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l
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1
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h
er
m
al
g
en
er
ati
n
g
u
n
it,
P
i=
Po
w
er
g
en
er
atio
n
o
f
th
i
th
er
m
al
g
e
n
er
ati
n
g
u
n
it
T
h
e
f
u
el
co
s
t is q
u
ad
r
atic
f
u
n
c
tio
n
s
o
it is
g
iv
e
n
as
i
gi
i
gi
i
i
i
c
P
b
P
a
P
F
2
)
(
(
2
)
Su
b
j
ec
ted
to
n
i
L
D
i
P
P
P
1
(
3
)
m
a
x
,
m
i
n
,
i
i
i
P
P
P
(
4
)
W
h
er
e
i
a
,
i
b
,
i
c
ar
e
f
u
el
co
s
t c
o
e
f
f
ici
en
ts
o
f
th
e
th
i
th
er
m
a
l g
e
n
er
atin
g
u
n
i
t,
i
P
=
T
h
e
r
ea
l
p
o
w
er
o
f
g
en
er
at
in
g
u
n
it
i
,
D
P
=
T
o
tal
lo
ad
d
em
an
d
,
L
P
=
T
o
tal
tr
an
s
m
is
s
io
n
li
n
e
lo
s
s
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m
i
n
,
i
P
=
T
h
e
m
i
n
i
m
u
m
g
en
er
atio
n
li
m
it o
f
u
n
it i
an
d
m
a
x
,
i
P
=
T
h
e
m
a
x
i
m
u
m
g
en
er
atio
n
li
m
it o
f
u
n
it
i
.
2
.
1
.
E
co
no
m
ic
Dis
pa
t
ch
P
ro
ble
m
w
it
h Va
lv
e
-
P
o
int
L
o
a
din
g
E
f
f
ec
t
Sin
u
s
o
id
al
f
u
n
ctio
n
s
ar
e
ad
d
ed
w
it
h
th
e
q
u
ad
r
atic
f
u
n
c
tio
n
o
f
f
u
el
co
s
t
to
r
ep
r
esen
t
t
h
e
v
alv
e
-
p
o
in
t
lo
ad
in
g
e
f
f
ec
t
s
.
I
t f
o
llo
w
s
a
s
[
1
3
]
-
[
15
].
))
(
*
s
i
n
(
*
)
(
m
i
n
2
i
i
i
i
i
i
i
i
i
i
i
P
P
f
e
P
c
P
b
a
P
F
(
5
)
W
h
er
e
i
e
an
d
i
f
ar
e
co
ef
f
icien
t
o
f
t
h
e
g
en
er
at
in
g
u
n
its
r
e
f
lecti
n
g
v
a
lv
e
-
p
o
in
t
lo
ad
in
g
ef
f
ec
ts
.
T
h
e
tr
an
s
m
is
s
io
n
li
n
e
lo
s
s
es a
r
e
w
r
itten
as
n
i
n
j
n
i
i
i
j
ij
i
L
B
B
P
P
B
P
P
1
1
1
00
0
(
6
)
W
h
er
e
B
ij
, B
0i
an
d
B
00
a
r
e
tr
an
s
m
i
s
s
io
n
li
n
e
lo
s
s
co
ef
f
ici
e
n
t
s
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
2
0
1
7
:
3
2
4
6
–
3
2
5
3
3248
3.
T
& L
B
AS
E
D
O
P
T
I
M
I
Z
A
T
I
O
N
A
L
G
O
R
I
T
H
M
T
ea
ch
in
g
a
n
d
L
ea
r
n
in
g
(
T
&
L
)
in
s
p
ir
ed
o
p
ti
m
izatio
n
p
r
o
ce
s
s
p
r
o
p
o
s
ed
b
y
R
ao
,
Sa
v
s
an
i
an
d
p
atel
[
1
6
]
-
[
1
8
]
.
T
h
e
T
ea
ch
in
g
a
n
d
L
ea
r
n
i
n
g
(
T
&
L
)
b
ased
o
p
ti
m
i
za
tio
n
is
a
m
e
ta
-
h
e
u
r
is
tic
p
o
p
u
latio
n
b
ased
s
ea
r
ch
alg
o
r
ith
m
li
k
e
H
S
A
,
A
n
t
C
o
lo
n
y
Op
ti
m
iza
tio
n
(
AC
O)
,
P
SO
an
d
A
r
tif
ic
ial
B
ee
C
o
lo
n
y
(
AB
C
)
.
T
h
e
T
ea
ch
in
g
an
d
L
ea
r
n
i
n
g
(
T
&
L
)
b
ased
o
p
tim
izat
io
n
m
et
h
o
d
is
a
s
i
m
p
le
m
ath
e
m
at
ical
m
o
d
el
to
s
o
lv
e
d
if
f
er
en
t
o
p
tim
izatio
n
p
r
o
b
le
m
s
.
I
n
th
is
w
o
r
k
co
n
ce
n
tr
ates
o
n
a
n
e
w
o
p
ti
m
izatio
n
alg
o
r
it
h
m
th
at
i
s
T
ea
ch
in
g
a
n
d
L
ea
r
n
i
n
g
(
T
&
L
)
b
ased
o
p
tim
izatio
n
.
I
n
co
r
p
o
r
a
ted
T
&
L
b
ased
o
p
ti
m
izatio
n
alg
o
r
ith
m
is
e
f
f
ec
ti
v
e
r
e
m
ed
y
f
o
r
d
im
i
n
is
h
i
n
g
t
h
e
f
la
w
s
i
n
tr
ad
itio
n
al
ap
p
r
o
ac
h
lik
e
p
r
o
v
in
cial
o
p
ti
m
a
l
tr
ap
p
in
g
,
in
ad
eq
u
ate
ef
f
ec
ti
v
e
to
id
en
tify
n
ea
r
b
y
ex
tr
e
m
e
p
o
in
ts
an
d
i
n
e
f
f
icien
t
m
ec
h
a
n
is
m
to
a
n
al
y
s
i
n
g
th
e
co
n
s
tr
ain
ts
.
A
cc
o
r
d
in
g
to
o
u
r
T
&
L
b
ased
o
p
tim
iza
tio
n
alg
o
r
ith
m
a
lear
n
er
ca
n
g
ai
n
s
k
n
o
w
led
g
e
i
n
t
w
o
w
a
y
s
: (
i)
b
y
teac
h
er
(
ca
lled
teac
h
er
p
h
a
s
e)
an
d
(
ii)
i
n
ter
ac
ti
n
g
w
it
h
t
h
e
n
ei
g
h
b
o
u
r
.
lear
n
er
s
(
ca
lled
lear
n
er
p
h
ase)
.
I
n
th
is
a
lg
o
r
ith
m
lear
n
er
s
ar
e
ca
lled
as
p
o
p
u
latio
n
.
Desi
g
n
v
ar
iab
le
ar
e
ca
lled
as su
b
j
ec
ts
o
f
th
e
lear
n
er
s
.
T
h
e
b
est lea
r
n
er
is
tr
ea
ted
as tea
ch
er
.
3
.
1
.
T
ea
cher
p
ha
s
e
P
u
p
il
g
ain
s
k
n
o
w
led
g
e
f
r
o
m
th
e
in
s
tr
u
c
to
r
ev
er
an
d
in
s
tr
u
cto
r
s
h
o
u
ld
i
m
p
r
o
v
e
t
h
e
m
ea
n
r
esu
lt
o
f
class
b
y
h
is
s
k
i
lls
.
T
h
e
b
est
lear
n
er
is
th
at
o
n
ce
k
n
o
w
led
g
e
i
s
eq
u
al
to
th
e
teac
h
er
s
k
n
o
w
le
d
g
e
m
ea
n
s
teac
h
er
m
ak
e
to
lear
n
er
s
to
r
ea
ch
h
i
s
k
n
o
w
led
g
e.
B
u
t
p
r
ac
ticall
y
is
n
o
t
p
o
s
s
ib
le
b
ec
au
s
e
a
ll
lear
n
er
s
ar
e
n
o
t
clev
er
er
.
T
h
is
f
o
llo
w
s
a
s
[
1
9
]
,
L
et
i
M
= Mean
i
T
=
T
ea
ch
er
at
an
y
iter
atio
n
i
.
i
T
Ma
k
es
th
e
m
ea
n
i
M
to
m
o
v
e
to
war
d
s
its
o
w
n
k
n
o
w
led
g
e
le
v
el,
th
er
e
f
o
r
e
i
T
ch
o
s
en
as
M
new
.
He
n
ce
th
e
b
e
s
t
lear
n
er
is
tr
ea
ted
as
teac
h
er
.
T
h
e
d
if
f
er
en
ce
o
f
t
h
e
c
u
r
r
en
t
m
ea
n
r
esu
l
t
o
f
ev
er
y
s
u
b
j
ec
t
an
d
th
e
co
r
r
esp
o
n
d
in
g
r
esu
lt o
f
t
h
e
teac
h
er
f
o
r
ev
er
y
s
u
b
j
ec
t is g
i
v
en
b
y
,
)
(
*
i
F
n
e
w
M
T
M
r
D
i
f
f
e
r
e
n
c
e
(
7
)
W
h
er
e
F
T
=
T
ea
ch
in
g
f
ac
to
r
.
I
t is
g
iv
e
n
as
f
o
llo
w
s
:
)]
1
2
(
*
)
1
,
0
(
*
1
[
r
a
n
d
r
o
u
n
d
T
F
(
8)
T
h
is
d
if
f
er
e
n
ce
m
o
d
i
f
ies t
h
e
e
x
is
t
in
g
s
o
l
u
tio
n
ac
co
r
d
in
g
to
th
e
f
o
llo
w
i
n
g
ex
p
r
ess
io
n
d
i
f
f
e
r
e
n
c
e
X
X
i
o
l
d
i
n
e
w
,
,
(
9
)
W
h
er
e
i
n
e
w
X
,
is
th
e
u
p
d
ated
v
al
u
e
o
f
i
o
l
d
X
,
.
A
cc
ep
t
i
n
e
w
X
,
3
.
2
.
L
ea
rner
ph
a
s
e
T
h
e
in
p
u
t
f
o
r
th
e
lear
n
er
p
h
as
e
is
th
e
teac
h
er
in
lear
n
er
p
h
a
s
e
lear
n
er
g
ain
s
k
n
o
w
led
g
e
le
ar
n
er
g
ain
s
k
n
o
w
led
g
e
b
y
t
w
o
w
a
y
s
:
o
n
e
is
g
ai
n
i
n
g
k
n
o
w
led
g
e
f
o
r
m
te
ac
h
er
an
d
o
th
er
is
b
y
s
h
ar
in
g
k
n
o
w
led
g
e
b
et
w
ee
n
lear
n
er
s
in
ter
ac
tio
n
.
T
h
e
lear
n
er
p
h
ase
is
s
h
o
w
s
as
f
o
llo
w
s
.
R
an
d
o
m
l
y
s
elec
t t
w
o
lear
n
er
s
an
d
w
h
er
e
i
≠
j
)
(
*
,
,
j
i
i
o
l
d
i
n
e
w
X
X
r
X
X
if
)
(
)
(
j
i
X
f
X
f
)
(
*
,
,
i
j
i
o
l
d
i
n
e
w
X
X
r
X
X
if
)
(
)
(
j
i
X
f
X
f
(
1
0
)
A
d
m
it i
f
it g
iv
e
s
b
etter
f
u
n
ctio
n
v
al
u
e
4.
CO
M
P
ARIS
O
N
O
F
T
&L
B
ASE
D
O
P
T
I
M
I
Z
A
T
I
O
N
AL
G
O
RI
T
H
M
WI
T
H
O
T
H
E
R
AL
G
O
RI
T
H
M
S
T
h
er
e
ar
e
s
ev
er
al
alg
o
r
ith
m
s
li
k
e
G
A
,
P
SO,
A
B
C
,
HS
A
,
etc.
T
h
e
p
r
o
p
o
s
ed
th
e
ef
f
ec
ti
v
e
n
es
s
o
f
T
&
L
b
ased
Op
ti
m
izatio
n
o
n
6
u
n
it
test
s
y
s
te
m
an
d
co
m
p
ar
ed
w
it
h
P
SO,
DE
,
HS
A
.
Fin
a
ll
y
T
&
L
b
ased
o
p
tim
izatio
n
tec
h
n
iq
u
e
g
i
v
es
th
e
h
ig
h
q
u
al
it
y
s
o
lu
t
io
n
.
T
h
e
f
lo
w
ch
ar
t
f
o
r
th
e
p
r
o
p
o
s
ed
T
L
B
O
alg
o
r
ith
m
is
s
h
o
w
n
in
F
ig
ur
e
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
C
o
mp
a
r
is
io
n
a
l I
n
ve
s
tig
a
tio
n
o
f Lo
a
d
Dis
p
a
tch
S
o
lu
tio
n
s
w
ith
TL
B
O
(
DS
N
M R
a
o
)
3249
Fig
u
r
e
1
.
Flo
w
C
h
ar
t o
f
T
&
L
b
ased
o
p
tim
izatio
n
alg
o
r
it
h
m
5.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
& DIS
C
USS
I
O
N
T
h
e
P
r
o
p
o
s
ed
T
&
L
b
ased
Op
ti
m
izat
io
n
al
g
o
r
ith
m
w
a
s
i
m
p
le
m
e
n
ted
f
o
r
t
w
o
ca
s
e
s
ca
s
e:
1
co
n
s
is
tin
g
6
-
B
aselo
ad
g
e
n
er
at
io
n
u
n
it
s
p
r
ef
er
r
i
n
g
lo
ad
in
g
v
alv
e
p
o
in
t
lo
ad
in
g
e
f
f
ec
t
a
n
d
lo
s
s
es.
T
h
e
T
&
L
b
ased
o
p
tim
izatio
n
alg
o
r
it
h
m
w
a
s
w
r
i
tten
u
s
i
n
g
M
A
T
L
A
B
8
.
5
(
R
2
0
1
6
b
)
r
u
n
n
i
n
g
o
n
i5
p
r
o
ce
s
s
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A
.
J.
W
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.
F
.
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,
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[2
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[3
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[4
]
D
.
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B.
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,
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IEE
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3
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[5
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d
A.
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.
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a
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,
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Res
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[6
]
M
.
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.
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ter
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Ch
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iza
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IET
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[8
]
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.
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,
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to
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Un
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[9
]
L
.
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g
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d
L
.
L
i,
“
A
n
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ti
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ti
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m
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[1
0
]
A
.
I
.
S
e
l
v
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k
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d
K.
T
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sh
k
o
d
i,
“
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n
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w
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a
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c
le
s
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m
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iza
ti
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so
lu
ti
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n
t
o
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o
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.
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[1
1
]
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No
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a
n
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d
H.
Ib
a
,
“
Dif
fe
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e
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d
is
p
a
tch
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ro
b
lem
s
,
”
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e
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tric
Po
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S
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ste
m
Res
e
a
rc
h
,
v
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l.
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8
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p
p
.
1
3
2
2
–
1
3
3
1
,
2
0
0
8
.
[1
2
]
N
.
Am
j
a
d
y
a
n
d
H
.
S
h
a
rif
z
a
d
e
h
,
“
S
o
lu
ti
o
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o
f
n
o
n
-
c
o
n
v
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x
e
c
o
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m
ic
d
isp
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tch
p
ro
b
lem
c
o
n
sid
e
rin
g
v
a
l
v
e
lo
a
d
in
g
e
ffe
c
t
b
y
a
n
e
w
m
o
d
ifi
e
d
d
iffere
n
ti
a
l
e
v
o
lu
ti
o
n
a
lg
o
rit
h
m
,
”
El
e
c
tric
a
l
Po
we
r
a
n
d
En
e
rg
y
S
y
ste
ms
,
v
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l.
3
2
,
p
p
.
8
9
3
–
903
,
2
0
1
0
.
[1
3
]
L
.
Wan
g
a
n
d
L
.
L
i,
“
A
n
e
ff
e
c
ti
v
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d
if
fe
re
n
ti
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rm
o
n
y
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e
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rc
h
a
lg
o
rit
h
m
f
o
r
th
e
so
lv
in
g
n
o
n
-
c
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v
e
x
e
c
o
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o
m
ic
lo
a
d
d
isp
a
tch
p
ro
b
lem
s
,
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e
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trica
l
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4
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p
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3
2
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3
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0
1
3
.
[1
4
]
L
.
D
.
S
.
Co
e
l
h
o
a
n
d
V
.
C
.
M
a
ria
n
i,
“
A
n
im
p
ro
v
e
d
h
a
rm
o
n
y
s
e
a
rc
h
a
lg
o
rit
h
m
f
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r
p
o
w
e
r
e
c
o
n
o
m
ic
lo
a
d
d
isp
a
tch
,”
En
e
rg
y
Co
n
v
e
rs
io
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a
n
d
M
a
n
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g
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me
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t
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0
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p
.
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5
2
2
–
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6
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0
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.
[1
5
]
D
.
Zo
u
,
e
t
a
l.
,
“
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v
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d
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ti
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lu
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lg
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m
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d
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tc
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p
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lem
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w
it
h
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r
w
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h
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t
v
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lv
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p
o
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t
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ff
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ts
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En
e
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,
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1
8
1
,
p
p
.
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7
5
–
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0
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0
1
6
.
[1
6
]
R.
V
.
Ra
o
,
e
t
a
l.
,
“
T
e
a
c
h
in
g
–
lea
rn
in
g
-
b
a
se
d
o
p
ti
m
iza
ti
o
n
:
A
n
o
v
e
l
m
e
th
o
d
f
o
r
c
o
n
stra
in
e
d
m
e
c
h
a
n
ica
l
d
e
sig
n
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s
,
”
Co
mp
u
ter
-
Ai
d
e
d
De
sig
n
,
v
o
l
.
4
3
,
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p
.
3
0
3
–
315
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2
0
1
1
.
[1
7
]
R.
V
.
Ra
o
,
e
t
a
l.
,
“
T
e
a
c
h
in
g
–
L
e
a
rn
in
g
-
Ba
se
d
Op
ti
m
iza
ti
o
n
:
A
n
o
p
ti
m
iza
ti
o
n
m
e
th
o
d
f
o
r
c
o
n
ti
n
u
o
u
s
n
o
n
-
li
n
e
a
r
larg
e
sc
a
l
e
p
ro
b
lem
s
,
”
In
fo
rm
a
ti
o
n
S
c
ien
c
e
s
,
v
o
l.
1
8
3
,
p
p
.
1
–
15
,
2
0
1
2
.
[1
8
]
R.
V
.
Ra
o
a
n
d
V
.
P
a
tel,
“
A
n
imp
ro
v
e
d
tea
c
h
in
g
-
lea
rn
in
g
-
b
a
se
d
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
f
o
r
so
lv
in
g
u
n
c
o
n
stra
in
e
d
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s
,
”
S
c
ien
ti
a
Ira
n
ica
D
,
v
o
l.
2
0
,
p
p
.
7
1
0
–
7
2
0
,
2
0
1
3
.
[1
9
]
S
.
Ba
n
e
rjee
,
e
t
a
l.
,
“
T
e
a
c
h
in
g
lea
rn
in
g
b
a
se
d
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
r
o
b
lem
c
o
n
sid
e
ri
n
g
v
a
lv
e
p
o
i
n
t
l
o
a
d
i
n
g
e
ff
e
c
t
,
”
El
e
c
tri
c
a
l
P
o
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r a
n
d
E
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e
rg
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p
.
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5
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.
[2
0
]
I
.
Cio
rn
e
i
a
n
d
E
.
Ky
ria
k
id
e
s,
“
A
GA
-
A
P
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S
o
l
u
ti
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f
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th
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Eco
n
o
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ic
Disp
a
tch
o
f
Ge
n
e
ra
ti
o
n
i
n
P
o
w
e
r
S
y
ste
m
Op
e
ra
ti
o
n
,
”
IE
EE
T
r
a
n
s
a
c
ti
o
n
s o
n
p
o
we
r sy
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ms
,
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l/
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e
:
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7
(1
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,
p
p
.
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,
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2
.
B
I
O
G
RAP
H
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S
O
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AUTH
O
RS
DSNM
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:
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re
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e
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e
d
h
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T
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h
.
a
n
d
M
.
T
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c
h
.
in
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f
ro
m
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T
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Un
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y,
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n
d
h
ra
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ra
d
e
sh
,
In
d
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M
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.
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