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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

Abstract Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Cost-optimal evaluation of centralized and distributed microgrid topologies considering voltage constraints

Optimal design of hybrid renewable mini-grids requires both economic and power quality considerations. Existing modeling approaches address these considerations via separate or loosely coupled models. Here, we extend REopt—a techno-economic optimization model developed at the National Renewable Energy Laboratory—to consider both within a single model. REopt formulates the design problem as a mixed-integer linear program that solves for a site's optimal technology mix, sizing, and operation to minimize life cycle cost. REopt has traditionally assumed a single node system. In the work presented here, we expand the REopt platform to consider multiple connected nodes with associated voltage constraints. In order to do this, we model power flow using a fixed-point linear approximation method. Additionally, we then use the model to explore design considerations of mini-grids in Sub-Saharan Africa. Specifically, we evaluate under what combinations of transmission line distance and capacity it is technically viable and economically preferable to build multiple isolated mini-grids versus an interconnected, centralized system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Ground-Truthing: Exploratory Borehole Characterization and Modeling to Verify and Expand Techno-Economic Evaluation of Earth Source Heat at Cornell U

This report documents the successful completion of DOE award DE-EE0009255: Ground- Truthing: Exploratory Borehole Characterization and Modeling to Verify and Expand Techno- Economic Evaluation of Earth Source Heat at Cornell University . Cornell University is evaluating the technical and economic feasibility of using deep direct-use (DDU) geothermal energy, referred to locally as Earth Source Heat (ESH), to serve as a renewable energy source for campus heating. To meet this objective, Cornell drilled an approximately 3-kilometer-deep exploratory borehole: the Cornell University Borehole Observatory (CUBO), or ESH-1. The team conducted an extensive suite of geophysical, hydrologic, thermal, mechanical, and geochemical measurements to evaluate subsurface conditions and geothermal development potential. This report summarizes the well drilling and testing tasks, findings, and conclusions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes

Hydrothermal liquefaction (HTL) has potential for converting abundant wet organic wastes into renewable fuels. Because HTL consists of a complex reaction network, deterministic, physics-based prediction of its biocrude yield is prohibitively difficult. Data-driven methods provide an alternative to the physics-based approach; however, rigorous testing must be performed to ensure the accuracy of predictions made by data-driven methods. To this end, a data set was assembled consisting of 570 data points appearing in the open literature. The data set was divided into training, validation, and test sub-sets and used for evaluating different machine learning regression approaches to predict biocrude yield. Among the tested algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost) predicted biocrude yields in a test set that had not been used for training with the greatest accuracy, with root mean square errors (RMSE) of 8.34 and 8.57, respectively. Further refinement of the Random Forest model reduced its RMSE to 8.07. In comparison, predictions of a series of literature models resulted in RMSE ranging from 9.16 in the most accurate case to 27.6 in the least accurate; most literature models yielded RMSE values > 10. Using biocrude yield predictions from the most accurate Random Forest model and a probabilistic economic analysis found that the model accuracy is sufficient to prioritize allocation of resources based on projected minimum fuel selling price. In our report the models and analysis represent a major advance in the ability to use readily available data to predict biocrude yields on new feedstocks that have not previously been studied.

42 ENGINEERING↗
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↗
DOE OSTI2025

Microwave-Assisted Dehydroaromatization of Flare Gas: Reactor Modeling, Plant-Wide Simulation and Economic Feasibility Analysis

Flaring is widely practiced in the oil, gas, and petrochemical sectors to ensure safety during upsets and maintenance but emits large amounts of GHGs, causing energy and economic losses. In the U.S., about one-third of Bakken gas (~250 MMSCFD) and ~100 MMSCFD from Eagle Ford are flared. Recovering this gas is essential for sustainability. Existing recovery methods—compression and reinjection (EOR), conversion to NGL, LNG/CNG, GTL, and GTW—are often limited by flowrate, composition, and variability, especially in unconventional wells. This study develops a microwave-assisted dehydroaromatization (DHA) process to convert flare gas into benzene, toluene, ethylene, and naphthalene. A laboratory reactor model is scaled up into a modular plant-wide system. Techno-economic (TEA) and life-cycle (LCA) analyses evaluate performance and sustainability, with sensitivity studies on plant capacity, electricity cost, and catalyst price confirming strong economic potential.

dehydroaromatization↗
DOE OSTI2024

Tax Credits for Clean Electricity: The Distributional Impacts of Supply-Push Policies in the Power Sector

We evaluate distributional and efficiency consequences of the bulk power clean electricity tax credits authorized by the 2022 Inflation Reduction Act. To do so, we link detailed electricity capacity expansion, computable general equilibrium, microsimulation, and air pollution models to estimate economic welfare and health incidence across demographic groups. We evaluate trade-offs between policy efficiency and income progressivity by comparing the tax credits to cap-and-trade policies. The tax credits encourage increased clean electricity investment, resulting in a reallocation of capital from elsewhere in the economy, higher prices for capital and other goods, lower power prices, and lower emissions. The tax credits yield progressive outcomes for economic welfare at the expense of efficiency while all modeled policies demonstrate progressivity in health impacts. The health benefits, absent climate benefits, exceed total policy costs and provide greater benefits for low-income and historically marginalized households given coincidence of household locations and emissions exposure intensity.

distributional impacts↗
DOE OSTI2023

WAVES (Wind Asset Value Estimation System) [SWR-23-81]

The Wind Asset Value Estimation System (WAVES) model is a coupling framework for core NREL techno economic analysis software models to estimate capital expenditures (ORBIT), operational expenditures (WOMBAT), and energy production (FLORIS) for offshore wind power plants. Existing workflows to couple the three models for lifecycle performance and cost estimation require a large amount of manual and error-prone setup to combine both shared inputs and dependent outputs, as such WAVES's primary functionality is to wrap the core logic for running standard modeling workflows to ensure shared settings and entangled results are correctly and efficiently combined every time. SEE ALSO: https://pypi.org/project/WAVES/

Hammond, Robert↗
DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard"

This dataset contains BEPAM model code and input data to replicate the outcomes for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard". The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output.gms. (BEPAM-Social cost model-ERL.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

Cost-Benefit Analysis↗
DOE OSTI2026

Technical, economic, and load-following capabilities assessment of grid-connected geothermal and geothermal-solar hybrid systems

The technical and economic performance as well as the load-following capabilities of grid-connected geothermal hybrid systems were assessed in this work. The analyzed geothermal hybrid configuration is composed of a binary geothermal plant integrated with a concentrating solar-thermal system and underground thermal energy storage (UTES) through a primary heat exchanger. Physics-based models for the hybrid system for plant generation capacities of 1, 25, and 50 MW were developed from validated models for each subsystem. Also, an economic model was developed that accounts for different hybrid system capabilities, solar field sizes, and thermal storage duration. The advantage of the geothermal hybrid system was assessed by comparing the performance with the baseline benchmark geothermal plant with a similar configuration and generation capacity. It was found that hybridizing geothermal plants with concentrating solar and thermal energy storage not only improves the thermal efficiency by up to 8 percentage points when additional heat from the solar-UTES loop rises the evaporator temperatures from 70 to 125 °C, but also enhances the load-following capability for the geothermal plant, which can meet a typical residential load profile with a power rate of change 0.25 kW/s with an absolute error under 13 kW for a 1 MW plant. Other benefits of hybridization include resource preservation and a potential LCOE reduction of up to 56% for a 50 MW geothermal hybrid plant having a 50% solar share, a 1.4 solar multiple, and 24-h storage capacity. The results presented in this work demonstrate that hybridizing geothermal systems transforms them into a flexible and cost-effective solution for addressing the dynamic requirements of modern electric grids.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Advancing Agrivoltaic Modeling With ADAM

Designing an agrivoltaic system presents a complex set of tradeoffs around PV system configuration, resulting performance, agricultural needs, and system economics. Developing tools for agrivoltaic analysis can assist system designers when making decisions related to these tradeoffs. We are developing the Agrivoltaics Design and Analysis Model (ADAM) as a free, publicly available web tool for agrivoltaics economics analysis. Features of ADAM include automated calculations for available agrivoltaic cropland, user-friendly configuration changes, inter-row irradiance calculations, and integrated economic modeling of energy and non-energy revenues. We will discuss the iterative process of agrivoltaic tool development including tradeoffs between accuracy and uncertainty, feature prioritization, and ease of use driven by our multi-disciplinary stakeholder design process. Finally, we will share modeling results from existing agrivoltaics systems as a preliminary case study.

14 SOLAR ENERGY↗
DOE OSTI2025

A framework for testing soil carbon dynamics post land-use transition in a multisector dynamics model

Soil carbon plays a crucial role in the global carbon cycle. Changes in land use can determine whether carbon is stored or is emitted into the atmosphere as carbon dioxide, which has broad implications for the human and Earth systems. These feedbacks to the carbon cycle and their socio-economic drivers are modelled by many global multisector dynamics models to project future possibilities for the human-Earth system. One notable model of this class is the Global Change Analysis Model (GCAM), which uses a simplified process to model soil organic carbon (SOC) content after land-use transition across 384 land units. While the current GCAM soil carbon framework is based on scientific principles, it has not been tested against experimental data. This work examines rates of SOC change from GCAM input data. Specifically, first order rate constants derived from model inputs were compared to values from two syntheses to assess GCAM’s accuracy. Welch’s t-tests and linear models were used to determine if rate constants were consistent across all tested geographical areas and land-use transition types. While we found that there was general agreement on the direction and magnitude (i.e., rate) of SOC change, the rate constant derived from GCAM and empirical values differed strongly in a subset of specific instances. These results indicate that GCAM’s current SOC dynamics during land use transition successfully capture broad patterns of change in this critical carbon pool, but should be interpreted with caution at finer spatial scales. One potential cause of these discrepancies is our highly aggregated variable, soil timescale, which could be made more granular to improve accuracy. When using economically rooted multisector dynamics models, such as GCAM, it is critical to understand such model limitations for representing specific Earth system processes.

carbon↗
DOE OSTI2025

Confinement Exploiting Arrays of Cross-Flow Turbines (ConExT) (Final Scientific/Technical Report)

Final technical report for the ARPA-E SHARKS project: Confinement Exploiting Arrays of Cross-Flow Turbines (ConExT). This involved collaboration by the University of Washington, University of Wisconsin, National Renewable Energy Laboratory, and Oberon Insights. Cost-effective, large-scale utilization of tidal and river current resources requires turbine arrays. Most array concepts involve multiple, staggered rows of turbines analogous to wind farms. However, in water, turbines that have an appreciable projected area (i.e., the projected area over a full revolution) relative to a channel’s cross-sectional area can theoretically extract far more energy than when operating in isolation. This suggests substantial benefits to a confinement-exploiting approach to array layout, in which turbines are more densely clustered in a single row spanning a channel. The objective of this project was to move this concept from theory to practice, as well as establishing environmental and economic trade-offs. This objective was accomplished through scale-model experimentation, high-fidelity computational fluid dynamic simulation, and integrated techno-economic modeling.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

useeio-infra-app

This GitHub repository hosts the source code to an RShiny web application that implements the U.S. Environmentally-Extended Input-Output (USEEIO) model in order to conduct a screening-level life cycle analysis. The USEEIO model is an open-source model developed and maintained by the Environmental Protection Agency (EPA), along with colleagues and contractors, and is available on GitHub (https://github.com/USEPA/USEEIO). The USEEIO model incorporates environmental data into pre-existing Economic Input-Output Models, which look at the interdependence of different economic industries. In this web application, the focus is on energy infrastructure applications; however, it can be used to implement USEEIO in any area for which this model is applicable.

AS↗
DOE OSTI2022

Technical and Economic Evaluation of the First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope via Machine Assisted History Matching

Polymer flooding has become globally established as a potential enhanced oil recovery method for heavy oils. To determine whether this technology may be useful in developing the substantial heavy oil resources on the Alaska North Slope, a polymer flood field pilot commenced at the Milne Point Unit in August 2018. This study seeks to evaluate the results of the field pilot on a technical and economic basis. A reservoir simulation model is constructed and calibrated to predict the oil recovery performance of the pilot through machine-assisted reservoir simulation techniques. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are introduced into the simulation model, forcing viscous fingering effects. In the ensuing polymer flood, these transmissibility contrasts are reduced to replicate the restoration of injection conformance during polymer flooding. Transmissibility contrasts are later reinstated to replicate fracture overextension interpreted in one of the producing wells. The calibrated simulation models produced at each stage of the history matching process are used to forecast oil recovery. These forecasts are used as input for economic analysis, incremental to waterflooding expectations. The simulation forecasts indicate that polymer flooding significantly increases the heavy oil production for this field pilot compared to waterflooding alone, yielding attractive project economics. However, meaningful variations between simulation scenarios demonstrate that a simulation model is only valid for prediction if flow behavior in the reservoir remains consistent with that observed during the history matched period. Critically, this means that a simulation model calibrated for waterflooding may not fully capture the technical and economic benefits of an enhanced oil recovery process such as polymer flooding. Subsequently, the simulation model and economic model are used in conjunction to conduct a sensitivity analysis for polymer flood design parameters, from which recommendations are provided for both the continued operation of the current field pilot and future polymer flood designs. The results demonstrate that a higher polymer concentration can be injected due to the development of fractures in the reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter demonstrates the decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding.

Keith, Cody↗
DOE OSTI2022

GREET-Based Interactive Life-Cycle Assessment of Biofuel Pathways: User Manual

An interactive, web-based tool was developed to streamline the process design for biofuel pathways that the Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) is developing. The tool utilizes the latest life-cycle analysis (LCA) data in the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model developed by Argonne National Laboratory (Argonne National Laboratory, 2021). It provides techno-economic analysis (TEA) modelers with a interative interface that generates real-time LCA results based on life-cycle inventory (LCI) data and generates useful insights into key emissions drivers. Economic implications of the LCA results are also included in the tool. Overall, the tool aims to assist TEA researchers with the development of economically viable and environmentally beneficial biofuel technologies. This manual introduces the interface and analysis capabilities of the tool.

09 BIOMASS FUELS↗
DOE OSTI2021

Systems Analysis Approach to Polyethylene Terephthalate and Olefin Plastics Supply Chains in the Circular Economy: A Review of Data Sets and Models

The environmental and economic impacts of implementing a circular economy in plastic waste supply chains are not well understood. The proposed systems analysis framework assesses environmental, social, and economic impacts of plastic waste supply chains in a circular economy. The first objective of this article is to identify datasets, models and knowledge gaps associated with waste plastic supply chain processes, mainly in the U.S. Our literature review indicated that the best datasets exist for virgin plastic resin production, mechanical recycling, landfilling, and incineration, with materials recovery facility being intermediate, and with chemical recycling the lowest. The second objective of this article is to develop an illustrative application of the framework by conducting a preliminary systems analysis of PET bottles with closed-loop recycling. Here, the preliminary systems analysis of PET bottles utilized a linear programming optimization method. Our optimization model indicated that both chemical and mechanical recycling processes are needed to achieve a true circular economy of PET bottles with the least greenhouse gas emissions, specifically reductions of 24% when compared with the linear economy. Good quality and standardized life cycle assessment and techno-economic analysis studies are needed to better understand the environmental, economic, and social impacts of advanced sorting and chemical recycling technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa (for corresponding manuscript: DOI: 10.1002/bbb.2148) PDF Files: Images of 1H NMR spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum). Suppression of the water peak was achieved using a NOESY-1D with presaturation, a recycle delay of 5 s, and a total of 64 scans. Spectra were acquired at 298 K and processed with automatic phase correction, baseline correction, and chemical shift referencing to TSP-d4. Images show all 1H data from 10 to 1ppm with inset spectra of region of interest (4.0 to 3.1 ppm). Text Files: Spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum) were converted into text files for plotting. Files contain 8192 points of raw spectral data from 12.23 to -2.78 ppm. The text file contains 4 columns of data and includes: Point number, Intensity, Hz, and ppm. Xcel Spreadsheet: HPLC measured monomeric sugar concentrations and bucketed 1H NMR data used to build monomeric sugar composition prediction models. Sugar composition in biomass determined from HPLC analyses are given in mg sugar/mg of biomass. Spectral bucketing was performed using Bruker’s AMIX software. Spectra were divided into 0.005 ppm buckets in the region of 3.10– 4.15 ppm for a total of 210 buckets. Headers for the bucketed data are the chemical shift in ppm of the center of the bucket. Bucketed data was used to build partial least squares models for subsequent predictions in The Unscrambler v. 10.5(CAMO A/S, Trondheim, Norway). The formation of methanol during hydrolysis interferes with the quantitative NMR analysis of sugars, so the methanol peak centered at 3.37 ppm and spanning four buckets (3.2925 – 3.2775 ppm) was set to zero for all spectra.

09 BIOMASS FUELS↗