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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
DOE OSTI2023

Hydrothermal but Not Mechanical Pretreatment of Wastewater Algae Enhanced Anaerobic Digestion Energy Balance due to Improved Biomass Disintegration and Methane Production Kinetics

This study used pilot-scale high-rate algae ponds to assess algal–bacteria biomass productivity and wastewater nutrient removal as well as the impact of mechanical and hydrothermal pretreatments on biomass disintegration, methane production kinetics, and anaerobic digestion (AD) energy balance. Mechanical pretreatment had a minor effect on biomass disintegration and methane production. By contrast, hydrothermal pretreatment significantly reduced particle size and increased the solubilized organic matter content by 3.5 times. The methane yield and production rate increased by 20–55% and 20–85%, respectively, with the highest values achieved after pretreatment at 121 °C for 60 min. While the 1st-order and pseudo-1st-order reaction equation models fitted methane production from untreated biomass best (R2 > 0.993), the modified Gompertz sigmoidal-type model provided a superior fit for hydrothermally pretreated algae (R2 ≥ 0.99). The AD energy balance revealed that hydrothermal pretreatment improved the total energy output by 25–40%, with the highest values for volume-specific and mass-specific total energy outputs reaching 0.23 kW per digester m3 and 2.3 MW per ton of biomass volatile solids. Additionally, net energy recovery (energy output per biomass HHV) increased from 20% for untreated algae to 32–34% for hydrothermally pretreated algae, resulting in net energy ratio and net energy efficiency of 2.14 and 68%, respectively.

09 BIOMASS FUELS↗
DOE OSTI2023

Lithium Metal Battery Quality Control via Transformer–CNN Segmentation

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations.

Quenum, Jerome (ORCID:0000000271265853)↗
DOE OSTI2023

Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH 4 ), remain challenging. We estimated LULUCF-related CH 4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH 4 emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH 4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH 4 . Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH 4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH 4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing Resilience in Lane Detection Methods: Infrastructure-Based Sensors and Traditional Approaches for Autonomous Vehicles

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions. We address this need by evaluating the influence of hazards on the resilience of three different lane detection methods in simulation: (1) traditional camera detection using a U-Net algorithm, (2) radar detections using infrastructure-based radar retro-reflectors (RRs), and (3) direct communication of lane line information using chip-enabled raised pavement markers (CERPMs). The performance of each of these methods is assessed using resilience engineering metrics by simulating the individual methods for each sensor technology’s response to related hazards in the CARLA simulator. Using simulation techniques to replicate these methods and hazards acquires extensive datasets without lengthy time investments. Specifically, the resilience triangle was used to quantitatively measure the resilience of the lane detection system to obtain unique insights into each of the three lane detection methods; notably the infrastructure-based CERPMs and RRs had high resistance to hazards and were not as easily affected as the vision-based U-Net. However, while U-Net was able to recover the fastest from the disruption as compared to the other two methods, it also had the most performance loss. Overall, this study demonstrates that while infrastructure-based lane keeping technologies are still in early development, they have great potential as alternatives to traditional ones.

Patil, Pritesh↗
DOE OSTI2024

Real World Use Case Evaluation of Radar Retro-reflectors for Autonomous Vehicle Lane Detection Applications

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical R2 score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.

Brown, Nicolas E.↗
DOE OSTI2020

Data from: No‐till establishment improves the climate benefit of bioenergy crops on marginal grasslands

Expanding biofuel production is expected to accelerate the conversion of unmanaged marginal lands to meet biomass feedstock needs. Greenhouse gas production during conversion jeopardizes ensuing climate benefits, but most research to date has focused only on conversion to annual crops and only following tillage. Here we report the global warming impact of converting USDA Conservation Reserve Program (CRP) grasslands to three types of bioenergy crops using no-till (NT) versus conventional tillage (CT). In three CRP fields planted to continuous corn, switchgrass, or restored prairie we established replicated NT and CT plots. For the two years following an initial soybean year in all fields, we found that, on average, NT conversion reduced nitrous oxide (N2O) emissions by 50% and carbon dioxide (CO2) emissions by 20% compared to CT conversion. Differences were higher in year 1 than in year 2 in the continuous corn field, and in the two perennial systems the differences disappeared after year 1. In all fields net CO2 emissions (as measured by eddy covariance) were positive for the first two years following CT establishment, but following NT establishment net CO2 emissions were close to zero or negative, indicating net C sequestration. Overall, NT improved the global warming impact of biofuel crop establishment following CRP conversion by over 20-fold compared to CT (-6.01 Mg CO2e ha-1 yr-1 for NT vs. -0.25 Mg CO2e ha-1 yr-1 for CT, on average). We also found that IPCC estimates of N2O emissions (as measured by static chambers) greatly underestimated actual emissions for converted fields regardless of tillage. Policies should encourage adoption of NT for converting marginal grasslands to perennial bioenergy crops in order to reduce carbon debt and maximize climate benefits.

09 BIOMASS FUELS↗
DOE OSTI2020

Using CESM-RESFire to understand climate–fire–ecosystem interactions and the implications for decadal climate variability

Large wildfires exert strong disturbance on regional and global climate systems and ecosystems by perturbing radiative forcing as well as the carbon and water balance between the atmosphere and land surface, while short- and long-term variations in fire weather, terrestrial ecosystems, and human activity modulate fire intensity and reshape fire regimes. The complex climate–fire–ecosystem interactions were not fully integrated in previous climate model studies, and the resulting effects on the projections of future climate change are not well understood. Here we use the fully interactive REgion-Specific ecosystem feedback Fire model (RESFire) that was developed in the Community Earth System Model (CESM) to investigate these interactions and their impacts on climate systems and fire activity. We designed two sets of decadal simulations using CESM-RESFire for present-day (2001–2010) and future (2051–2060) scenarios, respectively, and conducted a series of sensitivity experiments to assess the effects of individual feedback pathways among climate, fire, and ecosystems. Our implementation of RESFire, which includes online land–atmosphere coupling of fire emissions and fire-induced land cover change (LCC), reproduces the observed aerosol optical depth (AOD) from space-based Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products and ground-based AErosol RObotic NETwork (AERONET) data; it agrees well with carbon budget benchmarks from previous studies. We estimate the global averaged net radiative effect of both fire aerosols and fire-induced LCC at –0.59±0.52 W m –2 , which is dominated by fire aerosol–cloud interactions (–0.82±0.19 W m –2 ), in the present-day scenario under climatological conditions of the 2000s. The fire-related net cooling effect increases by ~170 % to –1.60±0.27 W m –2 in the 2050s under the conditions of the Representative Concentration Pathway 4.5 (RCP4.5) scenario. Such considerably enhanced radiative effect is attributed to the largely increased global burned area (+19 %) and fire carbon emissions (+100 %) from the 2000s to the 2050s driven by climate change. The net ecosystem exchange (NEE) of carbon between the land and atmosphere components in the simulations increases by 33 % accordingly, implying that biomass burning is an increasing carbon source at short-term timescales in the future. High-latitude regions with prevalent peatlands would be more vulnerable to increased fire threats due to climate change, and the increase in fire aerosols could counter the projected decrease in anthropogenic aerosols due to air pollution control policies in many regions. We also evaluate two distinct feedback mechanisms that are associated with fire aerosols and fire-induced LCC, respectively. On a global scale, the first mechanism imposes positive feedbacks to fire activity through enhanced droughts with suppressed precipitation by fire aerosol–cloud interactions, while the second one manifests as negative feedbacks due to reduced fuel loads by fire consumption and post-fire tree mortality and recovery processes. These two feedback pathways with opposite effects compete at regional to global scales and increase the complexity of climate–fire–ecosystem interactions and their climatic impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations

Water is the single most important element of life. Rainfall plays an important role in the spatial and temporal distribution of this precious natural resource, and it has a direct impact on agricultural production, daily life activities, and human health. One of the important elements that govern rainfall formation and distribution is atmospheric aerosol, which also affects the Earth's radiation balance and climate. Therefore, understanding how dust compositions and distributions affect the regional rainfall pattern is crucial, particularly in regions with high atmospheric dust loads such as the Middle East. Although aerosol and rainfall research has garnered increasing attention as both an independent and interdisciplinary topic in the last few decades, the details of various direct and indirect pathways by which dust affects rainfall are not yet fully understood. Here, we explored the effects of dust on rainfall formation and distribution as well as the physical mechanisms that govern these phenomena, using high-resolution WRF-Chem simulations (~1.5 km × 1.5 km) configured with an advanced double-moment cloud microphysics scheme coupled with a sectional eight-bin aerosol scheme. Our model-simulated results were realistic, as evaluated from multiple perspectives including vertical profiles of aerosol concentrations, aerosol size distributions, vertical profiles of air temperature, diurnal wind cycles, and spatio-temporal rainfall patterns. Rainfall over the Red Sea coast is mainly caused by warm rain processes, which are typically confined within a height of ~6 km over the Sarawat mountains and exhibit a strong diurnal cycle that peaks in the evening at approximately 18:00 local time under the influence of sea breezes. Numerical experiments indicated that dust could both suppress or enhance rainfall. The effect of dust on rainfall was calculated as total, indirect, and direct effects, based on 10-year August-average daily-accumulated rainfall over the study domain covering the eastern Red Sea coast. For extreme rainfall events (domain-average daily-accumulated rainfall of ≥ 1.33 mm), the net effect of dust on rainfall was positive or enhancement (6.05 %), with the indirect effect (4.54 %) and direct effect (1.51 %) both causing rainfall increase. At a 5 % significance level, the total and indirect effects were statistically significant whereas the direct effect was not. For normal rainfall events (domain-average daily-accumulated rainfall < 1.33 mm), the indirect effect enhanced rainfall (4.76 %) whereas the direct effect suppressed rainfall (-5.78 %), resulting in a negative net suppressing effect (-1.02 %), all of which were statistically significant. We investigated the possible physical mechanisms of the effects and found that the rainfall suppression by dust direct effects was mainly caused by the scattering of solar radiation by dust. The surface cooling induced by dust weakens the sea breeze circulation, which decreases the associated landward moisture transport, ultimately suppressing rainfall. For extreme rainfall events, dust causes net rainfall enhancement through indirect effects as the high dust concentration facilitates raindrops to grow when the water vapor is sufficiently available. Our results have broader scientific and environmental implications. Specifically, although dust is considered a problem from an air quality perspective, our results highlight the important role of dust on sea breeze circulation and associated rainfall over the Red Sea coastal regions. Our results also have implications for cloud seeding and water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Leveraging the signature of heterotrophic respiration on atmospheric CO 2 for model benchmarking

Spatial and temporal variations in atmospheric carbon dioxide (CO 2 ) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO 2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO 2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO 2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO 2 , in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations

Abstract. One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG.

Bhattacharya, Bimal K.↗
DOE OSTI2023

Soil organic matter diagenetic state informs boreal forest ecosystem feedbacks to climate change

The fate of soil organic carbon (SOC) in boreal forests is dependent on the integrative ecosystem response to climate change. For example, boreal forest productivity is often nitrogen (N) limited, and climate warming can enhance N cycling and primary productivity. However, the net effect of this feedback on the SOC reservoir and its longevity with climate change remain unclear due to difficulty in detecting small differences between large and variable carbon (C) fluxes needed to determine net changes in soil reservoirs. The diagenetic state of SOC – resulting from the physicochemical and biological transformations that alter the original biomolecular composition of detrital inputs to soil over time – is useful for tracing the net response of SOC at the timescales relevant to climate change not usually discernible from fluxes and stocks alone. Here, we test two hypotheses using a mesic boreal forest climate transect: (1) the SOC diagenetic state is maintained across this climosequence, and (2) the maintenance of the SOC diagenetic state is a consequence of coupled soil C and N cycling, signifying the role of enhanced N cycling supporting SOC inputs that maintain SOC stocks within the warmer-climate forests. Shifts in nonvascular to vascular plant inputs with climate observed in these and other boreal forests highlighted the need to carefully separate biogeochemical indicators of SOC source from those signifying diagenetic alteration. We thus evaluated and applied lignin biomarkers to assess the diagenetic alteration of SOC in these boreal forest organic soils and directly compared the lignin diagenetic state with that of soil organic nitrogen (SON) assessed through amino acid composition. The lignin diagenetic state remained constant across the climate transect, indicating a balance between the input and removal of lignin in these mesic boreal forests. When combined with previous knowledge of these forest ecosystems, including the diagenetic state of SON and direct measures of C fluxes and stocks, the results indicate a coupled increase in C and N cycling with climate warming that supports forest productivity and maintains SOC stocks. This balance could markedly shift as other factors begin to limit forest productivity (e.g., trace nutrients, water) with further climate change or affect forest nutrient allocation (e.g., forest age or compositional change). Further application of the approach presented here could be used to detect the limits of this and other ecosystem–climate feedbacks, by providing a tractable and parameterizable index of the lignin state across large spatial scales, necessary for ecosystem-scale parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗
DOE OSTI2025

TRAILS Output Files

Overview This data repository contains ZIP files that store compressed versions of the output of running the WaterPaths utility planning and management tool in the DU Re-Evaluation mode (to download the tool, please see this GitHub repository). The tool was used to simulate the six-utility North Carolina Research Triangle problem. Details on the contents of each ZIP file can be seen below. Data details Temporal range: Weekly data for 2,344 weeks from 2015 to 2060 (45 years). Spatial range: Six water utilities in the North Carolina Research Triangle region (0: Chapel Hil/OWASA, 1: Durham, 2: Cary, 3: Raleigh, 4: Pittsboro, and 5: Chatham) File types: CSV and OUT Different solutions available The solution numbers correspond to the different pathway strategies (henceforth referred to as "solutions") discussed in paper's main and supporting text (abstract and link to the paper here). They are as follows: Sol92: The Durham-focused pathway strategy Sol132: The Raleigh-focused pathway strategy Sol140: The regionally-robust pathway strategy Objectives files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Objectives_RDMXX_solsXX_to_XX.csv files. Each CSV file will consist of a row representing all the objective values for that specific solution, while every six columns represents the reliability, restriction frequency, infrastructure net present value ($ mil), peak financial cost, worst-case cost, and unit cost ($ per MG; in that order) for each of the six utilities. There will be 1,000 such files, denoting the performance of the six utilities across the 1,000 deeply uncertain states of the world (DU SOWs). Pathway files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Pathways_sXX_RDMXX.out file. Each OUT corresponds to the set of infrastructure being triggered in a specific DU SOW, and each file will have the name file will consist of four tab-delimited columns that are described as follows: Realization: The realization in which an infrastructure options being triggered utility: The utility currently triggering infrastructure week: The week in which a specific infrastructure option is being triggered infra.: The infrastructure option being triggered If the OUT file contains only the header line, no infrastructure was triggered for that specific DU SOW. Policies files These files can be obtained by unzipping Policies.zip. Each of the 1,000 CSV files within the unzipped folder will contain weekly water use restriction policies for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: 0rest_m: restriction multiplier for utility 0 (values between 0 and 1) 1rest_m: restriction multiplier for utility 1 (values between 0 and 1) 2rest_m: restriction multiplier for utility 2 (values between 0 and 1) 3rest_m: restriction multiplier for utility 3 (values between 0 and 1) 4rest_m: restriction multiplier for utility 4 (values between 0 and 1) 5rest_m: restriction multiplier for utility 5 (values between 0 and 1) 0transf: transfer volume for utility 0 (in MGD) 1transf: transfer volume for utility 1 (in MGD) 2transf: transfer volume for utility 2 (in MGD) 3transf: transfer volume for utility 3 (in MGD) 4transf: transfer volume for utility 4 (in MGD) 5transf: transfer volume for utility 5 (in MGD) Water Sources files These files can be obtained by unzipping WaterSources_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each water source for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xvolume: available water volume from source X (in MGD) Xs_area: surface area of source X (in ACF) Xdemand: demand drawn from a water source from source X (in MGD) Xup_spill: upstream spillage from source X (in MGD) Xww_inflow: wastewater inflow from source X (in MGD) Xcatch_inflow: upstream catchment inflow to source X (in MGD) Xevap: evaporation multiplier for source X (values between 0 and 1) Xds_spill: downstream spillage from source X (in MGD) X_Y_alloc_cap: the allocated capacity from source X to utility Y (values between 0 and 1) X_Y_alloc_dem: the allocated demand from source X to utility Y (values between 0 and 1) Xtrmt_alloc_Y: the allocated treatment capacity from source X to utility Y (values between 0 and 1) Utilities files These files can be obtained by unzipping Utilities_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each utility for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xst_vol: total available storage volume of utility X (in MG) Xcapacity: total storage capacity of utility X (in MG) Xnet_inf: : net inflow for all storage infrastructure for utility X (in MGD) Xst_rof: short term ROF for utility X (values between 0 and 1) Xst_stor_rof: short-term storage ROF for utility X (values between 0 and 1) Xst_trmt_rof: short-term treatment ROF for utility X (values between 0 and 1) Xlt_rof: long-term ROF for utility X (values between 0 and 1) Xlt_stor_rof: long-term storage ROF for utility X (values between 0 and 1) Xlt_trmt_rof: long-term treatment ROF for utility X (values between 0 and 1) Xrest_demand: restricted demand for utility X (in MGD) Xunrest_demand: unrestricted demand for utility X (in MGD) Xunfulf_demand: unfulfilled demand for utility X (in MGD) Xwastewater: wastewater return for utility X (in MGD) Xtreat_capacity: total treatment capacity for utility X (in MG) Xcont_fund: reserve (contingency) fund balance for utility X Xins_pout: insurance payout for utility X (% annual volumetric revenue) Xins_price: insurance price for utility X (% annual volumetric revenue) Xinfra_npv: infrastructure net present value for utility ($mil) Xst_vol: total available storage volume of utility X (in MG) Xdebt_serv: debt service for utility X (usually once per year if the infrastructure is triggered; % annual volumetric revenue) Xstor_vol: total stored volume (in MGD) Xobs_ann_dem: observed annual demand for utility X (in MGD) Xproj_dem: projected annual demand for utility X (in MGD) Xpv_debt_serv: present value of debt service payments for utility X (% annual volumetric revenue) Xgross_rev: gross revenue for utility X ($mil) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

Artificial Intelligence↗
DOE OSTI2021

Grid Service Values of Generic Marginal Building Flexibility in Modeled 2030 U.S. Power Systems

The datasets include the capacity, energy, and ancillary service values of a marginal kilowatt-hour (kWh) of generic, daily, shiftable building flexibility as a presumed market entrant in the 2030 U.S. power systems. The results should be interpreted along with the caveats listed in "Valuation of Building Flexibility: Grid Service Value for Initial Market Entrants in Projected 2030 United States Power Systems", which also documents the methods used for the study. The factors examined in the study include: grid scenario, region, original usage hour in the day (local time), and building flexibility parameters - efficiency, dissipation, and shifting window include max pre-shift and max post-shift. Filenames in the datasets: The filenames contain information on the grid scenario, and the building flexibility efficiency and dissipation. For example: MidCase_2030_efficiency1.25_dissipation0.05_value.csv contains all results under Mid RE 2020, efficiency = 1.25, and dissipation = 0.05. * MidCase = Mid RE Each .csv file contains: region: The location of the building flexibility, aligned with U.S. Energy Information Administration (EIA) National Energy Modeling System (NEMS) Electricity Market Module regions. max_pre_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift earlier. max_post_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift later. local_datetime: Original datetime of 1kWh of building energy consumption. local_orig_h: Original usage hour (1-24) of building energy consumption, ignores daylight saving. local_shift_to: The datetime to when building energy consumption shifts, if shifting happens. If no shifting occurs, this cell is left blank. energy: Net energy value of energy shifting. capacity: Net capacity value of energy shifting. shifting_value: Net energy plus capacity value of energy shifting. spin: Spin reserve value at the original datetime of consumption. flex: Flexible reserve value at the original datetime of consumption. reg: Regulation reserve value at the original datetime of consumption. total_profit: Assuming the building flexibility is capable of providing energy, capacity, and ancillary services, total profit is the maximum value of energy shifting value, spin reserve value, flexible reserve value, and regulation reserve value - one of the four choices at any given hour - because we do not allow its value to be double-counted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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

TEA and LCA Assessments of H2 Production from Coal, Coal/Biomass, and Biomass

This presentation was made at the "Gasification Technology Status and Pathways for Net-Zero Carbon Economy Workshop" held on November 30, 2022. The presentation primarily focuses on TEA/LCA results that NETL has developed for coal gasification w/ & w/o CCS as well as net-zero greenhouse gas coal/biomass gasification. Full reporting of these cases is publicly available in the NETL report titled "Comparison of Commercial, State-of-the-Art, Fossil-Based Hydrogen Production Technologies," April 2022. Additionally, the presentation describes current NETL work in support of the U.S. Department of Energy (DOE) Office of Fossil Energy and Carbon Management (FECM) Gasification Program in the areas of TEA/LCA/Markets analysis of biomass/MSW/waste plastics gasification to achieve cost reductions for net-zero H2 production.

Lewis, Eric↗
DOE OSTI2024

Recognizing Institutions that Prepare Students for 21st Century Building Careers

Buildings play a significant role in reaching the White House's goal of a net-zero emissions economy by 2050, so it is critical that building professionals are prepared to design and construct high-efficiency, low-carbon buildings powered by renewables. The U.S. Department of Energy's Zero Energy Design Designation recognizes post-secondary academic programs that prepare students for 21st century building careers. ZEDD is a three-year designation, and as of 2023 almost 40 programs of study from more than 25 institutions have been recognized and supported by DOE for their great work. The University of Cincinnati received the ZEDD designation for the Net Zero Energy Design Track in Architectural Engineering Program. Tracks, majors, minors, and certificates are all eligible for ZEDD. In recent interviews with faculty from the University of Cincinnati and Appalachian State University about their experiences with ZEDD, many common topics came up. Both faculty members mentioned their students who graduate with ZEDD on their resume are often the only people in their future design firms who understand and can create net zero energy designs.

buildings↗
DOE OSTI2025

Techno-Economic Viability of Flexible Dispatch of Unconventional Geothermal Systems

Flexible geothermal operations could boost project returns through the allocation of improved power purchase agreements and/or exploitation of power price arbitrage opportunities. In this study, we investigated the techno-economic feasibility of variable flow rate control and time-of-day pricing in closed-loop geothermal systems. We considered U-shaped multilateral system configurations and modeled a variety of technical system parameters. These designs were simulated using a slender-body theory (SBT) model for transient heat transfer and fluid flow. This subsurface model was integrated into the flexible geothermal economic model (FGEM) tool to evaluate the overall flexible geothermal system techno-economics. Future hourly ambient temperature conditions were based on the Sup3rCC dataset. Published datasets were used for future hourly wholesale electricity prices. We analyzed four operating strategies: 1) baseload operation, 2) seasonal dispatch (high flow rate during summer and nominal flow rate during the rest of the year), 3) net generation maximization by varying flow rate to maximize net power output, and 4) revenue maximization by varying flow rate to maximize revenue. We ran all four scenarios for a multiloop configuration with 12 lateral passes, 7-km vertical depth and 87-km total drilling length. Furthermore, we assumed a 60 degrees C/km geothermal gradient and ambient temperature and wholesale electricity prices for New Mexico as a typical state location. The nominal flow rate was set to 80 kg/s. When considering drilling costs of $1,000/m and a discount rate of 7%, the generation maximization scenario resulted in the lowest levelized cost of electricity (LCOE) of ~$150/MWh. When considering project return on investment (ROI), defined as lifetime net income divided by upfront capital costs, all flexible operation scenarios performed better than the base case scenario. The highest ROI of 80% was obtained with the revenue maximization scenario. With drilling costs of $200/m and a discount rate of 5%, the generation maximization scenario resulted in LCOE of $49/MWh.

flexible geothermal↗