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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 OSTI2021

Climate-Induced Tradeoffs in Planning and Operating Costs of a Regional Electricity System

Electricity grid planners design the system in order to supply electricity to end users reliably and affordably. Climate change threatens both objectives through potentially compounding supply- and demand-side climate-induced impacts. Uncertainty surrounds each of these future potential impacts. Given long planning horizons, system planners must weigh investment costs against operational costs under this uncertainty. Here, we developed a comprehensive and coherent integrated modeling framework combining physically-based models with cost-minimizing optimization models in the power system. We applied this modeling framework to analyze potential tradeoffs in planning and operating costs in the power grid due to climate change in the Southeast U.S. in 2050. We find that planning decisions that do not account for climate-induced impacts would result in a substantial increase in social costs associated with loss of load. These social costs are a result of under-investment in new capacity and capacity deratings of thermal generators when we included climate change impacts in the operation stage. Finally, these results highlight the importance of including climate change effects in the planning process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2021

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. Here, we also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.

42 ENGINEERING↗
DOE OSTI2022

North Carolina Water Utility Builds Resilience with Distributed Energy Resources

As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning

Hydrogen is pivotal in the transition to sustainable energy systems, playing major roles in power generation and industrial applications. Metal–organic frameworks (MOFs) have emerged as promising mediums for efficient hydrogen storage. However, identifying potential candidates for deployment is challenging due to the vast number of currently available synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for hydrogen storage applications. While previous screenings of MOF databases have predominantly emphasized high hydrogen capacities under cryogenic conditions, this study reveals that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, among the 9720 MOFs tested, 9692 MOFs achieve the lowest cost at temperatures between 170 K and 250 K and a pressure of 150 bar. Under these optimal conditions, 362 MOFs deliver a lower levelized cost of storage than 350 bar compressed gas hydrogen storage. Furthermore, this study reveals key material properties that result in low system cost, such as high surface areas (>3000 m2/g), large void fractions (>0.78), and large pore volumes (>1.1 cm3/g).

Hydrogen storage↗
DOE OSTI2024

Optimal Design Approaches for Cost-Effective Manufacturing and Deployment of Chemical Process Families with Economies of Numbers

Developing methods for rapid, large-scale deployment of carbon capture systems is critical for meeting climate change goals. Optimization-based decisions can be employed at the design and manufacturing phases to minimize costs of deployment and operation. Manufacturing standardization results in significant cost savings due to economies of numbers. Building off previous work, we present a process family design approach to design a set of carbon capture systems while explicitly including economies of numbers savings within the formulation. Our formulation optimizes both the number and characteristics of the common components in the platform and simultaneously designs the resulting set of carbon capture systems. Savings from economies of numbers are explicitly included in the formulation to determine the number of components in the platform. We show and discuss the savings we gain from economies of numbers.

Stinchfield, Georgia↗
DOE OSTI2026

Exploring the fusion power plant design space: comparative analysis of positive and negative triangularity tokamaks through optimization

The optimal configuration choice between positive triangularity (PT) and negative triangularity (NT) tokamaks for fusion power plants hinges on navigating different operational constraints rather than achieving specific plasma performance metrics. This study presents a systematic comparison using constrained multi-objective optimization with the integrated FUsion Synthesis Engine (FUSE) framework. Over 200 000 integrated design evaluations were performed exploring the trade-offs between capital cost minimization and operational reliability (maximizing q 95 ) while satisfying engineering constraints including 250 ± 50 MW net electric power, tritium breeding ratio > 1.1, power exhaust limits and an hour flattop time. Both configurations achieve similar cost-performance Pareto fronts through contrasting design philosophies. PT, while demonstrating resilience to pedestal degradation (compensating for up to 40% reduction), are constrained to larger machines (R 0 > 6.5 m) by the narrow operational window between L–H threshold requirements and the research-established power exhaust limit (P sol /R < 15 MW m –1 ). This forces optimization through comparatively reduced magnetic field (∼8 T). NT configurations exploit their freedom from these constraints to access compact, high-field designs (R 0 ~ 5.5 m, B 0 >12 T), creating natural synergy with advancing HTS technology. Sensitivity analyses reveal that PT’s economic viability depends critically on uncertainties in L–H threshold scaling and power handling limits. Notably, a 50% variation in either could eliminate viable designs or enable access to the compact design space. These results suggest configuration selection should be risk-informed: PT offers the lowest-cost path when operational constraints can be confidently predicted, while NT is robust to large variations in constraints and physics uncertainties.

FUSE framework↗
DOE OSTI2025

Equitable Urban Electric Vehicle Charging: Feasibility and Benefits of Streetlight Charging in Kansas City Right-of-Way

With an increasing global emphasis on sustainability, electric vehicles (EVs) play a crucial role in reducing urban pollution and carbon emissions. For EVs to be widely adopted, equitable and convenient access to charging infrastructure is essential. Equity in this paper refers to the proactive engagement with the community to ensure that the benefits of streetlight charging are distributed equitably across diverse neighborhoods in Kansas City, providing fair charging opportunities and resources to all community members. This research explores the potential of utilizing streetlights—ubiquitous elements of urban electrical infrastructure—as low-cost, equitable EV charging solutions. Compared with conventional chargers, streetlight charging offers several notable advantages, including proximity to roadways, potential boosts to the local economy, and easier usage due to city ownership. These chargers also leverage existing power setups to minimize costs and maximize efficiency by reutilizing existing structures. In this manuscript, we introduce a systematic framework to develop, analyze, and evaluate a scalable and cost-effective streetlight charging solution. Initially, we employ a two-tiered site selection framework that accounts for both charging demand and equity considerations to identify optimal locations for streetlight chargers. We then assess the feasibility of these chargers through observational data from 23 units installed in Kansas City, Missouri, comparing their performance with traditional chargers. Here, our evaluation extends to the environmental impact, comparing reduced gasoline consumption and greenhouse gas (GHG) emission reductions between streetlights and regular charging stations. Our findings reveal that streetlight charging stations offer faster charging speeds than conventional stations, validating their viability for EV charging. Additionally, the duration of stay at streetlight chargers tends to be shorter, likely due to parking constraints or associated costs. Furthermore, streetlight-powered chargers demonstrate considerable environmental advantages, achieving 11.94% greater avoided gasoline consumption and 11.24% higher GHG reductions compared with regular charging stations, highlighting their substantial environmental benefits.

42 ENGINEERING↗
DOE OSTI2021

Viral Inhibitors Derived from Macroalgae, Microalgae, and Cyanobacteria: A review of antiviral potential throughout pathogenesis

Viruses are abiotic obligate parasites utilizing complex mechanisms to hijack cellular machinery and reproduce, causing multiple harmful effects in the process. Viruses represent a growing global health concern; at the time of writing, COVID-19 has killed at least two million people around the world and devastated global economies. Lingering concern regarding the virus’ prevalence yet hampers return to normalcy. While catastrophic in and of itself, COVID-19 further heralds in a new era of human-disease interaction characterized by the emergence of novel viruses from natural sources with heretofore unseen frequency. Due to deforestation, population growth, and climate change, we are encountering more viruses that can infect larger groups of people with greater ease and increasingly severe outcomes. The devastation of COVID-19 and forecasts of future human/disease interactions call for a creative reconsideration of global response to infectious disease. There is an urgent need for accessible, cost-effective antiviral (AV) drugs that can be mass-produced and widely distributed to large populations. Development of AV drugs should be informed by a thorough understanding of viral structure and function as well as human biology. To maximize efficacy, minimize cost, and reduce development of drug-resistance, these drugs would ideally operate through a varied set of mechanisms at multiple stages throughout the course of infection. Due to their abundance and diversity, natural compounds are ideal for such comprehensive therapeutic interventions. Promising sources of such drugs are found throughout nature; especially remarkable are the algae, a polyphyletic grouping of phototrophs that produce diverse bioactive compounds. While not much literature has been published on the subject, studies have shown that these compounds exert antiviral effects at different stages of viral pathogenesis. In this review, we follow the course of viral infection in the human body and evaluate the AV effects of algae-derived compounds at each stage. Specifically, we examine the AV activities of algae-derived compounds at the entry of viruses into the body, transport through the body via the lymph and blood, infection of target cells, and immune response. We discuss what is known about algae-derived compounds that may interfere with the infection pathways of SARS-CoV-2; and review which algae are promising sources for AV agents or AV precursors that, with further investigation, may yield life-saving drugs due to their diversity of mechanisms and exceptional pharmaceutical potential.

Algae, antiviral, SARS-CoV-2, seaweed, cyanobacter↗
DOE OSTI2024

Viability of Additively Manufactured Electrodes for Lithium-Ion Batteries

As the global economy becomes increasingly electrified, the demand for batteries and energy storage is expected to rise significantly, particularly in the transportation and electricity sectors. Lithium-ion batteries (LIBs) are currently the most advanced and widely used technology in this field. Traditionally, LIBs are manufactured using simple 2D planar geometries to maximize production efficiency and minimize costs. However, this approach limits energy density due to the restricted design flexibility of the electrodes. Additive manufacturing (AM) offers a promising solution to enhance the energy density and efficiency of LIBs by enabling the design of architectures that reduce diffusive losses and allow for a greater amount of active material to be incorporated within the same device footprint, thereby minimizing the use of inactive materials. Different AM techniques come with their own set of limitations, including printing speed, material compatibility, and scale, which must be considered when designing electrodes. Scalable and cost-effective methods are particularly important for electric vehicle batteries, while achieving higher energy densities in microbatteries is crucial for the miniaturization of wearable electronics and medical devices. Here, in this study, we simulate various 3D porous electrode designs for LIBs using graphite and nickel manganese cobalt oxide (NMC) electrodes. These designs are selected to represent structures that could be produced using different AM techniques, such as direct ink writing, fused deposition modeling, and stereolithography. Our results indicate that at higher charging rates and increased areal mass loading, 3D structures can outperform traditional 2D electrodes, although the benefits may diminish with more complex designs that are harder to manufacture. The observed gains in energy density are attributed to improved electrode utilization and reduced diffusive energy losses. This comprehensive analysis of structure–performance relationships will provide valuable insights to guide future research on 3D designs, material selection, and AM techniques for additively manufactured battery electrodes.

25 ENERGY STORAGE↗
DOE OSTI2026

Financial-technical co-design for capital-intensive, resource-responsive energy systems

Because of their capital-intensive operation, wind energy systems that are competitive in terms of the cost of the energy that they produce lead to risk-reward trade-offs that make their business cases less favorable than those of conventional energy generation technologies. However, wind energy systems tend to be designed to maximize energy production or minimize cost of energy rather than to maximize their business cases. In this work, we attempt to exploit designs specifically tailored to business cases. We develop a novel framework for analyzing energy systems that ties their design variables to monthly operating incomes using simple models and historical hourly market and resource data. Using this approach, we demonstrate that for a wind site with abundant wind resource in the California Independent System Operator market, we can control the trade-off between mean and 5th percentile monthly returns by choosing the specific power of the turbine at a fixed modeled initial capital cost. Our framework gives a measure of the risk-reward spectrum of energy generation assets that could be built at a given site with respect to the sub-annual resource/market variation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Mobilizing mine lands for biobased decarbonization strategies

Over 163 000 ha of mine lands in Pennsylvania (PA) have the potential to produce willow as a feedstock for renewable energy generation. Each year these lands could produce between 454 000 and 907 000 dry Mg of willow, which can be a feedstock for bioenergy (i.e., biopower) with carbon capture and sequestration (BECCS) or sustainable aviation fuel (SAF) production. We used a spatially explicit model to explore cost-minimized allocation of willow biomass from abandoned mine lands (AML) to be used for BECCS or SAF production and to abate carbon dioxide. The results suggest that between 454 000 and 907 000 dry Mg of biomass can be produced on AML in PA at costs from $94 to $250 per dry Mg. This AML willow biomass could be combined with other available biomass and aggregated to ten facilities to produce 17.9 TWh of power; alternatively, it could produce 958 million liters of jet fuel, 638 million liters of renewable diesel, and 1.289 billion liters of naphtha, along with 0.735 TWh of electricity. From this analysis, in PA, under the BECCS pathway, up to 36 million Mg per year of CO2 could be abated at costs of up to $134 per Mg; under the SAF pathway, up to 17.5 million Mg per year of CO2 could be abated at costs up to $150 per Mg of CO2. The use of mine land willow as a supplemental feedstock for SAF and BECCS would need strong incentive programs if costs of production are to be comparable with production on agricultural land.

Wahlstrom, Mallory [Pennsylvania State University]↗
DOE OSTI2025

STREAM: A technology planning and capacity expansion model for the industrial sector

The Strategic Technology Roadmapping and Energy, Environmental, and Economic Analysis Model—STREAM—is an optimization-based modeling tool and analysis framework to assist with strategic planning and technology investments of the industrial sector. This open-source framework is written in Julia using the JuMP package, which enables users to model future “pathways” for incumbent and future production technologies, costs, fuels and energy carriers, and energy and non-energy environmental impacts from industries as they transform in pursuit of a robust and competitive manufacturing sector. The model starts with an initial stock of industrial production technologies and assets at a facility level and then determines pathways that minimize cost, subject to an array of possible constraints on demand, market shares, environmental flows, and other exogenously specified operational considerations such as capacity utilization rates or regional energy costs. Key features of the framework include flexibility to model a wide range of industries and industrial technologies/processes at varying levels of granularity, ability to perform parametric sensitivity analyses, and ability to visualize model results using visualization objects.

capacity expansion↗
DOE OSTI2021

Nth-plant supply: corn stover supplies and costs in a fleet of biorefineries

Feedstock cost and cost variability is expected to increase with the number of biorefineries. To quantify this effect, this spatial-economic analysis simulates feedstock cost and cost variability of an industry based on corn stover as a function of the number of biorefineries. Results are reported for nine scenarios (a base case and sensitivity analysis of four variables – harvest efficiency, sustainability constraints, opportunity cost, and corn grain yield) under deterministic and stochastic simulations, assuming biorefineries using 658 000 Mg (725 000 tons) year- 1 of corn stover in 2019. The resulting supply curves are highly elastic (i.e. little change in cost) for the first 50 of the 121 biorefineries, with price increases in subsequent biorefineries depending on scenario. In the base-case deterministic scenario, weighted-average stover costs are $66 Mg -1 ($60 ton- 1 ), $69 Mg -1 ($62 ton -1 ), and $156 Mg -1 ($142 ton -1 ), at the first, 60th, and 121st biorefineries, respectively. The stochastic simulations, subject to observed 30-year corn yield variability, follow a similar pattern, with price distributions that vary by scenario. The base-case stochastic simulations illustrate minimal cost variability for the first 60 biorefineries, but rapid increases in cost variability in the second half of potential biorefineries, with similar patterns observed in the other scenarios. Of the four variables explored, price was most sensitive to harvest efficiency, followed by sustainability constraints, corn yield, and opportunity cost. Results suggest that, under conventional logistics, about half of the US corn stover resource is reliably available with minimum cost increase and variability. Interactive visualization is available at https://doi.org/10.11578/1828779.

09 BIOMASS FUELS↗
DOE OSTI2026

Dual particle imaging using time-of-flight neutron classification

Fast-neutron imaging technology is well-suited for passive nuclear material monitoring, secondary inspection of flagged cargo, and wide-area search for lost neutron sources. However, imaging systems that use pulse shape discrimination for event classification require complex pulse waveform analysis. In this work, we evaluate time-of-flight (TOF) based particle classification as an alternative solution for fast-neutron imaging by classifying all events with a TOF above a maximum threshold as neutrons. We measured a Cf-252 source next to Cs-137 using a 12-bar organic-glass scintillator array. By varying the TOF thresholds for neutron identification, we demonstrate a clear trade-off between event yield and backprojection image fidelity, with stricter thresholds improving precision at the cost of statistics, TOF thresholded data generated an image that predicted the neutron source direction with 20% reduced mean central angle prediction error compared to a traditional pulse shape discrimination (PSD) method with comparable event count. Time-of-flight particle classification shows promise as an alternative to pulse shape discrimination systems for fast neutron imaging systems looking to minimize costs and size of electronics with comparable imaging quality. The sources used demonstrate that the method is effective in classifying measured neutrons in a measurement environment with 150 μCi Cs-137 and 1.6 × 10 6 n/s Cf-252 sources positioned at distances of 66 cm and 81 cm from the detector. Additionally, the method classifies low-energy neutron events that pulse shape discrimination removes, so a combination of both methods would result in a higher overall neutron event efficiency.

Heriot, William [Univ. of Michigan, Ann Arbor, MI ↗
DOE OSTI2026

Trace levels of PtRu on NiMo foam electrodes towards hydrogen evolution reaction

Electrochemical water splitting plays a critical role in high purity hydrogen production. To lower capital cost and energy consumption, efficient catalysts are required to boost the hydrogen evolution reaction (HER), especially in alkaline media. Although PtRu alloy materials are still considered the state-of-the-art catalyst for the HER under alkaline media, large-scale application is hindered by its scarcity and high cost. NiMo alloy catalysts can be potential Pt-free alternatives to drive the sluggish kinetics of the HER under alkaline media, but their HER activity is still not ideal due to the limitation of Volmer step. Herein, we investigated the impact of trace levels of PtRu catalysts on NiMo electrodes towards the HER by incorporating advantages of both PtRu and NiMo catalysts to minimize cost and maintain a promising HER activity. Trace levels of PtRu catalysts from 0.0173 to 0.2648 mg cm −2 were loaded on commercial NiMo foam electrodes by a facile electrodeposition technique utilizing ppm concentration levels of PtRu in electrolyte. The results show that 0.0173 mg cm −2 PtRu loading on NiMo electrode can significantly enhance HER activity compared to the pristine NiMo foam electrode (Tafel slopes drop from ∼170 to ∼60 mV dec −1 ) due to the remarkable decline of energy barriers towards water dissociation and hydrogen adsorption. Meanwhile, the rate determining step (RDS) switches from the Volmer step to the Heyrovsky step when the PtRu loading increases to 0.0675 mg cm −2 . Promising catalytic stability is observed at 100 mA cm −2 over the course of 50 h with a PtRu loading of 0.1198 mg cm −2 . In conclusion, this work demonstrates a potential strategy to decrease the cost and simultaneously maintain superior HER performance by integrating trace levels of PtRu catalyst with NiMo foam electrodes.

Electrochemical water splitting↗
DOE OSTI2024

Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗
DOE OSTI2021

The Co-Optimization of Sustainable Aviation Fuel: Cost, Emissions, and Performance

The combustion of petroleum-based fuels contributes to increases in atmospheric CO2, contributing to climate change. As other sectors electrify, current battery technology makes this impractical for the aviation industry. The fastest pathway to reducing the carbon contributions of aviation is to create low or no net carbon-emitting drop-in petroleum alternatives. This research explores 3 potential sorghum derived jet fuel molecules from 4 different production routes, hydro-processed esters and fatty acids (HEFA), and Jet A, to identify how blends including these fuels could offer improved performance (MJ/kg, MJ/L) and improved emissions (gCO2/MJ) while minimizing costs. Different applications will value each target metric differently. This research utilizes the Jet Fuel Blend Optimizer (JudO) to create a 4-dimensional Pareto front across potential solutions. The molecules considered will not offer reduced cost compared to conventional jet fuel (Jet A), but they can be utilized to create blends with higher specific energy, greater energy density, and reduced greenhouse gas emissions. Considerations including carbon credits, the sale of byproducts, and the valuation of improved performance will make the proposed molecules more commercially viable. At the lowest region of GHG solutions, coupled with equivalent LCFS approximations, JudO determined blends that could achieve as high as 69% overall carbon reductions at a premium of $0.34/L.

Feldhausen, John J.↗