Towards robust and scalable dispatch modeling of long-duration energy storage
Not Available
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
Despite their widespread use, the mechanisms governing the synthesis of zeolite catalysts are still poorly understood. A notable example of this problem is the uncertainty surrounding the influence of synthesis conditions on the placement of Al atoms in the zeolite framework which determines the active sites available for catalytic species. In this work, the role of the cis to trans isomer ratio of the OSDA N,N-dimethyl-3-5-dimethylpiperidinium on the energetics of 26 distinct Al pair distributions in SSZ-39 is examined both in the presence and absence of Na using density functional theory calculations. The initial orientation of the OSDA was found to have a significant impact on the final energies present, necessitating the screening of a large number of initial orientations with force field calculations and single point DFT calculations. Ground state energies were found to vary significantly with the ratio of cis to trans OSDAs with a Boltzmann distribution revealing the most likely Al pair distributions shift from sharing the same 8 membered rings to sharing the same double six membered rings to having no shared subunits as one increases the amount of cis OSDA present within the framework. The presence of Na was found to favor Al pair distributions where both Als occupied the same 6-membered ring. When an implicit solvent model was used to evaluate ground state energies the ideal Na sites shifted from 6-membered rings to empty SSZ-39 cages while OSDA positions and orientations remained largely the same. To provide insight on how kinetic factors may influence Al distributions, formation energies we calculated for connected double six membered rings. Further, these formation energies revealed a preference for Al pairs to occupy the same 4-membered ring which indicates kinetic and thermodynamic control may lead to different Al distributions in SSZ-39.
Because mixed-halide wide-bandgap (1.6-2.0 eV) perovskite solar cells suffer from operating instability related to light-induced halide segregation, it is of interest to study alternative means of bandgap widening. Perovskitoids combine wide bandgaps and structural stability resulting from face- or edge-sharing octahedral connections in their crystal structures. Unfortunately, there existed no prior reports of three-dimensional (3D) perovskitoids having direct bandgaps with optical absorption edges less than 2.2 eV. As the most significant predictor of perovskitoid bandgaps is the fraction of corner-sharing in their crystal structures, we hypothesized that increasing the amount of corner-sharing would access lower bandgaps than previously reported. Here, we accomplished this by mixing a spacer cation within the size range for 3D perovskitoid formation with a smaller perovskite-forming cation. We explored three spacer cations of different sizes: ethylammonium (EA), cyclopropylammonium (c-C3A), and cyclobutylammonium (c-C4A), combining these with methylammonium (MA), and found that the middle cation, c-C3A, pairs with MA to form a 3D perovskitoid with the formula (c-C3A) 3 (MA) 3 Pb 5 I 16 and a direct bandgap with an optical absorption edge at 2.0 eV. Solution-processed films of this perovskitoid showed improved light stability over mixed-halide perovskites, and solar cells based on these films exhibit increased maximum power point operating stability compared to reference mixed-halide devices.
Due to its dependence on a communication network, distributed secondary control of microgrids is susceptible to denial-of-service (DoS) attacks in channel shutdown mode, which may negatively impact the network connectivity and thus deteriorate the coordination and power sharing among distributed generators (DGs). Honeypot is a common method for cyber deception by introducing fake targets. However, in the context of microgrid, the misleading information spread by honeypots will also impact the system performance. This paper proposes an attack-resilient distributed control for AC microgrids utilizing virtual agents (VAs) to counteract both DoS edge and node attacks. The VAs are designed to not impact the system’s steady state during normal operation but to share information among neighboring real agents and serve as dummy targets for DoS attacks. The control with VAs is implemented by a primal-dual gradient based distributed algorithm to efficiently obtain a practical solution for voltage/frequency regulation and power sharing. The simulation results on a 4-DG test system and a modified IEEE 34-bus system show that 1) VAs do not impact the normal functionality of the test system, and 2) deploying VAs can enhance the resilience of the microgrid control against DoS edge and node attacks.
Microgrids have physical, electrical, and logical (data, network, and ownership) boundaries. To power unserved customer loads during an outage, microgrids can extend the traditional operational boundaries. This can become complex when considering microgrid-to-microgrid (M2M) interactions where sensitive information such as competitive microgrid operational data is not shared. This work proposes an optimization method coordinated between microgrid controllers and distribution management systems that limits data sharing. The method involves a competitive bidding strategy that maximizes unserved load coverage while minimizing resource utilization and sensitive operational data sharing among entities. The work is validated on a two-microgrid system with photovoltaic and energy storage systems and curves of load derived from real world residential buildings datasets. Results show that the proposed method, when applied for three distinct use cases of energy storage sufficiency to cover the predefined boundary and/or the expanded boundary, can successfully select and bid the available load coverage.
Due to the different device selections, aging levels, and thermal dissipation performance, some converters may take additional thermal stress on switching devices than others in paralleled converter systems, which will reduce system reliability. To address this problem, this paper proposes a power-sharing strategy with adaptive thermal management. First, the temperature-based power loss model and electrical-thermal model are established. Based on that, a high-accuracy IGBT junction temperature estimate considering the power loss-temperature coupling can be achieved. Further, the thermal-sharing for all the switching devices in paralleled converters can be achieved with the proposed adaptive thermal management strategy. The proposed strategy can change the power-sharing ratio adaptively according to the system operation conditions, which will contribute to the system reliability enhancement. The effectiveness of the proposed strategy is verified through PLECS thermal simulation and joint real-time simulation with Dspace and RT-box.
The development of scientific software—a cornerstone of long-term collaboration and scientific progress—parallels the development of other types of software but still poses distinct challenges, especially in high-performance computing. Although web searches yield numerous resources on software engineering, there is still a scarcity specifically for scientific software development. Here, this article introduces the Better Scientific Software site (https://bssw.io), a platform that hosts a community of researchers, developers, and practitioners who share their experiences and insights on scientific software development. Since 2017, this collaborative hub has gained traction within the scientific computing community, attracting a growing number of readers and contributors eager to share ideas and elevate their software development practices. In sharing the BSSw.io site’s story, we hope to encourage further growth of the BSSw.io community through both readership and contributors, with a long-term goal of fostering culture change by increasing emphasis on best practices in scientific software.
Here, we present the initial design of the matching dipole B2PF, a large aperture cosine-theta magnet for the Electron-Ion Collider (EIC). First we share an optimized 2D magnet cross-section based on a double-layer design with Nb-Ti Rutherford cable. This cross-section is optimized to meet the required field quality in the 120 mm coil aperture both at the nominal operating current and considering iron saturation effects during the ramp. Next, we share a first design of the 3D coil ends optimized for windability, field quality, and minimizing the conductor peak field. Based on the resulting peak field, we evaluate the short-sample margin of the design for an operating target of 6.3 T dipole field at 1.9 K. Finally, we share an initial mechanical design study which utilizes a key-and-bladder support structure capable of adjusting magnet preload.
High Performance Computing (HPC) centers provide resources to users who require greater scale to “get science done”. They deploy infrastructure with singular hardware architectures, cutting-edge software environments, and stricter security measures as compared with users’ own resources. As a result, users often create and configure digital artifacts in ways that are specialized for the unique infrastructure at a given HPC center. Each user of that center will face similar challenges as they develop specialized solutions to take full advantages of the center’s resources, potentially resulting in significant duplication of effort. Much duplicated effort could be avoided, however, if users of these centers found it easier to discover others’ solutions and artifacts as well as share their own. The FAIR principles address this problem by presenting guidelines focused around metadata practices to be implemented by vaguely defined “communities”; in practice, these tend to gather by domain (e.g. bioinformatics, geosciences, agriculture). Domain-based communities can unfortunately end up functioning as silos that tend both to inhibit sharing of solutions and best practices as well as to encourage fragile and unsustainable improvised solutions in the absence of best-practice guidance. We propose that these communities pursuing “science at scale” be nurtured both individually and collectively by HPC centers so that users can take advantage of shared challenges across disciplines and potentially across HPC centers. We describe an architecture based on the EOSC-Life FAIR Workflows Collaboratory, specialized for use with and inside HPC centers such as the Oak Ridge Leadership Computing Facility (OLCF), and we speculate on user incentives to encourage adoption. We note that a focus on FAIR workflow components rather than FAIR workflows is more likely to benefit the users of HPC centers.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to accurately assess mode share for trips within San Francisco (including trips to or from San Francisco, but excluding through trips) among city residents and residents of other Bay Area counties; provide additional trip details, including trip purpose information for each trip in the mode-share question series; and use the mode-share percentages collected on this survey in conjunction with total trip estimates provided by SFMTA to project the number of trips by mode on an average day in San Francisco.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.
The development of electric vehicle (EV) charging infrastructure is crucial for the widespread adoption of electric transportation. However, implementing such infrastructure is a complex task that requires consideration of factors such as space limitations, adherence to industry standards, grid capacity, and other technical and policy issues. This project seeks to create a framework for the efficient design of compact medium voltage (MV) extreme fast charging (XFC) stations for EVs. The station design involves the use of a solid-state transformer (SST) that connects to the MV distribution network, delivering power to a shared DC bus. This innovative approach eliminates the need for a step-down transformer to provide low-voltage service by connecting directly to the MV distribution network. Eliminating the low-frequency transformer not only reduces the system footprint and losses but also eliminates inrush currents during grid black-start. Additionally, placing power electronics directly on the distribution system allows for high-bandwidth filtering and power factor correction. The inclusion of a shared DC bus enables multiple charging dispensers and DC storage/generation units to connect, forming a DC microgrid. This setup facilitates power sharing with minimal conversion stages. The project showcases a DC distribution network protected by intelligent solid-state (SS) DC circuit breakers (DCCB) capable of isolating the smallest section of the faulted circuit much faster than existing mechanical solutions.
We extend an existing approach for efficient use of shared mapped memory across Chapel and C++ for graph data stored as 1-D arrays to sparse tensor data stored using a combination of 2-D and 1-D arrays. We describe the specific extensions that provide use of shared mapped memory tensor data for a particular C++ tensor decomposition tool called GentenMPI. We then demonstrate our approach on several real-world datasets, providing timing results that illustrate minimal overhead incurred using this approach. Finally, we extend our work to improve memory usage and provide convenient random access to sparse shared mapped memory tensor elements in Chapel, while still being capable of leveraging high performance implementations of tensor algorithms in C++.
The NEAMS Multiphysics Applications team aims at providing assessment of code useability and functionality for microreactor design and analyses, together with demonstration of their capabilities to properly capture the steady-state and time-dependent behavior of different microreactor concepts. In FY-24, significant progress was achieved in improving multi-physics models of several microreactors systems: HP-MR, GC-MR and KRUSTY. These efforts focused on solving more complex multiphysics problems enabled by enhanced tools capability, verifying and validating results obtained, providing feedback to developers for suggested improvements, and sharing these models to facilitate user training. A series of new multiphysics transients were completed on the HP-MR (using Griffin/BISON/Sockeye) with core startup transient, control drum inadvertent rotation accident, and hydrogen leakage from hydride moderator (also including SWIFT). On the GC-MR, a new full-core model was developed and analyzed through a series of new multiphysics (Griffin/BISON/SAM) transients to simulate moderator leakage (also including SWIFT), flow blockage and coolant depressurization. Additional and updated TRISO failure analyses were completed on the HP-MR unit-cell and GC-MR assembly models leveraging improved TRISO modeling capabilities. The amount of SiC failure following accidental transients at end-of-life was null. However, GC-MR assembly TRISO analysis highlighted Pd penetration rate can be problematic and may require design changes on the studied microreactor concept. The neutronics discrepancies observed on the KRUSTY model in previous years were resolved using hybrid set of Monte Carlo/Deterministic cross-sections. The multiphysics (Griffin neutronics / BISON thermal-mechanics) 15₵ insertion transient simulation displayed good agreement when comparing with experimental data. Initial modeling of the 30 ₵ reactivity insertion also displays promising results. Such close agreement provides important validation data that can be leveraged by the NEAMS program and by microreactor vendors to support licensing of their technology. Finally, important experience was gathered with the NEAMS tools leading to several user feedback shared with tools developers, especially with regards to MOOSE mesh generator and Griffin. This project led to many publications demonstrating modeling capabilities, and to three models shared on the Virtual Test Bed.