Wind Energy Yield Methods UpdateA white paper on validation and update of methods for performing pre-construction wind energy yield assessments in the European context

Aug 20, 2020

DISCLAIMER Copyright © 2020. All rights reserved.

Neither UL, UL-DEWI nor UL-AWST accept any responsibility or liability for the contents or any use which is made of this document. No representation is made regarding the completeness, methodology or current status of any information referred to in this document which is made available “as is” and without liability or responsibility of any kind (including in negligence). Neither UL, UL-DEWI nor UL-AWST shall in any way be responsible in connection with erroneous information or data provided to them by any third party or for any effects of such erroneous information or data whether or not contained or referred to in this document.

This document shall not be disclosed in any public offering memorandum, prospectus or stock exchange listing, circular or announcement without the express prior written consent of UL, UL-DEWI or UL-AWST.

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DOCUMENT CONTRIBUTORSAuthor

José VidalTechnical Director, Energy Advisory

Supporting Authors

Chris Ziesler, PhDDirector, Energy Advisory

Kai Mönnich, PhDSenior Engineering Lead, Energy Advisory

Reviewers

Michael Brower, PhDVice President, Renewables

Gill Howard-LarsenGlobal Director, Renewables Advisory Services

Thibaut LabondeBusiness Development Manager, Mediterranean

Anaïs Madaule,Senior Engineer, Energy Advisory

Stéphanie PhamTeam Leader, Energy Advisory

Jan RaabeTeam Leader, Energy Advisory

Santi VilaTeam Lead, Energy Advisory

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TABLE OF CONTENTSExecutive Summary 1

Introduction 3

UL-DEWI and UL-AWST methods 3

Validation of the UL-DEWI method Backcast database Method of analysis UL-DEWI results

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Validation of the UL-AWST method Backcast database Method of analysis UL-AWST results

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Analysis of deviations from operational energy yields 8

Harmonization Description and schedule of the phased approach MEASNET and accreditation Wind flow and wake modeling Wind flow modeling Wake effect modeling Upwind blocking effect in large wind farms Non-wake plant losses Availability Electrical Turbine performance Environmental Curtailments Uncertainty Effects of methods changes for harmonization

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Summary and conclusions 19

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LIST OF FIGURES

LIST OF TABLES

Figure 1: Distribution by country of the 50 windplants in the UL-DEWI backcast dataset 4

Figure 2: Distribution by turbine manufacturer of plants in the UL-DEWI backcast dataset 4

Figure 3: Distribution of production ratios (PR) for the UL-DEWI method 6

Figure 4: Proportion of projects for which the operational yield exceeds the estimated P-value of the pre-construction EYA. 6

Figure 5: Distribution by country of the 43 wind plants in the UL-AWST backcast dataset 7

Figure 6: Distribution of production ratios (PR) for the UL-AWST method 8

Figure 7: Frequency distribution of deviations using hybrid method 18

Figure 8: Frequency distribution of deviations using accredited method

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Figure 9: Proportion of projects for which the operational yield exceeds the estimated P-value of the pre-construction EYA for the hybrid and accredited methods 19

Table 1: Comparison of the UL-DEWI and UL-AWST Methods 3

Table 2: Characteristics of the 50 wind plants in the UL-DEWI backcast dataset 4

Table 3: Characteristics of the 43 wind plants in the UL-AWST backcast dataset 7

Table 4: Estimated contributions of various factors to deviations between predicted and observed production for the UL-DEWI and UL-AWST methods 10

Table 5: Loss assumptions for EYAs under the UL-DEWI, hybrid and accredited methods 17

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UL intends to adopt a harmonized method globally, while implementing certain regional market and project-specific adaptations. This white paper describes the harmonized method to be implemented for onshore wind projects where the current UL-DEWI method is being applied.

In a first step, the present study evaluates the accuracy of the UL-DEWI and the UL-AWST methods based on met mast measurements through a comparison with observed production from operational plants. The results of the analysis indicate that, for a sample of 50 plants in Europe and North Africa, the operational energy production is 6.2% below the preconstruction

EYA P50 from the UL-DEWI method, while for 43 plants in the same region (including 25 in common with the UL-DEWI dataset), the operational energy production is 1.3% higher than the preconstruction EYA P50 from the UL-AWST method. These findings are generally consistent with previous studies, and they support the need for adjustments in the UL-DEWI method, and especially in the plant losses, to reduce the observed gap.

Building on these findings, the harmonization process will be divided into two phases.

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Since the 1990s, UL has performed thousands of energy yield assessments (EYAs) globally, following one of two methods: the UL-DEWI method developed originally by the Europe-based firm DEWI, which was acquired by UL in 2012, and the UL-AWST method developed originally by the U.S.-based firm AWS Truepower, which was acquired by UL in 2016. The two methods differ in a number of respects, including wind flow model, wake model and plant loss assumptions. Since 2016, the UL-DEWI method has been applied mainly in Europe (except Portugal and Spain), North Africa, and the Middle East, while the UL-AWST method has been applied globally elsewhere.

EXECUTIVE SUMMARY

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Phase one

To align methodology and close the gap in the P50 from the UL-DEWI method, the following will be implemented in Phase one:

A hybrid method will be implemented for regions where the UL-DEWI method is presently being used. This method will retain the current UL-DEWI meteorological analysis, while adopting the UL-AWST method for wind flow and wake modeling, as well as some of the UL-AWST plant loss assumptions. New uncertainty estimates will also be adopted to achieve more accurate P-values. The current UL-AWST method will remain in place and unchanged for markets where it is presently being used.

In addition, certain region-specific adaptations will be implemented. Specifically, in order to address the TR6 standard in Germany, a TR6-accredited EYA method will be implemented referred to as the accredited method. The primary difference between the accredited method and the general hybrid method is that the accredited method will exclude six loss sub-categories.

• Both methods will continue to follow the International Electrotechnical Commission (IEC) and International Network for Harmonised and Recognised Measurements in Wind Energy (MEASNET) guidelines. Additionally, for accredited Energy Yield Assessments (EYAs), especially critical for the German market, UL will follow the TR6 guideline.

• The wind flow and wake models will be aligned to the ones currently in use by the UL-AWST method.

• Non-wake plant losses will be increased compared with the current UL-DEWI method. The average total loss for the hybrid method is 5.0% higher and the average total loss for the accredited method is 3.0% higher for the sample of 32 wind farms from the UL-DEWI dataset for which the hybrid and accredited methods could be applied.

• The losses for the hybrid method will reduce the gap in the P50 to approximately -1.3% and the losses for the accredited method will close the gap in the P50 to approximately -3.6%.

Phase two

The second phase of the harmonization will define the single UL method and toolset to perform an EYA based on wind measurement data in any part of the world. The principal although not the only focus will be on meteorological analysis to process the wind speeds from met masts and on wind flow modeling to determine the model to be utilized for complex terrain (meso-microscale combination and CFD) and to apply the most suitable model for each site on a case by case basis. Phase two will be implemented in Q1 2021.

UL will perform additional backcast studies in future years to assess the performance of the new methodology.

INTRODUCTIONWith wind developers and financial institutions operating increasingly on a global scale, UL intends to adopt a harmonized method globally, while implementing certain regional and project-specific adaptations to meet the requirements of specific markets. The harmonized method will be adopted in a two-phase approach, with the first phase rolled out late Q3 2020 and the second phase in Q1 2021.

A key step in the harmonization is the assessment of the accuracy of the current methods in use. The process of verifying energy production estimation methods against observed plant performance is called a backcast study. UL has performed several backcast studies for the UL-DEWI and UL-AWST methods over the years.1 This white paper presents the results of an updated assessment for both methods, based on met mast measurements,2 with an emphasis on projects in Europe.

The results of these assessments provide the basis for the changes in energy yield assessment methods which will be implemented in Phase One of harmonization and the work to be completed in Phase Two.

UL-DEWI AND UL-AWST METHODSThe UL-DEWI and UL-AWST methods were both conceived and designed to provide accurate forecasts of the long-term energy output from wind farms in the design stage. Both methods have evolved over the years to take advantage of new modeling techniques and new performance information as it has become available. They represent different approaches to the problem of how to arrive at an accurate estimate of the production of a wind farm using data from a limited period of meteorological measurements gathered from a limited number of on-site devices, often taken below the hub height of the turbines, and then applying a variety of models and assumptions to accurately characterize the wind resource and energy production of the whole farm over the long term.

Table 1 summarizes the main differences between the two methods. Some of the differences arise from the use of different software: for example, the UL-DEWI method employs WAsP, a linearized flow model, and CFD in complex terrain, while the UL-AWST method uses Sitewind, a coupled mesoscale-microscale flow model. Other differences are due to the sources of data most readily available or to engineering judgement, e.g., the UL-DEWI

1 See (a) For the UL-DEWI method: Schorer, T., Levée, P. (2013): Review of the Real Energy Production Data of Operating Wind Farms in Comparison to Former Predicted Energy Yields. DEWI MAGAZIN NO. 42. K. Mönnich, S. Horodyvskyy, F. Krüger: Comparison of Pre-Construction Energy Yield Assessments and Operating Wind Farm’s Energy Yields. Poster presentation WindEurope Summit 2016, Hamburg, Germany. Moennich, K., Horodyvskyy, S., Jimenez, B. (2017): Do Uncertainties and Losses in Pre-Finance Wind Farm Energy Assessments Fit to Reality? WindEurope Resource Assessment 2017 Workshop, Thursday 16 & Friday 17 March 2017, EICC, Edinburgh, UK. (b) For the UL-AWST method: Ziesler, C., O’Loughlin, B., Lightfoote, S., Bernadett, D., Brower, M. (2018): 2018 Backcast Study and Methods Update. Verifying and Updating UL AWS Truepower’s Methods for Performing Pre-Construction Wind Energy Production Estimates. UL AWS Truepower. White, E. (2009): Closing the Gap on Plant Underperformance. AWS Truewind. Bernadett, D., Brower, M., Van Kempen, S., Wilson, W., Kramak, B. (2012): 2012 Backcast Study: Verifying AWS Truepower’s Energy and Uncertainty Estimates. AWS Truepower.2 The UL-DEWI method includes two approaches to performing EYAs. The method that is evaluated here, like the UL-AWST method, uses wind resource measurements from on-site meteorological masts. The other method, known as “EYA based on General Wind Climate and verification turbines,” is based on operational data from nearby farms; it is primarily used in Germany, and is not considered in this analysis.

method uses monthly averages to calculate long-term correlations using the measure-correlate-predict (MCP) method, whereas the UL-AWST method uses hourly or daily averages. Finally, the two methods assume different plant losses. The UL-DEWI method largely follows long-established European standards for estimating losses, while the UL-AWST method employs loss assumptions developed independently from experience with US and Canadian projects.

The cumulative differences in the techniques and assumptions used by the two methods mean that they will produce different energy estimates for any given project. One of the main reasons for performing this study was to quantify and understand the accuracy of the different energy estimates in order to establish a sound basis for moving towards a unified approach.

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Characteristic UL-DEWI UL-AWST

Source of data Met mast(s), lidar(s) or (Germany) validation

by nearby wind farm if available

Met mast(s) or lidar(s)

Shear method (for met masts)

Modeled with WAsP or CFD

Extrapolated

Long-term correction

Monthly MCP Hourly or daily MCP

Wind flow model

WAsP supported by own scripts (simple terrain) or

CFD (complex terrain)

Mesoscale/Mass Consistent modeling

solution

Wake model Modified park Openwind deep-array wake model (DAWM)

Energy yield calculation

WAsP11 Openwind

Non-wake plant losses

FGW TR6 Rev10 and MEASNET Site

assessment plus experience

Derived mainly from U.S. experience

Table 1: Comparison of the UL-DEWI and UL-AWST methods

VALIDATION OF THE UL-DEWI METHODBackcast database

The present analysis compares pre-construction EYAs for plants then under development, created using the UL-DEWI method and data from met masts, with operational energy yield assessments (OP-EYAs) for the same plants now in operation. All the EYAs and OP-EYAs in the study were performed by UL.

In the first stage of the analysis, UL identified plants for which both EYAs and OP-EYAs were already available or could be performed. For prior studies, UL compared reported locations, rated capacities, turbine models and hub heights to ensure no significant changes had occurred between the time the EYA was performed and when the plant went into operation. Where the parameters differed, the plant was excluded. UL considered only plants with EYAs performed in 2009 or later, as they are the most relevant for assessing the accuracy of the UL-DEWI method going forward.

A total of 50 wind plants from Europe and North Africa qualified for the study. The selected plants are located mostly in France (39), but some are in Croatia (1), Finland (3), Germany (3), Morocco (1) and Poland (3), as shown in Figure 1.

The main criteria which have been considered to select the projects are the following:

• Availability of EYA and OP-EYA

• Quality of the on-site and production data

• Project age (recent projects were preferred)

• Project location (in a country where the UL-DEWI method is presently being applied)

The selected plants are sited in a variety of wind resource regimes and land cover. The predominant terrain type is simple.

A summary of key project characteristics is presented in Table 2, while Figure 2 shows the distribution of projects by turbine manufacturer. The range of plant sizes is diverse, although most of the plants can be considered small; 37 have 10 or fewer wind turbines. An average of 30.6 months of operational data was available from each project, meaning that this assessment is based on more than 125 plant-years of data in total.

Figure 1: Distribution by country of the 50 wind plants in the UL-DEWI backcast dataset

Parameter Range Average

Operational months 9 - 68 30.6

Plant capacity (MW) 4.6 – 72.6 18

Number of turbines 2 - 29 8

Turbine rated capacity (MW)

0.8 – 3.3 2.3

Table 2: Characteristics of the 50 wind plants in the UL-DEWI backcast dataset

Figure 2: Distribution by turbine manufacturer of plants in the UL-DEWI backcast dataset

EnerconFuhrländer

GE

Nordex

SenvionSiemens

Vestas

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Method of analysis

Since the goal of the study was to assess the accuracy of current EYA methods, UL applied adjustments, where necessary, to the estimated production from each prior EYA to account for changes in methods since it was performed. Prior OP-EYAs were also reviewed and adjusted to current practice where necessary. For plants with more than one EYA or OP-EYA in UL-DEWI’s database, the latest revision conforming to the commissioned wind farm characteristics was used.

To perform the long-term adjustments for the EYA and OP-EYA of each plant, the same reference data source and reference period were used with a few minor and inconsequential exceptions. This should eliminate most deviations between observed and predicted long-term production from this source.

The main changes in methods from prior studies, depending on age of prior study, were the following:

• WAsP 11.6 instead of WAsP 5.1

• Long-term correction period

• Long-term correction dataset

• Wake decay constant of 0.07 instead of 0.077 in the Modified Park model

• Adjusted utility grid and wind turbine maintenance availability losses (from approximately 1% before to 0% currently) as required by TR6

• Adjustments of default Generalized Wind Climate (GWC) heights to better represent measurement and hub heights, in order to have a better interpolation to the hub height.

• Air density correction based on on-site temperature, pressure and humidity

For each plant, a production ratio (PR), defined as the OP-EYA P50 (median-likelihood) estimate divided by the EYA P50 estimate, net of plant losses, was calculated and converted to a percentage. A ratio above 100% corresponds to an underestimation of the long-term plant production; a ratio below 100% indicates an overestimation. The mean and median of all ratios and their standard deviation, or spread, indicate the overall accuracy of the current UL-DEWI EYA method for the sample of projects considered. To help ensure that the sample was reasonably representative of present-day wind plants, only projects which reached commercial operation during the last 11 years were used and UL helped ensure that these had high quality on-site wind measurements and deployed turbine types and hub heights that are still relevant today. While the result may not be perfectly reflective of the outcome for all possible plants, UL considers that the sample size and selection criteria are sufficient to draw conclusions and implement the changes and harmonization proposed in response to UL’s findings.

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UL-DEWI results

The chart in Figure 3 shows the distribution of PR values for all 50 wind farms. The bars represent the number of plants that fall within each bin of width 2.5% centered on the indicated value. For example, 11 wind farms have a PR in the 90% bin, spanning values from 88.75% to 91.25%.

The mean PR is 93.8%, the median is 94.0%, and the standard deviation is 5.3%. The mean difference of -6.2% is the average deviation from 100%. The median deviation of -6.0% represents the deviation exceeded by half the plants.

The results indicate that the UL-DEWI method tends to overestimate plant production. While the tendency is moderate compared to the typical EYA uncertainty estimate for a single project (10%-15%), the average deviation over all projects is statistically highly significant (p < 0.001) based on the standard deviation and sample size. It is also consistent with deviations observed in prior UL-DEWI backcast studies.

Whereas the PR is based on the P50 or median-likelihood values of the operational and preconstruction estimates, project financing usually relies on a higher threshold of confidence such as the P75 or P90. Such probability-based estimates, or P-values, depend on the uncertainties in the EYAs as well as on their median or expected outcomes.

Figure 4 compares the observed probabilistic results to the predicted P-values. The orange line shows the proportion of projects (y-axis) found to be above the indicated P-value (x-axis). If the EYA P50 were unbiased and the uncertainty were accurately estimated, the orange line would fall on the dotted grey line of equality, i.e., 10% of the projects would be above the P10, 20% above the P20, and so on. The actual proportion is well below this ideal distribution up to about the P85. This occurs, in part, because the EYA method overestimates the P50. However, the red line in the same figure shows that even if the mean deviation in the P50 were removed from the distribution, the estimated uncertainties in the UL-DEWI method would be too high, resulting in too many projects exceeding the P-values above P50.

Considering these findings, it is necessary to address both the mean bias and the uncertainty of the UL-DEWI EYA method to align the production estimates with observed results over the full range of confidence levels.

Figure 4: Proportion of projects for which the operational yield exceeds the estimated P-value of the preconstruction EYA.

Figure 3: Distribution of production ratios (PR) for the UL-DEWI method

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VALIDATION OF THE UL-AWST METHODBackcast database

In parallel with the foregoing assessment of the UL-DEWI method, UL carried out a backcast study for the UL-AWST method against a comparable set of projects. Twenty five of the projects were from the UL-DEWI backcast database. These were projects for which it was possible to perform an independent EYA using the UL-AWST method. An additional 18 projects, all in France, came from the prior UL-AWST backcast study published in 2018.

The main characteristics of the plants are summarized in Table 3. The selected plants are located in France (39), Germany (1), Morocco (1) and Poland (2), as shown in Figure 5..

Method of analysis

The method of analysis for the UL-AWST validation was mostly the same as for the UL-DEWI validation, and its description will not be repeated here. Since all the EYAs were performed using the current UL-AWST method, no adjustments were needed to update the results. One relevant difference is that the long-term adjustments were performed independently for the EYAs and OP-EYAs, and therefore the reference data sources and periods of record were, in general, different between them. This introduces a possible additional source of deviation between the two assessments that was not present in the UL-DEWI analysis. However, the impact of the different long-term adjustments is judged to be small.

UL-AWST results

Figure 6 shows the distribution of PR values for the 43 wind farms assessed with the UL-AWST method. The width of the bins is again 2.5%. The mean PR is 101.3%, the median is 99.7%, and the standard deviation is 8.0%.

In contrast to the UL-DEWI method, the average deviation of +1.3% indicates a tendency of the UL-AWST EYA method to slightly underestimate the operational energy yield, though the result is not statistically significant considering the sample size and spread of the distribution. However, the median deviation of -0.3% shows that almost exactly half of the projects exceed the predicted P50. The significant difference between the average and median deviations is caused by the skew in the distribution towards the high side, as three projects have PR values above 115%.

Parameter Range Average

Operational months 12 - 63 25.9

Plant capacity (MW) 4.8 – 58.0 15.6

Number of turbines 3 - 29 7

Turbine rated capacity (MW)

0.8 – 3.3 2.1

Table 3: Characteristics of the 43 wind plants in the UL-AWST backcast dataset

Figure 5: Distribution by country of the 43 wind plants in the UL-AWST backcast dataset

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… the median deviation of -0.3% shows that almost exactly half of the projects exceed the predicted P50.

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ANALYSIS OF DEVIATIONS FROM OPERATIONALENERGY YIELDSUL assessed the potential causes of the observed deviations between actual and expected plant performance for both the UL-DEWI and UL-AWST methods. The potential causes include, in the EYAs, possible errors in the observed wind resource, shear from measurement height to hub height, long-term climate adjustments, wind flow modeling, predicted turbine performance, wake losses and non-wake plant losses; and in the OP-EYAs, possible errors in production data, long-term adjustments, and the analysis method. In general, EYAs carry the potential for larger errors than the OP-EYAs, and most deviations between observed and predicted production can be assumed to be from the EYAs.1

The following is UL’s analysis of the main factors considered up to the calculation of the gross or ideal energy production.

• Collection and validation of the meteorological data. While errors are possible in individual cases, the methods of data validation are well proven and unlikely to result in a significant

1 An exception worth noting is those factors potentially affecting the plant performance that typically fall outside the scope of the EYA. Examples include curtailments of plant production due to grid congestion and other reasons, changes in turbine control algorithms or airfoil characteristics that alter the power curve, and wake effects from neighboring plants that did not exist or were unknown at the time of the EYA. UL does not believe such factors significantly influence the results of this study.

bias. Average, validated wind speeds at measurement height were almost identical between the UL-DEWI and UL-AWST methods for the same plants.

• Shear adjustments. Three projects in the UL-AWST backcast database show an unusually large excess of production over the predicted. In these three cases, the measurement height is much below the turbine hub height, suggesting a problem in the shear adjustments. The removal of these three projects would reduce the mean PR by 1.4%. Two of the three projects are also part of the assessment of the UL-DEWI method and are likewise underestimated, but to a much smaller degree. However, a possible problem in vertical extrapolation has an uneven impact on projects, since many of them have measurements close to the hub height. In these latter cases, the difference would be negligible. It has been estimated that the average impact on the set of projects would be approximately 0.2%.

• Long-term climate adjustments. For the UL-DEWI method, in most cases, the same reference data sources and reference periods were used for the EYA and OP-EYA of each plant. This should eliminate most deviations between observed and predicted long-term production from this source. It was not possible to take the same approach for the UL-AWST method, however, and consequently the long-term adjustments may introduce larger deviations between EYA and OP-EYA results. UL believes this factor might account for a shift of ~0.4% in the mean PR for the UL-AWST method.

Figure 6: Distribution of production ratios (PR) for the UL-AWST method

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• Wind flow modeling. Given the relatively small size of most projects in this study and the simple terrain in which they are sited, wind flow modeling errors are likely to be small and unbiased. While the wind flow models used in the two methods differ substantially in their characteristics and performance, particularly in complex terrain and where there are thermal gradient effects, these differences are not believed to play a role in this analysis.

The following factors concern plant losses:

• Wake modeling. The UL-DEWI and UL-AWST methods employ different wake models. Among other differences, the UL-AWST Deep-Array Wake Model (DAWM) incorporates a model of two-way boundary-layer effects in plants with multiple rows of turbines, whereas the UL-DEWI Modified Park model considers only direct wake effects. Overall, DAWM tends to estimate a larger wake loss for larger projects and for sites with frequent thermally stable atmospheric conditions. The mean difference for the 25 projects in common between the two backcast databases was 0.8%.

• Upwind blocking effects. The current UL-DEWI method does not consider possible upwind blocking effects, which consider the impeding influence of wind plants on the available wind resource. Research indicates this loss could be as high as 3% depending on project size and other characteristics. However, the mean blocking effect estimated with the UL-AWST method for the 25 projects in common was 0.0% because of the relatively small size of most projects in the study.

• Turbine performance in IEC-compliant tests. Based on a review of numerous tests performed by UL and independent third parties, UL has found that wind turbines often produce less energy in IEC-compliant power curve verification tests than predicted based on their manufacturer-provided power curves. This finding is also supported by research published by other consultants. The UL-AWST method includes a turbine performance loss, whereas the UL-DEWI method does not. For the 25 plants in common between the two datasets, the UL-AWST loss is 1.7%.

• Environmental loss factors. UL finds that these losses tend to be greater than assumed in the UL-DEWI method. Specifically, degradation of blades caused by environmental conditions such as soiling is larger because the blade refinishing schedule is typically less frequent than assumed. The estimated impact is 0-1%.

• Wind turbine availability losses. Between 2014 and 2020, UL performed an analysis of energetic availabilities from SCADA data on 39 French and German projects. This analysis confirmed the 3% loss as currently applied in the UL-DEWI method and suggested by FGW TR6. The UL-AWST method instead, splits the wind turbine availability loss in three different sources: Contractual Turbine Availability (3%), Non-Contractual Turbine Availability (1.3%) and Long-term Availability Correlation with High Wind Events (0-3.5%),

calculated depending on the wind speed distribution and the power curve. These assumptions are supported by the analysis of SCADA data from more than 200 projects, largely from North America, where the mean time-based availability was 95.8%, while the estimated mean energy-based availability for 29 projects was 93.7%.

The difference of turbine availability loss between UL-DEWI and UL-AWST methods can range then from 1.3% to 4.8% for the same project, although in both cases the assumed or modeled values have been established based on actual wind farm operation data. The following two factors can be the reasons of such discrepancy:

◦ The operation of the wind farms might be different between Europe and North America, and more generally speaking between different parts of the world, leading to specific availability loss values.

◦ The accounting of the unavailability events and its transformation into energy loss follows different methods.

UL will continue to explore these differences through the analysis and comparison of SCADA data. Following this process, UL will be able to accurately assess the possible contributions of the two factors and adjust the availability loss estimation, if warranted.

• Curtailments of different origins. To the extent they were well documented, they were found to have an insignificant impact on the performance gap.

• Electrical losses. The default values of some systematic losses may be too high for the sample projects’ characteristics. Specifically, the default value of 2.4% electrical loss in the UL-AWST method may be high considering the small number of turbines and electrical design characteristics of the projects in the backcast databases, compared to the much larger projects typically encountered in the United States where the UL-AWST method was developed. On average, the difference in electrical losses between the UL-AWST and hybrid method compared with the values used in the UL-DEWI method is 0.6%. Analysis of SCADA and revenue meter data from European projects supports the UL-DEWI estimates.

• Miscellaneous availability losses. Various availability factors including substation and collection system, utility grid and site access losses, though individually small, contribute 0.2%-1.5% additional loss.

• Sub-optimal operation loss. The UL-DEWI method assumes plants are operated with optimal effectiveness, meaning deviations in yaw, speed, pitch and other control parameters are quickly identified and addressed and downtime is kept to a minimum. The effects of inefficient operating practices are not considered by the current method. The UL-AWST method estimates these losses to typically range from 0.5%-3.0%, with a typical value of 1.5%.

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Table 4 presents a synthesis of our findings, including estimates of the contribution of each factor to explain the deviations observed in both methods for the projects in the backcast database. A hyphen (-) indicates no significant contribution. Positive deviations indicate the factor causes the method to overestimate production, while negative deviations (in parentheses) indicate the opposite. While it is not possible, without much further investigation, to determine all the reasons for the observed differences between predicted and actual energy yield for either method, the factors identified here explain the great majority of them, and form a sound basis for adopting an updated, harmonized method incorporating features of both.

Potential contributing factor UL-DEWI UL-AWST

Meteorological Analysis

Data validation - -

Shear adjustment - (0.2%)

Long-term adjustment - (0.4%)

Wind flow modeling - -

Plant Losses

Wake effect 0.8% -

Upwind blocking effect 0.0% -

Turbine performance in IEC-conditions

1.7% -

Environmental factors 0.5% -

Wind turbine availability ? (?)

Curtailments - -

Electrical - (0.6%)

Miscellaneous grid 0.6% -

Sub-optimal operation 1.5% -

Total Effect 5.1% (1.2%)

Table 4: Estimated contributions of various factors to deviations between predicted and observed production for the UL-DEWI and UL-AWST methods

HARMONIZATIONDescription and schedule of the phased approach

The goal of method harmonization is twofold: to adopt a single method globally for the sake of consistency across all markets, and to reduce or eliminate the observed deviations identified in the present study, particularly in the countries where the UL-DEWI method is being applied.

The harmonization process is divided into two phases:

Phase one

Based on the results of the analyses presented in the previous section, UL will be introducing a hybrid method in Phase One for regions where the UL-DEWI method is presently being used. This method will retain the current UL-DEWI meteorological analysis, while adopting the UL-AWST method for wind flow and wake modeling, as well as some of the UL-AWST plant loss assumptions. This will greatly reduce the average deviation observed in the P50. New uncertainty estimates will also be adopted to achieve more accurate P-values. The current UL-AWST method will remain in place and unchanged for markets where it is presently being used.Certain region-specific adaptations will be implemented.

Specifically, in order to address the TR6 standard in Germany, a TR6-accredited EYA method will be implemented (referred to as the accredited method). The primary difference between the accredited method and the general hybrid method is that the accredited method will exclude six loss categories for which the TR6 standard defines different default values or that are not included in the TR6 framework: upwind blocking effect, availability of collection and substation, site access, grid availability, consumption of the extreme weather package, and sub-optimal performance losses.

Both the hybrid and accredited methods will continue to follow the IEC and MEASNET guidelines. Additionally, for accredited EYAs, especially critical for the German market, UL will follow the TR6 guideline.

The implementation of Phase One will occur on 01 October 2020 and is discussed in more detail later in this section.

Phase two

The second phase of the harmonization will adopt a single UL method and toolset to perform EYAs based on wind measurement data in any part of the world. The principal although not the only focus of the harmonization will be in two areas:

• Meteorological analysis. Although the present study shows a negligible difference in results of the UL-DEWI and UL-AWST meteorological analyses, UL will be adopting a single method. The steps used in both methods will be analyzed and the most accurate or practical approach for each step will be chosen.

The harmonized method is expected to be fully implemented in UL’s Windographer software. The use of a common single software and data format will be a step forward in the continued digitalization of UL’s advisory services.

• Wind flow modeling. A validation of the two types of models used by UL for complex terrain, Meso-micro scale combination and CFD, will be performed to assess their error characteristics in a wide range of situations. The ultimate goal is to establish criteria to identify which model is most suitable for any given site.

The implementation of phase two will occur in Q1 2021.

MEASNET and accreditation

Besides other services, UL through its office in Oldenburg, Germany, is accredited for the following services:

• Determination of Wind Potential and Energy Yield of Wind Turbines; and

• Determination of Site Quality.

The accreditation requires UL to follow the guidance set out by the following standards:

• FGW TR6: Determination of Wind Potential and Energy Yields

• MEASNET: Evaluation of Site-Specific Wind Conditions

• IEC 61400-12-1: Wind Turbines - Part 12: Power Performance Measurements of Electricity Producing Wind Turbines; and

• IEC 61400-1: Wind Turbines - Part 1: Design Requirements.

It is important to UL and many of UL’s customers that UL maintain energy yield assessment and site quality services as accredited services. UL therefore will continue to obtain accreditation and will follow the requirements set out in these guidelines. Most of the changes described in the following sections are in accordance with the above guidelines and follow recommendations within these guidelines, with only a few exceptions of the FGW TR6, which are noted accordingly.

Wind flow and wake modeling

Under Phase One, UL will introduce changes in the energy assessment process with the goals of reducing or eliminating the deviations in the present UL-DEWI method and moving closer to full harmonization. The following subsections describe the wind flow and wake models that will be adopted in countries where the UL-DEWI method is being used. They are already in use in the UL-AWST method.

Wind flow modeling

Linear wind flow models like WAsP1 are widely used to predict the spatial variation of the average wind speed, directional frequency distribution (wind rose), wind shear, and other boundary layer

1 Troen, I. (1990). “A High Resolution Spectral Model for Flow in Complex Terrain”. Proceedings from the 9th Symposium on Turbulence and Diffusion, Roskilde, Denmark.

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characteristics. WAsP is based on the theory of Jackson and Hunt.2 It came into wide use in the 1980s when the computing resource was very limited. It runs quickly while performing reasonably well where the wind is not significantly affected by steep slopes, flow separations, thermally driven flows, low-level jets, and other dynamic and nonlinear phenomena.

A second common wind flow modeling approach is Reynolds-average Navier Stokes (RANS) computational fluid dynamics (CFD) models. Commercial examples include Meteodyn WT and WindSim.3 Other versions have been developed or adapted by various organizations, including by UL, where it is applied in the UL-DEWI method in complex terrain. Unlike WAsP and related models, RANS CFD models handle turbulent flows and flow separations and related non-linear phenomena important in steep terrain.

The third approach to wind flow modeling is the use of mesoscale numerical weather prediction (NWP) models e.g., WRF, ARPS, MC2, KAMM, etc. In principle, fully compressible, non-hydrostatic NWP models can simulate and capture a broad range of meteorological phenomena from synoptic to micro scales, but the required computing power is substantial and increases rapidly with decreasing grid spacing. To circumvent this issue, NWP models are usually coupled with simpler “microscale” wind flow models to achieve a high spatial resolution. The microscale models used for this purpose include Jackson-Hunt-type models (e.g., WAsP, MsMicro,4 Raptor5 ) and mass-conserving models (e.g. WindMap,6 CALMET7 ). Two leading examples of such coupled mesoscale-microscale models are the KAMM/WAsP system developed by Risoe National Laboratory8 and the Sitewind system developed by AWS Truepower.

For the hybrid method, Sitewind will be the default modeling system used in simple and medium-complex terrain by global UL. In this approach the mesoscale model (WRF) is run for a sample of days in nested grids down to a resolution of 1 km. Then, the mean wind flow is downscaled to approximately 50 m grid spacing using the microscale model WindMap.9 Previous research has suggested that this approach is more accurate than the industry-standard WAsP and RANS CFD model over wind-project-scale distances, especially where mesoscale circulations have a significant impact on the spatial distribution of the wind resource.10 At smaller scales and in particularly steep terrain, RANS CFD may have an advantage.

2 Jackson, P.S. and J.C.R. Hunt (1975). “Turbulent Wind Flow over Low Hill”. Quart. J. R. Met. Soc., vol. 101, pp. 929-955.3 S. Ramechecandane, A. R. Gravdahl (2012). “Investigations on Wind Flow over Complex Terrain”, WIND ENGINEERING Volume 36, NO. 3, 2012, pp273-296.4 Taylor, P.A., J.L.Walmsley, J.R. Salmon (1983). “A Simple Model of Neutrally Stratified Boundary-Layer Flow over Real Terrain Incorporating Wave Number-Dependent Scaling”. Boundary-Layer Meteorology., vol. 26, pp. 169-189.5 Ayotte, K.W., P.A. Taylor (1995). “A Mixed Spectral Finite-Difference 3D Model of Neutral Planetary Boundary-Layer Flow over Topography. J. Atmos. Sci., vol. 52, pp. 3523-3537.6 Brower, M. (1999). “Validation of the WindMap Program and Development of MesoMap”. Proceeding from AWEA’s WindPower conference. Washington, DC, USA.7 Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino (2000). “A user’s guide for the CALMET meteorological model (version 5)”. Report from Earth Tech, Inc., Concord, Massachussets, USA. 332 pp.8 Frank, H.P. and L. Landbergh (1997). “Modeling the wind climate of Ireland”. Boundary Layer Meteorology, vol. 85, pp. 359-378.9 Brower, M., J.W. Zack, B. Bailey, M.N. Scwartz and D.L. Elliot (2004). “Mesoscale modeling as a tool for wind resource assessment and mapping”. Proceeding from the American Meteorological Society conference. Seattle, WA, USA.10 Reed, R., M. Brower, and J. Kreiselman (2004).”Comparing SiteWind with standard models for energy output estimation”. Proceedings from EWEC, London, UK.11 Mortensen, Niels G.; Landberg, Lars; Troen, Ib; Petersen, Erik L (1993): Wind Atlas Analysis and Application Program (WASP), Vol.1: Getting Started, Vol. 2: User’s Guide.12 I.Katic, J.Højstrup; N.O.Jensen (1986): A Simple Model for Cluster Efficiency, European Wind Energy Association Conference and Exhibition, 7-9 October 1986, Rome, Italy.13 Frandsen, S.T., Barthelmie, R.J., Pryor, S.C., Rathmann, O., Larsen, S. Højstrup, J. and Thøgersen, M. (2006): Analytical Modeling of Wind Speed Deficit in Large Offshore Wind Farms. Wind Energy, 9, 39-53.14 Openwind User Manual (2019). UL.15 Brower, M.C., Robinson, N.M. (2017): The Openwind Deep-array Wake Model, Development and Validation. UL AWS Truepower

Wake effect modeling

The wake loss estimation considers the influence of the wind turbine generators (WTGs) from the same wind farm and by known existing and planned neighboring wind farms. The calculated loss for each source is presented separately in the energy assessment results. The Park model implemented in the current WAsP software11 used in the UL-DEWI method is based on the N.O. Jensen wake model12 and accounts for the effect of multiple wakes on the velocity. The model determines the initial wind speed deficit behind the rotor using the conservation of momentum and mass and assumes a linear expansion of the wake.

In the past few years, researchers have become aware that the current generation of wake models including Park may underestimate wake losses in wind projects with multiple rows of wind turbines. The crux of the problem is that the leading wake models – including Park – ignore two-way interactions between the atmosphere and the turbines.13 Each turbine extracts energy from the wind passing through its rotor plane, creating a zone of reduced speed extending some distance downstream. Upstream and outside this zone of influence, it is assumed the ambient wind is unaffected.

Both theory and experiments suggest that, for large arrays of wind turbines, this assumption does not hold. The presence of numerous large wind turbines in a limited area can alter the wind profile in the planetary boundary layer outside the zone of direct wake effect, both within and around the array, thereby reducing the amount of energy available to the turbines for power production. Experimental data supporting this hypothesis comes mainly from offshore wind projects. Onshore, the effect is attenuated, but theory suggests it may nonetheless be significant in larger projects.

The deep-array wake model (DAWM) developed by UL and implemented in the Openwind14 plant design and optimization program is a modification that can be applied to any other wake model. The deep array effect emerges gradually as more rows of turbines are added. The Deep Array Eddy-Viscosity Wake Model15 (DAWM Eddy-Viscosity) used in the UL-AWST method will be the global UL default model for all energy assessments regardless of how many turbines are being modeled.

The introduction of the DAWM in the hybrid method will contribute to the reduction of the performance gap, because the wake loss is on average greater than that obtained with the Park wake model.

Upwind blocking effect in large wind farms

The upwind blocking effect describes the impeding influence of a large wind plant on the available wind resource. This effect is conceptually similar to how an obstruction in a stream causes the current ahead of it to slow down as the water is diverted around it. Since the effect operates upstream, it is not captured by standard wake models, including Park and DAWM, which address only downstream turbine influences.

The effect is not considered in the accredited method as it is not covered in the TR6 guidelines. For the hybrid method, UL will use the following upwind blocking adjustment adopted in the current UL-AWST method16:

• For wind farms less than 50 MW in rated capacity, or for wind farms with a single row of turbines in the prevailing wind direction, the loss is assumed to be 0%. This is the case for most of the projects in the present study.

• For wind farms greater than 50 MW in rated capacity, and with at least two rows of turbines in the prevailing wind direction, the loss is assumed to increase proportionate to farm size and the total wake loss of the project is up to a maximum value of 3.0%.

• A correction is made for farms with shallow arrays in the prevailing wind direction.

• Above 200MW, where the upwind blocking effect appears to reach a maximum in the numerical simulations, the loss is assumed to be a fixed proportion of the total wake loss or 3.0%, whichever is the lower value.

Non-wake plant losses

The following subsections describe in further detail the loss assumptions which will be applied in the hybrid and accredited methods. Where neither method is mentioned, the loss applies to both. The loss classification categories and subcategories follow in general the IEC 61400-15 Energy Loss Framework.

Availability

A plant or a single WTG is said to be available when it is capable of generating its full rated output, given sufficient wind. Availability losses, or downtime, occur when some turbines in a project, or the entire project, should be able to operate in the wind and temperature conditions at the site but are unable to for some reason.

The availability losses for the hybrid and accredited methods are similar and largely follow the TR6, rev. 10, guidelines.17

Turbine availabilityUL will continue to assume a turbine availability of 97.0%, which

16 Ziesler, C.D. (2018): “Large Wind Farm Blockage Loss – Calculation Methodology”. UL AWS Truepower17 Technical Guidelines for Wind Energy Plants (FGW e.V.), Part 6: “Determination of Wind Potential and Energy Yield”, revision 10 (TR6, rev.10).

is the estimated average availability during normal operation according to TR6, without consideration of environmental conditions. This includes planned maintenance, as it is often included in turbine availability warranties, and it is assumed that a full wrap maintenance contract exists. The 97.0% value has been confirmed by an internal SCADA data analysis performed by UL of 39 wind farms in Europe operating between 2014 and 2020, with an average of 4.4 years of operation since their commissioning date.

Balance of plant availabilityIn the accredited method, in accordance with TR6, rev.10, the balance of plant availability loss is considered to be negligible. However, UL will include the following two additional sources of downtime loss in the hybrid method:

• Availability of collection and substation: This loss accounts for outages of the collection system and substation. It has typically assigned a value of 0.2%, which corresponds to two events per year of eight hours average duration.

• Site access: Severe weather can limit access to some sites, which can reduce energy production because response times for repairs are increased. This loss will be estimated based on weather data and other site-specific information. For most onshore European projects, it will be negligible.

Grid availabilityOutages of the utility grid contribute to plant downtime. In the hybrid method, absent other information, this factor will be assigned a default value of 0.3%, which corresponds to four events per year of 6 hours average duration. For the accredited method, the TR6 loss value of 0.0% will be applied.

Electrical

Electrical efficiencyElectrical losses of collection and interconnection systems depend on the electrical design. A default value of 2.0% will be applied in both the hybrid method and the accredited method based on estimations and experiences from wind farms in Europe. Where a detailed electrical design calculation is available, UL will use the calculated loss instead of the default loss.

Facility parasitic consumptionFor the hybrid and accredited methods, power consumption for site lighting, O&M facilities, and other site facilities not associated with the turbines are not included as loss items and should be considered in the project’s financial modeling.

If the turbines are equipped with an extreme-weather package, the energy consumed by the equipment is accounted in the hybrid method as an addition to the electrical loss and is calculated from the equipment specifications and specific site conditions, unless the client verifies this consumption is being accounted for separately in the financial model. For the accredited method, the loss associated with the extreme weather package will not be considered and the energy consumed should be included in the project’s financial modeling.

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Turbine Performance

General power curve adjustmentIt is common practice to test a small number of newly installed wind turbines at a wind plant site under controlled conditions defined by the IEC. In the UL-AWST method, turbines are assigned a power curve loss derived from independently performed, IEC-compliant tests where a sufficient number of such tests are available.1 Where sufficient tests are not available, which is often the case for less common and/or new turbine models, a default loss value is assumed. The present default loss is 2.1%, based on a review of 69 tests of wind turbine models that do not qualify for turbine-specific power curve losses.2 The same value will be assumed in the hybrid and accredited methods.

Site-specific power curve adjustmentThe power curves provided by manufacturers typically assume horizontal flow. Where the incoming flow is inclined so it approaches the turbine from below, however, the power generated at a given wind speed is reduced. The UL-AWST method includes a site-specific power curve adjustment for inclined flow estimated from the terrain slope around the turbines. The same approach will be adopted in the hybrid and accredited methods.

High wind hysteresisFor most turbines, once the wind speed exceeds the turbine’s design cut-out speed and the machine shuts down, the control software waits until the speed drops below a lower speed threshold (the reset-from-cut-out speed) before allowing the turbine to restart. The resulting hysteresis loss is accounted for in the energy yield assessment and is calculated according to the TR6, Rev. 10, for both the hybrid and accredited methods.

Sub-optimal performanceSub-optimal performance refers to conditions that cause wind turbines to produce less power than they should for the inlet wind condition, and the deficit cannot be explained by known factors such as downtime, curtailment, inclined flow or blade degradation. Sub-optimal operation can occur when turbine control settings or inputs are incorrect or have been modified (to reduce turbine wear and tear, for example), causing the turbine to operate at less than its design efficiency or to operate in a reduced-power mode. It can also occur through control system faults or conditions that do not register as downtime.

It is important to note the difference between turbine availability losses and sub-optimal performance losses. When a turbine is shut down, the lost time in production should be accounted for in availability statistics. Sub-optimal performance refers to energy lost when the turbines are indicated by the SCADA records to be operating normally but are in fact producing less power than they should be.

UL has observed sub-optimal performance at numerous plants.

1 Ziesler, C., O’Loughlin, B., Lightfoote, S., Bernadett, D., Brower, M., (2018): “2018 Backcast Study and Methods Update. Verifying and Updating UL AWS Truepower’s Methods for Performing Pre-Construction Wind Energy Production Estimates. UL AWS Truepower”. To be used to calculate a turbine-specific loss, test results must be from the same turbine model or performance family (defined as a group of turbine models from the same manufacturer and with the same rotor diameter), installed in commercial wind farms (i.e., not prototypes), and the data analysis must be free from filtering for shear, turbulence, veer, inclined flow, or any other condition not included in the IEC filtering requirements. Tests performed by the manufacturer or by the plant owners without independent oversight are excluded. The loss is defined as the difference between the expected and measured Annual Energy Production (AEP) for a given test.2 Of the 69 tests cited here, 29 were performed by UL, while the remaining 40 were performed by independent firms.

Examples of issues UL has identified include:

• Incorrect pitch settings causing aerodynamic inefficiencies• Incorrect anemometer calibration constants, resulting in

turbines switching on at too high or too low a speed• Yaw misalignment caused by incorrect direction vane offsets• Load control strategies that cause the turbine to operate in a

reduced-power mode in order to reduce loads• Control software settings that slow the response of the

turbine to wind direction and speed changes to reduce wear and tear on yaw and pitch motors

• Temporary derating of turbines from delivering nameplate power either manually or by the SCADA system.

In some cases, analysis comparing the output of different turbines in the same project shows that sub-optimal performance is occurring, and yet it has not been possible from the data to identify the cause, or causes, of the issue.

For the hybrid method, UL will adopt the default loss factor from the UL-AWST method, which is 1.5%. Following the TR6 guideline, the default loss for the accredited method is zero.

Environmental

IcingThis loss reflects decreased rotor aerodynamic efficiency caused by the accumulation of ice on the turbines during plant operation, as well as turbine shutdowns caused by excessive ice accumulation. The distribution of allocated losses will follow TR6, Rev. 10, for both the hybrid and accredited methods. The icing losses are estimated on a project-specific basis from site weather data, including the expected frequency and duration of freezing precipitation and rime ice formation. If no site-specific icing data are available, icing maps will be used (for the accredited method, in Germany, the FGW map).

Blade degradationOver the lifetime of the wind turbines it can be expected that the rotor blades will not keep their ideal aerodynamic profile, and especially the leading edges and tips can degrade. Furthermore, dirt, insects, rime and ice,and aging of the rotor blade material can impact the blade aerodynamic efficiency. UL therefore assumes a small loss for rotor blade degradation. In the hybrid and accredited methods, UL will adopt the current UL-AWST method, which calculates the loss on a project-specific basis from the expected dust and insect accumulation in the area and frequency of precipitation.

Environmental shutdownThis loss accounts for the energy lost at machine stops due to environmental conditions. A possible low/high temperature shutdown project-specific loss value will be calculated for both the accredited and hybrid methods based on the energy that will be lost when the turbine shuts down due to temperatures outside the operating design envelope.

Curtailments

Environmental/permit curtailmentIf the wind farm is required to comply with certain operational standards due to environmental constraints, an environmental curtailment loss may be estimated on a project specific basis. Production may be curtailed due to habitat concerns e.g. birds or bats, noise constraints, shadow flicker and other issues.

Load curtailmentIf the manufacturer or an independent consultant specifies a wind turbine curtailment strategy to limit the wear and tear on certain turbines, the effects on the energy output of the wind farm will be estimated by UL for both hybrid and accredited methods. If the curtailment strategy information is not available, no loss will be applied in the accredited method. For the hybrid method, when turbines are spaced closer than three rotor diameters from each other, UL will estimate a representative loss (auto-curtailment) until a detailed curtailment strategy is specified by the manufacturer. At that time, a more detailed calculation of this loss can be performed.

Grid curtailmentIf the wind farm is forced to curtail production, loss of revenue could result from the sale of energy and/or loss of production incentives, and this curtailment will be considered by UL.

Operational strategiesA wind farm may be subject to occasional or regular up-rating, derating, optimization or shut-down not captured in the power curve or availability assumptions. UL will estimate the corresponding losses if data are available to support a specific value.

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Uncertainty

UL’s approach to uncertainties will be identical for both hybrid and accredited methods’ EYA. For Phase one of the harmonization process, measurement procedures will follow the UL-DEWI method and the determination of uncertainties related to measurements will be identical to current evaluations. The uncertainty of later stages of the EYA will be updated to reflect the methods changes, as explained in the following subsections.

Long-term adjustment and data reconstruction The uncertainty for the long-term adjustment includes data reconstruction uncertainty and is fully compliant to MEASNET and TR6 requirements. The approach considers the representativeness of the reference data for the site, length of common period of on-site measurement and reference data set, temporal consistency of the long-term data, method-inherent uncertainties, and long-term climate variability. The data reconstruction uncertainty is based on the UL-DEWI method. It depends on correlation quality and site and measurement characteristics. It also considers the representativeness radius as defined in MEASNET and TR6 and differences between measurement heights.

Vertical extrapolationIn the current method vertical extrapolation has been modeled using the WAsP software, which has been adjusted to the vertical wind shear on the site. In the future method, the vertical extrapolation will be using the measured wind shear on the site. The vertical extrapolation uncertainty is based on the uncertainty of the shear coefficient determination depending on wind speed uncertainties, distances between and number of anemometers used, site and flow characteristics and extrapolation distance. It considers the minimum measurement height of two-thirds of hub height as required by MEASNET and TR6. It fully complies with the other requirements as formulated in TR6.

Wind flow modeling (horizontal extrapolation)The horizontal wind flow modeling by WAsP will be replaced by the UL Sitewind model. Consequently, the uncertainty model used will be the one implemented in the UL Openwind software with small adjustments for close distances and considering the representativeness radius formulated in MEASNET and TR6. It is derived from an analysis of observed wind flow modeling errors for sites spanning a range of topographic and meteorological conditions following the concept of wind resource similarity.

Wind speed frequency distributionThis uncertainty component has been considered within the long-term uncertainty in the UL-DEWI method. Like the mean speed, the wind speed frequency distribution varies over time. UL research indicates that the inter-annual variability of the energy production directly related to the wind speed frequency distribution is typically about 1.4%. The estimated uncertainty in the long-term energy production estimate considers this factor along with the on-site period of record and the length of the evaluation period.

Power curve The uncertainty of the power curve follows a wind speed dependent uncertainty approach, based on the average of

current prototype measurement uncertainties, following TR6 requirements. The average of prototype measurement uncertainties has been scaled to consider that some share of the uncertainty has already been considered as loss. It includes the uncertainty of the applied power performance loss.

Wake losses As the used wake model will be changed to the DAWM model, the uncertainty of the determined wake losses will be calculated with UL’s Openwind software in the future in accordance with TR6 requirements.

Performance losses Uncertainties for other systematic losses will be considered as required by TR6.

The uncertainty methods that will be applied lead to identical total wind resource uncertainties on average and to 1% lower total energy uncertainties, compared to the UL-DEWI method for the sample of projects in the database. This reduction was achieved because of the findings from the UL analysis of power curves on commercially operational sites compared to prototype measurements. Consequently, part of the power curve uncertainties in the current method will be in the power curve losses in the new methods.

The uncertainty methods that will be applied lead to identical total wind resource uncertainties on average and to 1% lower total energy uncertainties …

Effects of Methods Changes for Harmonization Phase One

A comparison of the planned loss assumptions for the hybrid and accredited methods compared with the losses in the current UL-DEWI method is set out in Table 5 below:

* Except if the consumption of the extreme weather package is already included in the financial model

The impact of these changes has been tested in 32 wind farms from the UL-DEWI backcast database, which were those for which it was possible to apply the new wake and wind flow modeling method. The results are shown in Figure 7. For the hybrid method EYAs, the additional loss categories reduced the performance gap from -6.2% to -1.3%. For the selected projects, the mean PR obtained is 98.7%, the median is 97.6%, and the standard deviation is 7.1%. The average total plant loss for the sample of projects is 19.4%, an increase of 5.0% compared with the UL-DEWI method. Although this is insufficient to reduce the gap to zero, UL considers that the deviation obtained is low enough to be acceptable, and due to the intrinsic uncertainties of the methodology, a smaller deviation may not result in an improvement of the overall accuracy.

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Category UL-DEWI Method(Current)

Hybrid Method(Planned)

Accredited Method(Planned)

Wake effect

Internal wake effects External wake effects Future wake effects Upwind blocking effect

Calculated (Jensen)

0.0%

Calculated (DAWM)

Calculated 0%-3%

Calculated (DAWM)

0.0%

Availability

Turbine availabilityBalance of plant availability

• Availability of collection and substation

• Site access• Grid availability

3.0%

0.0%

0.0%0.0%

3.0%

0.2%

Calculated0.3%

3.0%

0.0%

0.0%0.0%

Electrical

Electrical efficiency Facility parasitic consumption (extreme weather package)

Calculated (default 2.0%)0.0%

Calculated (default 2.0%)Calculated*

Calculated (default 2.0%)0.0%

Turbine Performance

Sub-optimal performanceGeneric power curve adjustmentSite-specific power curve adjustment (inclined flow)High wind hysteresis

0.0%0.0%

0.0%Calculated

1.5%Calculated (default 2.1%)

CalculatedCalculated

0.0%Calculated (default 2.1%)

CalculatedCalculated

Environmental

Icing Degradation Environmental shutdown

Calculated0.5%0.0%

CalculatedCalculatedCalculated

CalculatedCalculatedCalculated

Curtailments

Load curtailment Grid curtailment Environmental curtailment Operational strategies

CalculatedCalculatedCalculatedCalculated

CalculatedCalculatedCalculatedCalculated

CalculatedCalculatedCalculatedCalculated

Table 5: Loss assumptions for EYAs under the UL-DEWI, hybrid and accredited methods

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The uncertainty methods that will be applied lead to identical total wind resource uncertainties on average and to 1% lower total energy uncertainties …

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Distribution histogram

Fitted normal distribution

Fitted normal centered on 100%

For the accredited EYAs, the average deviation is -3.6%. The mean PR obtained for the 32 projects is 96.4%, the median is 95.7%, and the standard deviation is 7.0%. The value of the average total losses is 17.4%, 3.0% more than the UL-DEWI method. The frequency distribution of deviations is shown in Figure 8.

Figure 8: Frequency distribution of deviations using accredited method

Figure 7: Frequency distribution of deviations using hybrid method

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Fitted normal distribution

Fitted normal centered on 100%

Figure 9 compares the observed probabilistic results to the predicted P-values for the hybrid and accredited methods. The proportion for the hybrid method is much closer to the ideal distribution along the range of P-values than the UL-DEWI method (Figure 4). However, it remains below it up to the P75 and exceeds it above the P75, a reflection of the residual mean deviation in the hybrid method. For the accredited method, the curve is in between that for the hybrid and UL-DEWI methods. UL is continuing to assess the uncertainties of both hybrid and accredited methods to improve the probabilistic results.

SUMMARY AND CONCLUSIONSIn a first step, the present study evaluates the accuracy of the UL-DEWI and the UL-AWST methods based on met mast measurements through a comparison with observed production from operational plants. The results of the analysis indicate that, for a sample of 50 plants in Europe and North Africa, the operational energy production is 6.2% below the preconstruction EYA P50 from the UL-DEWI method, while for 43 plants in the same region (including 25 in common with the UL-DEWI dataset), the operational energy production is 1.3% higher than the preconstruction EYA P50 from the UL-AWST method. These findings are generally consistent with previous studies, and they support the need for adjustments in the UL-DEWI method, and especially in the plant losses, to reduce the observed gap.

The harmonization process will be divided into two phases:

Phase one

To align methodology and close the gap in the P50 from the UL-DEWI method, the following will be implemented in Phase one:

A hybrid method will be implemented for regions where the UL-DEWI method is presently being used. This method will retain the current UL-DEWI meteorological analysis, while adopting the UL-AWST method for wind flow and wake modeling, as well as most of the UL-AWST plant loss assumptions. New uncertainty estimates will also be adopted to achieve more accurate P-values. The current UL-AWST method will remain in place and unchanged for markets where it is presently being used.

Certain region-specific adaptations will be implemented. Specifically, in order to address the TR6 standard in Germany, a TR6-accredited EYA method will be implemented (referred to as the accredited method). The primary difference between the accredited method and the general hybrid method is that the accredited method will exclude six loss sub-categories.

• Both methods will continue to follow the IEC and MEASNET guidelines. Additionally, for accredited EYAs, especially critical for the German market, UL will follow the TR6 guideline.

• The tools used to assess the downwind modification of the wind resource through its interaction with the wind turbines (wake effects) and the physical extrapolation of the wind resource from point measurements (wind flow modeling) will be aligned to the ones currently in use by the UL-AWST method.

• In order to close the gap in the P50 energy yield, UL will be increasing the losses compared with the current UL-DEWI method. The average losses for the hybrid method is a total of 5.0% higher than the current method and the average losses for the accredited method is a total of 3.0% higher than the current method for 32 wind farms considered.

• The losses for the hybrid method will close the gap in the P50 from the 2020 backcast study to -1.3% and the losses for the accredited method will close the gap in the P50 to -3.6%.

The implementation of Phase one will occur on 01 October 2020.

Phase two

The second phase of the harmonization will define the single UL method and toolset to perform an EYA based on wind measurement data in any part of the world. The principal although not the only focus will be on meteorological analysis to process the wind speeds from met masts and on wind flow modeling to determine the model to be utilized for complex terrain (meso-microscale combination and CFD) and to apply the most suitable model for each site on a case by case basis. Phase two will be implemented in Q1 2021.

UL will perform additional backcast studies in future years to assess the performance of the new methodology.

WHITE PAPER

Figure 9: Proportion of projects for which the operational yield exceeds the estimated P-value of the pre-construction EYA for the hybrid and accredited methods

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UL-DEWI Expected Hybrid Accredited

UL.com

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