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Wabash CarbonSAFE: Detailed Site Characterization Plan, Task 11.0, Technical Report
Wabash CarbonSAFE established that commercial-scale CO 2 storage in the Potosi Dolomite –Maquoketa Group storage complex associated with the Wabash Valley Resources (WVR) plant site near Terre Haute, IN is highly feasible. The CarbonSAFE project team performed this evaluation through extensive data acquisition and analysis including 2D seismic reflection data, wireline logs, well testing, and core/cuttings from the Wabash #1 stratigraphic test well (now plugged and abandoned). This document summarizes work detailed in separate Wabash CarbonSAFE reports and consolidates the geologic characterization, well testing, and storage complex modeling results for the Mt. Simon Sandstone and Potosi Dolomite, two distinct reservoirs characterized at the Wabash CarbonSAFE project site. The report then presents recommendations for the next steps for site characterization, identifies data gaps for future activities, and provides an overall assessment of site potential.
Solar Photovoltaic Curtailment Estimates 2018
This databook provides certain data behind figures and tables found in the NREL report "Solar PV Curtailment in Changing Grid and Technological Contexts." These data reflect estimates based on the best available data for solar photovoltaic curtailment.
U.S. Voluntary Green Power Market Data 2018
This data book provides certain data behind figures and tables found in the NREL presentation Status and Trends in the U.S. Voluntary Green Power Market 2018 Data. These data reflect estimates based on the best available data.
U.S. Voluntary Green Power Market Data 2019
This data book provides certain data behind figures and tables found in the NREL presentation, Status and Trends in the U.S. Voluntary Green Power Market (2019 Data). These data reflect estimates based on the best available data.
NREL Green Power Data 2020
This data book provides certain data behind figures and tables found in the NREL presentation "Status and Trends in the Voluntary Market (2020 Data)." These data reflect estimates based on the best available data.
Status and Trends in the U.S. Voluntary Green Power Market (2021 Data)
This data book provides certain data behind figures and tables found in the NREL presentation "Status and Trends in the Voluntary Market (2021 Data)." These data reflect estimates based on the best available data. Excluded Data This data book excludes data for certain figures using confidential survey data or purchased data for which NREL does not own the rights. In particular, renewable energy certificate (REC) price data are not included in this data book Rounding: Some estimates may be slightly inconsistent across tabs due to estimate rounding Citation for the data: E. O'Shaughnessy and J. Heeter. 2022. Status and Trends in the Voluntary Market. Golden, CO: NREL. For more information: contact Jenny Heeter (jenny.heeter@nrel.gov). For more information on voluntary green power markets see NREL's resources at https://www.nrel.gov/analysis/green-power.html. Acronyms CCA Community choice aggregation MWh Megawatt hour PPA Power purchase agreement RECs Renewable energy certificate
A Polarimetric Radar Analysis of Ice Microphysical Processes in Melting Layers of Winter Storms Using S-Band Quasi-Vertical Profiles
Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization Z H . QVP ML statistics are examined for two regimes in the ML data: Z H ≥ 20 dB Z and Z H < 20 dB Z . Regions of Z H ≥ 20 dB Z indicate locations of MLs collocated with enhanced differential reflectivity Z DR and reduced copolar correlation coefficient ρ hv , while for Z H < 20 dB Z a well-defined ML is difficult to discern using Z H alone. Evidence of large Z DR up to 4 dB, backscatter differential phase δ up to 8°, and low ρ hv down to 0.80 associated with lower Z H (from -10 to 20 dB Z ) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase K DP and maximum Z H in the ML; these are the first QVP observations of K DP in MLs documented at S band. Negative correlation occurs between minimum ρ hv in the ML and ML depth and between minimum ρ hv in the ML and the corresponding enhancement of Z H (Δ Z H = Z H max - Z H rain ).
IRA Energy Community Data Layers
Data, geospatial data resources, and the linked mapping tool and web services reflect data for two types of potentially qualifying energy communities: 1) Census tracts and directly adjoining tracts that have had coal mine closures since 1999 or coal-fired electric generating unit retirements since 2009. These census tracts qualify as energy communities. 2) Metropolitan statistical areas (MSAs) and non-metropolitan statistical areas (non-MSAs) that are energy communities for 2023 and 2024, along with their fossil fuel employment (FFE) status. Additional information on energy communities and related tax credits can be accessed on the Interagency Working Group on Coal & Power Plant Communities & Economic Revitalization Energy Communities website (https://energycommunities.gov/energy-community-tax-credit-bonus/). Use limitations: these spatial data and mapping tool may not be relied upon by taxpayers to substantiate a tax return position or for determining whether certain penalties apply and will not be used by the IRS for examination purposes. The mapping tool does not reflect the application of the law to a specific taxpayer’s situation, and the applicable Internal Revenue Code provisions ultimately control.
The Geology of The Mt. Simon Sandstone Storage Complex at the Wabash #1 Well, Vigo Co., Indiana (Subtask 7.2, Technical Report)
The Wabash CarbonSAFE project drilled the Wabash #1 stratigraphic test well (ID# 168045) at the Wabash Valley Resources (WVR) IGCC facility in Vigo County, Indiana, to characterize and evaluate the basal Cambrian Mt. Simon Sandstone for commercial-scale CO 2 storage near the site. This report presents an extensive geologic characterization of the Mt. Simon storage complex and relevant data collected from the Wabash #1 well, such as lithologic data collected from cuttings and core, geophysical logging, geomechanical analysis of core samples, and well testing and fluid sampling within the Mt. Simon Sandstone. The Mt. Simon storage complex comprises two major sections: the Mt. Simon Sandstone as the potential reservoir and the overlying Eau Claire Formation as its primary seal. Within the report, an extensive depositional, sedimentological, and geochronologic characterization of the Mt. Simon is included with supportive chapters on the regional geology and the geophysical, petrophysical, and petrologic data collected during the project. An overview of 2D seismic reflection data collected from and around the test well is presented. Also presented are chapters on the characterization of the sealing Eau Claire Formation, including a chapter on the capacity of the primary and secondary seals to the Mt. Simon as well as a chapter on geomechanical testing results of the Eau Claire Formation and Mt. Simon Sandstone. Some of the information discussed in this report was used in the development of static and dynamic geologic models of the Mt. Simon Sandstone storage complex. The static and dynamic modeling of CO 2 injection in the Mt. Simon Sandstone are discussed in a separate report (Dessenberger et al., 2022) under the Wabash CarbonSAFE project.
Leaf Nitrogen and Carbon Content, and Leaf Mass Per Area, Kougarok Road, Seward Peninsula, Alaska, 2018
Nitrogen and carbon content, leaf mass per area (LMA) and leaf water content (LWC) of leaves sampled from locations on the Kougarok mile marker 64 NGEE Arctic site, Seward Peninsula, Alaska. Samples were collected in July 2018 from Alnus viridis, Betula nana, Betula glandulosa, Arctostaphylos alpina and Salix pulchra. This data package includes leaf sample information and trait data (*.csv). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA207_flmd.csv. See data package NGA208 "Full spectrum 350-2500 nm canopy spectral reflectance, Seward Peninsula, Alaska, 2018" for linked canopy spectral reflectance data, dGPS locations and sample photographs. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset
Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest near Crested Butte, CO with remotely sensed data including imaging spectroscopy. The flight box design encompassed the watersheds, and flight design targeted a minimum of 30% overlap between adjacent flight lines. The total area surveyed was 334 km2 across 72 lines. Flight lines were oriented north-south for consistency in solar illumination geometry. Seven days were required to cover the flight area. Some areas were flown additional times to improve data quality or temporal coincidence between airborne acquisitions and field sampling. In total, 102 lines were collected and can be downloaded here: https://portal.nersc.gov/wfsfa/doi-10-15485-1617204/. The instrument used for the acquisition was an Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG), which records radiance within 426 contiguous spectral bands in the wavelength range from 380 to 2510 nm. The instrument was flown at a target aboveground altitude of 1000 m for a processed data spatial resolution of 1 m. These data are non-orthorectified, radiance data that require further processing prior to use as a surface reflectance data set.
Plant Physiology, Alder and Willow Species, Seward Peninsula, Alaska, 2019
Photosynthetic parameters (Vcmax and Jmax), CO2 response (ACi) curves and dark respiration (Rdark) measurements of tundra shrub species. Data were collected at the NGEE-Arctic Kougarok and Teller study sites on the Seward Peninsula, AK, USA in July 2019. Four species were measured using LI-COR LI-6400 portable gas exchange systems: Alnus viridis ssp. fruticosa, Salix richardsonii and Salix pulchra. The data package files include data files, metadata files, dGPS locations and the complete instrument output for all gas exchange measurements in .csv format. See the related data packages for foliar trait data (leaf mass per area, leaf nitrogen concentration), and leaf spectral reflectance data collected simultaneously on the same shrubs (NGA210, NGA212).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Autocorrelation and Multifractal Detrended Fluctuation Analyses Reveal Superdiffusive Mass Transport in Solvent-Filled Nanoporous Media
Fluorescence fluctuation spectroscopy experiments were conducted to better understand the complex mass transport dynamics of organic molecules in liquid-filled nanoporous media. Anodic aluminum oxide (AAO) membranes incorporating 10 and 20 nm diameter cylindrical pores were employed as model materials. Nile red (NR) dye was used as a fluorescent tracer. The dye was dissolved separately in ethanol and toluene at a concentration of 20 nM and used to fill the membrane nanopores. Confocal fluorescence microscopy was employed to capture photon intensity time series data reflecting apparent diffusion of the dye within the pores. Autocorrelation of these data revealed that NR diffusion within the membranes occurred over a broad range of time scales. The autocorrelation decays were fit to a model for one-dimensional diffusion incorporating both fast and slow components having apparent diffusion coefficients, D f and D s , differing by a factor of ∼100. The fast mechanism was attributed to hindered bulk-like diffusion in the central pore cavity, while slow diffusion likely involved absorption of the dye to the pore surfaces. Unfortunately, important evidence of diffusion anomalies is lost in the broad autocorrelation decays obtained. The method of multifractal detrended fluctuation analysis (MF-DFA) was applied to the same data as a means to overcome this limitation. MF-DFA revealed that time series acquired from within the nanopores were multifractal and exhibited evidence of anomalous superdiffusion, likely resulting from the participation of a desorption-mediated diffusion mechanism. Monte Carlo simulations of time series modeling desorption-mediated diffusion in cylindrical nanopores provided support for this assignment. Here, the new knowledge gained affords an improved understanding of hydrocarbon dynamics within nanoporous oil and gas shales.
Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots
Defining quantum dots in semiconductor-based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage device characterization and dot-tuning process, which first determines whether devices are functional and then attempts to tune the functional devices to the single or double quantum-dot regime. We show that automating well-established manual-tuning procedures and replacing the experimenter’s decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without premeasured input or manual intervention. The quality of measurement results and charge states are assessed by four binary classifiers trained with experimental data, reflecting real device behavior. We compare and optimize eight models and different data preprocessing techniques for each of the classifiers to achieve reliable autonomous tuning, an essential step towards scalable quantum systems in quantum-dot-based qubit architectures.
Leaf structural and chemical traits, and vegetation temperature and height, Seward Peninsula, Alaska, 2019.
Leaf nitrogen and carbon content, leaf water content (LWC) and leaf mass per area (LMA) of leaves, and vegetation height and temperatures sampled from locations on the Teller MM 27, Kougarok MM 64 and Kougarok MM 80 NGEE Arctic sites, Seward Peninsula, Alaska. These data were collected in support of ongoing NASA ABoVE AVIRIS data synthesis work. Samples were collected in July 2019 from 24 species. This data package includes leaf sample information and vegetation trait data (*.csv). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA210_flmd.csv. See data package NGA212 "Full spectrum 350-2500 nm leaf and canopy spectral reflectance, Seward Peninsula, Alaska, 2019" for linked spectral reflectance data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
The Influence of Aqueous Dispersivity on Transport of Radiological Material at LANL MDA G - 20450
Aqueous dispersivity is a phenomenon by which a solute moving in groundwater mixes with water that does not contain the solute and becomes diluted. Solutes take paths of varying lengths through pore networks or experience slower velocities due to interactions with pore walls. Aqueous dispersivity leads to breakthrough curves with earlier arrival than expected in purely advective systems and persistent 'tails,' or late-arriving, dilute concentrations of the solute. Dispersivity can be longitudinal (in the direction of flow) or transverse (orthogonal to flow). LANL has disposed of radioactive material at Material Disposal Area (MDA) G since the late 1950's. Most of the disposed materials at MDA G are low-level waste (LLW), but other types of radioactive wastes are disposed or stored at the site. For example, there is a collection of remote-handled transuranic waste (RH-TRU) waste in 33 shafts, which were intended to be stored retrievably. Aqueous-phase radionuclides included in models of contaminant transport at MDA G are subject to dispersive processes in porous media of the vadose zone and saturated zone. To model dispersivity at MDA G probabilistically, we created site-specific distributions of aqueous dispersivity. First, we performed a comprehensive review of peer-reviewed publications, reports from other performance assessments of MDA G, and documentation from sites that were considered similar based on expert opinion, such as Yucca Mountain. Data were included from a wide range of sources, so that distributions developed based on the data reflect the current state of knowledge in properties of the subsurface environment. Data were grouped based on value type (e.g., determined via experiment or used in a different model), rock type, zone type (saturated or unsaturated), and site, and were weighted in the distributions according to their relevance to the site and zone type. We combined Monte Carlo simulation and bootstrapping techniques to produce approximate distributions of mean aqueous dispersivity. Distributions of dispersivity for saturated and unsaturated zone transport are differentiated by data weight and the length scales over which transport occurs. We find that underestimating or disregarding transverse dispersivity may lead to overstating simulated well capture in the saturated zone at MDA G. Neglecting transverse dispersivity is a common assumption due in part to use of one-dimensional transport models that do not consider solute spreading orthogonal to flow. Also, we find that variability in longitudinal dispersivity is associated with substantial travel time alterations in the MDA G unsaturated zone and in simulated contaminant breakthrough times at a downstream observation plane in the MDA G saturated zone. Finally, at large scales (>1000 m), a linear trend between the dispersivity coefficient and length over which transport occurs may overestimate the dispersivity coefficient. Future modeling work with application to solute spreading could include interacting advective and diffusive zones in the highly fractured basalt below MDA G. (authors)
Cinematic reflectometry using QIKR, the quite intense kinetics reflectometer
The Quite Intense Kinetics Reflectometer (QIKR) will be a general-purpose, horizontal-sample-surface neutron reflectometer. Reflectometers measure the proportion of an incident probe beam reflected from a surface as a function of wavevector (momentum) transfer to infer the distribution and composition of matter near an interface. The unique scattering properties of neutrons make this technique especially useful in the study of soft matter, biomaterials, and materials used in energy storage. Exploiting the increased brilliance of the Spallation Neutron Source Second Target Station, QIKR will collect specular and off-specular reflectivity data faster than the best existing such machines. It will often be possible to collect complete specular reflectivity curves using a single instrument setting, enabling “cinematic” operation, wherein the user turns on the instrument and “films” the sample. Samples in time-dependent environments (e.g., temperature, electrochemical, or undergoing chemical alteration) will be observed in real time, in favorable cases with frame rates as fast as 1 Hz. Cinematic data acquisition promises to make time-dependent measurements routine, with time resolution specified during post-experiment data analysis. This capability will be deployed to observe such processes as in situ polymer diffusion, battery electrode charge–discharge cycles, hysteresis loops, and membrane protein insertion into lipid layers.