SEARCH · Search NASA

Results for “STORAGE AND NETWORKING SYSTEMS”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

Synchronous machines have traditionally acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, with the increased integration of distributed renewable resources and energy-storage technologies, there is a need to systematically acknowledge the dynamics of power-electronics inverters - the primary energy-conversion interface in such systems - in all aspects of modeling, analysis, and control of the bulk power network. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator, three-phase inverter, and a load. The inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

RAPIDS: Reconciling Availability, Accuracy, and Performance in Managing Geo-Distributed Scientific Data

In modern science, big data plays an increasingly important role. Many scientific applications, such as running simulations on supercomputers or conducting experiments on advanced instruments, produce huge amount of data at unprecedented speed. Analyzing and understanding such big data is the key for scientists to make scientific breakthroughs. However, data might become unavailable for scientists to access when outages or maintenance of the storage system occur, which severely hinders scientific discovery. To improve the data availability, data duplication and erasure coding (EC) are often used. But as the scientific data gets larger, using these two methods can cause considerable storage and network overhead.In this paper, we propose RAPIDS, a hybrid approach that combines the multigrid-based error-bounded lossy compression with erasure coding, to significantly reduce the storage and network overhead required for maintaining high data availability. Our experiments show that RAPIDS reduces the storage overhead by up to 7.5x and network overhead by up to 3x to achieve the same level of availability compared to the regular EC method. We improve RAPIDS by building two models to optimize the fault tolerance configurations and data gathering strategy. We demonstrate that RAPIDS significantly improves performance when running on many CPU cores in parallel or on GPUs.

Wan, Lipeng↗

DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗

DOE OSTI2018

Understanding the Mechanistic Behavior of Highly Charged Cellulose Nanofibers in Aqueous Systems

Mechanistic behavior and flow properties of cellulose nanofibers (CNFs) in aqueous systems can be described by the crowding factor and the concept of contact points, which are functions of the aspect ratio and concentration of CNF in the suspension. In the study reported herein, CNFs with a range of aspect ratio and surface charge density (380–1360 μmol/g) were used to demonstrate this methodology. It was shown that the critical networking point of the CNF suspension, determined by rheological measurements, was consistent with the gel crowding factor, which was 16. Correlated to the crowding factor, both viscosity and modulus of the systems were found to decrease by increasing the charge density of CNF, which also affected the flocculation behavior. Interestingly, an anomalous rheological behavior was observed near the overlap concentration (0.05 wt %) of CNF, at which the crowding factor was below the gel crowding factor, and the storage modulus (G') decreased dramatically at a given frequency threshold. This behavior is discussed in relation to the breakup of the entangled flocs and network in the suspension. The analysis of the mechanistic behavior of CNF aqueous suspensions by the crowding factor provides useful insight for fabricating high-performance nanocellulose-based materials.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Response of Integrated CO 2 Capture and Storage Systems in Saline Aquifers and Fractured Shale Formations to Changes in CO 2 Capture Costs

Fractured shale formations are new potential target reservoirs for CO 2 capture and storage (CCS) and provide several potential advantages over storage in saline aquifers in terms of storage capacity, leakage risk, and cost savings from brownfield development. Here, we used a geospatial-optimization, engineering-economic model to investigate the sensitivity of integrated CCS networks in Ohio, Pennsylvania, and West Virginia to reductions in CO 2 capture costs. The resulting reductions in CO 2 capture costs were based on hypothetical cases where technological innovation reduced CO 2 capture costs. There were also small differences in the spatial organization of the CCS deployment when the capture costs were reduced. We also found that the percent reduction in average cost of CCS systems became smaller as the CO 2 capture costs were decreased.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Synthetic Electricity Market Data Generation and HERON Use Case Setup of Advanced Nuclear Reactors Coupled with Thermal Energy Storage Systems

This study evaluates and optimizes advanced nuclear reactors coupled with thermal energy storage (TES) systems in an Integrated Energy System (IES) architecture to enable advanced nuclear power plants (A NPP) to participate in multi-commodity markets, thus enhancing their economic competitiveness. Nuclear-TES coupling scenarios studied herein are designed attenuate the nuclear heat dynamics and defer energy delivery to a later time, enabling the nuclear reactor to continue operating at or near steady-state design conditions as usual while also enabling flexible generation. Three A-NPPs, namely, an advanced light-water reactor (A LWR), a high temperature gas-cooled reactor (HTGR) and a liquid-metal fast reactor (LMFR) were selected as the initial use cases for demonstrating the technoeconomic of thermally balanced energy storage coupling design for thermal power extraction. Each of the reactor technologies were evaluated in two different electricity markets. Stochastic optimization approach was adopted which included the evaluation of price signals from the Pennsylvania-New Jersey-Maryland (PJM) market, and Electric Reliability Council of Texas (ERCOT), using an autoregressive moving average (ARMA) model. Risk Analysis Virtual Environment (RAVEN) tool and its dispatch optimization plugin, the Holistic Energy Resource Optimization Network (HERON), were used to perform dispatch and capacity optimization, using the price data provided by the ARMA models. The results from the Nuclear-TES use cases will be used to design and characterize dynamic integrated system behavior and feedback.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

dCache: Inter-disciplinary storage system

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters and long term data persistence on a tertiary storage. Though it was originally developed for the HEP experiments, today it is used by various scientific communities, including astrophysics, biomed, life science, which have their specific requirements. In this paper we describe some of the new requirements as well as demonstrate how dCache developers are addressing them.

Mkrtchyan, Tigran↗

DOE OSTI2020

Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework

In this report, a robust machine-learning-based computational framework that couples multi-layer neural network (MLNN) proxies and a multi-objective particle swarm optimizer (MOPSO) to design water-alternating-carbon dioxide injection (CO 2 -WAG) projects is presented. The proposed optimization protocol considers various objectives, including oil recovery and CO 2 storage volume. Expert MLNN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. When multiple objective functions are considered, two approaches are employed to treat the objectives: the weighted sum method and the Pareto-front-based scheme. A field-scale implementation focusing on tertiary recovery in the Morrow B formation at Farnsworth Unit (FWU) is presented. The developed Pareto-optimal solutions indicate the maximal available oil production can be 1.64 × 10 7 barrels and maximal carbon storage can achieve 2.35 × 10 7 tons. Trade-offs factor is defined to divide the constructed Pareto front into 4 sections with the trade-off factors’ value ranges from 0.35 to 49.9. This work also compares the optimum solution found by the aggregative objective function and the solution repository covered by the Pareto front that considers the physical and operational constraints and reduces uncertainties involved by the multi-objective optimization process. Our comparison indicates multiple solutions exist to satisfy the objective criteria of the WAG design, and these results cannot be found using the traditional weighted sum method. The Pareto front solution can provide more options for project designers, but decisions regarding necessary trade-offs must be made using the solution repository to balance the project economics and CO 2 storage amount.

10 SYNTHETIC FUELS↗

DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗

DOE OSTI2022

Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Real-Time Dispatcher for a Distributed Energy Resource Power Plant to Provide Grid Services [SWR-25-17]

This software dispatches the aggregated power of a distributed energy resource (DER) plant located on an electrical distribution network. It is designed to provide the following services to the grid in real-time: (i) voltage support of the distribution network, (ii) virtual power plant at the substation with power factor support, and (iii) operating reserves for automatic generator control for the transmission system. This dispatcher integrates with the local power plant controller and utilizes the battery energy storage system (BESS) to smooth the volatile net power output from the DERs. It can also integrate with a day-ahead scheduler to strategically charge and discharge.

Comden, Joshua [National Renewable Energy Laborato↗

DOE OSTI2019

Secure, Scalable Control and Communications for Distributed PV

An increasing number of jurisdictions are adopting Distributed Energy Resource (DER) interconnection standards which require photovoltaic (PV) inverters, energy storage systems, and other DER to include interoperable grid-support functionality. These functions provide grid operators the nobs to support local and bulk power system operations with DER equipment, but the associated grid operator-to-DER communications networks must be deployed with appropriate cybersecurity features. In some situations, additional security features may prevent control system scalability or increase communication latencies and dropouts. These unintentional consequences of the security features would therefore hinder the ability of the grid operator to implement specific control algorithms. This project evaluated the tradeoffs between power system performance and cybersecurity metrics for several grid services.

14 SOLAR ENERGY↗

DOE OSTI2019

Secure Scalable Control and Communications for Distributed PV (Final Technical Report)

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Generator Interconnection Cost Analysis in the Midcontinent Independent System Operator (MISO) territory

Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data from interconnection studies for the Midcontinent Independent System Operator (MISO), representing nearly 50% of all projects requesting interconnection from 2010 to 2020. Project-level cost summary data are available for download on this page. We find: -Average interconnection costs have grown as the number of interconnection requests have escalated -Projects that have completed all required interconnection studies have the lowest cost compared to applicants still actively working through the interconnection process or those that have withdrawn. -Broader network upgrade costs are the primary driver of recent cost increase. -Potential interconnection costs for wind, storage, and solar are larger than for natural gas -Larger generators have greater interconnection costs in absolute terms, but economies of scale exist on a per kW basis. -Interconnection costs vary by location Berkeley Lab will publish a series of short analytical papers of generator interconnection costs to the transmission system for MISO, PJM, SPP, ISO-NE and NYISO, which you can find at https://emp.lbl.gov/interconnection_costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Numerical modeling and sensitivity analysis of seawater intrusion in a dual-permeability coastal karst aquifer with conduit networks

Long-distance seawater intrusion has been widely observed through the subsurface conduit system in coastal karst aquifers as a source of groundwater contaminant. In this study, seawater intrusion in a dual-permeability karst aquifer with conduit networks is studied by the two-dimensional density-dependent flow and transport SEAWAT model. Local and global sensitivity analyses are used to evaluate the impacts of boundary conditions and hydrological characteristics on modeling seawater intrusion in a karst aquifer, including hydraulic conductivity, effective porosity, specific storage, and dispersivity of the conduit network and of the porous medium. The local sensitivity analysis evaluates the parameters' sensitivities for modeling seawater intrusion, specifically in the Woodville Karst Plain (WKP). A more comprehensive interpretation of parameter sensitivities, including the nonlinear relationship between simulations and parameters, and/or parameter interactions, is addressed in the global sensitivity analysis. The conduit parameters and boundary conditions are important to the simulations in the porous medium because of the dynamical exchanges between the two systems. The sensitivity study indicates that salinity and head simulations in the karst features, such as the conduit system and submarine springs, are critical for understanding seawater intrusion in a coastal karst aquifer. The evaluation of hydraulic conductivity sensitivity in the continuum SEAWAT model may be biased since the conduit flow velocity is not accurately calculated by Darcy's equation as a function of head difference and hydraulic conductivity. In addition, dispersivity is no longer an important parameter in an advection-dominated karst aquifer with a conduit system, compared to the sensitivity results in a porous medium aquifer. In the end, the extents of seawater intrusion are quantitatively evaluated and measured under different scenarios with the variabilities of important parameters identified from sensitivity results, including salinity at the submarine spring with rainfall recharge, sea level rise, and a longer simulation time under an extended low rainfall period.

58 GEOSCIENCES↗

DOE OSTI2019

Distribution Systems Planning and Analysis Framework for Indian Feeders - Partnership with BSES/BRPL

Integration of distributed energy resources (DER) such as photovoltaics, electric vehicles (EVs) and battery energy storage systems (BESS) is expected to rapidly increase in distribution systems in India. These grid edge resources can have adverse impacts on the grid and can provide additional revenue streams as well. To mitigate these impacts distribution planning and analysis is required. So this paper presents a framework which can be used to complete all steps involved in distribution planning and analysis. This framework uses data in the same format as is readily available with Indian utilities and processes it to generate detailed feeder models and loading profiles. This framework already includes multiple DER use cases such as peak shaving for BESS and EV demand modelling and can be easily extended to simulate a number of additional use cases. All of these use cases can be simulated in parallel for multi-year time series simulations using an integrated command line interface. A suit of grid readiness metrics are then evaluated for each simulation to determine required network upgrades and associated costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session One - Abridged

The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. This is Session 1 of 3.

25 - ENERGY STORAGE↗

DOE OSTI2025

BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session One

25 - ENERGY STORAGE↗