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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

HybridBOSSE (Hybrid Balance-of-System (BOS) Systems Engineering Model)

Hybrid Power Plants (HPPs) have the potential to increase the value of renewable energy systems and decrease their costs through shared development (e.g., permitting) and infrastructure (e.g., collection system). Prior work has identified potential cost savings and technical and economic performance improvements for solar plus storage plants. However, additional research is needed to understand cost drivers that are specific to hybrid wind plants. Here we analyze the potential for shared infrastructure cost savings at one type of hybrid plant: wind plus solar photovoltaic (PV). To perform this analysis, we developed a new open-source, Python-based cost modeling tool: the Hybrid Balance-of-System (BOS) Systems Engineering model (HybridBOSSE). HybridBOSSE is an extension of NREL's LandBOSSE tool.

Barker, Aaron↗
DOE OSTI2023

ABF Past Accomplishments - TEA/LCA

The Integrated Analysis task in the Agile BioFoundry (ABF) conducts techno-economic analysis (TEA) and life cycle assessment (LCA) to quantify the economics and environmental impacts of bioprocesses under development. The team from Argonne National Laboratory (ANL) and the National Renewable Energy Laboratory (NREL) develops TEA and LCA models of selected compound targets and hosts of interest to the ABF in an effort to provide an analysis-based foundation to the R&D. To date, the Integrated Analysis task has performed process modeling and analyses on multiple pairings of bioproducts and hosts, centered around major metabolic "beachheads" and "exemplar" molecules chosen for each of the metabolic pathways of interest. This presentation provides an overview of the approach used for TEA and LCA in the ABF and of the progress, outcomes, and impacts of TEA and LCA to support ABF goals, with a focus towards developing bio-based products that are both environmentally and economically viable.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2020

NREL's Capabilities in Biorefinery Analysis

NREL remains at the forefront of expertise in conducting process, techno-economic, and life cycle analyses to connect research with future commercial process integration, a critical step in the scale-up of biomass conversion technologies. Technology areas covered include lignocellulosic biorefineries, algae, CO2 utilization, waste-to-energy, aviation and marine fuels, refinery modeling and analysis, and the circular economy.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2024

Toward High-Throughput Deposition of III-V Materials and Devices Using Halide Vapor Phase Epitaxy: Preprint

III-V devices are used in countless applications due to their excellent physical properties. They could become more prevalent, especially in area-intensive applications such as solar power, if they can achieve significant cost decreases through increasing scale. The development of high-throughput growth systems can help to achieve this scale, leading to the use of III-V devices in areas where they are not currently economically feasible. Here, we describe a pilot production, pseudo inline HVPE reactor with the potential to greatly increase the throughput of III-V devices. We show computational modeling results that both informed system design and the understanding of the impact of different process parameters on the deposition. We show the throughput possibilities of this reactor with an example solar cell device design but note that this system is agnostic to the device structure and can be used to increase the throughput of lasers, LEDs, transistors, and more III-V devices.

high throughput↗
DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗
DOE OSTI2023

Modeling Tool Development and Validation for Solar Industry Process Heat Using Particle Thermal Energy Storage

U.S. industry sectors used 26.2 quadrillion Btu and accounted for 33% of total energy consumption in 2021 according to the Energy Information Agency. Industrial process heat accounts for 70% of industrial energy use with application temperatures ranging from 60 degrees -1100 degrees C. Industry processes, heavily relying on fossil fuels of cheap coal or natural gas, differ widely in operating conditions and load requirements which makes them difficult to standardize and imposes great challenges in decarbonization. Industry processes require reliable energy supply and vary widely in temperature ranges. Storing energy from renewable sources is necessary to improve reliability and to mitigate renewable intermittency when replacing carbon fuel-based heat supplies to achieve energy savings and reduce emissions. To this end, we have developed a particle-based thermal energy storage (TES) technology using low-cost and highly stable silica sand as a storage medium. The economic and performance-based analysis is key for renewable energy sources to reliably supply industry process heat and ultimately displace fossil fuels for decarbonization. The diversified industrial processes need case-by-case analysis and design. Therefore, an adaptive modeling tool is key for renewable power with energy storage to meet industry demands. Thus, a modeling tool to simulate a solar industry process heat system using the particle TES has been developed using the object-oriented equation-based language Modelica and the commercial platform of Modelon Impact. The Modelica-based software tool provides a general simulation environment for the design of reliable solar energy sources integrated with TES for various industrial process applications at different temperatures for economic competence with fossil fuels such as coal and natural gases. It uses both customized and standard component modeling modules in Modelon libraries for the flexibility to be adapted to a specific energy demand application. The particle TES system establishes a uniform energy supply platform with an efficient heat exchanger and particle thermal energy reservoir integrated with renewable powers. The particle TES system can provide a wide temperature range and can have a large storage temperature difference that increases storage energy density; therefore, it can be an adaptable energy storage system integrated with renewable power to supply 24/7 heat for industry decarbonization.

concentrated solar thermal↗
DOE OSTI2024

Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering

Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications.

Baquedano-Aguilar, Mario D.↗
DOE OSTI2021

Development of an enzyme cocktail to bioconvert untapped starch in sweet sorghum processing by-products: Part II. Application and economic potential

Demand for bioethanol and other biochemicals produced from agricultural crops has increased. Sweet sorghum has been identified as a promising bioenergy crop as it can produce starch, sugar, and fibrous biomass. During processing, starch accumulates in the juice sediments and clarification mud. And, while currently not recovered, it can be made accessible for fermentation by enzymatic hydrolysis. The enzymatic hydrolysis of starch in sweet sorghum processing streams was investigated, followed by fermentation of the resulting glucose to ethanol. The juice sediment contained sufficient starch to increase fermentable sugars by 127%, allowing a final ethanol titer of 11.1 vol%. The economics for fermentation of only dissolved sugars (Scenario 1) versus dissolved sugars and hydrolyzed starch (Scenario 2) were compared using a technoeconomic model. Fixed capital cost was found to be similar for both scenarios. For a set harvest period, Scenario 2 could produce 20% more ethanol; however, if the distillation by-product was dried and marketed as animal feed, Scenario 1 was more cost effective. A plant producing 5.6–5.7 Mkg of fuel ethanol per year had a minimum selling price of $0.67/kg of ethanol with concurrent production of animal feed and zero cost sweet sorghum processing. Finally, even with these optimistic assumptions, the estimated production cost was higher than the current wholesale price of fuel ethanol.

09 BIOMASS FUELS↗
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 OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗
DOE OSTI2026

Modeling Combined Heat and Power Systems in REopt

This webinar will explore how the National Laboratory of the Rockies' REopt(R) web tool can evaluate the techno-economics of combined heat and power (CHP) technologies and systems to assess performance and financial viability. REopt can analyze both standalone CHP systems, and systems paired with other on-site energy resources. Participants will gain practical insights into using REopt to optimize site energy strategies, reduce energy costs, and improve site energy security and outage recovery.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Virtual Engineering of Low-Temperature Conversion

In this work, we present the development of an overarching software framework and supporting multiphysics models to simulate the end-to-end process of biomass conversion. This virtual engineering (VE) software is designed with the goal of accelerating research and development and reducing risk for market-relevant biomass conversion processes. We currently support multiple models, computing paradigms, and fidelities representing the steps of feedstock pretreatment, enzymatic hydrolysis, and bioconversion. Although this VE approach was developed to support a biomass workflow, we have designed each component in a way that allows us to easily support new domains, unit models, and feedstocks. We begin by presenting the user-facing aspects of the VE software and highlight how simulated elements are defined and linked by VE functions. We then present an overview of the high-fidelity computational fluid dynamics (CFD) models developed to support our target domain before segueing into our efforts to develop accurate and fast surrogate models, capturing the salient outcomes from the CFD simulations in a significantly less computationally demanding manner. We then present how VE calculations interface with a commercial techno-economic analysis software, Aspen Plus. We conclude by presenting VE case studies that leverage these methods and discuss how our methods can be extended to support a wide variety of accelerated biofuel commercialization pathways in the future.

biofuel↗
DOE OSTI2020

Algae-Based Beneficial Re-use of Carbon Emissions Using a Novel Photobioreactor: a Techno-Economic and Life Cycle Analysis

Despite the many advantages of microalgae, the feasibility of large-scale cultivation requires significant amounts of carbon dioxide (CO 2 ) to enable high growth rates. A synergistic union typically proposed for the supply of CO 2 is the coupling of algal cultivation with emissions from power plants. This study investigates the sustainability of a novel microalgae platform coupled with coal-based flue gas. The proposed system consists of a novel photobioreactor (PBR) for the production of biomass followed by a two-stage dewatering process. Here, a systems model, which quantifies the CO 2 and energy consumption of the proposed system, was developed and the minimum biomass selling price (MBSP) was determined by a techno-economic analysis (TEA). TEA results indicate that a facility with the capacity to capture 30% of the emissions from a 1 MW power plant requires a biomass production of 1280 metric tonnes per year, which when scaled to a nth of kind facility, can produce biomass at a MBSP of $2322 per tonne. The environmental impact of the proposed facility was determined by a life cycle assessment methodology and results indicate a carbon capture potential of 1.16 x 10 4 metric tons of CO 2 equivalent. In addition, an energy analysis indicates a desirable net energy ratio of 0.1, which is lower than conventional PBRs. Discussion focuses on the requirements to reduce biomass production cost, including research investment areas for increasing productivity while decreasing energy requirements.

09 BIOMASS FUELS↗
DOE OSTI2020

Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles

With the intermittency that comes with electricity generation from renewables, utilizing dynamic pricing will encourage the demand-side to respond in a smart way that would minimize the electricity costs and flatten the net electricity demand curve. Determining the optimal dynamic pricing profile that would leverage distributed storage to flatten the curve is a novel idea that needs to be studied. Moreover, the economic feasibility of utilizing distributed electrical energy storage is still not given in the literature. Therefore, in this paper, a novel way of solving a citywide dynamic model using a bilevel programming algorithm is introduced. The problem is developed as a novel non-cooperative Stackelberg game that utilizes air-conditioning systems and electrical storage through the end-users to determine the optimal dynamic pricing profile. The results show that the combined effect of utilizing demand-side air-conditioning systems and distributed storage together can flatten the curve while employing the optimal dynamic pricing profile. An economic study is performed to determine the economic feasibility of 20 different cases with different battery designs and the level of solar penetration. Three metrics were used to evaluate the economic performance of each case: the levelized cost of storage, the levelized cost of energy, and the simple payback period. Most cases had levelized cost of storage values lower than 0.457 $/kWh, which is the lower bound available in the literature. Seven out of 16 cases have a simple payback period shorter than the lifetime of the system (25 years). The case with a 100 MW PV power plant and a battery storage of size 597 MWh, was found to be the most promising case with a simple payback period of 12.71 years for the photovoltaic plant and 19.86 years for the demand-side investments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

cabbi-bio/BECT-ABM

The emergence of the cellulosic bioeconomy requires not only adequate technological, economic, and policy advances, but also effective communication and coordination among the multiple stakeholders in the bioeconomy community. Aiming to facilitate stakeholder communication and collaboration for cellulosic bioeconomy growth, an agent-based model is developed as a community communication tool in this study. The tool simulates the behaviors and interactions of key stakeholders, as well as the responses and interactions between the stakeholders and biophysical, socioeconomic, and environmental conditions. The agent behavior rules are developed and validated based on behavior and economic theories and information attained from a stakeholder focus group study and a land use survey.

Yang, Pan↗
DOE OSTI2021

Multicriterion Benefit Evaluation of Deploying New Battery Technology with Increased Capacity at a Generic Nuclear Power

Nuclear power plant (NPP) safety improvements are routinely made by plant licensees and regulators. Examples of plant improvements include accident-tolerant fuel, diverse and flexible coping strategies, passive cooling systems, and increased battery capacity. A combined use of these plant improvements could lead to plant designs with enhanced resilience, allowing NPPs to better cope with both internal and external hazards and keep the plant operating safely, efficiently, and economically. This paper focuses on increased battery capacity and evaluates the potential costs and benefits of deploying batteries with increased capacities at a generic boiling water reactor (BWR) NPP. A multicriterion benefit evaluation methodology is used for the cost-benefit analysis. Ten alternatives for extending battery capacity are developed, including nine alternatives to provide additional direct-current power and one alternative to provide additional alternating-current power. Potential benefits of reducing plant risk are quantified through incorporating the alternatives into loss-of-offsite-power scenarios of the generic BWR probabilistic risk assessment model. Potential costs of implementing the alternatives are qualitatively discussed and ranked. The alternatives are then compared based on their impacts on plant risk and economics.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Multicriterion Benefit Evaluation of Deploying New Battery Technology with Increased Capacity at a Generic Nuclear Power Plant

This presentation is prepared for the DOE LWRS RISA Pathway Stakeholder Engagement Meeting on October 13-14, 2021. Nuclear power plant (NPP) safety improvements are routinely made by plant licensees and regulators. Examples of plant improvements include accident-tolerant fuel, diverse and flexible coping strategies, passive cooling systems, and increased battery capacity. A combined use of these plant improvements could lead to plant designs with enhanced resilience, allowing NPPs to better cope with both internal and external hazards and keep the plant operating safely, efficiently, and economically. This presentation focuses on increased battery capacity and evaluates the potential costs and benefits of deploying batteries with increased capacities at a generic boiling water reactor (BWR) NPP. A multicriterion benefit evaluation methodology is used for the cost-benefit analysis. Ten alternatives for extending battery capacity are developed, including eight alternatives to provide additional direct-current power and one alternative to provide additional alternating-current power. Potential benefits of reducing plant risk are quantified through incorporating the alternatives into loss-of-offsite-power scenarios of the generic BWR probabilistic risk assessment model. Potential costs of implementing the alternatives are qualitatively discussed and ranked. The alternatives are then compared based on their impacts on plant risk and economics.

99 GENERAL AND MISCELLANEOUS↗