Towards robust and scalable dispatch modeling of long-duration energy storage
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Guerra, Omar J.↗
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Results for “Scalable”
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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IPL-Annealed Mixed-Cation Perovskites with Robust Coating Window toward Scalable Manufacturing of Commercial Perovskite Solar Cells
Perovskite solar cells (PSCs) are a promising alternative solar technology, but the technical challenges of (1) stability/durability, (2) efficiency at scale, and (3) manufacturability must be overcome to achieve widespread PSC commercialization. The challenge of balancing solution ink formulation and scalable manufacturing is often overlooked in the literature, where focus is on adopting inks from processes that will not scale. In this study, we apply a classical roll-to-roll manufacturing perspective, utilizing both compositional engineering and intense pulsed light (IPL) annealing, to develop a mixed-cation perovskite ink with a robust coating window that simultaneously solve issues of stability and manufacturability for PSCs. Our method resulted in blade-coated, flexible, mixed-cation PSCs on ITO-PET substrates with a champion power conversion efficiency (PCE) efficiency of 16.7% using IPL annealing of the absorber layer and, to our knowledge, is one of the fastest processing methods for the perovskite layer. This overall reduction in processing time with a stable ink represents an advance toward the scaled production of perovskite solar cells on flexible substrates.
flexible perovskite solar cells↗
Scalable, low-cost synthesis of high volumetric capacity LiMn 0.5 Fe 0.5 PO 4 cathode for lithium-ion batteries
A facile, scalable, wastewater-free synthesis of high energy density LiMn0.5Fe0.5PO4 (LMFP) cathodes with high electrode press density is achieved by employing the spinel LiMnFeO4 (LMFO) as a precursor.
Reed, Seth↗
A Highly Effective Polysulfide-Trapping Approach for the Development of High Energy Density, Scalable Lithium-Sulfur Batteries
Lithium-sulfur (Li-S) batteries are identified as one of the most promising next-generation battery technologies owing to their high theoretical specific energy, sustainability, and affordability. However, the commercialization of Li-S batteries has been hindered by severe technical challenges, including the lithium polysulfide (PS) dissolution/shuttling effect, a major cause of fast capacity degradation over cycling. We demonstrated that, for the first time, nanolayer polymer coated high surface area porous carbons (NPCs) were coated directly on sulfur electrodes (NPC-S), which led to a high specific capacity of ∼1,600 mAh g −1 approaching the theoretical specific capacity limit in the NPC-S based Li-S batteries. The NPC-S based Li-S batteries maintained their large initial specific capacity gain compared with the Baseline-S based Li-S batteries (control) over extended cycles. A follow-on study indicated that the NPC-S approach is a necessary and critical step to boost the near-theoretical specific capacity while being stabilized over long cycles with a synergistic strategy. Our experimental and computational results suggest that NPC coated on sulfur electrodes provides not only an effective and strong PS-trapping power but also an increased redox reaction kinetics for sulfur ↔ PS’s conversions during battery charge and discharge, rendering the realization of near-theoretical discharge specific capacity in the NPC-S based Li-S batteries. The findings presented in this study may inspire a new, simple, low-cost, and commercially scalable approach, without adding any appreciable dead weight or volume to the batteries, in the effort to tackle the technical challenges facing SOA Li-S batteries.
25 ENERGY STORAGE↗
ActiveBAS: A Low-cost, Scalable Control Solution for Grid-Interactive Small and Medium Sized Commercial Buildings
This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.
24 POWER TRANSMISSION AND DISTRIBUTION↗
GFCCLib: Scalable and efficient coupled-cluster Green's function library for accurately tackling many-body electronic structure problems
Coupled-cluster Green’s function (GFCC) calculation has drawn much attention in the recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. However, GFCC calculations on scientific computing clusters usually suffer from expensive higher di- mensional tensor contractions in the complex space, expensive inter-process communi- cation, and severe load imbalance, which limits it’s routine use for tackling electronic structure problems. Here we present a numerical library prototype that is specifically designed for large-scale GFCC calculations. The design of the library is focused on a systematically optimal computing strategy to improve its scalability and efficiency. The performance of the library is demonstrated by the relevant profiling analysis of running GFCC calculations on remote giant computing clusters. The capability of the library is highlighted by computing a wide near valence band of a fullerene C60 molecule for the first time at the GFCCSD level that shows excellent agreement with the experimental spectrum.
Peng, Bo↗
Scalable uncertainty quantification for deep operator networks using randomized priors
Not provided.
Engineering↗
Scalable Frontal Oligomerization: Insights from Advanced Mass Analysis
Linear oligomers of dicyclopentadiene (DCPD) are reactive precursors for thermoplastic and thermoset materials. Unlike the foul-smelling parent monomer, oligomers composed of DCPD are odorless. With appropriate modification of the end-group or backbone chemistry, telechelic DCPD oligomers have potential utility as cross-linkers and as macromonomer precursors for block and graft copolymers. Most existing methods to produce oligo-DCPD, however, require solvent, are relatively slow, and necessitate air-free techniques. Here we show that frontal ring-opening metathesis oligomerization (FROMO) of neat DCPD and other norbornene derivatives rapidly generates hundreds of grams of material in minutes with catalyst loadings of 0.5 mM. This energy-efficient catalytic process utilizes the heat generated by the reaction to self-propagate oligomerization throughout the liquid monomer. FROMO employs a terminal olefin (e.g., styrene) in which a cross-metathesis reaction (i.e., chain transfer) competes with ring-opening metathesis (i.e., propagation). Kendrick mass analysis enables rapid identification and assignment of all the chain-end types present and quantifies the degree of branching resulting from the infrequent cyclopentene ring-opening reaction. This analytical technique also detects oligomer species derived from trace impurities in the monomer or chain-transfer agent that are otherwise difficult to observe with other characterization methods. Here, the obtained oligomers possess well-defined chain-ends and molecular weight distributions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.
Chemistry↗
The scalability of gadolinium-doped water-Cherenkov detectors for nonproliferation
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Physics - Nuclear physics and radiation physics↗
Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring
Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.
feature extraction↗
A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks
As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.
genetic algorithm↗
Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput
The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.
AWS↗
Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication
Writing large amounts of data concurrently to stable storage is a typical I/O pattern of many HPC workflows. This pattern introduces high I/O overheads and results in increased storage space utilization especially for workflows that need to capture the evolution of data structures with high frequency as checkpoints. In this context, many applications, such as graph pattern matching, perform sparse updates to large data structures between checkpoints. For these applications, incremental checkpointing techniques that save only the differences from one checkpoint to another can dramatically reduce the checkpoint sizes, I/O bottlenecks, and storage space utilization. However, such techniques are not without challenges: it is non-trivial to transparently determine what data has changed since a previous checkpoint and assemble the differences in a compact fashion that does not result in excessive metadata. State-of-art data reduction techniques (e.g., compression and de-duplication) have significant limitations when applied to modern HPC applications that leverage GPUs: slow at detecting the differences, generate a large amount of metadata to keep track of the differences, and ignore crucial spatiotemporal checkpoint data redundancy. This paper addresses these challenges by proposing a Merkle tree-based incremental checkpointing method to exploit GPUs' high memory bandwidth and massive parallelism. Experimental results at scale show a significant reduction of the I/O overhead and space utilization of checkpointing compared with state-of-the-art incremental checkpointing and compression techniques.
Tan, Nigel↗
Enabling Scalable VQE Simulation on Leading HPC Systems
Large-scale simulations of quantum circuits pose significant challenges, especially in the context of quantum chemistry, due to the number of qubits, circuit depth, and the number of circuits needed per problem. High-performance computing (HPC) systems offer massive computational capabilities that could help overcome these obstacles. We developed a high-performance quantum circuit simulator, called NWQ-Sim, and demonstrate its capability to simulate large quantum chemistry problems on NERSC's Perlmutter supercomputer. Integrating NWQ-Sim with XACC, we have executed QPE and VQE algorithms for downfolded quantum chemistry systems at unprecedented scales. Our work demonstrates the potential of leveraging HPC resources to advance quantum chemistry and other applications of near-term quantum devices.
Wang, Meng↗