Wind Power Simulation in the Gasp ésie Tim Weis, M.Sc. Sustainable Communities Group

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Sustainable Energy Solutions
Wind Power Simulation in the Gaspésie
Tim Weis, M.Sc.
Sustainable Communities Group
May 17, 2005
© 2005 Pembina Institute
www.pembina.org
Sustainable Energy Solutions
About the Pembina Institute
§ Environmental research institute
§ Focused on energy development, usage,
and minimizing their impact on the
environment
§ Offices in Calgary, Drayton Valley,
Edmonton, Gatineau and Vancouver
§ www.pembina.org
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© 2005 The Pembina Institute
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About the author
§ Researcher with the Pembina since 2002
§ Began work in wind energy in 1999 with Master’s
degree (Mechanical Engineering) focused on ice
adhesion to wind turbine blades
§ Private consulting for Yukon Energy and Aurora
Research Institute on Arctic applications of wind
energy
§ PhD research on renewable energy development
in First Nations communities
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© 2005 The Pembina Institute
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Gaspésie wind power simulation
§ Goal
Ÿ 8760 data series representative of the hourly output of
the overall wind development in the Gaspésie
§ Assumptions
Ÿ 3.17 TWh (net) delivered to grid by proposed wind farms
Ÿ 15% losses due to transmission, blade degradation,
turbine availability, etc.
Ÿ 2004 winds are representative
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© 2005 The Pembina Institute
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Methodology
§ “Single mast, single wind farm” approximation
§ 2004 Environment Canada data from Cap Chat
25
Hourly Wind Speed (m/s)
20
15
10
5
0
1-Jan
31-Jan
1-Mar
31-Mar 30-Apr 30-May 29-Jun
29-Jul 2 8 - A u g 2 7 - S e p 27-Oct 26-Nov 2 6 - D e c
Date/Time
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Methodology
§ Data scaled to a hub height of 80 m and to meet
the assumed overall output of 3.17 TWh
generated by 660 GE 1.5 MW turbines
1600
Output Power (kW)
1400
GE 1.5 sl/e
GE 1.5 s/se
Linear Average
1200
1000
800
600
400
200
0
0
5
10
15
20
25
Wind Speed (m/s)
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© 2005 The Pembina Institute
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Methodology
§ The wind turbines will be built throughout the
region, and will therefore experience the same
weather system at different times as it moves
through the region
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© 2005 The Pembina Institute
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Methodology
§ The geographic distribution was modeled by
averaging the data over a time scale
representative of the average speed weather
systems move across the region – smoothes wind
data
§ Smoothed wind data applied to the GE power
curve to simulate power output
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© 2005 The Pembina Institute
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Results
Average output: 362 MW
Capacity factor: 36%
Machines produce full power 15% of the year (1,275 hours)
No output for 7% (624 hours)
2500
100%
Power Output (MW)
Cumulative Frequency
90%
2000
1500
60%
50%
1000
40%
30%
500
20%
10%
850
800
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Power Output (MW)
www.pembina.org
750
700
650
600
550
500
450
400
350
300
250
200
150
100
0%
50
0
0
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Annual Frequency
80%
70%
Hours Per Year
§
§
§
§
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Comments
§ Broadly speaking the same approach used
by Hélimax1 (2005) and Hydro-Québec2
(1995)
§ A “single mast, single turbine” approach is a
worst-case scenario in terms of variability
1
Hélimax Énergie inc.
inc. Étude sur la valeur en puissance des 1 000 MW d’énergie éolienne
éolienne achetés par HydroHydro-Québec
Distribution
2
Lambert, R. & Marcotte, J., Évaluation de la valeur en puissance d’un parc d’éoliennes incluant l’effet de corrélation entre
le vent et la demande
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Comments
§ Abrupt cut-outs a limitation of methodology
900
Wind Output (MW)
800
700
600
500
400
300
200
100
0
1/1/04 0:00
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© 2005 The Pembina Institute
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Comments
§ Even in high winds machines will not all cut
out simultaneously
§ Built-in buffers prevent this
§ Local effects such as turbulence, wakes
from other machines and winds changing
direction mean that individual turbines will
not all “see” maximum wind speed at any
given moment
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© 2005 The Pembina Institute
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Comments
§ A smoothed power curve for a wind farm
simulates this phenomenon
§ Example:
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Questions?
Tim Weis
timw@pembina.org
(780) 485-9610
www.pembina.org
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© 2005 The Pembina Institute
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