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At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Where and When Does Solar Plus Storage Make Sense for Commercial Buildings?

As the capital cost of battery energy storage systems (BESS) declines, opportunities for commercial buildings to achieve net savings through peak demand management and energy arbitrage are emerging. National Renewable Energy Laboratory researchers modeled energy storage project economics - with and without accompanying solar photovoltaic systems - using local utility rates, ASHRAE climate zones, and solar intensity data to identify regions where these systems deliver life cycle savings now and in future cost scenarios.

battery energy storage systems↗

DOE OSTI2024

Streaming Large-Scale Microscopy Data to a Supercomputing Facility

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ⁠), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

4D-STEM↗

DOE OSTI2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗

DOE OSTI2023

Thermal conductivity measurement using modulated photothermal radiometry for nitrate and chloride molten salts

Molten salts are being used or explored for thermal energy storage and conversion systems in concentrating solar power and nuclear power plants. Thermal conductivity of molten salts is an important thermophysical property dictating the performance and cost of these systems, but its accurate measurement has been challenging, as evidenced by wide scattering of existing data in literature. The corrosive and conducting nature of these fluids also leads to time consuming sample preparation processes of many contact-based measurements. Here, in this work, we report the measurement of thermal conductivity of molten salts using a modulated photothermal radiometry (MPR) technique, which is a laser-based, non-contact, frequency-domain method adopted for molten salts for the first time. By unitizing the advantages of front side sensing of frequency-domain measurements and the vertical holder orientation, the technique can minimize the natural convection and salt creeping effects, thus yielding accurate molten salt thermal conductivity . The MPR technique is first calibrated using standard molten materials including paraffin wax and sulfur. It is then applied on measuring pure nitrate salts (NaNO 3 and KNO 3 ), solar salt (NaNO 3 –KNO 3 mixture), and chloride salt (NaCl–KCl–MgCl 2 ). The measurement results are compared with data from literature, especially those obtained from laser flash analysis (LFA). Our results demonstrate that the MPR is a convenient and reliable technique of measuring thermal conductivity of molten salts. Accurate thermal conductivity data of molten salts will be valuable in developing the next-generation high-temperature thermal energy storage and conversion systems.

14 SOLAR ENERGY↗

DOE OSTI2026

Operational Simulation of Multi-Functional Charging Station for Sustainable Transportation

As transportation systems move toward electrification and decarbonization, multifunctional charging stations (MFCS) are emerging as key infrastructure for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper presents a simulation model of an MFCS that integrates solar photovoltaic (PV), wind power, battery storage, hydrogen (\mathrm{H}_{2}) production, dual-pressure \mathrm{H}_{2} storage, fuel cells, and dynamic grid interactions. The model simulates daily operations using 5 -minute resolution data to capture realtime variability in renewable energy (RE), demand, and electricity prices. A flexible dispatch algorithm dynamically allocates energy for EV charging, \mathrm{H}_{2} production, storage, and grid transactions while respecting system constraints. Results show that the MFCS effectively prioritizes RE usage, minimizes waste, meets diverse energy demands, and achieves net operational profit. The model serves as a valuable decision-support tool for designing and optimizing integrated clean energy hubs for zero-emission transportation.

Gbadegoye, Jeremiah [University of Tennessee, Knox↗

DOE OSTI2021

Status Report on FY2022 Model Development within the Integrated Energy Systems HYBRID Repository

This publication details newly created energy storage models developed within the HYBRID Modelica repository as part of the Department of Energy Office of Nuclear Energy (DOE-NE) Integrated Energy Systems (IES) program, led by Idaho National Laboratory (INL). Model development to-date includes creation of dynamic systems-level models of a pebble bed high temperature gas reactor (HTGR), liquid air energy storage (LAES), and compressed air energy storage (CAES) in the IES-based HYBRID repository. Models are developed using the latest publicly available data and incorporate the possibility of control strategy inclusion for use with the existing IES modeling, analysis, and optimization toolset. Simulations showcase the abilities of each technology to flexibly operate in ways consistent with IES operation expectations. When these models are available, they can be utilized within different integrated energy park concepts to understand optimal system operation, control, and dispatching. Moreover, given the generic nature of the models, industrial partner technologies can be quickly added to the repository using the existing models as a basis. Additional dynamic models for thermal energy storage concepts can be developed and added to the HYBRID repository as needed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗

DOE OSTI2021

Ambient-Temperature Hydrogen Storage via Vanadium(II)-Dihydrogen Complexation in a Metal-Organic Framework

In this report, the widespread implementation of H 2 as a fuel is currently hindered by the high pressures or cryogenic temperatures required to achieve reasonable storage densities. In contrast, the realization of materials that strongly and reversibly adsorb hydrogen at ambient temperatures and moderate pressures could transform the transportation sector and expand adoption of fuel cells in other applications. To date, however, no adsorbent has been identified that exhibits a binding enthalpy within the optimal range of -15 to -25 kJ/mol for ambient-temperature hydrogen storage. Here, we report the hydrogen adsorption properties of the metal-organic framework (MOF) V 2 Cl 2.8 (btdd) (H 2 btdd, bis(1 H -1,2,3-triazolo[4,5- b ],[4',5'- i ])dibenzo[1,4]dioxin), which features exposed vanadium(II) sites capable of backbonding with weak π acids. Significantly, gas adsorption data reveal that this material binds H 2 with an enthalpy of -21 kJ/mol. This binding energy enables usable hydrogen capacities that exceed that of compressed storage under the same operating conditions. The Kubas-type vanadium(II)-dihydrogen complexation is characterized by a combination of techniques. From powder neutron diffraction data, a V-D 2 (centroid) distance of 1.966(8) Å is obtained, the shortest yet reported for a MOF. Using in situ infrared spectroscopy, the H-H stretch was identified, and it displays a red shift of 242 cm -1 . Electronic structure calculations show that a main contribution to bonding stems from the interaction between the vanadium d π and H 2 σ* orbital. Ultimately, the pursuit of MOFs containing high densities of weakly π-basic metal sites may enable storage capacities under ambient conditions that far surpass those accessible with compressed gas storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗

DOE OSTI2022

Chapter 8: Life-Cycle Testing and Analysis

Prior to a spacecraft launch, program mission assurance standards dictate that the flight battery power system should comply with mission requirements under the intended operating conditions. Ground life cycle testing (LCT) combined with an analysis on the electrical power system (EPS) battery is an empirical method used to demonstrate compliance to satellite service life requirements. The LCT compliance method adopted by the aerospace industry is based on demonstrating a space battery's life expectancy as a part of battery qualification. Real-time cell and lithium-ion battery (LIB) LCT data are commonly used for model inputs to EPS power and energy balance analyses, in LIB reliability analysis estimates, and to support on-orbit spacecraft mission life extension opportunities. This chapter describes the LCT planning steps, process approach, and analysis techniques commonly used to qualify space LIB power systems.

accelerated aging↗

DOE OSTI2023

First benchmarked electron cooling simulations from first principles

We present the first microscopic electron cooling simulations from first principles with accurate prediction of cooling time. These simulations were performed using our previously developed numerical method, PHAD, which is the first efficient large-scale collisional numerical method in beam physics. The simulation results are benchmarked with the experimental data of the low energy bunched electron cooling of ion beams at the storage ring CSRm at the IMP facility in China. Here, we have accurately considered the nonlinear dynamics in the whole accelerator system in addition to the electron cooling section. As a result, our simulations correctly reproduced cooling times of the experiments from first principles and without any tuning or fitting parameters in the code.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Visualization Tool for Comparing Low-Carbon Energy Options

This project developed a web based visualization tool for comparing current and future nuclear fuel cycle options to low-carbon and conventional energy technologies in the United States. While nuclear power is well established as the gold-standard for baseload power production and also as the technology with the lowest overall carbon intensity of any commercial form of electricity generation. This project develop a web-based tool for electricity producers and consumers to compare renewable and conventional energy technologies to the conventional and advanced nuclear fuel cycle options. The work leveraged data on land use, carbon intensity, unit cost and pricing data for renewables from the open literature. Input data from the Advanced Fuel Cycle Cost Basis report will be used for current and advanced nuclear power systems. We coupled these data to energy flow and lifecycle models for user-selected energy generation and storage types. We will display economic comparisons from the perspectives of generators and consumers, as well as carbon production, land use, and reliability comparisons.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Hydropower Energy Storage Capacity Dataset

The Hydropower Energy Storage Capacity (HESC) Dataset catalogues estimates of nominal energy storage capacity based on varying levels of detail. Dams and reservoirs selected were selected based on those reported in the National Inventory of Dams (NID 2019) and/or the Global Reservoir and Dam (GRanD v1.3) datasets. These data provide a foundation for understanding available resources at existing hydropower facilities and their potential to provide storage of energy and more flexible generation. Current estimates include Level 1 (based on maximum storage capacities and hydraulic head) and Level 2 (based on historical models or observations of reservoir volume and hydraulic head). For facilities where installed capacity is known, there are also estimates for discharge duration or the length of time when a facility could provide generation at a given capacity. Essential information used to calculate the energy storage capacity and discharge duration (volume, hydraulic head, and details about the sources or records used to obtain those parameters) and summaries of historical generation (for context) are also included.

13 HYDRO ENERGY↗

DOE OSTI2021

Voltage Control Strategy for Energy Storage System in Sustainable Distribution System Operation

Due to the increasing penetration of distributed energy resources (DERs) required for the sustainable distribution system, new voltage control strategy is needed by utilities. Traditional voltage control strategy can not support the increasing number of DERs in a coordinated and scalable manner to meet the operational voltage regulation requirement. Supported by the power electronics converter, the energy storage system can provide fast, smooth, and flexible voltage control services. In this paper, an effective and easy to implement sensitivity-based voltage control strategy is developed for the energy storage system. The developed control strategy is validated using an industrial feeder data in Northwest Washington. The proposed strategy can mitigate the voltage unbalance issue, improve the voltage profile, and correct power factors while supporting sustainable distribution system operation.

Zhang, Yue (ORCID:0000000241469716)↗

DOE OSTI2022

Magnetically Levitated and Constrained Flywheel Energy Storage System

The concept of using linear induction motors to lift, constrain, accelerate, and decelerate a large-scale flywheel is proposed, and some of the advantages are investigated. Calculations for a Magnetically Levitated Energy Storage System (MLES) are performed that compare a single large scale MLES with a current state of the art flywheel energy storage system in order to show the relative differences and advantages of such a system. The system that is used for comparison is a typical Beacon Power flywheel energy system. This is currently one of the state-of-the-art flywheel energy storage systems and so it can be used to provide a basic data set to compare to an MLES system.

VanOsdol, John↗

DOE OSTI2023

Informed Feature Selection for Data Clustering of CSP Plant Production

To make concentrating solar power (CSP) more cost competitive, rigourous optimizations must be run to improve plant design and operations. However, these optimizaitons rely on time consuming annual simulations that solve an electricity dispatch scheduling problem to maximize plant revenue. To reduce the runtime of annual dispatch simulations of CSP plants, a data clustering approach is utilized. This approach assumes that like days of revenue and electricity generation can be identified using weather and price data. Although weather and price are important factors for electricity production, this work investigates how thermal energy storage (TES) inventory at the beginning of a day, denoted as Si, can be used as a supplemental feature to group like days. A framework for creating and training a deep neural network to predict Si is proposed. This model is validated and assessed using eleven sets of testing data that were not used during training. Then, the data clustering approach is performed three seperate times with features of weather and price along with either Si from the neural network, Si from the full annual simulation, or no Si. Ultimately, the results suggest that using Si as an additional clustering feature improves the data clustering simulation accuracy by 1.4%.

Tuman, Matthew J. (ORCID:000900038772051X)↗

DOE OSTI2021

Data Analysis Approach for Large Data Volumes in a Connected Community

Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems are providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, and in-memory processing of large volumes of streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive and predictive data analysis for understanding live operation of the neighborhood, fault identification, and future opportunities for further optimization of load curves. We then demonstrate how we achieve live situational awareness of the connected neighborhood through a suite of visualization components. Finally, we discuss a few analytic dashboards that address questions such as peak load reductions obtained due to optimization, customer preference for automatic control of appliances (do they override the automatic control of HVAC?, etc.). 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Chinthavali, Supriya↗