impact of integrating the photovoltaic and wind
TRANSCRIPT
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Impact of Integrating the Photovoltaic and Wind
Energy Sources on Generation System
Reliability and Operation Economics
Amit lain, Jaeh o Choi, Member, IEEE, and Byounglin
Kim
Abstract-This pap er presents an appro ach to assess the
impact
on
the performance
of
generation system with the
integration
of
the photovoltaic and wind energy systems. The
fluctuating nature
of
the energy produced by these renewable
energy
sources
has different
effect on
the generalion system
reliab ility than the energy produced by the conven tional
generation units. The probability models for conventional,
photovoltaic and wind generalion units are created separately
treating them
as
three separate groups.
An
hourly load model
i s
employed for the present study, thus the probability models f o r
photovoltaic and wind energy units
a r e
modified hourly to
include the
ef fect
of fluctuation
in
generation capacity. Fina lly
these models are combined
to
evaluate the re liab ility index, l o s s of
load expectation
LOLE),
using discrete state algorithm and
impact
of
the integration
o f
wind and photovoltaic units
is
analyzed. The conve ntiona l fuel savings due to the
use
of
photovoltaic and wind energy
sources
are assessed to analyze the
impact on the operational
economics.
Index
Terms-Reliability analysis,
loss
of load expectation,
gen eration System, wind, photovoltaic, operatio nal
economics
1. INTRODUCTION
N
the last few years, non-conventional energy sources have
attracted major attention as
the
alternat ive source o f pow er
generation due to the continuous depletion o f the
conventional energy sources. Apart from this,
the
conventional
energy sources cause pollution and many other adverse
environmental impacts. New technologies fo r electrical pow er
from renewable energy sources like sun and wind have been
developed and considerable research are being done
to
improve them. By
their
very nature these power generation
sources are small, modular, and geographically distributed,
which clearly identify them as Distributed Generation (DG)
sources. The installed capacity o f wind power generation units
in com mercial operation was nearly
7000
M W b y t he e nd
of
year
2000 [ I ] , [ 2 ] .
Wi th the technological advancement,
I
The authors uauld like to
acknowledge
the financial suppon
provided
b y
BKZl
program
ofChungbuk Nat ional University
A m i t l a i n and Jaeho Choi
are
\Lath the Dupanmenl of Electrical and
Computer Engineering. Chungbuk Naiional University, Cheongu. Chungbuk
361-763 South Korea (e-mail. amii@power chungbuk ac k i and
photovoltaic (PV) systems are also being used now days for
commercial power generation fro m solar energy.
Photovoltaic and Win d systems differ considerably from the
conventional power generation technologies in their
performance and operating characteristics. With the
integrat ion o f PV and win d generat ion units i n the commercial
power system, the fluctua ting na ture of the energy produced by
these systems. have differe nt effect on the o verall system
relia bilit y than the energy produced by .conventional units.
Therefore, it becomes particularly important to study the
impact that they will have on the overall generation system
reliability. Equally important
i s to
assess the economical
impact
of
these sources on the system, as there i s no fuel cost
in case of P V and wind power generation
[ 3 ] .
Thi s paper presents a meth od for assessing the impact o f
integrating the PV and wind system on
the
overall electric
power generation system reliability. This is done by evaluating
the reliability index,
loss
o f oad expectation
(LOLE),
for the
power generation system with and without integration o f PV
and wind system in the overall electric power generation
system. Imp act
of
integration
of
PV and wind sources
on
the
overall system operational economics
i s
analyzed in terms of
conventional fuel savings due
to
the use of these
non-
conve ntion al sources.
The overall system is divided into three subsystems,
containing the conventional, ph otovoltaic and win d units, and
generation system proba bility models are bu ilt for each o f
these subsystems. These probab ility m odels o f PV and wind
systems are updated to incorporate
their
fluctuating energy
production and all three subsystems are combined using
the
Discrete State A lgor i thm ,to f in d the
loss
o f load expectation
for the hour under study. For assessing the impact on
operational economics, total energy generated by the PV and
wind units
i s
evaluated by using the hourly updated energy
generation. Then the conventional fuel saving i n quantity and
money terms, wh ich has been achieved because o f the
utilization
of
P V and w ind generation system,
i s
calculated by
using formulae that have been developed for this simulation.
chaigpoiber chungbuk.ac.ki)
Korea (e-mail bjkimQkiinch eon
ac
k r )
Byound in
K i m
IS
\ \ i rh Kimcheon college^ Kimchson City 740-704 South
0-7803-7459-21021517.00 2002
IEEE
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11.
PROBABILISTIC
MODELINGAND RELIABILITY INDEX
DEFINITION FOR
GENERATION SYSTEM
A . Probabilistic Mod el of Generation System
The avai labi l i ty o r random outages of generat ion units
i s
calculated as probability density function
on
the bases o f
the
histor ic data. The graphical representation o f
the
avai labi l i ty
o f
the generating capacity of a given
unit
on the bases of
histor ical data is represented as shown in Fig.1.
The reliability analysis for generation system
uses
a
capacity outage probabi l i ty table, whic h
i s
an array o f capacity
leve ls
and the associate probabilit ies o f existence. Th is i s
obtained by combining the generat ing
units
avai labi l i ty and
unavai labi l i ty
using
basic probabi l i ty concepts. From the
individual probability table,
we
prepare cumu lat ive probab i l i ty
table [ 6 ] . The
cumulat ive probabi l i ty
i s
the probabi l i ty of
f inding
a
quanti ty o f capacity outage equal to or
greater
than
the indicated amount. In this paper we are using a generation
system for the two state units and a recursive algorithm, w hich
adds the units sequentially,
is
used to bu i ld the capacity model
o f
the
generation system that
i s
represented as the cumulative
probabi l i ty table.
upt ime
B.
Recursive Algorithm
for
the Probabilistic Capacity Model
i /:L\ ofthe Generation System
The cumulat ive probabi l i ty o f a part icular capacity outage
unit of capacity CjM W and for ced
tate
o f X M W ,
after
the
outage rate U ,are added,
is given
b y
P ( X )
= (1 -U ,)P X)+ ,P X -c,
(1)
l l l l l l
where
P (X)
and
P(X)
denote the cumulative probabi l i t ies
of
I
Downstage 2
he capacity outage state o f M W before and after
the I
uni t
is
used.
ime
Stage21
*+T
mwntim
The above expression is ini t ial ized by
setting
P (X) = 1.0
for
X
5 0
ig
I
Random un i t performance
record
ignoring schedule oulage
The long-term average o f generat ion unit up time expressed
as a fraction o f the average cycle time gives the probabi l i ty
that
the generation capacity
of
the unit
wil l
be available, also
called the unit avai labi l i ty (denoted
asp). In
he same way, the
long-term average of the down
time
gives
the
probabi l i ty that
the generating
capacity ofthe
unit wil l
be unavailable (denoted
by
9),
also called as
the
un it forced outage rate (FOR).
The probabi l i ty
values p
and are probabi l i ty o f the unit
being in the up-state at any t ime and the probab i l i ty o f the
unit
in
the
downstate
at
any
time t
respectively
[ 4 ] , [SI.
The
forced outage capacity prob ability density function o f the
unit
i s depicted in Fig.
2.
q,
and
P (X) = 0 0
therwise.
P (X-CJ =
Outage capacity
(X - CJ
proba bi l i ty before the
i h unit
is
added.
C.
Reliability Index:
Loss of
Load Expectation
The technique used
to
determine whether
a generation
system satisfies a desired l eve l of rel iabi l i ty i s defined by
a
rel iabi l i ty index,
loss
o f load expectat ion
(LOLE). A
l os s o f
load
wil l
occur only when the system load
level
exceeds the
c apabi li t y o f the generating capacity remaining in service.
Loss
o f load expectation
is
the probabi l i ty o f the power
generating units o f
a
system bein g inadequate
to
meet
the
load
demand. The capacity outage probabi l i ty table o f the
generation system
is
convolved
with
system load
characteristics for calculating
the
loss of load e xpectation.
An
hourly peak load variation model is used where i t s hourly peak
load represents each hour; therefore, ind ividu al hou rly load
values
are
used.
L O i E
= 4
(c;
L , )
( 2 )
where
C,
=
available capacity on h o u r i
L ; =
forecast peak load
on
h o u r i
P,(C,
<
LJ =
probab i li ty o f
oss
of load on hour i
ou tage
Capacity MW) '
Fig. Forced outage capacity p robability density function
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Afte r computing the ho ur ly value
o f LOLE
for al l the hours f luctuat ing energy generation by creat ing two m-dimensional
vectors
MPV,
and
MWIND,
such
that:
nder study, the index for
the
entire per iod
is
computed by:
( 3 )
where
LOLE
=
4oss of
loa d expectation for period under study
nhs =numb er ofhours under s tudy
The
value
of L O L E i s in
hours.
where
C
WIND,PO WINDk
PRWIND
WIND, ,,
= 5 )
M PV,
,
PRPV
=
iIh
element o f
MPV,
= iIh
e le me nt o f C P v
=rated power
o f PV
subsystem
111. LOSS
OF L O A D
EXPECTATION FOR PV AND
WIND
ENERGY
cPv,
INTEGRATED
GENERATION SYSTEM
MWIND,., =
Ih
element
o f M W I N D A
CWIND,
= i h
lement
o f C W l N D
PRWIND =
rated power o fw in d subsystem
Modification of Probabilistic Model
of
PV and Wind
Generation Systems
The overall power generation system
i s
div ided into three
subsystems, conta ining the conventio nal,' photo voltaic and
win d units respectively, and capacity outage prob ability Each subsystem i s treated as multi-state unit and these
models are bui lt using a Recursive Alg ori thm for each ofthese subsystems are combined to calculate the
LOLE
for the hour
three subsystems. T w o m-dimensional vectors describe each
of
in question. The combinat ion
o f
hese multi-state units
results
the generation subsystem pro bab ility models as follow s: in states wit h capacities given by the equation:
/Iiscrefe
C,= i r h
element of t he
C
vector
C,,
= CC, CP Vi
+
C WIND,,
( 6 )
=
o ne o f he possible discrete capacity states
P, =
i h
element
of^ vector
where
= P(C
2 CJ
i, n
C,,,,
refer to the
states
i n
the
first,
second and third
subsystems respectively.
represent
an element in
three-dimensional array C
that constitutes
al l
possible capacity states o f the
=
probabi l i ty o f capacity
on
outage being equal to or greater
than
C;
m =
number ofgenerat ion
states
. .
combined system.
These generation subsystem probability models are
represented by the vectors
CC, P C , ~ PV, PPV
and
CWIND,
P
WIND,
A Discrete State Algo r i thm
i s
used for evaluating the
LOLE
o f the system for the hour under s tudy. The rel iabi l i ty index
for the
entire
per iod
i s
computed by the summation
of
a l l
hour ly values o f
LOLE.
The Discrete State Algor i thm i s
presentedas fol lows:
where,
CC
PC
=
capacity vector
o f
conventional subsystem
=
prob ability vector o f conventional subsystem
C P V
= capacity vector
o f P V
subsystem
PPV
=pro bab i l i ty vector of
PV
subsystem
CWIND
=capacity vector of wi nd subsystem
PWIND
= probabi l i ty
vector
of wind subsystem
I ) Initialize
by sett ing
where
LOLE, = 0 0
LOLE, = loss
of lo ad expec tat ion fo rk hour ofs tudy
The power output, o f the
PV
and wind subsystems are
calculated for each hour under study and vectors containing
the hourly output o f the
PV
and wind generat ion unit
subsystems are create d
as:
POPV,
=
power output
o f
the
PV
system during the
Kh
hour o f he per iod under s tudy
POWIND,
= power output o f he wind system dur ing
the Kh
hour of th e per iod under s tudy
The probabi l i ty model of
PV
and wind generation
subsystems
are
mo dif ied to take into account the effect
of
the
2) n = I
( 3 )
=
1
(4) CT =
CC,,
CPV,,, + CWINDn,,,nd
(7)
where
CT
=to tal capacity
nc
npv
mvind
CGC
=
number o f states
in
conventional subsystem
=
number o f states
in
PV
subsystem
=
number o f
states in
wind subsystem
=to tal capacity
in
conventional subsystem
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CPV,,
CWIND,,,,,, = total capacity
in
wind subsystem
=total capacity in PV subsystem
( 5 )
i 1
( 6 )
Con= CC,
CP?
+
CWIND,
If Cgn
s
equal to or more than (CT - oad for the hour, go
to
(9 ) .
7) =
+
1
If i is less than or equal to nc, go to (6) .
(MW)
50
20
12
8) If i
is
more than nc, go to (1 1)
9) b
=
i
where
b = boundary state defining the loss of load.
IO) LOLE, =
LOLE,+ PC,(PP?- PPQ+,) (PWIND
P WIND,+,
)
( l l ) j = j l I
I f j is less than or equal to npv. go to
( 5 ) .
1 2 ) n = n + l
I f n is
less
than or equal to nivind, go to ( 5 ) .
(M W ) r a t e ( ~ 0 ~ 7
2 100
0.05
2
40
0.08
3 36
0.08
IV. OPERATIONAL ECONOMIC ANALY SIS
When studying the impact of integrating the PV and wind
energy system with-conventional generation system on the
operational performance of overall generation system, it
is
very important to look into the operational economic aspects
of
using these renewable sources.
As
PV and wind units
replace some conventional generation units, the fuel that
would have been used for power generation is saved due to the
use ofth ese renewable energy sources.
In the present study, this conventional fuel saving is
evaluated to assess the impact
on
operational economics of
overall generation system. For this, total energy generated by
the PV and wind
units in
entire period under study (nhs hours)
is calculated by using the hourly modified PV and wind
generation capacities. Then the conventional fuel saving that
has been achieved because of the utilization of these
renewable energy sources
is
evaluated,
in
terms of quantity
and money, by using formulae:
Qua ntiy offuel saved
Cost
offuel saved
= p o w e r
in
MW which
is
replaced by PV units
=po wer in MW which is replaced by wind units
= efficiency of conventional generation unit
=
percentage of
full
rated capacity that is generated
by PV unit for a particular hour
= percentage of
full
rated capacity that is generated
by wind unit for a particular hour
=
lower calorific value of fuel used at the input of
conventional unit; KJ/Kg
=
cost of fuel used;
Rs /Kg
V.
SIMULATION RESULTS
FOR
C A S E
STUDIES
The reliability index evaluation and fuel savings assessment
approach has been applied
on
a sample test system with a total
capacity of
176
MW [ 7 ] . The test system consists of the units
shown in table 1 The study was conducted by replacing the
12
MW conventional units sequentially, by PV and wind units
and this is represented as the penetration level of the
PV
and
wind power in the overall electric generation system. The
penetration level indicates the percentage of the system
generation capacity that is
substituted by photovoltaic and
wind units. The details about the PV and wind units are given
in Appendices 1 and 11
[SI.
All the units contained in wind
system are taken to be identical and same is the case with all
PV units.
TABLE
I
DATA OR THE TESTSYSTEhl
UDES FOR
SIMULATION STUDY
lUnit capacity1 No. of units I Total capacity /Forced outage1
Three types of cases were used for the present study:
I )
Conventional subsystem and both photovoltaic and
(2) Conventional subsystem and photovoltaic subsystem
( 3 )
Conventional subsystem and wind subsystem
wind subsystems.
In each case the PV and wind generation units replaced
some conventional units. In case I ) the replaced conventional
power generation was equally divided between PV and wind
units. Then for different penetration levels, reliability indices,
energy generated and fuel saved due to the integration of these
renewable energy units have been simulated for four different
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Penetratio
n level
Case
I
Case2
Case3
Case
I
Case2
Case3
6 .8
15.38
1 7 . 2 9
1 3 . 4 8 I
1 .8
1 2 . 5
I 1 . 2
I?
I 10.8
I
14.6
I 6.97
3 .6 I 4 .9 I 2.3
13.6 ] 14.6 15.8 ] 13.4
I
4.9 5.3
4.5
20.5
I 2 1 . 9 1 2 3 . 7
1 2 0 . 1
I 7.4
I 8 .0 I
6.8
Cornnuison
of results
for
lune
1
20.5
I
16 1 1 2 1 . 9 I 10.5
I 5.4 I
7.4
I 3.5
Historic data
i s
taken for wind velocity, solar radiation, and
ambient temperature 191. Hourly load data using daily load
cycle was
used.
This simulation study i s based on the tacit
assumption that du ring one-month period unde r consideration,
wind speed, solar radiation, ambient temperature and load
patterns do not vary significantly fi om day-to-da y and same
daily statistics s used for the entire month [ IO ] .
V I . DISCUSSION
The results obtained from the simulation study for a l l three
cases for four differe nt months of a year a re ana lyzed to assess
the impact o f integrating
the
P V and win d energy systems to
the conventional generation system. For
this
purpose, a
comparative study o f the rel iab il i ty index
LOLE),
energy
generated and fuel saved, for al l the three types o f cases for
four differe nt m,onthsof a year with the increasing penetration
level, is done.
In the month o f March, the LOLE for the case (2) with PV
system i s higher with increase in penetration because power
generation duration for PV system
i s
only for
a
few hours hut
wind system generates power for a large number o f hours
though it
i s
lesser in amount than
the
power generated by P V
system. The energy generated by PV system is larger than
win d system because the P V system wi l l produce energy very
near to rated capacity for mid
noon
hours due to high solar
radiation while w ind velocity
i s
very near to cut-in velocity for
almost
al l
the hours so win d energy generation is small. So
f u e l saved by PV system i s more than the win d system.
LOLE
i s
the best for case (I) ystem in
al l
three cases and fuel
savings
is
also very good though it i s somewhat lower than the
case
2).
In the m onth o f June, LOLE for case 3)
i s
the best because
of th e fact that the wind velocity i s very high and remains very
near t o rated velocity for almost al l the hours. The fuel saved
and en ergy generated i s also highest in
this
case.
I n the mon th of September, case
I )
is best as far as LOLE
i s
concerned This
i s
due
to
the fact that w ind speed is lower in
this month and day length i s also small therefore both case
(2)
and case
(3)
could
not
generate the required power during the
high load times which is generated by their combination in
case (I). ut ene rgy generated and fuel saved in case (3)
i s
the
highest because wind speed remains in between cut-in and
rated velocity almost all the hours o f the day. Therefore, it
generates pow er a l l the time though at lower than rated power
and total energy generation in case
3
becomes highest in al l
the cases and same i s the case with
f u e l
savings.
In the month of December, both wind speed and solar
insolation are very low
so
power generated by them is also
low. But their combination in case I ) is quite good because
PV system generates quite good amount o f power in daytime
though it is only for a few hours wh ile in the nigh t time at least
some power will be available through the wind system. Thus,
there
wi l l always be some renewable power i n addition to
power generated
by
conv entiona l sources. Bu t fuel saved and
energy generated
i s
maximum
in
case (2) as the power
generated
is
very near to rated power in the
noon
hours so
energy generated in only
a
few hours i s greater than the low
level wind power generated i n larger number o f hours.
The study done for four months, characterizing different
weather condition of the years, shows that the generation
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system with both PV and wind units i.e. case I ) is better than
the systems with only either integration of PV units or wind
units with conventional generation system. In this case LOLE,
energy generated from renewable sources and fuel savings are
better than the case
2)
or case (3) when considering the
overall performance of the system in all the four characteristics
months of the year. Another important conclusion from the
study is that
LOLE
increases with increase of the renewable
energy penetration in the generation system because the effects
of
fluctuating energy from renewable energy become more
dominant. .Therefore, the level of PV and wind energy
integration in the conventional generation system at present
level of technology should not be very high when considering
their effect on the overall generation system reliability though
their integration have a positive impact on operational
economics due to conventional fuel savings.
VII. APPENDICES
A. Appendix I
Photovoltaic units of I MW capacity with
FOR
= 0.09.were
used in this paper. These units were modeled by a two state
model, and the power output of these units was calculated
using the following equations:
where
W = power output at plant
= module efficiency at ambient temperature for the
particular hour
A
= surface area of the total modules
q ?=
wiring efficiency
I, efficiency of power conditioning system
H i; =radiation on the tilted surface
Hho i:un nlradiation on the horizontal surface
= factor to correct horizontal incident radiation to that on
' =
standard efficiency of module
F, = matching factor
P
= temperature coefficient for the change in module
T,,, = temperature ofc ell
To
ambient temperature.
a tilted surface
efficiency
These equations are for a fixed tilt plant. For optimum
power generation in a whole year, the tilt is taken as the
latitude of the place.
B.
Appendix
II
I
I
I
I
- 2442
Wind turbine system units of
500
KW capacity with
FOR
=
0.03 were used in this paper. The wind speed data for these
units
is:
VCj = Cut-in velocity = 12.6 kmph
V, = Ratedvelocity = 32.4 kmph
V,,
=
Cut-off velocity
=
69.0 kmph
The power output of these udits was calculated using the
following equations:
0.0
0 < v < v,,
(15)
A i
B V + C V 2 vci
<
v
< v,
v, < v < v,,PRW
POW
=
0.0 v
>V<
where
V = wind velocity for the hour in question
PRW = rated power of the uni t
VIII.
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