Validation of annual wind energy production estimates using the WRF model at 50 wind farms

Transcription

1 Validation of annual wind energy production estimates using the WRF model at 50 wind farms Mark Žagar, Sven-Jesper Knudsen Vestas Wind Systems A/S, Aarhus, Denmark ICEM2015, June 23, 2015

2 Estimating future Annual Energy Production (AEP) Reducing uncertainty Uncertainty affects Choice of site Choice of turbine type / class Price of capital Operation strategy Service cost estimation Cost of Energy 1% Energy ~ 0.05 m/s 1% Uncertainty in yearly production estimate

3 Who is Vestas World s largest wind turbine manufacturer Technology & Service Solutions Service & Plant Solutions Plant Solutions Plant Siting & Forecasting Flow Modelling (CFD, Loads) Yavor Hristov, Greg Oxley, Seonghyeon Hahn, Niels Lauritsen, Jesper Graugaard Meteorology (NWP, Climatology) Line Gulstad, Mark Žagar, Tomislav Marić, Annika Collins Functional Excelence (Project Management, HPC, Statistics) Michael Nielsen, Martin Qvist, Frede Tøgersen

4 Complexity Introduction Approach Results Modeling Approaches Focus on information gain Life-time (20 years) expected conditions at the turbine locations Much more than average wind speed! Complex flow structures (Nocturnal LLJ, Coastal jets, ) Seasonal, Diurnal variability Non-neutral flow Information Information becomes increasingly difficult to add.

5 Complexity Introduction Approach Results Modeling Approaches Focus on information gain Incomplete model, Throw in more CPU More of the same - inefficient - little gain Effort Information

6 Complexity Introduction Approach Results Modeling Approaches Focus on information gain Incomplete model with fixes Bending the curve - approximate - new techniques - limited gain Information

7 Complexity Introduction Approach Results Modeling Approaches Focus on information gain Full physics models, Made for the atmosphere Perfect model - All-in-one - Scalable Information

8 Weather Research and Forecasting (WRF) model Skamarock et al., 2005

9 Simulating wind conditions on a site Increasing resolution introducing more terrain induced complexity 3km 1/3 km resolution Capacity (P/P full ) [%]

10 How accurate are the simulations? Verification with mast data 3km run Measurements usually 1 year High resolution (300m) simulations as well Not necessarily the same year LT correction necessary (standard practice) Campaign, Turkey: Significantly reduced prediction error with increased resolution. BIAS [m/s] MAE [m/s] STDE [m/s] 3 km km /3 km

11 Global selection of wind farms Varying terrain complexity, climate regimes, farm sizes

12 Wind farm production estimate Prediction, calibration, calculation P = P(V, ρ) W(t y ) = t y t=0 P(V t, ρ t ) t Model prediction for the mast location vs. Calculation using the measurements Correction factor 1 dot == 1 mast location

13 Predicted park production [MWh/y] Introduction Approach Results Wind farm production estimate Verification against production data Production data compensated for: Downtime Wake (based on standard wake models) Curtailed operations AEP Model only AEP Corrected using mast data MAE STDE EtotP WRF raw WRF corr 16.2% 18.2% 4.7% 8.0% 9.4% 2.7% Observed park production [MWh/y]

14 Flow modeling within a farm WRF as CFD Randomly selected turbines, used to calibrate Different WRF regimes solution driving the prediction errors Error of the individual production prediction on other turbines in the farm. Randomly selected turbines, used to calibrate WRF solution Farm production prediction from several realizations

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