TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 14, No. 1, April 2015, pp. 72 ~ 79
DOI: 10.115
9
1
/telkomni
ka.
v
14i1.757
0
72
Re
cei
v
ed
De
cem
ber 2
1
, 2014; Re
vi
sed
Febr
uary 17,
2015; Accept
ed March 5, 2
015
Demand Side Energy Management for
Linear
Progra
mming Method
N. Logana
th
an*, K. Laks
hmi
K.S.Rangas
am
y Co
lle
ge of T
e
chno
log
y
, KSR
Ka
lvi Na
gar, T
i
ruch
eng
od
e, Namaka
l-63
7 21
5
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: loguk
irsh@
g
mail.com
A
b
st
r
a
ct
T
h
is
pap
er
prese
n
ts an
o
p
ti
mi
z
a
ti
o
n
meth
od
to th
e D
e
man
d
S
i
d
e
En
ergy
Man
age
ment Syst
e
m
(DSEMS) of a
give
n cons
u
m
er (e.g.an
in
du
strial co
mp
o
u
n
d
or u
n
ivers
i
ty campus) w
i
th
respect to h
o
u
r
ly
electricity
pric
e
s
. T
he d
e
m
a
n
d
s ca
n b
e
s
u
p
p
lie
d thr
o
u
gh t
he
main
gri
d
a
nd stoc
hastic
Distribut
ed E
n
ergy
Reso
urces (
D
ERs), such
a
s
w
i
nd
and
s
o
lar
pow
er s
o
urces. T
o
s
o
l
v
e this
DSE
M
S pro
b
le
m
and
opti
m
i
z
at
ion a
l
gorith
m
b
a
se
d on Li
ne
ar Prog
ramming (L
P) appr
oach
has
bee
n i
m
pl
e
m
e
n
ted. T
he obj
e
c
tive
of the pro
pos
e
d
metho
d
is to
max
i
mi
z
e
th
e
utili
z
a
ti
on
of th
e cluster
of de
ma
nds. T
h
is
L
P
alg
o
rith
m a
l
l
o
w
s
the cluster
of
de
ma
nd to
bu
y, store
an
d s
e
ll
ener
gy at s
u
itab
le ti
mes
to ad
just the
h
ourly
loa
d
l
e
ve
l. T
o
evaluate the performanc
e of
the proposed algorit
hm
an IEEE 14 bus
sys
tem
was considered. The results
show
s that the cluster of de
mands
of e
nergy
man
a
g
e
m
e
n
t system usi
ng t
he pro
pos
ed a
ppro
a
ch incr
ea
sin
g
the efficiency
a
nd min
i
mi
z
i
n
g
the loss
es than
the existin
g
me
thods.
Ke
y
w
ords
: de
ma
nd si
de e
n
e
r
gy ma
na
ge
me
nt, demand r
e
s
pons
e, distrib
u
ted en
ergy res
o
urces.
Copy
right
©
2015 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introduc
tion
This pa
per
addresse
s u
ndersi
zed
si
ze ex
citing
energy syste
m
s am De
m
and Side
Energy M
a
n
ageme
n
t System
(DSEM
S) problem
within a
Sm
all Size
of
Electri
c
Ene
r
g
y
Manag
eme
n
t System (SS
EEMS)usin
g
Linea
r Progra
mming
(LP)
method. In th
is p
ape
r p
r
op
ose
a dem
and
re
spo
n
se mo
de
l for
a
clu
s
ter
of pri
c
e
-re
sp
o
n
sive
dema
n
d
s i
n
tercon
ne
cted th
rou
gh
an
SSEEMS. The work
ing of t
he
DSEMS of
this c
l
us
ter
of demands
is
as
follows
.
Demands supply
con
s
um
ption
informatio
n to the DSEM
S that is
in cha
r
ge fo
r th
eir en
ergy
supply. Given
the
deman
d ra
ng
es an
d functi
on inform
atio
n of
the different dema
nds, and based
on ene
rgy pri
c
e
informatio
n, the
DSEMS o
p
timally de
cid
e
t
he
hou
rly
energy con
s
u
m
ption fo
r e
a
c
h
dema
n
d
a
nd
sen
d
s the tot
a
l energy con
s
umptio
n to the ene
rgy su
ppliers.
Dema
nd Si
de
Energy ma
n
ageme
n
t in
cl
ude
s pl
anni
n
g
an
d o
p
e
r
ation of
ene
rgy
cou
p
led
prod
uctio
n
an
d co
nsumptio
n of units.
D
e
m
and
s offe
r
consumption i
n
ord
e
r to the
DSEMS that is
in charge fo
r
their e
n
e
r
gy
sup
p
ly. The
o
perat
io
n of
Demand
Side
Energy M
ana
gement Sy
stem
(DSEMS) is
cru
c
ial
for cl
uster of d
e
m
and
s
with th
eir e
n
e
r
gy supply. Based
on th
e po
wer
deman
d rang
e of a utility and p
o
wer
co
st inform
at
ion
,
the DSEMS
optimally de
cide
s the
hou
rly
energy co
nsumption fo
r
each dem
an
d and
determines th
e to
tal power
co
nsum
ption to
the
energy sources. It ha
s
co
nsid
ere
d
thre
e ene
rgy
sou
r
ce
s, n
a
mely, the main
gri
d
, photovoltai
c
and a win
d
p
o
we
r plant sy
stem. The group of dem
a
nds o
w
n
s
an
energy stora
ge ability to store
energy and t
o
utilize it at
suitable tim
e
s a
s
s
oon
as de
si
red.
Dema
nd
s offer con
s
umpti
on in
orde
r to th
e
DSEMS that i
s
in
ch
arg
e
fo
r their en
ergy
sup
p
ly. The
DSEMS opti
m
ally de
cide
s the
hourly
ene
rg
y con
s
um
ptio
n for ea
ch
d
e
mand
an
d
sen
d
the
total en
ergy
co
nsum
ption to
the
energy su
ppli
e
rs. T
he cl
ust
e
r of dem
and
s owns
an
en
ergy sto
r
ag
e facility to store
energy and t
o
con
s
tru
c
t u
s
e
of it at right period a
s
ne
ed
ed.
With high
pe
netration
of wind e
n
e
r
gy, the kn
o
w
le
d
ge of un
ce
rtainties a
hea
d
can b
e
extremely val
uable to
a n
u
mbe
r
of en
ergy
system
ope
ration
a
nd ma
nage
m
ent procedu
res,
inclu
d
ing
but
not limited t
o
, optimal o
p
e
ration
re
se
rve determi
na
tion [1], syst
em ste
ady-state
se
curity assessment [2], econo
mic g
enerati
on scheduli
ng and
dispatch [3], the excellent
approximatio
n and gen
era
lization
capa
bilities, neu
ra
l networks
(NNs) are widel
y used for wi
nd
energy forecasts [4]. It su
pport
s
m
a
rke
t
parti
cipat
ion
of
wind
gen
eration
sizin
g
and
control
of
flow batte
rie
s
[5]. Reliabilit
y benefits
of energy st
o
r
a
ge in a
sy
ste
m
with hi
gh
wind
pen
etrat
i
on
inclu
d
ing th
e
improvem
ent
of win
d
capa
city credit
a
r
e
qua
ntified in
[6], the impa
ct of storage
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Dem
and Side
Energ
y
Man
agem
ent for Linea
r Prog
ra
mm
ing Method (N. Log
anat
han)
73
improvin
g the
economi
c
p
e
rform
a
n
c
e,
reliability and
the integ
r
atio
n of
rene
wa
b
l
e source
s i
n
a
micro gri
d
-ba
s
ed
enviro
n
m
ent [7]. The o
peratin
g cost
categ
o
rie
s
h
a
v
e been m
o
d
e
lled u
s
ing th
e
approa
ch p
r
e
s
ente
d
in [8].
In a real-tim
e pri
c
ing based
DSM framew
ork, the billing
mechanism
is
of great
importa
nce
si
nce
it may
si
gnifica
ntly affect the
cons
umers
’
motivat
i
on to
partic
i
pate in the
DSM
program.
However,
there
has only
been limited work investigating
th
is im
portant billing issue.
[9]
Propo
se
d a simple billing
approa
ch, wh
ere the con
s
um
ers we
re
charg
ed in pro
portion
al to thei
r
total energy consumption f
o
r the
next op
eration
pe
riod
. To add
re
ss t
h
is p
r
obl
em [
10] pro
p
o
s
ed
a
new
billin
g
a
ppro
a
ch, whe
r
e ea
ch co
nsumer
is
cha
r
ged
ba
sed
o
n
hi
s/he
r in
st
antane
ou
s lo
ad in
each time
sl
ot du
ring
the
next o
peration p
e
ri
od, synchrono
us and asyn
ch
ronou
s algo
rithms
were respe
c
tively develop
ed in [1
0] a
nd [11]
fo
r t
he
con
s
um
ers to a
c
hi
eve
their
optim
al
strategi
es in
a distrib
u
ted
manne
r, pro
p
o
se
d ba
sed o
n
the proxima
l
decom
po
sition method [1
2].
In addition, th
ere exi
s
ts a t
w
o-way
com
m
uni
cation
s netwo
rk co
nn
ecting
e
a
ch
consume
r
to the e
nergy provid
er [1
3], DR is
able
to re
du
ce
pea
k d
e
man
d
, th
ereby
alleviat
ing the
nee
d
to
operate hig
h
-co
s
t high
-emi
ssi
on g
ene
rat
i
ng unit
s
[14], DR
ca
n be u
s
ed
as
a pe
rfect comple
m
ent
to the u
n
cert
ain rene
wa
bl
e en
ergy
re
source
s
su
ch
as win
d
[15]. Location
[1
6] repo
rted
that U.K.
DR pot
ential is able to red
u
ce its pe
ak
deman
d
by more tha
n
15
%. Renewabl
e energy is the
only sustai
na
ble
solutio
n
of se
cu
re
en
ergy
whi
c
h
i
s
e
n
viro
nme
n
tal frie
ndly
[17]. In rece
nt
decade, with
advan
ceme
nts in telecom
m
unication
s and the in
cre
a
sin
g
req
u
ire
m
ents of vari
ous
se
ctors
of th
e po
we
r i
ndu
stry for mo
nitoring
to
gr
id
as a scientific
a
nd practi
cal
solution
to
the
utility industry
[18]. The im
pact
of pri
c
e
-
based
DR
on
voltage p
r
ofil
e and
lo
sses
of a di
strib
u
tion
netwo
rk
wa
s
explore
d
in [1
9]. It have been different
a
s
pe
cts of the
netwo
rk
ope
ration, inclu
d
i
n
g
netwo
rk p
e
a
k
load, netwo
rk losse
s
, voltage prof
iles,
and service reliability, are
to be studi
ed
[20]. To solve this DSE
M
S proble
m
, this pape
r
prop
ose an
algorith
m
ba
se on a Li
n
ear
Programmin
g
(LP) m
odel
has
bee
n imp
l
emented to
t
a
ke full
adva
n
tage of the
effectivene
ss for
the g
r
ou
p of
dem
and
wit
h
value
to
a set of
co
nstrai
nts such a
s
mi
nimu
m daily
ene
rgy
con
s
um
ption,
highe
st an
d
lowe
st amo
u
n
t hourly l
o
a
d
levels, e
nergy stora
ge li
mits, and e
n
ergy
accessi
bility from the ma
in
grid and the DERs.
The pap
er i
s
stru
ctu
r
ed
as follows: Section 2 provide
s
the
demand
si
de ene
rgy
manag
eme
n
t system. Se
ction 3 provide
s
the Impl
eme
n
tation of LP method to sl
o
v
e the DSEMS
probl
em. Section 4 pre
s
ent
s the re
sult
s and an
alysi
s
. Section 5 p
r
o
v
ides the con
c
lu
sion.
2. Demand S
i
de Energ
y
Manag
e
ment Sy
stem
Dema
nd
side
energy man
ageme
n
t has forecast
fun
c
tion of ene
rgy-rel
a
ted produ
ction
and utilizatio
n units. The main obje
c
tives of D
SEM
S are re
sou
r
ce co
nservati
on, environm
ent
prote
c
tion
an
d p
r
ice
savin
g
s, at
the
sa
me time
as t
he u
s
e
r
s h
a
ve pe
rma
nent
acce
ss to
the
energy they required.
Figure 1. Block
Diag
ram o
f
DSEMS
It
is
essential
to
in
corpo
r
ate
th
e energy mana
gement in th
e
orga
nization
al arrang
eme
n
t,
thus the ene
rgy managem
ent can be i
m
pleme
n
ted.
Resp
on
sibilit
ies and the communi
catio
n
of
the resolutio
n
make
r mu
st be reg
u
larizing.
The
Wind and Sola
r energy is a
realisti
c ene
rgy
sup
p
ly which
ma
ke
high
-q
uality use of
wind
an
d
sol
a
r
ene
rgy.Block dia
g
ram o
f
dema
nd
sid
e
energy man
ageme
n
t sy
stem is
sho
w
n in Fig
u
re
1. This
met
hod
can
not
only su
pply
a
agre
e
me
nt of
low cost
and
high
reliabilit
y for a
qu
anti
t
y of are
a
wh
ere
ene
rgy
condu
ction
is
not
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 1, April 2015 : 72 – 79
74
suitabl
e su
ch
as limit re
si
stan
ce a
nd i
n
stall a n
e
w area
whi
c
h
resolution th
e eme
r
gen
cy
of
energy so
urces a
nd e
n
viro
nment poll
u
tion. It is very
compl
e
x to m
a
ke
use of th
e sol
a
r a
nd
wind
energy all cli
m
ate pre
s
e
n
tly during solar system or
wi
nd
system se
parately,
for the co
nstraint of
time and
a
r
e
a
. So a
struct
ure t
hat i
s
b
a
s
ed
on
re
ne
wabl
e resou
r
ce
s b
u
t at th
e eq
uivalent t
i
me
reliabl
e
is
ne
eded and
wi
nd/sol
a
r syst
em
with ba
ttery
sto
r
ag
e can meet
u
p
this con
s
trai
nt.
Providentially
, the probl
em
s can b
e
mo
derately ove
r
come
by inte
grating th
e re
sou
r
ces to fo
rm
DG sy
stem, u
s
ing the
stren
g
th of one so
urce to
overcome the limitation of the other sou
r
ce.
3. Implementation of LP
Metho
d
to Slov
e the DSEMS Problem
The
step by
step a
pproa
ch for math
em
atical
formula
t
ion of linea
r
prog
ram
m
ing
method
to solve ene
rgy manage
m
ent probl
em i
s
as follo
ws.
Step 1:
Input the de
mand vari
abl
es for
real ti
me
data an
d
pre d
e
termi
ned data
usi
ng Ne
ural
Network (NN) of the ener
gy
manage
ment
system.
Step 2:
Formul
ate th
e dema
nd fu
nction to
be
optimize
d
(m
aximum or
minimum
)
a
s
a linea
r
function of th
e different variable
s
.
Step 3:
Formul
ate th
e con
s
traint
s
of ene
rgy m
a
nagem
ent
system su
ch as
re
sou
r
ce
limi
t
ations,
market dema
nds, inter- rel
a
tion betwee
n
different de
mand vari
abl
es.
Step 4:
From the con
s
ide
r
ed
ca
se
study thirteen
diffe
rent types of deman
ds available an
d three
different type
s of en
ergy
sou
r
ces
avail
able. Let a
pq
denote th
e
numbe
r of u
n
its of en
erg
y
sou
r
ces q in t
he unit of demand
s p, q =1, 2, 3: p = 1,
2, 3, 4, 5, 6,
7, 8,
9, 10, 1
1
, 12, 13. Let
x
q
be the n
u
mb
er of u
n
its
co
nsum
ed fo
r d
e
mand. T
hen
the total nu
mber
of units of dema
n
d
s
I in
the prefe
rre
d sou
r
ce.
∑∑
a
x
(
1
)
Step 5:
Let b
p
be the numbe
r of uni
ts of minimu
m daily
requi
rement of the deman
d i and
it can
be express
e
d as
follows
:
∑∑
a
x
b
(
2
)
Whe
r
e q
= 1, 2, 3 . . . 13
Step 6:
For ea
ch
sou
r
ce q,
x
p
must
be either po
sitive or zero.
x
0
(3)
Whe
r
e q
= 1, 2, 3
Step 7:
Let c
p
b
e
the
ene
rgy man
ageme
n
t sy
stem outp
u
t of energy source q. Thu
s
the
total
output of ene
rgy mana
gem
ent system i
s
given belo
w
:
zc
x
c
x
+……
+
c
x
(4)
Step 8:
The mo
st importa
nt ch
ara
c
teri
stic o
f
Pr
ediction
Intervals (PIs) is thei
r coverag
e
prob
ability. PI coverage p
r
obability (PICP) is mea
s
u
r
ed by cou
n
tin
g
the numb
e
r of target values
covered by the con
s
tru
c
ted
PIs.
PICP
∑
c
(
5
)
PICP is a me
asu
r
e of valid
ity of PIs con
s
tr
u
c
ted
with an asso
ciate
d
confid
en
ce
level.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Dem
and Side
Energ
y
Man
agem
ent for Linea
r Prog
ra
mm
ing Method (N. Log
anat
han)
75
Step 9:
PI normali
ze
d average
d
width
(PINA
W
) a
sse
ss
es PIs
fr
om th
is
as
pe
c
t
a
n
d
me
as
ur
es
how
wide the
y
are:
PIN
A
W
∑
U
L
(
6
)
W
h
er
e U
p,
L
p
upp
er limit
and l
o
wer li
mit of de
man
d
, R is the
range
of th
e
unde
rlying
target d
e
fine
d a
s
the
differen
c
e
bet
ween it
s mini
mum a
nd m
a
ximum valu
es. PINA
W i
s
the
averag
e widt
h of PIs as a percenta
ge of
the underlyin
g target ra
ng
e.
4. Results a
nd Analy
s
is
The pro
p
o
s
e
d
LP metho
d
sim
u
lation
were develo
ped usi
ng M
A
TLAB
7.10
softwa
r
e
packa
ge a
n
d
the
system
config
uratio
n
is Intel
Co
re i
5
-24
10M P
r
o
c
e
s
sor
with
2
.
90 G
H
z spe
ed
and
4 GB
RA
M. In proposed
work
three energy
sources, 13 demands
and IEEE 14 bus sy
stem
con
s
id
ere
d
a
s
case stu
d
y, over spe
c
ified time
inte
rvals. The
co
mputational
result
s of EM
S
probl
em attai
ned by the propo
sed LP m
e
thod
for the
three en
ergy sou
r
ces a
nal
yzed.
4.1. Case s
t
u
d
y
– IEEE 14 Bus Sy
stem
This
study is accepted
a
w
ay
at the state of plann
ing, ope
ratio
n
, control an
d co
st-
effective fore
ca
st. They exist of use in dec
i
s
ive the magnitud
e
an
d phase angl
e of load buses,
and a
c
tive a
n
d
re
active
po
wer flow grea
ter than
cond
uction li
ne
s,
and a
c
tive a
n
d
re
active
po
wer
with the
pu
rp
ose
of b
e
inj
e
cted
at the
buses.
For th
is
wo
rk the li
near p
r
og
ram
m
ing m
e
thod
i
s
use
d
for mat
hematical an
alysis. The p
u
rpo
s
e
of thi
s
proj
ect is t
o
expand a
MATLAB pro
g
ram
maximize th
e
utilization
of the cl
uste
r o
f
deman
ds
when it i
s
subj
ected to
a
se
t of con
s
trai
n
t
s.
Figure 2 sh
o
w
s the total d
e
mand
s an
d the ene
rgy so
urces.
Figure 2. IEEE 14 Bus syst
em Network
This
LP al
go
rithm all
o
ws
the cl
uste
r o
f
deman
d to
buy, sto
r
e
and
sell
ene
rgy at
suitabl
e times to adju
s
t the hourly loa
d
level to analyze voltage
s, active and reactive po
we
r
c
ontrol on eac
h
buses
us
ed for IEEE 14 bus
s
y
s
t
ems
.
By primary IEEE 5 bus
s
y
s
t
em is
desi
gne
d by usin
g hand
calcul
ation
s
and com
p
a
r
ed
with MATLAB Program
re
sults a
nd the
n
IEEE 14 bus s
y
s
t
em MATLAB program is
exec
uted
with the c
o
ntribution data. This
type of
analysi
s
is u
s
eful for
solving the po
we
r flow
proble
m
in different
powe
r
syste
m
s whi
c
h
wil
l
useful to calculate t
he un
known qua
ntities.
1) Loa
d dem
and data:
This pa
per
co
nsid
ers the DSEMS demands lo
cated i
n
the K.S.Ranga
samy Coll
ege of
Tech
nolo
g
y (KSRCT
) ca
m
pus.
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046
TELKOM
NI
KA
Vol. 14, No. 1, April 2015 : 72 – 79
76
Table 1. Loa
d
Deman
d
Dat
a
@ KSRCT
H
O
U
R
Lig
ht &
fan
Mech
dep
A
cad
emic
bloc
k i
Te
x
Til
e
A
cad
e
mic
block ii
Bio-
tech
1
Com
me
-rcial
Mba
Bloc
k
Bio
-
tec
h 2
Ne
w
host
el
Ct
mai
n
bloc
k
It
par
k
Mo
t
-
ors
Tot
al
12.10
PM
114.
5
32.1 28.1
49.
1
5.4 37.8
11.9
74.6
7.1
28.4
30
64.5
10.
1
493.
6
1.51
PM
113.
2
31.9 63.1
28.
3
10.2 22.2
13.7
44.7
2.2
35.3
32.1
86.5
10.
4
493.
8
2.11
PM
109.
9
34.8 56.2
48.
8
11.2 25.9
13.2
75.2
6.8
38.2
68.1
93.7
10.
4
592.
4
3.26
PM
108.
4
29.3 43.5
40
10.5
32.6
13.2
75.4
7.2
28.1
34.9
85.5
9.8
518.
4
4.05
PM
86.2 25.6
35
30.
9
8.9
4.8
13.9
50.7
7.2
27.5
29.2
91.9 0 411.
8
5.20
PM
70.1
11
8
4.8
6.6
5.7
13.2
25.2
1.2
37.5
29.5
66.9 0 279.
7
6.10
PM
66.3 10.3
5.8
4.8
0.44
6.9
12.7
23.8
4.9
39.5
11.7
41
0.3
228.
4
7.54
PM
63.9 7.8
5.1
2.7
1
32
9.6
4.5
0.7
36.7
5.6
18
0.6
188.
2
8.10
PM
64.9 7.7
4.5
4.1
1.1
3.2
11.4
7.8
0.8
36.1
2.9
11.6
0.7
156.
8
9.37
PM
49.2 6.2
4.4
4
1
3.4
9.2
3.6
0.8
36.3
2.9
20
5.7
146.
7
10.30
PM
62.3 6.2
4.3
3.9
1
2.9
9
3.8
0.8
26.9
2.9
15.2
5.1
144.
3
11.11
PM
53 5.6
4.1
4
1
3.3
9
3.7
0.7
26.9
3
14.2
5.1
133.
6
12.48
AM
51.3 4.5
4.1
3.8
1.1
3.1
8.9
3.5
0.8
26.8
2.9
13.7
5.3
129.
8
1.26
AM
50.3
4.3
4.3
3.9
0.9
3
8.7
3.5
0.8
26.7
3
14.4 1 124.
8
2.40
AM
48.9 4.8
4.3
3.9
1
3
8.7
3.5
0.7
26.8
2.9
14
0.9
123.
4
3.29
AM
47.5 4.6
4.4
3.9
1
3.1
8.8
3.5
1.3
26.9
3
14
0.9
122.
9
4.19
AM
47.1
4.5
4
3.6
0.8
3.2
8.7
3.4
1.7
26.8
3
14.1 1 121.
9
5.05
AM
47.4 4.7
4.2
4
0.5
3.3
8.5
3.5
1.5
26.8
3
14.3
1.1
122.
8
6.47
AM
40.6 4.9
4.1
4.3
0
3.1
8.6
3.6
6.4
43.1
2.9
19.9
0.4
141.
9
7.53
AM
50.9 5.2
4
4.3
0
2.7
8.5
3.9
1.6
58.5
4.1
27
0
170.
7
8.50
AM
94.1 14.4
7.8
23.
3
3.3 21.1 9
48.7
3.4
41.9
10.4
55.2
0
332.
6
9.55
AM
106.
9
42.9 32.3
46.
4
9.7 33.8
12.4
81.9
10.
7
43.4
20.7
98.5 0 539.
6
10.35
AM
109.
5
34.2 48.4
43.
6
11.2 34
13.3
82.2
10.
2
45.5 30.5
99.9
12.
6
575.
1
11.55
AM
108.
8
35.2 55.2
41.
6
5.6 40.5
13.4
88.6
9.8
44.7
28.1
101.
2
9.6 582.
3
The
DERs and an energy st
orage facility are located at
bus
14.The
main grid is
con
n
e
c
ted in
to the bu
s3.
The total po
wer
su
pplie
d
from the
DE
Rs, m
a
in g
r
i
d
and
ene
rgy
storage facilit
y. The ava
ilable DSM capacity
is
taken as a
fr
action
of the
scheduled demand of
the corre
s
po
nding
hou
r. I
n
ad
dition, th
e committed
DSM
cap
a
cit
y
has to be
put ba
ck to t
h
e
deman
d du
ri
ng the same
day in ord
e
r
to t
he beh
avior of ene
rgy pri
c
e
se
nsitive sm
art
applia
nce
s
. If a hig
h
de
gre
e
of DE
Rs ge
neratio
n
i
s
co
nsid
ere
d
, the
main
cont
rib
u
tion of
DSM
is leveli
ng th
e loa
d
a
nd
redu
cing
the
DERs va
riabi
lity. The ho
u
r
ly load
level
for
different
deman
ds a
n
d
collecte
d
the
data on 22.09.2014 -
23.0
9
.2014 at 12 PM - 11AM from Thursd
ay
to Friday in KSRCT.
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TELKOM
NIKA
ISSN:
2302-4
046
Dem
and Side
Energ
y
Man
agem
ent for Linea
r
Prog
ra
mm
ing Method (N. Log
anat
han)
77
4.2. Simulink
Model for Proposed
DSEMS
The mo
del h
a
s d
e
tailed
si
mulation
with
a little time interval takes
more th
an 6
hours to
run.
Dema
nd
side
ma
nag
ement u
s
e
s
t
he ba
se
mo
del of lin
ear
prog
ram
m
ing
.
The
colle
cti
v
e
analysi
s
ju
st requi
re
s all t
he facto
r
s th
at were
furth
e
r in the p
a
st for each o
pportu
nity. This
cha
r
a
c
teri
stic allows thi
s
model to re
strict DE
Rs when the overload of it avoids a
c
hievin
g a
rea
s
on
able
solution (g
ene
ration gre
a
ter
than dema
n
d
)
.
Perman
ent
m
agnet syn
c
hronou
s gen
erators (PMS
G’
s) are lo
gicall
y use
d
in
sm
all wi
nd
turbines for
several reasons
collectively with hi
gh
efficiency, g
earle
ss, sim
p
le co
ntrol.
This
system p
r
o
c
e
dure a
s
sump
tion of wind turbin
e,
the maximum outp
u
t energy of wind ge
ne
rat
o
r
depe
nd
s on t
he greate
s
t tip sp
eed
ratio
.
A wind turbi
ne op
erate
s
by extract
kin
e
tic en
ergy from
the wi
nd
pa
ssi
ng th
rou
g
hout its wi
n
d
turbine
rot
o
r. Th
e MP
PT is
co
ntro
lled to t
r
a
c
k the
maximum e
n
e
rgy of
the
win
d
turbin
e.The
wind
prod
uces 29
0 volts
outp
ut voltage f
r
om
gene
rating
st
ation. The
wi
nd produ
ce
s 7.5 amp
s
o
u
tput cu
rrent
from
ge
nera
t
ing station.T
h
e
photo voltaic produ
ce
s 86
KW from generatin
g stat
i
on. The mai
n
grid produ
c
e
s
77 kW from
deman
d
side.
The
output
voltage an
d
current in th
e
ma
in g
r
id i
s
shown in
Figu
re 3. Th
e IT P
a
rk
con
s
um
ed 4
4
.
95 kW from
load
side.Th
e
output volta
ge an
d curre
n
t in the IT Park is
sho
w
n
in
Figure 4.
Figure 3. Output Voltage a
nd Cu
rrent-M
ain Grid fo
r Demand Sid
e
Figure 4. Output Voltage a
nd Cu
rrent-IT
Park for
De
mand Side
DSM
results i
n
reduction of
losses and proper
utilizati
o
n of
the
resources.
Thi
s
m
o
deling
sho
w
s flexibi
lity in gene
ration an
d lo
ad bal
an
ce
that evaluat
es
req
u
ire
m
ent con
s
ide
r
i
n
g
operational
a
nd
cap
a
city
cost. Th
e
cha
nge
s im
pact
only on
curtai
led d
e
man
d
becau
se th
e
only
con
s
trai
nt on
shifted d
e
ma
nd that affe
cts several
in
stants of time
i
s
for
ea
ch
da
y, the minimu
m
size that
ha
s
been
con
s
ide
r
ed to
ge
nera
t
e the
clu
s
ters.
In
o
r
de
r
to evaluate
th
e perfo
rman
ce
of
the model a
nd und
erstan
d the impa
ct of shi
fted DSM on the d
e
mand
-sup
ply balance, this
sub
s
e
c
tion p
r
ese
n
ts a
sim
p
le case stud
y for 24 ho
urs. The d
e
ma
nd is m
odel
e
d in a si
nu
soi
d
al
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 1, April 2015 : 72 – 79
78
way to p
r
ovid
e so
me
so
rt o
f
variation. Th
e dem
and
wo
uld not
be e
n
ough to
a
c
hie
v
e this result i
n
a practi
cal a
p
p
licatio
n, external
co
ntrol te
chni
que
s
wou
l
d be n
e
cessary. The Fi
gu
re 5
sh
ows th
e
total conn
ect
ed load, total con
s
um
ed po
wer fo
r
the feasibl
e
LP method of the time perio
d.
Figure 5. Peak Powe
r Con
s
umptio
n at KSRCT
5. Conclusio
n
The p
r
op
ose
d
metho
d
provides th
e a
c
tual
time m
onitor a
nd
control
of de
mand
side
manag
eme
n
t syste
m
. It
improve
s
th
e
pe
rform
a
n
c
es
of st
ru
cture
dem
and
to the
leve
l of
distrib
u
ted e
n
e
rgy resources diffu
sion.
The ph
ot
ovoltaic
a
nd wind model wa
s
consi
dered
u
s
i
n
g
MATLAB. The overloa
d
energy which is pro
d
u
c
ed
f
r
om ph
otovoltaic and wi
n
d
powe
r
plan
t it
transfe
rred to
the ele
c
tri
c
al
netwo
rk. T
h
e
energy
co
nsumption of IE
EE 14 bu
s sy
stem h
a
s
bee
n
determi
ned u
s
ing LP
meth
od
in
all
th
e buses and d
e
mand
i
s
sati
sfy
with
p
r
ote
c
tion system for
photovoltai
c
and
win
d
p
o
w
er pla
n
t was i
m
plem
en
ted. The
pro
posed
advan
ce
sati
sfies the
clu
s
ter
of de
mand
s in
the
energy
man
a
gement
syste
m
and
al
so i
m
prove
s
the
system
efficie
n
cy
and minimi
ze
s the lo
sses.
The exce
ss
energy from
distrib
u
ted e
n
e
rgy re
so
urces can al
so
be
store in the b
a
ttery and it could be utili
ze
by the load whe
n
there i
s
a demand of
energy.
Referen
ces
[1]
MA Matos, RJ
Bessa. S
e
ttin
g
the
o
perati
n
g res
e
rve
usi
n
g pr
oba
bi
listic
w
i
nd
e
ner
g
y
fo
recasts.
IEEE
T
r
ans. Energy
Syst.
2011; 26(
2): 594–
60
3.
[2]
RJ Bessa, MA
Matos, IC Costa,
L Bremerma
nn, IG Franchi
n, R Pestana,
N Machad
o, HP W
a
ldl,
C
W
i
chman
n
. Re
serve setti
ng
and
stead
y-st
a
t
e securit
y
as
sessment
usin
g
w
i
nd
en
erg
y
unc
ertaint
y
forecast: A case study
.
IEEE Trans. Sustain. Energy
. 201
2; 3(4): 827–
83
6.
[3]
YV Makarov, PV Etingov, J Ma, Z
Y
Huang
, K Subbara
o
. Incorporati
ng
uncerta
int
y
of w
i
nd
ener
g
y
gen
eratio
n for
e
cast into
ener
g
y
s
y
stem
op
e
r
ation, d
i
spatc
h
, and
unit co
mmitment proc
edur
es.
IEEE
T
r
ans. Sustain.
Energy.
20
11; 2(4): 433-
44
2.
[4]
K Bhask
a
r, S
N
Sin
gh. AW
N
N
-assiste
d
w
i
n
d
e
ner
g
y
f
o
rec
a
sting
usi
ng fe
ed-for
w
a
r
d
ne
ural
net
w
o
rk.
IEEE Trans. S
u
stain. Ener
gy
.
2012; 3(2): 3
0
6–3
15.
[5]
T
KA Brekken, A Yokoch
i, A v
on Jo
ua
nne, Z
Z
Yen,
HM Ha
pke, DA H
a
l
a
ma
y
.
Optim
a
l
ener
g
y
stora
g
e
sizing
and c
ont
rol for
w
i
n
d
en
erg
y
ap
plic
atio
ns.
IEEE Trans. Sustain. Ener
gy
. 2011; 2: 69
-77.
[6]
P W
ang, Z
Gao, L Bertling. O
peratio
nal a
d
equ
ac
y
stu
d
ies
of energ
y
s
y
s
t
ems
w
i
th
w
i
n
d
farms an
d
ener
g
y
stor
ag
e
s
.
IEEE Trans.
Energy Syst.
2012; 27: 2
377
–
238
4.
[7]
Y Xu, C
Sin
gh.
Ade
quac
y
a
n
d
eco
nom
y
an
al
ysis
of d
i
strib
u
tion s
y
st
ems i
n
tegrate
d
w
i
t
h
el
ectric e
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