offshore wind resource assessment colin morgan and graham gow garrad hassan and partners
TRANSCRIPT
Offshore wind resource assessment
Colin Morgan and Graham Gow
Garrad Hassan and Partners
www.garradhassan.com/offshore
Scenario 1Physical Modelling
• Immediate• Cheap
• Least accurate• Highly sensitive to station• Accuracy very difficult to
determine
+ Published
estimates
Scenario 2Physical and Statistical Modelling
• Rapid• Increased cost
• Platform accuracy?• Model accuracy in
coastal zone?• More time consuming
+ Published
estimates
Scenario 3Statistical Modelling
• Most accurate• Mast may be used for
later purposes?
• Costly• Time consuming
Overall schemeSite free wind speed
(ref. height)
Hub height
Ideal energy output
Topolosses
Wakelosses
Electrical losses
Net Energy Output
Other losses
Other Losses
1 - Unscheduled turbine downtime
2 - Waiting on weather time
3 - Scheduled WTG downtime
4 - Electrical system downtime
5 - Grid downtime
6 - Grid curtailment
7 - Environmental and Owner’s downtime
8 - Icing and blade degradation
9 - Columnar shutdown
Uncertainty in energy prediction10-yr average (gross of availability)
0%2%4%6%8%
10%12%14%16%
Sc
en
ari
o 0
-B
es
t p
rac
tic
eo
ns
ho
re
(1 y
ro
n-s
ite
me
as
ure
me
nt)
Sc
en
ari
o 1
-N
o o
ffs
ho
rem
ea
su
rem
en
t
Sc
en
ari
o 2
-E
xis
tin
g lo
ng
-te
rm o
ffs
ho
rem
ea
su
rem
en
t
Sc
en
ari
o 3
-O
ffs
ho
re S
ite
me
as
ure
me
nt
(1 y
r)
Forecasting - Project Overview
• Co-funded by UK Government
• Scottish Power
• Utility• Wind farm operator
• The Met Office
• Meteorological services
• Garrad Hassan
• Method development• Project management
Project Aims
• Stimulate UK forecasting activity
• Validation under UK conditions
• Addressed to individual wind farms
• Market trading
• Geographically transferable
Method
• NWP to site-specific
• Statistical modelling
• Adaptive
• Multi-inputs
• wind speed• direction• temperature• time of day• etc.
Site-specificmodels
Site-specificforecasts
Powermodels
Powerforecasts
“National”Forecasts
Results - 12 hr wind speed forecast
0
5
10
15
20
25
17 Nov 19 Nov 21 Nov 23 Nov 25 Nov 27 Nov 29 Nov
Win
d s
pee
d [
m/s
]
ActualMet OfficeSite model
Results - wind speedImprovement in error standard deviation over persistence
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
1 7 13 19 25 31
Forecast horizon [hour]
Imp
rove
men
t o
ver
per
sist
ence
[%
]
Overall0-5 m/s20-30 m/s
Results - 12hr power forecast
0
2000
4000
6000
8000
10000
12000
14000
17 Nov 19 Nov 21 Nov 23 Nov 25 Nov 27 Nov 29 Nov
Ou
pu
t p
ow
er [
kW]
ActualSite model
Results - power Improvement in error standard deviation over persistence
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
1 7 13 19 25 31
Forecast horizon [hour]
Imp
rove
men
t o
ver
per
sist
ence
[%
]
Overall0-20 % rated80-100 % rated
Implications for offshore projects• Flexible adaptable tool of proven accuracy
• Increasing importance for large wind farms
• Operators (O&M scheduling)• Owners• Grid companies
• Scope for tuning
• “Learning” time• Improved NWP modelling of offshore effects• Refined downtime modelling and forecasting• Portfolio effects
Construction Plant Market Survey and Database
www.garradhassan.com/offshore/database