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Driesse, Anton

Publications and source records attributed to Driesse, Anton.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Introducing the Baseline Performance Reference (BPR) for Irradiance in PV System Applications

Photovoltaic (PV) reference cells, modules, and arrays respond in a complex manner to the many variables that define their operating environment. PV reference cells for irradiance measurement have similarities but also differences to operational PV modules. For this reason, a more neutral and a well-defined generic PV reference cell is needed, whose characteristics are close enough to most operational PV devices to make stable performance indicators possible but whose characteristics are not necessarily identical to any of them. This poster describes a new well-defined reference quantity for outdoor PV measurements, called the baseline performance reference (BPR).

baseline performance reference↗
DOE OSTI2023

pvlib python: 2023 project update

pvlib python is a community-developed, open-source software toolbox for simulating the performance of solar photovoltaic (PV) energy components and systems. It provides reference implementations of over 100 empirical and physics-based models from the peer-reviewed scientific literature, including solar position algorithms, irradiance models, thermal models, and PV electrical models. In addition to individual low-level model implementations, pvlib python provides high-level workflows that chain these models together like building blocks to form complete “weather-to-power” photovoltaic system models. It also provides functions to fetch and import a wide variety of weather datasets useful for PV modeling. pvlib python has been developed since 2013 and follows modern best practices for open-source python software, with comprehensive automated testing, standards-based packaging, and semantic versioning. Its source code is developed openly on GitHub and releases are distributed via the Python Package Index (PyPI) and the conda-forge repository. pvlib python’s source code is made freely available under the permissive BSD-3 license. Here we (the project’s core developers) present an update on pvlib python, describing capability and community development since our 2018 publication (Holmgren, Hansen, & Mikofski, 2018).

14 SOLAR ENERGY↗
DOE OSTI2023

Global normal spectral irradiance in Albuquerque - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has measured global normal spectral irradiance nearly continuously from August 2013 to April 2018. During this time other broadband irradiance measurements (global horizontal, direct normal, diffuse horizontal and global normal) and weather variables were also recorded. For this dataset PV Performance Labs (PVPL) has pulled together data from both sources to assemble a full calendar year spectral data set for use in photovoltaic research. It is composed of eight continuous segments of different durations taken from the two-year period September 2013 to August 2015.

photovoltaics↗
DOE OSTI2023

The Baseline Performance Reference for Irradiance in PV System Applications

This report proposes the definition of a new baseline performance reference (BPR). The definition goes beyond existing standards pertaining to photovoltaic (PV) reference cells and devices to define the response under all possible operating conditions in the field. Field evaluations using BPR devices will be more sensitive to performance anomalies than pyranometers because they track PV system power output more closely. At the same time, they will be able to detect a broader range of performance anomalies than traditional matched reference devices, which might have matching defects. The BPR definition also opens the door to new practices in resource assessment and yield prediction. Solar resource data can be collected or modeled and validated directly as BPR irradiance, and PV system simulations based on BPR irradiance need fewer assumptions and less processing to obtain the effective irradiance on modules. As a result, lower uncertainty in yield assessments can be expected.

14 SOLAR ENERGY↗
DOE OSTI2023

Blind photovoltaic modeling intercomparison: A multidimensional data analysis and lessons learned

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and –3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

14 SOLAR ENERGY↗
DOE OSTI2023

PV Module Operating Temperature - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has an extensive test site where PV modules and other system components are deployed and monitored for testing and evaluation. For this dataset PV Performance Labs has assembled one year of measurements from the Systems Long-Term Evaluation (SLTE) project (formerly known as PV Lifetime) providing the main variables needed to investigate and validate PV module operating temperature models: irradiance, ambient temperature, wind speed and back-of-module temperature. For use with more advanced thermal modeling, an estimate of down-welling long-wave radiation is also included.

14 SOLAR ENERGY↗
DOE OSTI2023

Global horizontal spectral irradiance in Albuquerque - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has measured global horizontal spectral irradiance nearly continuously from September 2019 to March 2022. During this time other broadband irradiance measurements (global horizontal, direct normal, diffuse horizontal and global normal) and weather variables were also recorded. For this dataset PV Performance Labs (PVPL) has pulled together data from both sources to assemble a calendar year spectral data set for use in photovoltaic research. It is composed of twelve segments of 15 days each, beginning with January 1-15 and ending with December 1-15. The segments are all taken from the two-year period October 2019 to September 2021.

14 SOLAR ENERGY↗
DOE OSTI2023

PV module spectral response measurements - Data and Resources

"This dataset includes spectral response curves for 12 commercial silicon module types that are deployed at SNL in Albuquerque. The modules chosen for evaluation were originally purchased by SNL for the PV Lifetime project (renamed to Systems Long-Term Evaluation [SLTE]). The majority of those modules are deployed outdoors for long-term evaluation, but several modules of each type were placed in storage for future comparison purposes. One stored module of each type was sent to NREL for the spectral response measurements. NREL used the recently developed Module Quantum Efficiency (QE) test bed. What makes this system unique is that it scans the entire module automatically, taking one or more measurements on each cell. Using the mean of these measurements at each wavelength leads to improvements in spectral mismatch correction and module power measurements but having the individual measurements also makes it possible to identify outlier cells, which could be useful for investigating underperforming modules. All measurements were performed nominally at 25°C"

14 SOLAR ENERGY↗
DOE OSTI2023

PV module operating conditions and temperature measurements: an open dataset for PV research

This report describes the structure and content of an open dataset created for the purpose of testing and validating PV module temperature prediction models and their parameters. The dataset contains the main environmental parameters that affect temperature: irradiance, ambient temperature, wind speed and down-welling infrared radiation, as well as measured back-of-module temperature.

14 SOLAR ENERGY↗
DOE OSTI2023

Global horizontal spectral irradiance and module spectral response measurements: an open dataset for PV research

This report describes the creation process and final content of a spectral irradiance dataset for Albuquerque, New Mexico accompanied by a set of spectral response measurements for modules deployed at the same location. The spectral irradiance measurements were made using horizontally mounted spectroradiometers; therefore, they represent global horizontal irradiance. The dataset combines non-continuous spectroradiometer and weather measurements from a two-year period into a single calendar year. The data files are accompanied by extensive metadata as well as example calculations and graphs to demonstrate the potential uses of this database. The spectral response measurements were carried out by the National Renewable Energy Laboratory using 12 commercial silicon modules types that are undergoing long-term evaluation at Sandia National Laboratories in Albuquerque.

14 SOLAR ENERGY↗
DOE OSTI2022

International Photovoltaic Modeling Intercomparison [Slides]

Irradiance transposition models seem to perform well, except the Isotropic with -11.25 W/m 2 underestimation. Most temperature models could not capture behavior when ΔΤ between module and ambient is negative. Uncertainties due to derate factors: modelers overbudgeted resulting in significant power underestimation; maybe ~10% is appropriate for commercial systems but not lab-scale? Most software and models cluster together showing good reproducibility among participants. Modeler’s skills seem to be more important than the PV model itself (flat efficiency with irradiance, positive power temperature coefficients, etc.). Results and best practices will be communicated in a journal article.

14 SOLAR ENERGY↗
DOE OSTI2022

Improving Common PV Module Temperature Models by Incorporating Radiative Losses to the Sky

PV module operating temperature is the second-most important factor influencing PV system yield–after irradiance–and a substantial contributor to uncertainty in energy system yield predictions. Models commonly used to predict operating temperature in system simulations are based on a simplified energy balance that lumps together different heat loss mechanisms–including radiation–and assumes an overall linear behavior. Radiative heat loss to the sky is usually substantial, but modeling it accurately requires additional information about down-welling long-wave radiation or sky temperature and increases the complexity of temperature model equations. In this work we show how radiative losses to the sky can be separated into two parts to improve the accuracy of modeling without additional complexity. We also predict and demonstrate the variation of these losses at different tilt angles and show that the effective view factor is reduced by the non- isotropic distribution of down-welling long-wave radiation. Finally, we demonstrate substantial reduction in bias (MBE) and scatter (RMSE) when the new radiative loss term is added to the Faiman model using one year of measurements at Sandia National Labs.

14 SOLAR ENERGY↗
DOE OSTI2022

PV Reference Cells for Outdoor Use: Comparison of First-Year Field Measurements

Reference cells are widely used in the photovoltaic (PV) industry to measure irradiance. For field applications and outdoor use, a variety of products are on the market, and they are often perceived as low-cost alternatives to thermopile pyranometers. But reference cell characteristics, such as directional and spectral response, fundamentally differ from pyranometers, and measurements made by the former cannot be substituted for or directly compared with the latter. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory is currently in a multiyear effort to develop guidance and recommendations for the design and use of outdoor reference cells, with a view to reducing inconsistencies and measurement uncertainty. The core of this effort is the long-term deployment of multiple products from different manufacturers that are mounted at a fixed tilt, on a single-axis tracker, and on a dual-axis tracker. This report provides an analysis of the data collected during the first year of operation at the NREL location. An overall assessment is made by comparing the total energy measured by each instrument to the other instruments having the same orientation. These totals are calculated for three different scenarios: (1) using the factory calibration factors, (2) using the previously determined NREL Cell Lab calibration factors, and (3) using the newly determined field calibration factors derived from the recorded measurements. Because the true or correct measurement values are unknown, pair-wise comparisons are made. The results show that the difference between the highest and lowest measured energy can exceed 3% when using the factory calibration factors. Consistency among instruments improves when Cell Lab calibrations are used, and it further improves when using field calibration factors.

14 SOLAR ENERGY↗