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1. International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Author

Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, Luo, Wei, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, and Luo, Wei

Abstract

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

Published
2021

2. Assessment of Performance Loss Rate of PV Power Systems

Author

Integration of Photovoltaic Solar Energy, Energy and Resources, French, Roger H., Bruckman, Laura S., Moser, David, Lindig, EURAC, van Iseghem, Mike, ISI, Fraunhofer, Stein, Joshua S., Richter, Maurice, Herz, Magnus, van Sark, Wilfried, Baumgartner, Franz, Integration of Photovoltaic Solar Energy, Energy and Resources, French, Roger H., Bruckman, Laura S., Moser, David, Lindig, EURAC, van Iseghem, Mike, ISI, Fraunhofer, Stein, Joshua S., Richter, Maurice, Herz, Magnus, van Sark, Wilfried, and Baumgartner, Franz

Published
2021

3. International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Author

Integration of Photovoltaic Solar Energy, Energy and Resources, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, Luo, Wei, Integration of Photovoltaic Solar Energy, Energy and Resources, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, and Luo, Wei

Published
2021

4. International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Author

Integration of Photovoltaic Solar Energy, Energy and Resources, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, Luo, Wei, Integration of Photovoltaic Solar Energy, Energy and Resources, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, and Luo, Wei

Published
2021

5. Assessment of Performance Loss Rate of PV Power Systems

Author

Integration of Photovoltaic Solar Energy, Energy and Resources, French, Roger H., Bruckman, Laura S., Moser, David, Lindig, EURAC, van Iseghem, Mike, ISI, Fraunhofer, Stein, Joshua S., Richter, Maurice, Herz, Magnus, van Sark, Wilfried, Baumgartner, Franz, Integration of Photovoltaic Solar Energy, Energy and Resources, French, Roger H., Bruckman, Laura S., Moser, David, Lindig, EURAC, van Iseghem, Mike, ISI, Fraunhofer, Stein, Joshua S., Richter, Maurice, Herz, Magnus, van Sark, Wilfried, and Baumgartner, Franz

Published
2021

6. International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Author

Integration of Photovoltaic Solar Energy, Energy and Resources, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, Luo, Wei, Integration of Photovoltaic Solar Energy, Energy and Resources, Lindig, Sascha, Moser, David, Curran, Alan J., Rath, Kunal, Khalilnejad, Arash, French, Roger H., Herz, Magnus, Müller, Björn, Makrides, George, Georghiou, George, Livera, Andreas, Richter, Mauricio, Ascencio-Vásquez, Julián, van Iseghem, Mike, Meftah, Mohammed, Jordan, Dirk, Deline, Chris, van Sark, Wilfried, Stein, Joshua S., Theristis, Marios, Meyers, Bennet, Baumgartner, Franz, and Luo, Wei

Published
2021

7. Assessment of Performance Loss Rate of PV Power Systems

Author

Integration of Photovoltaic Solar Energy, Energy and Resources, French, Roger H., Bruckman, Laura S., Moser, David, Lindig, EURAC, van Iseghem, Mike, ISI, Fraunhofer, Stein, Joshua S., Richter, Maurice, Herz, Magnus, van Sark, Wilfried, Baumgartner, Franz, Integration of Photovoltaic Solar Energy, Energy and Resources, French, Roger H., Bruckman, Laura S., Moser, David, Lindig, EURAC, van Iseghem, Mike, ISI, Fraunhofer, Stein, Joshua S., Richter, Maurice, Herz, Magnus, van Sark, Wilfried, and Baumgartner, Franz

Published
2021

8. Direct nanoscale mapping of open circuit voltages at local back surface fields for PERC solar cells

Author

Longacre, Alexandra, Martin, Michael, Moran, Thomas, Kolosov, Oleg, Schneller, Eric, Curran, Alan J., Wang, Menghong, Dai, Jianfang, Bruckman, Laura S., Jaubert, Jean-Nicolas, Davis, Kristopher O., Braid, Jennifer L., French, Roger H., Huey, Bryan, Longacre, Alexandra, Martin, Michael, Moran, Thomas, Kolosov, Oleg, Schneller, Eric, Curran, Alan J., Wang, Menghong, Dai, Jianfang, Bruckman, Laura S., Jaubert, Jean-Nicolas, Davis, Kristopher O., Braid, Jennifer L., French, Roger H., and Huey, Bryan

Abstract

The open circuit voltage (VOC) is a critical and common indicator of solar cell performance as well as degradation, for panel down to lab-scale photovoltaics. Detecting VOC at the nanoscale is much more challenging, however, due to experimental limitations on spatial resolution, voltage resolution, and/or measurement times. Accordingly, an approach based on Conductive Atomic Force Microscopy is implemented to directly detect the local VOC, notably for monocrystalline Passivated Emitter Rear Contact (PERC) cells which are the most common industrial-scale solar panel technology in production worldwide. This is demonstrated with cross-sectioned monocrystalline PERC cells around the entire circumference of a poly-aluminum-silicide via through the rear emitter. The VOC maps reveal a local back surface field extending * 2 lm into the underlying p-type Si absorber due to Al in-diffusion as designed. Such high spatial resolution methods for photovoltaic performance mapping are especially promising for directly visualizing the effects of processing parameters, as well as identifying signatures of degradation for silicon and other solar cell technologies.

Published
2020

9. Climatic Rating of Photovoltaic Modules: Different Technologies for Various Operating Conditions

Author

Bonilla Castro, Johanna (author), Schweiger, Markus (author), Moser, David (author), Tanahashi, Tadanori (author), King, Bruce H. (author), Friesen, Gabi (author), Haitao, Liu (author), French, Roger H. (author), Vogt, M.R. (author), Bonilla Castro, Johanna (author), Schweiger, Markus (author), Moser, David (author), Tanahashi, Tadanori (author), King, Bruce H. (author), Friesen, Gabi (author), Haitao, Liu (author), French, Roger H. (author), and Vogt, M.R. (author)

Abstract

Photovoltaic Materials and Devices

Published
2020

10. Unsupervised Non-Intrusive Energy Disaggregation for Commercial Buildings

Author

Hossain, Mohammad A., Khalilnejad, Arash, Swanson, Rachel A., Mousseau, Jack, Pickering, Ethan M., French, Roger H., Abramson, Alexis R., Hossain, Mohammad A., Khalilnejad, Arash, Swanson, Rachel A., Mousseau, Jack, Pickering, Ethan M., French, Roger H., and Abramson, Alexis R.

Abstract

Commercial buildings alone are responsible for 36% of the total United States electricity consumption, and 30% of this electricity consumption is wasted on average. One of the greatest challenges in improving building energy efficiency lies in the ability to do simple and non-intrusive load disaggregation. In this paper, we propose a novel unsupervised, non-intrusive building energy disaggregation technique using only 15-minute interval whole-building energy consumption and weather data. The proposed disaggregation technique consists of an analysis loop with three steps. Step 1 analyzes the change in energy consumption in 15-minute intervals for every 3 degrees C temperature range to identify statistically significant temperature dependent and independent the equipment turn on-off states. In step 2, a probabilistic approach is developed using a Factorial Hidden Markov Model (FHMM) that combines equipment state transition matrices from the equipment turn on-off states of step 1 and an emission matrix representing all possible combinations of equipment energy consumption in each state. In step 3, FHMM identifies the best possible combinations of the equipment state and consumption for every point in time using an Expectation Maximization (EM) algorithm. Ultimately, specific equipment such as HVAC, plug load, and lighting, can be identified virtually without setting foot in the building. The proposed disaggregation technique is validated by comparing real-world, sub-metered building equipment energy consumption data to the whole building data. The results show that the method achieves at least 80% accuracy. Using the disaggregation technique to identify the breakdown of HVAC, lighting, and plug load, we compare the results from building population studies to identify potential areas of energy waste and generate an automated virtual energy audit report. The report provides personalized building recommendations such as improving HVAC operations, lighting, and equipment s

Published
2017

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