Muhammad Ali, Jovica V. Milanović

1. School of Electrical & Electronic Engineering Probabilistic Assessment Of Wind Farm Energy Yield Considering Wake Turbulence And Variable Turbine Availabilities Muhammad Ali, Jovica V. Milanović Manchester, UK Muhammad Ali – United Kingdom – RIF Session 4 – 0528

2. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

3. Presentation Outline • Background Information • Motivation (why it was done) • Methodology (how it has been done) • Case Study • Results • Conclusion Muhammad Ali – United Kingdom – RIF Session 4 – 0528

4. Wake effects • Kinetic energy in wind converted to electrical energy • Wind leaving turbine is reduced in speed and turbulent Background Information • 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

5. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

6. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

7. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

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

9. Results - 2 Distribution of wind speeds at each wind turbine (dots) and result from deterministic wake model (line) Gaussian WS distribution at WT 21 Results for WS = 10m/s and WD = 270 +/- 3 deg Muhammad Ali – United Kingdom – RIF Session 4 – 0528

10. Results - 3 • 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 Estimated total power produced at WS = 10m/s, WD = 0 to 360 deg. Deterministic model (line), Probabilistic model (dots) Muhammad Ali – United Kingdom – RIF Session 4 – 0528

11. Results - 4 • Energy Yield Comparison Using Deterministic and Probabilistic Wake Model • Inclusion of probabilistic nature of wind “converts” these loses into a range Muhammad Ali – United Kingdom – RIF Session 4 – 0528

12. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

13. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

14. Conclusion • A probabilisticwakemodel 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 estimateloss in energy yield • Energy loss of between 9% and 17% was calculated • Both techniques are wind farm layout and sitespecific Muhammad Ali – United Kingdom – RIF Session 4 – 0528

15. Thank you Muhammad Ali – United Kingdom – RIF Session 4 – 0528

16. Appendix (Background) - 1 • 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

17. Appendix (Background) - 2 • 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528

18. 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 Muhammad Ali – United Kingdom – RIF Session 4 – 0528