Search NASASearch

SEARCH · Search NASA

Results for “Loading”

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.

At least 145 records · Page 8

Characterization of 6 Li-loaded pulse-shape-discriminating plastic scintillators

Lithium-loaded organic plastic scintillators combine sensitivity to γ rays with the ability to detect both fast and slow neutrons, making them valuable for applications in nuclear security and in basic nuclear and particle physics. The goal of this work is to characterize the neutron response of two custom lithium-loaded organic plastic scintillators developed at Lawrence Livermore National Laboratory. Both are ternary polystyrene-based formulations containing 1.5 wt.% 6 Li salts of isobutyric acid, but they differ in their primary and secondary dye compositions: one uses m-terphenyl as the primary fluor and with Exalite 404 as the wavelength shifter, whereas the other uses 2,5-diphenyloxazole (PPO) and 9,10-diphenyl-anthracene, respectively. The temporal response of the scintillators was measured via time-correlated single photon counting for γ-ray and neutron events. The proton light yield was measured using the double time-of-flight technique from 1.3 to 15 MeV at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. For the slow neutron response, an AmBe source moderated with polyethylene was used, and the light output from the 6 Li(n,α)t reaction was characterized. Differences in ionization quenching and temporal response were observed between the two materials with the PPO-containing scintillator exhibiting higher ionization quenching. These results provide performance benchmarks that can guide the design and optimization of future lithium-loaded plastic scintillators for use in basic science and applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

deep learning

Solar Cell Metallization Wear is Sensitive to Loading Frequency

Here, we performed cyclic loading of photovoltaic laminates with precracked silicon cells to explore if and how loading frequency and contact pressure influence the ensuing gridline wear-out process. A measurement of parallel resistance across cracked gridlines on a laminated cell coupon was used as the metric for gridline electrical contact degradation. A statistical analysis of variance (ANOVA) analysis of the experimental results discerned that loading frequency is a more significant factor than contact pressure for gridline degradation.

14 SOLAR ENERGY

Community Geothermal: Soil Conductivity, Borehole Design, Energy Models, and Load Data for a Residential System Development - Hinesburg, VT

This dataset contains materials from the Coalition for Community-Supported Affordable Geothermal Energy Systems (C2SAGES) project, which evaluated the techno-economic feasibility of a community geothermal system for a residential development in Hinesburg, VT. The dataset includes detailed soil conductivity test reports, energy models, borehole design reports, hourly energy loads for heating, cooling, and hot water, and design layouts. EnergyPlus was used to model building energy loads, and Modelica software was applied for geothermal loop sizing based on these loads and soil conductivity results. Python scripts for network design further refined the models. Key files include PDF reports on borehole design (with projections for 1-year, 15-year, and 30-year systems), soil conductivity test results, EnergyPlus modeling outputs, and 2D/3D design drawings in PDF, DWG, and DXF formats. Python notebooks for network design and OnePipe model files are also provided, with Modelica required for viewing certain files. Outputs and modeling data are in various formats including CSV, JPG, HTML, and IDF, with units and data clearly labeled to support understanding of system design and performance for the proposed geothermal solution.

15 GEOTHERMAL ENERGY

Commercial and Residential Hourly Load Profiles for All Typical Meteorological Year 3 (TMY3) Locations in the United States

One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High-quality end-use load profiles (EULPs) provide this information and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy funded a 3-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute-resolution load profiles for all major residential and commercial building types and end uses across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

Proactive Regulatory Approaches to Electrification and Load Growth: Workshop Report

On July 10 and 11, 2024, Pacific Northwest National Laboratory and RMI led a workshop in Aurora, Colorado, to explore novel and proactive approaches to electrification and load growth while minimizing risks and costs to customers. Over the next decade, a unique opportunity exists to invest strategically in the electricity system to enable electrification across the transportation, industrial, and building sectors and respond to data and technology-based load growth. However, current utility and regulatory planning practices are insufficient to identify and enable the right investments, and work must be done to reduce the risk and decisional uncertainty faced by utility regulatory commissions and utilities. Ensuring timely electrification investments may require new approaches to address risk, uncertainty, prudence, and cost recovery. Understanding the decision-making process and information needs of utilities and regulators is critical. New policies (or application of policies), financial tools, systems analysis, regulatory mechanisms, and enhanced process transparency may be required. The workshop's goal was to identify proactive regulatory approaches for electrification and load growth that minimize costs and risks to customers. Our intention was that the conversations and the resulting solutions and takeaways would be specific and tactical rather than general and theoretical and that together we would create actionable next steps for key actors in the system, including utilities, regulators, thought leaders, researchers, and the U.S. Department of Energy (DOE). This report is intended to provide workshop attendees with a record and summary of the discussion and proposals raised at the workshop and to provide interested entities who did not attend, such as other regulators, policymakers, utilities, and U.S. DOE offices, with an understanding of what was discussed and with ideas to explore in their organizations.

24 POWER TRANSMISSION AND DISTRIBUTION

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION

Power Management Solution for Growing Loads at Airport Rental Facilities

Growing electrical loads across airport facilities could expand beyond local grid capacities. Coordinating flexible loads with generation and storage assets can reduce coincident peaks, mitigate costly upgrades, and reduce electricity costs. This report outlines how The Athena Team has developed a suite of tools, resources, and solutions to leverage load flexibility with energy assets to harness this value potential.

24 POWER TRANSMISSION AND DISTRIBUTION

MegaWatt Mayhem: Grid Operator Challenges Center Loads

This report provides a summary of the challenges faced by United States electricity grid operators in accommodating and anticipating the rapid deployment of large loads, particularly data centers, based on academic literature and industry working groups. The report highlights the unique requirements and operational characteristics of data centers, which differ significantly from traditional industrial loads. Key issues addressed utility planning considerations, with emphasis on the implications for grid operators, impacts to normal operations for grid operators, reliability considerations during periods of grid stress, and resilience considerations for the changing operational paradigms based on data centers. Real-world examples are used to highlight these challenges and the changes that grid operators must address. The findings underscore the necessity for coordinated efforts and innovative solutions from both grid operators and regulatory bodies to ensure the stable integration of large loads into the grid. This report is the first in a series that will explore the challenges of data center deployments based on several key power system perspectives.

24 - POWER TRANSMISSION AND DISTRIBUTION

Distributed Wind for Commercial Loads

Distributed wind can help meet on site energy and resilience needs for a wide variety of commercial loads .While it is a variable resource, it has predictable daily and annual production trends. The presence of on-site renewable generation can enhance resilience, supplying power for long periods of time without worrying about conserving fuel supply. The addition of properly sized storage or complementary solar resources can further reduce the challenges of intermittency and reduce the need for fuel-limited backup power during outage situations. This fact sheet explores further the energy and resilience requirements of commercial loads and how distributed wind may be a good match to meet their load needs.

20 FOSSIL-FUELED POWER PLANTS

LAF-Net: A Deep Residual and Cross-Attention Framework for Day-Ahead Load Forecasting: Preprint

Accurate day-ahead load forecasting is essential for reliable power system operations and market efficiency. System operators such as the Midcontinent Independent System Operator (MISO) rely on forecasts from multiple vendors, yet combining them effectively remains a persistent challenge due to vendor-specific biases. This paper presents a novel LSTM-Attention Fusion Network with Error Representation (LAF-Net) that enhances day-ahead hourly load forecasting through deep residual learning and multi-modal cross-attention. The proposed model builds a historical error memory from past vendor performance and dynamically queries it with future hour context to generate adaptive, hour-specific trust weights for each vendor. A bounded residual correction further refines forecasts by mitigating systematic and temporally localized errors. Tested on real MISO LBA data with multi-vendor forecasts, LAF-Net consistently outperforms the best vendor baseline across all 38 LBAs, achieving more than a 40% reduction in system-level mean absolute error (MAE) during peak load hours relative to the best vendor baseline.

24 POWER TRANSMISSION AND DISTRIBUTION

Micron Size NaCrO 2 Particles Enable High‐loading Dry‐processed Electrode for Sodium Ion Batteries

Dry-process fabrication using fibrillatable binder is emerging as a promising method to produce high-loading electrodes for energy storage applications, favored by its cost-efficiency and eco-friendliness. While previous studies have demonstrated the advantages of dry process over the traditional slurry method, there remains a gap in understanding how the particle size of active materials influences the mechanical and electrochemical performance of dry electrodes. In this study, four different particle size NaCrO 2 materials (Average size, S-NCO: 0.6 µm, M1-NCO 1.5 µm, M2-NCO: 4.4 µm, and L-NCO: 9.9 µm) are synthesized to investigate the effect of particle size on dry-processed high-loading electrodes. The findings reveal that the larger micron-sized (>4.4 µm) NCO dry films exhibit significantly improved tensile strength and electrochemical performance, primarily ascribed to the low film porosity, abundant inter-particle connection by the binder, comprehensive carbon coverage, and efficient percolation of the conductive pathway. Notably, a full cell incorporated with a high loading (5.2 mAh cm −2 ) and high active material ratio (96.5 wt.%) L-NCO film electrode demonstrates promising cycling stability and rate capability. Furthermore, these results provide valuable insights regarding the design and fabrication of dry-processed electrodes for future energy storage applications.

25 ENERGY STORAGE

Step-loaded creep testing of Zircaloy-4 cladding at higher temperatures in the α-phase

A refined understanding of zirconium-based cladding thermomechanical performance during rapid transients is essential for enhancing the safety and operation of light-water reactors. Traditional models for zirconium alloys under accident conditions generally assume that creep dominates fuel cladding performance. Here, these historic models have largely remained unchanged and serve as the basis for safety criteria development. As the U.S. nuclear industry pursues higher burnup levels, the increased release of fission gases during transients raises the risk of cladding failure in the low-temperature hcp α-phase, making the fidelity of these models of greater importance. Creep testing was conducted from 550–700°C with 25–120 MPa applied hoop stresses to investigate Zircaloy-4 deformation at accident-relevant temperatures in the α-phase. Step-loading was employed to capture creep behavior across a wide stress range from a single sample. The stress-strain rate data at higher temperatures (650 and 700°C) were well-described by isotropic versions of the Erbacher and Kaddour models, while the lower temperature data (550 and 600°C) were underpredicted by both anisotropic and isotropic model variants. Greater strain rates during the initial loading step at 650 and 700°C were attributed to recrystallization and grain growth of sub-micron crystallites. Yet, texture analysis revealed the basal split texture remained after testing. These observations produced results suggesting Zircaloy-4 claddings experience higher creep rates across the α-phase than previously thought, possibly related to dynamic anisotropy due to temperature dependent activation of deformation mechanisms, effects of biaxial loading, and variation in material condition between the current testing used in previous model development.

36 MATERIALS SCIENCE

Technical, economic, and load-following capabilities assessment of grid-connected geothermal and geothermal-solar hybrid systems

The technical and economic performance as well as the load-following capabilities of grid-connected geothermal hybrid systems were assessed in this work. The analyzed geothermal hybrid configuration is composed of a binary geothermal plant integrated with a concentrating solar-thermal system and underground thermal energy storage (UTES) through a primary heat exchanger. Physics-based models for the hybrid system for plant generation capacities of 1, 25, and 50 MW were developed from validated models for each subsystem. Also, an economic model was developed that accounts for different hybrid system capabilities, solar field sizes, and thermal storage duration. The advantage of the geothermal hybrid system was assessed by comparing the performance with the baseline benchmark geothermal plant with a similar configuration and generation capacity. It was found that hybridizing geothermal plants with concentrating solar and thermal energy storage not only improves the thermal efficiency by up to 8 percentage points when additional heat from the solar-UTES loop rises the evaporator temperatures from 70 to 125 °C, but also enhances the load-following capability for the geothermal plant, which can meet a typical residential load profile with a power rate of change 0.25 kW/s with an absolute error under 13 kW for a 1 MW plant. Other benefits of hybridization include resource preservation and a potential LCOE reduction of up to 56% for a 50 MW geothermal hybrid plant having a 50% solar share, a 1.4 solar multiple, and 24-h storage capacity. The results presented in this work demonstrate that hybridizing geothermal systems transforms them into a flexible and cost-effective solution for addressing the dynamic requirements of modern electric grids.

15 GEOTHERMAL ENERGY

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus

Promoting electrochemical rates by concurrent ionic-electronic conductivity enhancement in high mass loading cathode electrode

Enhancing the fast charging capacity of thick electrodes with high mass loading is imperative in expediting the widespread adoption of electric vehicles. Nonetheless, the insufficient charge transfer kinetics of thick electrodes hinder the movement of effective electrons and ions, hence diminishing capacity at high current rates. In this work, we applied sustainable and biodegradable cellulose nanocrystals (CNCs) as electrode additives. It is the first time to simultaneously improve the electronic conductivity by optimizing the carbon dispersion and establishing electron transfer networks, as well as boosting the ionic conductivity of electrodes by shortening the ion transfer pathway. Specifically, the LiNi 0.6 Mn 0.2 Co 0.2 O 2 electrodes incorporating 1% dual functional CNCs additive exhibit improved effective electrical conductivity from 0.11 to 0.16 S/m and risen effective ionic conductivity from 0.36 to 0.62 S/m, in comparison to counterpart electrodes without CNCs. Therefore, the 1% CNC electrode with a high mass loading of 27.0 mg/cm 2 delivers a discharge capacity of 128 mAh/g at 1 C, which is superior to that of the CNC-free electrodes (95 mAh/g). In short, this study presents a novel environmentally friendly, economically viable, and dual-functional electrode additive that enhances both electronic and ionic conductivities with the aim of facilitating the widespread adoption of fast-charging high mass loading electrodes.

25 ENERGY STORAGE

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN

Thermal loading effects on chalk hydromechanical behavior for nuclear waste disposal

Safe disposal of heat-generating nuclear waste depends on host rock stability under thermal, hydrological, and mechanical stresses. This study investigates the effect of thermal loading on mechanical behavior of the shallowly buried Ghareb formation chalk through triaxial and hydrostatic constant strain rate and creep tests at temperatures up to 100 ˚C and effective pressures up to 20.7 MPa. Experimental results show that thermal loading reduces the elastic moduli of chalk by 50–75%, and a transition occurs above 60 ˚C where creep rates increase rapidly. Water saturation nearly doubles the thermally induced strain compared to dry conditions and strongly decreases material rigidity. Thermal loading also leads to significant pore pressure increases under undrained conditions and leads to reductions in the apparent permeability during drained conditions. Laboratory experimental data were used to parameterize and develop a preliminary constitutive model for predicting future deformation during repository operations in the Ghareb. The strongly coupled effects – mechanical weakening, fluid pressure fluctuations, and permeability modification – demonstrate that elevated repository temperatures will have a pronounced effect on the near field Ghareb behavior during waste disposal operations. The findings indicate that the coupled interactions must be considered in predictive models and repository design to ensure long-term nuclear waste isolation and safety.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W