1. 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 Engineering Manchester, UK

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. Frankfurt (Germany), 6-9 June 2011 Muhammad Ali – United Kingdom – RIF Session 4 – 0528 Results - 1 Wind plot of WT 13 for incoming wind speed of 10m/s, Deterministic (Line), Probabilistic (dots) Layout of 49 turbine wind farm

9. Frankfurt (Germany), 6-9 June 2011 Muhammad Ali – United Kingdom – RIF Session 4 – 0528 Results - 2 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

10. 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)  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

11. 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%

12. 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

13. 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 0Case 1Case 2 EY difference (%)Ref.-8.65-17.3

14. 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

15. Frankfurt (Germany), 6-9 June 2011 Muhammad Ali – United Kingdom – RIF Session 4 – 0528 Thank you

16. 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

17. 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

18. 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