,
eq
u
al
to
P
t,
1,
min
an
d
P
t,
M
,
max
,
w
h
ic
h
ar
e
s
h
o
w
n
i
n
(
2
)
an
d
(
3
)
.
2
.
3
.
3
.
Co
ns
t
ra
ints
o
f
s
y
s
t
e
m
T
h
e
p
o
w
er
s
y
s
te
m
co
n
s
tr
ain
s
th
e
b
alan
ce
o
f
p
o
w
er
b
et
w
e
en
s
u
p
p
l
y
s
id
e
an
d
co
n
s
u
m
e
s
id
e.
I
f
th
e
b
alan
ce
is
ac
h
ie
v
ed
,
th
e
f
r
eq
u
en
c
y
o
f
t
h
e
s
y
s
te
m
ca
n
b
e
s
ta
b
le
an
d
s
y
s
te
m
i
s
w
o
r
k
i
n
g
s
ta
b
ly
.
T
h
e
s
u
p
p
l
y
s
id
e
co
n
s
is
ts
o
f
T
P
P
s
an
d
W
PP
s
w
h
er
e
co
n
s
u
m
e
s
id
e
is
lo
ad
d
e
m
an
d
.
T
h
e
b
alan
ce
co
n
s
tr
ain
t i
s
as f
o
llo
w
s
:
11
Nt
Nw
t
w
d
ema
n
d
tw
P
W
P
P
(
7
)
w
h
er
e
P
demand
is
t
h
e
ac
ti
v
e
p
o
w
er
o
f
all
lo
ad
s
i
n
th
e
s
y
s
te
m
.
3.
CUCK
O
O
SE
ARCH
A
L
G
O
RIT
H
M
(
CSA)
3
.
1
.
L
év
y
f
lig
hts
L
é
v
y
f
li
g
h
ts
i
s
a
p
o
w
er
s
ea
r
ch
tech
n
iq
u
e
o
f
C
S
A
th
a
n
k
to
th
e
u
s
e
o
f
a
n
in
f
i
n
ite
s
tep
s
iz
e
b
y
u
s
i
n
g
L
é
v
y
d
is
tr
ib
u
tio
n
.
T
h
e
tech
n
iq
u
e
is
u
s
ed
to
u
p
d
ate
n
e
w
s
o
l
u
ti
o
n
s
as s
h
o
w
n
i
n
t
h
e
f
o
llo
w
i
n
g
eq
u
atio
n
:
.
;
1
,
.
.
.
,
n
e
w
s
s
p
o
S
o
l
S
o
l
L
e
v
y
s
N
(
8
)
w
h
er
e
ε
is
s
ca
le
f
ac
to
r
; L
e
v
y
i
s
L
é
v
y
d
i
s
tr
ib
u
tio
n
[
3
3
]
; a
n
d
Np
o
is
p
o
p
u
latio
n
s
ize.
3
.
2
.
M
ut
a
t
io
n t
ec
hn
iqu
e
Mu
tatio
n
tec
h
n
iq
u
e
is
e
m
p
lo
y
ed
in
C
S
A
to
u
p
d
ate
n
e
w
s
o
lu
tio
n
s
f
o
r
t
h
e
s
ec
o
n
d
t
i
m
e.
Ho
w
e
v
er
,
t
h
e
tech
n
iq
u
e
d
o
es
n
o
t
u
p
d
ate
t
h
e
w
h
o
le
p
o
p
u
latio
n
b
u
t
it
s
el
ec
ts
s
o
l
u
tio
n
s
b
ased
o
n
t
h
e
c
o
m
p
ar
is
o
n
co
n
d
itio
n
b
et
w
ee
n
a
r
an
d
o
m
n
u
m
b
er
cs a
n
d
m
u
tatio
n
f
ac
to
r
MF.
T
h
e
tech
n
iq
u
e
is
s
h
o
w
n
as
f
o
llo
w
s
:
12
.
(
)
s
r
p
r
p
s
n
e
w
s
s
S
o
l
c
S
o
l
S
o
l
if
c
M
F
Sol
S
o
l
o
th
e
r
w
is
e
(
9
)
w
h
er
e
Sol
rp1
an
d
Sol
rp2
ar
e
p
ic
k
ed
s
o
lu
tio
n
s
b
y
u
s
i
n
g
r
an
d
o
m
izatio
n
.
4.
NUM
E
RICAL
R
E
SU
L
T
S
I
n
th
i
s
s
ec
tio
n
,
P
SO,
AL
A
a
n
d
C
S
A
ar
e
e
x
ec
u
ted
f
o
r
r
ea
ch
in
g
t
h
e
o
p
ti
m
al
s
o
lu
t
io
n
s
o
f
t
w
o
tes
t
s
y
s
te
m
s
.
E
ac
h
m
e
th
o
d
is
r
u
n
f
if
t
y
tr
ial
s
o
n
Ma
t
lab
p
latf
o
r
m
an
d
a
P
C
w
it
h
p
r
o
ce
s
s
o
r
o
f
2
.
2
GHz
an
d
R
AM
o
f
4
.
0
GB
.
T
h
e
d
etail
o
f
s
i
m
u
la
tio
n
r
esu
lt is
p
r
esen
ted
in
t
h
e
t
w
o
f
o
llo
w
in
g
s
ec
tio
n
s
.
4
.
1
.
T
he
f
irst
w
ind
-
t
her
m
a
l po
w
er
pla
nt
s
y
s
t
e
m
I
n
th
i
s
s
ec
tio
n
,
th
e
d
ata
o
f
ten
T
PP
s
w
it
h
MFS
s
an
d
w
it
h
o
u
t
VL
E
s
i
s
tak
e
n
f
r
o
m
[
3
4
]
w
h
il
e
th
e
d
at
a
o
f
W
P
P is
tak
en
f
r
o
m
[
3
5
]
.
T
h
e
r
ated
s
p
ee
d
o
f
w
i
n
d
an
d
t
h
e
r
ated
p
o
w
er
o
f
t
h
e
W
P
P
is
1
5
m
/s
a
n
d
1
2
0
MW
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
0
2
2
-
1028
1026
T
h
e
w
in
d
s
p
e
ed
at
th
e
s
c
h
ed
u
lin
g
h
o
u
r
is
1
4
m
/s
a
n
d
th
e
p
o
w
er
g
e
n
er
atio
n
o
f
th
e
W
P
P
is
1
0
8
MW
.
T
h
e
T
PP
s
an
d
t
h
e
W
P
P
s
u
p
p
ly
elec
tr
icit
y
to
a
2
4
0
0
MW
lo
ad
.
I
n
o
r
d
er
to
g
et
r
es
u
lts
f
o
r
P
SO,
AL
A
a
n
d
C
S
A
,
Np
o
an
d
Nit
(
w
h
ic
h
i
s
th
e
n
u
m
b
er
o
f
iter
atio
n
s
)
ar
e
r
esp
ec
tiv
el
y
s
et
to
2
0
an
d
1
0
0
f
o
r
P
SO
an
d
AL
A
,
an
d
1
0
an
d
1
0
0
f
o
r
C
S
A
.
T
h
e
r
es
u
lt
s
ar
e
s
u
m
m
ar
ized
i
n
Fi
g
u
r
e
s
2
an
d
3
.
F
ig
u
r
e
2
s
h
o
w
s
t
h
at
C
S
A
ca
n
r
ea
ch
th
e
s
m
al
lest
co
s
t
w
it
h
$
4
3
7
.
7
w
h
il
e
P
SO
is
th
e
w
o
r
s
t
m
et
h
o
d
w
i
t
h
th
e
h
i
g
h
est
co
s
t
o
f
$
4
4
1
.
4
.
Si
m
ilar
l
y
,
C
S
A
a
n
d
P
SO
ar
e
th
e
b
est
an
d
th
e
w
o
r
s
t
m
et
h
o
d
s
w
it
h
th
e
s
m
alle
s
t
an
d
h
ig
h
es
t
m
a
x
i
m
u
m
co
s
t.
T
h
e
co
m
p
ar
i
s
o
n
s
o
f
m
ea
n
co
s
t
o
f
5
0
r
u
n
s
ca
n
in
d
ic
ate
th
e
b
est
s
tab
ilit
y
o
f
C
S
A
s
in
ce
t
h
at
o
f
t
h
e
m
et
h
o
d
is
t
h
e
s
m
al
lest
.
Fi
g
u
r
e
3
ca
n
co
n
f
ir
m
t
h
e
b
e
s
t
s
t
a
b
i
l
i
t
y
o
f
C
S
A
o
n
e
m
o
r
e
t
i
m
e
s
i
n
c
e
a
l
l
r
u
n
s
o
f
C
S
A
h
a
v
e
m
u
c
h
l
e
s
s
c
o
s
t
t
h
a
n
t
h
o
s
e
o
f
P
S
O
a
n
d
A
L
A
.
Fig
u
r
e
2
.
C
o
m
p
ar
is
o
n
s
o
f
r
es
u
lt f
o
r
th
e
f
ir
s
t s
y
s
te
m
Fig
u
r
e
3
.
T
FF
C
co
m
p
ar
is
o
n
s
o
f
5
0
tr
ial
r
u
n
s
f
o
r
th
e
f
ir
s
t
s
y
s
te
m
4
.
2
.
T
he
s
ec
o
n
d
w
ind
-
t
her
m
a
l po
w
er
pla
nt
s
y
s
t
e
m
I
n
t
h
is
s
ec
tio
n
,
th
e
d
ata
o
f
ten
T
P
P
s
w
it
h
MF
Ss
a
n
d
V
L
E
s
is
tak
e
n
f
r
o
m
[
3
5
]
w
h
ile
t
h
e
d
at
a
o
f
W
P
P
is
ta
k
en
f
r
o
m
[
3
6
]
.
T
h
e
r
ated
s
p
ee
d
o
f
w
i
n
d
a
n
d
t
h
e
r
ated
p
o
w
er
o
f
t
h
e
W
P
P
is
1
5
m
/
s
a
n
d
8
0
MW
.
T
h
e
w
in
d
s
p
ee
d
at
t
h
e
s
ch
ed
u
li
n
g
h
o
u
r
i
s
1
4
.
5
m
/
s
a
n
d
t
h
e
p
o
w
e
r
g
en
er
atio
n
o
f
t
h
e
W
P
P
is
7
6
MW
.
T
h
e
T
PP
s
an
d
t
h
e
W
PP
s
u
p
p
ly
elec
tr
icit
y
to
a
2
7
0
0
MW
lo
a
d
.
Fo
r
r
u
n
n
in
g
P
SO,
AL
A
a
n
d
C
S
A
,
Np
o
an
d
Nit
ar
e
r
esp
ec
ti
v
el
y
s
et
to
2
0
an
d
2
0
0
f
o
r
P
SO
an
d
AL
A
,
an
d
1
0
an
d
2
0
0
f
o
r
C
SA
.
T
h
e
Fig
u
r
e
s
4
an
d
5
s
h
o
w
th
e
b
est
p
er
f
o
r
m
a
n
ce
o
f
C
S
A
b
ec
a
u
s
e
C
S
A
ca
n
r
ea
ch
t
h
e
s
m
al
lest
m
i
n
i
m
u
m
,
m
ea
n
a
n
d
m
ax
i
m
u
m
co
s
t.
AL
A
i
s
t
h
e
s
e
co
n
d
b
est
m
eth
o
d
w
h
er
ea
s
P
S
O
is
s
till
th
e
w
o
r
s
t
p
er
f
o
r
m
a
n
ce
m
et
h
o
d
.
T
FF
C
o
f
5
0
r
u
n
s
f
r
o
m
C
S
A
i
s
ap
p
r
o
x
im
atel
y
lied
o
n
a
l
in
e
w
h
i
le
th
a
t
o
f
P
SO
an
d
AL
A
h
av
e
t
h
e
h
i
g
h
f
lu
ct
u
atio
n
s
.
T
h
e
an
al
y
s
is
ca
n
lead
to
a
co
n
cl
u
s
io
n
t
h
at
C
S
A
i
s
s
u
p
er
io
r
to
P
SO
an
d
AL
A
f
o
r
th
e
s
y
s
te
m
w
it
h
MFS
s
,
V
L
E
s
an
d
w
in
d
p
o
w
er
p
lan
ts
.
O
p
ti
m
al
s
o
lu
t
io
n
s
o
f
t
h
e
t
w
o
s
y
s
te
m
s
ar
e
s
h
o
w
n
T
ab
le
A
1
in
A
p
p
en
d
i
x
.
Fig
u
r
e
4
.
C
o
m
p
ar
is
o
n
s
o
f
r
es
u
lt f
o
r
th
e
s
ec
o
n
d
s
y
s
te
m
Fig
u
r
e
5
.
T
FF
C
co
m
p
ar
is
o
n
s
o
f
5
0
tr
ial
r
u
n
s
f
o
r
th
e
s
ec
o
n
d
s
y
s
te
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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p
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g
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N:
2
0
8
8
-
8708
Op
tima
l g
en
era
tio
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fo
r
w
in
d
-
t
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erma
l p
o
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er p
la
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t sys
tems w
ith
mu
ltip
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fu
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s
o
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r
ce
s
(
P
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n
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in
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)
1027
5.
CO
NCLU
SI
O
N
I
n
th
i
s
p
ap
er
,
P
SO,
AL
A
a
n
d
C
S
A
s
o
lv
ed
t
w
o
w
i
n
d
-
t
h
er
m
a
l
p
o
w
er
p
lan
t
s
y
s
te
m
s
w
h
er
e
T
P
P
s
u
s
ed
m
u
ltip
le
f
u
el
s
o
u
r
ce
s
to
g
e
n
er
ate
elec
tr
icit
y
to
lo
ad
s
.
T
h
e
t
w
o
s
y
s
te
m
h
ad
t
h
e
s
a
m
e
n
u
m
b
er
o
f
p
o
w
er
p
la
n
ts
,
ten
T
PP
s
an
d
o
n
e
W
PP
in
w
h
ich
th
e
f
ir
s
t
s
y
s
te
m
d
id
n
o
t
co
n
s
id
er
v
al
v
e
lo
ad
in
g
ef
f
ec
t
s
o
n
T
PP
s
d
u
r
in
g
t
h
e
p
o
w
er
in
cr
ea
s
e
o
r
d
ec
r
ea
s
e
p
r
o
ce
s
s
b
u
t
t
h
e
s
ec
o
n
d
s
y
s
te
m
d
id
.
T
h
e
r
esu
lt
co
m
p
ar
i
s
o
n
i
n
d
icate
d
th
at
C
S
A
w
a
s
m
o
r
e
r
o
b
u
s
t t
h
a
n
P
SO a
n
d
AL
A
i
n
r
ea
ch
i
n
g
th
e
o
p
ti
m
al
p
o
w
er
g
en
er
atio
n
a
n
d
th
e
s
ea
r
c
h
s
t
ab
ilit
y
.
C
S
A
co
u
ld
r
ea
ch
t
h
e
s
m
al
lest
m
in
i
m
u
m
,
m
ea
n
a
n
d
m
a
x
i
m
u
m
co
s
t
a
m
o
n
g
t
h
r
ee
ex
ec
u
ted
m
et
h
o
d
s
.
I
n
ad
d
itio
n
,
th
e
co
s
t
f
r
o
m
f
i
f
t
y
r
u
n
s
o
f
C
S
A
w
as a
p
p
r
o
x
i
m
atel
y
lied
o
n
a
li
n
e
w
it
h
o
u
t
f
l
u
ct
u
atio
n
s
w
h
ils
t t
h
e
co
s
t o
f
P
SO a
n
d
AL
A
h
ad
h
ig
h
f
l
u
ct
u
atio
n
s
.
C
o
n
s
eq
u
en
t
l
y
,
C
S
A
i
s
r
ec
o
m
m
e
n
d
ed
as
a
p
o
w
er
f
u
l
s
ea
r
ch
a
lg
o
r
it
h
m
f
o
r
th
e
o
p
ti
m
a
l
g
en
er
atio
n
o
f
w
i
n
d
-
t
h
er
m
al
p
o
w
er
p
lan
t
s
y
s
te
m
s
.
AP
P
E
NDI
X
T
ab
le
A1
.
Op
tim
al
g
en
er
atio
n
o
f
th
e
t
w
o
s
y
s
te
m
s
f
o
u
n
d
b
y
P
SO,
AL
A
a
n
d
C
S
A
m
et
h
o
d
s
P
t
(
M
W
)
S
y
st
e
m 1
S
y
st
e
m 2
PSO
A
L
A
C
S
A
PSO
A
L
A
C
S
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P
1
1
6
8
.
9
1
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2
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7
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9
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0
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8
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3
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4
P
2
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6
1
9
7
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3
1
9
6
.
8
3
0
3
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2
2
7
3
.
6
2
7
1
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3
P
3
2
2
0
.
9
2
3
9
.
9
2
3
9
.
6
2
6
5
.
0
2
3
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6
2
3
7
.
5
P
4
2
2
0
.
2
2
3
2
.
2
2
3
0
.
4
2
9
8
.
8
2
4
4
.
9
2
7
0
.
0
P
5
2
2
5
.
7
2
1
4
.
1
2
2
3
.
7
2
3
5
.
8
2
4
4
.
4
2
3
5
.
9
P
6
2
2
3
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5
2
2
9
.
2
2
2
9
.
8
2
9
5
.
2
2
9
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9
2
7
8
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1
P
7
2
6
1
.
7
2
3
6
.
2
2
3
5
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1
2
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4
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2
3
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4
2
3
8
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5
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8
2
2
4
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2
2
3
0
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0
2
2
9
.
1
3
3
0
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4
3
9
4
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7
4
0
9
.
6
P
9
3
2
7
.
0
3
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8
3
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6
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5
2
0
8
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8
2
7
6
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6
2
6
2
.
8
P
10
2
1
1
.
1
2
2
9
.
5
2
2
1
.
2
2
4
4
.
9
2
3
1
.
7
2
1
1
.
8
RE
F
E
R
E
NC
E
S
[1
]
M
o
n
t
o
y
a
,
O.
D.,
G
il
-
G
o
n
z
á
lez
,
W
.
,
G
risa
le
s
-
No
re
ñ
a
,
L
.
,
Oro
z
c
o
-
He
n
a
o
,
C.
,
S
e
rra
,
F
.
,
“
Eco
n
o
m
ic
Disp
a
tch
o
f
BES
S
a
n
d
Re
n
e
w
a
b
le
G
e
n
e
ra
to
r
s
in
DC
M
icro
g
rid
s
Us
in
g
V
o
lt
a
g
e
-
De
p
e
n
d
e
n
t
L
o
a
d
M
o
d
e
ls,”
E
n
e
rg
ies
,
v
o
l.
1
2
,
n
o
.
2
3
,
p
.
4
4
9
4
,
2
0
1
9
.
[2
]
T
riv
e
d
i,
I.
N.,
Ja
n
g
ir,
P
.
,
B
h
o
y
e
,
M
.
,
a
n
d
Ja
n
g
ir,
N.,
“
A
n
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
a
n
d
m
u
lt
ip
le
e
n
v
iro
n
m
e
n
tal
d
isp
a
tch
p
r
o
b
lem
so
lu
ti
o
n
w
it
h
m
icro
g
rid
s
u
sin
g
in
terio
r
se
a
rc
h
a
lg
o
rit
h
m
,
”
Ne
u
ra
l
Co
mp
u
ti
n
g
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l.
3
0
,
n
o
.
7
,
p
p
.
2
1
7
3
-
2
1
8
9
,
2
0
1
8
[3
]
Al
-
Be
tar,
M
.
A
.
,
Aw
a
d
a
ll
a
h
,
M
.
A
.
,
Krish
a
n
,
M
.
M
.
,
“
A
n
o
n
-
c
o
n
v
e
x
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
p
r
o
b
lem
w
it
h
v
a
lv
e
l
o
a
d
i
n
g
e
f
f
e
c
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s
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b
r
i
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w
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f
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p
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i
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e
r
,
”
N
e
u
r
a
l
C
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d
A
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.
3
2
,
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p
.
12127
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2
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0
.
[4
]
G
n
a
w
a
li
,
K.,
Ha
n
,
K.
H.,
G
e
e
m
,
Z.
W
.
,
J
u
n
,
K.
S
.
,
Yu
m
,
K.
T
.
,
“
Eco
n
o
m
ic
Disp
a
tch
Op
ti
m
iz
a
ti
o
n
o
f
M
u
lt
i
-
W
a
ter
Re
so
u
rc
e
s: A
Ca
s
e
S
t
u
d
y
o
f
a
n
Isla
n
d
i
n
S
o
u
t
h
K
o
re
a
,
”
S
u
st
a
in
a
b
i
l
it
y
,
v
o
l.
1
1
,
n
o
.
2
1
,
p
.
5
9
6
4
,
2
0
1
9
.
[5
]
Da
s,
D.,
Bh
a
tt
a
c
h
a
ry
a
,
A
.
,
Ra
y
,
R.
N.,
“
Dra
g
o
n
f
l
y
A
l
g
o
rit
h
m
f
o
r
so
lv
in
g
p
r
o
b
a
b
i
li
stic
Eco
n
o
m
ic
L
o
a
d
Disp
a
tch
p
ro
b
lem
s,”
Ne
u
ra
l
Co
mp
u
t
in
g
a
n
d
Ap
p
li
c
a
ti
o
n
s
,
v
o
l.
3
2
,
p
p
.
3
0
2
8
-
3
0
4
5
,
2
0
2
0
.
[6
]
A
n
g
,
S
.
,
L
e
e
to
n
,
U.,
Ch
a
y
a
k
u
lk
e
e
re
e
,
K.,
Ku
lw
o
ra
w
a
n
ich
p
o
n
g
,
T
.
,
“
S
in
e
C
o
sin
e
A
lg
o
rit
h
m
f
o
r
Op
t
im
a
l
P
lac
e
m
e
n
t
a
n
d
S
izi
n
g
o
f
Distrib
u
ted
G
e
n
e
ra
ti
o
n
i
n
Ra
d
ial
Distrib
u
ti
o
n
Ne
t
w
o
rk
,
”
G
M
S
AR
N
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
v
o
l
.
1
2
,
p
p
.
2
0
2
-
212
,
2
0
1
8
.
[7
]
M
m
a
r
y
,
E.
R.
,
a
n
d
M
a
ru
n
g
sri,
B.
,
“
In
teg
ra
ti
o
n
o
f
M
u
lt
i
-
Re
n
e
w
a
b
le
En
e
rg
y
Distrib
u
ted
G
e
n
e
ra
ti
o
n
a
n
d
Ba
tt
e
ry
in
Ra
d
ial
Distrib
u
ti
o
n
Ne
tw
o
rk
s,”
GM
S
AR
N
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
,
v
o
l.
1
2
,
p
p
.
1
9
4
-
2
0
1
,
2
0
1
8
.
[8
]
M
m
a
r
y
,
E.
R.
,
a
n
d
M
a
ru
n
g
sri,
B.
,
“
M
u
lt
i
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
o
f
re
n
e
wa
b
le
d
istri
b
u
te
d
g
e
n
e
ra
ti
o
n
a
n
d
sh
u
n
t
c
a
p
a
c
it
o
r
f
o
r
tec
h
n
o
-
e
c
o
n
o
m
ic
a
n
a
ly
sis
u
sin
g
h
y
b
rid
in
v
a
siv
e
w
e
e
d
s
o
p
ti
m
iza
ti
o
n
,
”
GM
S
AR
N
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
,
v
o
l
.
1
2
,
p
p
.
24
-
33
,
2
0
1
8
.
[9
]
Ka
m
b
o
j,
V
.
K.,
Bh
a
d
o
ria,
A
.
,
a
n
d
Ba
th
,
S
.
K.,
“
S
o
lu
ti
o
n
o
f
n
o
n
-
c
o
n
v
e
x
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
p
ro
b
lem
f
o
r
s
m
a
ll
-
s
c
a
l
e
p
o
w
e
r
s
y
s
t
e
m
s
u
s
i
n
g
a
n
t
l
i
o
n
o
p
t
i
m
i
z
e
r
,
”
N
e
u
r
a
l
C
o
m
p
u
t
i
n
g
a
n
d
A
p
p
l
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c
a
t
i
o
n
s
,
v
o
l
.
2
8
,
n
o
.
8
,
p
p
.
2181
-
2192
,
2
0
1
7
.
[1
0
]
Ka
m
b
o
j,
V.
K.,
Ba
th
,
S
.
K.,
a
n
d
Dh
il
lo
n
,
J.
S
.
,
“
S
o
l
u
ti
o
n
o
f
n
o
n
-
c
o
n
v
e
x
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
p
r
o
b
lem
u
sin
g
G
re
y
W
o
l
f
Op
ti
m
iz
e
r,
”
Ne
u
ra
l
Co
mp
u
t
in
g
a
n
d
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
2
7
,
n
o
.
5
,
p
p
.
1
3
0
1
-
1
3
1
6
,
2
0
1
6
.
[1
1
]
Zh
o
u
,
J.
,
“
S
o
lv
in
g
e
c
o
n
o
m
ic
d
i
sp
a
tch
p
r
o
b
lem
w
it
h
p
iec
e
w
ise
q
u
a
d
ra
ti
c
c
o
st
f
u
n
c
ti
o
n
s
u
sin
g
L
a
g
r
a
n
g
e
m
u
lt
ip
li
e
r
th
e
o
ry
,
”
In
Pro
c
.
3
rd
ICCT
D
,
p
p
.
2
5
-
2
7
,
2
0
1
1
.
[1
2
]
Ka
u
r,
N.,
a
n
d
S
in
g
h
,
I.
,
“
Eco
n
o
m
ic
Disp
a
tch
S
c
h
e
d
u
li
n
g
u
sin
g
Clas
sic
a
l
a
n
d
Ne
w
to
n
Ra
p
h
so
n
M
e
t
h
o
d
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
n
g
in
e
e
rin
g
a
n
d
M
a
n
a
g
e
me
n
t
Res
e
a
rc
h
(
IJ
EM
R)
,
v
o
l.
5
,
n
o
.
3
,
p
p
.
7
1
1
-
7
1
6
,
2
0
1
5
.
[1
3
]
L
in
,
C.
E.
,
Ch
e
n
,
S
.
T
.
,
a
n
d
Hu
a
n
g
,
C.
L
.
,
“
A
d
irec
t
N
e
w
to
n
-
Ra
p
h
so
n
e
c
o
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o
m
ic
d
isp
a
tch
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r S
y
ste
ms
,
v
o
l.
7
,
n
o
.
3
,
p
p
.
1
1
4
9
-
1
1
5
4
,
1
9
9
2
.
[1
4
]
L
ian
g
,
Z.
X
.
,
a
n
d
G
lo
v
e
r,
J.
D.
,
“
A
z
o
o
m
f
e
a
tu
re
f
o
r
a
d
y
n
a
m
ic
p
ro
g
ra
m
m
in
g
so
lu
ti
o
n
to
e
c
o
n
o
m
ic
d
isp
a
tc
h
in
c
lu
d
in
g
tran
sm
issio
n
l
o
ss
e
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r S
y
ste
ms
,
v
o
l.
7
,
n
o
.
2
,
p
p
.
5
4
4
-
5
5
0
,
1
9
9
2
.
[1
5
]
Ra
y
,
S
.
,
“
Eco
n
o
m
ic L
o
a
d
Disp
a
tc
h
S
o
l
u
ti
o
n
u
si
n
g
In
terv
a
l
G
r
a
d
ien
t
Me
th
o
d
,
”
Ad
v
a
n
c
e
d
Res
e
a
rc
h
in
El
e
c
trica
l
a
n
d
El
e
c
tro
n
ic E
n
g
i
n
e
e
rin
g
,
v
o
l.
1
,
n
o
.
4
,
p
p
.
55
-
58
,
2
0
1
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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1028
[1
6
]
S
u
d
h
a
k
a
ra
n
,
M
.
,
Ra
j,
P
.
A
.
D.
V
.
,
a
n
d
P
a
la
n
iv
e
lu
,
T
.
G
.
,
“
A
p
p
li
c
a
ti
o
n
o
f
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
f
o
r
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
p
ro
b
lem
s,”
In
2
0
0
7
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
I
n
t
e
ll
ig
e
n
t
S
y
ste
ms
Ap
p
li
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a
ti
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s
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o
Po
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S
y
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ms
,
T
o
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e
,
Niig
a
ta,
p
p
.
1
-
7
,
2
0
0
7
.
[1
7
]
A
risti
d
is,
V
.
,
“
P
a
rti
c
le
S
w
a
r
m
O
p
ti
m
iza
ti
o
n
(
P
S
O)
tec
h
n
i
q
u
e
s
so
l
v
in
g
Eco
n
o
m
ic
L
o
a
d
Disp
a
tch
(EL
D)
P
ro
b
lem
,
”
J
o
u
rn
a
l
o
f
S
t
a
ti
sti
c
s
a
n
d
M
a
n
a
g
e
me
n
t
S
y
ste
ms
,
v
o
l.
1
1
,
n
o
.
4
,
p
p
.
7
6
1
-
7
6
9
,
2
0
0
8
.
[1
8
]
S
a
h
a
y
,
K.
B.
,
S
o
n
k
a
r,
A
.,
a
n
d
Ku
m
a
r,
A
.
,
“
E
c
o
n
o
m
ic
L
o
a
d
Disp
a
tch
Us
in
g
Ge
n
e
ti
c
A
lg
o
rit
h
m
Op
ti
m
iz
a
ti
o
n
T
e
c
h
n
iq
u
e
,
”
In
2
0
1
8
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
a
n
d
Util
it
y
Exh
ib
it
io
n
o
n
Gr
e
e
n
En
e
rg
y
fo
r
S
u
sta
in
a
b
l
e
De
v
e
lo
p
me
n
t
(
ICUE)
,
P
h
u
k
e
t,
T
h
a
il
a
n
d
,
2
0
1
8
,
p
p
.
1
-
5.
[1
9
]
K
a
u
r
,
A
.
,
S
i
n
g
h
,
H
.
P
.
,
a
n
d
B
h
a
r
d
w
a
j
,
A
.
,
“
A
n
a
ly
s
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s
o
f
e
c
o
n
o
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i
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o
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s
i
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g
g
e
n
e
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ic
a
lg
o
r
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t
h
m
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
A
p
p
l
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c
a
t
i
o
n
o
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I
n
n
o
v
a
t
i
o
n
i
n
E
n
g
i
n
e
e
r
i
n
g
&
M
a
n
a
g
e
m
e
n
t
(
IJ
A
I
E
M
)
,
v
o
l
.
3
,
n
o
.
3
,
p
p
.
2
4
0
-
246
,
2
0
1
4
.
[2
0
]
Ola
k
u
n
le,
A
.
O
.,
a
n
d
F
o
ll
y
,
K.
A
.
,
“
Eco
n
o
m
ic
L
o
a
d
Disp
a
tch
o
f
P
o
w
e
r
S
y
ste
m
Us
in
g
G
e
n
e
ti
c
A
l
g
o
rit
h
m
w
it
h
V
a
lv
e
P
o
i
n
t
Ef
f
e
c
t,
”
In
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
in
S
w
a
rm
In
tell
ig
e
n
c
e
,
v
o
l.
9
1
4
0
,
2
0
1
5
,
p
p
.
2
7
6
-
284
.
[2
1
]
Din
h
,
B.
H.,
a
n
d
Ng
u
y
e
n
,
T
.
T
.
,
“
N
e
w
so
lu
ti
o
n
s
t
o
m
o
d
ify
th
e
d
iff
e
re
n
ti
a
l
e
v
o
lu
ti
o
n
m
e
th
o
d
f
o
r
m
u
lt
i
-
o
b
jec
ti
v
e
lo
a
d
d
isp
a
tch
p
r
o
b
lem
c
o
n
sid
e
r
i
n
g
q
u
a
d
ra
ti
c
f
u
e
l
c
o
st
f
u
n
c
ti
o
n
,
”
In
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
d
En
g
i
n
e
e
rin
g
T
h
e
o
ry
a
n
d
A
p
p
li
c
a
t
io
n
s
,
v
o
l.
4
1
5
,
2
0
1
6
,
p
p
.
5
9
8
-
6
0
7
.
[2
2
]
P
h
a
n
V
a
n
Ho
n
g
T
.
a
n
d
T
ra
n
T
h
e
T
.
,
“
Eco
n
o
m
ic
Disp
a
tch
in
M
icro
g
rid
u
si
n
g
S
to
c
h
a
stic
F
ra
c
tal
S
e
a
rc
h
A
lg
o
rit
h
m
,
”
GM
S
AR
N
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
,
v
o
l.
1
1
,
p
p
.
1
0
2
-
1
1
5
,
2
0
1
7
.
[2
3
]
Ra
o
,
D
.
S
.
N
.
M
.
,
a
n
d
K
u
m
a
r,
N.,
“
Co
m
p
a
risio
n
a
l
In
v
e
stig
a
ti
o
n
o
f
L
o
a
d
Disp
a
tch
S
o
lu
ti
o
n
s
w
it
h
T
L
BO,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
E
n
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
7
,
n
o
.
6
,
p
p
.
3
2
4
6
-
3
2
5
3
,
2
0
1
7
[2
4
]
G
a
c
h
h
a
y
a
t,
S
.
K.,
Da
sh
,
S
.
K.,
a
n
d
Ra
y
,
P
.
,
“
M
u
lt
i
Ob
jec
ti
v
e
Dire
c
ted
Be
e
Co
lo
n
y
Op
ti
m
iza
ti
o
n
f
o
r
Eco
n
o
m
ic
L
o
a
d
Disp
a
tch
W
it
h
En
h
a
n
c
e
d
P
o
w
er
D
e
m
a
n
d
a
n
d
V
a
lv
e
P
o
i
n
t
L
o
a
d
in
g
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
&
Co
mp
u
ter
E
n
g
in
e
e
r
in
g
(
IJ
ECE
)
,
v
o
l.
7
,
n
o
.
5
,
p
p
.
2
3
8
2
-
2
3
9
1
,
2
0
1
7
[2
5
]
S
p
e
a
,
S
.
R.
,
“
S
o
lv
in
g
p
ra
c
ti
c
a
l
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
p
r
o
b
lem
u
sin
g
c
ro
w
s
e
a
rc
h
a
l
g
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
t
e
r E
n
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
4
,
p
p
.
3
4
3
1
-
3
4
4
0
,
2
0
2
0
.
[2
6
]
Ra
jes
h
,
K.,
a
n
d
V
isa
li
,
N.,
“
H
y
b
rid
m
e
th
o
d
f
o
r
a
c
h
iev
in
g
P
a
re
to
f
ro
n
t
o
n
e
c
o
n
o
m
ic
e
m
i
ss
io
n
d
isp
a
tch
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
4
,
p
p
.
3
3
5
8
-
3
3
6
6
,
2
0
2
0
.
[2
7
]
Ba
su
,
M
.
,
“
M
o
d
if
ied
p
a
rti
c
le
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
f
o
r
n
o
n
c
o
n
v
e
x
e
c
o
n
o
m
ic
d
isp
a
tc
h
p
ro
b
lem
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
Po
we
r
&
En
e
rg
y
S
y
ste
ms
,
v
o
l.
6
9
,
p
p
.
3
0
4
-
3
1
2
,
2
0
1
5
.
[2
8
]
Ba
la
m
u
ru
g
a
n
,
R.
,
S
u
b
ra
m
a
n
ian
,
S
.
,
“
H
y
b
ri
d
in
teg
e
r
c
o
d
e
d
d
if
fe
re
n
ti
a
l
e
v
o
lu
ti
o
n
-
d
y
n
a
m
ic
p
ro
g
ra
m
m
in
g
a
p
p
ro
a
c
h
f
o
r
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
w
it
h
m
u
lt
ip
le
f
u
e
l
o
p
ti
o
n
s,”
E
n
e
rg
y
Co
n
v
e
rs
io
n
a
n
d
M
a
n
a
g
e
me
n
t
,
v
o
l.
4
9
,
n
o
.
4
,
p
p
.
6
0
8
-
614
,
2
0
0
8
.
[2
9
]
Ch
ian
g
,
C.
L
.
,
“
I
m
p
ro
v
e
d
g
e
n
e
ti
c
a
lg
o
rit
h
m
f
o
r
p
o
w
e
r
e
c
o
n
o
m
ic
d
isp
a
tch
o
f
u
n
it
s
w
it
h
v
a
l
v
e
-
p
o
in
t
e
ff
e
c
ts
a
n
d
m
u
lt
ip
le f
u
e
ls,”
IEE
E
tra
n
s
a
c
ti
o
n
s o
n
p
o
we
r sy
ste
ms
,
v
o
l.
2
0
,
n
o
.
4
,
p
p
.
1
6
9
0
-
1
6
9
9
,
2
0
0
5
.
[3
0
]
He
tze
r,
J.,
Da
v
id
,
C.
Y.
,
a
n
d
B
h
a
tt
a
ra
i,
K.,
“
A
n
e
c
o
n
o
m
ic
d
isp
a
tch
m
o
d
e
l
in
c
o
r
p
o
ra
ti
n
g
w
in
d
p
o
w
e
r,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
e
n
e
rg
y
c
o
n
v
e
rs
io
n
,
v
o
l.
2
3
,
n
o
.
2
,
p
p
.
6
0
3
-
6
1
1
,
2
0
0
8
.
[3
1
]
Jo
se
,
J.
T
.
,
“
Eco
n
o
m
ic l
o
a
d
d
isp
a
t
c
h
in
c
lu
d
i
n
g
w
in
d
p
o
w
e
r
u
sin
g
B
a
t
A
lg
o
rit
h
m
,
”
In
2
0
1
4
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
s in
El
e
c
trica
l
En
g
in
e
e
rin
g
(
ICAE
E)
,
V
e
ll
o
re
,
2
0
1
4
,
p
p
.
1
-
4
.
[3
2
]
Am
a
ireh
,
A
.
A
.
,
A
l
-
Zo
u
b
i
,
A
.
S
.
,
a
n
d
Dib
,
N.
I.
,
“
T
h
e
o
p
ti
m
a
l
s
y
n
th
e
sis
o
f
sc
a
n
n
e
d
li
n
e
a
r
a
n
ten
n
a
a
rra
y
s,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
m
p
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
2
,
p
p
.
1
4
7
7
-
1
4
8
4
,
2
0
2
0
.
[3
3
]
A
b
d
u
l
-
A
d
h
e
e
m
,
W
.
R.
,
“
A
n
e
n
h
a
n
c
e
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iz
a
ti
o
n
a
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
9
,
n
o
.
6
,
p
p
.
4
9
0
4
-
4
9
0
7
.
2
0
1
9
.
[3
4
]
P
a
u
l,
K.,
a
n
d
K
u
m
a
r,
N.,
“
Cu
c
k
o
o
S
e
a
rc
h
A
lg
o
rit
h
m
f
o
r
Co
n
g
e
stio
n
A
ll
e
v
iatio
n
w
it
h
In
c
o
rp
o
ra
ti
o
n
o
f
W
in
d
F
a
rm
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
&
Co
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
8
,
n
o
.
6
,
p
p
.
4
8
7
1
-
4
8
7
9
,
2
0
1
8
.
[3
5
]
Zh
a
n
g
,
H.,
Yu
e
,
D.,
Xie
,
X
.
,
Do
u
,
C.
,
S
u
n
,
F
.
,
“
G
ra
d
ien
t
d
e
c
e
n
t
b
a
se
d
m
u
lt
i
-
o
b
jec
ti
v
e
c
u
lt
u
ra
l
d
if
f
e
re
n
ti
a
l
e
v
o
l
u
ti
o
n
f
o
r
sh
o
rt
-
term
h
y
d
ro
th
e
rm
a
l
o
p
ti
m
a
l
sc
h
e
d
u
li
n
g
o
f
e
c
o
n
o
m
ic
e
m
issio
n
w
it
h
in
teg
ra
ti
n
g
w
i
n
d
p
o
w
e
r
a
n
d
p
h
o
to
v
o
lt
a
ic
p
o
w
e
r,
”
En
e
rg
y
,
v
o
l.
1
2
2
,
p
p
.
7
4
8
-
7
6
6
,
2
0
1
7
.
[3
6
]
L
e
e
,
K.
Y.,
S
o
d
e
-
Yo
m
e
,
A
.
,
P
a
r
k
,
J.
H.
,
“
A
d
a
p
ti
v
e
Ho
p
f
ield
n
e
u
ra
l
n
e
tw
o
rk
s
f
o
r
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
,
”
IEE
E
tra
n
sa
c
ti
o
n
s
o
n
p
o
we
r sy
ste
ms
,
v
o
l.
1
3
,
n
o
.
2
,
p
p
.
5
1
9
-
5
2
6
,
1
9
9
8
.
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