Impact of Integrating the Photovoltaic and Wind

8/15/2019 Impact of Integrating the Photovoltaic and Wind

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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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2/6

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


=

number o f states

in

PV

subsystem

=

number o f

states in

wind subsystem

=to tal capacity

in


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4/6

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

2440 -


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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

2441


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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.

REFERENCES

H.

Lee Willir and Walter G

Scott,

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L. H. Stember, W.

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Documents

Impact of Integrating the Photovoltaic and Wind