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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 973 records · Page 54

Value Proposition of UV-Absorbers in PV Module Encapsulation

Various common crystalline silicon cell technologies were exposed to UVA radiation (1.24 Wm-2 nm-1 at 340 nm peak) on the front and back faces at 45 degrees Celsius for periods up to 3000 h, representing about 3 y of solar exposure in Phoenix, Arizona, USA. The resulting degradation of the open-circuit voltage and short-circuit current is presented. Of the various cell types examined, significant levels of degradation were seen in all cases. Less degradation was generally found after UV irradiation of cell fronts and older cell types, whereas a bifacial PERC type exposed on the rear showed about 25% degradation in short circuit current, attributable to lack of a diffused surface field. Selected cells were exposed to UV irradiation with the addition of long pass UV filters to replicate UV-absorbers in encapsulants. Modern cell designs are sensitive to UV-ID because of reduced or eliminated front and back surface field and increased dependence on high quality surface passivation. Single transformation of the independent variable (t, kW h/m^2) could be used to achieve a linear model of the data to extrapolate to 50 y. Solar Advisor Model (SAM) shows appropriate filtering of UV-irradiation can improve LCOE and net present value of plant. Some advanced cell types are seen to be UV-resistant (cell level solutions also exist). Solutions therefore exist on the cell, glass, and encapsulant level. Changes over time in each of these would also need to be considered (solarization, encapsulant browning...).

ENGINEERING,SOLAR ENERGY↗

Halide Perovskite Solar Photovoltaics

Technological progress in photovoltaic (PV) technologies provides hope that a comprehensive and desperately needed decarbonization of the energy sector is possible. Commercially successful PV technologies based predominantly on silicon wafer technology are reliable and cost-effective, but remain capital- and carbon-intensive. In this context, emerging PV technologies, such as metal-halide perovskites (MHPs), could further catalyze the energy transition by providing technological opportunities for even lower-cost, mass-producible, high-efficiency solar cells with a significantly reduced "carbon footprint." This themed issue of MRS Bulletin on "Halide perovskite solar photovoltaics summarizes the current state of the art, challenges, and opportunities of perovskite photovoltaics with contributions and perspectives from six expert teams worldwide. The topics covered provide a status update on perovskite PV, remaining hurdles to their deployment, and challenges to realizing the potential of this technology to impact climate goals. Articles in this collection address scalability of perovskite PV and prospects for industrial manufacturing; perovskite PV as an add-on technology on top of commercial silicon PV; environmental and sustainability considerations; and durability and reliability considerations. Further considerations include prospects of automation, coupled to artificial intelligence and machine learning, for accelerating material-based solutions to these outstanding challenges including the possibilities of discovering new absorber and device component materials to enable success and ultimately deployment of these next-generation PVs.

metal-halide perovskites↗

Annual Solar Irradiance Anomaly Features Over the USA During 1998-2017 Using NSRDB V3

Annual solar irradiance anomalies (or departures from the long-term mean annual value) have a direct impact on various phases of solar energy projects, from prefeasibility studies to technical deployment decisions. Anomalies can happen because of normal climate variability or exceptional weather patterns. This study investigates such anomalies for both global horizontal irradiance (GHI) and direct normal irradiance (DNI) using Version 3 of the National Solar Radiation Database (NSRDB V3) and surface irradiance measurements at eight U.S. locations. At each site, the annual anomaly is analyzed here by evaluating the irradiance deviation from the long-term average for each specific year from 1998–2017. A positive/negative anomaly indicates that the solar resource was higher/lower than the long-term average during that specific year. The results show that in most cases the anomaly is within ±5% for GHI and ±10% for DNI using either ground-based irradiance measurements or modeled data from the NSRDB.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Block Scale Rooftop Solar Technical Potential for the City of Orlando

The html maps are provided as supplementary information for the publication titled Parcel Scale Assessment of Rooftop Solar Technical Potential (NREL/PR-7A40-80780). The maps contain information on rooftop solar technical potential at the block scale for the city of Orlando in Florida. The rooftop solar technical potential information is based on data from two different datasets. The first dataset is LiDAR data for the city of Orlando obtained from the Orlando Utilities Commission (OUC) (Koebrich et al. 2021). The second dataset is a national parcel dataset (HIFLD 2020) which contains descriptive data and geometries for parcels in the U.S. Parcel scale data from both these datasets have been processed and aggregated to block scale to produce these html maps. The first html map (block scale developable roof area for Orlando) contains the developable roof area for solar. The second html map (block scale rooftop solar technical potential for Orlando) contains the rooftop solar technical potential in units of kilowatts as well as additional information on the most common building use type and the most common building occupancy type for the block. These html maps are provided to demonstrate the proof-of-concept analysis conducted for Orlando.

14 SOLAR ENERGY↗

Annual Solar Irradiance Anomaly Features Over the USA During 1998-2017 Using NSRDB V3: Preprint

Annual solar irradiance anomalies (or departures from the long-term mean annual value) have a direct effect on various phases of solar energy projects, from prefeasibility to deploy-ment. Anomalies can happen because of normal climate varia-bility or exceptional weather patterns. This study investigates such anomalies for both global horizontal irradiance (GHI) and direct normal irradiance (DNI) using Version 3 of the National Solar Radiation Database (NSRDB V3) and surface irradiance measurements at eight U.S. locations. At each site, the annual anomaly is analyzed here by evaluating the irradi-ance deviation from the long-term average for each specific year during 1998–2017. A positive anomaly indicates that the solar resource was higher than the long-term average during that specific year, and vice versa in the case of a negative anomaly. The results show that, in most cases, the anomaly is within ±5% for GHI and ±10% for DNI, using either ground-based irradiance measurements or modeled data from the NSRDB.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Spectroscopic Investigation of Light-Induced Degradation Paramagnetic Defect in Czochralski Silicon

We report on the electron paramagnetic resonance (EPR) spectra of Cz and FZ Si in different stages of light-induced degradation. Firstly, we show that the paramagnetic signal at low temperature (10K) is dependent on the state of light-induced degradation performed at room temperature and on the sample doping. The light-degraded state in p-Cz Si shows strong, sharp paramagnetic defect signatures compared to samples in the annealed state. When an excitation light is applied during the EPR measurement, additional structures appear in the spectra. In contrast, the wide EPR active signature related to boron interaction with lattice, is present in the annealed state but diminishes after light exposure. Finally, we show that the degraded paramagnetic signal returns when annealed samples are exposed to ambient light. We suggest that these changes in the electron paramagnetic resonance spectra is related to the boron-oxygen defect that causes light-induced degradation.

41 EE - Solar Energy Technologies Office (EE-4S)↗

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↗

2020 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies

Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This updated version of the workbook provides data for the 2020 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. A website documents this data at https://atb.nlr.gov .

02 PETROLEUM↗

PVcircuit [SWR-22-26]

The software contains objects that are building blocks for PV modeling and interactive data fitting based on: Optoelectronic models for tandem/multijunction solar cells including resistive and luminescent coupling; simulation of modules composed of 2T, 3T, and 4T tandem solar cells; and energy yield analysis of PV systems composed of tandem solar cells.

Geisz, John↗

PVcircuit v0.0.6 [SWR-22-26]

The software contains objects that are building blocks for PV modeling and interactive data fitting based on: Optoelectronic models for tandem/multijunction solar cells including resistive and luminescent coupling; simulation of modules composed of 2T, 3T, and 4T tandem solar cells; and energy yield analysis of PV systems composed of tandem solar cells.

Geisz, John↗

Advances in Mixed Tin‐Lead Narrow‐Bandgap Perovskites for Single‐Junction and All‐Perovskite Tandem Solar Cells

Abstract Organic–inorganic metal‐halide perovskites have received great attention for photovoltaic (PV) applications owing to their superior optoelectronic properties and the unprecedented performance development. For single‐junction PV devices, although lead (Pb)‐based perovskite solar cells have achieved 26.1% efficiency, the mixed tin‐lead (Sn‐Pb) perovskites offer more ideal bandgap tuning capability to enable an even higher performance. The Sn‐Pb perovskite (with a bandgap tuned to ≈1.2 eV) is also attractive as the bottom subcell for a tandem configuration to further surpass the Shockley–Queisser radiative limit for the single‐junction devices. The performance of the all‐perovskite tandem solar cells has gained rapid development and achieved a certified efficiency up to 29.1%. In this article, the properties and recent development of state‐of‐the‐art mixed Sn‐Pb perovskites and their application in single‐junction and all‐perovskite tandem solar cells are reviewed. Recent advances in various approaches covering additives, solvents, interfaces, and perovskite growth are highlighted. The authors also provide the perspective and outlook on the challenges and strategies for further development of mixed Sn‐Pb perovskites in both efficiency and stability for PV applications.

14 SOLAR ENERGY↗

A life cycle assessment of e-hydrogen production using proton-exchange membrane water electrolysis coupled with desalination in Saudi Arabia

Hydrogen, considered a crucial element in the transition towards a sustainable energy future, offers the potential to mitigate greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. Here, this study explores the viability of hydrogen production using proton exchange membrane water electrolysis (PEMWE) as a key driver of decarbonization within the Vision 2030 framework in the Kingdom of Saudi Arabia. A first-of-a-kind life cycle assessment (LCA) of electrolytic hydrogen (e-hydrogen) production using PEMWE in the Kingdom is performed. As the hydrogen will be produced in a freshwater scarce region, the inclusion of water desalination processes adds an important dimension to the assessment, reflecting the local context and resource availability. Two main renewable energy scenarios are assessed: solar energy through photovoltaics (PV) and wind energy through onshore turbines. The global warming potential (GWP) results indicate a GHG emissions reduction of up to 95 % compared to the state-of-the-art steam methane reforming process if the electrolysis process is powered exclusively by renewable electricity. The scenarios powered by solar and wind energy result in 3.66 and 0.76 kg CO 2 eq/kg H 2 , respectively. The metal depletion is assessed to consider the requirement of rare materials, with a 7.19 × 10 −2 kg Cu eq/kg H 2 for the solar scenario and 2.82 × 10 −2 kg Cu eq/kg H 2 for the wind scenario. A contribution analysis reveals that the majority of emissions in both scenarios originate from the electricity used for electrolysis, with the electrolyser itself contributing minimally. The absolute impact of the water desalination process is the same in both scenarios; however, it appears more prominent in the wind-powered case due to the significantly lower overall emissions in that scenario. The findings underscore the importance of renewable energy integration and process optimization in minimizing environmental impacts and advancing the sustainability of e-hydrogen production.

08 HYDROGEN↗

2021 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies

Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This version of the workbook provides the final updates to data for the 2021 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. The data for 2015 - 2020 is located on https://data.nrel.gov. In 2021 and going forward, the data is cloud optimized and provided in the OEDI data lake. A website documents this and future data at https://atb.nrel.gov.

Array↗

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗