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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Advanced Hydropower and PSH Capacity Expansion Modeling (Final Report on HydroWIRES D1 Improvements to Capacity Expansion Modeling)

Hydropower and pumped-storage hydropower (PSH) have played a key role in providing flexible, low-carbon electricity to the U.S. electricity system for over a century. As variable generation (VG) deployment increases the demand for flexible, dispatchable generation, it is important to use all available methods for understanding how hydropower and PSH interfaces with VG in a future low-carbon grid. Capacity expansion models (CEMs) of electricity systems are often used to study future electricity scenarios, but these tools often have difficulty representing site- and technology-details of hydropower and PSH due to limited spatial, temporal, or process resolution. This report demonstrates a set of model advancements to improve hydropower and PSH representations in CEMs and other models that consider hydropower and PSH's role in electricity systems. Model advancements include new data integration to define closed-loop PSH resource availability and cost, along with site-level parameterizations of existing PSH capacity and energy storage specifications. Shifting energy across seasons for both hydropower and PSH is explored to demonstrate the potential value of long-duration storage. New representations of hydropower upgrades offer opportunities to increase dispatchability, add pumps, or independently add capacity or energy depending on what is most valuable. New model structures and data are demonstrated individually and in combination to observe their impact on model results and provide initial insights into what is most important for analysts, electricity system planners, and hydropower decision-makers to consider when assessing future roles of hydropower. Improving flexibility of existing hydropower assets has the potential to reduce CO 2 emissions through complementing VG technologies and/or improve electricity system economics by reducing the need to deploy higher-cost systems. Large-scale energy storage is also shown to provide opportunities for balancing seasonal differences in VG, particularly solar photovoltaics (PV). Initial analysis with a new closed-loop PSH resource and cost dataset also demonstrate the potential for new PSH deployment to offer new grid flexibility opportunities. Future analysis and modeling of hydropower's role in the U.S. and other electricity systems could include methodological improvements based on those discussed here along with sensitivity analysis to better represent current and future potential hydropower and PSH flexibility. Improvements to geospatial and site-level data can further improve the understanding of how hydropower and PSH can be upgraded and operated in response to future electricity system needs. This work lays the groundwork to use advanced planning models to explore the range of roles hydropower and PSH can play in the grid of the future.

13 HYDRO ENERGY↗
DOE OSTI2024

Multi-scale impacts of climate change on hydropower for long-term water-energy planning in the contiguous United States

Climate change impacts on watersheds can potentially exacerbate water scarcity issues where water serves multiple purposes including hydropower. The long-term management of water and energy resources is still mostly approached in a siloed manner at different basins or watersheds, failing to consider the potential impacts that may concurrently affect many regions at once. There is a need for a large-scale hydropower modeling framework that can examine climate impacts across adjoining river basins and balancing authorities (BAs) and provide a periodic assessment at regional to national scales. Expanding from our prior assessment only for the United States (US) federal hydropower plants, we enhance and extend two regional hydropower models to cover over 85% of the total hydropower nameplate capacity and present the first contiguous US-wide assessment of future hydropower production under Coupled Model Intercomparison Project phase 6’s high-end Shared Socioeconomic Pathway 5-8.5 emission scenario using an uncertainty-aware multi-model ensemble approach. We present regional hydropower projections, using both BA regions and US Hydrologic Subregions (HUC4s), to consistently inform the energy and water communities for two future periods—the near-term (2020–2039) and the mid-term (2040–2059) relative to a historical baseline period (1980–2019). We find that the median projected changes in annual hydropower generation are typically positive—approximately 5% in the near-term, and 10% in the mid-term. However, since the risk of regional droughts is also projected to increase, future planning cannot overly rely on the ensemble median, as the potential of severe hydropower reductions could be overlooked. The assessment offers an ensemble of future hydropower generation projections, providing regional utilities and power system operators with consistent data to develop drought scenarios, design long duration storage and evaluate energy infrastructure reliability under intensified inter-annual and seasonal variability.

13 HYDRO ENERGY↗
DOE OSTI2024

Measurements of Normalized Differential Cross Sections of Inclusive η Production in e + e − Annihilation at Energy from 2.0000 to 3.6710 GeV

Using data samples collected with the BESIII detector operating at the BEPCII storage ring, the cross section of the inclusive process e + e − → η + X , normalized by the total cross section of e + e − → hadrons , is measured at eight center-of-mass energy points from 2.0000 to 3.6710 GeV. These are the first measurements with momentum dependence in this energy region. Our measurement shows a significant discrepancy compared to the existing fragmentation functions. To address this discrepancy, a new QCD analysis is performed at the next-to-next-to-leading order with hadron mass corrections and higher twist effects, which can explain both the established high-energy data and our measurements reasonably well. Published by the American Physical Society 2024

Physics↗
DOE OSTI2024

In the Mix : A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science

As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.

25 ENERGY STORAGE↗
DOE OSTI2022

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Characterization of Cycle-Aged Commercial NMC and NCA Lithium-ion Cells: I. Temperature-Dependent Degradation

Lithium-ion batteries are widely used in applications from consumer electronic devices to stationary energy storage. Appropriate management of batteries is challenging due to limited data on their performance and materials degradation. Previous studies have focused on characterization of single cells under specific operating conditions. In the present work, commercial 18650 lithium-ion cells with LiNi x Mn y Co 1-x-y O 2 (NMC) and LiNi x Co y Al 1-x-y O 2 (NCA) positive electrodes were characterized by a wide range of electrochemical and materials techniques after cycling at 15, 25, or 35 °C to ∼80% capacity. The NCA cells exhibit weak temperature dependence in their cycle aging and materials degradation. The NMC cells exhibited increased capacity fade and materials degradation as ambient temperature decreased. All cells exhibited loss of lithium inventory as their primary degradation mode. However, the NCA cells only showed evidence of solid electrolyte interphase (SEI) growth whereas the NMC cells showed signs of Li plating at 15 °C, transitioning to SEI growth at 35 °C. The NMC cells displayed signs of loss of active material at the positive electrode at lower temperatures, suggesting that Li plating is correlated to additional processes that increase the rate of degradation. These results highlight the importance of avoiding broad generalizations about Li-ion battery temperature dependence.

25 ENERGY STORAGE↗
DOE OSTI2021

PyBLM (Python-based Battery Life Model) [SWR-21-50]

PyBLM is a battery life model to predict the capacity fade of two home battery energy storage systems, manufactured by LG and Tesla. The battery life model is built from experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. PyBLM is developed in Python and formatted to work in conjunction with another NREL software named TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies).

Mishra, Partha↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2023

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data‐Driven Machine Learning

Abstract Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid–solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data‐driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency.

25 ENERGY STORAGE↗
DOE OSTI2023

Data Projection of the High Temperature Electrolysis System in the Dynamic Energy Transport and Integration Laboratory using Dynamic System Scaling

For nuclear power to be flexible in a functioning Integrated Energy System (IES), excess produced heat must be stored or utilized during times of low power demand to ensure a load factor of 1 while load balancing. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is one facility that is under development to emulate IES conditions on the engineering-scale, planned to conduct virtual real time operations with industry-scale facilities, and is currently testing thermal storage and high temperature electrolysis. As part of the study to develop a method to preprocess input signals or postprocess output signals between systems of different scales via Dynamical System Scaling (DSS), the current research is one of the continued efforts branching from the data projection activity conducted for the Thermal Energy Distribution System and currently engages the High Temperature Electrolysis (HTE) System in DETAIL. The HTE SOEC electrical, fluid, and thermal dynamics Figure of Merits (FOM) were identified, governing equations and closure relations were successfully scaled, and relations between FOM scaling ratios were determined. Setting the scaling objectives to reform existing data to project a data set that doubly accelerated the electrolysis process while preserving the produced amount of hydrogen was generated for the full transient. The calculated boundary conditions were inlet temperature, stack current, and inlet steam mass flow rate at 1470 K, 121.1 A, and 1.886 g/s, respectively. The research outcomes demonstrated an output signal postprocessing case accelerating the hydrogen production without changing geometry, number of cells, and partial pressures.

08 HYDROGEN↗
DOE OSTI2023

Scaling Outlooks on Integrated Energy Systems

The report provides research summaries of two developed theories: data projection to enable multi-scale facility digital interactions and introducing the Evaluation Model Development and Assessment Process (EMDAP) approach to Integrated Energy Systems (IES) design. For the data projection, a two-system scaling case including interactions between thermal storage and hydrogen production is provided. The corresponding facilities are the thermal energy distribution system (TEDS) facility and high temperature steam electrolysis (HTSE) facility at the Dynamic Energy Transport and Integration Laboratory (DETAIL) in Idaho National Laboratory (INL). For the EMDAP adaption to system design, a modified process of the EMDAP was developed and applied to indirect two-tank heat storage system design. System phenomena were identified, ranked, and scaled to discover helpful key relations that would determine the ideal component geometry.

08 HYDROGEN↗
DOE OSTI2026

Modeling Framework for Data Center

This chapter highlights the critical need for advanced modeling of data centers due to their rapidly increasing energy consumption and impact on grid reliability. Driven by the demand for AI applications, data centers are projected to consume a significant portion of US energy by 2028, putting stress on an already challenged power grid. The chapter emphasizes the importance of "fast" time-scale models to understand the dynamic interactions between data centers and the grid, especially given the rapid power fluctuations of AI workloads. It outlines a modeling framework that includes both offline and real-time EMT domain simulations, detailing the necessary representations for various components like utility interfaces, transformers, IT loads, UPS, cooling loads, Battery Energy Storage Systems (BESS), generators, protection systems, and higher-level control systems. While standard simulation tools like PSCAD offer basic models, custom development is often required to accurately capture the unique and fast-changing behaviors of modern data centers. The chapter also discusses key metrics and test cases for validating these models, focusing on transient load responses, protection relay coordination, and demand flexibility. Finally, it addresses the challenges of modeling large-scale data centers, such as computational complexity and the trade-off between model fidelity and practicality, suggesting hybrid modeling approaches as a solution. The overarching goal is to create a robust framework that helps assess data center impacts on grid stability, identify vulnerabilities, and inform the development of standards for reliable integration of these large loads into the bulk power system.

25 ENERGY STORAGE↗
DOE OSTI2021

Thermal Energy Storage 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 concrete, latent heat, and packed-bed thermocline energy storage technologies for deployment 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 cyclically charge and discharge when exposed to time-varying boundary conditions. In addition, low-level surrogate models for some of the thermal energy storage technologies were created using Python. These lower order Python files are much cheaper to run (i.e., computationally faster) and thus operate well when incorporated within the stochastic optimization problems run for the IES program. Moreover, economic data was collected for use within the INL-developed Framework for Optimization of ResourCes and Economics (FORCE). This information is incorporated within the FORCE platform. This work has resulted in creation of systems-level models for concrete, latent heat, and thermocline thermal energy storage systems with associated control systems. Now that 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 (e.g., Storworks Power, EnergyNest) 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.

25 ENERGY STORAGE↗
DOE OSTI2022

Charting Hydropower's Role in the Next-Generation Grid

To build a 100% clean energy power sector, the United States is adding more energy storage and variable renewable energy sources, like solar power and wind energy, to the grid. Hydropower and pumped storage hydropower (PSH) can help with both. These technologies already play a key role in providing flexible, low-carbon electricity to the U.S. power grid, and this role will become even more valuable as that grid evolves. That's why researchers at the National Renewable Energy Laboratory (NREL) are analyzing how the U.S. electricity sector could invest in hydropower and PSH using new data and modeling capabilities.

HYDRO ENERGY↗
DOE OSTI2021

Data‐Driven Safety Risk Prediction of Lithium‐Ion Battery

Abstract Inevitable safety issues have pushed battery engineers to become more conservative in battery system design; however, battery‐involved accidents still frequently are reported in headlines. Identifying, understanding, and predicting safety risks have become priorities to further accelerate technology and industry development. However, diverse loading scenarios, significantly varied stress‐induced short circuit mechanisms, and highly coupled mechanical–electrochemical safety behaviors have remained grand challenges. Herein, the safety risk is termed as the probability of the mechanical triggering of an internal short circuit, to reflect the safety related behaviors of lithium‐ion batteries. Based on a mechanical model and experimental results, a sufficient dataset is generated consisting of strain states and their corresponding safety risks, covering both cylindrical and pouch cells, various states of charges, and loading conditions. Machine‐learning tools combined with the established finite element mechanical model are applied to predict the safety risks of the cells. The results achieve a high level of accuracy on the test data (the relative error of the average short circuit prediction deviation is less than 6.2%.). This work underpins the safety risk concept and highlights the promise of physics combined with data‐driven modeling methodology to predict the safety behaviors of energy storage systems.

Jia, Yikai↗
DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗
DOE OSTI2023

2023 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies

These data provide the 2023 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

Array↗
DOE OSTI2022

Development and Validation of Resistance-Capacitance Model (RCM) for Phase Change Material (PCM) Embedded in 3D Periodic Structures

The low thermal conductivity of Phase Change Materials (PCM) can be improved with extended surfaces such as additively manufactured 3D periodic lattice structures. Three different aluminum alloy-based lattices (base sizes 10, 20, and 40 mm) with average porosity of 0.95 filled with paraffin wax, with a nominal phase change temperature of 55°C, were experimentally investigated. In this work, a computationally efficient 2D Resistance Capacitance-based model (RCM) was developed for predicting the thermal characteristics of these geometries. Non-uniform porosity in the PCM-metal domain was estimated using image processing and served as model input. The solver does not solve for higher-order physics as in CFD but still can provide a good prediction of thermal resistance and energy storage at a very low computational cost. The simulation-to-real-time factor for this geometry is of the order of 10-4, while CFD simulations typically have a real-time factor greater than 1. The model was validated against the experimental data for melting under three different heat fluxes (6250 W/m2, 12500 W/m2, and 18750 W/m2). The mean deviation of the predicted average PCM temperature was between 1.34 K-2.81 K for different cases. The maximum average temperature deviation of 5.45 K was observed for the 20 mm geometry at the highest heat flux test condition. The effects of natural convection were neglected in the model, but the predicted PCM temperature and energy storage still showed good agreement with the experimental data.

25 ENERGY STORAGE↗