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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Annual Supply Chain for Photovoltaics (ASC-PV) in the United States: 2024 in Review

This report analyzes U.S. PV and BESS supply chains and costs in 2024, for PV module and battery technologies, structural and electrical balance of system (BOS) components, as well as PV recycling. The report concludes with an analysis of technology installation trends, government support for domestic manufacturing, manufacturing jobs, and the domestic content of PV systems installed in the United States in 2024.

14 SOLAR ENERGY↗
DOE OSTI2026

PV Operations Software Transparency: A PVMAC Industry Snapshot

The rapid growth of photovoltaic (PV) deployment has increased reliance on software platforms for monitoring, workflow automation, diagnostics, and performance analytics. As these tools play a central role in asset management and operations and maintenance (O&M), greater transparency in methodologies, data handling, and validation practices benefits the broader PV ecosystem. To better understand current practices and identify opportunities for improved clarity and interoperability, 24 software providers contributed detailed responses through the PV O&M Analytics Collaborative (PVMAC) initiative, the first structured questionnaire of its kind in the industry, covering onboarding, interoperability, data quality, diagnostics, AI/ML, and other operational categories. These providers represent over 1.1 TW of solar assets under management. The analysis shows broad adoption of digital twins, AI/ML, and API integrations, but also highlights challenges in onboarding processes, inconsistent definitions and methodologies, variability in key performance indicator (KPI) calculations, and limited independent validation. Greater standardization, clearer documentation, and stronger validation frameworks could improve transparency, comparability, and trust across PV operations software platforms.

14 SOLAR ENERGY↗
DOE OSTI2026

Education for PV Modeling Professionals: Survey of Educators

We report a survey of instructors at universities offering courses that address PV performance modeling. We confirm our earlier finding that most courses lack the level of detail sought by the professional community. We recommend that the PV Performance Modeling Collaborative (PVPMC), or a university, sponsor a committee to produce a curriculum outline for PV system performance modeling, and that the PVPMC solicit industry for well-documented examples of PV system designs and accompany

Hansen, Clifford [Sandia National Laboratories (SN↗
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

Digital Twin Empowered PV Power Prediction

The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper proposes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical data set, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long-short-term memory model) for capturing the hidden spatial-temporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real data set is conducted to access the effectiveness of the proposed method.

14 SOLAR ENERGY↗
NASA NTRS1983

Evolution of integrated panel structural design and interfaces for PV power plants

The evolution of integrated photovoltaic (PV) panel design at ARCO Solar is discussed. Historically, framed PV modules of about 1 x 4-ft size were individually mounted in the field on fixed support structures and interconnected electrically with cables to build higher-power arrays. When ARCO Solar saw the opportunity in 1982 to marry its PV modules with state-of-the-art heliostat trackers developed by ARCO Power Systems, it became obvious that mounting individual modules was impractical. For this project, the framed modules were factory-assembled into panels and interconnected with cables before being mounted on the trackers. Since then, ARCO Solar made considerable progress and gained substantial experience in the design and fabrication of large PV panels. Constraints and criteria considered in these design activities included static and dynamic loads; assembly and transportation equipment and logistics, structural and electrical interfaces, and safety and grounding concerns.

Arnett, J. C.↗
NASA NTRS1989

Space shuttle Production Verification Motor 1 (PV-1) static fire

All inspection and instrumentation data indicate that the PV-1 static test firing conducted 18 Aug. 1988 was successful. With the exception of the intentionally flawed joints and static test modifications, PV-1 was flight configuration. Fail-safe flaws guaranteeing pressure to test the sealing capability of primary O-rings were included in the aft field joint, case-to-nozzle joint, and nozzle internal Joint 5. The test was conducted at ambient conditions, with the exception of the field joints and case/nozzle joints which were maintained at a minimum of 75 F. Ballistics performance values were within specification requirements. The PV-1 motor exhibited chamber pressure oscillations similar to previously tested Space Shuttle redesigned solid rocket motors, particularly QM-7. The first longitudinal mode oscillations experienced by PV-1 were the strongest ever measured in a Space Shuttle motor. Investigation into this observation is being conducted. Joint insulation performed as designed with no evidence of gas flow within unflawed forward field joints. The intentionally flawed center and aft case field joint insulation performance was excellent. There was no evidence of hot gas past the center field joint capture feature O-ring, the case-to-nozzle joint primary O-ring, or the aft field joint primary O-ring. O-ring seals and barriers with assured pressure at the flaws showed erosion and heat effect, but all sealed against passage of hot gases with the exception of the aft field joint capture feature O-ring. There was no evidence of erosion, heat effect, or blowby on any O-ring seals or barriers at the unflawed joints. Nozzle performance was nominal with typical erosion. Post-test examination revealed that the forward nose ring was of the old high performance motor design configuration with the 150-deg ply angle. All nozzle components remained intact for post-test evaluation. The thrust vector control system operated correctly. The water deluge system, CO2 quench, and other test equipment performed as planned during all required test operations.

Source record↗
NASA NTRS2000

PV Theories For Planetary Atmospheric Circulations

Potential vorticity (or PV) has become an important tool for the diagnosis and modeling of the Earth's atmospheric and oceanic circulations. More recently, the application of PV thinking to numerical simulations and spacecraft observations of other atmospheres, including those of Mars, Venus, and Titan, has encouraged the hope for a unified understanding of planetary circulation regimes encompassing a wide range of rotation, stratification, and forcing parameters. Specifically, the accumulated evidence suggests that zonal-mean winds and temperatures at upper tropospheric levels approximate a state of zero potential vorticity within the bounding latitudes of the westerly jets, while the poleward regions of cyclonic shear conform to a PV state that is well mixed with respect to its polar limit. This review of the prospects for conceptual planetary circulation models will explore the possibility that the zonal-mean state of an atmosphere can be calculated in terms of a simple link between the latitudinal PV and potential temperature variation at an upper baroclinic steering level.

Allison, Michael↗
DOE OSTI2021

Optimal Sizing of an Electric Vehicle Charging Station with Integration of PV and Energy Storage

This paper proposes an optimization model for the optimal configuration of an grid-connected electric vehicle (EV) extreme fast charging station considering integration of photovoltaic (PV) and energy storage. The proposed model minimizes the annualized net cost (i.e., maximizes the annualized net profit) of the extreme fast charging station, including investment and maintenance cost of charging ports, PV and energy storage, net cost of purchasing energy from utility and selling energy to EV customers, degradation cost of energy storage and demand charge. The decision variables are number of charging ports, capacity of invested PV and the power and energy ratings of invested energy storage. The Erlang-loss system is adopted to model the EV mobility. Results of numerical simulations indicate that investment of PV and energy storage could increase the annualized profit of the extreme fast charging station. In addition, the impacts of various parameters on the optimal solution are investigated by sensitivity analysis.

Liu, Guodong↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Quantifying Energy Flows in PV Circularity Processes: Preprint

As sustainability and end-of-life management become a hot topic to timely address in the PV community, a proper evaluation of the benefits of circular pathways such as recycling, reuse, and remanufacturing has not been performed holistically beyond material flows or LCA analysis. Energy flows are critical for evaluating energy generation technologies. Previously they have been used to compare renewables to fossil generation and then between PV technologies. This paper brings energy flows to bear on reaching circular pathways for PV. The energy flows to track energy usage, generation, and losses complementary to the mass flows are presented for silicon through the leverage of the PV ICE framework.

circular economy↗
DOE OSTI2023

Structure and Electronic Properties of Mesopores in Si PV Devices with PLEO Contacts

All current state-of-the-art silicon photovoltaic (PV) devices employ some flavor of passivating contact structure to minimize detrimental recombination while providing good conductivity. These properties are most often provided by a dielectric layer (SiNx, Al2O3, or SiOx) that is in contact with the crystalline silicon (c-Si) substrate. The effectiveness of these layers is heavily dependent on the nanoscale structure of both the dielectric layers and the crystalline silicon interface. As an example, the tunneling probability for charge carriers across thin SiOx layers, a process that is critical to maintain high conductivity, is extremely sensitive to the SiOx thickness. SiOx with a thickness below 2 nm readily allows charge carrier tunneling, whereas it is impeded for greater thicknesses. In the latter case it has been shown that modifications to the thermal processing schedule can induce disruptions, or pinholes, in thick SiOx layers that allow transport of charge carriers while maintaining excellent passivation. However, this requires higher temperatures processes that may inhibit widespread adoption by industry. Recently, another option has been presented that may circumvent the necessity for high temperature processing, namely metal assisted chemical etching (MACE). MACE relies on deposition of Ag nanoparticles onto the SiOx layers with subsequent electroless etching to form mesopores in the SiOx layers that are analogous to the pinholes created with high temperature processing. The size and density can be controlled based on the Ag nanoparticle deposition conditions. The resulting contact structure is known as polysilicon on locally etched oxide (PLEO) contacts. The nanoscale structure of the mesopores in PLEO contacts define the device level observables such as recombination current (Jo) and junction resistance. In this work we present atomic resolution transmission electron microscopy (TEM) analysis of mesopores in PLEO contacts formed with a different processing conditions to connect provide insight into how the nanoscale structure of the SiOx layer influences PV device properties. Additionally, we have previously used electron beam induced current (EBIC) to probe local transport properties in Si PV devices with a SiOx layer containing pinholes due to high temperature processing. Using EBIC we were able to directly show the enhance charge carrier transport through the pinholes. Here we also employ EBIC to study non-uniformities in charge carrier recombination and transport associated with mesopores in PLEO contacts and compare these results with our previous work on devices with pinholes in the SiOx formed through high temperature processes. The products of this work provide critical information that is required to both further optimize performance of Si PV devices with PLEO contact and to drive future adoption of this technology by industry.

ENGINEERING,SOLAR ENERGY↗
DOE OSTI2023

Measuring Sustainability of PV in Energy Transition: Mass, Energy, and Circularity

Energy transition to a carbon-free energy system is crucial for global decarbonization and underpins circular economy (CE) goals. Photovoltaic (PV) technology is required for energy transition, but manufacturing and circular pathways can be material, energy, and carbon-intensive. Therefore, we need a prioritization of sustainability strategies for PV evolution. In this study, we compare sustainable PV module designs in terms of their entailed mass, energy, circularity, and carbon impacts to achieve Energy Transition. Results will support decision-makers through data-backed scenario and tradeoff analyses offering prioritization of sustainable PV module designs and CE pathways for meeting Energy Transition goals.

carbon↗
DOE OSTI2023

Industry Facing PV Degradation Prediction Tool and Database to Enable a 50-Year Life Module

The Photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 years. Furthermore, research efforts aim for up to 50 years of service life while keeping performance degradation at a minimum for decades of outdoor weathering - putting additional pressure on improving the accuracy of long-term durability and reliability assessments. Here there is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. A graphical user interface will aid in the understanding of the results. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database to perform geospatial degradation analysis in the US and worldwide. Our repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry.

degradation↗
DOE OSTI2023

Extreme Weather Events and the Impact on PV Time Series Data

The impact of extreme weather events on PV performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1% of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60 % loss showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events comparing PLR before and after these weather events. In addition, hail events caused a higher PLR for 2 out of 3 systems. More data are required to better quantify the impact, but these first results illustrate the substantial risk these events pose short-and long-term.

degradation↗
DOE OSTI2023

Automatic voltage regulation application for PV inverters in low-voltage distribution grids – A digital twin approach

This paper proposes a hierarchical coordinated control strategy for PV inverters to keep voltages in low-voltage (LV) distribution grids within specified limits. The top layer of the proposed architecture consists of the designed automatic voltage regulation (AVR) application, which has access to voltage measurements and grid parameters from the LV distribution grid, both current and historical. The AVR application solves a constrained optimization problem, which provides a set of local control set-points that bring the voltage across the grid within bounds. The middle layer consists of a local Volt/VAR controller, which is adjusted by the AVR app, while the bottom layer is the inner-loop controller of the PV inverter. The proposed method not only improves the voltage quality in the grid but also manages the reactive power outputs of PV inverters efficiently. Further, a digital twin of the cyber-physical system has also been employed that interacts with the control system to ensure its appropriate operation. The effectiveness of the proposed methodology is demonstrated on a representative low-voltage feeder located in Denmark.

24 POWER TRANSMISSION AND DISTRIBUTION↗