Scheduling and Forecasting

8/14/2019 Scheduling and Forecasting

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RES Forecasting and Scheduling

125/05/11

Dr. Naran M. Pindoriya

Assistant Professor, EE DepartmentIndian Institute of echnology !andhinagar


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25/05/11 2

v Short"term !eneration Scheduling

v Forecasting Methodologies

v #oad Forecasting $S#F%

v &ind Speed forecasting

v Solar Po'er Forecasting

al( outline


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!eneration Scheduling

The thermal generation scheduling comprises of two

tasks:

the unit commitment, which determines the on/offschedules of thermal generators;

other is the power dispatch which distributes the sstem

load demand to the committed generators

The optimal thermal generation scheduling re!uireseffectivel performing the above two tasks to meet theforecasted load demand over a particular time hori"on,

satisfing a large set of operating constraints andmeeting certain ob#ectives$

25/05/11 %


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Multi"o)*ecti+e !eneration Scheduling

&i'ob#ective optimi"ation:

1( minimi"ing the sstem operation cost and

2( minimi"ing the emission cost,

while satisfing all the e!ualit and ine!ualit constraints overthe scheduling period$

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Minimizing the system operation cost

Minimizing the emission cost


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Multi"o)*ecti+e !eneration Scheduling

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Constraints

System power balance:

System spinning reserve requirements:

Unit minimum up/down times:

Unit generation limits:


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oncepts of Multi"-)*ecti+e-ptimiation

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! process of simultaneousl optimi"ing two or moreconflicting ob#ectives sub#ect to certain constraints$+tcan be stated as minimi"ation(:

-ub#ect to:

! The main goal of multi'ob#ective optimi"ation is to find aset of values which ield bestcompromise solutions among all the ob#ective functions

! These set of solutions is referred to as the .areto'optimalset

( ) ( ) ( ) ( )[ ]1 2minimize , , . . . , mx f x f x f x=r r r r

/

( ) 0; 1,2, . . . ,i

g x i k =

( ) 0; 1,2,. . . ,jh x j p= =+ne!ualit constraints(!ualit constraints(

* * * *

1 2, , . . . , nx x x x1 1= 1 1r


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oncepts of Multi"-)*ecti+e-ptimiation

25/05/11

Pareto

optimal front

f1 andf2 are to be

minimized

better

be

tt

er

Dominated solution Nondominated solution

F

F is feasibleperformance space

f1

f

2

! The vector corresponding tothe solution included in the.areto'optimal set are said tobe non'dominated b othersolutions(

! or a given .areto'optimalset, the correspondingob#ective function values inthe ob#ective space are called

the .areto front


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M-EA for day"ahead generation scheduling

25/05/11 3

4nupam Trivedi, N. M. Pindoriya, ipti -rinivasan, and eepak -harma, 67ulti'ob#ectivevolutionar 4lgorithm for a'4head Thermal 8eneration -cheduling,9 IEEE Congress onEvolutionary Computation, ew rleans,


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M-EA for day"ahead generation scheduling

25/05/11 >

min

2 2max

min max2 2

2 2 2 2max min

2 2max

2 2

1 ;

;

0 ;

F F

F FF F F

F F

F F

= <


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Stochastic Short"term Scheduling

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-)*ecti+e1 De+elopment of ad+anced optimiation techni2ues and computational

Intelligent tools for short"term scheduling in po'er system under uncertainty3 intermittent

char. of energy sources and demands

De+eloping 4 formulation )ased on stochastic programming Models for unit outages, load and RES uncertainties Pro)a)ilistic reser+e criterion

on+erterSystem

operationincluding short"term scheduling

decision

#oaddemand

.? 4rra nerg storage

@indturbine

7ainsuppl


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ont5d

25/05/11 11

&eather+aria)les

6istoricaldata sets

&ind and Solargeneration

capacity forecast

#oad 7 priceforecasting 7

modeling

Multi"o)*ecti+egeneration scheduling

System parameters and constraints

7aAimi"e reliabilit, securit and efficienc

7inimi"e the fuel and emission cost

-ub#ect to: .ower balance e!uation, power flow constraint and

reserve constraints 8enerators conventional, B-( capacit limit Bamp up/down limit and minimum up/down'time limit,

etcC


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Modified IEEE RS"89

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%nit 12&1'

()1*+-#

%nit 21

1)1 -#

/%S 1+

/%S 1

/%S 21 /%S 22

/%S 2(

/%S 20/%S 1*/%S 1!

/%S 1

/%S 1'

/%S 1(

slac bus#

/%S 12/%S 11/%S 2'

/%S ( /%S * /%S 10 /%S !

/%S /%S

/%S '

/%S 1 /%S 2

/%S +

cable

cable

Sync.

3ond.

%nit 1&2

2)20 -#

%nit (&'

2)+! -#

%nit &!

2)20 -#

%nit +&2)+! -#

%nit *&11

()100 -#

%nit 1&1*

)12 -#

%nit 20

1)1

-#

%nit 22

1)'00 -# %nit 2(

1)'00 -#%nit 2'&2*

!)0 -#

%nit (0&(1

2)1 -#

%nit (2

1)(0 -#

138 kV

230 kV

WECS

(125 MW)

PVCS

(50 MW)

D

otal :00 M& RES is added

through ;"mission line atdifferent )uses $0 and>:% 'hich are ha+ing relati+elylarge EPNS +alues.

#oad )usEPNS$M&%

#oad )usEPNS$M&%

1 3$0*2 10 3$>5

2 3$10* 1% %$)*>

% >$)0 1) >$*2)

) 3$1>> 15 *$03)

5 3$13) 1* 3$1*2

* 3$5%2 13 $1>> *$>>> 1> 3$1%

3 3$>* 20 3$1%0

> >$1%%

otal installed capacity1


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25/05/11 1%

PV

module

PV

module

PV

module

DC

AC

Arrayo

f

PV

Cells

PV

module

PV

module

PV

module

DC

AC

500 !

500 !

" #VA$ %&.5 V'(V side)

PV/Battery Module

DC

AC

DC

AC

500 !

500 !

storage

storage

String

Capacity : 1 MW

Two strings, !"

#odules/string, $""

W/#odule

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

PV/Battery Module

1%" MVA,

$&'%/1$(V (V

)rid

PV/Battery Module

PSopology


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P Modeling

25/05/11 15

G$7$ 4twa, $$l'-aadan, 7$7$4$ -alama, and B$ -eethapath 6ptimal Benewable Besources 7iA foristribution -stem nerg Foss 7inimi"ation,9 + Tran$ .ower -stems, vol$ 25, no$ 1, eb$ 2010$


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&ind ur)ine modeling

25/05/11 1*

Hhanan -ingh and 4$ Fago'8on"ale", 6Beliabilitmodeling of generation sstems including

unconventional energ sources,9 + Transactions on.ower 4pparatus and -stems, ?ol$ .4-'10), o$ 5, 7a1>35$

G$7$ 4twa, $$l'-aadan, 7$7$4$ -alama, and B$-eethapath 6ptimal Benewable Besources 7iA foristribution -stem nerg Foss 7inimi"ation,9 + Tran$.ower -stems, vol$ 25, no$ 1, eb$ 2010$

( )

( )

0 ci co

ci

ci r

r ci

r co

V V and V V

V VPOW PRW V V V

V V

PRW V V V

=


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Introduction to Forecasting models

Time -eries 7odels

4rtificial +ntelligence 7ethods

eural etwork@avelet'based 44daptive @avelet eural etwork

25/05/11 1


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ime Series Methods

4B74 7odel

-easonal 4B+74 7odel

25/05/11 13


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Artificial Neural Net'or( $ANNs%

1>

+nspiration originates from the desire to model the

wa the human brain works and createsophisticated artificial sstems that are capable of

intelligent computations, similar to the

computations of the biological neurons in the brain

structures$

4 is a mathematical model that simulates thefunction of human brain$

BClac( )o;model I +dentif the compleA and non'

linear relationship


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ANN ont5d 1 opology

eed forward 4

ata from input to output units isstrictl feed forward

Becurrent 4

contain feedback connections

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ANN

M#P

FFNN RNN

RCF


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ANN ont5d

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+

=

bxwf

n

i

ii

1

Output

Inputs

Hidden

!"e#

!"e#

$ei%&ts

Input

$ei%&ts

( )xe

xf+

=1

1


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ANN ont5d 1 raining Paradigms

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4 Training

-upervised

network is trained b

providing it with input

and matching output

patterns$

+nput'output pairs can

be provided b aneAternal teacher, or b

the sstem which

contains the neural

network self'

an output( unit is

trained to respond to

clusters of pattern

within the input

sstem is supposed to

discover statisticall

salient features of the

input population

or eA$ : -7


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ANN ont5d 1 raining Paradigms

25/05/11 2%

+nputfeature

s

euraletwork

Targetfeature

s

-upervisedTraining4lgorithm

rrorvector

@eight Jbias

ad#ustment

' K

&ack'propagationsupervised trainingalgorithm


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ANN 'ith 'a+elet transforms

25/05/11 2)

L &a+elet")ased NN

L &a+elet Neural Net'or( $&NN%1com)ines thetime"fre2uency localiation characteristic of 'a+elet

and learning a)ility of NN into a single unit Adapti+e &NN $A&NN%1H@T based activation function Fi;ed grid &NN: @T based activation function

NNData

4nput-a5elet

Decomposition

Predicted

6utput

NN

-a5elet

7econstruction

NN


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A&NN Model

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x1

xn

w1

w2

w

m

v1

vn

j

ij

y

g

7eAican'hat wavelet has beenused as a mother wavelet

@avelet famil can begenerated b

The n' wavelet basis function

utput of 4@

4@ has been trained using

back'propagation learning

algorithm$

( ) ( ) 20.2

1 ;ix

i ix x e i n

=

( )

22

0.

, 1 ;

i ij

ij

ij i j

x b

ai ij

ij a b i

ij

x bx e j ma

1 1

1 1

1 11 1

1 11 11 1= = 1 11 11 11 11 1

( ),1

i j ij

n

j a b i

i

x =

=

1 1

m n

j j i i

j i

y w v x g= =

= + +1 1

8 8! 8' 82 0 2 ' ! 80.

0

0.

(x

x

b 9 0

b 9 1b 9 2

-8 -6 -4 -2 0 2 4 6 8

-0.5

0

0.5

1

(x

x

a = 2

a = 1

a = 0.5


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Short term load forecasting

25/05/11 2*

N'M' Pind#i"!, S.N. Sin:, and S.;. Sin:, a5elet transforms >it feed8for>ard neural net>or,?!n"erna"iona# $o%rna# of &merging ec"ric Power

'y("em(, 5ol. 1, no. 1, 2010.


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Short"erm #oad Forecasting

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! Input +aria)le selection for S#F

Auto"correlation Function $AF%1mutual dependency)et'een +alues of the same time series at differenttime periods

4H for Foad series of =an$'200

0 2' ' +2 ! 120 1'' 1! 1280.'

80.2

0

0.2

0.'

0.!

0.

1

@a: t81, ... t812#

SampleAutocorrelation

L(t) and

L(t-1)Between L(t)

and L(t-24)Between L(t)

and L(t-168)

peaks at multiple of 2), in

4H indicates dailseasonalit

+nput variables:

1 2 (

2( 2' 2 ' +2 !

120 1'' 1!+ 1! 1! 12

1 2 ( 2'

, , ,

, , , , , ,

, , , , , ,, , , ,

h h h

h h h h h h

h h h h h h

h h h h h

) ) )

) ) ) ) ) )

) ) ) ) ) )* * * * *

1 11 1

1 11 11


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Sliding &indo' oncept

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! ase study 1 alifornia EM $Gear @008%

-easons: @inter ec$ I eb$( and -ummer =une I4ug$(

Training: )3 das previous to the da to be forecasted

Training is based on 6sliding window9 concept, to

incorporate most recent information$

*!"

*!+

*!%

*!

n

Training '&, days) Test

day "

day +

day %

day


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ANN )ased Regression

25/05/11 2>

1 2 ( 2', , , ,

h h h h h* * * * *

M#PNN$@0">0">%

H 0.

Training convergence

1 100 200 (00 '00 000

0.0

0.1

0.1

0.2

0.2

No.of iterations

,S$

A-NN

@PNN

1

1

100

ac" for+h h

ac"h h

x x

M,P& + x=

=

2

2

1

1100

ac" for+h h

e ac"h h

x xM,P&

+ x

=

1 1= 1 1

1 11 1

-mall value gives moreprecise prediction


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S#F Results

25/05/11 %1

(our-ahead forecast

1 2' ' +2 *! 120 1'' 1!

20

22

2'

2!

2

(0

(2

Bour index

@oadC-#

Actual

3A4S6

A-NN

on Due Du-ed Sat Sun=ri

ay-ahead forecast

1 2' ' +2 *! 120 1'' 1!

20

22

2'

2!

2

(0

(2

Bour index

@oadC-#

Actual

3A4S6A-NN

=ri SunSat-ed D 2uDue,on

=orecastodels

Bour8aead forecast Day8aead forecast

-AP$ 108

'#-AP$ 108'#

@PNN 1.(0+ 1.((1 1.!*0 1.!!

3A4S6 1.2+0 1.20! 1.++' 2.'(

A-NN 0'1* 0'+35 1'38* 1'31

2

,e week 2

,e week


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25/05/11 %2

(A forecast AP/ using A!

(!) Hu#,!&e!d se-ected $eekd!" (.) Hu#,!&e!d se-ected $eekend

(c) /!",!&e!d se-ected $eekd!" (d) /!",!&e!d se-ected $eekend

1 1( 1+ 21 2'

20

22

2'

2!

2

(0

(2

Bour index

@oadC-#

Actual

A-NN

1 1( 1+ 21 2'

20

22

2'

2!

2

(0

Bour index

@oadC-#

Actual

A-NN

1 1( 1+ 21 2'20

22

2'

2!

2

(0

(2

Bour index

@oa

dC-#

Actual

A-NN

1 1( 1+ 21 2'20

22

2'

2!

2

(0

Bour index

@oa

dC-#

Actual

A-NN


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Short term 'ind Speed forecasting

25/05/11 %%

M$ &haskar and -$ $ -ingh, 6 @ind -peed orecasting using 7B4based 4daptive @avelet eural etwork,9 1*th .-H 2010


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Decomposed 'ind series

25/05/11 %)


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Input +aria)le selection

25/05/11 %5

-1

0

1

A

C

F

-1

0

1

A

C

F

-1

0

1

A

C

F

-1

0

1

A

C

F

0 1 2 2 4 3 6 48 60 7 2 8 4 9 6 10 0-1

0

1

La g

A

C

F

*&

.&

.%

.+

."

N 1,2,CC$$,>O

N 1,2,%,11,12,1%,2)O

N 1,2,CC,O

N 1,2,CC$,*O

N 1,2,CC$,*O

+nput ?ariables


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Results . ont5d

etwork parameters used

+nitial learning Bate and 7omentum .arameters :0$1 and 0$5 for 4@( ;

0$5 and 0$5 for (

7- 8oal set : 0$0001 or( 500 iterations

+n case of validation as an earl stopping criterionthe maAimum fails are set to 100$


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Results . ont5d

Hlick to edit 7aster teAt stles

-econd level

Third level

ourth level

ifth level

using and 4@ without 7B4

a ahead hourl @ind orecasting


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Results . ont5d

3

8

133

S4

-1

0

1

D4

-1

0

1

D3

-1

0

1

D

2

-1

0

1

D

1

1 5 10 15 20 243

8

13

hours

w

ind

series

Hlick to edit 7aster teAt stles-econd level

Third level

ourth level

ifth level

using 7B4 based a ahead hourl @ind orecasting


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Results . ont5d

ail 74. and 7ean rror


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R lt t5d


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Results . ont5d

25/05/11 )2

@eekl 74. and 7ean rror


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Short term Solar po'er forecasting

-tatistical 4pproach

4 based 7ethod

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Solar Po'er !enerating apacity


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Solar Po'er !enerating apacityForecast

25/05/11 ))

Fin .ho aing and ipti -rinivasan,6stimation of solar power generatingcapacit,9 In proc. of 11th IEEE International

Conference on Probabilistic Methods ppliedto Po!er "ystems #PMP" $%1%&, =une 1)'1,2010, -ingapore$

Focation:1deg 13 min latitude( and 10% deg )*min longitude(

Eori"ontal

+nsolation5min and 1 hrdata(


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Data Analysis

25/05/11 )5

t5d


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ont5d

25/05/11 )*

Mde-s MAPE

ASHAE (O#i%in!-) 1(.!!1

Mdiied ASHAE '.1(0

Meine- (O#i%in!-) 1.(1

Mdiied Meine- 1.((

#!dient descent 1.2+

#!dient descent $it&entu

1.11

e4en.e#%,M!#u!#dtptii6!tin

1.20

4-EB4 and 7einel 7odelsare developed b


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ANN for Solar Po'er Forecasting

25/05/11 )

-hambhavi 8upta, 64 neural network application for short term prediction of solar energ generation in-ingapore, 4 Technical .aper,


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Results

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&. &. with momentum

Results ont5d


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Results ont5d

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Results ont5d


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Results ont5d

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Thank ou for

our kind

attention PPP

Questions R

Scheduling and Forecasting

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