Wind Resource Analysis

Transcription

1 Wind Resource Analysis An Introductory Overview MGA/NWCC Midwestern Wind Energy: Moving It to Markets July 30, 2008 Detroit, Michigan Mark Ahlstrom 1

2 WindLogics Background Founded supercomputing background Atmospheric modeling and visualization US Air Force Operational Weather Squadrons Israeli Air Force Operational Forecasting System Harvard University Air Quality Modeling DOE Real-time Wind Field Monitoring NASA Meteorological Data Assimilation Experience in fine-scale forecasting systems Applied these advanced modeling and analysis technologies to wind energy since 2002 Subsidiary of FPL Energy since September

3 One Day of Weather 3 Inner grid from July 4, minute resolution at 30 min/sec Showing wind vectors (90 m AGL) and cloud water/precipitation isosurface

4 Atmospheric Complexity The atmosphere is so complex So how does this work? Solar Radiation Convection Moisture Fluxes Turbulence Evaporation Surface Heat Condensation 4

5 Gridded 3D Weather Data Integrates all available data sources, from the surface to the upper atmosphere, into a unified and physically consistent state of all grid cells at a given point in time. Over 160 weather variables collected from: Surface / METAR station data Oceanographic buoys Ship reports Aircraft (over 14,000 ACARS/day) NOAA 405 MHz profilers Boundary-layer (915 MHz) profilers Rawinsondes (balloon soundings) Reconnaissance dropwinsonde RASS virtual temperatures SSM/I precipitable water GPS total precipitable water GOES precipitable water GOES cloud-top pressure GOES high-density vis. cloud drift wind GOES IR cloud drift winds GOES cloud drift winds VAD winds: WSR-88D NEXRAD radars 5

6 Meteorological Models Numerical gridded representation of the laws of physics Conservation relations Mass Energy Momentum Water, etc. Physical processes Radiation Turbulence Soil/ocean interactions, etc. Use lots of fast computers Partial differential equations Gridpoint difference values Step all points through time using very small steps (a few seconds per step) 6

7 Nesting Modeling Techniques Modeling fills the gaps in both space & time Inner Grids Outer Grids 7

8 Averaging of Long-term Results 8

9 Variability in the Day 9

10 Variability over Years (Annual Energy ) 2002) 10

11 Influence of Local Terrain Example showing wind speed in color, wind direction as streamlines. Data Sources: WindLogics Archive Local Test Towers Hi-Res. Terrain / Land Cover Process: Detailed Windfield Modeling Result: 30 meter grid 50 meter hub height 30m Grid (5x6 km) 11

12 Influence of Height Production estimate in GWh per year at multiple heights 30m Grid (5x6 km) 30m Grid (5x6 km) 50m Height 80m Height 12

13 Complexity of Wind Energy Location & terrain make big difference Power in the wind is proportional to the cube of wind speed, so great value in optimizing location, layout & height Many characteristics to consider Shear (speed increase with height) Diurnal & seasonal patterns Long-term interannual variability Planning, financing & operating issues A large investment with a 25-year timeline Variability on many time scales Implications for utility operations 13

14 Challenges Can t afford a full gridded array of met towers one or two will have to suffice Can t wait forever one or two years measurement will have to suffice Anemometers are never perfect Diurnal patterns don t match hub height Airport Anemometers are too low, blocked by trees and vegetation Airport Anemometers are too far away 100 m 80 m Data Gaps, Icing, Calibration, Spares 7 or 8 years is NOT long-term data 50 m Measuring above 50 meters is too Met Tower expensive Wind Turbine Airport Anemometer Challenges of distance, height, time & space 14

15 Risk Uncovered in Long Term Analysis Normalized Speed vs. Year Facility Planning Phase 1 st Year of Operations 15

16 Avoiding Risk Seek All Available Data Apply Best Technologies Invest Today for Future Returns 16

17 Available Data On Site Met Tower Data National/Global Data Regional Weather Data Regional Tower Data Model-based Data 17

18 Data Integration is the Key Tower Data for Point Accuracy Model-based Wind Data for 3D Understanding Use Models as a Foundation for Integration 18

19 The Snapshot Prior to Construction Understanding the resource, variability & risk 19

20 The Continuous Process & Timeline Construction Hourly Weather Forecast Regional Prospecting Met. Tower Placement Virtual Tower Site Assessment Enhanced Tower Data Project Engineering Turbine Selection Financial Due Diligence Micro Siting Planning and Weather Forecast Maintenance Planner Long-term Financial Forecast Operations Project Timeline Wind Maps/GIS Initial Studies Site Visits Detailed Maps/GIS Preliminary Layouts Forecasting Operational Assessment Data Collection Data Analysis Long Term Normalization Micrositing Capacity Factor Predictions 20

21 Questions & Discussion Time series showing forecast with wind speed and cloud cover Mark Ahlstrom, CEO