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At least 145 records · Page 8

Quantifying Volume Change in Porous Electrodes via the Multi-Species, Multi-Reaction Model

Automotive manufacturers are working to improve individual cell and overall pack design by increasing their performance, durability, and range, while reducing cost; and active material volume change is one of the more complex aspects that needs to be considered during this process. As the time from initial design to manufacture of electric vehicles is decreased, design work that used to rely solely on testing needs to be supplemented or replaced by virtual methods. As electrochemical engineers drive battery and system design using model-based methods, the need for coupled electrochemical/mechanical models that take into account the active material change utilizing physics based or semi-empirical approaches is necessary. In this study, we illustrated the applicability of a mechano-electrochemical coupled modeling method considering the multi-species, multi-reaction model as popularized by Verbrugge and Baker. To do this, validation tests were conducted using a computer-controlled press apparatus that can control the press displacement and press force with precision. The coupled MSMR volume change model was developed and its applicability to graphite and NMC cells was illustrated. The increased accuracy of the model considering the coupled MSMR volume change approach shows in the importance of accounting for individual gallery volume change behavior on cell level predictions.

25 ENERGY STORAGE↗

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

Comprehensive Study of the Potential of Extracting and Processing Critical Minerals from Coal-Based Resources - Phase I

The Phase I report prepared for the Department of Energy addresses U.S. Executive Order 13817 titled A Federal Strategy to Ensure Secure and Reliable Supplies of Critical Minerals, issued on December 20, 2017, that lists 35 critical minerals that are vulnerable to supply disruption. A comprehensive review of each of the minerals was conducted to determine the criticality based primarily on extractability from coal-based resources. Several other factors were also considered such as gaps in supply and demand, use in current technology, and the existence of viable substitutes. It was determined that the critical minerals that show highest potential for extraction from coal-based resources are lithium, rare earth elements (REEs), cobalt, and manganese. All four of the critical minerals listed serve an important role in the technology industry, have few substitutes, and have a heavy import reliance. Most notably are lithium, which is widely used in the electric vehicle industry, and the REEs which can be found in virtually all electronic devices. The research of critical mineral extraction from coal-based resources was completed using a combination of literature review from public sources as well as cooperation from coal mines and power plants across the United States. Samples collected from six different geographical locations across the U.S. were subjected to sample preparation (i.e., pH measurement, moisture content, particle size analysis) and characterization studies using Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) and Scanning Electron Microscopy, Energy Dispersive X-Ray Spectroscopy (SEM-EDX) instruments. 27 samples of coal waste materials such as refuse, sludge, and fly ash were tested to characterize the rare earth element concentration by total rare earth elements (TREEs), heavy rare earth elements (HREEs), and light rare earth elements (LREEs). Of the 27 samples tested, 22 contained a TREE concentration higher than the threshold of 300 ppm, which is considered a viable feedstock material. 3 samples contained less than 300 ppm of TREEs; however, they were within 20 ppm of the threshold, and could potentially be considered viable sources in the future pending the advancement of more efficient extraction technologies. 2 of the 27 samples had significantly low TREE concentrations, which does not imply any potential for being a source for REEs. For the minerals identified as most critical in the literature review, a conceptual process flow diagram (PFD) was developed for their extraction from different coal-based feedstocks. The process targets selective recovery of one commodity (i.e., rare earths, lithium, cobalt, and manganese) via several hydrometallurgical separation methods. By identifying potentially extractable coal-based critical mineral resources, a study of the current and future market environments for each critical mineral, and a review of current processing methodologies for critical mineral extraction from coal-based resources, the foundation has been laid to further characterize and explore new resources and extraction techniques. As reliance on technologies in industries such as the production of electronic devices, batteries, and alloys containing critical minerals utilizing critical minerals continues to increase, a sound understanding of our nation’s dependence on and even the global criticality of certain critical minerals, will serve as a catalyst for innovation in virtually all fields of science.

01 COAL, LIGNITE, AND PEAT↗

Collaborative Computing Support for Analysis Facilities Exploiting Software as Infrastructure Techniques

Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked software, it is possible for multiple institutes to collaborate more effectively since resource details are abstracted away through various forms of hardware and software virtualization. In this whitepaper we will outline the development of two analysis facilities: "Coffea-casa" at University of Nebraska Lincoln and the "Elastic Analysis Facility" at Fermilab, and how utilizing platform abstraction has improved the development of common software for each of these facilities, and future development plans made possible by this methodology.

97 MATHEMATICS AND COMPUTING↗

Dispatch Informed Hydrogen Production

The cost of hydrogen production is projected to decline over the next several decades alongside the widespread deployment of energy constrained generating resources and electrification. While today’s system can utilize fast responding thermal generating assets to answer to changes in demand and energy constrained resource output, the system of tomorrow will more likely rely upon price responsive demands and virtual power plants. Since hydrogen is anticipated to be a primary generating fuel of the future while also providing a large demand base for its production, it is reasonable to evaluate the potential performance for hydrogen-electric coordination issues.

Brewer, John↗

Demonstrating the data center as a flexible grid asset using a C-HIL setup

Increasing data center demand is outpacing grid infrastructure development. Artificial intelligence workloads and hyperscale cloud growth are creating unprecedented demand for power, while traditional grid expansion faces multiyear development timelines. Verrus is developing an innovative datacenter solution for this challenge, data centers that act as active grid-supportive assets rather than passive loads. Our approach integrates a novel grid-aware power flow management system with battery energy storage systems(BESS) into a microgrid-controlled, medium-voltage power distribution architecture that delivers critical capabilities, such as: * Fast response to grid disturbances such over/ under voltage or over/ under frequency * Demand flexibility that can service requests from the utility within 10 s * Uninterrupted transition to islanded operation during grid outages * Continuous uptime assurance for compute loads while maintaining all customer service level agreements. Through Verrus' strategic partnership with the National Renewable Energy Laboratory (NREL), these capabilities were validated using NREL's Advanced Research on Integrated Energy Systems (ARIES) virtual emulation environment to model a 70-MW grid-interactive data center. This paper outlines the design, methodology, and results of this emulated deployment, demonstrating that data centers can provide both critical load resilience and ancillary grid support without compromising uptime requirements. Specifically, we present a digital real time simulation of a 70 MW data center integrated with a physical microgrid controller, and demonstrate the data center response in the event of a grid voltage and frequency event, utility demand response request and utility outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Aggregation and Grid Security Workshop Report

The Aggregation and Grid Security Workshop - held on June 17-18, 2025, National Laboratory of the Rockies (NLR) in Golden, Colorado - brought together approximately 40 external stakeholders from the energy sector, including VPP owner/operators, aggregators, OEMs, utilities, testing & certification labs, trade associations, and cybersecurity vendors. Led by key facilitators, the workshop focused on addressing cybersecurity challenges and enhancing grid resilience for aggregated Distributed Energy Resources (DERs) and Virtual Power Plants (VPPs). The workshop was catalyzed by recognition that traditional, rearward-looking regulatory frameworks are insufficient to keep pace with technological change. There is a "missing understanding" of risk, an "absent security basis" for managing it, and an "untenable responsibility" due to unclear ownership and requirements. The workshop aimed to shift the mindset from reacting to past crises to proactively preparing for emerging threats, fostering forward resilience through risk simulation and collaborative action. This report summarizes the outcomes of the workshop, marking it a significant step toward a secure, reliable and affordable energy future.

14 SOLAR ENERGY↗

Uncertainty Quantification in High-Low Dynamic System Coupling using RAVEN and TRANSFORM

This work demonstrates new functionality and applications stemming from the development of high-fidelity to low-fidelity (high-low) coupling for system simulations and to further explore the capabilities of the Risk Analysis Virtual Environment (RAVEN) in the performance of uncertainty quantification in this kind of high-low coupled system models. The work builds from previous work on high-low coupling that utilized COBRA-TF (CTF), the high-fidelity subchannel analysis code, with a low fidelity model built in ORNL’s TRANSFORM, the system analysis code, utilizing the Functional Mock-Up Interface (FMI). Steady-state and transient analysis examples using the high/low coupled models generated from CTF and TRANSFORM/FMI are investigated. The workflows for both steady-state and transient coupled simulations are described. A steady-state parameter sweep and uncertainty analysis of the primary flow rates and reactor power are demonstrated. Likewise, a transient pump trip and power ramp sensitivity studies are also demonstrated. This work elucidates some of the potential benefits and future needs of using RAVEN for high/low system coupling analysis of energy systems. It also shows some of the difficulties that can be encountered in coupling system simulations.

Williams, Wesley↗

Systems and methods for data analytics for virtual energy audits and value capture assessment of buildings

A system may provide virtual energy audits of one or more target buildings. The system may retrieve weather data and energy usage data specific to a given target building from a weather server and a utility server, respectively. The system may store predefined building characteristics corresponding to the given target building in local memory. Based on the weather data, energy usage data, and/or predefined building characteristics, the system may generate one or more building markers that characterize the energy usage and efficiency of the given target building. Building efficiency diagnostics and energy conservation prognostics may be generated based on the building markers and may be sent by the system to be displayed via a user interface of a client device. The energy conservation prognostics may include one or more energy conservation measure recommendations and corresponding predicted cost/energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Zooming in on virtual commutes: Telecommuting impacts on mobility and sustainability

Motivated by a societal shift towards remote work and rapid advancement in information and communication technologies, this study examines the impact of telecommuting on urban mobility across the nine counties of the San Francisco Bay Area, California. Utilizing a behaviorally realistic integrated agent-based transportation model and recent data on telecommuting patterns, we simulate the impact of multiple telecommuting scenarios on transportation system outcomes. This provides a comprehensive picture of the impact of remote work on travel costs and accessibility of all travelers, factoring in changes in mode use and congestion. We analyze how telecommuting influences broader societal factors such as transportation energy consumption. Our findings indicate that increased telecommuting reduces overall person miles traveled and transportation energy consumption. Furthermore, telecommuting results in externality benefits by improving accessibility and reducing commute times for non-telecommuters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Risk-informed Hierarchical Control of Behind-the-Meter DERs with AMI Data Integration (Final Technical Report)

This project addresses several key barriers to implement the next generation demand response applications and provides a clear understanding of implementing hierarchical and standalone control using AMI data. Through this program, Eaton has developed and tested a meter-as-a-controller prototype with the help of other partners--- National Renewable Energy Laboratory (NREL), Electric Power Research Institute (EPRI), Pecan St Inc. (PSI), and Delaware Electric Cooperative (DEC). The controller can utilize residential controllable loads such as heating, ventilation, and air conditioner (HVAC), electric water heater and distributed energy resources like solar PV and battery energy storage systems for off-setting the demand that is required from the grid, thus providing reliable grid-services for demand reduction or peak shaving. The controller is also capable of coordinating the resources of the premises for better management and energy efficiency while meeting the comfort bound of the premises owner as quality-of-service. The development has been demonstrated in a three virtual-home setup at system performance lab of NREL with real appliances (HVAC, electric water heater, solar PV, and battery). The technology has also been proved through laboratory and field demonstration with successful interconnectivity (e.g., end-to-end communication and data exchange) between the residential appliances and utility through the RF network at Delaware Electric Co-op (DEC) in Delaware.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

A transition from generation on demand to consumption on demand is one of the solutions to overcome the many limitations associated with the higher penetration of renewable energy sources. Such a transition, however, requires a considerable amount of load flexibility in the demand side. Demand response (DR) programs can reveal and utilize this demand flexibility by enabling the participation of a large number of grid-interactive efficient buildings (GEB). Existing approaches on DR require significant modelling or training efforts, are computationally expensive, and do not guarantee the satisfaction of end users. To address the aforementioned limitations, this software code considers a scalable hierarchical model-free transactional control approach that incorporates elements of virtual battery, game theory, and model-free control (MFC) mechanisms. The developed approach separates the control mechanism into upper and lower levels. The MFC modulates the flexible GEB in the lower level with guaranteed thermal comfort of end users in response to the optimal pricing and power signals determined in the upper layer using a Stackelberg game integrated with aggregate virtual battery constraints. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable even on small, embedded devices. This software code enables the use of residential and small-size commercial buildings to offer a potentially substantial source of ancillary grid services that are currently underutilized. A hierarchical, model-free transactive building control provides a smooth interface between the grid service requests of utilities and the reliable control required by participating buildings. Model-free control, which supports distributed control architecture, will be a more scalable solution that can be deployed in neighborhood-size systems as well as individual buildings. The proposed approach contributes to the body of knowledge on two main aspects. First, it couples the MFC with the game-theoretic control and proposes a scalable model-free transactive control approach. MFC does not require any modeling effort or model training for the various building loads. This is very beneficial since deriving an accurate model for every single unit participating in DR programs and obtaining all the parameters about the units (e.g., thermal coefficients, standby losses) are infeasible. Also, MFC is computationally efficient, easily deployable even on small, embedded devices, and can be implemented in real time. Second, it integrates the concept of virtual battery into DR via the Stackelberg game. The concept of virtual battery enables efficient coordination and aggregation of a large number of flexible GEB with guaranteed thermal comfort of end users.

Olama, Mohammed [Oak Ridge National Lab. (ORNL), O↗

Enumeration as a Tool for Structure Solution: A Materials Genomic Approach to Solving the Cation-Ordered Structure of Na 3 V 2 (PO 4 ) 2 F 3

While powder diffraction methods are routinely utilized to optimize structural models for compounds whose crystal structures are known, the determination of unknown structures is far more challenging. When the unknown structure is large, structure solution can become a virtually intractable problem using standard structure solution methodologies, especially when the space group cannot be unambiguously resolved. One such system is the promising Na-ion battery cathode material Na 3 V 2 (PO 4 ) 2 F 3 whose high temperature and room temperature structures were previously solved, but whose more complex low-temperature structure could not be determined. Here, a novel materials genomic approach is demonstrated for the solution of the unknown 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 in which enumeration methods are first used to generate a large number (~3,000) of trial structures based on plausible orderings of Na ions and then automated Rietveld refinements are carried out to optimize each of these trial structures. Based on both the analysis of the ensemble of optimized trial structures and the density functional theory energy minimization of selected trial structures, the 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 is best described as belonging to the space group A2 1 am with unit cell dimensions of a = 9.01928(4), b = 27.1379(1), c = 10.73307(5). The 100 K unit cell has a large volume of 2627.07(2) Å 3 with Z = 12 and 33 independent crystallographic sites (9 Na, 3 V, 3 P, 12 O, and 6 F) that is 3x and 6x larger than the room- and high-temperature polymorphs of this phase, respectively. Finally, the novel methods described here will be generally applicable for the solution of the complex cation-ordered structures that commonly occur for battery materials.

36 MATERIALS SCIENCE↗

Real-Time Dispatcher for a Distributed Energy Resource Power Plant to Provide Grid Services [SWR-25-17]

This software dispatches the aggregated power of a distributed energy resource (DER) plant located on an electrical distribution network. It is designed to provide the following services to the grid in real-time: (i) voltage support of the distribution network, (ii) virtual power plant at the substation with power factor support, and (iii) operating reserves for automatic generator control for the transmission system. This dispatcher integrates with the local power plant controller and utilizes the battery energy storage system (BESS) to smooth the volatile net power output from the DERs. It can also integrate with a day-ahead scheduler to strategically charge and discharge.

Comden, Joshua [National Renewable Energy Laborato↗

Future of Water Infrastructure and Innovation Summit

Energy and water systems are interdependent, and the U.S. Department of Energy (DOE) has invested in energy and water for several years, including the Energy-Water Desalination Hub (led by the National Alliance for Water Innovation, NAWI), and research and development (R&D) in resource recovery from wastewater, among other areas. The Advanced Manufacturing Office (AMO) at the U.S. Department of Energy (DOE) held The Future of Water Infrastructure and Innovation Summit to inform the understanding of future opportunities in the water space. The virtual summit was held on October 27 and 28, 2020. The organizers gathered information from a diverse group of relevant water and wastewater stakeholders representing academia, industry, government, nongovernmental organizations, and local/regional utilities. Topics from Day 1 discussions included: desalination, water and wastewater treatment/recovery, produced water, industrial management of water, and hydropower, conveyance, and water systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Release of a High Temperature Engineering Test Reactor (HTTR) Steady State Multiphysics Model to the Virtual Test Bed

In response to climate change, global governments and private industry have established a common goal of achieving net-zero emissions by 2050 \cite{osti_1865910}. This goal requires a reassessment of current energy demands and production methods. Reducing emissions at an affordable cost while maintaining grid reliability requires a nationwide collaborative effort among government and industry in the United States. Nuclear power is the leading low-carbon electricity generation method. In the past 50 years, the use of nuclear power has reduced carbon dioxide emissions by over 60 gigatons and has played a crucial role in the security of energy supply~\cite{IEA}. In the U.S., nuclear power accounts for 20\% of the electrical supply and provides energy reliably. Advanced reactors will operate at higher temperatures, operate more efficiently, utilize more energy stored within fuel, and reduce the amount of waste produced \cite{osti_1616270}. To face these challenges and goals, the U.S. Department of Energy has created an initiative to focus on the modeling and simulation tools to support future nuclear power plant design, licensing, and operations. The Virtual Test Bed (VTB)~\cite{vtb2023} was launched by the National Reactor Innovation Center (NRIC) in collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to support the advanced nuclear reactor community. The VTB involves teams from both Idaho National Laboratory and Argonne National Laboratory and aims to provide example models for a broad range of both current and future advanced reactor designs. A feature of the VTB is the automatic testing of these models to ensure continued functionality as simulation tools are further developed. The VTB and the advanced reactor models documented there are important resources for this initiative. This work describes the inclusion of a new model on the VTB---a High Temperature Engineering Test Reactor (HTTR) steady-state model \cite{LABOURE2023109838}.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Mapping the Path Forward to Next Generation Algal Technologies: Workshop on Understanding the Rules of Life and Complexity in Algal Systems

Algal technology offers enormous potential as a source of renewable biomass and bioproducts for fuels, biochemicals, nutrients, and numerous other applications. While tremendous opportunities exist, significant challenges are also present in harnessing and utilizing this valuable and potentially sustainable bioproduction platform. Funded by a joint partnership between the Bioenergy Technologies Office (BETO) at the Department of Energy (DOE) and the National Science Foundation (NSF), the recent virtual workshop, "Understanding the Rules of Life: Complexity in Algal Systems Workshop," was organized to bring together thought leaders from academia, national laboratories and other government agencies, and industry to identify key challenges and propose research strategies to overcome these barriers. Two parallel sessions were undertaken: 1) algae strain and toolkit development and 2) algal ecosystem and microbiome dynamics, in order to advance algae engineering and cultivation efforts. In each session, attendees discussed the most pressing challenges from the perspective of model systems and omics analysis. Themes such as genetic engineering toolboxes, translation from the laboratory to outdoor cultivation, functional genomics, metabolomics, algal ecology, consortial design, microbiome dynamics, mathematical models, and data analytics emerged from the discussion. Overall, this workshop has served to elucidate the key hurdles and many opportunities present as we continue to advance algal science and technology from the laboratory and into the commercial realm.

09 BIOMASS FUELS↗