muhammad ali, jovica v. milanović

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



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



Presentation Outline

Background Information Motivation (why it was done) Methodology (how it has been done) Case Study Results Conclusion



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



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



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



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



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



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



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



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%



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



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



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



Thank you


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



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




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

muhammad ali, jovica v. milanović

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