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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Momentum power spectrum of SDSS galaxies by massE cosmic ruler: 2.1×improvement in measure of growth rate

ABSTRACT Peculiar motion of galaxies probes the structure growth in the universe. In this study, we employ the galaxy stellar mass-binding energy (massE) relation with only two nuisance parameters to build the largest peculiar-velocity (PV) catalogue to date, consisting of 229 890 ellipticals from the main galaxy sample (MGS) of the Sloan Digital Sky Survey (SDSS). We quantify the distribution of the massE-based distances in individual narrow redshift bins (dz = 0.005), and then estimate the PV of each galaxy based on its offset from the Gaussian mean of the distribution. As demonstrated with the Uchuu-SDSS mock data, the derived PV and momentum power spectra are insensitive to accurate calibration of the massE relation itself, enabling measurements out to a redshift of 0.2, well beyond the current limit of z = 0.1 using other galaxy scaling laws. We then measure the momentum power spectrum and demonstrate that it remains almost unchanged if varying significantly the redshift bin size within which the distance is measured, as well as the intercept and slope of the massE relation, respectively. By fitting the spectra using the perturbation theory model with four free parameters, fσ8 is constrained to fσ8 = 0.459$^{+0.068}_{-0.069}$ over Δz = 0.02–0.2, 0.416$^{+0.074}_{-0.076}$ over Δz = 0.02–0.1, and 0.526$^{+0.133}_{-0.148}$ over Δz = 0.1–0.2. The error of fσ8 is 2.1 times smaller than that by the redshift space distortion (RSD) of the same sample. A Fisher matrix forecast illustrates that the constraint on fσ8 from the massE-based PV can potentially exceed that from the stage-IV RSD in late universe (z<0.5).

Shi, Yong (ORCID:0000000286146275)↗
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
DOE OSTI2023

United States Utility-Scale PV Supply Curves 2023

This data packet contains supply curves, hourly generation profiles, and a composite siting exclusion TIFF for utility-scale PV across the contiguous United States. The supply curves offer comprehensive metrics such as capacity (MW), generation (MWh), levelized cost of energy (LCOE), levelized cost of transmission (LCOT), and more for each reV site (~60,000 sites). Hourly generation profiles are available for each reV site and can be matched to the available capacity in the supply curve (refer to the Jupyter Notebook). The composite exclusion TIFF is a single file that delineates areas where PV installations are permissible based on various siting assumptions. This data packet contains information for the Reference and Limited siting scenarios. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

2023↗
DOE OSTI2023

Durable Module Materials (DuraMAT) Consortium (Final Technical Report)

The DuraMAT Consortium brings together DOE national lab and university research capabilities with the photovoltaic (PV) and supply-chain industries to accelerate a sustainable, just, and equitable transition to zero carbon electricity generation by 2035 through our five core objectives: development of a central data resource for PV modules, multi-scale and multi-physics modeling, disruptive acceleration science, forensic tools for fielded modules, and materials solutions for more durable, reliable, and resilient modules.

14 SOLAR ENERGY↗
NASA NTRS2016

Analysis of the Electronic Crosstalk Effect in Terra MODIS Long-Wave Infrared Photovoltaic Bands Using Lunar Images

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key sensors among the suite of remote sensing instruments on board the Earth Observing System Terra and Aqua spacecrafts. For each MODIS spectral band, the sensor degradation has been measured using a set of on-board calibrators. MODIS also uses lunar observations from nearly monthly spacecraft maneuvers, which bring the Moon into view through the space-view port, helping to characterize the scan mirror degradation at a different angles of incidence. Throughout the Terra mission, contamination of the long-wave infrared photovoltaic band (LWIR PV, bands 27-30) signals has been observed in the form of electronic crosstalk, where signal from each of the detectors among the LWIR PV bands can leak to the other detectors, producing a false signal contribution. This contamination has had a noticeable effect on the MODIS science products since 2010 for band 27, and since 2012 for bands 28 and 29. Images of the Moon have been used effectively for determining the contaminating bands, and have also been used to derive correction coefficients for the crosstalk contamination. In this paper, we introduce an updated technique for characterizing the crosstalk contamination among the LWIR PV bands using data from lunar calibration events. This approach takes into account both the in-band and out-of-band contribution to the signal contamination for each detector in bands 27-30, which is not considered in previous works. The crosstalk coefficients can be derived for each lunar calibration event, providing the time dependence of the crosstalk contamination. Application of these coefficients to Earth-view image data results in a significant reduction in image contamination and a correction of the scene radiance for bands 27- 30. Also, this correction shows a significant improvement to certain threshold tests in the MODIS Level-2 Cloud Mask. In this paper, we will detail the methodology used to identify and correct the crosstalk contamination for the LWIR PV bands in Terra MODIS. The derived time-dependent crosstalk coefficients will also be discussed. Finally, the impact of the correction on the downstream data products will be analyzed.

Wilson, Truman↗
DOE OSTI2021

Ganged-PV System Evaluation

The following report contains data and data summaries collected for the SkySun LLC elevated Ganged PV arrays. These arrays were fabricated as a series of PV panels in various orientations, suspended by cables, at the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories (SNL). Starting in February of 2021, Sandia personnel have collected power and accelerometer data for these arrays to assess design and operational efficacy of varying ganged- PV configurations. The purpose of this power data collection was to see how the various array orientations compare in power collection capability depending on the time of day, year, and the specific daily solar direct normal irradiance (DNI). The power data was collected as a measurement of the power output from the various series strings. The project team measured direct current (DC) voltage and current from the respective arrays. The accelerometer data was collected with the purpose of demonstrating potential destructive mode shapes that could take place with each of the arrays when exposed to high winds. This allowed the team to evaluate whether impacts with respect to specific array orientations using suspended cables is a safe design. All data collection was performed during calendar year 2021.

14 SOLAR ENERGY↗
DOE OSTI2022

PV Window Building Study

Data associated with the paper “Photovoltaic Windows to Offset the Intensive Energy and Carbon Footprints of Highly Glazed Buildings” by Vincent M. Wheeler, Janghyun Kim, Tom Daligault, Bryan Rosales, Chaiwat Engtrakul, Robert C. Tenent, and Lance M. Wheeler

14 SOLAR ENERGY↗
DOE OSTI2020

Global Techno-Economic Performance of Bifacial and Tracking Photovoltaic Systems

Although most of the current photovoltaic (PV) system installations use monofacial modules with fixed-tilt mounting structures, bifacial modules, trackers, and the combination of them are also getting more attention as potential candidates to further reduce the PV levelized cost of electricity (LCOE). This work is then the first to present a worldwide analysis on the yield potential and cost-effectiveness of PV farms composed of monofacial fixed-tilt and single/dual (1T/2T) tracker installations, as well as their bifacial counterparts. Our approach starts by estimating the irradiance reaching the front and rear surface of the modules for the different system designs (validated based on data from real PV systems and results from the literature) to estimate their energy production (row-row shading is neglected). Subsequently, the overall system cost during their 25-year lifetime is factored in, and LCOE is obtained. The results reveal that bifacial-1T installations increase energy yield by 35% and reach the lowest LCOE for the majority of the world (93.1% of the land area). Although dual axis trackers achieve the highest energy generation — especially for bifacial modules — their cost is still too high, and are therefore not as cost-effective. Sensitivity analyses based on the Monte Carlo and region sensitivity approach are performed to analyze the impact of input assumptions on the calculated LCOE. These reveal that our conclusions are robust in general but exact configuration to choose depends on specific site conditions. This investigation is not only of interest to the scientific community, but also can be used as a guide for PV installation companies and investors to determine the most suitable technology for a particular location.

14 SOLAR ENERGY↗
NASA NTRS2016

Assessments and Applications of Terra and Aqua MODIS On-Orbit Electronic Calibration

MODIS has 36 spectral bands located on four focal plane assemblies (FPAs), covering wavelengths from 0.41 to 14.4 micrometers. MODIS bands 1-30 collect data using photovoltaic (PV) detectors and, therefore, are referred to as the PV bands. Similarly, bands 31-36 using photoconductive (PC) detectors are referred to as the PC bands.The MODIS instrument was built with a set of on-board calibrators (OBCs) in order to track on-orbit changes of its radiometric, spatial, and spectral characteristics. In addition, an electronic calibration (ECAL) function can be used to monitor on-orbit changes of its electronic responses (gains). This is accomplished via a series of stair step signals generated by the ECAL function. These signals, in place of the FPA detector signals, are amplified and digitized just like the detector signals. Over the entire mission of both Terra and Aqua MODIS,the ECAL has been performed for the PV bands and used to assess their on-orbit performance. This paper provides an overview of MODIS on-orbit calibration activities with a focus on the PV ECAL, including its calibration process and approaches used to monitor the electronic performance. It presents the results derived and lessons learned from Terra and Aqua MODIS on-orbit ECAL. Also discussed are some of the applications performed with the information provided by the ECAL data.

Xiong, Xiaoxiong↗
DOE OSTI2025

OPET Control (Open PV Electrical Tool Control Software) [SWR-25-44]

Software interface for controlling and collecting data from the Open PV Electrical Tool (OPET) device. This repository contains everything relating to the control of the OPET device including source code. If you are looking for the hardware or firmware software repositories, links are below: https://github.com/NREL/opet-hardware https://github.com/NREL/opet-firmware

McDanold, Byron [National Renewable Energy Laborat↗
DOE OSTI2024

Data-Driven Model for Photovoltaic Generation: Comparison with Physical Models Using a Microgrid in Puerto Rico

Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model’s performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.

R pesante colón, Marcos↗
DOE OSTI2022

Field Experience Detecting PV Underperformance in Real Time Using Existing Instrumentation

Maintenance at large-scale photovoltaic plants employs a mix of preventative and corrective maintenance practices. Large outages, such as an inverter tripping offline, are often easy to detect. More subtle sub-inverter faults and failures can accumulate and go unnoticed for months or years. A software-based fault detection method has been developed to analyze commonly measured data from large-scale PV plants for more timely detection of subtle underperformance. The method has been demonstrated on eight datasets from large-scale plants with high accuracy of detection. Results are validated using aerial infrared scanning. String outages are detected with a true positive rate of 73 percent and tracker issues are detected with a true positive rate of 88 percent. The developed method can be uniformly applied to photovoltaic plants across a range of scales and configurations to assess performance, quickly detect underperformance, and determine the source and location of failures. The results inform and improve operations and maintenance at PV plants, ultimately aiding in improved affordability, reliability, availability, and resiliency of solar electricity.

14 SOLAR ENERGY↗
DOE OSTI2022

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗
DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗