BC HYDRO UNDERTAKING NO. 43 · Task 2 – Measured Data & Model Validation Province-Wide Wind Resource Data Inventory > Independent Power Producer (IPP) Data • 51 sites • Mostly

Exhibit B-43

BC Hydro 2008 LTAP Hearing

BC HYDRO UNDERTAKING NO. 43

HEARING DATE: March 3, 2009 TRANSCRIPT REFERENCE: Volume 11, Page 1986, Line 9 to Page 1987, Line 8

REQUESTOR: Commission Panel Chair QUESTION: With respect to the response to BCUC IR 2.157.1, Exhibit B-3, please provide the information from the initial phase of the wind integration study that was shared with IPPs. RESPONSE: The presentation materials and draft reports on the draft Wind Data Study Results provided to wind independent power producers on February 6, 2009 are attached. IPP participants at the February 6, 2009 presentation were invited to provide comments and feedback by March 11, 2009, BC Hydro may revise its materials to reflect the received comments at a later date.

BC

Hyd

ro W

ind

In

teg

rati

on

Pro

ject

Win

d D

ata

Stu

dy

Res

ult

s

Pre

sent

atio

n

Feb

ruar

y 6,

200

9

Attachment 1 to BCH Undertaking No. 43

Page 1 of 38

2

Pro

ject

Tea

m

•DN

V Gl

obal

Ener

gy C

once

pts In

c. -S

eattle

, WA

>W

ind en

ergy

tech

nolog

y con

sultin

g and

engin

eerin

g firm

•Ro

n Nier

enbe

rg -

Cama

s, W

A>

Indep

ende

nt co

nsult

ing m

eteor

ologis

t

•3T

IER

–Sea

ttle, W

A>

Meso

-scale

atmo

sphe

ric m

odeli

ng an

d for

ecas

ting s

ervic

es fir

m


Page 2 of 38

3

Pro

ject

Del

iver

able

s

•Th

eore

tical

Wind

Pow

er P

rojec

t Site

s•

Time S

eries

Wind

Data

for E

ach T

heor

etica

l Pro

ject

•Tim

e Ser

ies P

ower

Data

for E

ach T

heor

etica

l Pro

ject

•Fo

reca

sted W

ind D

ata fo

r Eac

h The

oreti

cal P

rojec

t


Page 3 of 38

4

Pro

ject

Ove

rvie

w

2 km

R

esol

utio

n(1

0 ye

ars)

Win

d T

ime

Ser

ies

Dat

a fo

r E

ach

The

oret

ical

Pro

ject

Qua

lity

Con

trol

led

Obs

erva

tiona

l D

ata

from

30

Loca

tions

Pow

er T

ime

Ser

ies

Dat

a fo

r E

ach

The

oret

ical

Pro

ject

The

oret

ical

Pro

ject

s D

elin

eate

d an

d In

stal

led

Cap

acity

Est

imat

ed

2 km

R

esol

utio

n W

ind

Map

6 km

R

esol

utio

n(3

0 ye

ars)

GIS

Ana

lysi

s

Mo

del

Val

idat

ion

Val

idat

ion

Rep

orts

Win

d M

od

elin

g

For

ecas

ted

Win

d D

ata

for

Eac

h T

heor

etic

al P

roje

ct


Page 4 of 38

5

Tas

k 1

–W

ind

Mo

del

ing

Peac

eNo

rthwe

st

North

Coa

st

South

ern I

nterio

r

Vanc

ouve

r Isla

nd


Page 5 of 38

6

Tas

k 2

–M

easu

red

Dat

a &

Mo

del

Val

idat

ion

Prov

ince-

Wide

Wind

Res

ource

Data

Inve

ntory

>Ind

epen

dent

Powe

r Pro

duce

r (IP

P) D

ata

•51

sites

•Mo

stly t

all to

wers

(30 m

or ta

ller)

>BC

Hyd

ro M

et To

wers

•18

sites

•30

m an

d 50 m

towe

rs>

Envir

onme

nt Ca

nada

Wea

ther S

tation

s•

117 s

ites

•Mo

st 10

m to

wers


Page 6 of 38

7

Tas

k 2

–M

easu

red

Dat

a &

Mo

del

Val

idat

ion

•Ho

w me

asur

ed da

ta we

re us

ed:

>Ke

y par

amete

rs for

comp

ariso

n:•

Mean

wind

spee

d•

Wind

spee

d cor

relat

ion (d

aily a

nd m

onthl

y)•

Wind

dire

ction

•Di

urna

l patt

ern

•Fr

eque

ncy d

istrib

ution

para

meter

s>

Loca

tions

of to

wers

are n

ot pr

esen

ted in

the p

ublic

repo

rt


Page 7 of 38

8

Tas

k 2

–M

easu

red

Dat

a &

Mo

del

Val

idat

ion

Diur

nal W

ind

Spee

d Va

riabi

lity


Page 8 of 38

9

Tas

k 2

–M

easu

red

Dat

a &

Mo

del

Val

idat

ion

Mont

hly W

ind

Spee

d Va

riabi

lity


Page 9 of 38

10

Tas

k 2

–M

easu

red

Dat

a &

Mo

del

Val

idat

ion


Page 10 of 38

11

Qu

esti

on

s?

•Ne

xt ste

p: Pr

oject

delin

eatio

n and

insta

lled c

apac

ity

estim

ates


Page 11 of 38

12

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio

n

•Ob

jectiv

e: Cr

eate

a bala

nced

selec

tion o

f theo

retic

al pr

ojects

ac

ross

the P

rovin

ce

>Al

lows f

lexibi

lity fo

r integ

ratio

n ana

lysts

to mo

del v

ariou

s dev

elopm

ent

scen

arios

•IU

P loc

ation

s use

d as a

guide

, but

other

area

s also

co

nside

red


Page 12 of 38

13

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio

n

•30

MW

mini

mum

proje

ct siz

e•

Favo

rable

terra

in (ri

dges

perp

endic

ular t

o pre

vailin

g wind

s)•

No of

ficial

confl

icting

land

uses

(nati

onal

parks

, co

nser

vanc

ies, e

tc.)

•Ar

eas w

ith 20

% or

grea

ter sl

ope e

xclud

ed•

Proje

ct cla

ssific

ation

crite

ria:

Rea

dily

A

vaila

ble

Am

bit

iou

s

Min

imu

m W

ind

Sp

eed

6.5

m/s

6.0

m/s

Max

imu

m D

ista

nce

fro

m

Tra

nsm

issi

on

50 k

m20

0 km


Page 13 of 38

14

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio

nAttachment 1 to BCH Undertaking No. 43

Page 14 of 38

15

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio


Page 15 of 38

16

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio


Page 16 of 38

17

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio


Page 17 of 38

18

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio


Page 18 of 38

19

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio


Page 19 of 38

20

Tas

k 3

–W

ind

Po

wer

Pro

ject

Del

inea

tio


Page 20 of 38

21

Tas

k 3

–W

ind

Po

wer

Pro

ject

Cap

acit

y E

stim

atio

n

•W

ind T

urbin

e Tec

hnolo

gy A

ssum

ption

s>

Clas

s I tu

rbine

assig

ned t

o are

as w

ith m

odele

d wind

spee

ds >

9 m/

sfor

sit

e suit

abilit

y con

sider

ation

s

•Tu

rbine

spac

ing:

>Ar

ray:

4D by

15D,

assu

ming

93 m

rotor

diam

eter

>Ri

dgeli

ne: 4

D, as

sumi

ng 93

m ro

tor di

amete

r•

Each

mod

el gr

id po

int w

ithin

a pro

ject a

ssign

ed a

spec

ific

numb

er of

turb

ines

Des

ign

Cla

ssif

icat

ion

IEC

Cla

ss I

IEC

Cla

ss II

Tur

bine

mod

elV

esta

sV

90S

iem

ens

2.3

Nam

epla

te c

apac

ity3

MW

2.3

MW

Rot

or d

iam

eter

90 m

93

m


Page 21 of 38

22

Tas

k 3

–W

ind

Po

wer

Pro

ject

Cap

acit

y E

stim

atio


Page 22 of 38

23

Tas

k 3

–W

ind

Po

wer

Pro

ject

Cap

acit

y E

stim

atio

n

Cat

ego

ryN

um

ber

of

Pro

ject

s

To

tal

Inst

alle

d

Cap

acit

y (M

W)

Van

cou

ver

Isla

nd

Rea

dily

Ava

ilabl

e11

1311

Am

bitio

us3

138

To

tal

1414

49

No

rth

Co

ast

Rea

dily

Ava

ilabl

e7

922

Am

bitio

us5

3288

To

tal

1242

11

So

uth

ern

Inte

rio

r

Rea

dily

Ava

ilabl

e25

3809

Am

bitio

us6

600

To

tal

3144

09

Pea

ce

Rea

dily

Ava

ilabl

e27

3410

Am

bitio

us21

2703

To

tal

4861

13

Pro

vin

ce T

ota

l

Rea

dily

Ava

ilabl

e70

9452

Am

bitio

us35

6730

To

tal

105

1618

2


Page 23 of 38

24

Qu

esti

on

s?

•Ne

xt ste

p: Pr

oject

wind

and p

ower

time s

eries

data

sets


Page 24 of 38

25

Tas

k 4

–P

roje

ct W

ind

Dat

a G

ener

atio

n

NWP

Mode

l Sim

ulatio

ns

Glo

bal

Wea

ther

A

rch

ive

1948

-pre

sen

t

•T

ime

serie

s W

ind

Dat

a

•W

ind

map

Hig

h R

eso

luti

on

T

erra

in, S

oil

and

V

eget

atio

n D

ata

INP

UT

SO

UT

PU

TS


Page 25 of 38

26

Tas

k 4

–P

roje

ct W

ind

Dat

a G

ener

atio

n

•NW

P Mo

del S

imula

tions

>W

eathe

r Res

earch

and F

orec

astin

g (W

RF) m

odel

>Mo

del c

onfig

urati

on te

sts us

ing m

easu

red d

ata>

30 ye

ar, h

ourly

(18 k

m re

solut

ion) d

ata>

10 ye

ar, 1

0-mi

nute

(6 km

reso

lution

) data

>1 y

ear,

10-m

inute

(2 km

reso

lution

) data

>“D

ata bl

endin

g”to

prod

uce 1

0 yea

r, 10

-minu

te (2

km re

solut

ion)

data;

and 3

0 yea

r, mo

nthly

(6 km

reso

lution

) data

sets


Page 26 of 38

27


Page 27 of 38

28


Page 28 of 38

29


Page 29 of 38

30

Note:

Wind

spee

ds sh

own a

re as

mod

eled (

i.e. n

ot ad

justed

)


Page 30 of 38

31

Tas

k 4

–P

roje

ct W

ind

Dat

a G

ener

atio

n

•Tim

e ser

ies w

ind da

ta se

ts we

re ex

tracte

d and

av

erag

ed fo

r eac

h the

oreti

cal p

rojec

t•

Adjus

ted fo

r wind

spee

d bias

>

Only

for ce

rtain

area

s of th

e Pea

ce do

main

[as pr

eviou

sly di

scus

sed

in Ta

sk 2]


Page 31 of 38

32

Tas

k 4

–P

roje

ct P

ow

erD

ata

Gen

erat

ion

•IN

PUTS

:>

Mode

led T

ime S

eries

Wind

Data

for E

ach T

heor

etica

l Pro

ject

>W

ind P

rojec

t (Gr

oss t

o Net)

Ene

rgy L

oss A

djustm

ent

>Ai

r Den

sity-s

pecif

ic Po

wer C

urve

s>

Stati

stica

l Adju

stmen

t (SC

ORE-

lite)


Page 32 of 38

33

Tas

k 4

–P

roje

ct P

ow

erD

ata

Gen

erat

ion

•W

ind P

rojec

t (Gr

oss t

o Net)

Ene

rgy L

oss A

djustm

ent

En

erg

y L

oss

Cat

ego

ryE

xam

ple

Lo

ss

Rou

tine

Mai

nten

ance

0.4%

Fau

lts1.

9%

Min

or C

ompo

nent

Fai

lure

1.7%

Maj

or C

ompo

nent

Fai

lure

1.5%

Bal

ance

of P

lant

Dow

ntim

e0.

5%

Arr

ay/W

ake

6.5%

Ele

ctric

al L

ine/

Tra

nsfo

rmer

2.5%

Bla

de S

oilin

g0.

5%

Wea

ther

(lig

htni

ng, i

cing

, etc

.)3.

0%

Tur

bule

nce

and

Con

trol

s1.

0%

Bla

de D

egra

datio

n0.

4%

Pow

er P

erfo

rman

ce0.

3%

Co

mb

ined

Lo

sses

18.5

%

>18

.5% en

ergy

loss

>11

.6% w

ind sp

eed r

educ

tion


Page 33 of 38

34

Tas

k 4

–P

roje

ct P

ow

erD

ata

Gen

erat

ion

•SC

ORE-

lite>

Wind

proje

ct ou

tput d

irectl

y der

ived f

rom

meso

scale

mode

led w

ind

spee

ds is

exce

ssive

ly sm

ooth

>Go

al is

to mo

dify m

odele

d pow

er da

ta so

that

ramp

ing

char

acter

istics

mor

e clos

ely ap

prox

imate

thos

e obs

erve

d in r

eality


Page 34 of 38

35

Tas

k 4

–P

roje

ct P

ow

erD

ata

Gen

erat

ion


Page 35 of 38

36

Tas

k 4

–P

roje

ct P

ow

erD

ata

Gen

erat

ion

Cat

ego

ryN

um

ber

of

Pro

ject

sT

ota

l In

stal

led

C

apac

ity

(MW

)80

m W

ind

Sp

eed

R

ang

e (m

/s)

80 m

Hu

b H

eig

ht

Cap

acit

y F

acto

r R

ang

e

Van

cou

ver

Isla

nd

Rea

dily

Ava

ilabl

e11

1311

6.8

to 8

.225

% to

34%

Am

bitio

us3

138

6.3

to 7

.120

% to

27%

To

tal

1414

496.

3 to

8.2

20%

to

34

%

No

rth

Co

ast

Rea

dily

Ava

ilabl

e7

922

6.5

to 7

.220

% to

28%

Am

bitio

us5

3288

6.6

to 8

.825

% to

34%

To

tal

1242

116.

5 to

8.8

20%

to

34

%

So

uth

ern

Inte

rio

r

Rea

dily

Ava

ilabl

e25

3809

6.5

to 7

.720

% to

31%

Am

bitio

us6

600

6.3

to 6

.419

% to

23%

To

tal

3144

096.

4 to

7.7

19%

to

31

%

Pea

ce

Rea

dily

Ava

ilabl

e27

3410

6.6

to 9

.623

% to

41%

Am

bitio

us21

2703

6.3

to 9

.920

% to

39%

To

tal

4861

136.

3 to

9.9

20%

to

41

%

Pro

vin

ce T

ota

l

Rea

dily

Ava

ilabl

e70

9452

6.5

to 9

.620

% to

41%

Am

bitio

us35

6730

6.3

to 9

.919

% to

39%

To

tal

105

1618

26.

3 to

9.9

19%

to

41

%


Page 36 of 38

37

Tas

k 4

–P

roje

ct P

ow

er D

ata

Gen

erat

ion

•Fo

reca

sted W

ind D

ata>

Clim

atolog

icalF

orec

asts

>Pe

rfect

Fore

casts

>NW

P Fo

reca

sts


Page 37 of 38

38

Qu

esti

on

s?


Page 38 of 38

BC

Hyd

ro W

ind

In

teg

rati

on

Pro

ject

Nex

t Ste

ps:

Nex

t Ste

ps:

Win

d Im

pact

Stu

dies

Pla

nW

ind

Impa

ct S

tudi

es P

lan

byby

Ziad

Sha

ww

ash

&Zi

ad S

haw

was

h &

Ala

a A

bdal

laA

laa

Abd

alla

Gen

erat

ion

Res

ourc

e M

anag

emen

t, B

C H

ydro

Gen

erat

ion

Res

ourc

e M

anag

emen

t, B

C H

ydro

& C

ivil

Engi

neer

ing

& C

ivil

Engi

neer

ing

–– UB

CU

BC

Win

d D

ata

Stud

y:

Win

d D

ata

Stud

y:

Stak

ehol

der E

ngag

emen

t Ses

sion

St

akeh

olde

r Eng

agem

ent S

essi

on

Feb

. 6, 2

009

Feb

. 6, 2

009


Page 1 of 8

2

Gen

erat

ion

Op

erat

ion

s (G

O)

Imp

act

Stu

dy

Ove

rall

Goa

l: U

nder

stan

dth

e po

tent

ial c

onse

quen

ces o

f in

tegr

atin

g w

ind

pow

er in

the

BC

Hyd

ro p

ower

syst

em.

Obj

ectiv

es:

1.R

evie

w th

e st

ate-

of-th

e-ar

t met

hodo

logi

es to

ass

ess t

he im

pact

s of i

nteg

ratin

g w

ind

into

pow

er sy

stem

s, in

par

ticul

ar h

ydro

-dom

inat

ed p

ower

syst

ems.

2.A

sses

s the

cap

abili

ty a

nd th

e co

st to

inte

grat

e w

ind

reso

urce

s in

the

curr

ent a

nd

in th

e ex

pand

ed B

C H

ydro

pow

er sy

stem

.

3.D

evel

op a

n un

ders

tand

ing

and

asse

ss th

e ex

pect

ed c

hang

es in

cur

rent

ge

nera

tion

oper

atio

ns p

ract

ices

and

pro

cess

es th

at w

ill b

e re

quire

d w

ith

incr

easi

ng w

ind

pene

tratio

n &

diff

eren

t win

d di

vers

ity sc

enar

iosf

or a

rang

e of

w

ater

& w

ind.


Page 2 of 8

3

Gen

erat

ion

Op

erat

ion

s Im

pac

t S

tud

y

To a

ccom

plis

h th

ese

obje

ctiv

es, G

O st

udy

will

:1.

Det

erm

ine

the

regu

latio

n an

d lo

ad fo

llow

ing

anci

llary

serv

ice

requ

irem

ents

an

d th

eir c

osts

for d

iffer

ent w

ind

pene

tratio

n an

d di

vers

ity le

vels

und

er

curr

ent a

nd a

ltern

ate

futu

re sy

stem

scen

ario

s

2.D

eter

min

e th

e un

it co

mm

itmen

t im

pact

s and

cos

ts in

the

scen

ario

s

3.D

eter

min

e th

e ef

fect

s of w

ind

pow

er fo

reca

stin

g ca

pabi

litie

s

4.D

eter

min

e ge

nera

tion

oper

atio

nal b

ottle

neck

s of i

nteg

ratin

g w

ind

pow

er

(cap

acity

and

ram

ping

cap

abili

ty)

5.In

vest

igat

e th

e im

pact

s of w

ind

pow

er c

urta

ilmen

t


Page 3 of 8

4

Gen

erat

ion

Op

erat

ion

s Im

pac

t S

tud

y

Syst

em w

ide

impa

cts c

an b

e ca

tego

rize

d in

to th

e fo

llow

ing

mai

n ar

eas:

•Sys

tem

stab

ility

: (tim

e sc

ale

seco

nds t

o m

inut

es).

Diff

eren

t win

d tu

rbin

e ty

pes h

ave

diff

eren

t con

trol

char

acte

ristic

s & d

iffer

ent i

mpa

cts a

nd su

ppor

t for

the

syst

em in

nor

mal

and

syst

em fa

ult s

ituat

ions

. Thi

s are

a al

so in

clud

e vo

ltage

man

agem

ent r

equi

rem

ent,

reac

tive

rese

rve.

•Reg

ulat

ion

and

load

follo

win

g:(ti

me-

scal

e: se

cond

s to

half

an h

our)

. Add

ress

es h

ow th

e va

riabi

lity

and

unce

rtain

ty o

f win

d po

wer

will

aff

ect t

he a

lloca

tion

and

use

of re

serv

es in

the

syst

em.

•Eff

icie

ncy

and

unit

com

mitm

ent:

(tim

e sc

ale:

hou

rs-d

ays)

. Add

ress

es h

ow th

e pr

oduc

tion

varia

bilit

y an

d fo

reca

stin

g er

rors

of w

ind

pow

er w

ill im

pact

gen

erat

ion

syst

em e

ffic

ienc

y an

d un

it co

mm

itmen

t dec

isio

ns.

•Ade

quac

y of

pow

er g

ener

atio

n:(ti

me

scal

e: se

vera

l yea

rs).

This

cat

egor

y is

con

cern

ed w

ith th

eto

tal

pow

er c

apac

ity a

vaila

ble

durin

g pe

ak lo

ad si

tuat

ion.

It a

lso

addr

esse

s the

tota

l ene

rgy

supp

ly o

f the

ele

ctric

sy

stem

for a

giv

en a

rea

•Tra

nsm

issi

on a

dequ

acy

and

effic

ienc

y:(ti

me

scal

e ho

urs t

o ye

ars)

. Add

ress

es th

e im

pact

s of w

ind

pow

er o

n th

e re

gion

al tr

ansm

issi

on sy

stem

, inc

ludi

ng tr

ansm

issi

on lo

sses

or b

enef

its, d

epen

ding

on

the

loca

tion

of w

ind

pow

er p

lant

s and

the

regi

onal

load

, and

the

corr

elat

ion

betw

een

win

d po

wer

pro

duct

ion

and

load

con

sum

ptio

n.


Page 4 of 8

5

Gen

erat

ion

Op

erat

ion

s Im

pac

t S

tud

yFi

gure

1. I

mpa

cts o

f win

d po

wer

on

pow

er sy

stem

s. (I

EA

, 200

7)


Page 5 of 8

6

Gen

erat

ion

Op

erat

ion

s Im

pac

t S

tud

yS

öder

, L. a

nd H

oltti

nen,

H. (

2008

) 'O

n m

eth

odol

ogy

for

mo

delli

ng w

ind

po

we

r im

pact

on

pow

er s

yste

ms'

,In

t. J.

Glo

bal E

nerg

y Is

sues

, Vol

. 29,

Nos

. 1/2

, pp.

181-

198.

Su

mm

ary

of

IEA

WIN

D T

ask

24 I

nte

gra

tio

n o

f W

ind

an

d H

ydro

po

wer

sys

tem

s

Item

12

34

5

AA

im o

f stu

dy

Wha

t hap

pens

with

X

GW

hw

ind

Ho

w m

uch

win

d is

pos

sibl

e

MM

etho

d to

per

form

st

udy

Add

win

d en

erg

yW

ind

also

re

plac

es c

apac

ityO

pti

mal

sys

tem

des

ign

IIm

bala

nce

calc

ulat

ion

Onl

y w

ind

Win

d +

load

Win

d +

load

+ p

rod

uct

ion

BB

alan

cing

loca

tion

Ded

icat

ed s

ourc

eF

rom

the

sam

e re

gion

Als

o o

uts

ide

reg

ion

GG

rid li

mit

on

tran

smis

sio

nN

o lim

itsC

onst

ant M

W li

mits

Con

side

r vo

ltage

N-1

crit

eria

Dyn

amic

si

mu

lati

on

UU

ncer

tain

ty

trea

tmen

tT

ran

smis

sio

n

mar

gin

sH

ydro

in

flo

w u

nce

rtai

nty

Win

d fo

reca

sts:

U3:

non

eU

4: p

ersi

sten

ceU

5: b

est

po

ssib

le

U6:

load

fo

reca

sts

con

sid

ered

U7:

th

erm

al

po

wer

ou

tag

es

con

sid

ered

HH

ydro

po

wer

m

odel

ing

Hea

d h

eig

ht

con

sid

ered

Hyd

rolo

gic

al c

ou

plin

g

incl

ud

edH

ydro

log

ical

res

tric

tio

ns

incl

ud

edA

vaila

bili

ty o

f w

ater

co

nsi

der

ed

TT

herm

al p

ow

er

mod

elin

gR

amp

rat

es

con

sid

ered

Sta

rt/s

top

co

sts

con

sid

ered

Eff

icie

ncy

va

riat

ion

co

nsi

der

edH

eat

pro

du

ctio

n

con

sid

ered

WW

ind

pow

er

mod

elin

gF

ew

win

d sp

eed

time

serie

sM

any

win

d po

we

r tim

e se

ries

Tim

e se

ries

sm

oo

thin

g

con

sid

ered

All

ow

co

ntr

olla

ble

w

ind

po

wer

SS

imul

atio

n m

ode

l of

oper

atio

nD

eter

min

istic

si

mul

atio

n, o

ne c

ase

Det

erm

inis

tic s

imul

atio

n se

vera

l cas

esS

toch

asti

c si

mu

lati

on

se

vera

l cas

es

RR

esol

utio

n of

tim

eD

ay/

we

ekH

our

Min

ute

/sec

PP

ricin

g m

eth

odC

osts

of f

uels

, etc

.P

rices

for

trad

ing

with

ne

ighb

our

sM

arke

t act

or s

imul

atio

nM

arke

t d

ynam

ics

incl

ud

ed

DD

esig

n of

rem

ain

ing

syst

emC

onst

ant r

ema

inin

g sy

stem

Op

tim

ised

rem

ain

ing

p

rod

uct

ion

Op

tim

ised

rem

ain

ing

tr

ansm

issi

on

Per

fect

tra

din

g r

ule

s

MA

GE

NT

A B

OL

D=

Söd

eran

d H

oltti

nen

'sre

com

men

datio

nB

C H

ydro

's a

ppr

oach


Page 6 of 8

7

Gen

erat

ion

Op

erat

ion

s Im

pac

t S

tud

y:M

od

elin

g M

eth

od

olo

gy

Tim

e Hor

izon

Sec–

Min

Hour

–Day

–W

eek

Mont

h -Y

ears

AGC

Optim

izer

STOM

GOM

& HY

SIM

Shor

t –Te

rm O

pera

tions

Mid-

Long

Ter

mOp

erat

ions

Incre

menta

l regu

lation

& lo

ad

Incre

menta

l regu

lation

& lo

ad

follow

ing re

serve

s, ra

mp up

/ fol

lowing

rese

rves,

ramp

up/

down

down

Shor

t term

syste

m op

erati

ons a

nd th

e Sh

ort te

rm sy

stem

oper

ation

s and

the

cost/

lost

oppo

rtunit

y of a

ncilla

ry co

st/ lo

st op

portu

nity o

f anc

illary

servi

ces a

nd w

ind fo

reca

sting

erro

rsse

rvice

s and

wind

fore

casti

ng er

rors

Wind

ener

gy in

fusion

s, se

ason

al W

ind en

ergy

infus

ions,

seas

onal

& mi

d&

mid --

term

ener

gy sh

ifts an

d ter

m en

ergy

shifts

and

impa

cts on

ener

gy an

d cap

acity

impa

cts on

ener

gy an

d cap

acity


Page 7 of 8

8

Gen

erat

ion

Op

erat

ion

s Im

pac

t S

tud

y

The

mai

n de

liver

able

s of t

he st

udy

will

incl

ude:

1.A

det

aile

d de

scrip

tion

of th

e hy

dro

gene

ratin

g sy

stem

and

the

trans

mis

sion

sy

stem

and

the

mai

n as

sum

ptio

ns u

sed

in th

e st

udy.

2.

Ass

essm

ent o

f win

d re

sour

ces o

n re

gula

ting

and

capa

city

rese

rves

.3.

Ass

essm

ent o

f the

impa

cts o

f win

d en

ergy

ram

p ra

tes o

n th

e sy

stem

.4.

Det

aile

d m

odel

s’ou

tput

s and

ana

lysi

s of t

he re

sults

. 5.

The

unit

cost

of i

nteg

ratin

g w

ind

ener

gy in

to th

e B

C H

ydro

syst

em.

6.W

ind

pene

tratio

n bo

ttlen

eck

and

limits

. 7.

Ass

essm

ent o

f pot

entia

l win

d de

velo

pmen

t lim

its th

at c

ould

aris

efr

om th

e bo

ttlen

ecks

iden

tifie

d ab

ove.

8.

Ass

essm

ent o

f the

mai

n ch

ange

s to

exis

ting

hydr

o sc

hedu

ling

and

oper

atio

n pl

anni

ng a

ppro

ach

that

will

be

requ

ired

to in

tegr

ate

win

d in

to th

e B

C H

ydro

syst

em.

9.Th

e in

crem

enta

l im

pact

s of i

ncre

asin

g w

ind

deve

lopm

ent s

cena

rioso

n th

e in

tegr

ated

BC

syst

em g

ener

atio

n op

erat

ions

.


Page 8 of 8

BC

Hyd

ro W

ind

In

teg

rati

on

Pro

ject

Win

d D

ata

Stu

dy

Res

ult

s

Cha

ract

eriz

atio

n of

BC

’s

Win

d R

esou

rce:

Pre

limin

ary

Ana

lysi

s

Feb

ruar

y 6,

200

9


Page 1 of 11

2

Ove

rvie

w o

f A

nal

ysis

•Int

er-a

nnua

l•

Seas

onal/

Month

ly•

Hour

ly•

10-M

inute

•An

alysis

base

d on a

ll mod

elled

proje

cts


Page 2 of 11

3

Inte

r-an

nu

al V

aria

bili

ty

Annu

al-av

erag

ed C

Fsof

All P

rojec

ts In

Each

Reg

ion

Note:

The

lowe

r and

uppe

r whis

kers

repr

esen

t the l

owes

t datu

m sti

ll with

1.5 I

QR of

the l

ower

qu

artile

and t

he hi

ghes

t datu

m sti

ll with

in 1.5

IQR

of the

uppe

rqua

rtile,

resp

ectiv

ely. D

ata po

ints

outsi

de th

e 1.5

IQR

are r

epre

sente

d by r

ed cr

osse

s (ou

tliers)

.


Page 3 of 11

4

Inte

r-an

nu

al V

aria

bili

ty:

Win

d v

sH

ydro

Note:

The

Integ

rated

Sys

tem W

eighte

d Infl

ow is

a su

mmar

y mea

sure

of the

amou

nt of

inflow

that

has b

een

obse

rved a

t eac

h of th

e res

ervo

irs, e

ach w

eighte

d by a

spec

ific W

eighti

ng F

actor

to ac

coun

t for t

he di

ffere

nt en

ergy

conv

ersio

n rate

s at d

iffere

nt pla

nts in

the B

C Hy

dro s

ystem

.


Page 4 of 11

5

Sea

son

al/M

on

thly

Var

iab

ility

Month

ly-av

erag

ed C

Fsof

All P

rojec

ts In

Each

Reg

ion


Page 5 of 11

6

Diu

rnal

Var

iab

ility

-D

ecem

ber

Hour

ly-av

erag

ed C

Fsof

All P

rojec

ts In

Each

Reg

ion


Page 6 of 11

7

Diu

rnal

Var

iab

ility

-Ju

ly

Hour

ly-av

erag

ed C

Fsof

All P

rojec

ts In

Each

Reg

ion


Page 7 of 11

8

Ho

url

y S

tep

Ch

ang

e C

har

acte

rist

ics

•Ma

ximum

+ve

hour

ly ra

mps

rang

e fro

m 52

%

to 94

% of

insta

lled

capa

city

•Ma

ximum

–ve

hour

ly ra

mps

rang

e fro

m 49

%

to 85

% of

insta

lled

capa

city

Note:

Bin

width

is 5%

of na

mepla

te ca

pacit

y.


Page 8 of 11

9

Exa

mp

le o

f 10

-min

Dat

a:

Fo

ur

Ran

do

m P

roje

cts

on

a R

and

om

Day

Note:

PST

= U

TC -

8


Page 9 of 11

10

10-m

in S

tep

Ch

ang

e C

har

acte

rist

ics

•Ma

ximum

+ve

10-

min s

tep ch

ange

ra

nge f

rom

23%

to

94%

of in

stalle

d ca

pacit

y•

Maxim

um –v

e10-

min s

tep ch

ange

ra

nge f

rom

31%

to

81%

of in

stalle

d ca

pacit

y

Note:

Bin

width

is 1%

of na

mepla

te ca

pacit

y.


Page 10 of 11

11

Qu

esti

on

s?Attachment 3 to BCH Undertaking No. 43

Page 11 of 11

DNV Global Energy Concepts Inc. 1809 7th Avenue, Suite 900 Seattle, Washington 98101 USA Phone: (206) 387-4200 Fax: (206) 387-4201 www.globalenergyconcepts.com www.dnv.com

DRAFT

BC Hydro Wind Data Study

CSRP0009

CONFIDENTIAL

February 5, 2009

Prepared for:

British Columbia Hydro & Power Authority 333 Dunsmuir Street

Vancouver, BC V6B 5R3


Page 1 of 53

DRAFT BC Hydro Wind Data Study CSRP0009

DNV Global Energy Concepts Inc. i February 5, 2009

Approvals

Prepared by Date Reviewed by Date

Version Block Version Release Date Summary of Changes

DRAFT


Page 2 of 53


DNV Global Energy Concepts Inc. ii February 5, 2009

Table of Contents

INT RODUCTION......................................................................................................................... 1

OVERVIEW OF PROJECT SCOPE ......................................................................................... 2

SECTION 1 - WIND MODELING ............................................................................................. 4

1.1 DOMAIN SELECTION ........................................................................................................ 4 1.2 NWP MODEL SIMULATIONS ............................................................................................ 6

1.2.1 Input Data................................................................................................................ 6 1.2.2 Model Configuration Test Simulations................................................................... 7 1.2.3 Data Modeling ........................................................................................................ 9

SECTION 2 - MEASURED DATA AND MODEL VALIDATION ...................................... 11

2.1 MEASURED DATA INVENTORY....................................................................................... 11 2.1.1 IPP Met Data......................................................................................................... 11 2.1.2 BC Hydro Met Data .............................................................................................. 11 2.1.3 Environment Canada Met Data............................................................................. 12

2.2 SELECTED VALIDATION SITES ....................................................................................... 12 2.3 DATA QUALITY CONTROL ............................................................................................. 12 2.4 MODEL VALIDATION ..................................................................................................... 13

SECTION 3 - WIND POWER PROJECT DELINEATION AND INSTALLED CAPACITY ESTIMATION ...................................................................................................... 18

3.1 PROJECT DELINEATION CRITERIA .................................................................................. 18 3.2 PROJECT DELINEATION METHODOLOGY........................................................................ 21

3.2.1 Wind Map Validation ........................................................................................... 23 3.3 THEORETICAL PROJECTS................................................................................................ 29 3.4 PROJECT CAPACITY ESTIMATION................................................................................... 33

3.4.1 Wind Turbine Technology Assumptions .............................................................. 33 3.4.2 Wind Turbine Density Assumptions..................................................................... 37 3.4.3 Final Project Estimated Capacities ....................................................................... 37

SECTION 4 - PROJECT WIND AND POWER DATA GENERATION............................. 40

4.1 PROJECT WIND DATA .................................................................................................... 40 4.1.1 Accounting for Wind Speed Bias ......................................................................... 41

4.2 PROJECT POWER DATA .................................................................................................. 41 4.2.1 Accounting for Losses .......................................................................................... 41 4.2.2 Power Curves ........................................................................................................ 42 4.2.3 SCORE-lite ........................................................................................................... 43 4.2.4 Final Project-Average Power Data Sets................................................................ 44

4.3 FORECASTED WIND DATA ............................................................................................. 45

APPENDIX A – CONFIDENTIAL TABLES AND FIGURES

APPENDIX B – OUTLINED PROJECTS AND INSTALLED CAPACITY BY DOMAIN


Page 3 of 53


DNV Global Energy Concepts Inc. iii February 5, 2009

List of Figures

Figure 1. Flowchart of Project Process ........................................................................................... 3 Figure 2. Model Domains ............................................................................................................... 5 Figure 3. Transmission Lines and 50 km and 200 km Ranges ..................................................... 19 Figure 4. Examples of Outlined Projects ...................................................................................... 22 Figure 5. Vancouver Island Domain 1-year Wind Map (August 2007 through July 2008) ......... 25 Figure 6. North Coast Domain 1-year Wind Map August 2007 through July 2008..................... 26 Figure 7. Southern Interior Domain 1-year Wind Map August 2007 through July 2008............. 27 Figure 8. Adjustments to Peace Domain 1-year Wind Map August 2007 through July 2008 ..... 28 Figure 9. Vancouver Island Domain Theoretical Projects............................................................ 29 Figure 10. North Coast Domain Theoretical Projects................................................................... 30 Figure 11. Southern Interior Domain Theoretical Projects........................................................... 31 Figure 12. Peace Domain Theoretical Projects............................................................................. 32 Figure 13. North Coast Domain Theoretical Projects and Assigned Turbine Type ..................... 35 Figure 14. Peace Domain Theoretical Projects and Assigned Turbine Type ............................... 36 Figure 15. Example of GIS Project Installed Capacity Estimation .............................................. 38

List of Tables

Table 1. Configurations Using the Advanced Research WRF Core............................................... 8 Table 2. Observational Data Sets Used for Configuration Tests .................................................... 9 Table 3. Selected Model Configurations for Each Domain............................................................ 9 Table 4. Model Validation Sites Summary of Correlations.......................................................... 15 Table 5. Model Validation Sites Summary of Wind Resource Comparisons............................... 16 Table 6. Project Delineation Criteria ............................................................................................ 18 Table 7. Wind Map Period (August 2007 through July 2008) Average Wind Speed Relative to

the 10-Year Average ............................................................................................................. 24 Table 8. Turbine Specifications .................................................................................................... 34 Table 9. Projects and Installed Capacity by Domain.................................................................... 39 Table 10. Example of Energy Losses for Large Scale Wind Projects .......................................... 42 Table 11 Approximate Air Densities for Each Domain................................................................ 43 Table 12. Installed Capacity, Average Wind Speed, and Capacity Factor for All

Theoretical Projects .............................................................................................................. 45


Page 4 of 53


DNV Global Energy Concepts Inc. 1 February 5, 2009

Introduction The British Columbia Hydro and Power Authority (BC Hydro) is the largest electric utility in British Columbia, serving approximately 95% of the Province's population and 1.7 million customers. BC Hydro's various facilities generate between 43,000 and 54,000 gigawatt hours (GWh) of electricity annually, depending on prevailing water levels. Electricity is delivered through a network of 18,336 kilometers (km) of transmission lines and 55,705 km of distribution lines. About 90% of BC Hydro's generation is presently produced by hydroelectric means. Currently, no utility-scale wind farms are in operation in the Province of British Columbia (BC). As a result of its fall 2006 Call for Power, BC Hydro has signed Electricity Purchase Agreements (EPA) with three wind farms in BC; Dokie, Bear Mountain, and Mt. Hays . The wind independent power producers (IPP) industry is growing in BC and more wind projects are expected to be bid into future power Calls. As an intermittent resource, wind energy offers some unique challenges for the operation and planning of the generation and transmission system. BC Hydro and the British Columbia Transmission Corporation (BCTC) are carrying out wind integration studies, which aim to assess the potential impacts of new wind generation on the electricity system. A necessary first step for the wind integration studies is to gather wind data and information across the Province that are representative of possible future wind generation and develop a data set for use in subsequent integration analyses. Under contract number Q7-3472, DNV Global Energy Concepts, Inc. (DNV-GEC) was selected by BC Hydro to help fulfill this need by providing wind energy consulting services and supplying synthesized wind and wind power production data.

Project Team

DNV-GEC is the prime contractor for this project, with specified work subcontracted to 3TIER®, Inc. (3TIER), a meso-scale atmospheric modeling and forecasting services firm, and Mr. Ron Nierenberg, a consulting meteorologist in the wind energy industry. DNV-GEC coordinated all tasks and maintained communication with the subcontracted parties throughout the project’s duration.

Confidential Information

Some of the information used for this study was acquired from IPPs operating within BC. This information is considered proprietary, and its use is limited by non-disclosure agreements (NDA) between BC Hydro and the participating IPPs. Consequently, any such information, for example the locations of met towers and statistics derived from meteorological (met) data, is not listed in this report. Confidential information important to the study’s results can be found in Appendix A. The tables and figures contained therein are available only to BC Hydro, and are meant for internal use only.


Page 5 of 53



Overview of Project Scope The objective of this study was to assess the characteristics of wind resources in regions of British Columbia that are likely to experience significant wind energy development in the future. This was done by synthesizing wind speed, wind direction and wind project power data that accurately represent the spatial and temporal characteristics of realistically located and sized wind farms that might be built in each region. This information will serve as input to a wind integration analysis being undertaken by BC Hydro and the BCTC. Specific objectives of the study were to:

o Synthesize wind resource data for selected regions of the Province and validate the data with acquired observational data.

o Synthesize wind resource and power production data for theoretical wind power project sites in selected regions of the Province. The selected temporal resolutions for the study were 10 years of 10-minute data and 30 years of monthly data.

o Simulate wind generation forecasts for theoretical wind power project sites. In order to achieve these objectives, the project was separated into the following tasks, which are described in detail in the following sections:

Task 1: Wind Modeling

Task 2: Measured Data and Model Validation

Task 3: Wind Power Project Delineation and Installed Capacity Estimation

Task 4: Project Wind and Power Data Generation The general project process is shown in Figure 1. The presented flowchart is a simplified overview, and not all details of the study are shown. More detailed information about each step can be found in the text of this report.


Page 6 of 53



Gross to Net Energy

Adjustments

Air Density Specific

Power Curves

Statistical Correction

(SCORE-lite) for 10-

Year Data Set

IUP LocationsTopographic and

Elevation Data

Pre-defined

Criteria

2 km

Resolution

(10 years)

“Gross” Wind Time Series Data for

Each Theoretical Project

(10 years of 10-minute and 30

years of monthly)

Observational Data from 30

Locations Collected and Quality

Controlled

30 Years of Monthly Power

Time Series Data for Each

Theoretical Project

Theoretical Projects

Delineated and Installed

Capacity Estimated

2 km Resolution

Wind Map

Meteorological Data Modeled Over 5 Domains

18 km

Resolution

(30 years)

IPP BC Hydro EC

Validated

Wind Map

GIS Analysis

10 Years of Hourly Power

Time Series Data for Each

Theoretical Project

Task 3 – Theoretical

Project Delineation and

Capacity Estimation

Task 4 – Theoretical Project Wind

and Power Data Generation

Task 2 – Measured Data and Model

Validation

Validation Reports

Task 1 – Data Synthesis

Forecasted Wind Data for

Each Theoretical Project

ProcessIntermediate

Product

Final

ProductInput

KEY:

Figure 1. Flowchart of Project Process


Page 7 of 53



Section 1 - Wind Modeling

The objective of this task was to synthesize wind data over areas of potential wind power project development in British Columbia. The complete modeled wind data set served as a source for wind resource and power production time series data sets at specific locations (i.e. theoretical wind project sites), as described in Sections 3 and 4. To complete this task, the Project Team followed the procedures outlined below.

1.1 Domain Selection

The geographic scope of this study was focused on areas in the Province where wind power development potential is greatest. Five geographic areas (domains) were selected for inclusion in the study, and wind data were synthesized by 3TIER for each domain using a numerical weather prediction (NWP) model. The five domains were selected based on their development potential, as indicated by the presence of land secured by Investigative Use Permits (IUPs), and on DNV-GEC’s and Ron Nierenberg’s knowledge of the wind resource potential and development activities throughout the Province. IUPs are issued by the Province of British Columbia’s Integrated Land Management Bureau to prospective developers and permit investigation of an area’s wind resource. The five resulting domains, shown in Figure 2, cover the key areas where wind developers are investigating potential wind power projects.


Page 8 of 53



Figure 2. Model Domains


Page 9 of 53



Although data were synthesized for the Northwest domain, this region was ultimately removed from the analysis because no locations met the project delineation criteria described in Section 3. The data synthesized over this domain are available to BC Hydro for future use, if needed.

1.2 NWP Model Simulations

To estimate the wind resource across the five domains, mesoscale NWP simulations were used to recreate the weather for recent historical years. NWP simulations utilize the global weather archive of historical weather data (described in Section 1.2.1) to initialize and continuously adjust numerical simulation models of the atmosphere. In addition, high-resolution data sets describing the land surface are also a required by mesoscale NWP models. The simulation models are run on supercomputers and essentially integrate the regional observational data sets and the high-resolution land surface data sets into a comprehensive representation of the region’s wind resource. The input wind resource data sets are carefully time-synchronized to capture hour-by-hour variations in the wind speed so that wind plant energy output can be analyzed against other time dependent variables, such as utility load and system operations. Although these models are similar or identical to the models meteorologists commonly use to predict the weather, the use of supercomputers and high-resolution land surface data allow for variations in the spatial resolution, including predictions at a finer geographic resolution in specific areas of interest. For this project, the Weather Research and Forecasting (WRF) model was used to perform the NWP simulations. The WRF model is generally considered to be the most advanced mesoscale NWP model in North America; it has superseded the previous industry standard, the MM5 model, and represents the next generation in mesoscale NWP models. The WRF model framework has been developed in a collaborative partnership between federal agencies and universities, so it is actively supported by a large research and operational community. The WRF model is designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 4-dimensional variational (4DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. Consequently, the WRF model is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers.

1.2.1 Input Data Global Weather Archive: The main input data for wind resource assessment simulations are historic global weather archives, which are maintained by operational weather forecasting centers around the world including the United States National Center for Environmental Prediction (NCEP). These global archives represent the overall state of the atmosphere over the entire planet and are themselves the result of a sophisticated computer analysis of available surface and upper air observations. Each time period of analysis combines tens of thousands of individual measurements around the globe into a consistent physical state. Due to the necessity to represent the entire globe, the NCEP/NCAR (National Center for Atmospheric Research) re-analysis data set is maintained at a relatively coarse horizontal resolution and, by itself, does not contain the level of detail necessary to resolve the wind flow patterns over smaller geographic regions or over a single project. However, these data do provide a good representation of the history of large-scale spatial patterns in the atmosphere (i.e., the


Page 10 of 53



position of high and low pressure systems; the location of the jet stream) as well as the general state of the ocean (e.g., sea surface temperatures) and land surface condition (e.g., soil moistures). Combining these coarse resolution re-analysis data with high-resolution land-use data and a mesoscale NWP model allows regional and site specific wind fields to be accurately reconstructed. High Resolution Terrain, Soil and Vegetation Data: To accurately resolve regional wind fields requires an ability to model the interaction of large scale weather systems with the varied terrain, land-use and vegetation of the region. An accurate representation of the local terrain is also important for resolving thermally driven circulations caused by differential heating and cooling of the land surface. 3TIER has customized the WRF model to ingest both the U.S. Geological Survey (USGS) GTOPO30 dataset, which provides a global 30-second (roughly 900 m) digital representation of land surface topography, as well as higher-resolution 3-second (roughly 90 m) terrain datasets such as those available from the Shuttle Radar Topography Mission. In addition, WRF employs a 30-second global 24-category land use map (USGS), a 5-min soil texture (FAO) and a 0.15-degree monthly climatology green vegetation fraction (NESDIS). These data sets were used to describe the height and roughness of the earth’s surface for the period of simulation.

1.2.2 Model Configuration Test Simulations The WRF model can be configured to better represent actual physical processes, given a certain region, resolution, and application. For this study, four different model configurations were tested at nine validation sites, and the configuration producing the best results in each domain was selected. Parameters of the four configurations are shown in Table 1.


Page 11 of 53



Table 1. Configurations Using the Advanced Research WRF Core

Configuration

Planetary Boundary Layer Parameterization

Land Surface Physics

Parameterization

Radiation Physics

Parameterization

A Yonsei University Thermal Diffusion Scheme

RRTM/Dudhia

B Yonsei University Unified NOAA land-surface

model RRTM/Dudhia

C Mellor-Yamada-

Janjic TKE Scheme

Thermal Diffusion Scheme

RRTM/Dudhia

D Yonsei University Thermal Diffusion

Scheme CAM/CAM

Configuration A is used as the baseline model configuration with Configurations B, C, and D all having a single parameter of deviation. Configuration B uses the Oregon State University land surface model, a more sophisticated physical process model for estimating surface fluxes. Configuration C uses the Mellor-Yamada-Janjic boundary layer parameterization, which features explicit prognostic equations for boundary layer turbulence. Configuration D uses NCAR’s community atmospheric model’s (CAM) radiation. Configurations B, C, and D should theoretically perform better than Configuration A. However, the increased sophistication in the models introduces additional assumptions and unconstrained parameters that can adversely affect the accuracy of the model. The test simulations covered the entire model domain at a grid resolution of 6 km, and were run for a number of days in each month (each test represented about 90 days). The results from these simulations were compared to nine observational data sets from across the province, each with measurement heights at or above 50 m above ground. These 9 observational data sets were a subset of the 30 selected observational sets used for model validation, as described in Section 2, and were selected for the configuration study because they were among the first observational data sets quality checked and ready to be used. Of the nine data sets, one represented the Vancouver Island domain, three represented the North Coast domain, two represented the Southern Interior domain, and three represented the Peace domain. Details about the nine observational data sets, including measurement height and period of record used for the configuration comparisons, are shown in Table 2. A configuration validation report was produced for each comparison and was provided to BC Hydro before decisions were made regarding which configuration would be used for each domain. Based on the results of the configuration validation tests, a single configuration was selected for each domain. The selected configurations are shown in Table 3.


Page 12 of 53



Table 2. Observational Data Sets Used for Configuration Tests

Site Name Site Owner Measurement

Height (m)

Months of Data Used for

Configuration Test

Vancouver Island

VI-IPP-3 IPP 50 10

North Coast

NC-IPP-3 IPP 60 12

NC-IPP-4 IPP 60 12

NC-IPP-5 IPP 60 6

Southern Interior

SI-IPP-2 IPP 80 8

SI-IPP-3 IPP 60 9

Peace Domain

P-IPP-3 IPP 50 12

P-IPP-4 IPP 40 12

P-IPP-5 IPP 40 12

Table 3. Selected Model Configurations for Each Domain

Domain Selected Model Configuration

Vancouver Island C North Coast C Southern Interior C Peace B

1.2.3 Data Modeling The WRF model was run using the selected configurations over each domain. Separate simulations were conducted to generate 2 km, 6 km , and 18 km resolution data. All WRF simulations utilized a nested grid configuration. Specifically, the 2 km resolution simulation was run using 54 km, 18 km, 6 km, and 2 km resolution nested grids. The 6 km and 18 km simulations used the same nested grid configuration, implementing the necessary number of grids: 3 nested grids for the 6 km simulations, and 2 nested grids for the 18 km simulations. The innermost model domain for all simulations covered the five domains of this study— Vancouver Island, the North Coast, Southern Interior, Peace, and Northwest—and all innermost domains incorporated a buffer zone in each direction to avoid grid edge effects. The 10-year, 10-minute time series data were derived from two separate simulations: a 1-year numerical simulation (August 2007 through July 2008) with a horizontal grid resolution of 2 km, and a 10-year numerical simulation (August 1998 through July 2008) with a horizontal grid resolution of 6 km. The model data from the 10-year simulation were stored at a 10-minute interval. At each of the theoretical wind project sites, these modeled data sets were then


Page 13 of 53



statistically combined to produce 2 km resolution, 10-year, 10-minute time series data sets over the period August 1998 through July 2008. Likewise the 30-year, monthly-mean time series data were derived from two simulations: a 10-year, 6 km resolution model simulation (used above), and a 30-year, 18 km resolution simulation (August 1978 through July 2008). The 30-year model simulation data were stored at an hourly interval. At each theoretical wind project site, these modeled data sets were then statistically combined to produce 6 km resolution, 30-year, monthly mean time series data sets over the period August 1978 through July 2008.


Page 14 of 53



Section 2 - Measured Data and Model Validation

The objective of this task was, to the extent possible with available data, to validate the NWP model results in each domain by comparing modeled to measured data at several locations, and specifically to identify geographic areas where the model substantially over- or under-predicted wind speeds. These results were then used, where necessary, to adjust the wind speeds modeled at theoretical wind power project sites, as described in Section 4.

2.1 Measured Data Inventory

DNV-GEC performed a comprehensive survey of available measured met data throughout the Province and selected 30 locations to serve as validation sites at which the acquired measured data were compared to data modeled at the same location. The available measured data sources included tall met towers (> 10 m) owned by IPPs, tall met towers operated by BC Hydro between 2000 and 2004, and 10-m met towers operated at airports and other monitoring stations across the Province by Environment Canada (EC).

2.1.1 IPP Met Data DNV-GEC contacted IPPs investigating wind power project development in BC to solicit their participation in the Study by sharing proprietary met data collected at monitoring stations throughout the Province. The IPPs contacted were identified as potential participants because they had either attended a stakeholder’s meeting hosted by BC Hydro in Vancouver, BC, on July 4, 2008, or because they were known by the Project Team to be pursuing wind power projects within the Province. As compensation for participation, BC Hydro agreed to share with all participating IPPs the data sets modeled at their tower locations that were selected for use in the study. Of the 10 IPPs solicited, 7 agreed to participate in the study. These IPPs were asked to provide DNV-GEC with a list of geographic location, monitoring heights, and period of record for each met tower they have or had installed in the Province. Data sets from a total of 51 met towers were ultimately offered. The locations and details of all 51 IPP towers are shown in Table A-1 in Confidential Appendix A. From the 51 towers, 20 were selected for use in the study. DNV-GEC acquired raw data files for these 20 towers and in turn transferred the data files to BC Hydro. BC Hydro and each participating IPP jointly signed an NDA before the data were transferred. The NDA prohibits the public disclosure of any information derived from the data sets, except under specified conditions. As such, information taken directly or calculated from the 20 IPP data sets is not presented in this report, except in confidential tables in Appendix A.

2.1.2 BC Hydro Met Data BC Hydro installed 18 tall met towers in locations throughout the Province from 2000 to 2004. The locations were biased toward regions that were expected to offer viable wind energy development sites, specifically the Vancouver Island, North Coast, Southern Interior, and Peace regions. The met towers included 12 50-m towers, 5 30-m towers, and 1 lighthouse. DNV-GEC had previously acquired these data sets from BC Hydro and had performed basic quality check procedures. As such, DNV-GEC was familiar with many of the data sets and knew that several


Page 15 of 53



showed apparent data quality issues, including malfunctioning vanes and anemometers. As a consequence, preference was given to IPP towers in situations where BC Hydro and IPP met towers were sited near one another. BC Hydro towers were only selected for inclusion in the study to either extend the geographic or temporal range of the collective measured data. Seven BC Hydro towers were ultimately selected for inclusion in the study, and the data quality of all of these was confirmed before they were used.

2.1.3 Environment Canada Met Data Environment Canada manages 117 weather monitoring stations at airports, lighthouses, and other government sites across the Province. Data from these met towers are generally collected at 10 m, a height that is, in general, too low to accurately indicate wind turbine hub height wind speeds. However, the data can be useful in providing qualitative checks on the modeling results if no other data sets are available. The Vancouver Island, North Coast, Southern Interior, and Peace domains were well represented by IPP and BC Hydro tall tower met data, so no EC data sets were selected for those domains. There were no IPP or BC Hydro data sets available for the Northwest domain, so EC data sets were used to represent this area. Data from all three land-based EC stations located in the Northwest domain were selected for use in the study.

2.2 Selected Validation Sites

From all available data sets, DNV-GEC selected 30 measured data sets to serve as validation sites, and 3TIER completed a validation study for each of these, as described in Section 2.4. Details about each selected site are shown along with correlation parameters in Table 4. More detailed information about and the locations of the 30 sites are presented in Table A-2 and Figure A-1 in Confidential Appendix A. These 30 sites were selected from the entire pool of available data sets based on the following factors: representation across the largest possible geographic and elevation spread within the domains of interest; appropriate micro-siting, i.e., avoiding towers in valleys and favoring towers in realistic, well-exposed development areas; highest possible measurement heights; and longest possible period of record. Ultimately, 20 validation comparisons utilized IPP data sets and the remainder utilized data sets from BC Hydro towers and EC weather stations.

2.3 Data Quality Control

DNV-GEC quality checked all 30 measured data sets selected for the validation study. Data from all tall towers (i.e., the IPP and BC Hydro data sets) were compiled and quality checked using methods consistent with DNV-GEC’s standard practices. Each data set was scrutinized using automated and visual checks to identify and remove erroneous data. Wind speed data were considered invalid due to icing if the temperature was near or below freezing and an additional criterion was met, such as the wind vane or anemometer standard deviation equaling zero for consecutive records or the average wind speed being lower than expected, relative to the wind speeds at other levels. Wind vane data were considered invalid due to icing if the standard deviation was zero for several consecutive records when temperatures were near or below freezing. To the extent possible, DNV-GEC filtered the wind speed data to select the values most representative of the free-stream wind speed. If a tower had more than one wind speed sensor at


Page 16 of 53



the upper measurement height, the “unwaked” wind speed value was used. The unwaked wind speed was determined by selecting the wind speed value from the least tower-influenced sensor (i.e., the “primary sensor”) by default, except in times when that sensor was downwind of the tower or the data were erroneous, in which case the wind speed from the “secondary” sensor was selected. If a tower only had one sensor at the upper measurement height, data from that sensor were considered representative of the free-stream air flow. In other words, the data were not corrected for tower effects. If the upper level sensor or sensors were malfunctioning for a substantial amount of the tower’s period of record, lower level sensors were used instead. If possible, the wind direction was also checked for consistency against other nearby towers. Data from the three 10-m EC station towers were not quality checked to the same degree as the tall tower data sets. As a result of both the low height of the towers and the possibility of obstructions near the tower, low wind speeds are common. Consequently, icing and other events causing erroneous data are difficult to identify and remove. In addition, as there are no other sources of quality near-surface met data in the area, the accuracy of the measured data could not be verified. The entire data set for all three EC towers was visually inspected to ensure that there were no obvious sensor malfunction issues. Nine of the resulting quality-controlled data sets were used to select the model configurations (as described in Section 1.2.2). All 30 quality-controlled data sets, including the 9 used for configuration testing, were used for the model validation studies.

2.4 Model Validation

To validate the model’s performance, 3TIER compared the 30 quality-controlled, measured data sets to the 10-year simulated data from the NWP model at the same location for the same period of record. The validation studies focused on the model’s ability to reproduce the observed variability of the wind resource at daily and monthly time scales, while preserving the distribution of hourly wind speeds and the diurnal characteristics of the wind. Consequently, investigated parameters included monthly-mean wind speeds, hourly distribution of wind speeds, hourly distribution of wind direction, and diurnal characteristics of the wind resource. The results from each of the 30 comparisons were summarized in validation reports, which were provided to BC Hydro. Information about each of the validation sites and the resulting modeled to observed data correlations are shown in Table 4. Qualitative and quantitative comparisons of the investigated wind resource parameters are shown in Table 5. The measured met data sets were not allowed to influence the raw model simulations, and remained separate from the modeling process prior to the validation comparison studies. The listed wind speeds in Table 5 incorporate all available observational data, which may over- or under-represent certain periods of the year depending on the data set’s period of record and recovery rate. As wind speeds can vary on an annual average basis by ~3% to ~6% depending on location, the listed wind speeds may not necessarily be representative of long-term annual means. Therefore, they should not be interpreted as estimates of the true wind speeds for the site, but rather a verification of the model’s ability to reproduce the available observed wind speeds. For the remaining statistics in both Table 4 and Table 5, any month or hour missing more than 25% of the available observations was not included in the comparison.


Page 17 of 53



In Table 5, the relative difference in wind direction between the modeled and observed data is described under “Modeled versus Observed Wind Rose.” If the two wind roses were similar (i.e., within 20 degrees), “OK” is listed. If the predominant wind direction of the two wind roses was different, the degrees difference—modeled relative to observed—is shown. For example, if the modeled wind rose showed predominant winds from the northwest (315 degrees), and the observed data showed predominant winds from the west (270 degrees), the value listed in the table is +45 degrees. Table 5 also provides qualitative measurements of the model’s performance in matching the observed diurnal patterns of the observed data sets. These judgments consider both the relative magnitude of diurnal fluctuation and the time of average high and low winds. If the modeled diurnal pattern matched the observed pattern well in both respects, “Excellent” is listed. If both patterns were relatively similar, “Good” is listed. If one of the two qualities was substantially different, “Fair” is listed. Finally, if neither the magnitude nor timing of the diurnal wind speed pattern matched the observed data, “Poor” is listed.


Page 18 of 53

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Page 19 of 53

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All of the investigated parameters are important considerations for wind project development and may be useful for subsequent integration studies. However, given the scope of this study and the amount of information currently available, it was not possible to utilize all of the resulting statistics and accurately correct for possible biases in the model over large geographic areas. For example, while a bias in diurnal wind pattern may be qualitatively detectable at a single validation site, it is not possible to quantify the bias and apply a correction to the modeled data over a large geographic area. For the purposes of this study, and specifically for generating wind project resource and production data, wind speed bias was the only parameter considered indicative of geographically large-scale patterns in the model results. Consequently, if the validation reports indicated a strong bias in average wind speed in certain geographic areas, the wind resource data synthesized for those areas were adjusted accordingly (as described in Section 4.1.1). The wind speed bias for each of the 30 validation points is shown geographically at the point of measurement in Table A-2 in Confidential Appendix A. The validation studies from the Vancouver Island, North Coast, and Southern Interior domains did not show any consistent bias in modeled wind speeds. Consequently, the modeled wind speeds were considered to be representative of actual wind speeds for the entire domains. However, validation studies for the Peace domain, showed substantial model underestimation of wind speeds at several locations. The sites where the wind speed bias approached or exceeded (-20%) are primarily located on high elevation ridgelines in the Muskwa and Hart Mountain Ranges (subdivisions of the Canadian Rockies), which stretch across the domain from northwest to southeast. Consequently, the modeled data used to represent potential projects outlined in these areas were adjusted upwards by 20%. The validation reports from other areas of the domain, specifically the area north and east of the high altitude ridgelines, consisting of the Rocky Mountain Foothills and the Peace River Block, did not show a clear or consistent trend in model wind speed estimation, so the wind data modeled for those areas were not adjusted.


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Section 3 - Wind Power Project Delineation and Installed Capacity Estimation

The objectives of this task were to identify potential project areas and their estimated capacity in each of the five domains. Each project was classified as either “readily available” or “ambitious” based on pre-determined criteria for mean annual wind speed and distance to transmission. The project capacities estimated here, and the NWP modeled time series wind data from within the outlined project boundaries (described in Section 1) were used to model power time series data for each project, as described in Section 4.

3.1 Project Delineation Criteria

DNV-GEC identified potential wind power project areas throughout the five domains. The domains were screened, and theoretical projects were outlined based on pre-determined criteria. Each outlined project was identified as either “readily available” or “ambitious,” based on criteria developed in cooperation with BC Hydro. The categories and associated criteria were developed in cooperation with BC Hydro, and are listed in Table 6.

Table 6. Project Delineation Criteria

Readily Available Ambitious Minimum Long-Term Wind Speed Average 6.5 m/s 6.0 m/s

Maximum Distance from Transmission 50 km 200 km

Maximum Slope Areas with 20% or greater slope

excluded. Areas with 10-20% slope avoided when possible.

The minimum annual project-average wind speed threshold for all projects was 6.0 m/s, according to the wind map created by 3TIER and validated as described in Section 3.2.1; projects with annual project-average wind speeds of 6.5 m/s or greater were considered Readily Available, and those with a project-average of 6.0 to 6.5 m/s were considered Ambitious. All projects had to be located within at least 200 km of existing transmission lines, based on data received from BC Hydro. Projects within 50 km of transmission were considered Readily Available and those within 50 to 200 km were considered Ambitious. The locations of transmission lines are shown in Figure 3, along with 50 and 200 km ranges. All of the areas of potential development interest, as indicated by the geographic concentration of IUPs, fall within 200 km of transmission lines, so the distance to transmission criteria did not effectively cut any potential future projects. However, wind speed and/or slope criteria did eliminate many potential project areas within IUPs from consideration.


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Figure 3. Transmission Lines and 50 km and 200 km Ranges

In addition to the above categorized considerations, all project areas had to meet several other criteria. Project areas were not outlined on very steep terrain or unbuildable terrain (i.e., wetlands). The maximum slope for a project area was 20%, and areas with slopes between 10% and 20% were avoided where possible. If a project area was flat in general, but contained isolated areas of steep terrain, the assigned percent of usable land used for project capacity


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estimation was decreased. Likewise, if a project area contained lakes or wetlands, the assigned percent of usable land was decreased. For projects identified on ridgelines, the top of the ridge had to have a slope of less than 20% for a width of approximately 100 m or more, an area generally wide enough to permit wind turbine installation. Projects were not outlined on ridgelines running parallel to the known predominant wind direction, as substantial energy would be lost as a result of turbine-turbine waking. If the wind direction was unknown in a certain area because no met data had been collected within a reasonable distance, the project ridgeline could be oriented in any direction. Finally, areas within provincial or national parks, and protected areas (such as conservancies and biodiversity areas) were not considered. DNV-GEC set a limit of 30 MW as the minimum installed capacity, or “size,” of each identified wind power project. While projects with less than 30 MW installed capacity technically could be constructed, historical experience has shown that larger projects generally prevail due to economies of scale. Admittedly, the minimum project size is a grey area, but a cut-off value needed to be specified for reducing variables in this study. The assumed turbine spacing used to calculate the installed capacity for each project was approximately 4 rotor diameters (D) between turbines within the same row and 15D between rows, i.e., 4D by 15D, assuming the Siemens 2.3 MW turbine. See Section 3.4.2 for a more detailed discussion of assumed turbine spacing. The resulting minimum project area was approximately 6 square km, and the minimum ridgeline length was 5 km. If a certain area satisfied all criteria, but the potential project area was too small to support 30 MW, the area was dropped from consideration in the study. Other factors that may affect the feasibility of a real-life project development, but that were not outlined in the study’s criteria, were not considered. For example, some outlined projects are located in or near areas with conflicting uses, such as popular vacation or recreational areas, and consequently may not receive support from local residents. While such considerations are important for a real-life development, they are both difficult to assess comprehensively and dynamic in nature, i.e., they may change as a result of new legislation, shifts in public acceptance, and efforts and/or concessions made by the developer. Consequently, any attempt to exclude project areas on such grounds would be based on speculation. Similarly, project areas located in seemingly “difficult” locations such as small, remote islands were also included in the study as long as they satisfied the project selection criteria. In summary, only concrete physical factors, such as the wind resource, basic constructability (as indicated by terrain slope), and distance to transmission were used to screen potential project areas. The final set of selected project areas represents all areas across the Province that met the pre-determined criteria. Cumulatively, the final set of projects is not a Province-wide capacity estimate, and should not be used as-is for a provincial supply curve. Likewise, the locations of individual outlined project areas do not indicate the overall feasibility or potential economic viability of a certain area for wind power development. Rather, the final set of outlined project areas represents a balanced selection of theoretical and realistic project areas across the Province, as dictated by the pre-determined screening process. The final selection of realistically sized projects identified over a wide geographic area allows for additional screening and sensitivity analyses, and was created specifically to serve the purposes of subsequent integration studies.


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3.2 Project Delineation Methodology

DNV-GEC visually identified potential wind power project areas, per the above criteria, by utilizing digital land and wind speed data and GIS technology. The primary data sets used include: 90-m resolution digital elevation model (DEM) data from GeoBase (a service overseen by the Canadian Council on Geomatics); 1:250k spatial resolution topographic data procured from the CanMatrix service of Natural Resources Canada; 2-km resolution wind maps produced by 3TIER for each domain showing annual wind speeds for the August 2007 through July 2008 year; and the locations of transmission lines and all IUPs in the Province (as of May 7, 2008), as provided by BC Hydro. The 30 model validation reports and 10-year data sets modeled at each of the validation locations were also used to validate the wind map, as described in Section 3.2.1. To expedite the project identification process, only areas currently being investigated for wind project development, as indicated by the presence of IUPs, were considered. Projects were outlined either in or near current IUPs. DNV-GEC also surveyed areas far from current IUPs for sites that satisfied the project criteria and determined that there were no substantial areas that had been missed. Consequently, project delineation efforts remained focused in or near existing IUPs. In addition, projects were outlined at each of the three wind power projects accepted under BC Hydro’s 2006 Request for Proposals (RFP)—Dokie, Mt Hays, and Bear Mountain—the locations of which were provided to DNV-GEC by BC Hydro. Depending on the topography, projects were either drawn as a line (for steeper areas where turbines would likely be installed on top of a ridge), or area (for flatter areas, where turbines would likely be installed in an array). A single project could consist of a single line/area or multiple lines/areas, depending on topography. Figure 4 shows an example of an outlined project area. During the project delineation process, each project area was assigned a “constructible area” percentage that was later used to calculate the project’s capacity. If a certain outlined project area had some unconstructible areas, such as lakes, wetlands, or steep terrain, the constructible area percentage was decreased. If a particularly large area could accommodate a very large project (i.e., 500 MW or more) or two or more smaller projects, it was broken up into several smaller projects when possible.


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3.2.1 Wind Map Validation The wind maps used for project delineation in each domain were developed by 3TIER based on the single year (August 2007 through July 2008), 2-km resolution NWP model results. Although 10-year average 2-km resolution wind maps would have been desirable, the computational cost of creating the necessary data over such large areas was prohibitive for the budget of this study. 10-year, 2-km resolution data (i.e., the results of blending the 2-km and 6-km resolution model results) were only created for individual points, not for the entire domain. As such, the single-year wind maps were used, and were validated by comparing the single-year simulated wind speed average to the 10-year simulated average at 20 validation points. The results of this exercise are shown in Table 7. Negative numbers indicate that the average wind speed from the wind map year was low compared to the 10-year average by the shown percent, whereas positive numbers indicate that the wind map wind year was relatively high. As shown, the bias in the single-year wind speed compared to the 10-year long-term wind speed was minimal for the Vancouver Island and North Coast domains, so the single-year wind maps for those domains were considered representative of long-term wind speed averages. The results for the Southern Interior domain showed a consistent trend that the August 2007 through July 2008 wind year was approximately 2.5% higher than the 10-year average. Consequently, the Southern Interior single-year wind map was adjusted downwards by 2.5% to better represent the long-term average. The average bias for the Peace domain was 1.7%, with individual sites ranging from 0.3% to 2.5%. Given the range of results, and the overall model biases in the Peace domain (as indicated by the validation studies), DNV-GEC did not feel that additional adjustments to the single-year wind map would improve the accurate application of project delineation wind speed criteria. Consequently, no adjustment was made to the wind map for the Peace domain.


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Table 7. Wind Map Period (August 2007 through July 2008) Average Wind Speed Relative to the 10-Year Average

Site Name 1-Year Wind Map Average

Wind Speed Bias

Vancouver Island VI-IPP-1 0.6% VI-IPP-2 -1.5% VI-IPP-3 0.6% Average -0.1% North Coast NC-IPP-1 1.5% NC-IPP-2 1.1% NC-IPP-3 0.2% NC-IPP-4 0.4% NC-IPP-5 -0.2% NC-IPP-6 0.8% Average 0.6% Southern Interior SI-IPP-1 2.9% SI-IPP-2 2.6% SI-IPP-3 2.6% Average 2.7% Peace P-IPP-1 2.4% P-IPP-2 2.5% P-IPP-3 1.8% P-IPP-4 0.3% P-IPP-5 2.0% P-IPP-6 1.3% P-IPP-7 1.5% P-IPP-8 2.0% Average 1.7%

The wind maps were also adjusted if necessary to account for biases in the simulated wind speed data, as indicated in the validation reports. As no consistent wind speed bias was evident in the Vancouver Island, North Coast, or Southern Interior domains, the wind maps for these domains were not adjusted. These wind maps are shown in Figure 5, Figure 6, and Figure 7. For the specific areas of the Peace domain where a low wind speed bias was evident in the modeled wind resource data, specifically on the high-altitude ridgelines of the Hart and Muskwa Ranges, the wind map was adjusted upwards by 20%. The Peace domain wind map, including the area where an adjustment was applied, is shown in Figure 8. For project areas identified in the remainder of the domain, where there was no clear wind speed bias in the simulated data or no available validation reports, no adjustment was made to the wind map.


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Figure 5. Vancouver Island Domain 1-year Wind Map (August 2007 through July 2008)


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Note: Wind speeds shown, including within 20% Wind Speed Adjustment Zone, are as-modeled.

Figure 8. Adjustments to Peace Domain 1-year Wind Map August 2007 through July 2008


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3.3 Theoretical Projects

The theoretical projects resulting from the above methodology and criteria are shown for each domain in Figure 9, Figure 10, Figure 11, and Figure 12. Projects classified as “Readily Available” are shown in green and projects classified as “Ambitious” are shown in red. No distinction is made in these maps between projects classified as “Ambitious” due to average wind speeds of less than 6.5 m/s and those located more than 50 km from transmission.

Figure 9. Vancouver Island Domain Theoretical Projects


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Figure 12. Peace Domain Theoretical Projects


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Much of the Peace region is characterized by high altitude, narrow ridgelines, so most of the identified projects in that domain were classified as “ridgeline” projects. Areas satisfying the study’s project criteria in the other three domains were primarily suitable for array-type projects, although some ridgeline projects were also identified in all domains. (No areas satisfied the study’s project criteria in the Northwest domain.)

3.4 Project Capacity Estimation

To estimate the installed capacity of each identified project, DNV-GEC made assumptions regarding the size and type of wind turbines that are likely to be deployed in the future. Although, realistically speaking, a wide variety of turbine models will be utilized in the different wind power projects to be developed across the Province, it was necessary to adopt consistent turbine model assumptions for the sake of modeling. In addition to assumptions on turbine models, DNV-GEC also made assumptions about the turbine spacing used in the outlined projects. Although turbine spacing in actual projects will vary as a result of many factors, including topography and development objectives, DNV-GEC applied a consistent spacing scenario to all projects for the sake of ease of modeling.

3.4.1 Wind Turbine Technology Assumptions One of two wind turbine models—the Siemens 2.3-93 (Class II) and the Vestas V90 (Class I)—was assigned to each project area based on the project’s anticipated average wind speed. These two specific turbine models were selected because they represent well the size and performance of turbines that will likely be deployed in the foreseeable future. Both turbines utilize full span pitch control and variable speed operation which are consistent with the current state of the art. At this stage of technology development, DNV-GEC expects further refinement of turbine technology to be made in evolutionary steps as opposed to revolutionary changes in fundamental design architecture. Evolutionary changes are expected in all major components and systems and will most likely focus on reducing costs and improving reliability. Improvements in energy conversion efficiency are likely to be limited because aerodynamic, mechanical, and electrical efficiencies have already attained a very high level, and are approaching fundamental limits on these efficiencies. Commercial land-based turbines in North America are currently in the 1.5 - 3.0 MW size range. Although larger models exist in Europe (up to 5 MW), these turbines are not well established in the industry. Furthermore, the terrain complexity found across the majority of potential development areas in the Province may prove to be a challenge for transportation and construction of turbines larger than ~3 MW. One potential off-shore project was identified for this study. Although it is possible that larger wind turbines (>3 MW) may be considered for this project, there is currently no North American offshore wind turbine precedent. Because of this, and also to simplify the domain-wide energy modeling, the project was assigned the Vestas V90 (Class I) wind turbine. Wind turbines are designed to operate within a specific envelope of loads dictated by the machine design and environmental conditions in which they operate. Specific wind turbine designs are governed by international standards promulgated by the International Electrotechnical Commission (specifically IEC 61400, Edition 3). Wind turbine designs fall into three basic classifications, Class I, Class II, and Class III which are broadly segregated by the


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annual average wind speed at the location where the turbine is installed1. IEC Class II wind turbines are designed for sites with annual average wind speed up to 8.5 m/s at Standard Atmospheric conditions. IEC Class I wind turbines are designed for sites with annual average wind speeds up to 10 m/s at Standard Atmospheric conditions. These ranges mean that a Class II designed wind turbine could be found unsuitable if placed at a site that exceeds the turbine loading capacity. If this occurs, a Class I wind turbine should be used instead. Given the range of expected wind speeds in BC, most identified project areas will utilize IEC Class II turbines, represented in this study by the Siemens 2.3-93. There are also some higher wind speed areas where Class I wind turbines will likely be utilized, which are represented in this study by the Vestas V90. The IEC design classifications of the two example wind turbines are presented in Table 8. Although commercial success of the V90 turbine in North America has been limited at this point, its power performance characteristics reflect the expected performance of turbines designed to IEC Class I conditions.

Table 8. Turbine Specifications

Design Classification IEC Class I IEC Class II Turbine model Vestas V90 Siemens 2.3

Nameplate capacity 3 MW 2.3 MW

Rotor diameter 90 m 93 m

IEC standards for turbine site suitability dictate a range of acceptable wind speeds, presuming Standard Atmospheric conditions. However, it is ultimately the loading conditions on the turbines under site-specific conditions, and not necessarily the wind speed, that dictate whether a turbine is suitable for a given location. Since a large majority of potential wind energy development sites in BC are found at elevations greater than 1,000 m, reduced air density at these altitudes mitigate some of the structural loads. Based on DNV-GEC’s experience, our analytical tools and our analysis of BC development areas, we have estimated the threshold between Class II and Class I wind turbines is 9 m/s. This wind speed threshold is greater than the 8.5 m/s limit associated with IEC Class II due to the effects of reduced air density. Therefore, for this study, areas with average wind speeds lower than 9 m/s were assigned the Class II turbine (Siemens 2.3-93). Projects with average wind speeds greater than 9 m/s were assigned the Class I turbine (Vestas V90). Due to the limitations of estimating the exact simulated project-average wind speed from the wind speed maps, a certain project was assigned a Class I turbine if most of the project area had wind speeds of 9 m/s or greater. Consequently, it may be possible that some projects assigned a Class I turbine may ultimately have a simulated project-average wind speed slightly lower than 9 m/s. Several projects in the Peace domain and an off-shore project in the North Coast domain had indicated wind speeds high enough to necessitate the Class I turbine. The theoretical project areas and associated turbine types for the North Coast and Peace domains are shown in Figure 13 and Figure 14, respectively. All projects in the Vancouver Island and Southern Interior domains were assigned the Class II turbine. 1 The appropriate turbine class for a particular site also depends on other factors—including turbulence intensity and the extent of inclined flow—that were not considered in this study.


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Figure 14. Peace Domain Theoretical Projects and Assigned Turbine Type


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3.4.2 Wind Turbine Density Assumptions To estimate the installed capacity of each outlined project, DNV-GEC made assumptions about the density of wind turbines likely to be installed in projects throughout the Province. A standard turbine density was calculated for each type of outlined project (areas and ridgelines), based on the Province’s expected wind patterns, the assumed turbine model rotor diameters, and standard wind power project buffers. The observed and simulated wind resource patterns across the five domains indicate that most regions experience unidirectional flow patterns. Project turbine arrays in such wind regimes tend to be “rectangular,” i.e., turbines are relatively close together within a single row, and rows are relatively far apart. Conversely, a multidirectional wind pattern requires a more “square” turbine arrangement, with turbine spacing matching row spacing more closely to minimize the effects of inter-turbine wakes. Because of the observed unidirectional wind patterns in most of the Province, a “rectangular” layout of 4D by 15D (4 rotor diameters distance between turbines within a row, and 15 rotor diameters distance between turbine rows) was assumed for all project areas identified in the study. This assumption implies that this arrangement, or configurations with similar turbine density, is likely to be used for wind power projects throughout the Province. This layout spacing results in a turbine density of approximately 2.3 turbines/km2 of constructible land area. For projects identified on ridgelines, spacing between turbines is assumed to be 4D, and spacing between turbine “rows” will be dictated by local terrain features. Given this, and the wind turbine model assumptions outlined above, the resulting ridgeline turbine density used in this study is approximately 2.7 turbines per km of ridgeline.

3.4.3 Final Project Estimated Capacities Given the objectives of this study, specifically to produce time series power data representative of actual wind farm output, the above turbine density assumptions were applied to each outlined project area on a modeled grid cell (4 km2) basis. Assigning turbine density on a grid cell basis, as opposed to assigning a standard turbine density across an entire project area, retained a turbine distribution more representative of actual project distributions. For example, an actual wind power project may have a maximum turbine density on higher elevation or easily constructible areas, and a lower effective turbine density on the perimeters of the project (simply because the perimeter of the project by definition consists of both project and non-project area). Consequently, grid cells on the perimeter of the project (i.e. grid cells that contained less “amount of project”) were assigned less turbines than grid cells in the center of a project. To calculate the number of turbines per grid cell, the “amount of project” (either km for ridgelines or km2 for areas) outlined within each grid cell was calculated using GIS software. Figure 15 illustrates the GIS methodology. This “amount of project” value was then multiplied by the turbine density assumptions described above, resulting in a number of turbines within each grid cell. For example, if a certain grid cell contained 2.6 km2 of project area, the number of turbines within that grid cell was calculated as [2.6 km2 x 2.3 turbines/ km2], or 6 turbines. To comply with limitations of the power modeling process, the number of turbines per grid cell was rounded to 0, 3, 6, or 10. As a result of this adjustment, the installed capacity for each domain


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changed slightly from the case if the un-rounded turbine numbers were used. The effect on the Province-wide installed capacity was a 3% reduction. The assumptions used to estimate each project’s installed capacity (turbine size, turbine location, turbine spacing and project boundaries) are reasonable approximations based on experience from actual wind power projects. However, these factors can change significantly from the initial assumptions to the final layout. Consequently, the change in project capacities that resulted from rounding the turbine numbers at each grid point are considered by DNV-GEC to fall within the range of reasonable project alterations that usually occur in the development process and the deviation from the base assumptions was considered acceptable.

Figure 15. Example of GIS Project Installed Capacity Estimation


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The final number of outlined projects and installed capacities for each domain are shown in Table 9. A complete table of all identified projects and their individual estimated installed capacities is available in Appendix B. As shown, the Peace region resulted in the highest estimated installed capacity. The North Coast and Southern Interior regions also had substantial estimated capacity, although over one third of the North Coast capacity is attributed to a single, potential offshore project. The potential North Coast offshore project is classified as “Readily Available” because it meets the study criteria for that category. However, it may face additional technical challenges not considered in this study. Compared to the other three domains, relatively few projects were identified on Vancouver Island.

Table 9. Projects and Installed Capacity by Domain

Category Number of Projects Total Installed Capacity (MW)

Vancouver Island Readily Available 3 138 Ambitious 11 1311 Total 14 1449 North Coast Readily Available 5 3288 Ambitious 7 922 Total 12 4211 Southern Interior Readily Available 6 600 Ambitious 25 3809 Total 31 4409 Peace Readily Available 21 2703 Ambitious 27 3410 Total 48 6113


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Section 4 - Project Wind and Power Data Generation

The objective of this task was to produce the final products of the study: modeled, historical wind resource and power data, as well as forecasted wind resource data, at each identified theoretical wind power project. To create the 30-year (monthly temporal resolution) and 10-year (10-minute temporal resolution) project average wind resource time-series data, the wind resource data synthesized and validated in Section 1 and Section 2, were extracted and averaged for each theoretical project location outlined in Section 3. The resulting project wind resource data were crossed with appropriate power curves to produce project time-series power data. For the 10-year (10-minute) data set, statistical corrections were applied to the power data. These statistical corrections, developed by 3TIER and referred to as the SCORE-lite process, incorporate short-term variability that is observed in operating wind project power data and often under-represented in modeled data. Finally, three individual wind energy forecasts were computed for each of the projects outlined in Section 3. These forecasts—including a climatological forecast, a perfect forecast, and an NWP-based forecast–are described below in Section 4.3.

4.1 Project Wind Data

As described in Section 1, a 10-year, 2 km resolution time series data set was created at grid points across the entire extent of each of the five domains. Grid points located within or near an outlined project area were extracted and used to calculate the average wind resource data set for that particular project. The project-average data set was calculated by weighting the data from each extracted grid point by the number of turbines assigned to each of those grid points. For example, if a particular project had one grid point with six turbines and four grid points with three turbines, the six-turbine grid point would receive twice the weight in the wind speed average calculation as other grid points. Project locations in the 30-year, 6 km resolution data set were defined using a single point near each project’s center. The simpler approach for defining projects, as compared to the 10-year data set, was used because of the coarse resolution of the 30-year data set; a more detailed project definition approach would not necessarily improve model results. As described in Section 1.2.3, the 10-year (10-minute) and 30-year (monthly) project average wind data sets were created in separate models runs. The 10-year data sets were derived by blending together data from a 1-year, 2 km resolution simulation with a 10-year, 6 km resolution simulation. The 30-year data sets were derived by blending together data from the 10-year, 6 km resolution simulation with a 30-year, 18 km resolution model simulation. Because of the different resolutions of the 10-year and 30-year data sets, the wind speed results for each data set may differ significantly. Consequently it is not expected that project-average wind speeds from each data set will match each other. While the blended 10-year, 2 km resolution project data are meant to realistically represent the wind speeds at each wind power project, the blended 30-year, 6 km model data are meant to identify long-term trends. Average values from the 30-year data set may not necessarily represent realistic project wind speeds, but this does not limit their usefulness to provide information on the long-term variability at each project site.


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4.1.1 Accounting for Wind Speed Bias After the modeled time series data were extracted for each grid point within a project, wind speeds were adjusted to account for biases in the modeled wind speed data, if necessary, as indicated by the validation study. The modeled wind speed bias for all 30 validation sites was shown in Table 5, and a discussion of the validation study results was presented in Section 2.4. To briefly reiterate the results, no clear wind speed bias was evident in the validation results for the Vancouver Island, North Coast, and Southern Interior domains, so no adjustments were made to the modeled wind data for these domains. For the Peace region, validation sites on high altitude ridgelines in the Muskwa and Hart Ranges showed an approximate -20% bias, so the wind speeds at projects in these areas were adjusted upwards by 20%. Validation results from the rest of the Peace domain did not show a significant bias, so the modeled data for projects in those areas were not adjusted. Several projects were identified on the western edge of the domain, near Williston Lake. No validation data sets were available for this area, so the modeled data for these projects were not adjusted.

4.2 Project Power Data

Power time series data for each project was effectively calculated by multiplying the wind time series data extracted for each grid point within the project (as described above) by the appropriate power curve (as described below) and by the number of turbines assigned to that grid point (as assigned in Section 3.4.3). However, before this calculation, the extracted wind time series data were adjusted to account for wind speed biases if necessary, and also to account for project energy losses, as described below. In addition, statistical corrections (SCORE-lite process) were applied to the final power time series data set to account for variability characteristic of actual project output.

4.2.1 Accounting for Losses DNV-GEC applied a wind speed reduction factor to account for project energy losses. The total net energy delivered by a wind power project at the revenue meter is always lower than the theoretical gross energy value calculated from the power curve as a result of system energy losses incurred within the project. To illustrate typical causes of project energy losses, Table 10 shows standard loss categories and example values for each category. Although the actual loss factor for each category can vary substantially between projects, the listed values are reasonable estimates for the losses typically incurred at large scale wind projects in North America. Based on DNV-GEC’s experience with energy estimates for modern wind power projects, cumulative project energy loss tends to range from ~12% to ~25%, depending on specific project circumstances. For the purposes of this study, a standard loss factor of 18.5% was assumed.


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Table 10. Example of Energy Losses for Large Scale Wind Projects

Energy Loss Category Example Loss Routine Maintenance 0.4% Faults 1.9% Minor Component Failure 1.7% Major Component Failure 1.5% Balance of Plant Downtime 0.5% Array/Wake 6.5% Electrical Line/Transformer 2.5% Blade Soiling 0.5% Weather (lightning, icing, etc.) 3.0% Turbulence and Controls 1.0% Blade Degradation 0.4% Power Performance 0.3%

Combined Losses 18.5%

Note: Losses are based on energy not time. Each of the listed categories is mutually exclusive (meaning they do not all happen at the same time); however, the aggregate effect over the long term is represented by the combined loss value. Transmission system outages or curtailment are not accounted for in this breakdown. If these conditions are expected to be present in certain regions of BC, then accounting for their impact will be necessary in subsequent transmission studies. Instead of applying losses directly to the final power data, DNV-GEC adjusted the wind speed data to retain the full range of power values a project is likely to see. If straight losses were applied to the power data itself (as opposed to a correlated adjustment to the wind speed data, as was done here), the resulting power data would not reach rated power. This would prove problematic for the purposes of this study, since maintaining the full range of power outputs from 0 to rated power is crucial for subsequent integration analyses. To appropriately adjust wind speed data to account for energy loss, DNV-GEC had to assume a standard relationship between energy and wind speed. The amount by which a project’s energy output changes as a result of a change in wind speed will vary depending on both the shape of the project’s wind speed frequency distribution and on the specific turbine model. Given the range of wind speeds and frequency distribution shapes at all identified projects in this study, a range of ratios between energy and wind speed will be seen. Investigations into the ratio of percent change in energy to percent change in wind speed using measured wind speed data from several locations in the Province ranged from 1.4 to 1.8. For this study, a moderate ratio of 1.6 was used to account for the range of wind speeds and distribution shapes seen across the Province. Given this, and the assumed energy loss factor of 18.5%, the associated wind speed reduction factor was calculated as 11.6% [18.5% / 1.6 = 11.6%]. Consequently, all project wind speed data was adjusted downwards by 11.6% before being used to calculate the power data sets.

4.2.2 Power Curves The amount of power generated by a wind turbine at a given wind speed varies as a function of air density. In areas with relatively high air density (typically lower elevations and/or colder climates) the power generated by a turbine is greater than it would be in an environment with


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lower air density. Consequently, using a turbine power curve that correlates to a site-specific air density is important for accurately calculating the power output of a turbine at that particular location. Project areas in the Vancouver Island and North Coast domains are relatively low in elevation (near sea level) and will consequently have air densities at or near a Standard Atmosphere (1.225 kg/m3). Projects in the Peace and Southern Interior domains are primarily located at elevations higher than 1000 m where air density is lower. For this study, the air density at the outlined project locations in each domain were estimated from the long term (30 year) simulated data sets produced at each of the 30 validation sites. The resulting air densities assigned to each domain are listed in Table 11.

Table 11 Approximate Air Densities for Each Domain

Domain Power Curve Air Density

(kg/m 3)

Vancouver Island

North Coast 1.225

Southern Interior

Peace 1.1

Power curves for both air densities (for both turbine models) were provided by DNV-GEC to 3TIER for power data modeling. The power curves for the Vancouver Island and North Coast domains (Standard Atmosphere; 1.225 kg/m3 air density) are available directly from the manufacturers. The power curves for the 1.1 kg/m3 air density were created by DNV-GEC by interpolation from the Standard Atmosphere power curve, per IEC standard 61400-12. In addition to using air density-adjusted power curves for each domain, additional adjustments were made during the power conversion process based on the air density during each 10-minute record. Power values were computed using the effective wind speed value during each 10-minute record, where the effective wind speed is defined as Ve = V * (density / reference density)^ (1/3).

4.2.3 SCORE-lite Numerical weather prediction models have a tendency to produce wind speed time series that are excessively smooth, i.e., they do not produce sufficient wind speed variation at short timescales. As a result, the overall behavior of wind plant output directly derived from mesoscale modeled wind speeds and put through a rating curve results in excessively smooth plant output. However, this simple conversion technique is still largely regarded as the industry standard. An alternative technique, the Statistical Correction to Output from a Record Extension (SCORE), was proposed in a paper presented at the IEEE Power Engineering Society General Meeting in 20072. SCORE has now been implemented in five different studies, representing several gigawatts (GW) of modeled potential wind energy installations. The SCORE process uses observed statistical deviations from a mean value to create probability density functions of deviation from some

2 C. W. Potter, H. A. Gil and J. McCaa, “Wind Power Data for Grid Integration Studies”, Proc. 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA. Paper No. 07GM0808, Jun. 2007.


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central point. It is run on each turbine and produces a time series of data for each turbine that then gets summed to sub-project groupings or summed up to entire project output. However, trying to run a probabilistic process on all turbines for this project would be extremely time-consuming and the turbine locations would need to be approximated, meaning the individual turbine locations would provide no extra information. To solve this problem SCORE-lite was developed. SCORE-lite models each grid point, instead of each turbine and this is achieved by sampling from the original SCORE probability density functions multiple times and using these multiple-resampled data to develop new probability density functions that represent multiple turbines instead of one. The goal of SCORE-lite is to take the “rated” power output, calculated by converting wind speed to power output through a simple rating curve, and modifying it such that the overall ramping characteristics more closely approximate those observed in reality. Previous validation studies have shown that the SCORE-lite process results in a more realistic number of ramps without any appreciable loss of accuracy in modeling the diurnal cycle. The SCORE-lite process was applied to the “rated” power values calculated for the 10 year, 10-minute data sets. The resulting power values are expected to better match the overall ramping observed in operating wind project power data. SCORE-lite was not used for the 30 year, monthly power data, as inter-hour ramping patterns are not a concern for calculating a monthly average. The smoothness of the “rated” power data is only a concern for fine time-scale ramping considerations, and does not affect the magnitude of a monthly power average.

4.2.4 Final Project-Average Power Data Sets A summary of the calculated project-average wind speeds and capacity factors (as calculated from the 10 year, 10-minute power data) for each domain are shown in Table 12. More detailed information, broken down by individual project, is presented in Appendix B.


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Table 12. Installed Capacity, Average Wind Speed, and Capacity Factor for All Theoretical Projects

Category Number of Projects

Total Installed Capacity

(MW)

80 m Wind Speed Range

(m/s)

80 m Hub Height Capacity Factor

Range

Vancouver Island

Readily Available 11 1311 6.8 to 8.2 25% to 34%

Ambitious 3 138 6.3 to 7.1 20% to 27%

Total 14 1449 6.3 to 8.2 20% to 34% North Coast


Ambitious 5 3288 6.6 to 8.8 25% to 34%

Total 12 4211 6.5 to 8.8 20% to 34% Southern Interior


Ambitious 6 600 6.4 19% to 23%

Total 31 4409 6.4 to 7.7 19% to 31% Peace


Ambitious 21 2703 6.3 to 9.9 20% to 39%

Total 48 6113 6.3 to 9.9 20% to 41%

As explained in Section 4.1 the 30-year, monthly project data sets were synthesized using a separate model, and it was not expected that the 30-year data match the results of the 10-year data. Due to the finer model spatial resolution (2 km) used, the 10-year data sets are expected to have wind speed, and consequently power, magnitudes more representative of actual conditions. In fact, monthly wind speed averages in the 6 km, 30-year data sets were generally lower than those for the same project and time period in the 10-year data sets. This is explained by the fact that the theoretical wind projects are sited at optimal wind speed locations, whereas the modeling node points are fixed to a 6 km grid, which encapsulates the coarse resolution average wind speed. The 30-year monthly averages are more valuable for identifying long-term variation and trends in wind speeds than for assessing the wind speed magnitude for any project.

4.3 Forecasted Wind Data

Three individual wind energy forecasts were created for each theoretical project. These forecasts include climatological forecasts, perfect forecasts, and NWP-based forecasts, and were selected to represent the dynamic behavior and error patterns of state-of-the-art wind forecasting systems commonly used for wind project and power operations management. By providing perfect, climatological, and NWP-based forecasts, stakeholders will be able to assess the increased skill an NWP-based forecast system provides at each outlined project as compared to using no forecast, a climatological forecast, or a persistence forecast (using the perfect forecast time series data). In addition, stakeholders will be able to analyze the expected error and uncertainty associated with each of the three forecast types.


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Climatological Forecasts: These forecasts are based on the expected average conditions and are often used as a baseline to evaluate the performance of other forecasting techniques. NWP-based forecasts typically outperform climatological forecasts for forecast lead times of less than about five days. Consequently, at the 24-hour ahead timescale, a climatological forecast provides a conservative estimate of forecast skill. It is important to note that the errors from a climatological forecast differ from the errors from a state-of-the-art NWP forecast. The maximum error for a climatology forecast will be smaller than the maximum error from a NWP forecast, but the mean error will be larger. Ideally, climatological forecasts would be derived from on-site observational data. However, since observational data are not available for the theoretical projects, the climatological forecasts were derived from the 10-year project-average time series data described above in Section 4.1. The climatological forecasts were obtained by computing the average diurnal cycle for each calendar month of the year. Thus, the climatological forecasts are essentially a month by hour, or “12 by 24,” table averaged over the 10-years of modeled wind resource data. Perfect Forecasts: Perfect forecasts attempt to forecast what will actually happen. For this study, the forecasted information was the modeled project-average time series. Clearly, perfect forecasts cannot be produced in a real-time environment, since the future is not known. However, these forecasts are essential in wind integration studies because they enable project stakeholders to evaluate the value of other forecasting techniques and to study the effects of wind power variability on the transmission network in case the future is perfectly known. As explained for climatological forecasts, perfect forecasts are ideally computed from observational data, but for this study they were derived from the 10-year project-average time series wind data. To compute the perfect forecasts, the 10-minute project-average time series were averaged to an hourly time frame, and the hourly average was calculated as hour-ending. NWP Forecasts: The third forecasted data set consisted of actual NWP forecasts. 3TIER ran an additional set of WRF simulations to generate the NWP forecasting data. The WRF model was configured to produce once-daily forecast runs initialized at 00Z with a forecast horizon of 4 days (96 hours) over the period 2005-2007. This forecast horizon is sufficient to produce 24 hour forecasts for each hour in the day out to three days in advance. The forecast simulations used archived global forecast grids from the Global Forecast System (GFS) model for initialization, and had a horizontal grid spacing of 6 km. The power conversion process for each forecast was based on the power curve of the turbines and the number of turbines at each project. The purpose of the NWP forecast data is to represent, in general, the skill level of an NWP-based forecast at each project; therefore, no losses or other adjustments were made to the NWP forecast data. The advantage of a NWP-based forecast is that spatial and temporal correlations of the forecast error will be similar to those in a real-time NWP-based forecast system. For example, timing errors in the forecast are likely to be similar for nearby sites.


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DNV Global Energy Concepts Inc. B-1 February 5, 2009

Appendix B – Theoretical Projects Summary by Domain Note: Wind Speed and Capacity Factor are based on 10 year, 10-minute power data sets

Project Name Category

Approximate Distance to

Transmission (km)

Wind Speed

Adjustment

Estimated % Usable

Land

Assigned Turbine

Type

Capacity (MW)

Wind Speed at 80 m (m/s)

Capacity Factor at 80 m

Vancouver Island

VI09 Ambitious 50 0% 75% Siemens 2.3 55.2 7.1 27%

VI10 Ambitious 50 0% 100% Siemens 2.3 34.5 6.7 25%

VI11 Ambitious 10 0% N/A Siemens 2.3 48.3 6.3 20%

VI02 Readily Available 45 0% 50% Siemens 2.3 200.1 6.9 26%









VI14 Readily Available 10 0% N/A Siemens 2.3 34.5 8.2 34%

VI15 Readily Available 35 0% N/A Siemens 2.3 41.4 7.4 29%



Transmission (km)

Wind Speed

Adjustment

Estimated % Usable

Land

Assigned Turbine

Type

Capacity (MW)

Wind Speed at 80 m (m/s)


North Coast

NC01 Ambitious 70 0% 60% Siemens 2.3 560.4 7.4 31%


NC04 Ambitious 85 0% 100% Vestas V90 2031.0 8.8 34%



NC07 Readily Available 25 0% N/A Siemens 2.3 117.3 6.9 24%

NC08 Readily Available 10 0% 90% Siemens 2.3 195.5 6.5 20%

NC09 Readily Available 30 0% 50% Siemens 2.3 333.5 7.2 28%






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Transmission (km)

Wind Speed

Adjustment

Estimated % Usable

Land

Assigned Turbine

Type

Capacity (MW)

Wind Speed at 80

m (m/s)


Southern Interior

SI02 Ambitious 10 0% 60% Siemens 2.3 69.0 6.4 20%






SI01 Readily Available 20 0% 80% Siemens 2.3 351.9 6.5 20%











SI18 Readily Available 35 0% N/A Siemens 2.3 117.3 7.3 27%















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Transmission (km)

Wind Speed

Adjustment

Estimated % Usable

Land

Assigned Turbine Type

Capacity (MW)

Wind Speed at

80 m (m/s)

Capacity Factor at

80 m

Peace PC01 Ambitious 130 20% N/A Siemens 2.3 151.8 7.4 29% PC02 Ambitious 140 20% N/A Siemens 2.3 138.0 7.0 25% PC03 Ambitious 145 20% N/A Siemens 2.3 62.1 8.7 38% PC04 Ambitious 130 20% N/A Siemens 2.3 103.5 8.5 36% PC05 Ambitious 125 20% N/A Siemens 2.3 96.6 8.9 39% PC06 Ambitious 105 20% N/A Vestas V90 243.0 8.6 32% PC07 Ambitious 120 20% N/A Siemens 2.3 117.3 7.7 30% PC08 Ambitious 90 20% N/A Siemens 2.3 41.4 8.0 34% PC09 Ambitious 70 20% N/A Vestas V90 207.0 9.1 34% PC10 Ambitious 55 20% N/A Vestas V90 297.0 9.0 35% PC11 Ambitious 85 20% N/A Vestas V90 126.0 9.5 37% PC12 Ambitious 75 20% N/A Siemens 2.3 96.6 8.0 34% PC13 Ambitious 75 20% N/A Vestas V90 135.0 9.9 39% PC14 Ambitious 55 20% N/A Vestas V90 144.0 9.2 37% PC22 Ambitious 130 0% N/A Siemens 2.3 207.0 6.3 20% PC23 Ambitious 80 0% N/A Siemens 2.3 55.2 7.1 25% PC29 Ambitious 0 0% N/A Siemens 2.3 89.7 6.4 22% PC30 Ambitious 20 0% N/A Siemens 2.3 230.0 6.4 20% PC37 Ambitious 75 20% N/A Vestas V90 72.0 8.7 31% PC43 Ambitious 50 20% N/A Siemens 2.3 41.4 8.3 37% PC45 Ambitious 70 0% N/A Siemens 2.3 48.3 6.5 22% PC15 Readily Available 35 20% N/A Vestas V90 108.0 9.2 35% PC16 Readily Available 45 20% N/A Vestas V90 99.0 9.5 37% PC17 Readily Available 10 0% N/A Siemens 2.3 103.5 7.4 30% PC18 Readily Available 5 20% N/A Siemens 2.3 138.0 8.5 39% PC19 Readily Available 25 20% N/A Vestas V90 117.0 9.4 37% PC20 Readily Available 50 20% N/A Siemens 2.3 158.7 9.0 41% PC21 Readily Available 15 20% N/A Vestas V90 99.0 9.4 36% PC24 Readily Available 50 0% N/A Siemens 2.3 117.3 6.8 23% PC25 Readily Available 20 0% N/A Siemens 2.3 158.7 7.2 27% PC26 Readily Available 5 20% N/A Vestas V90 126.0 8.7 34% PC27 Readily Available 15 20% N/A Siemens 2.3 110.4 7.4 28% PC28 Readily Available 20 20% N/A Vestas V90 153.0 9.6 40% PC31 Readily Available 0 20% N/A Siemens 2.3 241.5 8.2 36% PC32 Readily Available 10 0% 90% Siemens 2.3 151.8 6.6 23% PC33 Readily Available 5 20% N/A Siemens 2.3 69.0 7.9 35% PC34 Readily Available 25 0% N/A Siemens 2.3 351.9 6.9 25% PC35 Readily Available 25 20% N/A Siemens 2.3 117.3 8.5 38% PC36 Readily Available 20 0% N/A Siemens 2.3 172.5 6.7 24% PC38 Readily Available 5 0% N/A Siemens 2.3 131.1 6.7 24% PC39 Readily Available 5 0% N/A Siemens 2.3 186.3 7.0 26% PC40 Readily Available 5 20% N/A Siemens 2.3 117.3 7.8 32% PC41 Readily Available 10 20% N/A Vestas V90 45.0 9.0 33% PC42 Readily Available 20 20% N/A Vestas V90 63.0 9.0 35% PC44 Readily Available 30 20% N/A Siemens 2.3 34.5 7.9 33% PC46 Readily Available 0 20% N/A Vestas V90 54.0 8.4 30% PC47 Readily Available 0 20% N/A Siemens 2.3 34.5 7.9 33% PC48 Readily Available 25 20% N/A Siemens 2.3 151.8 8.3 36%

Note: Wind speeds shown are adjusted upwards by 20% for the applicable projects.


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BC HYDRO UNDERTAKING NO. 43 · Task 2 – Measured Data & Model Validation Province-Wide Wind Resource Data Inventory > Independent Power Producer (IPP) Data • 51 sites • Mostly