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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrothermal liquefaction of wastewater-grown algae to produce synthetic aviation fuel: A combined experimental study and techno-economic assessment

Large-scale algae farms may someday become a consistent source of biomass feedstock for biofuels. Near-term supplies of algal biomass are available at certain water resource recovery facilities as algae cultivation is used as a method for nutrient recovery from specific effluent streams. Algae grown as a service shifts the value to the service rather than its sole use as a feedstock, which could enable the provision of algal biomass at low to no cost to biofuel producers. Hydrothermal liquefaction (HTL) can readily upgrade wet feedstock slurries, such as algae, to produce a carbon-enriched biocrude. The HTL biocrude can be hydrotreated and distilled, producing a variety of distillate fuels, including synthetic aviation fuel (SAF). We present a pathway, showing the experimental production of SAF from wastewater-grown algae via HTL, along with a techno-economic assessment to identify opportunities for process improvements. Critical quality attributes of the SAF, such as density, viscosity, surface tension, and freeze point, were estimated within the expected fuel experience ranges when compared against petroleum jet fuel. The average minimum fuel selling price of fuels from wastewater-grown algae for breakeven economics was $\$9.04$ per gasoline gallon equivalent (GGE). The sale of co-products such as struvite fertilizers and cement additives can add revenue to reduce the net cost. Ultimately, the selling price is influenced by the scale of the HTL processing facility. Adjusting estimations in the process scale, algae yield, and capital cost estimation can lower the price to $\$6.51$/GGE or raise it to $13.07/GGE.

Biofuels↗
DOE OSTI2020

Severe weather, utility spending, and the long-term reliability of the U.S. power system

There has been a limited amount of peer-reviewed literature on long-term trends in electricity reliability including the underlying factors that impact reliability across the United States. In this analysis, we considered up to 16 years of data from 203 U S. utilities—representing about 70% of electricity sales. Annual frequency of interruptions for an average customer—at the regional and U.S. national-level—has generally decreased over this timeframe. But we do not find that there is a statistically significant trend in the annual duration of interruptions for an average customer. We find that more explicit measures of severe weather are correlated with reliability. We are able to explain 7% and 16% of past variation in the reliability metrics system average interruption duration and frequency indices, respectively, is due to severe weather—a significant improvement over earlier studies. We find that current year spending by utilities is correlated with worse reliability and that cumulative spending over the preceding three years is correlated with better reliability. Finally, we demonstrate that using a statistical instrument to represent the annual frequency of interruptions for an average customer can greatly improve analysis of trends in the annual duration of interruptions for an average customer.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Impact of residential PV adoption on Retail Electricity Rates

The price of electricity supplied from home rooftop photo voltaic (PV) solar cells has fallen below the retail price of grid electricity in some areas. A number of residential households have an economic incentive to install rooftop PV systems and reduce their purchases of electricity from the grid. A significant portion of the costs incurred by utility companies are fixed costs which must be recovered even as consumption falls. Electricity rates must increase in order for utility companies to recover fixed costs from shrinking sales bases. Increasing rates will, in turn, result in even more economic incentives for customers to adopt rooftop PV. In this paper, we model this feedback between PV adoption and electricity rates and study its impact on future PV penetration and net-metering costs. We find that the most important parameter that determines whether this feedback has an effect is the fraction of customers who adopt PV in any year based solely on the money saved by doing so in that year, independent of the uncertainties of future years. These uncertainties include possible changes in rate structures such as the introduction of connection charges, the possibility of PV prices dropping significantly in the future, possible changes in tax incentives, and confidence in the reliability and maintainability of PV. (C) 2013 Elsevier Ltd. All rights reserved.

Cai, DWH↗
DOE OSTI2017

Market-driven automotive industry compliance with fuel economy and greenhouse gas standards: Analysis based on consumer choice

This paper explored factors that affect market-driven compliance with both Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) standards (together called the National Program) in the United States for phase I 2012–2016 and phase II 2017–2025. We considered a consumer-choice-based simulation approach, using the MA3T model, to estimate the market acceptance of fuel efficiency (FE) technologies and alternative fuel technologies as reflected by new sales of light-duty vehicle (LDV). Because both full and extremely low FE valuations are common in the literature, we use a moderate assumption of a 10-year perceived vehicle lifetime at a 7% annual discount rate in the baseline and include both extreme views (5 years and 15 years) in the sensitivity analysis. The study focuses on market-driven compliance and therefore excludes manufacturers’ cross-subsidization. The model results suggest that the LDV industry is able to comply with both standards even without cross-subsidization and with projected high technology cost, mainly thanks to the multiple credit programs and technology advancements. The compliance robustness, while encouraging, however is based on moderate market assumptions, such as Annual Energy Outlook 2016 Reference oil price projection and moderate FE consumer valuation. Finally, sensitivity analysis results reveal two significant risk factors for compliance: low oil prices and consumers’ FE undervaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Scenario-based analysis of electric vehicle adoption in the United States: Technology, infrastructure, and electricity pricing

This work investigates the impact of battery technology advancement, charging infrastructure development, and time-of-use (TOU) electricity pricing on vehicle adoption by 6 powertrain types in the United States through 2050. Using the Market Acceptance of Advanced Automotive Technologies (MA3T) model, we simulate 15 scenarios, examining individual cost factors and their combinations. We assess outcomes through market share, consumer surplus, and energy consumption. Results show that battery cost reductions are the strongest driver of EV adoption, increasing 2050 battery electric vehicle (BEV) share by 27 percentage points over baseline, raising annual consumer surplus by $511 per household, and reducing cumulative energy consumption by 16,610 trillion Btu. These gains are two to five times larger than those from other individual factors. Reducing home charging installation costs produces moderate impact, while TOU pricing alone yields only small gains, raising 2050 BEV market share by 1–2 percentage points. However, when cost factor improvements are combined, their effects are amplified beyond simple additivity. Pairing modest battery cost reductions with charging installation cost reductions and TOU pricing results in the largest 2050 BEV sales combined impact. The analysis demonstrates that moderate progress targeting multiple cost barriers may be more impactful than focusing on any single barrier.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

National forest timber bids and export price interlinkages in the USA: The bounds testing approach

We examine the interrelationships of national forest timber bid and log export prices for Douglas-fir (Region 6) in 2003–2021 and loblolly pine (Region 8) using time-series stationarity and bounds-testing approaches. The analysis shows the difference in time series properties across different geographies and time scales. Bid prices for these dominant species of timber in each of two Forest Service regions are found to be stationary, while log export prices for those same species appear to be non-stationary. Bounds testing conducted with autoregressive distributed lag modeling methods provides strong evidence for a cointegrating relationship between timber bid prices and log export prices in Forest Service Region 6. In Region 8, evidence for a cointegrating relationship was weak. Here, the share of salvage wood in national forest timber sales is a statistically significant control variable in all models estimated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Development and testing of a PEM SO 2 -depolarized electrolyzer and an operating method that prevents sulfur accumulation

The hybrid sulfur (HyS) cycle is being developed as a technology to generate hydrogen by splitting water, using heat and electrical power from a nuclear or solar power plant. A key component is the SO 2 -depolarized electrolysis (SDE) cell, which reacts SO 2 and water to form hydrogen and sulfuric acid. SDE could also be used in once-through operation to consume SO 2 and generate hydrogen and sulfuric acid for sale. A proton exchange membrane (PEM) SDE cell based on a PEM fuel cell design was fabricated and tested. Measured cell potential as a function of anolyte pressure and flow rate, sulfuric acid concentration, and cell temperature are presented for this cell. Sulfur accumulation was observed inside the cell, which could have been a serious impediment to further development. A method to prevent sulfur formation was subsequently developed. As a result, this was made possible by a testing facility that allowed unattended operation for extended periods.

08 HYDROGEN↗
DOE OSTI2021

Improving chiller performance and energy efficiency in hydrogen station operation by tuning the auxiliary cooling

In a hydrogen station that operates with direct fueling through the use of a 700 bar boost compressor, the outlet hydrogen temperature can significantly increase, stressing the chiller system. Here, this paper evaluates improvements that can be made to the auxiliary cooling system integrated with the compressor. Both theoretical modeling and experiments were performed at Cal State LA Hydrogen Research and Fueling Facility. The findings suggest that adjusting the auxiliary closed-loop cooling system from 15 °C to 10 °C reduced the station energy consumption and decreased the demand on the station chiller that needed to provide –20 °C hydrogen at the hose. The overall energy consumption for a single fueling reduced by between 2.86 and 9.43% for the set of experiments conducted. After the temperature of the closed-loop cooling system was reduced by 5 °C, the boost compressor outlet temperature dropped from 46-50 °C–40 °C and consequently at the hose the hydrogen temperature declined by 3 °C. Results were scaled up with a forecast on the number of daily refueling events. With a low number of daily fuelings, the proposed set-up showed a minor influence on the overall station energy consumption. However, the benefits were more pronounced for a connector station with sales at 180 kg/day, where the energy efficiency improved by between 1.4 and 5.5%, and even more so for a higher capacity station at 360 kg/day, where the improvement was between 2.9 and 8%.

08 HYDROGEN↗
DOE OSTI2025

Renewable hydrogen horizon: Geospatial techno-economic feasibility and life cycle greenhouse gas analysis in the Middle East and North Africa

Renewable hydrogen is receiving increasing attention for its potential as a flexible energy carrier in sectors such as transportation and industry. Specific cost and carbon intensity (CI) of renewable hydrogen production vary largely based on the location, owing to differences in renewable energy resources, as well as the supply chain dynamics. This study maps the techno-economic and life cycle greenhouse gas emissions of renewable hydrogen production in the Middle East and North Africa region, leveraging abundant solar and wind resources. The work investigates the variability in hydrogen costs and CI, optimally sizing proton-exchange membrane (PEM) electrolyzers to account for partial and cyclic loading, and explores standalone versus grid-connected systems. PEM capacity ratios of 52 %–63 % for photovoltaic (PV) systems and 28 %–82 % for wind systems were identified as optimal, with hydrogen production costs ranging from $\$3.8$-$\$4.8$/kg for PV and $2.0-$7.0/kg for wind. CIs span from 1.9 to 3.7 kg CO 2 ,eq /kg H 2 for PV and 0.4–7.7 kg CO 2,eq /kg H 2 for wind systems. The study highlights significant cost and CI reductions achievable with technological advancements and co-product revenue from oxygen and excess electricity sales.

Carbon Intensity↗
DOE OSTI2020

Conceptual Design and Rationale for a New Agrivoltaics Concept: Pastured-Raised Rabbits and Solar Farming

Land-use conflicts created by the growth of solar photovoltaics (PV) can be mitigated by applying the concept of agrivoltaics, that is, the co-development of land for both PV and agricultural purposes, to commercial-scale solar installations. In this study, we present a conceptual design for a novel agrivoltaic system based on pasture-fed rabbit farming and provide the technical, environmental and economic analyses to demonstrate the viability of the concept. Included in our analysis are the economic advantages to the PV operator of grazing rabbits at a density sufficient to control vegetative growth, thus reducing the economic and environmental costs of mowing; the dual-revenue stream from the sale of both rabbits and electricity, contrasted with estimates of the capital-investment costs for rabbits co-located with, and also independent of, PV; and the economic value to the rabbit farmer of higher colony-growth rates (made possible by the shading and predator protection provided by the PV arrays and of reduced fencing costs, which are the largest capital cost, by being able to leverage the PV systems for rabbit fencing. We also provide an environmental analysis that suggests that rabbit-PV farming is a pathway to a measurable reduction in agriculturally-generated greenhouse-gas emissions. Here, our calculations indicate that the co-location of solar and rabbit farms is a viable form of agrivoltaics, increasing overall site revenue by 2.5%-24.0% above projected electricity revenue depending on location and rental/ownership of rabbits, while providing a high-value agricultural product that, on a per weight basis, has significantly less environmental impact than cattle.

14 SOLAR ENERGY↗
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Techno-economics of hydrocarbon fuel production and recyclables recovery from landfill-destined municipal solid waste: AI-enhanced materials recovery facility design

Sustainable aviation fuels (SAF) production from cellulosic paper fractions of municipal solid waste (MSW) destined for landfills has strong potential to advance environmental, social, and economic sustainability across the aviation and waste sectors. This study proposes an artificial intelligence-enabled material recovery facility (AI-MRF) design to efficiently characterize, separate, process, and convert recovered paper waste from MSW into intermediate chemicals and SAF. The AI-MRF, designed to process 233,091 metric tons of MSW annually, integrates smart manufacturing technologies including AI, visual and hyperspectral imaging, multi-sensor data, and traditional sorting systems. Well-characterized and sorted cellulosic paper waste was utilized for chemical and fuel production scenarios, while clean plastics, metals, and glass were considered for recycling. Conversion of paper waste into intermediate sugars achieved a net present value (NPV) of up to $\$67$ million. For sugar-to-SAF production scenarios, the minimum fuel selling price (MFSP) was calculated at $\$6.11$ per gasoline gallon equivalent (GGE) when excluding recyclable revenue, and $\$4.03$ per GGE when halving recyclable revenue. The MFSP was further reduced to $\$1.96$ per GGE when accounting for SAF sales and recyclables. Nationally, this approach could yield about 2 billion GGE of hydrocarbon fuel annually from available MSW in the United States.

09 BIOMASS FUELS↗
DOE OSTI2017

Are policy incentives for solar power effective? Evidence from residential installations in the Northeast

State incentives for solar power have grown significantly in the past several years. This paper examines the effectiveness of policy incentives to increase residential solar photovoltaic (PV) capacity. We use county-level panel data and control for demographic characteristics, solar resources, and pro-environmental preferences. Results show that among financial incentives, rebates have the most impact with an additional $1 per watt rebate increasing annual PV capacity additions by close to 50%. Factors that affect financial returns to solar PV such as electricity price and solar insolation are also found to be significant. Results also point to a significant positive relationship between hybrid vehicle sales and residential PV capacity growth, indicating the importance of pro-environmental preferences as a predictor of solar PV demand. Back of the envelope calculations suggest that the cost of carbon mitigation through rebates is around $184 per ton of CO2.

14 SOLAR ENERGY↗
DOE OSTI2023

Prevalence of Listeria monocytogenes , Salmonella spp., Shiga toxin-producing Escherichia coli , and Campylobacter spp. in raw milk in the United States between 2000 and 2019: A systematic review and meta-analysis

Raw (unpasteurized) milk is available for sale and direct human consumption within some states in the United States (US); it cannot be sold or distributed in interstate commerce. Raw milk may contain pathogenic microorganisms that, when consumed, may cause illness and sometimes may result in death. No comprehensive review for prevalence and levels of the major bacterial pathogens in raw milk in the US exists. The objective of the present research was to systematically review the scientific literature published from 2000 to 2019 to estimate the prevalence and levels of Listeria monocytogenes, Salmonella spp., Shiga toxin-producing Escherichia coli (STEC), and Campylobacter spp. in raw milk in the US. Peer-reviewed studies were retrieved systematically from PubMed®, Embase®, and Web of ScienceTM. The unique complete nonduplicate references were uploaded into the Health Assessment Work Collaborative (HAWC). Based on the selection criteria, twenty studies were included in the systematic review and meta-analysis. Comprehensive Meta-Analysis (CMA) was used for statistical analyses, specifically, random effects meta-analyses were used to synthesize raw bulk tank milk (BTM) and milk filters (MF) data. Data from studies using culture and non–culture-based detection methods were included. Forest plots generated in CMA (Biostat, Englewood, NJ) were used to visualize the results. The average prevalence (event rate) of L. monocytogenes, Salmonella spp., STEC, and Campylobacter spp. in raw BTM in the US was estimated at 4.3% (95% confidence intervals [CIs], 2.8–6.5%), 3.6% (95% CIs, 2.0–6.2%), 4.3% (95% CIs, 2.4–7.4%), and 6.0% (95% CIs, 3.2–10.9%), respectively. Estimated prevalence was generally larger in MF than in BTM. There was not enough data to perform a meta-analysis for the prevalence or levels of pathogens in raw milk from retail establishments or other milk categories.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Predicting content consumption from content-to-content relationships

As the majority of Internet traffic today is attributed to content-centric applications, there has been ever-increasing demand for highly scalable and efficient content delivery. An accurate prediction on future content consumption is essential for such demand. To address such an issue, this paper introduces a new computational approach, Content Network (CN) that can capture the relations among contents, and its potential applications. We conduct a measurement study to investigate how contents are inter-related from the viewpoint of content spreading on one of the popular BitTorrent portals: The Pirate Bay. Based on the large-scale dataset that contains 18 K torrents and 9 M users, we construct the CN and investigate its structural properties. Our key finding is that contents in the same community in the CN (i) belong to the same content category with 94% probability, (ii) are uploaded by the same content publisher with 76% probability, and (iii) have the similar titles with 51% probability, which implies that contents in the same community collectively contain common (shared) interests of users. Our trace-driven study demonstrates that the proposed CN model is useful in (i) content recommendation for increasing sales and (ii) content caching for networking efficiency. We believe our work can provide an important insight for content stakeholders, e.g., content providers for efficient publishing strategies, network engineers for networking efficiency, or content marketers for accurate recommendation.

97 MATHEMATICS AND COMPUTING↗