We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
supports HTML5 video
Published byNatalie O'Leary
Modified over 4 years ago
Eric White, Director of EngineeringWind Resource Assessment and Energy Production: From Pre-Construction to Post Construction May 2008 Eric White, Director of Engineering AWS Truewind, LLC 463 New Karner Road Albany, NY © 2007 AWS Truewind, LLC
AWS Truewind - OverviewIndustry Leader & Consultant for 20,000+ MW Full spectrum of wind farm development and evaluation services Wind Assessment, Mapping, Engineering, Performance Assessment, Forecasting In business 25 years Project roles in over 50 countries Albany, New York based; 55+ employees © 2007 AWS Truewind, LLC
Topics Wind resource assessment process Energy production modelingWind Farms Measurements Siting Wind resource assessment process Energy production modeling Effects on wind farm siting and design Operational plants and actual yields © 2007 AWS Truewind, LLC
Establishing Project ViabilityWind Resources Determine: Project Location & Size Tower Height Turbine Selection & Layout Energy Production annual, seasonal on- & off-peak Cost of Energy/Cash Flow Warranty Terms Size of Emissions Credits The wind energy industry is more demanding of wind speed accuracy than any other industry. © 2007 AWS Truewind, LLC
Power in the Wind (Watts)= 1/2 x air density x swept rotor area x (wind speed)3 A V3 Density = P/(RxT) P - pressure (Pa) R - specific gas constant (287 J/kgK) T - air temperature (K) Area = r2 Instantaneous Speed (not mean speed) kg/m3 m2 m/s Knowledge of local wind speed is critical to evaluating the available power © 2007 AWS Truewind, LLC
Wind Shear The change in horizontal wind speed with heightA function of wind speed, surface roughness (may vary with wind direction), and atmospheric stability (changes from day to night) Wind shear exponents are higher at low wind speeds, above rough surfaces, and during stable conditions Typical exponent () values: : water/beach : gently rolling farmland : forests/mountains Wind Shear Profile V2= 7.7 m/s V1 = 7.0 m/s Z2= 80 m Z1= 50 m Wind speed, and available power, generally increase significantly with height V2 = V1(Z2/Z1) = Log10 [V2/V1] Log10 [Z2/Z1] © 2007 AWS Truewind, LLC
Wind Resource Assessment Handbook Fundamentals for Conducting a Successful Monitoring ProgramPrepared for: National Renewable Energy Laboratory 1617 Cole Boulevard Golden, CO NREL Subcontract No. TAT Prepared By: AWS Scientific, Inc. 255 Fuller Road Albany, NY April 1997 Published by NREL Peer reviewed Technical & comprehensive Topics include: Siting tools Measurement instrumentation Installation Operation & maintenance Data collection & handling Data validation & reporting Costs & labor requirements © 2007 AWS Truewind, LLC
Summary of Wind Resource Assessment ProcessIdentify Attractive Candidate Sites Collect >1 yr On Site Wind Data Using Tall Towers Adjust Data for Height and for Long-Term Climatic Conditions Use Model to Extrapolate Measurements to All Proposed Wind Turbine Locations Predict Energy Output From Turbines Quantify Uncertainties © 2007 AWS Truewind, LLC
Siting Main Objective: Identify viable wind project sitesMain Attributes: Adequate winds Generally > 7 hub height Access to transmission Permit approval reasonably attainable Sufficient land area for target project size 30 – 50 acres per MW for arrays 8 – 12 MW per mile for single row on ridgeline © 2007 AWS Truewind, LLC
Sources of Wind Resource InfoExisting Data (surface & upper air) usually not where needed use limited to general impressions potentially misleading Modeling/Mapping integrates wind data with terrain, surface roughness & other features New Measurements site specific using towers & other measurement systems © 2007 AWS Truewind, LLC
Alternative Sources of Wind Speed DataVisual Indications and local knowledge Beaufort Scale Wind Speed Estimated by Visual Effects on Land Features Accuracy : 15% Without Height Adjustment National Weather Service (NWS) Measurements of Wind Speeds for Weather Conditions Accuracy : +/- 1 m/s up to 10 m/s % above 10 m/s Environmental Protection Agency Accuracy: 0.25 m/s < 5 m/s 5% > 2 m/s not to exceed 2.5 m/s World Meteorological Organization Accuracy: 0.5 m/s < 5 m/s 10% > 5 m/s Wind Industry expects 1-2% speed measurement accuracy! Alternative sources and techniques have large uncertainty; the wind industry is more demanding of accuracy than traditional users © 2008 AWS Truewind, LLC © 2007 AWS Truewind, LLC 11
Old and new wind maps of the DakotasModern Wind Maps Utilize mesoscale numerical weather models High spatial resolution ( m grid = 3-10 acre squares) Simulate land/sea breezes, low level jets, channeling Give wind speed estimates at multiple heights Extensively validated Std error typically 4-7% GIS compatible Reduce development risks Old and new wind maps of the Dakotas Source: NREL © 2007 AWS Truewind, LLC
Wind Mapping © 2007 AWS Truewind, LLC
Typical Monitoring TowerHeights up to 60 m Tubular pole supported by guy wires Installed in ~ 2 days without foundation using 4-5 people Solar powered; cellular data communications © 2007 AWS Truewind, LLC
How and What To Measure Anemometers, Vanes, Data Loggers, MastsMeasured Parameters wind speed, direction, temperature 1-3 second sampling; 10-min or hourly recording Derived Parameters wind shear, turbulence intensity, air density Multiple measurement heights best to measure at hub height can use shorter masts by using wind shear derived from two other heights to extrapolate speeds to hub height Multiple tower locations, especially in complex terrain Specialty measurements of growing importance Sodar, vertical velocity & turbulence in complex terrain © 2007 AWS Truewind, LLC
Predicting Long-Term Wind Conditions From Short-Term MeasurementsMeasure - Correlate - Predict Technique Measure one year of data on-site using a tall tower Correlate with one or more regional climate reference stations Need high r2 Reference station must have long-term stability Upper-air rawinsonde data may be better than other sources for correlation purposes Predict long-term (7+ yrs) wind characteristics at project site Airport A Regression y = x R 2 = Airport B Regression y = x = 0.875 Airport C Regression y = x = 5 10 15 20 25 Reference Station Mean Wind Speed (m/s) Project Site 60 m Wind Speed (m/s) Airport A Airport B Airport C This plot compares a site’s hourly data with three regional airport stations. A multiple regression resulted in an r2 of 0.92. © 2007 AWS Truewind, LLC
Conceptual Project Estimated Net Capacity Factor ~ 29.0 – 31.5%Software tools (WindFarmer, WindFarm, WindPro) are available to optimize the location and performance of wind turbines, once the wind resource grid within a project area is defined. Estimated Net Capacity Factor ~ 29.0 – 31.5% © 2007 AWS Truewind, LLC
Elements of Energy Production Analysis & ReportingSite/Instrument Description Wind Data Summary Long-term Speed Projection Turbine Power Curve Turbine Number & Layout Gross Energy Production Loss Estimates Uncertainty Analysis Net Annual Energy Production (P50, P75, P90, etc.) © 2007 AWS Truewind, LLC
Influences on Uncertainty(Typical Range of Impact on Lifetime Energy Production) Measured Speed Shear Climate Resource Model Plant Losses (2-4%) Sensor Types, Calibration & Redundancy, Ice-Free, Exposure on Mast, # of Masts (1-3%) Height of Masts, Multiple Data Heights, Sodar, Terrain & Land Cover Variability (4-9%) Measurement Duration, Period of Reference Station, Quality of Correlation (5-10%) Microscale Model Type, Project Size, Terrain Complexity, # of Masts, Grid Res. (1-3%) Turbine Spacing (wakes), Blade Icing & Soiling, Cold Temp Shutdown, High Wind Hysteresis, etc. © 2007 AWS Truewind, LLC
So What’s a P90? For a function with an assumed normal probability distribution, P50 = mean of the distribution P90 = the point where 90% of the results are expected to be above Both are important © 2007 AWS Truewind, LLC
Operational Plant Performance:Understanding operational wind farm performance can be non-trivial Typical Plant Operational Data Project Availability Plant Production Need to separate wind variability from other effects to understand real long term expectations © 2007 AWS Truewind, LLC
Characteristics of an Operational AssessmentInformation from actual operations improves estimate Many sources of uncertainty can be removed (measured speed, shear, resource model, plant losses, actual turbine performance) Monthly farm level numbers help smooth and linearize results (Power vs. wind speed) Climatological adjustment with reference station data to provide long term trends Can focus directly on the bottom line data (Revenue Meter) Typical Wind Farm Power Curve (After correcting for Availability) 100% Avail Plant Output Nacelle Average Wind Speed © 2007 AWS Truewind, LLC
Case Comparison - Reference Wind12 month Rolling Average Wind Speed Nacelle Average Trends are similar Annualized Wind Speed shows low period in 04 and 05; should expect low production Wind Speed (m/s) Ref Station 2 Ref Station 1 © 2007 AWS Truewind, LLC Month
Case Comparison – Operational Performance© 2007 AWS Truewind, LLC
A Few Words on Plant Underperformance - Understanding the IssuePlant underperformance vs preconstruction estimates is a real and significant issue for the industry Many plants averaging ~ 8 to 10% below projections Contributions from numerous sources Can be hard to pinpoint and evaluate Wind variability complicates the analysis Many aspects can be addressed Need to peel back the onion to understand the real issues involved - Studies in process © 2008 AWS Truewind Confidential © 2007 AWS Truewind, LLC
Early Findings – Some Key ContributorsRegional Climate and Variability Resource Assessment Campaign Biases Actual Plant Availability As-Built Plant Characteristics Changes from Plan Sub-Optimal Operation Many contributors – across the project development cycle © 2008 AWS Truewind Confidential © 2007 AWS Truewind, LLC
Summary Wind conditions are site-specific and variable, but predictable over the long term. Accuracy is important. Wind resource assessment programs must be designed to maximize accuracy. Combination of measurement and modeling techniques can give projections close to those experienced in actual operations. Operational plant evaluations can be used to improve projections © 2007 AWS Truewind, LLC
1 TIME OF DAY CORRELATIONS FOR IMPROVED WIND SPEED PREDICTIONS ANDREW OLIVER, PhDKRISTOFER ZARLING VP TECHNOLOGIESWIND DATA ANALYST 7 TH MAY 2009.
© 2007 AWS Truewind, LLC Wind Resource Assessment and Energy Production: From Pre-Construction to Post Construction July 2007 Eric White, Director of Engineering.
[ ] Preliminary Results of Full-Scale Monitoring of Hurricane Wind Speeds and Wind Loads on Residential Buildings Peter L. Datin Graduate Research Assistant.
1 An Overview of Small Wind Energy Today Shawn Shaw The Cadmus Group, Inc. Sierra Club Northeast Committee Fall Energy Conference 10/12-10/14/2007.
WIND ENERGY R&D TOPICS FOR THE COOPERATION BETWEEN EU AND LATIN AMERICAN ENTITIES. Emilien Simonot Technical secretariat Rio de Janeiro,
Wind: Energy measurement and analysis services in FMI
Chuck Kutscher National Renewable Energy Laboratory Geothermal Power Potential Energy and Climate Mini-Workshop November 3, 2008.
Introduction to modelling extremes
INVESTIGATION OF LOCAL STATISTICAL CHARACTERISTICS OF TURBULENT WIND FLOW IN ATMOSPHERE BOUNDARY LAYER WITH OBSTACLES Yuriy Nekrasov, Sergey Turbin.
The three most important considerations for development of wind farms are: LAND with good to excellent wind resource CONTRACT to sell electricity produced.
Chapter 13 – Weather Analysis and Forecasting
Seasonal Normal Weather Base
WIND INSIGHT a wind power forecasting tool for power system security management Dr Nicholas Cutler 21 March 2013
Understanding & Meeting Needs of the Renewable Energy Industry Priorities, Expectations and Roles 2009 AMS Summer Community Meeting August 12, 2009 Norman,
Capacity Planning For Products and Services
POTOMAC WIND ENERGY Presents: Small Wind Turbines for residential and business use. May 2008.
Layout Optimisation Brings Step Change in Wind Farm Yield Dr Andrej Horvat, Intelligent Fluid Solutions Dr Althea de Souza, dezineforce Come and visit.
WP3 - Energy yield estimation of wind farm clusters DANIEL CABEZÓN CFD Wind Engineer CENER (National Renewable Energy Center of Spain) Support by.
Wind Resource Assessment
Preliminary Impacts of Wind Power Integration in the Hydro-Qubec System.
© 2018 SlidePlayer.com Inc. All rights reserved.