Numerical Modelling for Optimization of Wind Farm Turbine Performance M. O. Mughal, M.Lynch, F.Yu, B. McGann, F. Jeanneret & J.Sutton Curtin University, Perth, Western Australia 19/05/2015 COOPERATIVE RESEARCH CENTRE FOR CONTAMINATION ASSESSMENT AND REMEDIATION OF THE ENVIRONMENT
OVERVIEW OF PRESENTATION Acknowledgements Introduction Objective Methodology Weather Research Forecasting (WRF) Sensitivity Analysis Coherent Doppler LIDAR (CDL) versus WRF Comparison Coupling WRF to OpenFOAM Conclusions Future work Q & As SCHEME OF PRESENTATION
ACKNOWLEDGEMENTS Cooperative Research Centre for Contamination Assessment and Remediation of Environment (CRC CARE), Australian Government This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia." Department of Environment Regulation (DER), Government of Western Australia Dr Peter Rye, DER, Government of Western Australia Associate Professor Diandong Ren, Curtin University ACKNOWLEDGEMENTS
INTRODUCTION Significance of short term numerical forecasting (STF) Significance and role of CDL in STF Mesoscale model shortcomings at wind farm scale Significance of meso and microscale model coupling Proposed technique Wind energy grid integration challenges INTRODUCTION
THE LAKE TURKANA WIND FARM Lake Turkana wind farm characteristics 325 MW Located in Marsabit district, near Lake Turkana Kenya, East Africa 140 to 700 km (width) 610 to 1524 m (elevation above sea level) CRC CARE & DER joint campaign (2009) to map the wind field using CDL Three masts [~40 m] and CDL used for numerical model validation Mast B (38 m) Mast C (46 m) Mast A (39 m) INTRODUCTION
COHERENT DOPPLER LIDAR Lockheed Martin WindTracer 1.6 µm Doppler- Lidar employed in determining wind fields at Lake Turkana State of the art eye safe technology with a range of 8-12 km 250 km 2 coverage for winds COHERENT DOPPLER LIDAR
LAKE TURKANA TOPOGRAPHY Topographic Height (m) Topographic Height (m) INTRODUCTION
OBJECTIVES Develop a short term forecasting technique Investigate on the use of an optimised configuration of (WRF) software Evaluate model performance using CDL and mast observations Improve wind farm forecasting by applying a coupled WRF and CFD (OpenFOAM) model Improve initialization data for WRF using CDL observations Achieved In Progress OBJECTIVE
METHODOLOGY WRF sensitivity analysis and validation via in situ and CDL observations Coupling optimised WRF model with OpenFOAM for prediction of micro-scale wind for improving turbine energy output estimation Evaluating impact using comparisons with in situ meteorological measurements WRF initialization data tuning incorporating CDL observations Achieved In Progress METHODOLOGY
WRF SENSITIVITY ANALYSIS WRF performance validation in Western Australia Sensitivity analysis conducted at Lake Turkana, Kenya site Sensitivity analysis includes testing initialization fields, physical & parametrization schemes, grid configurations, terrain complexity and satellite data Validation criteria based on Root Mean Square Error (RMSE) and Correlation Coefficient between in situ and predicted winds Considering Mast as true measurements Best results obtained through changing initialization fields WRF SENSITIVITY ANALYSIS
SENSITIVITY ANALYSIS RESULTS Results of sensitivity analysis at Lake Turkana European Centre for Medium-Range Weather Forecasts (ECMWF) initialization field selected with 70 km horizontal resolutions 60 model levels 6 hourly temporal resolution Configuration 4 domains 36 model levels Domain Size km x km Geographic Resolution Grid Resolution km Time Step s Domain 1 1593 x 1593 10` 27 30 Domain 2 918 x837 5` 9 10 Topographic Height (m) Domain 3 459 x 297 2` 3 3.33 Domain 4 126 x 82 30 1 1.11 WRF SENSITIVITY ANALYSIS
SENSITIVITY ANALYSIS RESULTS Wind Speed ms -1 Mast A vs WRF Mast mean 11.03 WRF mean 11.38 RMSE 1.66 Correlation Coefficient 0.69 Comparison of wind speed comparison between mast A (10 mins sampling), WRF predicted wind(10 mins sampling) and CDL at 39 m height (Time UTC) CDL vs WRF RMSE 2.24 Correlation coefficient 0.60 LIDAR Mean 10.5 Mast A VS WRF Wind Direction Mast mean 113.305 WRF mean 104.37 RMSE 12.019 Correlation Coefficient 0.44 Comparison of wind speed comparison between mast A (10 mins sampling) and WRF predicted wind (10 mins sampling) at 39 m height (Time UTC) WRF SENSITIVITY ANALYSIS
CDL TERRAIN FOLLOWING WIND MAP COMPARISON WITH WRF Zoomed in data at CDL location WRF Wind Speed (m/sec) CDL WRF COMPARISON CDL Terrain Following Map
LAKE TURKANA CDL-WRF COMPARISON AT PROPOSED TURBINE LOCATIONS WRF-CDL at proposed turbine locations suggests WRF has captured the wind speeds well spatially and even at locations other than masts Turbine location away from the mountain suggests terrain complexity is reduced and better RMSE values compared with mast-wrf comparison are achieved. RMSE 1.23 ms -1 Correlation Coefficient 0.84 RMSE 1.14 ms -1 Correlation Coefficient 0.81 CDL WRF COMPARISON
WRF TO OPENFOAM COUPLING Reliable micro-siting and cost energy estimation demands meso micro scale model coupling In STF coupled model can ingest inputs from WRF forecast running in real mode and use them to predict turbulence structures affecting wind speed ensuring reliable forecast. Spatial and temporal grids are, in general, non-matching WRF grid moves in the vertical with time-dependent pressure variation. Ambiguous mechanism for transferring turbulent energy from one code to the other The validation of these models is also difficult as the measurement data available is limited WRF TO OPENFOAM COUPLING
WRF TO OPENFOAM COUPLING Proposed technique surpasses other techniques having real time data from CDL. The solver can handle complex terrain features e.g. topography, temperature & pressure variations etc. Capability to capture a complete wind profile from WRF CDL data can be further ingested to improve initial conditions from WRF to bring fluxes up to right values at solver boundaries The software performance is tested in Lake Turkana and the results are validated with CDL The results are not an artefact of nudging in WRF Coupled WRF-CFD predicts turbulence structures due to topography catastrophic for turbines WRF Coupled CFD WRF TO OPENFOAM COUPLING
WRF TO OPENFOAM COUPLING Terrain extracted from SRTM data 4X4X2 km mesh (wind direction SE) Neumann and Dirichlet conditions applied to boundaries Simulation conducted on a Cray XC40 (24 nodes each with 64GB RAM) Results agree well with CDL data as compared to WRF WRF TO OPENFOAM COUPLING
CONCLUSIONS WRF modelling (without coupling) at Lake Turkana in a data sparse region of complex terrain typically achieves RMSE in speed of 1.6 ms -1 and direction of 12 and a correlation coefficient of 0.69 when validated against mast observations Comparison of WRF predictions with CDL demonstrates an improved performance of the model with RMSE in wind speed of 1.2 ms -1, and correlation coefficient 0.84 respectively when validated against meteorological observations; a 25% improvement in wind prediction and a 22% improvement in the correlation coefficient (against in situ mast data). The retrieval of vertical winds is showing significant skill with CFD / OpenFOAM having a low standard deviation of 3.35 ms-1 compared to WRF s of 4.71 ms-1 when validated against CDL winds CONCLUSIONS
FUTURE WORK Promote the advantages of CDL in wind farm research particularly in their potential operational role in enhancing the prediction of winds for improved infrastructure protection (e.g. turbulence, severe wind gusts) and for improved lead time energy generation management. It is intended to integrate local observations from CDL to improve the initialization fields for WRF Analyse the impact of integrating CDL data and assessing its impact on the knowledge of wind speed and direction information Determining the best criteria for judging a numerical model output in terms of wind speed and direction FUTURE WORK
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