Frankfurt (Germany), 6-9 June 2011 Muhammad Ali, Jovica V. Milanović Muhammad Ali – United...
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Transcript of Frankfurt (Germany), 6-9 June 2011 Muhammad Ali, Jovica V. Milanović Muhammad Ali – United...
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali, Jovica V. Milanović
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Probabilistic Assessment Of Wind Farm Energy Yield Considering Wake Turbulence And
Variable Turbine Availabilities
School of Electrical & Electronic EngineeringManchester, UK
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
What has been done
Developed a probabilistic wake model To estimate range of wind speeds that turbine/s under
wake can face
Analysed the effect of variable turbine availabilities inside a wind farm on the Energy yield
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Presentation Outline
Background Information Motivation (why it was done) Methodology (how it has been done) Case Study Results Conclusion
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Background Information Wake effects
Kinetic energy in wind converted to electrical energy Wind leaving turbine is reduced in speed and turbulent
Wake modelling Complex models - FEM,CFD- difficult to use, time consuming Analytical models - easier to use, simpler
‘Effective’ mean wind speed Wind speed that affects the
power output of a turbine Wind turbine availability
Amount of time in a year the turbine is operational
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Motivation (why it was done) - 1
In wind power industry ‘analytical’ wake models are commonly used but they are Deterministic
These models only provide same ‘mean’ wind speeds through formulas
In reality, turbines under wake can face a range of effective wind speeds due to atmospheric conditions and wind farm dynamics
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Motivation (why it was done) - 2 Therefore a ‘dynamic wake model’ to estimate range of
possible wind speeds at turbine/s downwind was needed
Dynamic behaviour is simulated by turbulence model previously used for mechanical loading of turbines
Developed model is simpler and faster
Handles detailed wake modelling: Single, partial and multiple wakes
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Methodology Combined two models:
Jensen’s deterministic wake model S. Frandsen’s turbulence model
Mean wind speed calculated using Jensen’s model Range of speed variation calculated using S. Frandsen’s
model Perform Monte Carlo to obtain range of wake wind speeds
at each turbine
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Results - 1
7
6
5
4
3
2
1
14
13
12
11
10
9
8
21
20
19
18
17
16
15
28
27
26
25
24
23
22
35
34
33
32
31
30
29
42
41
40
39
38
37
36
49
48
47
46
45
44
43
0o Northθ
Wind plot of WT 13 for incoming wind speed of 10m/s, Deterministic (Line), Probabilistic (dots)
Layout of 49 turbine wind farm
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Results - 2
49 42 35 28 21 14 70
1
2
3
4
5
6
7
8
9
10
Wind Turbine Number
Win
d S
peed
(m
/s)
3 4 5 6 7 8 9 100
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Data
Den
sity
WS at WT 5Normal Dist
Results for WS = 10m/s and WD = 270 +/- 3 deg
Distribution of wind speeds at each wind turbine (dots) and result from deterministic wake model (line)
Gaussian WS distribution at WT 21
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Results - 3
Estimated total power produced at WS = 10m/s, WD = 0 to 360 deg. Deterministic model (line),
Probabilistic model (dots)
0 40 80 120 160 200 240 280 320 36020
25
30
35
40
45
50
55
60
65
Wind Direction (degrees)
Win
d P
ow
er
(MW
)
Range of wind power at fixed wind speed of 10m/s obtained through Monte Carlo Simulations
Useful when operator has WS and WD forecast for the next few minutes e.g. for the next 30-min and a range of power output from the WF is required to adjust generation dispatch
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Results - 4
Energy Yield Comparison Using Deterministic and Probabilistic Wake Model
Inclusion of probabilistic nature of wind “converts” these loses into a range
EY ignoring wake effects
EY with deter. wake model
EY with prob. wake model
Reference -15.41%-15.41% +/-
0.2%
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Effect of turbine availabilities on energy yield
Turbines mostly under wake suffer greater fatigue damage than those in free stream wind
Level of wake faced by each turbine is calculated
Amount of time they remains under wake is also calculated
Availabilities are allocated to each turbine in the farm
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Results
Steps of 5% and 10% reduction in availability is assumed in Case 1 and Case 2 respectively. Case 0 is 100% availability of all turbines
Better than assuming same availability factor for all WTs
Case 0 Case 1 Case 2
EY difference (%) Ref. -8.65 -17.3
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Conclusion A probabilistic wake model is developed which should model
dynamic characteristic of wind inside a wind farm Gives range of instantaneous power output estimation when
wind speed and direction forecast is available Useful for generation dispatch or spinning reserve allocation
Concept of variable turbine availabilities is presented Useful during prefeasibility study to estimate loss in energy yield Energy loss of between 9% and 17% was calculated Both techniques are wind farm layout and site specific
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Thank you
Frankfurt (Germany), 6-9 June 2011
What is Effective wind speed? Wind speed that affects the power output of a single
turbine Example
A wind turbine faces different levels of wind speeds from one tip of rotor to the another (dist ~ 80m). Top hat distribution
The power produced is dependant on wind interactions at every point at the rotor, i.e. if described as a single value it is the effective wind speed
Appendix (Background) - 1
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Frankfurt (Germany), 6-9 June 2011
Atmospheric conditions and internal wind farm dynamics Effect of wind shear Effect of variable surface roughness Vortices of turbine upfront turbines Mixing of ambient air Mixing of wakes (further down in the row) Temperature Air density
Appendix (Background) - 2
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Frankfurt (Germany), 6-9 June 2011
Muhammad Ali – United Kingdom – RIF Session 4 – 0528
Appendix (Methodology) - 3
Turbulence Intensity:
I is calculated using S. Frandsen’s model and is the mean wake wind speed calculated using Jensen’s model
is the standard deviation, calculated for every incoming wind speed
Through Monte Carlo a range of possible wind speeds incident at a turbine is determined
I U
U