Oklahoma Wind Power Initiative (OWPI) Tim Hughes (OU) Mark Shafer (OU) Troy Simonsen (OU) Jeremy Traurig (OU) Nick Mirskey (OU) Steve Stadler (OSU) Pete.

Slides:



Advertisements
Similar presentations
Minimum temperature mapping in complex terrain for fruit frost warning Jin I. Yun Kyung Hee University Suwon, Korea.
Advertisements

Matthew Hendrickson, and Pascal Storck
Wind Energy Update October 6, OG&E’s Commitment to Wind Energy (2007) Make Oklahoma a national leader in renewable energy Mission Accomplished!
Using for Pollutant Dispersion Andrea Vignaroli – University of Perugia.
Difficulties Integrating Wind Generation Into Urban Energy Load Russell Bigley Shane Motley Keith Parks.
1 Modelled Meteorology - Applicability to Well-test Flaring Assessments Environment and Energy Division Alex Schutte Science & Community Environmental.
Announcements Read Chapter 7 Quiz on HW 3 Today
Introduction to the ISC Model Marti Blad NAU College of Engineering.
The Wind Energy Center at UMASS University of Massachusetts Patrick Quinlan Associate Director UMass Wind Energy Center Amherst, Massachusetts Site Considerations:
Wind Power APES What makes a good wind power site? Powering a Nation – Roping the Wind.
Uncertainty in Wind Energy
Kongiganak Wind Farm Picture by Puvurnaq Power Company Josh Craft Assistant Wind Program Manager Alaska Energy Authority (907)
CLIMAT (CLIMAT TEMP) History: 1935 – IMO (International Meteorological Organization) that mean monthly values of the main climatological elements at certain.
The Boundary Layer Wind Tunnel Laboratory University of Western Ontario London, Ontario, Canada, N6A 5B9 Mapping of Topographic Effects on Maximum Sustained.
©2005 Austin Troy. All rights reserved Lecture 3: Introduction to GIS Understanding Spatial Data Structures by Austin Troy, Leslie Morrissey, & Ernie Buford,
Energy from Wind. Power Power: Rate at which energy is delivered Power = Energy Time Measured in Watts (W), kilowatts (kW), or horsepower Power is an.
Totara Bank project 2008 Energy Postgraduate Conference Léa Sigot - Sylvain Lamige Supervisor: Attilio Pigneri.
Dynamic thermal rating of power transmission lines related to renewable resources Jiri Hosek Institute of Atmospheric Physics, Prague, Czech Rep.
Kingston, MA Shadow Flicker Study Elizabeth King Wind Analyst Chester Harvey GIS Specialist 256 Farrell Farm Rd. Norwich, VT Ph:
Geostatistical approach to Estimating Rainfall over Mauritius Mphil/PhD Student: Mr.Dhurmea K. Ram Supervisors: Prof. SDDV Rughooputh Dr. R Boojhawon Estimating.
The use of the Mesonet in Oklahoma agriculture Clint Dotson Precision Ag April 16, 2007.
Overcoming Barriers Expanding the Market for Small Wind Energy Systems Small Wind 102: Economics Making the numbers work Small Wind 103: Siting Issues.
Spatial Analysis.
The diagram shows weather instruments A and B.
NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy operated by the Alliance for Sustainable.
Article #3 New York Windmills Setbacks: Minimum distance from residence for construction of turbines. Determined locally – community, county, state. Typical.
Wolf-Gerrit Früh Christina Skittides With support from SgurrEnergy Preliminary assessment of wind climate fluctuations and use of Dynamical Systems Theory.
Renewable Energy Research Laboratory University of Massachusetts Prediction Uncertainties in Measure- Correlate-Predict Analyses Anthony L. Rogers, Ph.D.
ECE 7800: Renewable Energy Systems
Downscaling and its limitation on climate change impact assessments Sepo Hachigonta University of Cape Town South Africa “Building Food Security in the.
Article #3 New York Windmills
1/26 APPLICATION OF THE URBAN VERSION OF MM5 FOR HOUSTON University Corporation for Atmospheric Research Sylvain Dupont Collaborators: Steve Burian, Jason.
Site Selection in the Oklahoma Mesonet Mark A. Shafer Oklahoma Climatological Survey University of Oklahoma.
AWS Truewind Methodology Timeline of AWS Truewind participation Key points Wind resource modeling Estimation of plant output Validation and adjustment.
Energy from Wind. The Rating Game Turbine Ratings are weird Bergey XL.1 Rated Power 1 kW Does it produce 1000W all of the time? NO! Only a small percentage.
Quality control of daily data on example of Central European series of air temperature, relative humidity and precipitation P. Štěpánek (1), P. Zahradníček.
Estimating the Optimal Location of a New Wind Farm based on Geospatial Information System Data Dec Chungwook Sim.
The Analysis of Boundary Layer Refractivity Using the CSU-CHILL Radar David Coates.
CE-QUAL-W2 Shasta Modeling
Environmental Business Council December 17, 2009
An Improved Global Snow Classification Dataset for Hydrologic Applications (Photo by Kenneth G. Libbrecht and Patricia Rasmussen) Glen E. Liston, CSU Matthew.
EWEC06 ( , Athens) Numerical Site Calibration on a Complex Terrain and its Application for Wind Turbine Performance Measurements Toshiyuki SANADA.
Relationship between time, displacement, velocity, acceleration. Kinematic.
Wildfire Risk Assessment: Western Travis County, Texas Integrating GIS and Fire Modeling Jennifer Perry CE 394K GIS.
Estimating Soil Moisture Using Satellite Observations in Puerto Rico By Harold Cruzado Advisor: Dr. Ramón Vásquez University of Puerto Rico - Mayagüez.
Energy from Wind.
Unit 2 Measuring the Earth Mapping. Size and Shape Almost a perfect sphere- slight flattening in the polar regions and a slight bulging at the equatorial.
Physics and Astronomy Outreach Program at the University of British Columbia Renewable And Clean Energy Wind Turbines Multiple-Choice Questions.
Corn Yield Comparison Between EPIC-View Simulated Yield And Observed Yield Monitor Data by Chad M. Boshart Oklahoma State University.
 Energy is a major input for overall socio- economic development of any society  The prices of the fossil fuels steeply increasing  So renewables.
SnowSTAR 2002 Transect Reconstruction Using SNTHERM Model July 19, 2006 Xiaogang Shi and Dennis P. Lettenmaier.
The Oklahoma Climatological Survey & The Oklahoma Mesonet Mark Shafer Director of Climate Information Oklahoma Climatological Survey February 20, 2004.
Advanced Numerical Techniques Mccormack Technique CFD Dr. Ugur GUVEN.
Using satellite data and data fusion techniques
Torkil Veyhe, Hans Georg Beyer, Barður Niclasen
1Weidinger, T., 2Costa, A. A., 3Lajos, T., 4Kiss, Á.,
What is in our head…. Spatial Modeling Performance in Complex Terrain Scott Eichelberger, Vaisala.
Assessment of wind power resource in Belgrade region
Integrated Renewable Energy Transition Capabilities
Project progress report WP2
WindNinja Model Domain/Objective
Torkil Veyhe, Hans Georg Beyer, Barður Niclasen
Data management: 10 minute data, 8760 hours Data Q/C, error checking
Precision Agriculture an Overview
Introduction to Hands-on Activities
INFLUX: Comparisons of modeled and observed surface energy dynamics over varying urban landscapes in Indianapolis, IN Daniel P. Sarmiento, Kenneth Davis,
by Mark Meo University of Oklahoma
ALL the following plots are subject to the filtering :
The NWS and Climate Offices
Wind direction and speed, Wind is named from the direction it is coming from.
Presentation transcript:

Oklahoma Wind Power Initiative (OWPI) Tim Hughes (OU) Mark Shafer (OU) Troy Simonsen (OU) Jeremy Traurig (OU) Nick Mirskey (OU) Steve Stadler (OSU) Pete Earls (OSU)

Wind Energy: Cost of Wind-Generated Electricity 1980 to 2005 Levelized Cents/kWh

OWPI GOALS: Resource Assessment Policy Study Outreach Educational programs Community meetings Promote Economic Development

The Oklahoma Mesonet

115 Active stations, spaced ~32 km 5-minute resolution data Standard Meteorological variables following WMO standards 10-meter wind speed and direction; scaled to 50-meters using: U/U r = (Z/Z r ) 1/6 Rural sites; generally good fetch conditions data used in study

OWPAI

Oklahoma Wind Resource Maps Purple = class 5 or better, blue = class 4, lt. blue = class 3

Steps for Developing Models Review Mesonet site surroundings to qualify “fetch conditions” ofsite, using: –aerial photos (DOQQs) –vegetation (LU/LC) –site panorama photos Assign subjective ratings of ‘poor’, ‘fair’, ‘good’ or ‘excellent’

NORMAN Air Photo (zoomed) 250 m 500 m NORM wind rose

Steps for Developing Models Combine information from: Mesonet Station data (wind, pressure, temperature - 735,000 readings of each per station)  DEM elevation data  Vegetation data (roughness) Input into two different models: analytical model (Windmap) empirical model (using neural networks for non-linear relationship)

WindMap Software (Analytical) INPUTSOUTPUTS DEM Data Elevation Grid LULC (GAP) Map Roughness Grid 10 Meter Winds ArcView* WINDMAPWINDMAP Final Winds Map Power Density Map Turbine Output Map *A GIS Software Package

MODELED LONG-TERM AVERAGE WIND POWER DENSITY 50 METERS (164 FT.) Above Ground Level OWPI DRAFT 9/2001 Analytical model output

Neural Network (Empirical) Correlate wind power values calculated at Mesonet sites, with neural network scheme, to: –site elevations –north and south terrain exposures –north and south average roughness Get equations for wind power density as function of the above Fill in grid for whole state

Average Wind energy rose using wind data from 78 stations with ‘good-excellent’ rating on fetch conditions Wind energy in N + S wedges = 89% of total Realizable energy from turbines: > 95% from N & S 34 deg 146 deg 326 deg 214 deg North Wedge South Wedge

Sample calculated WPDs and elevation, terrain exposure, and roughness averages + 57 more …….

Wind Power Map for Oklahoma (Empirical Model using Neural Networks)

Analytical Model Findings Initial run underestimated wind power density at most Mesonet sites Linear regression of predicted vs. calculated wind power density yielded correction factor of 1.33 Better agreement with field data from validation site, but still under- estimates in Southeast Oklahoma

Empirical Model Findings Emphasizes ridge lines (areas of good exposure Low Roughness Good Terrain Exposure Compared to WindMap and Tower data, likely underestimates, especially in Southeast Oklahoma

NREL Resource Maps Purple = class 5 or better, blue = class 4, lt. blue = class 3

OWPI DRAFT 9/2001 Analytical Model

Empirical Model

OWPI’s Oklahoma Wind Climatology Products

Wind Climatology Cheyenne Mesonet Site Station ID: CHEY Class 3 Site (January ’94 – December ’00) Average 10 m Wind Speed = 5.70 m/s (12.8 mph) Average 10 m Power Density = 189 W/m 2

Cheyenne Wind Energy Rose

Cheyenne

For information on OWPI: Oklahoma Wind Power Initiative Contact Tim Hughes: For information on OREC: Oklahoma Renewable Energy Council