National Center for Atmospheric Research Research Applications Laboratory Renewable Energy

Transcription

1 National Center for Atmospheric Research Research Applications Laboratory Renewable Energy Dr. Sue Ellen Haupt, Director & Dr. Branko Kosovic, Program Manager Weather Systems & Assessment Program Research Applications Laboratory Boulder, CO April 22, 2014

2 Meteorology Needs for Power Production Siting Assessing the Resource Maintenance Operations Power Forecasts

3 Resource Assessment & Siting

4 Estimates of Wind Power & Its Variability Mean 80-m Wind Speed StDev 80-m Wind Speed

5 Estimates of Solar Power & Its Variability Mean Surface Downward Shortwave Radiation StDev Surface Downward Shortwave Radiation

6 Current Climate Wind Speed by Region, Time of Day, and Season Daily Time Slices Morning Afternoon Evening Night (Local Time) Seasons Summer Fall Winter Spring June-August September- October November- February March-May

7 Projected Change Future Climate - Wind

8 Projected Change Future Climate - Solar

9 Maintenance

10 Variability at Hub Height is Substantial Courtesy Ned Patton

11 Wake Effects of an Array of Wind Turbines Turbine wakes highlighted by sea fog Branko Kosovic

12 Varying Flow Local Scale Significant flow separation Courtesy of Ned Patton NCAR/MMM

13 Additional Offshore Challenges: Wave Generated Winds Moving waves Ocean Waves Generate their Own Wind Field that Persists to Hub Height Courtesy Peter Sullivan

14 Extreme Conditions & Hurricane Models 62 m Large Eddy Simulations Can integrate modeled hurricane forces models with turbine design models Courtesy Rich Rotunno Courtesy George Bryan

15 Operations - Power Forecasting Photo Credits: George Young Amy Haupt Sue Haupt

16 Wind Power Forecasting Necessary for Effective Grid Integration Day Ahead forecasting Short-term forecasting Thus, an effective forecasting system should target both Cedar Creek Wind Farm, Northeast Colorado Photo by Carlye Calvin, UCAR

17 Xcel Energy Variable Energy Forecasting System Center Data NAM, GFS, HRR, RAP, ECMWF, GEM Wind Farm Data Nacelle wind speed Generator power Node power Met tower Availability CSV Data Statistical Verification Operator GUI WRF RTFDDA System Ensemble System Solar Energy Forecast Supplemental Wind Farm Data Met towers Wind profiler Surface Stations Windcube Lidar Dynamic, Integrated Forecast System (DICast ) VDRAS (nowcasting) Expert System (nowcasting) Extreme Weather Events Wind to Energy Conversion Subsystem Potential Power Forecasting Data Mining for Load Estimation Probabilistic and Analog Forecast Meteorologist GUI WRF Model Output

18 DICast Integrator System Dynamic Integrated forecast System WRF RTFDDA WRF Ensemble Mean Integrator Nacelle Winds Error MM5 Ensemble Mean... GFS GEM NAM Other Model Data Wind speed example 10-15% decrease in error Turbine Power Prediction Wind Power Forecast

19 Customized Power Conversion Curves Observation-based power curves represent the site better than manufacturers power curves Gerry Wiener

20 Output Graphical User Interface

21 Wind Energy Ramp Event Nowcasting VDRAS Variational Doppler Radar Analysis System Need to provide time-of-arrival and magnitude of wind energy ramp. Jenny Sun

22 Doppler Radar Retrieval 10/12/2010 Wind Energy Ramp Case Copyright 2013 University Corporation for Atmospheric Research

23 Wind Power Forecast Value for Xcel Energy Forecasted MAE Percentage Savings Improvement ($000,000) * 16.80% 10.57% 37.1% $36.8 *Data through November, 2013 Also: saved > 238,000 tons CO2 (2011) Drake Bartlett, Xcel

24 Load Forecasting and Distributed Solar Energy Forecasting Power [MW] Hour Actual Forecast Load (MW) Time

25 A Public-PrivateAcademic Partnership to Advance Solar Power Forecasting Photo Credit: Tyler McCandless

26 Approach to Solar Power Forecasting 48

27 MADCast Multi-sensor Advective Diffusive forecast Tom Auligne; Xu et al. (Adv. in Atmos. Sci. 2014) AIRS IASI MODIS GOES Sounder GOES Imager Multi-sensor

28 WRF-Solar Improvements in: Radiation Schemes Shallow Convection Schemes Cloud Microphysics Satellite and specialized data assimilation Run Real-time for Demonstration Smaller droplet size increases albedo, less shortwave reaching the ground. Dudhia Hacker Thompson Deng & Stauffer Ruiz Arrias Guymardt Auligne

29 Summary Understanding atmospheric variability is critical to optimizing power production Meteorological modeling is needed to help deal with the issues Siting Maintenance Real-time forecasts

30 Dr. Sue Ellen Haupt Dr. Branko Kosovic