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Title: International Collaboration Framework for the Calculation of Performance Loss Rates: Data Quality, Benchmarks, and Trends

Abstract

Within the IEA PVPS Task 13, a group of experts representing several leading R&D centres, universities and industry companies, is developing a framework for the calculation of Performance Loss Rates (PLR) on a large number of commercial and research PV power plants and related weather data coming from various climatic zones. Various methodologies are applied for the calculation of PLR which are benchmarked in terms of uncertainties and 'true' values. The aim of the international collaboration is to show how to calculate the PLR on high quality (high time resolution, reliable data, irradiance, yield, etc.) and on low quality data (low time resolution, only energy data available). Various algorithms and models, along with different time averaging and filtering criteria, can be applied for the calculation of PLR each of which can have an impact on the results. The approach considers three pathways to ensure broad collaboration and increase the statistical relevance of the study: i) use of shared methodologies on shared time series, ii) use of confidential methodologies on shared time series, iii) use of shared methodologies on confidential time series. The data is used for benchmarking activities and to define which methodologies clusters around a 'true value' of PLR.more » The combination of metrics (PR or power based) and methodologies are benchmarked in terms of deviation from the average value and in terms of standard deviation. The combination of temperature corrected PR with the use of Year on Year (YOY) or Seasonal Trend decomposition using Loess (STL) performs well for all systems; other methodologies seems to be more depending on the filtering applied. Another set of data is represented by the IEA PVPS Task 13 'PV performance database' which includes more than 120 PV plants from different climates. The applied methodologies were STL and the YoY approach. STL results in an averaged PLR over all systems of -0.78%/a while YoY yields -0.63%/a. STL is better suited if the time series data are of higher resolution and high-quality weather data are available. One important outcome of this study is the setting of a framework for the contribution to a clear and structured quality classification for PV time series data and a guideline for PLR calculations in dependency of the data categorization.« less

Authors:
 [1];  [2];  [3];  [3];  [4];  [1];  [5];  [6];  [7];  [8];  [9];  [10]; ORCiD logo [11]
  1. Institute for Renewable Energy - EURAC
  2. RSE S.p.A.
  3. Case Western Reserve University
  4. TUV Rheinland Energy GmbH
  5. University of Cyprus
  6. Fraunhofer ISE
  7. 3E
  8. EDF Lab les Renardieres
  9. Utrecht University
  10. Sandia National Laboratories
  11. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1603253
Report Number(s):
NREL/CP-5K00-75090
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 36th European Photovoltaic Solar Energy Conference and Exhibition, 9-13 September 2019, Marseille, France
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 42 ENGINEERING; degradation; PV system; PV system performance; performance loss rate

Citation Formats

Moser, D., Bertani, D., Curran, A. J., French, R. H., Herz, M., Lindig, S., Makrides, G., Muller, B., Richter, M., Van Iseghem, M., van Sark, W., Stein, J. S., and Deline, Christopher A. International Collaboration Framework for the Calculation of Performance Loss Rates: Data Quality, Benchmarks, and Trends. United States: N. p., 2019. Web. doi:https://dx.doi.org/10.4229/EUPVSEC20192019-5BO.5.1.
Moser, D., Bertani, D., Curran, A. J., French, R. H., Herz, M., Lindig, S., Makrides, G., Muller, B., Richter, M., Van Iseghem, M., van Sark, W., Stein, J. S., & Deline, Christopher A. International Collaboration Framework for the Calculation of Performance Loss Rates: Data Quality, Benchmarks, and Trends. United States. doi:https://dx.doi.org/10.4229/EUPVSEC20192019-5BO.5.1.
Moser, D., Bertani, D., Curran, A. J., French, R. H., Herz, M., Lindig, S., Makrides, G., Muller, B., Richter, M., Van Iseghem, M., van Sark, W., Stein, J. S., and Deline, Christopher A. Mon . "International Collaboration Framework for the Calculation of Performance Loss Rates: Data Quality, Benchmarks, and Trends". United States. doi:https://dx.doi.org/10.4229/EUPVSEC20192019-5BO.5.1.
@article{osti_1603253,
title = {International Collaboration Framework for the Calculation of Performance Loss Rates: Data Quality, Benchmarks, and Trends},
author = {Moser, D. and Bertani, D. and Curran, A. J. and French, R. H. and Herz, M. and Lindig, S. and Makrides, G. and Muller, B. and Richter, M. and Van Iseghem, M. and van Sark, W. and Stein, J. S. and Deline, Christopher A},
abstractNote = {Within the IEA PVPS Task 13, a group of experts representing several leading R&D centres, universities and industry companies, is developing a framework for the calculation of Performance Loss Rates (PLR) on a large number of commercial and research PV power plants and related weather data coming from various climatic zones. Various methodologies are applied for the calculation of PLR which are benchmarked in terms of uncertainties and 'true' values. The aim of the international collaboration is to show how to calculate the PLR on high quality (high time resolution, reliable data, irradiance, yield, etc.) and on low quality data (low time resolution, only energy data available). Various algorithms and models, along with different time averaging and filtering criteria, can be applied for the calculation of PLR each of which can have an impact on the results. The approach considers three pathways to ensure broad collaboration and increase the statistical relevance of the study: i) use of shared methodologies on shared time series, ii) use of confidential methodologies on shared time series, iii) use of shared methodologies on confidential time series. The data is used for benchmarking activities and to define which methodologies clusters around a 'true value' of PLR. The combination of metrics (PR or power based) and methodologies are benchmarked in terms of deviation from the average value and in terms of standard deviation. The combination of temperature corrected PR with the use of Year on Year (YOY) or Seasonal Trend decomposition using Loess (STL) performs well for all systems; other methodologies seems to be more depending on the filtering applied. Another set of data is represented by the IEA PVPS Task 13 'PV performance database' which includes more than 120 PV plants from different climates. The applied methodologies were STL and the YoY approach. STL results in an averaged PLR over all systems of -0.78%/a while YoY yields -0.63%/a. STL is better suited if the time series data are of higher resolution and high-quality weather data are available. One important outcome of this study is the setting of a framework for the contribution to a clear and structured quality classification for PV time series data and a guideline for PLR calculations in dependency of the data categorization.},
doi = {https://dx.doi.org/10.4229/EUPVSEC20192019-5BO.5.1},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

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