1 / 22

Wind Energy Meteorology

Wind Energy Meteorology. Overview and Case Study. Introduction. Wind power meteorology bridges gaps between meteorology, climatology, and geography Main elements Siting Resource Assessment Short Term Prediction. The Elements: .

benita
Télécharger la présentation

Wind Energy Meteorology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Wind Energy Meteorology Overview and Case Study

  2. Introduction • Wind power meteorology bridges gaps between meteorology, climatology, and geography • Main elements • Siting • Resource Assessment • Short Term Prediction

  3. The Elements: • Siting: estimation of mean power produced by a particular turbine at one or more specific locations • Regional Assessment: estimation of potential output from a group of turbines distributed over an area • Forecasting – “it’s possible to construct a methodology by combining numerical weather prediction models with micro-siting models to predict the power output from specific wind farms up to 48 hours ahead

  4. What information is necessary? • Understanding of both small and large • scale boundary layer meteorology: • Wind profiles • Wind Shear • Turbulence and Gust • Extreme Winds • Sources: • On-site measurements • Synoptic networks • Re-analysis projects

  5. History • Interest in wind energy meteorology developed in the early 70s • Wind turbine development boomed in the 1980s, but were often dismantled after a few years due to poor design • Politics created (and maintains) a fluctuating market

  6. Market/Academic Reactions • 1778-80 Danish Wind Atlas • Never finished, realized hat in order for the map to be coherent, resolution would have to be impossibly high for the time period • Due to variation caused by heavy dependence of winds on topographical features • This method was adapted later “Wind Atlas Method”, but in table/graph form. • In 1981, the European Commission began its first project in wind energy modeled after the Danish Wind Atlas, an approach that was instantly labeled as impossible, prompted definition of different landscape types • It’s important to keep in mind that this is Pre-PC. • European Wind Atlas published 1989, one year after WASP

  7. Weather and Wind Climate • Boundary Layer Meteorology – processes in the boundary layer • Calm, clear nights – 100m • “Fine summer day” – up to 2000m • Luckily, mostly been able draw from prior boundary layer meteorology knowledge, and turbine designers haven’t even been able to make use of it all • This has changed; detailed, realistic models, like those used to model 3D turbulence over a rotor blade are very difficult • “As for now, no firm evidence of global [climate] change has been given”

  8. Boundary Layer Winds • Wind velocity gradient • Smaller in unstable conditions • Greater in stable conditions • Varying terrain causes certain layers of the flow to speed up/slow down, altering the wind profile • Behind a turbine, the flow speed decreases, forming strong shear layers near the wake boundary

  9. Gusts and Turbulence • Generally normalized and expressed in terms of the SD of fluctuations over 10-60 min period • In horizontally homogeneous terrain, intensity of turbulence is a function of height, roughness length, and stability, expressed as percentage • Flat open Grassland: 13% • Sea: 8% • Complex terrain: >20% • Sensitive to averaging time, since turbulence generally has low frequency • Intuitively, wakes have higher turbulence levels and decreased average wind velocity, causing to higher turbulence intensities.

  10. Gusts and Turbulence • Due to the time it takes for eddies to be altered, turbulence changes lag for up to many hours behind the initial cause • Effects on turbulence • Terrain inhomogeneities, vertical distortion • Changing roughness, first small scale, then large

  11. Case Study An examination of San Diego Gas & Electric Meteorology Applications and Obstacles

  12. Overview • SDGE provides electricity and natural gas to San Diego and southern Orange counties • 3.4 million customers over 4100 sq miles • Recent accolades include most intelligent AND most reliable utility in the country • It’s not always sunny and 75 in SoCal

  13. San Diego Gas & ElectricMeteorology Picture Courtesy of WildNaturImages.com Brian D’Agostino Picture Courtesy of NOAA Picture Courtesy of Ted Walton

  14. Who cares? • Electric Regional Ops • Electroic Distribution Ops • Electric Grid Ops • Demand Response Programs • Emergency Ops Center • Gas Distribution • Customer Service • Sunrise Aviation • Sunrise Construction • Gas Transmission • Energy Supply and Distribution SDGE Meteorology Group’s forecasts are delivered to more than 600 email addresses every day in many departments, including the following:

  15. Why is meteorology important? • Risks to utilities include: • Wildfires • Winter Storms • Heat Waves • Monsoonal Thunderstorms Picture Courtesy of Weather.com Picture Courtesy of the Union Tribune

  16. Tech Used to Minimize Risk: Anemometer measures wind speed/gust • Weather Stations: • 128 station MesoNet • Supports operational decisions • 8 Portable Weather Stations • Data collection in 10 minute increments • All data is public • 6 remote weather cameras • 130,000 data points/day • Supports real time operations, forecasting, and research Temperature, Relative Humidity Sensor Datalogger, Communications Dead- Fuel Moisture Sensor

  17. SDGE MesoNet

  18. California Energy Supply/Demand

  19. Acknowledgements: “Wind Power Meteorology” Petersen, Mortensen, Landberg, Hojstrup and Frank 1997 Special Thanks: Brian D’Agostino - San Diego Gas & Electric

More Related