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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Investigation of Ozone Sources in California Using AJAX Airborne Measurements and Models: Implications for Stratospheric Intrusion and Long Range Transport

High ozone concentrations at low altitudes near the surface were detected from airborne Alpha Jet Atmospheric eXperiment (AJAX) measurements on May 30, 2012. We investigate the causes of the elevated ozone concentrations using the airborne measurements and various models. GEOS-chem and WRF-STILT model simulations show that the contribution from local sources is small. From MERRA reanalysis, it is found that high potential vorticity (PV) is observed at low altitudes. This high PV appears to be only partially coming through the stratospheric intrusions because the air inside the high PV region is moist, which shows that mixing appears to be enhanced in the low altitudes. Considering that diabatic heating can also produce high PV in the lower troposphere, high ozone is partially coming through stratospheric intrusion, but this cannot explain the whole ozone concentration in the target areas of the western U.S. A back-trajectory model is utilized to see where the air masses originated. The air masses of the target areas came from the lower stratosphere (LS), upper (UT), mid- (MT), and lower troposphere (LT). The relative number of trajectories coming from LS and UT is low (7.7 and 7.6, respectively) compared to that from LT (64.1), but the relative ozone concentration coming from LS and UT is high (38.4 and 20.95, respectively) compared to that from LT (17.7). The air mass coming from LT appears to be mostly coming from Asia. Q diagnostics show that there is sufficient mixing along the trajectory to indicate that ozone from the different origins is mixed and transported to the western U.S. This study shows that high ozone concentrations can be detected by airborne measurements, which can be analyzed by integrated platforms such as models, reanalysis, and satellite data.

low altitude↗
DOE OSTI2022

Enhanced Frequency Support Scheme of Generic Inverter-Based Resource Models for Renewable-Dominated Power Grids

The frequency response of SG-dominated power grids is predictable ahead of an occurrence of a frequency event because the frequency response of SGs is consistent, and it can be inferred from the swing equation [1]. However, increasing the portion of IBRs in an SG-dominated power grid might make the characteristics of the conventional power grids no longer valid because this changing resource mix affects grid dynamics and controls [2]. Thus, maintaining these characteristics greatly benefits the control and operation of the power grids with high penetration of IBRs. To maintain these characteristics in IBR-dominated power grids, IBRs should have frequency response capability similar to that of an SG. The WECC modeling validation subcommittee has developed generic IBR models for large system planning [3]-[5]. These models can represent various vendors' dynamic behavior for WTG, PV, and ESS [5]. The current generic IBR models approved by WECC can provide frequency response only from droop control loops in REPC models [6], [7]. The contribution of the loops is proportional to the frequency deviation from the nominal frequency. Thus, it presents an insufficient contribution to arrest frequency variation compared to the frequency response of SGs because it allows a high ROCOF in the early stage of frequency events. This shortfall will become greater as the PL of IBRs increases in power grids. Controller enhancement for the generic IBR models is required to secure the frequency stability under high PL of IBRs as in the SG-dominated power grids. This paper proposes a control extension for the generic IBR models to enhance the frequency support capabilities and discusses the classification of frequency support for the different types of IBR considering their operating constraints. An inertial control scheme is implemented in the REPC and REEC models of the generic IBR models to achieve these objectives. The inertial control scheme includes the following stages: Control area data acquisition, inertia time constant estimation, IBR-related constraint check, IBR contribution determination, and inertial response provision. In the scheme, a REPC acquires control area data from a system operator and estimates a total inertia time constant for the control area the applicable IBR power plant belongs. Then, the estimated inertial time constant is transferred to each IBR controller—REEC—within the power plant. Each REEC checks the availability of applicable IBR for inertial response participation. If the IBR is available, the REEC amplifies the estimated inertial time constant to utilize it for inertial response provision. In this way, the proposed inertial response scheme extends the functionality of the generic IBR models to provide SG-like frequency response within their constraints. Various scenarios considering different IBR types, IBR penetration levels, and frequency control schemes were simulated and compared in an IEEE 39-bus system using PSCAD simulator to verify the effectiveness of the proposed scheme.

Kim, Jinho↗
DOE OSTI2022

Storage Futures Study: Key Learnings for the Coming Decades

This report is the final in NREL's Storage Futures Study, a multiyear research project that explored the role and impact of energy storage in the evolution and operation of the U.S. power sector. The SFS examined the potential impact of energy storage technology advancement on the deployment of utility-scale storage and the adoption of distributed storage, and the implications for future power system infrastructure investment and operations. The research findings and supporting data were published across a series of six reports, culminating in this final, seventh publication that draws upon findings from across the study, previous work, and additional analysis to identify eight key learnings about the future of energy storage and its impact on the power system. The key learnings can help policymakers, technology developers, and grid operators prepare for the coming way of energy storage deployment.

25 ENERGY STORAGE↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2021

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↗
DOE OSTI2021

Benchmarks of Global Clean Energy Manufacturing, 2014-2016

The Clean Energy Manufacturing Analysis Center (CEMAC), sponsored by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE), provides objective analysis and up-to-date data on global supply chains and manufacturing of clean energy technologies. This updated version of the Benchmarks of Global Clean Energy Manufacturing sheds light on several fundamental questions about the global clean technology manufacturing enterprise: How does clean energy technology manufacturing impact national economies? What are the economic opportunities across the manufacturing supply chain? What are the global dynamics of clean energy technology manufacturing?

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Analyses to Inform Equitable Investment Prioritization

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. Megan Day and Liz Ross, along with their co-authors and supported by NREL's Sustainable Communities Catalyzer, identified areas where disadvantaged community indicators intersect with high potential for renewable energy deployment. Through a geospatial intersection of energy burden, environmental hazard, and sociodemographic data with the technical generation potential and levelized cost of energy for multiple renewable energy technologies, we identified trends across disadvantaged community indicators and renewable energy deployment potential. Combining metrics across several tools, including the State and Local Planning for Energy (SLOPE) platform, the Low-Income Energy Affordability (LEAD) tool, and the Environmental Justice Screening and Mapping (EJSCREEN) tool, we compiled a dataset that can be used to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantage communities.

catalyzer↗
NASA NTRS2003

Assessment and Control of Spacecraft Charging Risks on the International Space Station

Electrical interactions between the F2 region ionospheric plasma and the 160V photovoltaic (PV) electrical power system on the International Space Station (ISS) can produce floating potentials (FP) on the ISS conducting structure of greater magnitude than are usually observed on spacecraft in low-Earth orbit. Flight through the geomagnetic field also causes magnetic induction charging of ISS conducting structure. Charging processes resulting from interaction of ISS with auroral electrons may also contribute to charging albeit rarely. The magnitude and frequency of occurrence of possibly hazardous charging events depends on the ISS assembly stage (six more 160V PV arrays will be added to ISS), ISS flight configuration, ISS position (latitude and longitude), and the natural variability in the ionospheric flight environment. At present, ISS is equipped with two plasma contactors designed to control ISS FP to within 40 volts of the ambient F2 plasma. The negative-polarity grounding scheme utilized in the ISS 160V power system leads, naturally, to negative values of ISS FP. A negative ISS structural FP leads to application of electrostatic fields across the dielectrics that separate conducting structure from the ambient F2 plasma, thereby enabling dielectric breakdown and arcing. Degradation of some thermal control coatings and noise in electrical systems can result. Continued review and evaluation of the putative charging hazards, as required by the ISS Program Office, revealed that ISS charging could produce a risk of electric shock to the ISS crew during extra vehicular activity. ISS charging risks are being evaluated in ongoing ISS charging measurements and analysis campaigns. The results of ISS charging measurements are combined with a recently developed detailed model of the ISS charging process and an extensive analysis of historical ionospheric variability data, to assess ISS charging risks using Probabilistic Risk Assessment (PRA) methods. The PRA analysis (estimated frequency of occurrence and severity of the charging hazards) are then used to select the hazard control strategy that provides the best overall safety and mission success environment for ISS and the ISS crew. This paper presents: 1) a summary of ISS spacecraft charging analysis, measurements, observations made to date, 2) plans for future ISS spacecraft charging measurement campaigns, and 3) a detailed discussion of the PRA strategy used to assess ISS spacecraft charging risks and select charging hazard control strategies

Koontz, Steve↗
DOE OSTI2023

Quantifying Uncertainty in PV Energy Estimates Final Report

Uncertainty in PV energy estimates is "one of the most critical areas of lack of understanding" according to independent engineers, financiers, PV model developers, and other industry stakeholders. The primary problem is a lack of rigorous, transparent, widely accepted methods for quantifying uncertainty in energy production estimates. Uncertainty in energy production estimates arises from variability of the solar resource, inexact PV performance models and their parameters, and system reliability considerations. Uncertainty in annual energy production is frequently calculated for larger projects in order to quantify financial risk. Key statistics for energy, such as the P-values "P50" and "P90" (the annual energy values that are exceeded in future years with 50\% and 90\% probability, respectively) are used by financing institutions to calculate the repayment risk for the project. The current methods to estimate these statistics are typically proprietary, specialized, and involve significant post-processing of commercial performance model results. This black-box approach leads to inconsistent P-value estimates from different parties, which reduces investors' confidence in the results. Since the financial community bases its risk assessment on these estimates, reduced confidence increases perceived project risk, and consequently financing costs. The goal of this project was to establish a set of best practices for quantifying uncertainty in energy production estimates, including identifying what sources of uncertainty must be considered with clear definitions and metrics, determining which sources are the biggest drivers of uncertainty, and providing a computationally efficient framework for combining different sources of uncertainty that is flexible enough to accommodate substitutions of data or methods when better information is available. We engaged a wide set of stakeholders to ensure industry endorsement and adoption, and leveraged complementary projects investigating individual sources of uncertainty in great detail, as well as others' work that started down this path.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2022

Community Planning for Solar: Conducting a Community Solar Survey

This guide is designed to assist community officials, volunteers, and regional planning agency staff in conducting a survey of residents to learn about attitudes and development preferences towards solar energy within the community. The guide will provide the key steps, timelines, distribution options, ethics, and considerations that should be made in developing a survey distribution strategy. The guide also contains an overview of various question types and response categories, with considerations for the type of data that are needed for the study. The guide concludes with recommendations for managing data and databases, data visualization, and reporting results. The appendix includes samples of materials used in a solar energy survey, from invitation letters to the survey. This document is intended to provide a practical guide for implementing a survey in communities that are proactively planning for solar development. The guide offers a practical “how-to” plan for conducting a survey. It may be advantageous to consult with an expert in survey development who will be familiar with any methods and techniques described in this guide, but this is not necessary. This guide offers basic considerations and examples of questions and analyses to help a community understand preferences of the community.

14 SOLAR ENERGY↗
DOE OSTI2021

The Evolving Solar Energy Innovation Ecosystem in Puerto Rico

This report presents initial findings from the research project, “Developing socially and economically generative, resilient PV-energy systems for low- and moderate-income communities: Applications for Puerto Rico.” This material is based upon work supported primarily by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technology Office Fiscal Year 2018 Funding Program award number DE-EE0008570 and in part by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC-1041895. This project is a joint effort of Arizona State University’s Center for Energy and Society; the University of Puerto Rico-Mayaguez’s Center for Hemispheric Cooperation in Research and Education in Engineering and Applied Science, Sustainable Energy Center, and National Institute for Energy and Island Sustainability; and the National Renewable Energy Laboratory. The project seeks to understand the place of energy in low-income communities in Puerto Rico, the opportunities for solar energy to provide solutions to the energy challenges facing these communities, and the capacity of the solar energy innovation ecosystem in Puerto Rico to deliver relevant solar solutions for these communities. We define the solar energy innovation ecosystem as the diverse actors and stakeholders whose collective networks and actions—and the flows among them of ideas, knowledge, data, influence, money, and equipment, as well as the rules that govern those flows—are essential to the success of renewable energy markets and projects. This report presents results from the first year of the project’s research, which has concentrated on several key goals: (1) characterizing the trajectory of solar energy development in Puerto Rico in the three years since Hurricane María; (2) mapping and characterizing the current solar energy innovation ecosystem in Puerto Rico, including the technical potential for solar energy in Puerto Rico, and the business and policy landscape shaping solar developments; and (3) beginning to characterize the broad landscape of community energy initiatives underway in Puerto Rico. To pursue these goals, we engaged in a systematic review of solar energy developments in Puerto Rico and conducted interviews with over 100 members of the solar energy innovation, including policy, business, finance, research, and civil society leaders, as well as solar users. Solar technical potential data for Puerto Rico was developed by the National Renewable Energy Laboratory (NREL) and is publicly available at: https://data.nrel.gov/submissions/144. The documentation for the NREL dataset is included as Appendix A.

14 SOLAR ENERGY↗
DOE OSTI2020

Utility-Scale Solar Data Update (2020 Edition) [Slides]

Berkeley Lab’s 2020 update of utility-scale solar data and trends provides an overview of key trends in the U.S. market, with a focus on 2019. Highlights of this year’s update include: More than 4.5 GWAC of utility-scale solar (i.e., ground-mounted solar projects larger than 5 MWAC) achieved commercial operations in 2019, bringing cumulative capacity to 29 GWAC. Projects are spread across all 10 regions that we track, though more heavily concentrated in the sunniest regions. The median installed cost of projects that came online in 2019 fell to $\$$1.4/WAC ($\$$1.2/WDC), down 20% from 2018 and down by more than 70% from 2010. 77% of projects and 88% of capacity added in 2019 used single-axis tracking. Average capacity factors range from 17% in the least-sunny regions to 30% where it is sunniest. Single-axis tracking adds roughly five percentage points to capacity factor in the regions with the strongest solar resource. Nationwide average levelized power purchase agreement (PPA) prices fell to $\$$24/MWh in 2019, down 17% from 2018 and more than 80% since 2010. Thirty-nine projects (totaling 4.2 GWAC) in our PPA price sample include battery storage (totaling 2.3 GWAC and 9.5 GWh). In the “lower 48” states, a number of these recent PV+battery PPAs have been inked in the mid-$\$$20/MWh range (levelized in 2019 dollars).Not including the 30% investment tax credit (ITC), the median levelized cost of energy (LCOE) from utility-scale PV has declined by 85% since 2010, to $\$$40/MWh in 2019. When the 30% ITC is included within the LCOE calculation, the median LCOE closely tracks the median PPA price over time. In higher-penetration markets like CAISO, the value of solar has been declining, but falling PPA prices have largely kept pace, preserving solar's net value. At the end of 2019, there were nearly 370 GW of solar in interconnection queues from all 7 ISOs and 30 additional utilities across the country. Nearly a third of this proposed solar capacity is paired with battery storage.

14 SOLAR ENERGY↗
DOE OSTI2020

Panel-Segmentation [SWR-21-18]

Panel-Segmentation contains the scripts for automated metadata extraction of solar PV installations, using satellite imagery coupled with computer vision techniques. In this package, the user can perform the following actions: *Automatically generate a satellite image using a set of lat-long coordinates, and a Google Maps API key. Users would need to set up a Google Cloud account and get a Maps Static API key. Please refer to Setting Up Google Maps Static API Key section for this process. *Perform image segmentation on the satellite image, to locate the solar array(s) in the image on a pixel-by-pixel basis, using an image segmentation model (panel_detection_model.pth). Get classification of the installation (rooftop, ground mounted fixed-tilt or tracking, carport, etc). *Perform azimuth estimation on each solar array cluster in the masked image. *Detect solar panels and get its latitude, longitude, and address within a geographic bounding box through the SOL-Searcher Pipeline. *Detect and calculate hurricane damage on solar installations given pre-hurricane and post-hurricane satellite imagery through the Hurricane Detection Pipeline. *Detect and calculate hail damage on solar installations given satellite imagery through the Hail Detection pipeline. *Convert NOAA MESH (Maximum Estimated Size of Hail) grib2 files into kml or geojson files. *Estimate tilt and azimuth of a solar array by processing USGS LiDAR data for the array’s location.

Edun, Ayobami↗
DOE OSTI2026

Solar Radiation Research Laboratory (SRRL) Core Project Final Report: Fiscal Years 2022-2024

The Solar Radiation Research Laboratory (SRRL) at the National Laboratory of the Rockies (NLR) is a world-leading solar calibration and measurement facility and maintains and disseminates the World Radiation Reference (essentially the W/m2) for the United States that is essential for traceable and accurate measurements of solar radiation at all solar generation facilities. SRRL operates two calibration facilities that meet International Standards Organization-17025 (ISO-17025) standards and provide unique high-quality calibrations to NREL and other U.S. Department of Energy laboratories. The Baseline Measurement System (BMS) at SRRL provides a high-quality record of solar irradiance and surface meteorological conditions. SRRL capabilities are used to develop (1) improved methods for the calibration of solar radiometers; (2) new standards through the ISO, the International Electrotechnical Commission (IEC), and the American Standards for Testing of Materials (ASTM) International; (c) solar radiation and meteorological models; and (d) advanced instrumentation and methods for operating solar measurement stations. The SRRL datasets are also critical for the validation of new models and datasets, such as the National Solar Radiation Database (NSRDB). The research and development of solar radiation measurement systems and resource modeling techniques are essential for advancing the scientific basis for producing reliable resource data. Specifically, the spatial, temporal, and spectral (wavelength dependency) characteristics of the solar resource are required in several different time frames for various project phases.

14 SOLAR ENERGY↗
DOE OSTI2020

Streamlining Energy Sprawl: Assessment of Geothermal Impacts on Public Lands

Renewable energy power generation, including geothermal, solar photovoltaics (PV), and wind, can have significant land use impacts depending on the technology and the size of the facility. At the same time, innovations across these renewable technologies are working to improve plant efficiency per acre of disturbance. Through a survey of satellite imagery and other sources, this analysis reevaluates the impact of geothermal power generation and examines the variations in land use for different geothermal technologies (binary, flash, and dry steam). Geothermal operations on Bureau of Land Management (BLM) sites serve as the focus for this analysis - i.e., only active sites with federal mineral ownership for which the BLM receives royalties from power production. The results show significant variation in direct land disturbance across the operations sites and across these three geothermal technologies. Although there is variation across plants within the same geothermal technology category, dry steam plants (The Geysers) were shown to have the lowest impact, in acres per megawatt capacity and acres per gigawatt-hour generation, followed by flash steam and binary plants. Overall, direct land impacts relative to capacity and generation have declined 16-27% for facilities constructed since 2000 as compared to those constructed prior, even though most new facilities are binary plants (which traditionally had higher land disturbance). Additionally, capacity factors based on actual net production data were found to average only 50% across all operations sites, suggesting that previous estimates based on nameplate capacity only may lead to an undercounting of generation-based land disturbance metrics. Should the geothermal industry capture additional market share, improving land disturbance metrics through accurate net production and capacity data and further minimizing impacts will be increasingly important as competition over land use intensifies.

GEOTHERMAL ENERGY↗
DOE OSTI2022

EVSE Cybersecurity and Resilience

Consequence-driven Cybersecurity Analysis for Extreme Fast Charging Electric Vehicle Infrastructure Electric vehicle (EV) development and associated charging infrastructure are expected to advance rapidly. Thirty percent of all global vehicle sales may be EVs and hybrid EVs by 2025, and they will rely on increasingly sophisticated strategies for grid integration. Next-generation EV charging infrastructure is expected to include interconnected renewable resources, such as photovoltaic (PV) arrays and battery storage systems, along with grid-edge devices. Although distributed energy resources (DERs) are useful in several ways, such as peak shaving at high demand times and backup supply for added resilience, the integration of vehicle charging and DERs could create more avenues for cyberattack. Physical and/or remote access to EV charging station components, including charge ports, power electronics, controllers, and local generation (e.g., PV and energy storage) could be paths to cause power fluctuations, leading to altered operations at the charging station, escalated privileges to administrative systems, exfiltration of financial information (including personally identifiable information), and reduced grid stability. One compromised EV supply equipment component can open the door to a variety of exploitable vulnerabilities. Cloud computing and mobile application control have the potential to expand the threat surface to non-repudiation and firmware integrity challenges. Vendor clouds have access to hundreds of chargers, and if compromised, can scale the attack surface exponentially. The high power and voltage levels of xFC infrastructure (e.g., 400 kW at 1000- V DC) increase the hazards and ability to impact the grid and vehicles more than lower-power charging systems. Legacy communications systems and protocols could also put EV infrastructure at risk of cyberattacks requiring a robust patch management process. Communications networks link EVs and chargers to several stakeholders - including charging station operators, grid operators, vendors/manufacturers, and aggregators - who have both physical and network access to share information for control, monitoring, and analytics. Information in these networks that is vulnerable to compromise includes the state of charge, charging duration, payment information, electricity price, and load control. Analyzing and prioritizing these interconnections risks could help address cybersecurity related to data leakage and manipulation.

charging↗