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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integration of Utility Distributed Energy Resource Management System and Aggregators for Evolving Distribution System Operators

With the rapid integration of distributed energy resources (DERs), distribution utilities are faced with new and unprecedented issues. New challenges introduced by high penetration of DERs range from poor observability to overload and reverse power flow problems, under-over-voltages, maloperation of legacy protection systems, and requirements for new planning procedures. Distribution utility personnel are not adequately trained, and legacy control centers are not properly equipped to cope with these issues. Fortunately, distribution energy resource management systems (DERMSs) are emerging software technologies aimed to provide distribution system operators (DSOs) with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources. However, as DERMS technology is still emerging, its definition is vague and can refer to very different levels of software hierarchies, spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems (called utility DERMS). Although they are all frequently simply called DERMS, these software technologies have different sets of tools and aim to provide different services to different stakeholders. This paper explores how these different software technologies can complement each other, and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM’s existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model: Preprint

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM’s existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Development of a Discrepancy Checker for the Digital Twin in a Supervisory Control System for a Thermal Energy Delivery System

Defined as a virtual representation of a physical object, process, or service, and used to support real-world decision-making, a digital twin (DT) can be utilized to combine classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems, and to enable optimal autonomous operations. However, a DT’s usefulness largely depends on its ability to adequately mirror the state of its physical counterpart, and this adequacy should be reflected by the level of uncertainty in the underlying simulation models when estimating and predicting quantities of interest (QOIs). Moreover, simulation models in a DT may involve multiple fidelities of representations—ranging from physics-based models to data-driven ones—but classical uncertainty quantification (UQ) methods struggle to handle numerous uncertainty sources, nor are they designed for real-time applications. This work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system applied to a thermal energy delivery system (TEDS) at Idaho National Laboratory. The discrepancy checker was developed using metadata from an automated DT development process, and these metadata included different combinations of physical model forms and model parameters, training data and hyperparameters for surrogate models, and design parameters for supervisory control systems. Next, correlations between the uncertainty results and the metadata were established and then applied to the DT operations. The discrepancy checker evaluates the discrepancies between model predictions from virtual and sensor measurements and backtraces them to the corresponding major sources of uncertainty. The discrepancy checker showed reasonable performance in detecting discrepancies and diagnosing sources of uncertainty in testing scenarios.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

CCE Phase 2: System-of-System Analysis

During Phase 1, Consequence Prioritization, the CCE Team identified High Consequence Events (HCEs) that can be accomplished through cyber means to impact critical functions, services, and processes. During Phase 2, System-of-Systems Analysis (SoS Analysis), the CCE Team will conduct a systematic review and analysis of information related to the equipment, systems, processes, operations, maintenance, testing, and procurement practices based on the HCEs identified in Phase 1.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Light‐driven catalysis with engineered enzymes and biomimetic systems

Abstract Efforts to drive catalytic reactions with light, inspired by natural processes like photosynthesis, have a long history and have seen significant recent growth. Successfully engineering systems using biomolecular and bioinspired catalysts to carry out light‐driven chemical reactions capitalizes on advantages offered from the fields of biocatalysis and photocatalysis. In particular, driving reactions under mild conditions and in water, in which enzymes are operative, using sunlight as a renewable energy source yield environmentally friendly systems. Furthermore, using enzymes and bioinspired systems can take advantage of the high efficiency and specificity of biocatalysts. There are many challenges to overcome to fully capitalize on the potential of light‐driven biocatalysis. In this mini‐review, we discuss examples of enzymes and engineered biomolecular catalysts that are activated via electron transfer from a photosensitizer in a photocatalytic system. We place an emphasis on selected forefront chemical reactions of high interest, including CH oxidation, proton reduction, water oxidation, CO 2 reduction, and N 2 reduction.

Edwards, Emily H.↗
DOE OSTI2021

Multielectrode Array Sensors to Enable Long-Duration Corrosion Monitoring and Control of Concentrating Solar Power Systems

Molten mixtures of MgCl2-KCl-NaCl are promising heat transfer fluid (HTF) candidates for the next generation of concentrating solar power systems (Gen3 CSP), because of their low cost, high decomposition temperature, and favorable heat transfer characteristics. However, if the chemistry of the salts is not properly controlled, molten chlorides can cause corrosion of high-temperature metal alloys, such as Inconel (R), in CSP systems. Here we demonstrate a corrosion monitoring system that can be used to monitor the health of the structural alloys over long durations. The monitoring system includes a multielectrode array voltammetry sensor that measures the redox potential of molten MgCl2-KCl-NaCl mixture as well as the presence of corrosion products and impurities. The sensors have been successfully operated in static molten salt environments for long durations, and are capable of detecting corrosion products, such as Cr2+ and Fe2+, quantitatively at low concentrations (i.e., approximately 100 ppm) with less than 4% error.

Concentrated solar power↗
DOE OSTI2022

Microwave Heating-Induced Temperature Gradients in Liquid–Liquid Biphasic Systems

Microwaves (MWs) can enable the electrification and intensification of chemical manufacturing. They have been applied to various unit separations, such as drying, distillation, and extraction, entailing gas–liquid and solid–liquid systems. However, a limited quantitative understanding of MW-heated liquid–liquid biphasic systems related to extraction exists. This work measures the temporal and spatial temperature difference between an aqueous and an organic phase in batch and continuous microfluidic modes. We demonstrate permanent temperature differences between phases over 35 °C and spatiotemporal periodic and quasiperiodic oscillations modulated by the flow patterns. The temperature differences are primarily driven by the faster absorption rate of MW irradiation by the aqueous phase versus the slower heat transfer from the aqueous phase to the organic phase. These are amplified by low specific interfacial area and modifications of the electromagnetic field. We employ a multiphysics model to predict the temperature difference in a batch system. The model is in good agreement with the experiments. We demonstrate a strong effect of input power, dielectric properties of organic solvents, the volume of solvents, and the volume ratio between phases on the temperature difference. A simple analytical model describes the temperature difference and provides design principles. Furthermore, the combined approach offers new insights into the design and optimization of the MW-heated biphasic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Combined Force-Frequency Sampling for Simulation of Systems Having Rugged Free Energy Landscapes.

An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.

Sevgen, Emre↗
DOE OSTI2022

Quantifying Energetic and Entropic Pathways in Molecular Systems

When examining dynamics occurring at nonzero temperatures, both energy and entropy must be taken into account to describe activated barrier crossing events. Furthermore, good reaction coordinates need to be constructed to describe different metastable states and the transition mechanisms between them. Here we use a physics-based machine learning method called state predictive information bottleneck (SPIB) to find nonlinear reaction coordinates for three systems of varying complexity. SPIB is able to correctly predict an entropic bottleneck for an analytical flat-energy double-well system and identify the entropy- and energy-dominated pathways for an analytical four-well system. Lastly, for a simulation of benzoic acid permeation through a lipid bilayer, SPIB is able to discover the entropic and energetic barriers to the permeation process. Given these results, we thus establish that SPIB is a reasonable and robust method for finding the important entropy, energy, and enthalpy barriers in physical systems, which can then be used to enhance the understanding and sampling of different activated mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Initial Land Use/Cover Distribution Substantially Affects Global Carbon and Local Temperature Projections in the Integrated Earth System Model

Initial land cover distribution varies among Earth system models, an uncertainty in initial conditions that can substantially affect carbon and climate projections. We use the integrated Earth System Model to show that a 3.9 M km2 difference in 2005 global forest area (9–14% of total forest area) generates uncertainties in initial atmospheric CO2 concentration, terrestrial carbon, and local temperature that propagate through a future simulation following the Representative Concentration Pathway 4.5. By 2095, the initial 6 ppmv uncertainty range increases to 9 ppmv and the initial 26 PgC uncertainty range in terrestrial carbon increases to 33 PgC. The initial uncertainty range in annual average local temperature of -0.74 to 0.96 °C persists throughout the future simulation, with a seasonal maximum in Dec-Jan-Feb. These results highlight the importance of accurately characterizing historical land use and land cover to reduce overall initial condition uncertainty.

Di Vittorio, Alan↗
DOE OSTI2022

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

Abstract Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cyber‐physical system testbed for power system monitoring and wide‐area control verification

The electric power system is intrinsically a cyber‐physical system (CPS) with power flowing inthe physical system and information flowing in the cyber‐network. Testbeds arecrucial for understanding the cyber‐physical interactions and provideenvironments for prototyping novel applications. This study proposes afour‐layer architecture for CPS testbeds with emphases on communication networkemulation and networked physical components. A configurable software‐definednetwork is employed to bridge physical components with wide‐area applicationsfor closed‐loop control. In order to distribute physically coupled devices intomultiple software simulations, this study proposes a data broker setup based ona distributed messaging environment to achieve low‐latency data streaming. Thedecoupled design with data streaming allows for building testbed components asmodules and running them in a distributed manner. Case studies verify the databroker setup for low‐latency sensing and actuation, as well as the communicationemulation setup for the desired network latency. Also illustrated is a replayattack scenario using synchrophasors in the Western Electricity CoordinatingCouncil (WECC) 181‐bus system for demonstrating the closed‐loop cyber‐physicalsimulation capability of the testbed.

Cui, Hantao↗
DOE OSTI2022

System-Level Comparison of Sodium and Salt Systems in Support of the Gen3 Liquids Pathway

This paper presents the comparison of the annual performance and the Levelised Cost of Energy (LCOE) of two high- temperature solar power tower configurations using a 565MWth chloride salt receiver and a 540 MWth liquid sodium receiver. Each power tower consists of a sodium or salt receiver, a 175 m high concrete tower, and a two-tank chloride salt system for thermal energy storage (TES). The power plants were simulated using SolarTherm to estimate their energy per year, capacity factor, and LCOE. The simulation was verified against the System Advisor Model (SAM), showing that the LCOE calculation differences are within ±1.0%. Results showed that the sodium-receiver system yields 69.60 USD/MWh real LCOE, versus 78.55 USD/MWh for the chloride salt system. Results also showed that sodium wins here due to a smaller and more efficient receiver, enabled by higher flux limits and reduced field size, leading to less mirror reflective area and site improvement cost.

capacity factor↗
DOE OSTI2024

Source shape estimation for neutron imaging systems using convolutional neural networks

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

Machine learning, neutron imaging, source reconstr↗
DOE OSTI2021

Prediction of crystal structures and motifs in the Fe–Mg–O system at Earth’s core pressures

Abstract Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe–O, Mg–O, and Fe–Mg binary systems under pressure have been investigated, there are still very few studies of the Fe–Mg–O ternary system at relevant Earth’s core and super-Earth’s mantle pressures. Here, we use the adaptive genetic algorithm (AGA) to study ternary Fe x Mg y O z phases in a wide range of stoichiometries at 200 GPa and 350 GPa. We discovered three dynamically stable phases with stoichiometries FeMg 2 O 4 , Fe 2 MgO 4, and FeMg 3 O 4 with lower enthalpy than any known combination of Fe–Mg–O high-pressure compounds at 350 GPa. With the discovery of these phases, we construct the Fe–Mg–O ternary convex hull. We further clarify the composition- and pressure-dependence of structural motifs with the analysis of the AGA-found stable and metastable structures. Analysis of binary and ternary stable phases suggest that O, Mg, or both could stabilize a BCC iron alloy at inner core pressures.

Wang, Renhai↗