Wayne O. Miller

Transcription

1 Wayne O. Miller Livermore, CA USA This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

2 ^ & Field Data

3 Dynamic Power Curve is a key deliverable Ensemble Estimates 10 4 m wind farm 10 2 m turbine Resource forecasting Power generation Power forecasts with uncertainty bounds Error & Sensitivity 3

4 The Dynamic Power Curve will deliver more accurate power forecasts for developers and utilities in a simplified model Power = f(dynamic inflow) Deliverable will be a statistical model of power output under complex inflow states Replace the static power curve Trained by SCADA data with known inflow state Parameter space is general (turbulence, shear, terrain, ) Will require live or forecast inflow data to adapt (hence DYNAMIC) We consider this a Reduced Order Model for wind power forecasting. The big data crunching done upfront. Three major R&D activities: 1. Field data campaigns extract inflow characteristics + power output 2. Wind/power simulations to explore full range of parameters 3. Training and evaluating statistical models 4

5 Original work on power curve errors performed at LLNL by Wharton & Lundquist Lundquist and Wharton, 2009, IEA Experts Meeting on SODAR and LIDAR; Wharton, Lundquist, Sharp, Crescenti, and Zulauf, 2009, AGU Fall Meeting; Wharton and Lundquist, 2010, DOE Technical report and in Wind Energy 5

6 Field Campaigns 6

7 We run field campaigns at many partner locations Altamont Pass (California) forecast uncertainty / power curve Tehachapi (California) ramp predictions from distributed data De Smet (South Dakota) short term forecasting assessment San Francisco Delta (California) wind farm characterization ARM (Oklahoma) turbulence assessment/ power curve Sweetwater (Texas) farm wake studies / power curve Columbia basin (Washington) resource assessment NREL (Colorado) wake imaging / power curve Central valley (California) surface moisture 7

8 We use LLNL Site 300 for intensive meteorological observations and forecasting studies in complex terrain LLNL (3 km 2 ) San Francisco (~40 km) Site 300 instrumentation: LIDAR Flux tower Site 300 (30 km 2 ) N Meteorological tower Available data: Temperature Relative humidity Soil moisture Surface heat flux Radiation data 8

9 Dual LIDARs can correct for flow acceleration over terrain 9

10 LIDAR + SCADA will define & provide V&V for our efforts 11/25/ /30/

11 WRF domain (3 level nesting) Comparison data from wind farm 11

12 Power Simulation 12

13 Relevant space & time scales cannot all be resolved with current instrumentation, so simulation extends the range Simulate shorter scales Measure longer scales Dynamic Loads Utility Power Turbine Power Convection Inflow Turbulence Ramps 10-2 s 10-1 s 10 0 s 10 1 s 10 2 s 10 3 s 10 4 s 10 5 s 60 Hz seconds minutes hours days 13

14 Turbulence Energy NWP LES Our wind HPC codes span the scales needed for full resolution Numerical Weather Prediction Δx > 10 km Mesoscale 10km > Δx > 100m Large-Eddy Simulation (LES) 100m > Δx > 1m Computational Fluid Dynamics (CFD) 10m > Δx > 0.01m Nested mesoscale simulations WRF WRF-IBM WRF-CGWIND WRF-HELIOS 14

15 Ensemble WRF prediction of wind and power at Tehachapi WIND SPEED TURBINE POWER 15

16 WRF-IBM (Immersed Boundary Method) -For complex terrain and urban environments POC: Katie Lundquist IBM is a non-conforming grid technique, which eliminates the coordinate transformation and the associated numerical errors. Developed at LLNL with LDRD & DOE funding Working with Prof. Tina Chow UC Berkeley Gives WRF better accuracy at high resolutions/fine scales Urban IBM Grid Scalar Release 16

17 CGWIND -HPC tool for complex terrain with turbine arrays 4th Order 2nd Order Under Development: WRF atmospheric inflow Lifting line turbine arrays

18 We have demonstrated the first atmospheric LES inflow into a rotor CFD code -WRF to HELIOS WRF atmospheric inflow on HELIOS CFD boundaries HELIOS nested and adaptive grids Collaborators: U. Wyoming U. S. Army

19 HELIOS simulation showing auto mesh refinement on wake & blade dynamics

20 Statistical Modeling 20

21 Want to develop a model for wind power as a function of various inputs: Wind power = f(wind speed, direction, season, stability, height, ) (Wharton & Lundquist ERL 2012) 21

22 The surrogate model (SMV in this case) is able to reproduce a computer model quite well and with fewer parameters (11 compared to 28) 22

23 node diameter V i / V tot (main effects) edge width V ij / V tot (interactions) (only two-way interactions shown, but higher orders can also be displayed on the same graph) Sensitivity analysis is essential for dimension reduction as it helps determine which inputs are most important and in what way

24 Basic model: Y(x) = (x) = g j (x)β j + Z(x) x = inputs (wind speed, direction, stability, etc.) Y = response (wind power) g j = known regression functions, β j = unknown regression coefficients, Z(x) = error process ˆf p å j=1 An interpolator model, i.e., fits the observed data exactly and interpolates the rest of the input space 24

25 Model: Y(x) = (x) = a j B j (x) x = inputs (wind speed, stability, etc.) Y = response (wind power) a j = unknown coefficients, B j = basis functions ˆf Nonparametric regression model, an extension of the usual linear regression (see Friedman, Annals of Statistics, 1991) p å j=1 Not an interpolator, i.e., does not have to go through the observed points 25

26 Questions? 26

27 The wind power function f is complex, so one approach is to approximate it Use the available data for wind power and the corresponding inputs (wind speed, stability, etc.) to fit a statistical model to predict wind power at the combinations of inputs we do not have wind power data for Gaussian Process Model (GPM), Multivariate Adaptive Regression Spline (MARS), Support Vector Machine (SVM) regression and Neural Networks (NN) are some possibilities for such a statistical model 27

28 GPM advantages: - Inference about predicted output is much more natural with GPM than MARS (because it is parametric unlike MARS) - An interpolator model, i.e., the fitted surface is forced to go through the observed data, unlike MARS MARS (and regression model) advantages: - Much more natural at handling non-stationary processes than GPM - Scales slightly better than GPM with number of parameters and observations Thus, the choice of the model strongly depends on the nature of the response surface, which will become more clear as we analyze the available data 28

29 Proof-of-concept UQ experiments with the Weather Research and Forecasting Model 2-day wind speed forecasts at 80-meters Ensemble spread from 11 parameters Enabled with LLNL s UQ Pipeline technology Scheme PBL (MYJ) Surface Layer (MYJ) Land Surface (Noah) Parameter prt rb fb2 alph epsl czil fh01 fh02 z0mult albmult smmult Default UQ Range [0.7, 1.3] [1.5, 1.644] [2.37, 3.80] [0.1, 0.3] [0.1, 0.32] [0, 1] [0.7, 1.3] [0.7, 1.3] [0.7, 1.3] [0.8, 1.2] [0.8, 1.2]

30 Proof-of-concept: UQ of turbine power forecast 2-day power output forecasts Ensemble spread from uncertainties in 11 WRF parameters ensemble members --- median of ensemble 30