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

Conference · Mon Sep 09 00:00:00 EDT 2019

DOI:https://doi.org/10.4229/EUPVSEC20192019-5BO.5.1· OSTI ID:1603253

Moser, D. ^[1]; Bertani, D. ^[2]; Curran, A. J. ^[3]; French, R. H. ^[3]; Herz, M. ^[4]; Lindig, S. ^[1]; Makrides, G. ^[5]; Muller, B. ^[6]; Richter, M. ^[7]; Van Iseghem, M. ^[8]; van Sark, W. ^[9]; Stein, J. S. ^[10]; Deline, Christopher A  ^[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)

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.

• Research Organization: National Renewable Energy Lab. (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1603253
• Report Number(s): NREL/CP-5K00-75090
• 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

Related Subjects

14 SOLAR ENERGY
42 ENGINEERING
degradation
PV system
PV system performance
performance loss rate