Wind Flow Modeling Uncertainty
Theory and Application to
Monitoring Strategies and Project Design
The uncertainty of wind resource and energy production estimates is a critical element in wind project financing. Although wind flow modeling uncertainty is often a large contributor to the total uncertainty, it is rarely quantified rigorously. This can lead to an underestimation or overestimation of the financial risks of the project. In addition, the variation of the wind flow modeling uncertainty across a site is generally unknown and, as a consequence, is ignored in the process of designing a wind project. Consequently, the turbine layout may not be optimal, leading to larger-than-expected errors in energy production forecasts.
This report presents a theoretical framework for understanding wind flow modeling uncertainty and illustrates some applications of this framework in plant design software. The uncertainty model is derived from an analysis of observed wind flow modeling errors for sites spanning a range of topographic and meteorological conditions. Our research shows that, with the appropriate model, it is possible to (a) quantify the variation of wind flow modeling uncertainty across a project site in a physically reasonable and statistically defensible way; (b) design monitoring campaigns to minimize the wind flow modeling uncertainty for a particular buildable area; and (c) optimize a wind project layout to maximize the PXX production (where XX is any confidence threshold such as 75%, 90% or 95%), as appropriate for its particular financing model. Read more »
A Study of Wind Speed Variability Using Global Reanalysis Data
One of the main factors determining the uncertainty in the predicted energy production of a wind project is the variability of the wind resource. This is often represented by the inter‐annual variability (IAV). IAV calculated from meteorological station records may not be representative of that experienced by wind projects. Over the past several years, three second‐generation reanalysis data sets have become available. There is reason to believe that with their greater spatial resolution, as well as improvements in data assimilation methods, these new data sets will perform better for wind energy applications than first generation reanalysis data sets released in the 1990s; the National Center for Atmospheric Research (NCAR)/National Centers for Environmental Prediction (NCEP) Global Reanalysis (NNGR). To test this hypothesis, AWS Truepower set out to (a) assess the suitability of the data sets for estimating (IAV), and (b) select the best data set to create a global map of IAV for use in wind energy studies. Read More
Reducing Uncertainty in Solar Energy Estimates: Mitigating Energy Risk Through On-site Monitoring
With solar photovoltaic (PV) projects, a major area of risk is quantifying the expected annual energy production and uncertainty. One of the most significant drivers is the uncertainty in solar irradiance data.
With more financial stakeholders becoming aware of the risks of using modeled data alone to estimate energy and project cash flows, the collection of on‐site measurements is coming to the forefront as a critical area for project planning and evaluated in project due diligence.
To demonstrate how solar irradiance data affects uncertainty in energy production estimates, a case study was conducted for 11 sites in the U.S. to show how energy estimates using only modeled data compared to those derived from on‐site measured data correlated to a long‐term reference. Read More
The Openwind Deep-Array Wake Model: Development and Validation
This paper describes a deep‐array wake model (DAWM) developed by AWS Truepower and implemented in the openWind plant design and optimization program. The paper discusses the theoretical background of the approach, its specific application in DAWM, and validation of the model at two power plants, one offshore and one onshore, where operational turbine output data are available. Read More
2012 Backcast Study: Verifying AWS Truepower’s Energy and Uncertainty
The 2012 Backcast Study is part of AWS Truepower’s continuous process improvement program, which seeks to ensure that the company’s energy estimation methods are as accurate as possible. This study finds the AWS Truepower’s current methods are broadly in line with operational experience. However, a mean windiness-corrected “underperformance gap’ of 3.6% (+/- 1.4%) is observed. Also, AWS Truepower observes good overall agreement between its current uncertainty estimates and the spread of the 2012 Backcast results. However, the uncertainty in long-term average production is found to be slightly lower than the corresponding variance in actual production. Lessons learned are incorporated into AWS Truepower’s methods. Read More
Wind Resource Maps and Data: Methods and Validation
This report describes the methods and models behind the high resolution wind resource maps and the validation of subsequent data available through AWS Truepower’s Wind Site Assessment Dashboard. Read More
Description of the MesoMap® System
The following paper describes the components of AWS Truepower’s proprietary wind modeling system, MesoMap®. Read More
Wind Flow Model Performance: Do More Sophisticated Models Produce More Accurate Wind Resource Estimates?
This study characterizes mean flow at four sites with different terrain complexity and wind climates (e.g. mesoscale circulations). In this paper five numerical wind flow models are compared: WAsP, Meteodyn WT, WindMap / openWind Enterprise, SiteWind and ARPS. Read More
Correction Factors For NRG #40 Anemometers Potentially Affected by Dry Friction Whip: Characterization, Analysis, and Validation
Over 50,000 NRG #40 anemometers manufactured between May 2006 and December 2008 are potentially affected by a self-excited vibratory phenomenon termed Dry Friction Whip. The following report proposes adjustments to the NRG #40 anemometer data. Read More
SMART Solar Resource Assessments
This paper provides an overview of why solar resource assessments are conducted, what are the best practices for desktop studies, on‐site monitoring programs, and field activities. While the primary focus of the paper is on solar resource analysis, significant influences on energy assessment and conversion calculations are noted. Read More
Wind Flow Modeling Uncertainty: Quantification and Application to Monitoring Strategies and Project Design
The uncertainty of wind resource and energy production estimates is a critical element in wind project financing. Wind flow modeling uncertainty is an especially important contributor to the total uncertainty, though one that is rarely quantified rigorously. In addition, the variation of the wind flow modeling uncertainty across a site is often overlooked and, as a consequence, goes unaccounted for in the process of designing a wind project. The result is that the energy production estimate may not represent a true P50, and the uncertainty may not be minimized, leading to unnecessary risk for the project owner, investor, or lender.
This report presents a conceptual framework for understanding wind flow modeling uncertainty and illustrates some applications of this framework in the openWind® wind project design software. Read More
New U.S. Wind Energy Potential Estimates: Background and Explanation of Changes from Prior Estimates
AWS Truewind and the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) have developed new wind potential estimates for each of the lower 48 states of the United States. The estimates indicate the potential megawatts of rated capacity that could be installed in each state in various ranges of gross capacity factor (without losses), assuming a generic turbine model with a hub height of either 80 m or 100 m. The gross capacity factor (CF) data were developed by AWS Truewind from high-resolution wind resource maps and modeled historical wind speed frequency distributions. The analysis of wind potential, including land area exclusions, was carried out by NREL. This document is aimed to provide transparency to all industry stakeholders. Read More
Evaluating Solar Energy Plants to Support Investment Decisions
As solar projects increase in quantity and scale, capital to fund the projects will be increasingly challenging to acquire. Coupled with tighter capital markets, an accurate resource and energy production analysis plays a critical role in presenting a “bankable” report to financial institutions. The methods applied to conduct a thorough analysis require the input of several factors including: solar resource assessment, energy production projections, and their associated uncertainties. This paper provides guidance on how these factors are evaluated as an independent engineering component of bankable assessments for both developers and equity/debt participants. Read More
An Analytical Correction to Treat NRG #40 Data Affected By Dry Friction Whip: Characterization, Analysis and Validation
Over 50,000 NRG #40 anemometers manufactured between May 2006 and December 2008 are potentially affected by a self-excited vibratory phenomenon termed Dry Friction Whip. The following report suggests adjustments to the NRG #40 anemometer data to reduce errors. Read More
OpenWind Theory and Validation
The purpose of this document is to describe the equations that govern openWind's energy capture and wake loss calculations. In addition, the document presents the results of validation tests carried out by AWS Truepower to confirm that the equations are sound and have been applied correctly. Read More
NRG 40 Transfer Function Recommendation
January 8, 2010 Read More
Closing the Gap on Plant Underperformance: A Review and Calibration of AWS Truepower’s Energy Estimation Methods
July 2009, Revised 2012
AWS Truepower undertook an extensive effort to validate and calibrate its wind energy production estimation methods using data from 11 wind projects with a combined 45 years of operational experience. Read More
Using Simulated Wind Data from a Mesoscale Model in MCP
Since field measurement campaigns for proposed wind projects typically last no more than a few years, the observed meteorological data collected on site often deviate from the long‐term climatology. To reduce the uncertainty associated with climate variability, a statistical relationship is typically established between a monitoring site and one or more reference stations using a technique known as Measure‐Correlate‐Predict (MCP).
Proper use of MCP requires reference stations with long data records collected at the same location and height, with the same equipment, and in relatively unchanging surroundings. Identifying reference stations meeting these stringent criteria is becoming increasingly difficult, especially considering that the National Weather Service (NWS) is replacing cup anemometers with ice‐free ultrasonic anemometers at its Automated Surface Observing System (ASOS) stations.
One approach that offers promise of mitigating these problems is to drive a mesoscale model with a consistent set of observational data. Though the concept is similar to reanalysis, the combination of higher model resolution and care in the selection of stations used in the simulations should improve on those data. Towards this end, we have created windTrends, a modeled dataset covering North America at 20‐km resolution from 1997 to the present, and have conducted a thorough evaluation of its suitability for MCP and other applications. This study summarizes our findings. Read More
Observed Rotor-Plane Wind Profiles Derived From Sodar Measurements: Potential Impact on Turbine Power Performance
The aim of this paper is to illustrate the ways in which the hub height wind power density is not necessarily representative of the integrated rotor-plane power density, and to suggest that sodar can play a role in addressing the variability in power curves and power performance. Read More