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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-5000-72523

May 2019

Distributed Wind Resource 

Assessment Framework: Functional 

Requirements and Metrics for 

Performance and Reliability Modeling

Heidi Tinnesand and Latha Sethuraman

National Renewable Energy Laboratory
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Technical Report

NREL/TP-5000-72523

May 2019

Distributed Wind Resource 

Assessment Framework: Functional 

Requirements and Metrics for 

Performance and Reliability Modeling

Heidi Tinnesand and Latha Sethuraman

National Renewable Energy Laboratory

Suggested Citation 

Tinnesand, Heidi, and Latha Sethuraman. 2019. Distributed Wind Resource Assessment 

Framework: Functional Requirements and Metrics for Performance and Reliability 

Modeling. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-72523.

https://www.nrel.gov/docs/fy19osti/72523.pdf.
NOTICE

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy 

Technologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
iii

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List of Abbreviations and Acronyms

DOE U.S. Department of Energy

DWRA Distributed Wind Resource Assessment

IEC International Electrotechnical Commission

kW kilowatt

NREL National Renewable Energy Laboratory

NYSERDA New York State Energy Research and Development Authority

REAP Rural Energy for America Program

TI turbulence intensity
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Executive Summary

Over the past several years, various efforts have highlighted the variability in project 

performance and accuracy of performance prediction methods for distributed wind projects.

These include:

• The 2016 and 2017 U.S. Department of Energy (DOE) distributed wind market reports, 

which show capacity factors for distributed wind turbine installations ranging from ~1% 

to 49%

• A total of 292 projects under the United States Department of Agriculture’s Rural Energy 

for America Program (REAP), where an analysis of that data showed a 3-year average

actual vs. predicted power production ranging from 62% to 134%, depending on project 

size. Note: Because of the federal funding application process, REAP projects have more 

strict requirements than distributed wind projects in general.

Improving the predictability and reliability of wind power generation will reduce costs by 

focusing investment to more lucrative installations and reducing investments with a low return 

due to poor performance. Developing tools to improve return on investments will reduce risk and 

naturally attract additional capital at lower financing rates into the distributed wind sector, a key 

cost reduction opportunity highlighted in the distributed wind future market assessment 

conducted by the National Renewable Energy Laboratory (NREL) (U.S. Department of Energy

2015).

To improve resource assessment predictions, we first need to evaluate the accuracy of current 

methods and understand the impact of specific input parameters and common assumptions made 

during the modeling process. A comprehensive, robust, and data-driven analysis of these inputs 

and their relative effectiveness at predicting performance will lead to improved models and best 

practices. As a result of the cost-prohibitive nature of commercial resource assessment tools, the 

distributed wind industry has developed independent tools. However, the distributed wind 

industry has historically lacked representative atmospheric and turbine performance data to 

validate and benchmark existing methodologies for predicting project performance and site 

suitability. There is also no industry-standardized methodology to document procedures, 

assumptions, or validation efforts.

Quantifying and refining the accuracy of project performance estimates would directly address 

several of the key challenges identified by industry stakeholders in 2015 as part of the distributed 

wind resource assessment (DWRA) workshop held by DOE/NREL. These efforts are also crosscutting for several other facets of the distributed wind portfolio, including:

• Turbine reliability: better understanding of site turbulence and extreme winds

• Operations and maintenance costs: better understanding of general site characteristics

• Grid integration: better prediction of power output

• Site assessment: simpler, more accurate, and lower cost

• Cost of capital and perception of financial risk: higher accuracy of production estimates.
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By generating a parameter framework and a functional loss and uncertainty approach, we will be 

able to highlight the impacts of various measurement or modeling approaches to operational 

performance. This could drive future R&D efforts that would have a large impact on achieving 

programmatic objectives, mainly improving consumer confidence and lowering the levelized 

cost of energy from distributed wind systems.

The focus of the DWRA performance framework presented in this report is to first clarify the 

key parameters that define the wind resource for any distributed wind turbine project and then 

describe the loss and uncertainty factors associated with long-term performance of wind turbine 

projects. Next it will identify and define the key parameters required to analyze the operational 

performance of a project. This framework could then be the foundation for understanding the 

predictability of the power and revenue from a project.

Wind project performance is defined in terms of the potential energy production as well as the 

uncertainty of the energy production estimate. Key parameters include:

• Wind resource

o Wind speed

o Wind shear

o Turbulence intensity (TI)

o Wind direction

o Extreme wind parameters

o Air density

o Other resource factors (inflow/terrain, roughness, obstacles, and interannual 

variability).

• Wind project characteristics

o Project configuration

o Wind turbine specifications

o Project costs.

• Wind project losses

• Energy production estimate uncertainty.

The first step toward validation of various models and approaches will be to obtain a better 

understanding of key input parameters related to distributed wind applications in resource 

assessment techniques. Quantifying the impact of different resource parameters and loss 

categories on performance will allow DWRA professionals to make choices about the degree to 

which parameters need to be measured and which models should be used to achieve an 

acceptable level of accuracy for the production estimate.

The next step is to characterize the key parameters that should be included in an operational 

performance data set to evaluate pre-construction energy estimates. These include:
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• Turbine power

• Turbine status signals

• Wind resource parameters.

The sensitivity analysis was one step toward developing a clearer understanding of the priority 

and required accuracy for the identified parameters but will require further investigation to 

understand the ultimate impact on project economics. From the analysis it was clear that wind 

speed and direction are essential parameters for any performance estimate, followed by air 

density, veer, and turbulence intensity, particularly for small wind turbines. While density 

doesn’t vary greatly on a particular site, the air density varies significantly across the United 

States, and developing a better understanding impact of density on the power performance of 

commercially available wind turbines will be highly valuable.

Near-term next steps in this research effort will likely include the following:

• Validating existing models to understand their limitations and appropriate uses

• Assessing current rules of thumb 

• Combining the results of multiple research efforts to provide better industry-wide 

guidance on modeling methodologies and appropriate assumptions

• Improving and better documenting methodologies for assessment of interannual 

variability; understanding under which conditions the different methodologies are most 

applicable

• Performing additional sensitivity analysis on resource parameters

• Evaluating costs associated with various resource assessment methods

• Estimating uncertainty and financial impact of the various evaluation methods.

Following the completion of these activities, there should be an effort to support the distributed 

wind industry in assessing the accuracy and precision of existing or new performance assessment 

tools. Select approaches for conducting these assessments are being discussed.
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Table of Contents

1 Overview................................................................................................................................................ 1

1.1 Objectives and Priorities ............................................................................................................... 2

1.2 Scope ............................................................................................................................................. 3

1.3 Summary of Wind Resource Assessment Approaches ................................................................. 3

2 Wind Project Performance Estimation............................................................................................... 7

2.1 Wind Resource .............................................................................................................................. 9

2.1.1 Wind Speed .................................................................................................................... 10

2.1.2 Wind Shear..................................................................................................................... 10

2.1.3 Turbulence Intensity....................................................................................................... 11

2.1.4 Wind Direction............................................................................................................... 11

2.1.5 Extreme Wind Parameters.............................................................................................. 12

2.1.6 Air Density ..................................................................................................................... 12

2.1.7 Ambient Temperature .................................................................................................... 13

2.1.8 Terrain Effects, Roughness, and Obstacles.................................................................... 13

2.1.9 Interannual Variability ................................................................................................... 14

2.2 Wind Project Characteristics....................................................................................................... 14

2.3 Loss Framework.......................................................................................................................... 15

2.4 Uncertainty Framework............................................................................................................... 16

3 Resource Assessment Tools, Models, and Methods...................................................................... 17

3.1 Current DWRA Approaches (Fields, Tinnesand, and Baring-Gould 2016)................................ 17

3.1.1 Model-Based Approach.................................................................................................. 17

3.1.2 Measurement-Based Approach ...................................................................................... 18

4 Wind Project Performance Assessment .......................................................................................... 20

4.1 Assessment Parameters............................................................................................................... 21

4.1.1 Operational Measurements............................................................................................. 21

4.1.2 Operational Loss Parameters.......................................................................................... 23

4.2 Recent Assessments .................................................................................................................... 25

4.2.1 Commercial Wind Projects: Results from the DOE’s A2E Performance, Risk, and 

Uncertainty Framework.................................................................................................. 25

4.2.2 Distributed Wind Projects: REAP and NYSERDA Project Performance Results from 

the Distributed Wind Market Report.............................................................................. 26

5 Sensitivity Analysis............................................................................................................................ 28

5.1 Analytic Hierarchy Process......................................................................................................... 28

5.2 FAST Analysis............................................................................................................................ 30

5.2.1 Approach ........................................................................................................................ 32

5.2.2 T-1 Results..................................................................................................................... 36

5.2.3 T-2 through T-4 Results................................................................................................. 40

6 Benchmark Datasets .......................................................................................................................... 41

7 Conclusions and Recommendations ............................................................................................... 42

References................................................................................................................................................. 44

Appendix.................................................................................................................................................... 46
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List of Figures

Figure 1. Process map for DWRA models.................................................................................................... 5

Figure 2. Project production probability ....................................................................................................... 7

Figure 3. Recommended turbine exclusion zone ........................................................................................ 14

Figure 4. IEC 61400-15 energy loss framework......................................................................................... 15

Figure 5. IEC 61400-15 energy uncertainty framework ............................................................................. 16

Figure 6. Single sensitivity plots from distributed wind future market assessment.................................... 21

Figure 7. Range of typical project losses as a percent of gross production ................................................ 23

Figure 8. Preliminary results from the Performance, Risk, and Uncertainty Framework project............... 25

Figure 9. REAP and NYSERDA results: operational turbine performance vs. pre-construction energy 

estimate .................................................................................................................................. 26

Figure 10. Resource and turbine input parameters presented for consideration ......................................... 29

Figure 11. Contribution from top five resource assessment factors............................................................ 30

Figure 12. Power performance curves for turbines T-1 through T-4.......................................................... 32

Figure 13. Sample histogram of mean elementary effects of all parameters on mean electric power........ 36

Figure 14. T-1 sensitivity to stability.......................................................................................................... 37

Figure 15. Sensitivity rankings of mean electric power to different wind parameters (a) Bin 1 (b) Bin 2 (c) 

Bin 3....................................................................................................................................... 38

Figure 16. Sensitivity rankings for standard deviation in electric power to different wind parameters (a) 

Bin 1 (b) Bin 2 (c) Bin 3 ........................................................................................................ 39

Figure 17. T-2 sensitivity to stability.......................................................................................................... 46

Figure 18. Sensitivity rankings for mean electric power to different wind parameters (1) Bin 1 (b) Bin 2 

(c) Bin 3 ................................................................................................................................. 48

Figure 19. Sensitivity rankings for standard deviation in electric power to different wind parameters (a) 

Bin 1 (b) Bin 2 (c) Bin 3 ........................................................................................................ 49

Figure 20. T-3 sensitivity to stability.......................................................................................................... 50

Figure 21. Sensitivity rankings for mean electric power to different wind parameters (a) Bin 1 (b) Bin 2 

(c) Bin 3 ................................................................................................................................. 52

Figure 22. Sensitivity rankings for standard deviation in electric power to different wind parameters (a) 

Bin 1 (b) Bin 2 (c) Bin 3 ........................................................................................................ 53

Figure 23. T-4 sensitivity to stability.......................................................................................................... 54

Figure 24. Sensitivity rankings for mean electric power to different wind parameters (a) Bin 1 (b) Bin 2 

(c) Bin 3 ................................................................................................................................. 56

Figure 25. Sensitivity rankings for standard deviation of electric power to different wind parameters (a) 

Bin 1 (b) Bin 2 (c) Bin 3 ........................................................................................................ 56
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List of Tables

Table 1. Impact of Key Parameters on Energy Estimate Based on Approach.............................................. 6

Table 2. Project Volume for Each Turbine Class ....................................................................................... 26

Table 3. Turbine Input Parameters.............................................................................................................. 29

Table 4. Resource Factors that Impact Turbine Location Wind Speed ...................................................... 29

Table 5. Resultant Contribution.................................................................................................................. 30

Table 6. Basic Specifications for the Four Turbines Analyzed .................................................................. 31

Table 7. Paramater Definitions for T-1 for Sensitivity Analysis................................................................ 35

Table 8. Sensitivity Ranking for T-1 Based on the Total Number of Appearances ................................... 36

Table 9. Sensitivity Rankings for Turbine T-2 ........................................................................................... 46

Table 10. Parameter Definitions for Simulating T-2 .................................................................................. 47

Table 11. Sensitivity Rankings for Turbine T-3 ......................................................................................... 50

Table 12. Parameter Definitions for Simulating T-3 .................................................................................. 51

Table 13. Sensitivity Rankings for Turbine T-4 ......................................................................................... 54

Table 14. Parameter Definitions for Simulating T-4 .................................................................................. 55
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1 Overview

The focus of the distributed wind resource assessment (DWRA) performance framework is to 

first clarify the key parameters that define the wind resource for any distributed wind turbine 

project and then describe the loss and uncertainty factors associated with long-term performance

of wind turbine projects. This framework could then be the foundation for understanding the 

predictability of the power and revenue from a project. 

Over the past several years, multiple efforts have highlighted the variability in project 

performance and accuracy of performance prediction methods for distributed wind projects. The 

2016 and 2017 U.S. Department of Energy distributed wind market reports illustrate the wide 

range of capacity factors for an assortment of distributed wind turbines. 

Recent investigation into state and national incentive program data for 292 projects has 

highlighted the significant performance variability for turbines across all sizes of distributed 

project installations. Most of the projects were funded through the Rural Energy for America 

Program (REAP) and New York State Energy Research and Development Authority

(NYSERDA) programs in which applicants were required to work with a trained installer and 

use technology with proven performance, though not necessarily certified turbines (U.S. 

Department of Energy 2015). In general, the same cannot be said for most distributed projects 

nationwide. Details of the data are included in Section 4.2.

In addition to numerous other examples, including several case studies in the recent site 

assessment report (Olsen and Preus 2015), these data provide a compelling justification for 

investigating and validating resource assessment and performance prediction methodologies. 

These activities would also be in line with the findings of the 2015 wind resource assessment 

workshop and survey, which were summarized in a state of the industry report published by 

NREL in 2016 (Fields, Tinnesand, and Baring-Gould 2016). Through that work, industry 

stakeholders identified nine challenges that, if solved, would provide a high return on investment 

for the distributed wind industry. These challenges include:

• Limited access to public data

• Minimal data, methodologies, and guidelines available for resource and site assessment 

validation and benchmarking

• Lack of education and outreach opportunities for the DWRA industry

• Need for better ways to access and incorporate site data for distributed wind projects

• Complexity and cost of Measure-Correlate-Predict approaches result in a lack of multiyear resource information used in project assessments

• Lack of robust methods for scaling wind data to typically lower hub heights for 

distributed wind projects

• Absence of standardization in DWRA methods

• Minimal focus on turbine site suitability

• Instrumentation, measurement systems, and data processing are too costly for many 

distributed wind projects.
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Quantifying and refining the accuracy of project performance estimates would directly address 

several of these challenges and be cross-cutting for several other facets of the distributed wind 

portfolio.

Improving the predictability and reliability of wind power generation and operations will reduce 

costs and potentially establish a framework to attract new capital to the distributed wind industry 

sector. This report is the first part of a research effort to investigate the impact that improved 

resource assessment and energy estimation for distributed wind can have on:

• Consumer confidence

• Cost of capital 

• Cost of ownership and operations

• Perception of financial risk

• Levelized cost of energy.

In general, resources published expressly for the distributed wind industry are limited. Many of 

the ideas used today have been modified from the utility wind industry and adjusted to smaller 

turbines based on the experience of industry experts. This paper seeks to align common 

challenges between DWRA and utility-scale resource assessment in hopes that the knowledge 

transfer can be seamless, efficient, and tailored to distributed wind industry needs. While the 

wisdom of experienced wind practitioners is valuable, the lack of distributed wind industryspecific resources can hinder consistency, training, and long-term industry expansion. These 

challenges demonstrate the need to pursue more rigorous DWRA processes. Improved methods 

could result in having the ability to quantify the impacts of various parameters on the resultant 

energy production and turbine reliability.

As with any distributed wind analysis, the following discussion is complicated by the breadth of 

the industry, from small single-kilowatt turbines to multiple utility-scale turbines installed behind 

the meter. To address this diversity, this work will employ the same distributed wind size classes 

described in the Distributed Wind Market Demand Model (NREL) and wherever possible link the 

approach or discussion to the relevant turbine size. The four turbine classes from that work are:

• Residential: up to 20 kilowatts (kW)

• Commercial: greater than 20 kW to 100 kW

• Mid-size: greater than 100 kW to just under 1 megawatt

• Large: 1 megawatt and greater (in a distributed application).

Utility-scale will be used to refer to all non-distributed wind turbine projects.

1.1 Objectives and Priorities

The DWRA performance framework will benefit industry stakeholders in the following ways:

• Establish a common framework for identification and quantification of key resource 

assessment parameters for predicting project performance
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• Identify key parameters that should be included in a benchmark dataset or model 

validation processes to evaluate the accuracy of any resource assessment toolset

• Illustrate the connection among resource assessment, operational performance, and 

financial frameworks.

1.2 Scope

At present there is no systematic method used by the industry to validate the results of resource

assessments except when financial incentive fund managers require production information. In 

some cases, post-installation assessments are completed if there is a large discrepancy between 

power generation by the turbine and predicted values, but this is performed on a case-by-case 

basis with limited direct efforts to insure the accuracy of future assessments (Fields, Tinnesand, 

and Baring-Gould 2016).

There is also no industry-standardized methodology to document procedures, assumptions, or 

validation efforts. Generally, there is a strong correlation between the level of effort in 

conducting the resource assessments and the size of the project, with smaller (residential and 

commercial) projects requiring low-cost, more automated, or user-derived processes for at least 

initial project screening while larger projects (mid-size and large) can afford and require 

expanded efforts that would include on-site measurements (Fields, Tinnesand, and Baring-Gould

2016).

The DWRA performance framework is designed to facilitate the evaluation of wind resource 

assessment methodologies and wind project performance models that generate energy production 

estimates. As a next step, this framework could be used to link energy production estimates with 

financial models. For example, the solar photovoltaic industry has experienced massive growth

in recent years, in part due to business and financial model innovations including shared solar 

systems (solar gardens or community solar), third-party ownership (leasing), solar-secured loans, 

securitization, and other financing mechanisms. There is no reason these models can't also be

applied to the distributed wind market. However, a key component to the adoption of these 

models is certainty in the cash flows from solar projects; distributed wind projects would likely 

need to realize similar predictability (Lowder et al. 2015), which is not currently the case.

By generating a parameter framework and a functional loss and uncertainty approach, we will be 

able to highlight the impacts of various measurement or modeling approaches to operational 

performance. This could drive future R&D efforts that would have the largest impact for 

achieving programmatic objectives, mainly improving consumer confidence and lowering the 

levelized cost of energy from distributed wind systems. The DWRA performance framework 

proposed herein combined with further investigation into the impact of various factors on wind 

project performance will offer a foundation for quantifying performance and uncertainty and the 

resultant impact on project performance.

1.3 Summary of Wind Resource Assessment Approaches

Figure 1 summarizes the various wind resource assessment approaches to illustrate the 

progression of complexity when moving from one approach to the next and to identify steps for 

which there are common data references, tools, and processes. Looking at the range of 

approaches in this way, it is possible to see opportunities to adapt various steps to improve the 
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accuracy of the simple or complex model-based approaches, which in turn improves the accuracy 

for residential, commercial, and mid-size distributed wind turbine project assessments.

While there are clearly defined turbine sizes for residential, commercial, mid-size, and large 

distributed wind projects, the same cannot be said for wind resource assessment approaches. 

Smaller projects tend to use simpler approaches; however, numerous other factors drive this 

choice. Because of this, future analyses will focus on quantifying the accuracy and impact of 

each element of the approach such that the end user can make choices based on the precision 

desired.

Figure 1 is a high-level snapshot of industry approaches for DWRA. This reflects current 

understanding and will continue to be refined with industry feedback.
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Figure 1. Process map for DWRA models
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The number of parameters that could or should be considered for any wind project depends on a 

variety of factors but is primarily related to project or turbine size and requirements from various 

funding agencies or financial institutions. Of all the parameters described below, the most critical 

is wind speed as it has the largest impact on wind turbine power production. After wind speed, 

the priority of parameters becomes much more nuanced. Table 1 suggests a framework for 

identifying high-, medium-, and low-priority parameters to consider for various project sizes or 

approaches. The sensitivity analysis described in Section 5 is one step toward developing a 

robust decision-making framework for analyzing distributed wind project performance. Further 

investigation will be required to understand the precise impact of these parameters.

Table 1. Impact of Key Parameters on Energy Estimate Based on Approach

Simple-Model

(Residential, 

Commercial)

Complex-Model

(Commercial, MidSize)

Instrument-Based

(Mid-Size, Large)

Resource:

Wind speed High High High

Wind shear High High Medium

Turbulence Medium Medium High

Wind direction High High High

Air density Low Low High

Terrain effects Medium Medium High

Roughness Medium Medium High

Obstacles High High High

Interannual variability Medium Medium High

Losses:

Availability High High High

Wake effects High High High

Electrical Medium Medium High

Turbine performance High High High

Curtailments and operational 

strategy

Low Low High

Environmental High High High

Uncertainty:

Plant performance Project specific Project specific High

Vertical extrapolation Project specific Project specific High

Spatial variation Project specific Project specific High

Site measurement Project specific Project specific High

Historic wind resource Project specific Project specific High

Project lifetime variability Project specific Project specific High

Other Project specific Project specific Project specific
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2 Wind Project Performance Estimation

Wind project performance is defined in terms of the potential energy production as well as the 

uncertainty of the energy production estimate. In other words, the wind project performance 

model represents the range of potential outcomes for energy production values, including the 

best-case and worst-case scenarios. Figure 2 illustrates the framework used by utility-scale 

developers to describe the potential range of outcomes for project stakeholders. The gross energy 

estimate is calculated from all the parameters detailed in Sections 2.1 and 2.2. Once this gross 

estimate has been calculated, the potential project losses are estimated using the International 

Electrotechnical Commission (IEC) 61400-15 loss framework described in Section 2.3. Finally, 

the range of outcomes or uncertainty for the net energy estimate is estimated using the IEC

61400-15 uncertainty framework described in Section 2.4. Once the result and likelihood of 

various outcomes are understood, an assessment of the risk for project owners or third-party 

investors can be initiated.

Figure 2. Project production probability
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Therefore, to assess the range of outcomes, the following items must be accounted for: 

• Wind resource (Section 2.1)

o Wind speed

o Wind shear

o TI

o Wind direction

o Extreme wind parameters

o Air density

o Other resource factors.

• Wind project characteristics (Section 2.2)

o Project configuration

o Wind turbine specifications

o Project costs.

• Wind project losses (Section 2.3)

• Energy production estimate uncertainty (Section 2.4).

The approach above allows representation of the wind project with relatively simple parameters 

and assumptions, mapping to all aspects of the wind project technology as well as the preconstruction and operational activities. For example, we can model the use of improved 

instrumentation or resource models by decreasing the uncertainty of onsite measurement data

and models, spatial variation, etc. Alternately we can model the stakeholder impacts of improved 

models through improved loss assumptions as well as the uncertainty about estimates and losses.

Although this approach was developed for the utility-scale wind industry, a simplified model 

could be established for the distributed wind industry as described in the following sections. 

The temporal duration and resolution of energy production should match the required financial 

analysis needs. Financial institutions for distributed wind projects seldom impose requirements, 

but it is expected that as the industry evolves and new financing structures become available for 

projects, this will be increasingly important. In this scenario, wind projects will need to satisfy a 

variety of stakeholders, including the project owner, incentive program managers, financiers, and 

power offtakers. Each of these entities has a unique interest in projects and unique time horizons 

for investment, ranging from long-term averages to hourly. At this time, sub-hourly resolutions 

do not appear to be driven by stakeholder needs but may be included as sub-hourly aggregation 

can impact total wind project performance model accuracy. Therefore, the wind project 

performance model should be able to represent the average, median, and standard deviation wind 

project production on the following time scales:

• Multi-year: represents project lifetime production estimates

• One year: allows correlation to long-term data sets to adjust for intra-annual variability

• Monthly: illustrates season variability
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• Daily: highlights seasonal variation and large-scale, weather-driven events

• Hourly: exposes diurnal variability

• Minutes: facilitates investigation of turbulence intensity at a particular location

• Seconds: enables assessment of wind turbine aerodynamic performance in relation to 

referenced power output assessments.

Many of these time scales are challenging to predict for a variety of reasons. It is therefore 

recommended that simple annualized methods should be considered as core requirements, but 

more detailed time series methods are a recommended evolution of the total modeling capability.

Most distributed wind projects use multi-year time scales exclusively. However, if financing 

requirements change or there is increased focus on turbine reliability and reduction of operations 

and maintenance costs, analysts may need to consider additional time scales.

The first step in understanding the financial impact of uncertainty in a measurement campaign—

either in terms of project financing or payback in general—is to first understand the critical 

factors that affect turbine performance. This includes the wind resource and associated losses, 

which are introduced in the following sections.

2.1 Wind Resource

Understanding the wind resource at a proposed project location can be achieved in several ways, 

but the primary method is to directly measure the resource at the precise location(s) or to 

measure the resource at a nearby location and extrapolate that resource to the proposed project 

height and location(s). If the proposed project is a single turbine and the wind resource is 

measured at the proposed turbine location, then the basic measured parameters are sufficient to 

characterize the wind resource at that site, in the measured timescale. However, the measured 

data must be corrected to account for the long-term behavior of the wind resource at the site. If 

there are multiple turbines on a project, the wind resource is measured at a distance from the 

proposed turbine location, or the measurement duration is a year or less, the resource should be 

adjusted using the second set of parameters. For most parameters, there is a range of approaches, 

which loosely correspond to the three approaches summarized in Section 1.3. Each section 

begins with a description of the utility-scale approach, followed by the most common distributed 

wind approach, and ends with a summary of the range of approaches.

Parameters that can be directly measured or calculated from measurements include the following 

basic parameters:

• Wind speed

• Wind shear

• TI

• Wind direction

• Air density.

The following parameters are important to consider for adjusting measured or modeled wind 

resource in the temporal and spatial domains:
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• Inflow angle/terrain effects

• Roughness 

• Obstacles

• Interannual variability.

2.1.1 Wind Speed

Understanding the wind speed at a prospective turbine site is the most important factor to 

consider when deciding whether to install a wind turbine because the power produced changes 

with the cube of the wind speed. 

The wind speed can be characterized in several ways, and the method chosen will impact the 

precision of the energy estimate. The most precise method for determining the site wind speed is 

to take measurements on site for a year or more and then correct the data using a long-term data 

source (see Section 2.1.9 for more information). Alternatively, the wind can be characterized 

using an annual frequency distribution—typically described by the Weibull distribution with a 

scale parameter “A” and a shape parameter “k.” The simple annual average wind speed for a 

typical meteorological year is the most basic method of characterizing the wind at a site. This is 

typically a value taken from a wind resource map and can be useful for larger projects when 

performing the initial site assessment to locate a good site within a region. 

If on-site instruments are out of the question, it is best to use a site-specific wind distribution 

rather than the simple annual average because two sites with the same average wind speed but 

disparate distributions will have very different energy estimates. The discrepancy between the 

two values depends on the magnitude of the difference between the two distributions.

The two primary DWRA methods of characterizing the wind are to look at the simple average 

annual wind speed, typically used when estimating potential energy production at a residential 

site, or a frequency distribution, which is typically used on commercial or mid-size turbine 

projects. The simple annual average can only provide a general indication of the energy that 

could be produced and, as such, should be used with caution. To more fully understand the 

potential energy generation, it is important to understand the site-specific distribution of the wind 

speeds. This can be obtained through direct measurements of the site or through a variety of 

model-based approaches.

simple average  frequency distribution  time series data.

2.1.2 Wind Shear

Wind shear is the change in wind speed with height. Typically, the wind speed continues to 

increase as the distance increases above the ground, although this is not always the case. Wind 

shear is used in wind resource assessments when the wind speed is measured or estimated using 

a model at a height other than the hub height of the proposed turbine. In either of these cases, the 

wind shear profile is used to convert the wind speed to the height of interest. There are numerous 

choices to make regarding how to apply shear to the height of interest, as the value can vary by 

season, time of day, etc. The wind shear can be used to select the appropriate hub height for a 

given location to minimize the levelized cost of energy. 
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Shear is particularly important and challenging in the DWRA space as most distributed wind

turbine deployments are sited in the lowest portions of the atmospheric surface layer. 

Additionally, high wind shear can introduce unbalanced loads on the rotor plane and thereby 

impact turbine reliability.

Typically, for distributed wind sites, the wind shear exponent, or in some cases a simple 

multiplication factor, is selected from reference tables that provide values based on the 

surrounding surface characteristics (i.e., forest or plains) and is often a single average shear 

value. A more complex approach uses an average shear value for each direction sector. There are 

a variety of sources for these tables with varying levels of validation. Some are derived from 

logarithmic wind profiles; others like the Wisconsin’s Focus on Energy work are based on 

extensive empirical data (Wegley Orgill, and Drake 1980 and Olsen and Preus 2015). 

Displacement height is another factor used exclusively in DWRA to adjust the effective distance 

from the ground to hub height when a turbine is sited near a forest. This parameter is a rule of 

thumb based on the forest density and is generally approximated at 67% to 75% of canopy height 

for deciduous to dense evergreen forests, respectively (Chiras 2010). This new height above the 

canopy is then used to apply the shear exponent to reach the turbine hub height.

single average based on site vegetation  average shear by wind direction sector based on site 

vegetation  measured shear at project location.

2.1.3 Turbulence Intensity

TI is the ratio of the wind speed standard deviation to the mean wind speed, measured over a 

period of 10 minutes. In general, the TI decreases with increasing wind speed. The TI at 15 m/s

(IEC 2006) is typically used to determine the suitability of a turbine at a proposed wind site. TI 

impacts the energy produced by a wind turbine as well as the longevity of the turbine.

The impacts of TI on a small turbine’s energy production are not particularly well documented, 

but there is evidence to support improved turbine performance in certain atmospheric turbulence 

conditions. Additionally, TI is a primary driver of fatigue loads for wind turbines. As such, it is 

critical to understand the relationship between turbine designs and site-specific TI; however, this 

relationship currently is not well quantified for most small wind turbines.

Generally speaking, the turbulence intensity at a prospective distributed wind project is estimated 

using the surrounding surface roughness and then adjusted up or down based on various tables or 

rules of thumb (Wegley, Orgill, and Drake 1980 and Olsen and Preus 2015) for nearby obstacles.

TI can reduce annual energy output anywhere from 15% to 25% because the small wind turbine 

power curves are typically measured at sites with lower TI than is seen at installed locations. TI 

is a major issue for small turbines because of their low height relative to surrounding obstacles 

and surface effects(Olsen and Preus 2015).

no TI  site average TI  TI for each operational wind speed.

2.1.4 Wind Direction

Understanding where the wind comes from throughout the year is important when siting a wind 

turbine; this knowledge allows the developer to avoid siting the turbine near obstacles or 
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obstructions that would reduce the wind speed coming from that direction or would induce wake 

or other losses. Understanding wind direction is also important because it is a major component 

in the calculation of the potential energy production on the site. For example, the wind may come 

from the north for most of the year, but the highest wind speeds come from the south for part of 

the year; this may mean that the most energy incident on the site is from the south, and the 

turbine should be sited accordingly.

A wind rose provides a more complete picture of the wind direction on site by graphically 

presenting the wind direction on a compass, with 12 to 16 sectors. The wind rose can represent 

pure wind direction frequency or combine the wind speed and wind direction parameters to 

construct a more complete picture of the wind resource. The latter two can be represented either 

as a percentage of time the various wind speeds blow in each direction sector or the percentage 

of the total energy that is generated from each direction sector. This is critical for properly 

evaluating a site.

If wind direction information is available for a distributed wind project, it is typically a single 

predominant direction sector rather than a wind rose, as described above. Not having access to 

all parameters can result in errors and uncertainty when planning distributed wind projects, 

especially for multi-turbine projects.

single dominant direction (simple average)  wind rose  time series data.

2.1.5 Extreme Wind Parameters

Within the IEC 61400-2 standard, which covers the engineering and safety of small turbines, 

extreme wind parameters are identified. These include extreme wind speed, extreme operating 

gust, extreme direction change, extreme coherent gust, and extreme coherent gust with direction 

change. These parameters are derived from onsite measurements or models that have the 

required data fidelity and are primarily important to consider in the context of turbine reliability. 

Certification loads modeling assumes specific maximum values for these parameters.

The 50-year extreme wind speed is generally considered to be the “survival” wind speed design 

limit for any wind turbine class. It is based on the reference wind speed, which is the extreme 10-

minute average wind speed expected with a 50-year recurrence. 

This parameter is seldom included in current DWRA and site assessment processes.

There is no progression of data complexity here. These parameters are only available with highresolution time series wind speed data.

2.1.6 Air Density

Air density impacts the power production of a turbine; for utility-scale turbines, several power 

curves are available, based on the site’s average air density. The air density is calculated using 

measured temperature and pressure. Air density can also have significant implications for 

reliability. A turbine may be able to withstand higher winds if the air density is decreased.

Density-specific power curves aren’t typically available for non-utility-scale wind turbines, 

which can contribute to increased uncertainty in the energy estimate.
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no air density  average site air density from nearby station  time series air density.

2.1.7 Ambient Temperature

In addition to being used in the calculated air density, temperature is one of the key 

measurements for understanding the amount of icing, or ice days, that a site will experience, 

which is a valuable input in the loss framework. However, other data are required to calculate an 

accurate estimate of icing days. This can be accomplished with humidity data or the ability to 

look at heated and unheated anemometer data.

Temperature is seldom included in current DWRA processes.

no temperature  average site temperature from nearby station  time series temperature.

2.1.8 Terrain Effects, Roughness, and Obstacles

Inflow angle, roughness, and obstacles near a proposed project location all have an impact on the 

wind flow, relative to the measured flow characteristics, if the wind flow isn’t measured at the 

precise project location. Many of these effects are modeled for utility-scale projects using 

commercially available software. 

Terrain effects include the contribution of the change in elevation in the near vicinity and the 

inflow immediately preceding the turbine in all directions. If the terrain is flat between the 

measured wind resource and the proposed turbine location and no other factors affect the wind 

flow (i.e., changes in roughness or obstacles), the measured wind resource can be extrapolated to 

the proposed turbine location. The accuracy of this approach decreases with increasing distance.

Surface roughness affects the wind speed near that surface. As the height above the surface 

increases, the wind speed increases. The rate at which the wind speed increases with height is a 

function of the roughness. A rough surface, such as a dense forest, will slow the wind just above 

the trees much more than wind flow just above a smooth surface, such as open water.

Obstacles impact the airflow by partially blocking or otherwise disturbing the flow. This 

disturbed flow is often referred to as wake. Waked flow reduces wind speed, affects wind 

direction, and increases turbulence.

In general, the effects from terrain, roughness, and obstacles are estimated in DWRA protocols, 

in part due to the prohibitive expense of the software packages described above and the lack of 

time series data to use as inputs. How these effects are estimated depends on a site assessor’s 

experience with a particular area to make a judgment as to whether the area will be augmented or 

diminished from the modeled wind speed.

Estimates of the effect of surface roughness can be extracted from various tables, such as Tables 

1 and 2 in the Siting Handbook for Small Wind (Wegley, Orgill, and Drake 1980).

For an obstacle that has a height of “H,” the zone of disturbance is generally estimated to be 2H 

upwind of the obstacle, 20H downwind, and 2H tall, as shown in Figure 3. This rule of thumb for 

residential-scale turbines dates to studies from 1964 and 1979 (Van Eimern et al. 1964 and Frost 

and Nowak 1979).
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Figure 3. Recommended turbine exclusion zone

2.1.9 Interannual Variability

The wind resource varies from year to year, and depending on the timescale of the wind resource 

data, the measured data need to be corrected to a long-term reference data source to properly 

account for the variability of the resource over the project life. This variability can have 

significant implications to project viability and should be accounted for. A 2013 study by AWS 

Truepower indicated that interannual variability varies by location with values ranging from 3% 

to 10% across the globe, looking at three reanalysis datasets (Brower et al. 2013). It is therefore 

recommended to use interannual variability for the project area, rather than a standard rule of 

thumb. There are a variety of methods to place measured and modeled data into long-term 

historical context. 

For the distributed wind industry, the discussion of interannual variability is complex. The wind 

maps or model-based approaches are based on wind speed averages for multiple years, so it is a 

long-term average of sorts; but for annual payback it’s challenging to determine what the 

variability might look like year to year, which can have an impact on meeting production 

guarantees or various loan or lease payment scenarios. The nature of averaging also obscures 

visibility into the magnitude and frequency of extreme wind events, which can impact turbine 

reliability.

2.2 Wind Project Characteristics

Wind project performance models require basic wind project characteristics. These 

characteristics include:

• Project configuration (if there are multiple turbines, as in larger community-scale 

projects, or several large turbines installed behind the meter)

o Rated capacity

o Number of turbines

o Geometric array.

• Wind turbine specifications

o Rated capacity

o Power curve

o Hub height
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o Rotor diameter

o Control parameters (e.g., high temperature shutdown).

• Project costs to estimate the levelized cost of energy if desired

o Installed turbine cost

o Balance of plant costs

o Operations and maintenance costs.

The project configuration is a function of the project design; the wind turbine characteristics and 

project costs can be obtained from the turbine manufacturer or previous experience with 

purchase agreements and historical operations and maintenance costs.

Operations and maintenance costs are likely the largest unknown in the distributed wind 

industry, due to a lack of individual owners’ previous experience to rely on.

2.3 Loss Framework

Wind project losses describe the physical drivers of the difference between gross energy 

production and net energy production. These can include effects such as electrical losses, wake 

losses, icing or blade soiling, and more. The wind project performance model should include a 

broad loss framework, which can account for a variety of technology improvements such as icing 

reduction approaches or improved models or instrumentation. In the absence of a distributed 

wind-specific loss framework, it is recommended to use and implement the IEC 61400-15 

consensus loss framework, as shown in Figure 4.

Figure 4. IEC 61400-15 energy loss framework

While most of the items in the loss framework will likely apply to any distributed wind project, a 

few may have a larger impact on the project performance than others for the distributed wind

sector. The largest impact will likely result from: 
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• Turbine availability

• External wake effects

• Turbine performance (all categories)

• Environmental (all categories).

2.4 Uncertainty Framework

Wind project uncertainty quantification methods are used to describe the likelihood of potential 

outcomes for potential investors and other project stakeholders. The distribution of energy 

production outcomes is of interest as it can have dramatic impacts on cash flows, investment 

security, and profitability. The wind project uncertainty model should follow the current industry 

consensus that is the IEC 61400-15 uncertainty framework, as shown below in Figure 5. The 

model should also be able to combine uncertainties and perform Monte Carlo simulations for 

populating uncertainty distributions. However, this level of effort is beyond the scope of typical 

distributed wind industry projects.

Figure 5. IEC 61400-15 energy uncertainty framework

Similar to the loss framework, all variables in the IEC 61400-15 Energy Uncertainty Framework 

are applicable to any distributed wind project to some degree, though the contribution from 

various factors will vary greatly depending on the models and other tools used, the source of 

wind input data, turbine type, how well the turbine was sited, and other factors.
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3 Resource Assessment Tools, Models, and Methods

There are numerous commercial and open access wind plant production models available, such 

as OpenWind, WindFarmer, and WindPro. Most of these tools focus on wind project design and 

offer simple energy production, energy loss, and uncertainty models as part of the software 

package. While many of these commercial models have the capability to input and address the 

parameters described in Section 2, they often come with expensive licenses and little opportunity 

for customization, integration with other systems (e.g., systems engineering), or automation. 

As a result of the cost-prohibitive nature of these commercial resource assessment tools, the 

distributed wind industry has developed independent tools. However, the industry lacks 

representative atmospheric and turbine performance data to validate and benchmark existing 

methodologies for predicting project performance and site suitability. New standard test cases 

and guidelines could include discussion of which tools, processes, and methodologies are 

appropriate for specific site conditions and the limitations of various approaches.

3.1 Current DWRA Approaches (Fields, Tinnesand, and Baring-Gould 

2016)

The DWRA workshop and survey in 2015 facilitated by DOE/NREL demonstrated that there are 

many approaches for conducting a wind resource assessment at a potential distributed wind site. 

These approaches vary by levels of complexity, cost, and company engagement; however, there 

are two basic categories of wind resource assessment that are commonly used in the distributed 

wind space: models and onsite measurements. The most common tools include wind maps, 

desktop spreadsheets, and desktop linear models. The following is a summary of the two 

principal approaches discussed during the DWRA workshop.

3.1.1 Model-Based Approach

A model-based approach uses pre-existing datasets such as wind maps or re-analysis1 data as an 

input for an energy assessment. This input is then modified for a specific turbine location using 

scaling2 models, expert judgment, or rules of thumb to determine the impacts of site-specific 

conditions such as terrain, roughness, and obstacles. This approach does not include onsite 

measurements but can include a site visit for site assessment refinement purposes. There is a 

large degree of variability in this approach, from the simple use of publicly available annual 

average wind speed estimates to detailed, site-specific physical modeling. This approach could 

also include the assessment of measured data from a nearby location to better understand local 

conditions.

 

1 Re-analysis refers to a family of data products that describe the long-term state of the global atmosphere. These 

data products are often used to initialize site-specific wind resource models. Examples are MERRA, ERA-Interim, 

CFSR, and National Center for Environmental Protection/National Center for Atmospheric Research Reanalysis 

Project; see https://reanalyses.org/ for more information. 2 Scaling refers to the process in which measured or modeled data are adjusted using defined parameters to a 

different height above the ground. In simple terms, it is a process that uses the power law to adjust the reference 

height of data, typically downward to a lower altitude above ground for distributed wind applications; but in almost 

all cases, it is not a simple process. It should be noted that downscaling is typically used in the creation of all 

modeled data because numerical weather prediction models calculate wind speeds at very high altitudes. 

Downscaling methods and models are typically business sensitive and therefore difficult to compare.
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The model-based approach is predicated on numerical weather prediction models for the major 

data input. Numerical weather prediction can be defined as reanalysis data (Modern Era 

Retrospective-Analysis for Research and Applications, ERA-Interim, and Climate Forecast 

System Reanalysis) or a finer-scale data product generated using the Weather Research & 

Forecasting Model. The data from the numerical weather prediction datasets are then used to 

create a static wind map or made available, typically for a fee, on a web-based portal. The

outputs from these products are meteorological statistics such as annual average wind speed or 

wind speed and direction frequency distributions, temperature, and air density, respectively. 

These meteorological statistics are then corrected or scaled to the turbine height and location

using a process called down-scaling that considers terrain, surface roughness, obstacles, and 

other local conditions. This down-scaling can be in the form of empirical rules of thumb or sitespecific models such as Wind Atlas Analysis and Application Program or computational fluid 

dynamics. Finally, an energy prediction is formed using a wind turbine power curve or an annual 

energy production look-up table. 

3.1.2 Measurement-Based Approach

On-site measurements can also be combined with other model or analytical tools (aspects of the 

first approach) to perform higher-resolution assessments, reducing the overall uncertainty of the 

energy and site condition estimate. The on-site measurements can include meteorological towers 

or remote sensing devices. In general, larger projects (typically mid-size or large turbines) tend 

to require on-site measurements to ensure that energy estimates are more accurate and likely to 

meet the projected energy output and therefore the economics of the selected turbine.

The measurement-based approach is recognized across the industry as being more accurate for 

predicting energy production as well as site suitability. Instruments are, however, more 

expensive and more time consuming than traditional model-based approaches, which has led to 

limited uptake in the distributed wind sector. 

The measurement-based approach generates its own site-specific wind statistics and largely 

eliminates the need for site-specific corrections accounting for terrain, surface roughness, and 

obstacles. There is typically an additional step known as Measure-Correlate-Predict to adjust the 

relatively short-term measurements to a long-term representation of the wind statistics based on a 

modeled or other nearby, long-term reference wind measurement station. This long-term 

representation is then combined with the power curve to generate gross energy estimates, and 

these estimates are subsequently run through a loss and uncertainty estimation process to arrive 

at the net annual energy production and expected variability.

One of the least agreed-upon aspects of on-site measurements is when they should be used to 

measure the wind resource of a potential project. Opinions vary on the minimum size at which 

on-site measurements should be initiated, ranging from 50 kW to 750 kW, with outliers on both 

ends suggesting that on-site measurements are never realistic for distributed wind projects or are 

realistic only for projects greater than 750 kW. Increased site complexity is also a driver that

leads to an expanded use of on-site measurement. 
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Additional topics during the discussion included the role of remote sensing versus tower-based 

measurements, and the minimum duration of on-site measurements required for the MeasureCorrelate-Predict process to provide high-quality results. At present there is scarce analysis to 

guide a simplified Measure-Correlate-Predict process for the distributed wind industry, but this 

will become increasingly important as the industry grows and more distributed projects utilize

mid-size and larger turbines.
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4 Wind Project Performance Assessment

There are three primary steps for calculating and then evaluating preconstruction energy 

estimates vs. operational performance. The first step is to calculate the bulk results, defined as a 

percentage of metered production over predicted performance. This is the simplest step and will 

not consider any unusual operational conditions that may be outside of the performance frame. 

Next, if operational data exist for the project, the sources of variation can be explored and 

evaluated. This process will take more time and data sources to complete. Finally, there is an 

opportunity to approximate the maximum possible accuracy that could have been obtained for 

any project by eliminating extenuating issues and events that cannot reasonably be predicted, 

such as catastrophic failures/serial defects, natural disasters, or unusual curtailment.

To make a complete assessment of the operational performance of a wind project, one needs a 

robust measurement campaign for the turbine including power production, turbine status signals, 

and measurements for the wind resource, as described in Section 2.1. These operational 

parameters are described in Section 4.1.1. Details of the project characteristics and operational 

data related to the loss parameters estimated during the pre-construction evaluation will also 

need to be assessed. Without wind resource measurements and turbine status signals during 

operation, it is infeasible to evaluate the cause of any variation in performance from the 

preconstruction energy estimate.

Unless the causes of variation are understood, it is difficult to refine the models for estimating 

performance on prospective projects. Unexplained variation then leads to high levels of 

uncertainty in the preconstruction energy assessment, as detailed in the uncertainty framework in 

Section 2.4. Uncertainty leads to unpredictable power generation, which impacts consumer 

confidence, reliability of the revenue from the project, and the cost of ownership. Improving the 

predictability and reliability of wind power generation will reduce costs by focusing investment 

to more lucrative installations. Developing tools to improve return on investments will naturally

attract capital into the distributed wind sector, a key cost reduction opportunity highlighted in the 

distributed wind future market assessment conducted by NREL (Lantz et al. 2016) and illustrated 

in Figure 6. 

Understanding the sources of variation during operation can be challenging. The first step toward 

developing capability in this area is to ensure that each operating project has a robust datamonitoring platform. Implementing monitoring has a twofold benefit: not only does it improve 

the capacity to verify the preconstruction energy estimate but it also enables maintenance 

divisions to provide timely and cost-effective O&M services.
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Figure 6. Single sensitivity plots from distributed wind future market assessment

4.1 Assessment Parameters

This following two sections describe the parameters required to evaluate the operational 

performance of a project and determine the causes of variation between the pre-construction 

energy assessment and actual performance.

4.1.1 Operational Measurements

A minimum of three parameters must be measured in order to evaluate operational performance 

of a project: turbine power, turbine status, and on-site wind resource.

4.1.1.1 Power 

The only required parameter for calculating the predicted vs. actual performance of a turbine is 

the power output of the turbine. There are several ways to measure this data and various 

locations in the system where this measurement can be taken.

On a commercial wind farm, unless testing under a formal power performance measurement, the 

power is measured at or near individual turbines or at the point of metering for the entire site, 

which can encompass many turbines. 

For single utility turbines, the power measurement can be taken in the turbine nacelle where the 

value is the maximum total power output from the turbine. The measurement can be read from 

the turbine’s data acquisition system or measured independently by installing a secondary set of 

voltage taps, current transformers, and a watt transducer. Other common locations to measure 

power are at the base of the turbine or the power off-take location.
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For small wind systems the power output from the turbine is typically measured at the residential 

electric panel.

Regardless of how many turbines are being evaluated or the size of the turbine under 

consideration, the key consideration is to ensure that the location of the measurement reflects the 

location for the initial evaluation, which would take into consideration items such as electrical 

line losses if measuring the power anywhere other than the nacelle.

4.1.1.2 Turbine Status Signals

Turbine status signals are critical to quantifying the amount of performance variation due to 

variables related to turbine operation such as curtailment or downtime. Signals can include 

turbine operational state, grid conditions, manual stops, or restrictions. Wind turbine operational 

state could include indications of states such as emergency, failure, stop, pause, warning or 

running. Curtailment is typically related to parameters outside of the turbine, such as utility or 

environmental regulations. Downtime captures all turbine maintenance, scheduled or 

unscheduled. Curtailment and downtime are both typically included in the preconstruction 

energy estimate but have high levels of uncertainty due to many factors.

Curtailment and downtime are challenging to quantify for utility-scale and small wind turbines 

due to the lack of dedicated signals. For utility-scale turbines, often more than one signal or 

channel can exist, which capture curtailment and downtime events. For small wind turbines 

monitoring systems are extremely limited; as such there is seldom a status signal.

4.1.1.3 Wind Resource 

It is critical to measure the wind resource at an operational wind turbine site to capture the actual 

wind speeds and environmental conditions after the turbine is installed. Without this information, 

it is impossible to separate and calculate the impact on power production from the wind speed 

during the year apart from other factors such as the turbine performing different from the 

manufacturer’s specification or the turbine having low availability (high downtime) due to 

unexpected faults and unscheduled maintenance.

There are two potential locations to measure wind speed near an operational wind turbine: a 

nacelle-mounted anemometer and an anemometer mounted on a nearby meteorological (met) 

mast. The wind speed from a met mast is typically more accurate than the measurement on top of 

a nacelle because it is in a position that is free of obstructions and is not impacted by the 

turbine’s rotor.

For power performance measurement in the utility sector, the standard is to use a met mast 

upwind of the turbine in the primary measurement direction. According to the IEC standard (IEC 

2005), if the terrain between the met mast and the turbine is complex, there will be a calibration 

between two met masts prior to testing. The turbine manufacturer uses a similar method when 

determining the relationship between the upwind free stream wind speed and the wind seen by 

the nacelle-mounted anemometer.

For the typical distributed wind turbines, there are no wind speed measurements available.
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4.1.2 Operational Loss Parameters

Project losses during operation include those areas identified in the Energy Loss Framework in 

Section 2.3—this covers availability, wake, suboptimal turbine performance, curtailment, etc.—

and are described in more detail below. In general, project losses are difficult to measure and 

therefore quantify, but Figure 7 gives a range of typical losses for commercial utility-scale 

projects based on a review of published literature.

Figure 7. Range of typical project losses as a percent of gross production

4.1.2.1 Availability

Availability is the metric by which we understand how often the turbine is ready and able to 

produce power. Availability falls into three general categories: turbine, balance of plant, and 

grid. From the IEC specification for time-based availability, it is the “fraction of a given 

operating period in which a [wind turbine] is performing its intended services within the design 

specification.” Historically the industry has calculated availability as a time-based metric but has 

moved toward more energy-based availability calculations on the basis that the wind resource is 

variable and as such the turbine’s availability is more valuable during some periods than others.

One of the main advantages of energy-based availability calculations is the ability to detect 

poorly scheduled maintenance. 

Availability is measured using a variety of turbine status signals, as described in Section 4.1.1.2.

4.1.2.2 Wake Effects

Wake effects cover the loss of wind speed and increase in turbulence intensity as a result of 

upstream obstacles. From a utility perspective, wake losses are typically defined as a result of 

upwind turbines disturbing and slowing down the free stream wind speed. Wake can be 

generated internally (within the wind farm being studied) or externally (from a neighboring wind 

farm, if any). Additionally, there is the possibility of future wake effects as more wind turbines 

or wind farms are built. For small wind turbines, the wake is more often from buildings and other 

obstacles such as trees in the environment. It is also important to consider future wakes in this 
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environment over the 20-year life of the project—these will arise from installation of new 

structures and trees growing.

There is no standards-based way to estimate wake effect losses, but the typical utility industry 

approach is to model wind speeds at each location, calculate the turbine power output in that 

wind field, calculate the wakes from each turbine, and use the new wind speed to estimate power 

at each wind turbine location (Clifton, Smith, and Fields 2016). In the small wind industry, wake 

effects are approximated using rules of thumb, as described in Section 2.1.8.

There is no operational validation of wake losses in the utility or small wind industry. 

4.1.2.3 Electrical

Electrical losses fall into two main categories: electrical efficiency and facility parasitic 

consumption. Specific losses include losses in collector lines, transformers, other site equipment, 

and transmission. These losses are therefore directly impacted by the site design.

This is the simplest loss parameter to quantify during project operation. It can be calculated by 

obtaining a power measurement at each turbine and another at the point of common coupling or 

the meter to capture the electrical losses between the point of generation and the distribution 

grid. It is the same procedure for utility-scale turbines or projects and small wind turbines.

4.1.2.4 Turbine Performance

Wind turbine power curves are typically generated from models and based on ideal conditions, 

which are not representative of average installed turbine conditions. Many factors may be 

included here: load- or noise-reducing control modes, high turbulence, high inflow, complex 

terrain, high wind shear or veer, and high-wind-speed hysteresis.

Turbines typically fall 1% to 4% short of advertised power curves under IEC-compliant 

conditions (Brower 2012).

Operational turbine performance is difficult to evaluate due to the lack of wind resource 

measurements measuring the free-stream wind speed in all directions at each turbine. However, 

utility projects have more funding to use remote sensing to validate operational performance and 

may have one or more onsite met towers to validate a performance and improve models.

Small wind turbines tend to lack operational wind resource measurements.

4.1.2.5 Curtailment 

Curtailment is the deliberate management of the wind plant to reduce the amount of energy

compared to what is possible from the available resource. This may occur because of grid

limitations; environmental or permitting requirements (e.g., to prevent bird or bat deaths or to 

prevent flicker); or deliberate operational strategies, including directional curtailment/wind 

sector management.

For utility projects, curtailments are difficult to predict due to the high degree of uncertainty as to 

what may be built in the future (grid curtailments) and a lack of knowledge about a plant 

operator’s future actions. Environmental curtailment is typically quantified during the permitting 

process and has less uncertainty. In addition to the difficulty in predicting and estimating 
25

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curtailment, these periods are difficult to filter out without having time series data for analysis; 

even with data, turbine signals that can clearly identify curtailment events are rare.

Curtailment is infrequent or nonexistent for small wind systems but may be applicable for single 

utility systems behind the meter.

4.1.2.6 Environmental 

Environmental losses include those due to icing, blade degradation, environmental shutdown, 

and exposure due to changes in obstacles near the installed turbine, such as the growth or 

clearing of trees or the addition or destruction of surrounding buildings.

4.2 Recent Assessments

Two recent validation efforts quantify aggregate performance for commercial and distributed 

wind projects. Each study encompasses several hundred turbine-years. The first results are from 

an ongoing study at NREL and the second are from analysis at Pacific Northwest National 

Laboratory that looked at REAP and NYSERDA data.

4.2.1 Commercial Wind Projects: Results from the DOE’s A2E Performance, 

Risk, and Uncertainty Framework

NREL recently began a study to evaluate the current performance of utility-wind project preconstruction energy estimates and validate the current third-party methodologies for energy yield 

assessments for the North American market. Figure 8 illustrates the preliminary findings of the 

study. The mean bias for a subset of North American wind projects from 2006 to 2016 is 10.8%.

This represents an over-prediction of energy from the projects relative to their actual measured 

production. This 10.8% number controls for extreme events and long-term performance.

Figure 8. Preliminary results from the Performance, Risk, and Uncertainty Framework project
26

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4.2.2 Distributed Wind Projects: REAP and NYSERDA Project Performance 

Results from the Distributed Wind Market Report

Figure 9 compares the actual versus projected performance for the REAP and NYSERDA 

projects broken out by turbine size class: small certified turbines, small non-certified 

turbines, mid-size turbines, and large turbines. The centerline in each box plot represents the 

median value for each turbine class. The whiskers show the extent of the first and fourth quartile 

for the data. The plots represent a different number of projects for each turbine class, as detailed 

in the 2017 Distributed Wind Market Report. 

Table 2. Project Volume for Each Turbine Class

Turbine Class Number of Projects

Small: certified 129

Small: non-certified 105

Mid-size 7

Large 12

Figure 9. REAP and NYSERDA results: operational turbine performance vs. pre-construction 

energy estimate

The project-specific details that drive each project’s actual energy generation amounts were not 

available for review, but in general, the amount of annual energy production that can be achieved 

by a distributed wind project is driven by many variables, including the project’s available wind 

resource, siting, and availability.

In these datasets, the projects using large turbines over-perform compared to the projects’ 

projected performance at the time of incentive application.
27

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In contrast, the projects using small and mid-size turbines in these datasets tend to underperform 

compared to the projects’ projected performance. The limited sample size for the mid-size 

turbine class may account for some of the discrepancy between projected and actual performance 

for this turbine size class. 

The certified small wind turbines exhibit a higher average of percent of projected production 

(89%) than the non-certified small wind turbines (64%), but this difference cannot be attributed 

solely to the turbine technology. Actual performance, compared to predicted performance, is 

driven by many factors including turbine technology, wind resource assessment methodologies, 

actual versus predicted wind resource, micrositing issues, and turbine availability.
28

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5 Sensitivity Analysis

Quantifying the impact of various resource parameters and loss categories on performance could 

enable DWRA professionals to make choices about which parameters need to be measured and 

to make rough assessments of the accuracy of models. For example, having accurate wind speed 

measurements has a large impact on the accuracy of estimated power production and barometric 

pressure has much less of an impact, but are there other parameters that are important? How 

much does a small error in parameter estimation impact the accuracy and uncertainty of the 

production estimate?

This effort comprised two separate activities: a qualitative analysis using the analytic hierarchy 

process and a quantitative analysis using FAST. Neither study has provided a conclusive, 

ordered list from which to make definitive selections for which parameters to measure and at 

which level of accuracy, but they have provided some initial insight into which elements are 

most important in this process.

5.1 Analytic Hierarchy Process

The interaction between site-specific details and wind resource parameters is myriad, nuanced,

and complex. To begin to decouple the complex interactions, NREL conducted an expert 

elicitation utilizing the Analytic Hierarchy Process. Analytic Hierarchy Process is a structured 

technique for organizing and analyzing complex decisions, based on mathematics and human 

input. 

For the Analytic Hierarchy Process, NREL assembled a group of wind industry experts that 

included turbine manufacturers, academics, consultants, and resource assessment specialists. The 

exercise was conducted in two parts. Part one was conducted with all study participants and 

NREL and DOE staff. The objective was to discuss the proposed list of wind resource and 

turbine input parameters for estimating turbine performance assessments and to determine 

whether there were missing or extraneous factors. 

The initial discussion allowed each participant to weigh in on the proposed list of parameters.

The group generally agreed on the list of resource factors, although all participants mentioned 

the variability in relative impact depending on the configuration of a site. From this discussion, it 

appeared that of the turbine input parameters, turbulence and density had little impact (a few 

percent each) on turbine performance relative to the wind speed and could therefore be ignored.

Veer—the variability in wind direction—came up as a factor that impacts performance for multimegawatt turbines but probably has little impact on small or mid-size turbine performance. 

Overall, the discussion highlighted the complexity inherent in estimating performance, given the 

breadth of the distributed wind industry turbine sizes and factors such as turbine availability, site 

complexity, and a specific turbine’s control mechanisms.

Based on the results of part one, part two of the analysis focused on the parameters that impact 

the turbine location wind speed (of which there were five key parameters: off-site or reference 

wind speed, wind shear, obstacles, roughness, and elevation contours). The Analytic Hierarchy 

Process framework was applied to these remaining parameters. Wind direction was removed 

from the sensitivity analysis because rather than impacting the turbine output, it is the spatial 

framework by which all other parameters must be analyzed. This is easiest to visualize in the 
29

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case of obstacles but is true for all other parameters. Figure 10 shows all the parameters that were 

considered during the analysis.

Figure 10. Resource and turbine input parameters presented for consideration

Table 3 through Table 5 show the results from the expert elicitation. Table 3 is the approximate

contribution from the three elements assumed to impact turbine power output. Table 4 presents

the results of the Analytic Hierarchy Process, showing the median value (the relative impact or

contribution) for each of the five parameters based on the 10 participant responses.

Table 5 provides the weighted average of the full list of resource factors. Figure 11 illustrates the 

percentage contribution for the top five factors listed in Table 5.

Table 3. Turbine Input Parameters

Input Variables Contribution %

Turbine Location Wind Speed 90%-96%

TI ~2%-5%

Density ~2%-5%

Table 4. Resource Factors that Impact Turbine Location Wind Speed

Input Variables Contribution %

Offsite/Reference wind speed 45.8%

Obstacles 23.2%

Contours 16.9%

Roughness 9.1%

Wind shear 8.6%
30

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Table 5. Resultant Contribution

Input Variables Contribution %

Wind direction Outside of analysis

Reference wind speed 41.2%

Obstacles 20.9%

Contours 15.2%

Roughness 8.1%

Wind shear 7.7%

TI ~2%-5%

Density ~2%-5%

Figure 11. Contribution from top five resource assessment factors

5.2 FAST Analysis

The analysis utilized FAST (Jonkman) for power performance predictions of turbines under 

different wind conditions and a technique called elementary effects, a simple methodology for 

screening parameters and assessing sensitivity to different wind conditions. The sensitivity 

analysis is based on a one-at-a-time approach in which each parameter of interest is varied 

individually while holding all other parameters fixed. The study evaluated the impact of wind 

speed, turbulence, air density, wind veer (change in wind direction with height), shear stability 

class, and roughness for four turbine models available in FAST and typical ranges for each 

parameter.

Wind turbine power curves are estimated using the average binned wind speeds at hub height as 

suggested by the IEC (IEC 2005). However, several other meteorological parameters such as 

wind shear, turbulence intensity, wind veer, and changes in air density are expected to influence 

the certified power curves (Farkas 2011). While emphasis is placed on accurate wind speed 

measurements at hub height, these additional atmospheric conditions can also play a role in 

turbine productivity. So far, few studies have attempted to evaluate these sensitivities for small 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

Wind speed Obstacles Contours Wind shear Roughness

% Contribution

Resource Factors
31

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turbines. This analysis examined the effects of these other atmospheric conditions on power 

performance of small wind turbines in an effort to reduce uncertainties in performance estimates

for small turbines. NREL’s Turbsim (Kelley and Jonkman) was used to simulate different sets of 

stochastic full field turbulences that the turbines may encounter during normal operation. These

wind fields were then used to simulate and analyze the power output for four small wind 

turbines, including a three-blade horizontal-axis rotor, pitch and stall regulated, upwind and

downwind rotor turbines using NREL’s aero-servo-elastic tool, FAST (Jonkman). FAST 

provided estimates for turbine power response for three sets of wind speed bins and atmospheric 

stability.

Four turbines—T-1, T-2, T-3, and T-4—were chosen to represent the range of distributed wind 

turbines. These turbines were rated at 10 kW, 85 kW, 225 kW, and 1.5 MW; the main 

specifications are listed in Table 6.

Table 6. Basic Specifications for the Four Turbines Analyzed

Turbine T-1 T-2 T-3 T-4

Rating 10 kW 85 kW 225 kW 1,500 kW

Hub height 25.0 m 43.21 m 31.5 m 80 m

Rotor diameter 6.7 m 19.2 m 27 m 77 m

Cut-in wind speed 3.1 m/s 4.0 m/s 3.0 m/s 3.1 m/s

Cut-out wind speed none 25 m/s 25 m/s 25 m/s

Power control Fixed pitch Stall Pitch Pitch

T-1 is a three-bladed, fixed pitch, small furling wind turbine with a rated output power of 10 kW

(Corbus and Meadors 2005). T-2 is a stall-regulated, three-bladed, horizontal-axis downwind 

turbine. T-3 is a fixed speed, variable pitch, three-bladed wind turbine. T-4 is a variable speed, 

variable pitch, three-bladed upwind wind turbine. The power performance curves for these 

turbines are shown in Figure 12.
32

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Figure 12. Power performance curves for turbines T-1 through T-4

To examine the potential sensitivities of mean power performance of these turbines to different 

wind conditions, the team used the elementary effects approach adopted by Robertson et al.

(2018). Elementary effects is a screening method in sensitivity analysis (Saltelli et al. 2008) that 

provides for qualitative sensitivity analysis measures to rank input factors in order of their 

importance. Each parameter of interest is varied individually while holding all other parameters 

fixed. A derivative is then calculated based on the measured change in mean power (or standard 

deviation in power) for a measured change in the input parameter (e.g., shear). The change in 

power statistics for a given input parameter is examined at different locations in the input 

parameter hyperspace. In other words, only one parameter is varied at a time, but this variation is 

performed multiple times using different values for the other input parameters. 

5.2.1 Approach

The following describes the analysis procedure for T-1, which was repeated for all four turbines.

For the purpose of this analysis, 19 relevant wind parameters were chosen to represent the 

variety of inflow conditions that the turbines may encounter during operation. Eighteen of these 

parameters were chosen based on NREL’s prior study on wind parameter sensitivity by 

Robertson et al. (2018). The study utilized the Veers model for describing and generating the 

wind characteristics for simulating the wind turbine. The model entails 18 parameters described 

by the mean wind profile (defined using two parameters: wind shear, α, and wind veer, β), 

velocity spectrum (defined using integral length scale parameters for the three velocity 

components Lk, and standard deviations σk with k =u,v and w), point-to-point spatial coherence 

(defined using decrement and offset parameters ak and bk and coherence exponent γ), and 

component-to-component correlations (PCuv, PCuw, PCvw). Furthermore, air density was included 

to study variation from the standard value (ρ=1.0 kg/m3).

The parameters were binned on three mean wind speeds at hub height, from cut-in to near-rated,

as well as atmospheric stability for neutral (N), stable (S), and unstable (U) conditions. 

0 5 10 15 20 25

Mean wind speed (m/s)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0

50

100

150

200

250

300

350

400

Electrical power (kW)

T-4

T-3

T-2

T-1
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The basic approach for performing an elementary effects analysis requires a sampling of the 19 

parameters and different starting evaluation points where derivatives are calculated to obtain a 

good assessment of their overall sensitivity. The sampling was designed to represent all possible 

values that the parameter of interest could assume (e.g., α). Then an approach to vary the 

parameters was selected. This study used the Sobol sequence for starting points. (Sobol

sequences belong to the family of quasi-random sequences that are designed to generate samples 

of multiple parameters as uniformly as possible over the multi-dimensional parameter space.)

The goal was to develop understanding of the sensitivity of power performance, say YYoo

(particularly, mean and standard deviation in power), to different combinations of the 19 input 

parameters. 

YYoo = ff(UU1,UU2 ... UU19) (1)

For a given sampling of an input parameter Ui, the elementary effect of Ui on YYoo is found by 

varying Ui by a normalized amount, ∆iiiiii such that 

EEEEiiii = YYoo(UU1,...,UUiiii−1,UUiiii+∆,UUiiii+1,...UU19)−YYoo(UU1,UU2,...... UU19)

∆iiiiii

(2)

Yo (Uib) the average of mean /standard deviation of electrical power in wind speed bin ‘b’ across 

‘s’ random realizations for a certain input parameter, i = {α, β, Lu, Lv , Lw , σu, σv, σw,au, av, aw, bu, 

bv , bw,γ, PCuw, PCuv, PCvw, ρ}

∆iiiiii =+/- 0.1*(UUiiiiiiiiii − UUiiiiiiiiii) (3)

The method provides two sensitivity measures for each input factor. The mean of EEib denoted 

by μii

∗ is helpful for assessing the overall importance of an input factor on the model output; and 

standard deviation of EEib denoted by σii describes non-linear effects and interactions between 

the parameters. Since we are interested in screening the parameters, we chose to examine the 

mean of elementary effects and used it as a measure for sensitivity. If there are R different 

sampling points, the mean of the absolute value of EEib across R different sampling points is 

given by

μii

∗ = 1

rr ∑ �EEEEiiii

jj � RR

jj=1 (4)

The sensitivity analysis was run three times across three wind regions, assuming a mean wind 

speed within each bin (5, 8, and 11 m/s). The process was repeated three times for each wind 

speed bin representing the different stability conditions. An accurate stratification of atmospheric 

stability will depend on several wind parameters, and there are different indices that can be used 

(e.g., the gradient (or bulk) Richardson number, and the Monin-Obukhov length, L). Depending 

on which metric is used and the approach applied to compute the index, they can generate quite 

different stability climates (Barthelmie 2015). For this study, measurements or models for virtual 

heat flux changes necessary to derive estimates of L were not available. To simplify the problem, 

three stability conditions were defined based on shear, turbulence intensity, and integral length 

scales. It was assumed that stable conditions were associated with combinations of low 

turbulence and high shear. Integral length scales were assumed to be relatively small, resulting in 

narrower wakes in stable conditions. Unstable conditions were characterized by high turbulence 
34

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and low shear. In addition, large integral length scales were assumed to act to widen wakes. 

Moderate shear and turbulence represented neutral conditions with intermediate values assumed 

for integral length scales.

For each of the parameter settings identified, 10 turbulent wind files (i.e., independent timedomain realizations from 10 seeds) were generated in TurbSim. A 6 x 6 square grid was 

considered to span the entire rotor plane of the turbine. FAST was then used to simulate a model 

of the 10-kW wind turbine using the wind files developed. Besides normal aero-elastic features 

of FAST, furling capability was enabled (Jonkman and Hansen). For the analysis, 19 parameters

with 50 starting points were examined. From the 10 turbulence seeds for each sampled parameter 

space, the sensitivity analysis was conducted nine times (three wind speed bins (w) times

stability classes). In total, 90,000 simulations were performed. Table 7 illustrates the min/max 

range used for turbine T-1.
35

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Table 7. Paramater Definitions for T-1 for Sensitivity Analysis

Mean 

velocity

Mean 

wind 

profile

Velocity spectra, turbulence standard 

deviations

Spatial coherence parameters and component-to-component 

velocity correlations (Turbulence)

Air 

den

sity

α β Lu Lv Lw σu σv σw au av aw bu bv bw γ PCuw PCuv PCvw ρ

Class 5 m/s

N min 0.1 -5 100 100 100 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 5 124 124 70 0.65 0.65 0.3 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.05 0.05 0.05 5.0 4.0 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 1.0 5 20 15 10 0.495 0.495 0.2 7.0 5.0 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 160 135 135 0.655 0.655 0.655 7.0 2.5 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 5 200 200 100 3.0 3.0 2.0 8.0 5.0 5.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

8 m/s

N min 0.1 -5 71 71 71 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 5 240 240 120 0.65 0.65 0.3 7.0 3.0 3.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.05 0.05 0.05 8.0 2.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 1.0 5 70 70 35 0.49

5

0.49

5

0.2 10 5.0 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 241 241 241 0.655 0.655 0.655 12 10 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 5 400 400 400 3.0 3.0 2.0 15 12 5.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

11 m/s

N min 0.1 -5 71 71 71 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 5 240 240 120 0.65 0.65 0.3 7.0 3.0 3.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.05 0.05 0.05 8.0 2.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 1.0 5 70 70 35 0.49

5

0.49

5

0.2 10 5.0 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 241 241 241 0.655 0.655 0.655 12 10 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 5 400 400 400 3.0 3.0 2.0 15 12 5.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

From each simulation, the power response (mean and standard deviation of electric power) was 

extracted and averaged across the 10 seeds. The elementary effect of each parameter on power 

response was then computed using (2). The mean of elementary effects given by (4) was then 

computed for each bin across all stability classes. The parameters that resulted in highest values 

of the mean elementary effects were identified as outliers using the 2-sigma rule. Figure 13

shows a sample histogram of elementary effects-means for mean electric power for a single bin 

during neutral stability conditions. A count of the total number of outliers contributed by each 

parameter was then extracted to rank relative sensitivities.
36

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Figure 13. Sample histogram of mean elementary effects of all parameters on mean electric power

5.2.2 T-1 Results

For the 10-kW turbine T-1, air density and veer have the greatest impact on power performance 

in stable and neutral atmospheric conditions. In unstable atmospheric stability, secondary 

turbulence parameters become more dominant and more evenly impactful. Across all wind speed 

bins, standard deviation of electric power is most sensitive to u-component velocity standard 

deviation (TI). Table 8 identifies the top parameters across all three bins for T-1 identified as the 

top 25% of the total number of outliers (based on the 2-sigma rule explained above) across all 

stability classes.

Table 8. Sensitivity Ranking for T-1 Based on the Total Number of Appearances

Response Bin 1 Bin 2 Bin 3

1 2 1 2 1 2

Power-mean ρ β ρ - ρ β

Power-standard 

deviation 

σu - σu - σu Lu

Figure 14 illustrates the impact of stability classes on mean power for T-1 predicted for a certain 

combination of parameters at an air density of 1.05 kg/m3. A significant reduction at 8 m/s and 

11 m/s is observed when wind conditions are unstable, whereas a marginal increase in power at 

11 m/s is noted when conditions were stable.

0 0.05 0.1 0.15 0.2

Elementary Effect mean ( i

)

0

50

100

150

200

Counts

Outliers

 + 2
37

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Figure 14. T-1 sensitivity to stability

4 5 6 7 8 9 10 11 12

Mean wind speed (m/s)

0

1

2

3

4

5

6

7

8

Mean Electrical Power (kW)

Neutral

Stable

Unstable
38

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Figure 15. Sensitivity rankings of mean electric power to different wind parameters (a) Bin 1 (b) 

Bin 2 (c) Bin 3

(a)

(b)

(c)
39

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Figure 16. Sensitivity rankings for standard deviation in electric power to different wind 

parameters (a) Bin 1 (b) Bin 2 (c) Bin 3

(a)

(b)

(c)
40

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5.2.3 T-2 through T-4 Results

Results for turbines T2-T4 are detailed in Appendix A and follow the same pattern as turbine T1. For each turbine, there is a table detailing the most impactful resource assessment parameters, 

a graph illustrating the impact of stability on power performance, and then two pages of plots 

illustrating the contribution of each input parameter on power output for the three wind speed 

and three atmospheric stability classes.

Changes to air density has the most impact on power performance for turbines T-2 and T-3 in 

stable and neutral atmospheric conditions. However, in unstable atmospheric stability, secondary 

turbulence parameters become more dominant for T2 and T3, similar to T1. For T-4, secondary 

turbulence parameters dominate in all atmospheric conditions, resulting in power performance 

being relatively insensitive to changes in air density. 

It may be inferred that air density, standard deviation (TI), and to a lesser degree atmospheric 

stability have the most influence on turbines T-1 through T-3. The behavior is markedly different 

in T-4, in which veer, shear and TI are prominent in all wind speed cases that were examined. 

The stability stratification is apparent in all the turbines.
41

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6 Benchmark Datasets

Developing independent validation datasets that highlight key DWRA-specific conditions is

critical for assessing project performance estimation models because it is unusual for distributed 

wind turbine installations to have operational wind resource measurements, turbine status 

signals, or power measurements. The portfolio should include a variety of site configurations 

covering a range of conditions; e.g., flat to complex terrain, open prairies to forested regions, and 

open fields to urban environments. It should also cover a variety of commercially available 

turbine types.

NREL is collecting datasets for this purpose, beginning with datasets from the small wind 

turbine testing centers across the country and other public institutions. These datasets are 

generally in terrain that is relatively benign, due to the nature of power performance testing 

requirements. There is still a need to discover datasets with turbine power and wind resource 

measurements in areas of complex terrain. 

If you have a dataset you would like to contribute to this compendium, please email NREL 

through the WINDExchange website or at windexchange@nrel.gov.
42

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7 Conclusions and Recommendations

The team has identified the key parameters of a DWRA performance framework, including key 

wind resource parameters, operational measurements, losses, and uncertainties. This framework 

could be used as the foundation for understanding the predictability of the power and revenue 

from a project. Improving the predictability and reliability of wind power generation and 

operations could reduce costs and potentially establish a framework to attract new capital into the 

wind energy space. 

In general, improving the accuracy of DWRA performance predictions could reduce costs by:

• Improving the overall distributed wind fleet performance 

• Enabling a reliable tiered screening methodology, with different methodologies available 

depending on the level of complexity of the site under consideration and the level of 

accuracy required by project developers and financial stakeholders

• Reducing the cost of capital and project financing by decreasing project risks through 

better precision and accuracy of energy predictions

• Expanding state or federal incentive support through improved and consistent project 

performance.

However, before performance estimates can be improved, we need to understand how accurate 

the industry’s current estimates are today. In the process of documenting the key parameters, it 

became clear that many of the adjustments to the raw wind speed and other parameters are based 

on rules of thumb or expert experience. It is likely that expert analysts within a region have a 

good sense of how to apply these adjustments, but a more rigorous approach to analyzing key 

parameters is needed as practitioners expand their market space, new individuals and businesses 

join the industry, and alternative financing mechanisms are considered.

The above discussion details the various approaches, tools, and models used to assess the wind 

resource at a prospective site, the expected energy production, and how to evaluate the accuracy 

of production estimates. It also lays out the distributed wind-specific gaps, challenges, and 

opportunities. Seeing the three primary methods for resource assessment mapped side by side, 

illustrated in Figure 1, it is easier to identify differences between the processes and where there 

may be tools or processes that can be distilled from the larger wind project approaches. Some of 

the key parameters and losses are being accounted for in the distributed wind space, while others 

are not. 

The sensitivity analysis is one step toward developing a clearer understanding of the priority and 

required accuracy for the identified parameters but will require further investigation to 

understand the ultimate impact on project economics. From the analysis, it was clear that wind 

speed and direction are essential parameters for any performance estimate, followed by air 

density, veer, and turbulence intensity, particularly for small wind turbines. While density 

doesn’t vary greatly on a particular site, the air density varies significantly across the United 

States, and developing a better understanding impact of density on the power performance of 

commercially available wind turbines will be highly valuable.
43

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The next step for our collective understanding is to quantify the accuracy of each approach or 

tool and develop a consistent way to communicate this information with various stakeholders. 

Some of the tasks could include:

• Validating existing models to understand their limitations and appropriate uses

• Assessing current rules of thumb 

• Verifying the entire site assessment process, not just the models used to perform site 

assessment

• Combining the results of multiple research efforts to provide better industry-wide 

guidance on modeling methodologies and appropriate assumptions

• Improving and better documenting methodologies for interannual variability assessment;

understanding under which conditions the different methodologies are most applicable

• Performing additional sensitivity analysis on resource parameters

• Evaluating costs associated with various resource assessment methods

• Estimating uncertainty and financial impact of the various evaluation methods

• Building a decision matrix to aid in determining the most appropriate resource 

assessment method

• Developing distributed wind-specific loss and uncertainty frameworks

• Curating standard test cases and guidelines to facilitate discussion of which tools, 

processes, and methodologies are appropriate for specific site conditions and the 

limitations of various approaches

• Documenting potential project size thresholds that warrant different levels of assessment 

processes.
44

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References

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Pryor, S.C. 2015. The role of atmospheric stability/turbulence on wakes at the Egmond aan Zee 

offshore wind farm, J. Phys.: Conf. Ser. 625 012002.

Brower, Michael, Michael Barton, Llorenç Lledó, Jason Dubois. 2013. A Study of Wind Speed 

Variability Using Global Reanalysis Data. AWS Truepower, LLC.

Brower, Michael. 2012. “Actual vs. Predicted Plant Production: The Role of Turbine 

Performance.” Slides presented at the AWEA Wind Resource Assessment Workshop, 

Philadelphia, PA, 2012.

Chiras, D. 2010. Wind Power Basics. New Society Publishers.

Clifton, Andrew, Aaron Smith, and Jason Fields. 2016. Wind Plant Preconstruction Energy 

Estimates: Current Practice and Opportunities. NREL/TP-5000-64735. National Renewable 

Energy Laboratory, Golden, CO. https://www.nrel.gov/docs/fy16osti/64735.pdf.

Corbus, D., and M. Meadors. 2005. Small Wind Research Turbine: Final Report. NREL/TP-500-

38550. https://www.nrel.gov/docs/fy06osti/38550.pdf.

Farkas, Zeno. 2011. Considering Air Density in Wind Power Production. Available from: 

https://arxiv.org/abs/1103.2198v1.

Frost, W., and D. Nowak. 1979. Handbook of Wind Turbine Generator Siting Techniques 

Relative to Two-Dimensional Terrain Features. Prepared for Pacific Northwest Laboratory by 

FWG Associates, Inc., Tullahoma, TN 37388.

IEC 61400-2: 2006, Ed 2. “Design requirements for small wind turbines.”

IEC 61400-12-1: 2005 Ed 1. “Power Performance measurements of electricity produced wind 

turbines.”

Fields, Jason, Heidi Tinnesand, and Ian Baring-Gould. 2016. Distributed Wind Resource 

Assessment: State of the Industry. NREL/TP-5000-66419. National Renewable Energy 

Laboratory, Golden, CO. https://www.nrel.gov/docs/fy16osti/66419.pdf.

Jonkman, Jason, and Craig Hansen. 2004. Development and Validation of an Aeroelastic Model 

of a Small Furling Wind Turbine, NREL/CP-500-39589.

Jonkman, Jason. FAST, An aeroelastic computer-aided engineering (CAE) tool for horizontal 

axis wind turbines. https://nwtc.nrel.gov/wind-tools/computer-aided-engineering-caetools?page=1.

Kelley, Neil, and Bonnie Jonkman. Turbsim: A stochastic, full-field, turbulence simulator 

primarily for use with InflowWind/AeroDyn-based simulation tools. Available online: 

https://nwtc.nrel.gov/TurbSim.
45

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Lantz, Eric, Benjamin Sigrin, Michael Gleason, Robert Preus, and Ian Baring-Gould. 2016. 

Assessing the Future of Distributed Wind: Opportunities for Behind-the-Meter Projects. 

NREL/TP-6A20-67337. National Renewable Energy Laboratory, Golden, CO. 

https://www.nrel.gov/docs/fy17osti/67337.pdf.

Lowder, Travis, Paul Schwabe, Ella Zhou, and Douglas Arent. 2015. Historical and Current 

U.S. Strategies for Boosting Distributed Generation. NREL/TP-6A20-64843. National 

Renewable Energy Laboratory, Golden, CO. https://www.nrel.gov/docs/fy16osti/64843.pdf.

Olsen, Tim, and Robert Preus. 2015. Small Wind Site Assessment Guidelines. NREL/TP-5000-

63696. National Renewable Energy Laboratory, Golden, CO. 

https://www.nrel.gov/docs/fy15osti/63696.pdf.

Robertson, A., L. Sethuraman, J. Jonkman and J. Quick. Assessment of Wind Parameter 

Sensitivity on Ultimate and Fatigue Wind Turbine Loads. Presented at the American Institute of 

Aeronautics and Astronautics SciTech Forum, Orlando, Florida January 8–12, 2018.

Saltelli, Andrea, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora 

Gatelli, Michaela Saisana, and Stefano Tarantola. 2008. Global Sensitivity Analysis, The Primer.

http://www.andreasaltelli.eu/file/repository/A_Saltelli_Marco_Ratto_Terry_Andres_Francesca_

Campolongo_Jessica_Cariboni_Debora_Gatelli_Michaela_Saisana_Stefano_Tarantola_Global_

Sensitivity_Analysis_The_Primer_Wiley_Interscience_2008_.pdf.

U.S. Department of Energy. 2015. 2015 Distributed Wind Market Report. PNNL-25636. Pacific 

Northwest National Laboratory, Richland, WA. 

https://www.energy.gov/eere/wind/downloads/2015-distributed-wind-market-report.

Van Eimern, J., R. Karschon, L.A. Razumova, and G.W. Robertson. 1964. Windbreaks and 

Shelterbelts. Technical Note 59, World Meteorological Organization, Geneva, Switzerland.

Wegley, H.L., M.M. Orgill, and R.L. Drake. 1980. A Siting Handbook for Small Wind Energy 

Conversion Systems.
46

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Appendix

T-2 Results

For the 85-kW turbine T-2, only density stood out from the other parameters as having a large 

impact on turbine power performance during stable and neutral atmospheric stability.

Table 9. Sensitivity Rankings for Turbine T-2

Response T-2

Bin 1 Bin 2 Bin 3

Ranking 1 2 1 2 1 2

Power-mean ρ - ρ - ρ -

Power-standard deviation σu - σu bw ρ σu

Figure 17. T-2 sensitivity to stability

4 5 6 7 8 9 10 11 12

Mean wind speed (m/s)

5

10

15

20

25

30

35

40

45

50

55

Mean Electrical Power (kW)

Neutral

Stable

Unstable
47

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Table 10. Parameter Definitions for Simulating T-2

Mean 

velocity

Mean 

wind 

profile

Velocity spectra, turbulence standard 

deviations

Spatial coherence parameters and component-to-component 

velocity correlations (Turbulence)

Air 

den

sity

α β Lu Lv Lw σu σv σw au av aw bu bv bw γ PCuw PCuv PCvw ρ

Class 5 m/s

N min 0.1 -5 100 100 100 0.50 0.50 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 15 124 124 70 0.65 0.65 0.3 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.05 0.05 0.05 5.0 4.0 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 1.0 15 20 15 10 0.50 0.50 0.2 7.0 5.0 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 160 135 135 0.66 0.66 0.66 7.0 2.5 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 15 200 200 100 3.0 3.0 2.0 8.0 5.0 5.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

8 m/s

N min 0.1 -5 71 71 71 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 15 240 240 120 0.65 0.65 0.3 7.0 3.0 3.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.05 0.05 0.05 8.0 2.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 1.0 15 70 70 35 0.49

5

0.49

5

0.2 10 5.0 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 241 241 241 0.66 0.66 0.66 12 10 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 15 400 400 400 3.0 3.0 2.0 15 12 5.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

11 m/s

N min 0.1 -5 71 71 71 1.1 1.1 1.1 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 15 240 240 120 1.43 1.43 0.75 7.0 3.0 3.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.11 0.11 0.11 8.0 2.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 0.6 15 70 70 35 1.09 1.09 1.09 10 5.0 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.1 -5 241 241 241 1.44 1.44 1.44 12 10 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 15 400 400 400 2.5 2.5 1.75 15 12 5.0 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15
48

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 18. Sensitivity rankings for mean electric power to different wind parameters (1) Bin 1 (b) 

Bin 2 (c) Bin 3

(a)

(b)

(c)
49

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Figure 19. Sensitivity rankings for standard deviation in electric power to different wind 

parameters (a) Bin 1 (b) Bin 2 (c) Bin 3

(a)

(b)

(c)
50

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Results T-3

For the 225-kW turbine T-3, air density stood out from the other parameters as having the 

biggest impact on turbine power performance.

Table 11. Sensitivity Rankings for Turbine T-3 

Response T-3

Bin 1 Bin 2 Bin 3

Ranking 1 2 1 2 1 2

Power-mean ρ - ρ - ρ -

Power-standard deviation ρ Lu ρ bu ρ σu

Figure 20. T-3 sensitivity to stability

4 5 6 7 8 9 10 11 12

Mean wind speed (m/s)

0

20

40

60

80

100

120

140

160

180

Mean Electrical Power (kW)

Neutral

Stable

Unstable
51

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Table 12. Parameter Definitions for Simulating T-3

Mean 

velocity

Mean 

wind 

profile

Velocity spectra, turbulence standard 

deviations

Spatial coherence parameters and component-to-component 

velocity correlations (Turbulence)

Air 

den

sity

α β Lu Lv Lw σu σv σw au av aw bu bv bw γ PCuw PCuv PCvw ρ

Class 5 m/s

N min 0.1 -5 100 100 100 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 15 124 124 70 0.65 0.65 0.3 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min -5 2 2 2 0.05 0.05 0.05 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 15 20 15 10 0.495 0.495 0.2 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 160 135 135 0.655 0.655 0.655 1.5 1.5 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 15 200 200 100 3.0 3.0 2.0 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

8 m/s

N min 0.1 -5 71 71 71 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 15 240 240 120 0.65 0.65 0.3 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.05 0.05 0.05 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 1.0 15 70 70 35 0.49

5

0.49

5

0.2 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 241 241 241 0.655 0.655 0.655 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 15 400 400 400 3.0 3.0 2.0 5 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

11 m/s

N min 0.1 -5 71 71 71 1.1 1.1 1.1 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 15 240 240 120 1.43 1.43 0.75 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

S min 0.2 -5 2 2 2 0.11 0.11 0.11 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 1.0 15 70 70 35 1.09 1.09 1.09 5.0 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15

U min -0.8 -5 241 241 241 1.44 1.44 1.44 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 15 400 400 400 2.5 2.5 1.75 5 2.5 2.5 0.08 4.5e-3 0.011 1 0.5 6.0 1.0 0.15
52

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Figure 21. Sensitivity rankings for mean electric power to different wind parameters (a) Bin 1 (b) 

Bin 2 (c) Bin 3

(a)

(b)

(c)
53

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Figure 22. Sensitivity rankings for standard deviation in electric power to different wind 

parameters (a) Bin 1 (b) Bin 2 (c) Bin 3

(a)

(b)

(c)
54

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Results T-4

For the 1.5-megawatt turbine, wind veer stood out from the other parameters as having the 

biggest impact on turbine mean power whereas turbulence had the most impact on standard 

deviation.

Table 13. Sensitivity Rankings for Turbine T-4 

Response T-4

Bin 1 Bin 2 Bin 3

Ranking 1 2 1 2 1 2

Power-mean β α β α β σu

Power-standard deviation σu - σu - σu -

Figure 23. T-4 sensitivity to stability

4 5 6 7 8 9 10 11 12

Mean wind speed (m/s)

0

200

400

600

800

1000

1200

1400

Mean Electrical Power (kW)

Neutral

Stable

Unstable
55

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Table 14. Parameter Definitions for Simulating T-4

Mean 

velocity

Mean 

wind 

profile

Velocity spectra, turbulence standard 

deviations

Spatial coherence parameters and component-to-component 

velocity correlations (Turbulence)

Air 

den

sity

α β Lu Lv Lw σu σv σw au av aw bu bv bw γ PCuw PCuv PCvw ρ

Class 5 m/s

N min 0.1 -15 300 300 300 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 30 650 650 650 0.65 0.65 0.3 5.0 4.0 2.5 0.08 4.5e-3 0.011 0 0.5 6.0 1.0 0.15

S min 0.2 -15 2 2 2 0.05 0.05 0.05 3.5 4.0 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 1.2 30 250 250 250 0.495 0.495 0.2 5.0 5.0 2.5 0.08 4.5e-3 0.011 0 0.5 6.0 1.0 0.15

U min -1.2 -15 700 700 700 0.655 0.655 0.655 3.5 2.5 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 30 1000 1000 1000 3.0 3.0 2.0 5.0 3.0 3.5 0.08 4.5e-3 0.011 0 0.5 6.0 1.0 0.15

8 m/s

N min 0.1 -15 300 300 300 0.5 0.5 0.5 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 30 650 650 650 0.65 0.65 0.3 7.0 3.0 6.0 0.08 2.5e-3 0.011 0 0.5 6.0 1.0 0.15

S min 0.2 -15 2 2 2 0.05 0.05 0.05 7.0 4.0 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -

0.15

max 1.2 30 250 250 250 0.49

5

0.49

5

0.2 11 7.5 6 0.08 2.5e-3 0.011 0 0.5 6.0 1.0 0.15

U min -1.2 -15 700 700 700 0.655 0.655 0.655 7 3.0 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 30 1000 1000 1000 3.0 3.0 2.0 11.

0

4.0 3.0 0.08 2.5e-3 0.011 0 0.5 6.0 1.0 0.15

11 m/s

N min 0.1 -15 300 300 300 1.1 1.1 1.1 1.5 1.5 2.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.19 30 650 650 650 1.43 1.43 0.75 9.0 3.0 4.0 0.08 2.5e-3 0.011 0 0.5 6.0 1.0 0.15

S min 0.2 -15 2 2 2 0.11 0.11 0.11 7.0 4.0 3.0 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.6 30 250 250 250 1.09 1.09 0.9 18 12.5 7.5 0.08 2.5e-3 0.011 0 0.5 6.0 1.0 0.15

U min -0.1 -15 700 700 700 1.441 1.441 1.441 7 3 2.5 0.0 0 0 0 -3.5 -4.5 -2.7 -0.15

max 0.09 30 1000 1000 1000 2.5 2.5 1.75 18 5 7.5 0.08 2.5e-3 0.011 0 0.5 6.0 1.0 0.15
56

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Figure 24. Sensitivity rankings for mean electric power to different wind parameters (a) Bin 1 

(b) Bin 2 (c) Bin 3

(a)

(b)

(c)
57

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Results

The initial discussion allowed each participant to weigh in on the proposed list of resource and

turbine input parameters. The group generally agreed on the list of resource factors, although all

members mentioned the variability in relative impact depending on the particular configuration

of a site. From this

discussion it was clear that of the turbine input parameters, turbulence and density had such little

(a)

(b)

(c)

Figure 25. Sensitivity rankings for standard deviation of electric power to different wind 

parameters (a) Bin 1 (b) Bin 2 (c) Bin 3

(a)

(b)