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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
Page 9 of 38
10
Tas
k 2
–M
easu
red
Dat
a &
Mo
del
Val
idat
ion
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
nAttachment 1 to BCH Undertaking No. 43
Page 15 of 38
16
Tas
k 3
–W
ind
Po
wer
Pro
ject
Del
inea
tio
nAttachment 1 to BCH Undertaking No. 43
Page 16 of 38
17
Tas
k 3
–W
ind
Po
wer
Pro
ject
Del
inea
tio
nAttachment 1 to BCH Undertaking No. 43
Page 17 of 38
18
Tas
k 3
–W
ind
Po
wer
Pro
ject
Del
inea
tio
nAttachment 1 to BCH Undertaking No. 43
Page 18 of 38
19
Tas
k 3
–W
ind
Po
wer
Pro
ject
Del
inea
tio
nAttachment 1 to BCH Undertaking No. 43
Page 19 of 38
20
Tas
k 3
–W
ind
Po
wer
Pro
ject
Del
inea
tio
nAttachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
Page 21 of 38
22
Tas
k 3
–W
ind
Po
wer
Pro
ject
Cap
acit
y E
stim
atio
nAttachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
Page 23 of 38
24
Qu
esti
on
s?
•Ne
xt ste
p: Pr
oject
wind
and p
ower
time s
eries
data
sets
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
Page 26 of 38
27
Attachment 1 to BCH Undertaking No. 43
Page 27 of 38
28
Attachment 1 to BCH Undertaking No. 43
Page 28 of 38
29
Attachment 1 to BCH Undertaking No. 43
Page 29 of 38
30
Note:
Wind
spee
ds sh
own a
re as
mod
eled (
i.e. n
ot ad
justed
)
Attachment 1 to BCH Undertaking No. 43
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]
Attachment 1 to BCH Undertaking No. 43
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)
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
Page 34 of 38
35
Tas
k 4
–P
roje
ct P
ow
erD
ata
Gen
erat
ion
Attachment 1 to BCH Undertaking No. 43
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
%
Attachment 1 to BCH Undertaking No. 43
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
Attachment 1 to BCH Undertaking No. 43
Page 37 of 38
38
Qu
esti
on
s?
Attachment 1 to BCH Undertaking No. 43
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
Attachment 2 to BCH Undertaking No. 43
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.
Attachment 2 to BCH Undertaking No. 43
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
Attachment 2 to BCH Undertaking No. 43
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.
Attachment 2 to BCH Undertaking No. 43
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)
Attachment 2 to BCH Undertaking No. 43
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
Attachment 2 to BCH Undertaking No. 43
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
Attachment 2 to BCH Undertaking No. 43
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
.
Attachment 2 to BCH Undertaking No. 43
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
Attachment 3 to BCH Undertaking No. 43
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
Attachment 3 to BCH Undertaking No. 43
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)
.
Attachment 3 to BCH Undertaking No. 43
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
.
Attachment 3 to BCH Undertaking No. 43
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
Attachment 3 to BCH Undertaking No. 43
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
Attachment 3 to BCH Undertaking No. 43
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
Attachment 3 to BCH Undertaking No. 43
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.
Attachment 3 to BCH Undertaking No. 43
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
Attachment 3 to BCH Undertaking No. 43
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.
Attachment 3 to BCH Undertaking No. 43
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
Attachment 4 to BCH Undertaking No. 43
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
Attachment 4 to BCH Undertaking No. 43
Page 2 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
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
Attachment 4 to BCH Undertaking No. 43
Page 3 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
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
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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.
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DRAFT BC Hydro Wind Data Study CSRP0009
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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.
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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
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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.
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Figure 2. Model Domains
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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
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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.
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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.
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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
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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.
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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
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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
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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.
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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.
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0.61
2.
26
SI-
IPP
-3
IPP
60
9
95%
0.
98
0.53
0.
89
1.58
M
errit
t B
C H
ydro
50
18
94
%
0.84
0.
82
0.81
1.
68
Pea
ce
P
-IP
P-1
IP
P
60
31
93%
0.
86
0.71
0.
84
1.45
P
-IP
P-2
IP
P
30
7 79
%
0.46
0.
48
0.73
2.
46
P-I
PP
-3
IPP
50
37
86
%
0.94
1.
80
0.87
2.
52
P-I
PP
-4
IPP
40
27
76
%
0.98
1.
25
0.87
2.
07
P-I
PP
-5
IPP
40
25
84
%
0.91
2.
14
0.88
3.
00
P-I
PP
-6
IPP
30
22
77
%
0.57
1.
73
0.60
2.
96
P-I
PP
-7
IPP
35
12
79
%
0.92
1.
34
0.87
2.
42
P-I
PP
-8
IPP
30
18
92
%
0.91
0.
39
0.86
1.
37
Aas
en
BC
Hyd
ro
50
21
94%
0.
82
0.54
0.
84
1.39
E
rbe
BC
Hyd
ro
50
22
89%
0.
70
0.55
0.
59
1.99
M
t War
tenb
e B
C H
ydro
50
14
88
%
0.93
0.
45
0.89
1.
50
Nor
thw
est
Sm
ither
s E
C
10
120
39%
0.
53
0.57
0.
51
1.13
S
tew
art
EC
10
12
0 26
%
0.05
1.
12
0.27
2.
34
Ter
race
E
C
10
120
68%
0.
29
1.26
0.
25
2.58
Not
e: A
ctua
l per
iod
of r
ecor
d m
ay b
e lo
nger
than
list
ed fo
r E
C to
wer
s, b
ut o
nly
10 y
ears
of d
ata
wer
e us
ed in
this
stu
dy.
Attachment 4 to BCH Undertaking No. 43
Page 19 of 53
DR
AF
T B
C H
ydro
Win
d D
ata
Stud
y C
SRP
0009
D
NV
Glo
bal E
nerg
y C
once
pts
Inc.
16
F
ebru
ary
5, 2
009
Tab
le 5
. Mod
el V
alid
atio
n S
ites
Sum
mar
y of
Win
d R
esou
rce
Com
paris
ons
Per
iod
of R
ecor
d W
ind
Dis
trib
utio
n
Wei
bull
Sca
le (
A)
and
Sha
pe (
k) F
acto
rs
Site
Nam
e
Mod
el
Win
d S
peed
B
ias
O
bser
ved
A
Obs
erve
d k
Mod
eled
A
M
odel
ed
k M
odel
ed v
ersu
s O
bser
ved
Win
d R
ose
Qua
litat
ive
Diu
rnal
Pat
tern
C
ompa
rison
Van
couv
er Is
land
V
I-IP
P-1
1%
6.
85
1.85
6.
93
1.78
P
rimar
y di
rect
ion
at +
45; S
econ
dary
OK
G
ood
VI-
IPP
-2
-8%
7.
33
2.15
8.
01
2.00
O
K
Exc
elle
nt
VI-
IPP
-3
-9%
6.
10
1.78
5.
62
1.79
O
K
Goo
d Jo
rdan
7%
4.
94
1.75
5.
33
1.82
O
K; N
E w
inds
und
erre
pres
ente
d E
xcel
lent
R
umbl
e 2%
7.
50
1.69
7.
61
1.50
-6
5 P
oor
Nor
th C
oast
NC
-IP
P-1
3%
8.
36
1.66
8.
73
1.95
+2
5 F
air
NC
-IP
P-2
-7
%
7.33
1.
54
6.90
1.
76
OK
G
ood
NC
-IP
P-3
0%
7.
89
1.69
7.
96
1.87
O
K
Goo
d N
C-I
PP
-4
14%
7.
92
1.81
9.
26
1.90
O
K
Exc
elle
nt
NC
-IP
P-5
-8
%
6.83
1.
49
6.40
1.
61
OK
; Sec
onda
ry d
irect
ion
off b
y -6
0 P
oor
NC
-IP
P-6
-9
%
11.4
9 1.
92
10.5
8 1.
98
OK
F
air
Mt H
ays
6%
6.06
1.
41
6.57
1.
74
OK
F
air
Sou
ther
n In
terio
r
SI-
IPP
-1
-43%
5.
67
1.49
3.
99
1.64
O
K
Exc
elle
nt
SI-
IPP
-2
14%
6.
44
2.48
7.
51
2.38
O
K; S
econ
dary
dire
ctio
n no
t rep
rese
nted
E
xcel
lent
S
I-IP
P-3
4%
7.
85
1.89
8.
21
1.98
O
K
Exc
elle
nt
Mer
ritt
-15%
6.
33
1.75
5.
47
1.69
G
ood;
Sec
onda
ry N
E c
ompo
nent
in m
odel
ed
data
is n
ot p
rese
nt in
obs
erve
d da
ta
Exc
elle
nt
Pea
ce
P-I
PP
-1
9%
6.43
2.
03
7.06
2.
21
+30
Poo
r P
-IP
P-2
3%
9.
54
2.31
9.
79
2.26
O
K
Exc
elle
nt
P-I
PP
-3
-24%
10
.04
2.05
8.
10
2.29
P
redo
min
ant d
irect
ion
OK
; Sec
onda
ry m
issi
ng
Poo
r P
-IP
P-4
-1
8%
9.12
2.
01
7.77
2.
01
+25
Fai
r P
-IP
P-5
-2
6%
10.6
5 1.
90
8.46
2.
21
OK
P
oor
P-I
PP
-6
-24%
8.
16
1.88
6.
56
1.88
+4
5 G
ood
P-I
PP
-7
-20%
8.
40
1.81
7.
11
2.12
O
K
Poo
r P
-IP
P-8
0%
6.
90
1.86
6.
91
2.15
O
K
Poo
r A
asen
6%
6.
12
1.84
6.
56
2.08
O
K
Poo
r E
rbe
7%
5.67
1.
78
6.14
2.
08
OK
F
air
Mt W
arte
nbe
-2%
8.
05
1.83
7.
90
1.98
O
K
Poo
r
N
orth
wes
t
Sm
ither
s 33
%
3.23
2.
07
3.6
2.02
O
K
Fai
r S
tew
art
60%
2.
97
1.74
3.
54
1.34
P
oor
Poo
r
Ter
race
2%
5.
02
1.79
4.
97
1.49
F
air;
Sec
onda
ry N
com
pone
nt in
mod
eled
dat
a is
not
pre
sent
in o
bser
ved
data
P
oor
Attachment 4 to BCH Undertaking No. 43
Page 20 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 17 February 5, 2009
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.
Attachment 4 to BCH Undertaking No. 43
Page 21 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 18 February 5, 2009
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.
Attachment 4 to BCH Undertaking No. 43
Page 22 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 19 February 5, 2009
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
Attachment 4 to BCH Undertaking No. 43
Page 23 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 20 February 5, 2009
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.
Attachment 4 to BCH Undertaking No. 43
Page 24 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 21 February 5, 2009
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.
Attachment 4 to BCH Undertaking No. 43
Page 25 of 53
DR
AF
T B
C H
ydro
Win
d D
ata
Stud
y C
SRP
0009
D
NV
Glo
bal E
nerg
y C
once
pts
Inc.
22
F
ebru
ary
5, 2
009
F
igur
e 4.
Exa
mpl
es o
f Out
lined
Pro
ject
s
Attachment 4 to BCH Undertaking No. 43
Page 26 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 23 February 5, 2009
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.
Attachment 4 to BCH Undertaking No. 43
Page 27 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 24 February 5, 2009
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.
Attachment 4 to BCH Undertaking No. 43
Page 28 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 25 February 5, 2009
Figure 5. Vancouver Island Domain 1-year Wind Map (August 2007 through July 2008)
Attachment 4 to BCH Undertaking No. 43
Page 29 of 53
DR
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d D
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SRP
0009
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NV
Glo
bal E
nerg
y C
once
pts
Inc.
26
F
ebru
ary
5, 2
009
F
igur
e 6.
Nor
th C
oast
Dom
ain
1-ye
ar W
ind
Map
Aug
ust 2
007
thro
ugh
July
200
8
Attachment 4 to BCH Undertaking No. 43
Page 30 of 53
DR
AF
T B
C H
ydro
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d D
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y C
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0009
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Glo
bal E
nerg
y C
once
pts
Inc.
27
F
ebru
ary
5, 2
009
F
igur
e 7.
Sou
ther
n In
terio
r D
omai
n 1-
year
Win
d M
ap A
ugus
t 200
7 th
roug
h Ju
ly 2
008
Attachment 4 to BCH Undertaking No. 43
Page 31 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 28 February 5, 2009
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
Attachment 4 to BCH Undertaking No. 43
Page 32 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 29 February 5, 2009
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
Attachment 4 to BCH Undertaking No. 43
Page 33 of 53
DR
AF
T B
C H
ydro
Win
d D
ata
Stud
y C
SRP
0009
D
NV
Glo
bal E
nerg
y C
once
pts
Inc.
30
F
ebru
ary
5, 2
009
F
igur
e 10
. Nor
th C
oast
Dom
ain
The
oret
ical
Pro
ject
s
Attachment 4 to BCH Undertaking No. 43
Page 34 of 53
DR
AF
T B
C H
ydro
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d D
ata
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y C
SRP
0009
D
NV
Glo
bal E
nerg
y C
once
pts
Inc.
31
F
ebru
ary
5, 2
009
F
igur
e 11
. Sou
ther
n In
terio
r D
omai
n T
heor
etic
al P
roje
cts
Attachment 4 to BCH Undertaking No. 43
Page 35 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 32 February 5, 2009
Figure 12. Peace Domain Theoretical Projects
Attachment 4 to BCH Undertaking No. 43
Page 36 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. 33 February 5, 2009
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
Readily Available 7 922 6.5 to 7.2 20% to 28%
Ambitious 5 3288 6.6 to 8.8 25% to 34%
Total 12 4211 6.5 to 8.8 20% to 34% Southern Interior
Readily Available 25 3809 6.5 to 7.7 20% to 31%
Ambitious 6 600 6.4 19% to 23%
Total 31 4409 6.4 to 7.7 19% to 31% Peace
Readily Available 27 3410 6.6 to 9.6 23% to 41%
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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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%
VI03 Readily Available 40 0% 50% Siemens 2.3 310.5 7.1 28%
VI04 Readily Available 25 0% 50% Siemens 2.3 62.1 7.0 27%
VI05 Readily Available 20 0% 50% Siemens 2.3 255.3 6.9 26%
VI06 Readily Available 15 0% 50% Siemens 2.3 117.3 7.0 27%
VI07 Readily Available 10 0% 75% Siemens 2.3 165.6 7.2 31%
VI08 Readily Available 20 0% 75% Siemens 2.3 41.4 6.8 25%
VI12 Readily Available 5 0% 90% Siemens 2.3 48.3 7.5 30%
VI13 Readily Available 10 0% 60% Siemens 2.3 34.5 7.2 28%
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%
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
North Coast
NC01 Ambitious 70 0% 60% Siemens 2.3 560.4 7.4 31%
NC02 Ambitious 50 0% 40% Siemens 2.3 234.6 7.0 28%
NC04 Ambitious 85 0% 100% Vestas V90 2031.0 8.8 34%
NC05 Ambitious 155 0% 40% Siemens 2.3 262.2 6.6 25%
NC06 Ambitious 185 0% 50% Siemens 2.3 200.1 6.9 27%
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%
NC10 Readily Available 15 0% N/A Siemens 2.3 96.6 7.0 26%
NC11 Readily Available 25 0% N/A Siemens 2.3 75.9 6.6 22%
NC12 Readily Available 20 0% N/A Siemens 2.3 75.9 7.2 27%
NC13 Readily Available 5 0% N/A Siemens 2.3 27.6 6.9 28%
Attachment 4 to BCH Undertaking No. 43
Page 51 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. B-2 February 5, 2009
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
Southern Interior
SI02 Ambitious 10 0% 60% Siemens 2.3 69.0 6.4 20%
SI06 Ambitious 5 0% 60% Siemens 2.3 131.1 6.4 21%
SI08 Ambitious 0 0% 50% Siemens 2.3 117.3 6.4 20%
SI09 Ambitious 0 0% 75% Siemens 2.3 96.6 6.4 20%
SI11 Ambitious 10 0% 50% Siemens 2.3 138.0 6.4 23%
SI33 Ambitious 35 0% 75% Siemens 2.3 48.3 6.4 19%
SI01 Readily Available 20 0% 80% Siemens 2.3 351.9 6.5 20%
SI03 Readily Available 20 0% 60% Siemens 2.3 151.8 6.5 22%
SI04 Readily Available 5 0% 75% Siemens 2.3 96.6 6.9 25%
SI05 Readily Available 10 0% 70% Siemens 2.3 144.9 6.7 23%
SI10 Readily Available 10 0% 80% Siemens 2.3 117.3 7.0 26%
SI12 Readily Available 5 0% 50% Siemens 2.3 186.3 7.3 28%
SI13 Readily Available 20 0% 50% Siemens 2.3 338.1 6.7 22%
SI14 Readily Available 20 0% 75% Siemens 2.3 82.8 7.0 27%
SI15 Readily Available 25 0% 60% Siemens 2.3 303.6 7.0 25%
SI16 Readily Available 5 0% 30% Siemens 2.3 662.4 6.8 23%
SI17 Readily Available 40 0% 90% Siemens 2.3 48.3 7.7 29%
SI18 Readily Available 35 0% N/A Siemens 2.3 117.3 7.3 27%
SI19 Readily Available 35 0% N/A Siemens 2.3 55.2 6.9 25%
SI20 Readily Available 20 0% 100% Siemens 2.3 41.4 7.3 28%
SI22 Readily Available 5 0% 100% Siemens 2.3 48.3 7.0 24%
SI23 Readily Available 20 0% N/A Siemens 2.3 193.2 7.7 31%
SI26 Readily Available 10 0% 80% Siemens 2.3 103.5 6.8 24%
SI27 Readily Available 5 0% N/A Siemens 2.3 89.7 7.2 26%
SI28 Readily Available 20 0% 75% Siemens 2.3 89.7 7.3 28%
SI29 Readily Available 20 0% N/A Siemens 2.3 117.3 6.9 25%
SI30 Readily Available 15 0% 50% Siemens 2.3 151.8 6.8 25%
SI31 Readily Available 25 0% 75% Siemens 2.3 144.9 6.6 22%
SI32 Readily Available 10 0% N/A Siemens 2.3 34.5 6.9 25%
SI37 Readily Available 5 0% N/A Siemens 2.3 34.5 6.7 23%
SI38 Readily Available 5 0% N/A Siemens 2.3 103.5 6.5 20%
Attachment 4 to BCH Undertaking No. 43
Page 52 of 53
DRAFT BC Hydro Wind Data Study CSRP0009
DNV Global Energy Concepts Inc. B-3 February 5, 2009
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
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.
Attachment 4 to BCH Undertaking No. 43
Page 53 of 53