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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

Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis (BSA) project uses systems thinking and analysis to assess how techno economics, research and development, deployment strategies, policy, and market conditions affect the potential development trajectories of the developing bioenergy industry. This project informs researchers, decision makers, and industry by identifying opportunities for and constraints to industrial development and quantifying important industry metrics (e.g., energy, economic, environmental) towards a sustainable domestic bioenergy system. One of the tools used in this project, the Bioenergy Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. Analysis products from this effort enable the development of the bioenergy industry by (1) encouraging policy-makers to explore multiple levers simulating outside impacts on biofuels production, identifying policy actions; (2) improving industry's understanding of growth potential under different market conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with analysis tools and analyses that can be adapted to meet research and teaching objectives, thus connecting students with careers that build the industry.

bioenergy↗
DOE OSTI2024

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop: Preprint

Geothermal cost and performance evaluation implemented via technoeconomic assessment (TEA) modeling is critical for the Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for the Annual Technology Baseline (ATB). The ATB data are inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL’s reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the US generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on technoeconomic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next generation technologies such as closed loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

Annual Technology Baseline↗
DOE OSTI2022

Multi-Level Impacts of Extreme Climate-Related Events on Food Supply Chain: Idaho Potato's Case Study

Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Harmonic Modeling, Data Generation and Analysis of Power Electronics-Interfaced Residential Loads

The share of electronics-based residential load is expected to rise as devices such as variable frequency drives (VFDs), electric vehicle chargers, and inverter-based distributed energy resources (DERs), e.g., photovoltaic (PV) systems become more common. These loads may introduce significant harmonics into power networks that need to be closely studied in order to perform accurate load modeling and forecasting. However, it can be difficult to obtain harmonic-rich voltage and current data - necessary for identifying accurate load models - for residential electrical loads. Recognizing this need, we identify and model a set of electronics-based end-use loads and DERs in an electromagnetic transients program (EMTP) tool for a residence with a single- phase split-phase supply. Further, a procedure is developed to model harmonic interactions between end-use loads connected to the same non-ideal supply voltage in a residential setting. Finally, a harmonic-rich dataset produced via the proposed procedure is utilized to identify frequency coupling matrix (FCM) based load model. Numerical results demonstrate the accuracy of the model, and explore model identifiability with limited data points.

harmonics, power quality, load modeling↗
DOE OSTI2021

U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2021 [Slides]

Our benchmarking method includes bottom-up accounting for all necessary system and projectdevelopment costs incurred when installing residential, commercial, and utility-scale systems, and it models the Q1 2021 costs for such systems, excluding any previous supply agreements or contracts. In general, we attempt to model the typical installation techniques and business operations from an installed-cost perspective, and our benchmarks are national averages. The residential PV-only benchmark and the commercial rooftop PV-only benchmark average costs by inverter type (string inverters, string inverters with direct current [DC] optimizers, and microinverters), weighted by inverter market share. The residential PV-only benchmark is further averaged across small installer and national integrator business models, weighted by market share. All benchmarks include variations—accounting for the differences in size, equipment, and operational use (particularly for storage)—that are currently available in the marketplace. All benchmarks assume nonunionized construction labor; residential and commercial PV systems predominantly use nonunionized labor, and the type of labor required for utility-scale PV systems depends heavily on the development process. All benchmarks assume the use of monofacial monocrystalline silicon PV modules. Benchmarks using cadmium telluride or bifacial modules could result in significantly different results. The data in this annual benchmark report inform the formulation of and track progress toward the U.S. Department of Energy (DOE) Solar Energy Technologies Office’s Government Performance and Reporting Act cost targets.

14 SOLAR ENERGY↗
DOE OSTI2025

The DREAM approach to demand-side emissions reductions in Indonesia, 2020–2060

Indonesia’s current energy system modeling is heavily focused on the supply side, but emissions reductions in the demand sector have a significant impact on advancing Indonesia’s ambitious emissions reductions goals. To address the gap, we develop a new modeling tool DREAM Indonesia based on a bottom-up, technology-rich demand side framework and formulate projections of demand-side emissions reductions in Indonesia in 2020–2060. We find that demand-side energy efficiency and electrification can halve the growth rate of final energy demand to 1.4% annually over 2020–2060 and reverse the growing trend of emissions. The feasibility of full electrification by 2060, coupled with rapid adoption of existing technologies, positions the building sector as a model for achievable decarbonization and a cornerstone of Indonesia’s emissions reductions ambitions. In the industrial sector, extensive emissions reductions of 87% by 2060 (compared to business-as-usual) are achievable through energy efficiency improvements, alongside enhanced material efficiency measures including optimized material usage, low-carbon substitutions, innovative technologies, and increased circularity. In the transportation sector, balancing final energy demand by incorporating energy efficiency improvements across all transport modes, along with electrification particularly in road transportation, could decrease the emissions by up to 82% in 2060 compared to business-as-usual. This study provides insights and modeling approaches for rapidly growing Asian economies as well as other developing countries facing combined development and decarbonization challenges.

DREAM Indonesia↗
DOE OSTI2020

A large-scale, agent-based simulation of metropolitan freight movements with passenger and freight market interactions

This study summarizes the first stage in the implementation of an agent-based freight modeling system that has a global representation of agents and detailed modeling of a large-scale transportation network. The model is used to evaluate the transportation and energy impacts of goods movement across urban and national scales. The framework is implemented within POLARIS, a C++-based Planning and Operations Language for Agent-based Regional Integrated Simulation, which consists of an activity-based modeling (ABM) and dynamic traffic assignment (DTA) system that has robust features for passenger travel. This platform provides a tool to model interactions among consumers, producers, and the transportation system. The main objective of this initial implementation is to implement a freight model within POLARIS following an agent-based paradigm with behavioral and simulation methods. This paper presents the initial framework and illustrates the application of the model. Building upon earlier works, a parcel location assignment algorithm for business establishments in the population is documented, along with a method for estimating establishment production and consumption volumes. In addition to population generation, other features of the model include push-pull supply chains, multimodal path choice, choice of transportation logistics node, and dynamic traffic assignment. A module with e-commerce supply and demand was also developed to analyze the effects of e-commerce delivery on last-mile energy use and congestion.

agent-based freight↗
DOE OSTI2022

Storm DEPART(Damage Estimate Prediction And Recovery Tool)

Damage prediction, materials needed, and resource allocation modeling capability in order to support pre-incident planning and preparation by predicting damage to the power generation capacity, transmission grids, distribution networks, and communications assets. The capabilities will be developed by multiple factors to include wind bands, storm surge, and flooding forecasts to participant’s assets which will result in a report of predicted damages. Additional factors to include available participants infrastructure data to include class, age, construction, location, wind rating, and additional information not in the public domain. Based on predicted damages, the output will be a bill of materials (BOM) to support short-term recovery operations. The extensiveness and level of detail for this BOM will depend on the replacement configuration specifications provided for each individual participant asset. Any limiting factors for the model will be applied related supply chain and resource constraints.

Klett, Mary↗
DOE OSTI2026

R&D GREET Battery Carbon Footprint Calculator

The Battery Carbon Footprint (CF) Calculator was developed to help U.S. battery manufacturers meet the carbon footprint reporting requirements of the EU Battery Regulation (EU) 2023/1542. The calculator incorporates several major battery carbon footprint frameworks, including the Joint Research Centre's Rules for the Calculation of the Carbon Footprint of Electric Vehicle Batteries (CFB-EV), RECHARGE's Product Environmental Footprint Category Rules for High Specific Energy Rechargeable Batteries for Mobile Applications (PEFCR), the Catena-X Product Carbon Footprint Rulebook (CX-PCF Rules), Battery Pass's Battery Carbon Footprint: Rules for Calculating the Carbon Footprint of the "Distribution" and "End-of-Life and Recycling" Life Cycle Stages, the Global Battery Alliance's Greenhouse Gas Rulebook: Generic Rules, Version 2.1, and the Ministry of Economy, Trade and Industry's draft Carbon Footprint Calculation Method for Automotive Batteries. The tool pairs these frameworks with foreground data from Argonne's R&D GREET models and integrates user-supplied background data covering battery manufacturing and supply chain activities. By bringing multiple international methodologies together in a single platform, the calculator enables manufacturers to evaluate product carbon footprints, improve data consistency, and prepare for evolving regulatory compliance and global market reporting requirements.

Zhang, Jingyi↗
DOE OSTI2024

The Cost of Offshore Wind Energy in the United States From 2025 to 2050

This study presents estimates of the levelized cost of energy (LCOE) of offshore wind energy throughout major U.S. coastal regions between a time frame of 2025 - 2050. The LCOE modeling accounts for impacts of supply chain shocks, inflation, and rising interest rates on cost. Given the near-term uncertainty in these factors, we present three possible scenarios driven by how uncertainty in costs, technology, and deployment may evolve over time. The cost increases reported by industry in recent years will likely be felt over next several years, but we expect long-term cost reductions enabled by growing offshore wind deployment and industry learning. This study helps inform decision-makers about the potential role that offshore wind energy can play in future clean energy strategies.

17 WIND ENERGY↗
DOE OSTI2020

The Economic Accessibility of CO2 Sequestration through Bioenergy with Carbon Capture and Storage (BECCS) in the US

Bioenergy with carbon capture and storage (BECCS) is one strategy to remove CO2 from the atmosphere. To assess the potential scale and cost of CO2 sequestration from BECCS in the US, this analysis models carbon sequestration net of supply chain emissions and costs of biomass production, delivery, power generation, and CO2 capture and sequestration in saline formations. The analysis includes two biomass supply scenarios (near-term and long-term), two biomass logistics scenarios (conventional and pelletized), and two generation technologies (pulverized combustion and integrated gasification combined cycle). Results show marginal cost per tonne CO2 (accounting for costs of electricity and CO2 emissions of reference power generation scenarios) as a function of CO2 sequestered (simulating capture of up to 90% of total CO2 sequestration potential) and associated spatial distribution of resources and generation locations for the array of scenario options. Under a near-term scenario using up to 206 million tonnes per year of biomass, up to 181 million tonnes CO2 can be sequestered annually at scenario-average costs ranging from $62 to $137 per tonne CO2; under a long-term scenario using up to 740 million tonnes per year of biomass, up to 737 million tonnes CO2 can be sequestered annually at scenario-average costs ranging from $42 to $92 per tonne CO2. These estimates of CO2 sequestration potential may be reduced if future competing demand reduces resource availability or may be increased if displaced emissions from conventional power sources are included. Results suggest there are large-scale opportunities to implement BECCS at moderate cost in the US, particularly in the Midwest, Plains States, and Texas.

09 BIOMASS FUELS↗
DOE OSTI2021

Material flows and GHG emissions from housing stock evolution in US counties, 2020–60

The evolution of housing stocks determines demand for construction materials and energy, and associated emissions of greenhouse gasses (GHGs). The contribution of construction to building life-cycle emissions is growing as buildings become more energy efficient and the energy supply decarbonizes. A housing stock model is developed for counties in the United States using dynamic vacancy rates which endogenously influence stock out- and inflows. Stocks of three house types and 10 construction cohorts are projected for all contiguous US counties for the period 2020–60. Inflows and outflows of construction materials are then estimated along with GHG emissions associated with material production and construction activities in scenarios defined by stock turnover rates, population share by house type, and floor area characteristics of new houses. The results provide new insights into the drivers of construction-related emissions at local and national levels, and identify opportunities for their reduction. Demolition material flows grow in relation to construction material flows over the analysis period. Increasing the stock turnover rate increases future floor area per person, material requirements, and emissions from construction. Scenarios with reduced floor area and more multifamily homes in new construction have lower floor area growth, material requirements, and emissions from construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Electromagnetic Transient Modeling of Data Centers

This report serves as a user manual for the accompanying EMT model library developed by the National Laboratory of the Rockies (NLR) for various equipment in large data centers. The EMT model library enables detailed modeling of large data center loads for conducting grid stability studies. The EMT model library for data centers include detailed models of a 5.5 kW power supply unit (PSU), a 2.5 uninterruptible power supply (UPS), a 260 MW gas turbine-generator, a 500 kW motor load, and a 33 kW IT rack. These components represent all major equipment in data centers that need to be modeled for performing grid stability studies for data centers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling

To meet ambitious global decarbonization goals, electricity system planning and operations will change fundamentally. With increasing reliance on variable renewable energy resources, energy storage is likely to play a critical accompanying role to help balance generation and consumption patterns. As grid planners, non-profit organizations, non-governmental organizations, policy makers, regulators and other key stakeholders commonly use capacity expansion modelling to inform energy policy and investment decisions, it is crucial that these processes capture the value of energy storage in energy-system decarbonization. Here we conduct an extensive review of literature on the representation of energy storage in capacity expansion modelling. We identify challenges related to enhancing modelling capabilities to inform decarbonization policies and electricity system investments, and to improve societal outcomes throughout the clean energy transition. Additionally, we further identify corresponding research activities that can help overcome these challenges and conclude by highlighting tangible real-world outcomes that will result from pursuing these research activities. Capacity expansion modelling (CEM) approaches need to account for the value of energy storage in energy-system decarbonization. A new Review considers the representation of energy storage in the CEM literature and identifies approaches to overcome the challenges such approaches face when it comes to better informing policy and investment decisions.

25 ENERGY STORAGE↗
DOE OSTI2021

Supply analysis of preferential market incentive for energy crops

This analysis explores the valuation of feedstock quality attributes of switchgrass and miscanthus – two energy crops poised for future expansion – and compares the relative economic availability of these two crops under two scenarios: (i) uniform price assumptions (i.e., no incentive for quality), and (ii) a scenario of a price premium based on convertibility (i.e., an incentive for quality). Given data on cellulose content, hemicellulose content, and their relative convertibility, miscanthus is expected to be 11% more efficient at conversion to biofuels than switchgrass under the biochemical conversion route. Based on this scenario of improved conversion efficiency and associated profit, we simulate an 11% price premium for miscanthus over other feedstocks in a base-case scenario. By adding this price premium, supplies of miscanthus increase over the base case by about 4 million (44%), 94 million (64%), and 166 million (94%) tons in year 0, 10, and 20 after simulated contracts for production are initiated respectively. These results emphasize that custom simulations are needed to quantify feedstock availability if supplies are intended to reflect grower response to industry demands for feedstock quality specifications. Farmers can grow ‘peas or carrots’, and price signals from biorefineries will influence what energy crops they produce. Recognizing that the energy crop mix is tractable according to quality characteristics is relevant both for near-term and long-term biofuels research and development. Here, we recommend accounting for market preferences for quality attributes when estimating potential future supplies of energy crops.

09 BIOMASS FUELS↗
DOE OSTI2026

BLEECAM™ (Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials) [SWR-25-125]

The National Laboratory of the Rockies' (NLR) Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials (BLEECAM™) is an open-source, integrated decision-support tool for evaluating the impacts, risks, and trade-offs across U.S. and global materials supply chains. Funded by the U.S. Department of Energy, BLEECAM supports supply chain and market analysis. The tool integrates multi-objective supply chain optimization, system dynamics, network design, lifecycle assessment, techno-economic modeling, and social impact assessment methods to evaluate how supply chains evolve over time, geography, and deployment scenarios. BLEECAM also supports analysis related to energy infrastructure, data centers and digital infrastructure, advanced manufacturing, and other sectors that depend on critical materials.

Khalifa, SherifA. [National Laboratory of the Rock↗
DOE OSTI2021

Analysis of a new multispecies tumor growth model coupling 3D phase-fields with a 1D vascular network

In this study, we present and analyze a mathematical model for tumor growth incorporating ECM erosion, interstitial flow, and the effect of vascular flow and nutrient transport. The model is of phase-field or diffused-interface type in which multiple phases of cell species and other constituents are separated by smooth evolving interfaces. The model involves a mesoscale version of Darcy’s law to capture the flow mechanism in the tissue matrix. Modeling flow and transport processes in the vasculature supplying the healthy and cancerous tissue, one-dimensional (1D) equations are considered. Since the models governing the transport and flow processes are defined together with cell species models on a three-dimensional (3D) domain, we obtain a 3D–1D coupled model.

3D–1D coupled blood flow models↗
DOE OSTI2023

Data-driven modeling of municipal water system responses to hydroclimate extremes

Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗