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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Thermal Energy Storage Selection for Near Term Nuclear Integration

Thermal energy storage (TES) integration with existing and future light water reactors (LWRs) has the capability to increase the deployment options and uses for the large amounts of heat produced in nuclear reactor cores. The present method of exclusively producing electricity has become less economical with increases in renewable energy penetration leading to times of low or negative electricity prices. Some response to this new grid reality has been to begin load following nuclear reactors, an unideal choice due to the relatively small modular cost of producing nuclear energy compared to the revenue that could be received by using all of the heat produced. To support research in this area, Idaho National Laboratory (INL) has joined with Utah Associated Municipal Power Systems (UAMPS) on the Carbon Free Power Project (CFPP) to contract the Joint Use Modular Plant (JUMP). CFPP is planned to be a 12-module NuScale operating site built on INL property, with the first module dedicated to JUMP for research, development, and demonstration purposes. The JUMP module coming online around the beginning of fiscal year 2027 in the current project timeline. One primary goal of JUMP is to demonstrate TES integrated with an operating LWR in a real-world environment. TES integration will allow for the demonstration of nuclear power’s capability to participate in grid load following without reactor cycling, process heat integration, and electrical ancillary market opportunities. The goal of this research is to support both SMR deployment opportunities as well as support LWR retrofit capabilities. In a discussion limited to load following or energy arbitration, “charging” and “discharging” can be simply defined. TES charging refers to time when energy is removed from the nuclear reactor power conversion cycle and directed to be stored in the TES storage medium resulting in less available energy available for grid output. Discharging refers to the process of releasing energy stored in the TES medium and having an increase in grid. This paper quantifies energy storage technologies for use with nuclear reactors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

SPC-2734 Chromalox Oil Heater System Procurement Specification

This specification will supply the necessary technical information to the vendor to allow them to provide the system we need for the Thermal Energy Delivery System (TEDS) construction. TEDS is being constructed within the dynamic energy transport and integration laboratory (DETAIL) facility to test heat transfer components, distribution systems, instruments, and controls that can be monitored and controlled for hybrid generation of electrical power and/or non-electrical products. TEDS is designed to operate as a part of an integrated system or independently to demonstrate component operations, to develop and validate thermal energy transport models and control systems, and to study thermal energy inertia and storage. Within the integrated system, TEDS will be connected to the INL Real Time Digital Simulator (RTDS) test platform to develop and demonstrate monitoring and control systems and to investigate real-time, hardware-in-the-loop response characteristics relative to grid operations. As the basis of the TEDS, Chromalox (the vendor) has been chosen to design and supply a heater, pump, storage tanks, and pressure vessel due to their experience with industrial-scale heat transfer oil systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Evidence of Completion of Milestone 3: Experimental Testbed

Construction and deployment of the experimental testbed encountered scheduling delays, and completion was subsequently further delayed by curtailment of LANL operations due to the COVID–19 pandemic. Between March and October, LANL was in a state of “Limited Operations” such that only certain “mission–critical” work was performed with approximately 25% on–site staffing. In spite of this, much of the lab space preparation was executed and the five initial model house” units were constructed and installed during this period. Still, the pace of construction was impacted by difficulty in coordinating personnel and minimizing contact between workers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Decarbonization of Residential Building Energy Supply: Impact of Cogeneration System Performance on Energy, Environment, and Economics

Electrical and thermal loads of residential buildings present a unique opportunity for onsite power generation, and concomitant thermal energy generation, storage, and utilization, to decrease primary energy consumption and carbon dioxide intensity. This approach also improves resiliency and ability to address peak load burden effectively. Demand response programs and grid-interactive buildings are also essential to meet the energy needs of the 21st century while addressing climate impact. Given the significance of the scale of building energy consumption, this study investigates how cogeneration systems influence the primary energy consumption and carbon footprint in residential buildings. The impact of onsite power generation capacity, its electrical and thermal efficiency, and its cost, on total primary energy consumption, equivalent carbon dioxide emissions, operating expenditure, and, most importantly, thermal and electrical energy balance, is presented. The conditions at which a cogeneration approach loses its advantage as an energy efficient residential resource are identified as a function of electrical grid’s carbon footprint and primary energy efficiency. Compared to a heat pump heating system with a coefficient of performance (COP) of three, a 0.5 kW cogeneration system with 40% electrical efficiency is shown to lose its environmental benefit if the electrical grid’s carbon dioxide intensity falls below 0.4 kg CO 2 per kWh electricity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1974

Ocean tides from Seasat-A

Two procedures for the evaluation of global tides from SEASAT-A altimetry data are elaborated: an empirical method leading to the response functions for a grid of about 500 points from which the tide can be predicted for any point in the oceans, and a dynamic method which consists of iteratively modifying the parameters in a numerical solution to Laplace tide equations. It is assumed that the shape of the received altimeter signal can be interpreted for sea state and that orbit calculations are available so that absolute sea levels can be obtained.

Hendershott, M. C.↗
DOE OSTI2016

An Efficient Sampling-Based Method for Sensitivity and Uncertainty Analysis Through RAVEN

Sensitivity analysis (SA) and uncertainty quantification (UQ) are now widely recognized as essential parts of analyses for complex nuclear systems. Plenty of well-established methodologies are already available for performing SA and UQ, such as Monte Carlo analysis, adjoint-based method, and variance decomposition. The focus of this summary is on developing an efficient sampling-based approach to perform SA and UQ through RAVEN. RAVEN (Risk Analysis and Virtual control Environment) is a software framework able to perform SA and UQ, currently under development at the Idaho National Laboratory. In literature, a number of approaches have been developed to accelerate the process of SA and UQ for large complex systems, such as principal component analysis (PCA) of input space, input-output correlation analysis, and intersection subspace methods. The fundamental idea is to identify the dominant active directions in the parameter space prior to conducting SA and UQ. The premise is that the number of active directions is much less than the dimensionality of input space. In this work, the PCA or Karhunen-Loeve expansion or SVD method is implemented in RAVEN to identify the active directions. This approach is also combined with the forward sampling approaches, such as Monte Carlo, Grid, Latin Hypercube, and Response Surface Design sampling approaches that are currently available in RAVEN, to perform SA and UQ more efficiently. The summary is organized as follows. In the following section, we present the PCA-based forward sampling approach. The effectiveness and efficiency are discussed in the numerical results section. The paper then ends with a concluding summary. In this summary, we have presented PCA-based forward sampling approach to perform SA and UQ. The goal is to reduce the computational overhead for repetitive executions of complex nuclear systems that are generally required when performing SA and UQ. The control of correlations between input parameters is implemented in RAVEN to better represent the input relationships. In addition, we are currently examining our implementations with more realistic models, and we are also focusing on implementation of some new high-dimensional input reduction techniques in RAVEN. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
NASA NTRS1995

Coupled Ablation, Heat Conduction, Pyrolysis, Shape Change and Spallation of the Galileo Probe

The Galileo probe enters the atmosphere of Jupiter in December 1995. This paper presents numerical methodology and detailed results of our final pre-impact calculations for the heat shield response. The calculations are performed using a highly modified version of a viscous shock layer code with massive radiation coupled with a surface thermochemical ablation and spallation model and with the transient in-depth thermal response of the charring and ablating heat shield. The flowfield is quasi-steady along the trajectory, but the heat shield thermal response is dynamic. Each surface node of the VSL grid is coupled with a one-dimensional thermal response calculation. The thermal solver includes heat conduction, pyrolysis, and grid movement owing to surface recession. Initial conditions for the heat shield temperature and density were obtained from the high altitude rarefied-flow calculations of Haas and Milos. Galileo probe surface temperature, shape, mass flux, and element flux are all determined as functions of time along the trajectory with spallation varied parametrically. The calculations also estimate the in-depth density and temperature profiles for the heat shield. All this information is required to determine the time-dependent vehicle mass and drag coefficient which are necessary inputs for the atmospheric reconstruction experiment on board the probe.

Milos, Frank S.↗
DOE OSTI2026

A practical control strategy for demand flexibility with ensured occupant comfort in grid-interactive efficient buildings

This study proposes a practical and simplified demand response (DR) control strategy from the perspective of grid-interactive efficient buildings (GEBs), aiming to secure demand flexibility while ensuring occupant thermal comfort. Focusing on summer on-peak periods, a linear demand response (LDR) strategy that integrates cooling setpoint adjustment and lighting dimming was designed, and its performance was quantitatively evaluated. A case study was conducted using EnergyPlus-based simulations for a U.S. DOE small office prototype building under summer on-peak weather conditions. Compared with a conventional rapid demand response (RDR) strategy, the proposed LDR approach gradually reduced electrical loads while maintaining occupant thermal comfort indices, including predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD), within acceptable comfort ranges. Quantitative analysis of demand flexibility using the grid-interactive impact index (GII) and the flexibility strength index (FSI) showed that the LDR strategy provided approximately 12.5% demand flexibility during DR periods and achieved an electricity cost reduction of about 8.8%. In addition, the results of the part-load ratio (PLR)-based cooling system performance analysis showed that, in the on-peak period, the LDR strategy exhibited improved cooling performance compared with the baseline. Overall, this study shows the potential of a practical DR control strategy that can simultaneously achieve occupant comfort and demand flexibility without relying on complex advanced control technologies.

Jung, Dong Eun↗
DOE OSTI2018

Insensitivity of the Cloud Response to Surface Warming Under Radical Changes to Boundary Layer Turbulence and Cloud Microphysics: Results From the Ultraparameterized CAM

We study the cloud response to a +4K surface warming in a new multi- scale climate model that uses enough interior resolution to begin explicitly resolving boundary layer turbulence (i.e. Ultra-Parameterization or UP), and the associated low clouds. UP's predictions are compared against those from standard Super-Parameterization (SP). The mean global cloud feedback turns out to be remarkably neutral across all of our simulations, despite some radical changes in both cloud microphysical parameter settings and cloud resolving model grid resolution. The overall cloud response to warming is the positive low-cloud feedbacks over land, negative feedbacks at high latitudes, and weak feedbacks over the low-latitude oceans. The most distinct effects of UP appear to be the result of tuning decisions impacting high latitude cloud feedback. UP's microphysics are tuned to optimize the model present-day, top-of-atmosphere radiation fluxes against CERES observations, by lowering the cloud ice-liquid phase-shift temperature ramp, adjusting the ice/liquid autoconversion rate, and increasing the ice fall speed. This reduces baseline high-latitude low cloud amounts, damping the phase change cloud feedback at high latitudes, leading to a slightly more positive global cloud feedback compared to SP. A sensitivity test that isolates these microphysical impacts from UP's grid resolution conrms that the microphysical settings are mostly responsible for the dierences between SP and UP cloud feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Taxonomy for Modeling Demand Response Resources

Demand response resources are an important component of modern grid management strategies. Accurate characterizations of DR resources are needed to develop systems of optimally managed grid operations and to plan future investments in generation, transmission, and distribution. The DOE Demand Response and Energy Storage Integration Study (DRESIS) project researched the degree to which demand response (DR) and energy storage can provide grid flexibility and stability in the Western Interconnection. In this work, DR resources were integrated with traditional generators in grid forecasting tools, specifically a production cost model of the Western Interconnection. As part of this study, LBNL developed a modeling framework for characterizing resource availability and response attributes of DR resources consistent with the governing architecture of the simulation modeling platform. In this report, we identify and describe the following response attributes required to accurately characterize DR resources: allowable response frequency, maximum response duration, minimum time needed to achieve load changes, necessary pre- or re-charging of integrated energy storage, costs of enablement, magnitude of controlled resources, and alignment of availability. We describe a framework for modeling these response attributes, and apply this framework to characterize 13 DR resources including residential, commercial, and industrial end-uses. We group these end-uses into three broad categories based on their response capabilities, and define a taxonomy for classifying DR resources within these categories. The three categories of resources exhibit different capabilities and differ in value to the grid. Results from the production cost model of the Western Interconnection illustrate that minor differences in resource attributes can have significant impact on grid utilization of DR resources. The implications of these findings will be explored in future DR valuation studies.

Olsen, Daniel↗
DOE OSTI2021

Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event’s source region by an impedancebased analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locallyinduced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.

14 SOLAR ENERGY↗
DOE OSTI2020

Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.

Durden, David J.↗
DOE OSTI2019

H2@Scale: Economic Potential of Hydrogen as an Energy Intermediate

The H2@Scale concept envisions hydrogen as an energy intermediate across multiple energy sectors. The concept is motivated both by the opportunity to produce hydrogen from low-cost energy resources, and by the many potential applications of hydrogen, including as energy for transportation - used directly in fuel cell electric vehicles (FCEVs), as a feedstock for synthetic fuels, and to upgrade oil and biomass - feedstock for industry (e.g., for ammonia production, metals refining, and other end uses), heat for industry and buildings, and electricity storage. A key hydrogen production opportunity is using variable renewable electricity (i.e., electricity generated by wind turbines or solar photovoltaic panels) during hours when generation exceeds the load on the grid as a source of low-cost, dispatch constrained electricity (LDE). In addition to producing low-cost hydrogen, electrolysis can also serve as a form of responsive load on the electric grid, which can enhance grid stability, reduce curtailment, and create a supplementary revenue stream for generators. The future use and adoption of hydrogen across end-use applications depends both the cost of hydrogen production and on each application's threshold price (i.e., the maximum price each application will pay for hydrogen - above that price, the application has an alternative that is more economic). We estimate economic potential of the hydrogen market - the quantity of hydrogen at an equilibrium price at which suppliers are willing to produce and consumers are willing to buy the same quantity of hydrogen - by developing supply and demand curves for hydrogen production and use. The demand curves are based on the threshold price for various quantities of hydrogen in each market, which is affected by projected prices for alternatives that could provide the same service (e.g., fuel for vehicles, feedstock for metals refining). The supply curves include hydrogen produced via natural gas steam methane reforming, high-temperature electrolysis coupled with nuclear power plants, biomass gasification, and low-temperature electrolysis using LDE. We use a range of market and technology-advancement assumptions to estimate the economic potential for five scenarios in the mid 21st century timeframe, and the corresponding impacts on energy use and emissions. We estimate hydrogen's economic potential to be 14-48 MMT/yr in the contiguous United States - corresponding to 1.5-5 times current annual consumption - depending on key assumptions about the future market conditions and the success of hydrogen production research and development (R&D) in our five scenarios. As a result of the estimated economic potential markets, we also approximate that grid-integrated electrolysis can increase the penetration of wind generation in the electricity market by providing a value stream for LDE, and that total U.S. fossil fuel use could decline below a scenario with high renewable penetration on the grid. We find that hydrogen has potential to provide technical and economic synergies when integrated across multiple sectors of the U.S. energy system, but continued R&D, infrastructure, and market development for LDE are required.

08 HYDROGEN↗
DOE OSTI2015

Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethink the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

A grid quality manipulation system

A grid quality manipulation system is described. The elements of the system are the measures by which quality is assessed, the computer graphic display of those measures, and the local grid manipulation to provide a response to the viewed quality indication. The display is an overlaid composite where the region is first covered with colors to reflect the values of the quality indicator, the grid is then placed on top of those colors, and finally a control net is placed on top of everything. The net represents the grid in terms of the control point form of algebraic grid generation. As a control point is moved, both the grid and the colored quality measures also move. This is a real time dynamic action so that the consequences of the manipulation are continuously seen.

Lu, Ning↗