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DOE OSTI2025

Efficient Quantum Gibbs Samplers with Kubo–Martin–Schwinger Detailed Balance Condition

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than being treated as an approximation to weakly coupled system-bath unitary dynamics. Recently, Chen, Kastoryano, and Gilyén (arXiv:2311.09207) introduced the first efficiently implementable Lindbladian satisfying the Kubo–Martin–Schwinger (KMS) detailed balance condition, which ensures that the Gibbs state is a fixed point of the dynamics and is applicable to non-commuting Hamiltonians. This Gibbs sampler uses a continuously parameterized set of jump operators, and the energy resolution required for implementing each jump operator depends only logarithmically on the precision and the mixing time. In this work, we build upon the structural characterization of KMS detailed balanced Lindbladians by Fagnola and Umanità, and develop a family of efficient quantum Gibbs samplers using a finite set of jump operators (the number can be as few as one), akin to the classical Markov chain-based sampling algorithm. Compared to the existing works, our quantum Gibbs samplers have a comparable quantum simulation cost but with greater design flexibility and a much simpler implementation and error analysis. Moreover, it encompasses the construction of Chen, Kastoryano, and Gilyén as a special instance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

3D modeling of deep borehole electromagnetic measurements with energized casing source for fracture mapping at the Utah Frontier Observatory for Research in Geothermal Energy

Here, we present a 3D numerical modelling analysis evaluating the deployment of a borehole electromagnetic measurement tool to detect and image a stimulated zone at the Utah Frontier Observatory for Research in Geothermal Energy geothermal site. As the depth to the geothermal reservoir is several kilometres and the size of the stimulated zone is limited to several 100 m, surface-based controlled-source electromagnetic measurements lack the sensitivity for detecting changes in electrical resistivity caused by the stimulation. To overcome the limitation, the study evaluates the feasibility of using a three-component borehole magnetic receiver system at the Frontier Observatory for Research in Geothermal Energy site. To provide sufficient currents inside and around the enhanced geothermal reservoir, we use an injection well as an energized casing source. To efficiently simulate energizing the injection well in a realistic 3D resistivity model, we introduce a novel modelling workflow that leverages the strengths of both 3D cylindrical-mesh-based electromagnetic modelling code and 3D tetrahedral-mesh-based electromagnetic modelling code. The former is particularly well-suited for modelling hollow cylindrical objects like casings, whereas the latter excels at representing more complex 3D geological structures. In this workflow, our initial step involves computing current densities along a vertical steel-cased well using a 3D cylindrical electromagnetic modelling code. Subsequently, we distribute a series of equivalent current sources along the well's trajectory within a complex 3D resistivity model. We then discretize this model using a tetrahedral mesh and simulate the borehole electromagnetic responses excited by the casing source using a 3D finite-element electromagnetic code. This multi-step approach enables us to simulate 3D casing source electromagnetic responses within a complex 3D resistivity model, without the need for explicit discretization of the well using an excessive number of fine cells. We discuss the applicability and limitations of this proposed workflow within an electromagnetic modelling scenario where an energized well is deviated, such as at the Frontier Observatory for Research in Geothermal Energy site. Using the workflow, we demonstrate that the combined use of the energized casing source and the borehole electromagnetic receiver system offer measurable magnetic field amplitudes and sensitivity to the deep localized stimulated zone. The measurements can also distinguish between parallel-fracture anisotropic reservoirs and isotropic cases, providing valuable insights into the fracture system of the stimulated zone. Besides the magnetic field measurements, vertical electric field measurements in the open well sections are also highly sensitive to the stimulated zone and can be used as additional data for detecting and imaging the target. We can also acquire additional multiple-source data by grounding the surface electrode at various locations and repeating borehole electromagnetic measurements. This approach can increase the number of monitoring data by several factors, providing a more comprehensive dataset for analysing the deep-localized stimulated zone. The numerical analysis indicates that it is feasible to use the combination of the energized casing and downhole electromagnetic measurements in monitoring localized stimulated zone at large depths.

58 GEOSCIENCES↗

DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗

DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Development of Innovative Non-Concentrated Alloy (NCA) Fuel Cladding for Advanced Nuclear Reactors

As the nuclear energy sector advances toward next-generation reactors, the need for high-performance fuel cladding materials has become increasingly urgent. Traditional alloys like zirconium and stainless steel are reaching their performance limits under higher temperatures, more corrosive coolants, and extended irradiation. This report presents the development of a new class of fuel cladding materials based on Non-Concentrated Alloys (NCAs) ? multi-element systems designed to deliver enhanced mechanical strength, corrosion resistance, and radiation tolerance. Through a combination of computational modeling (Computer Coupling of Phase Diagrams and Thermochemistry: CALPHAD), simulation-guided alloy selection, and experimental fabrication via arc melting and spark plasma sintering (SPS), three strategic alloy design paths were explored: (1) FeCrAl-based NCAs, (2) refractory-lean neutron-efficient alloys, and (3) equimolar high-entropy compositions. Microstructural analysis confirmed the formation of stable body-centered cubic BCC_A2 phases, while mechanical testing demonstrated hardness values significantly exceeding those of conventional cladding materials. The results highlight the tunability of NCA systems and their potential for balancing strength and ductility ? a critical consideration for in-reactor performance. Looking forward, future work will focus on thermomechanical optimization, CALPHAD refinement, and benchmarking against industry standards to enable scalable deployment. This work not only advances the science of nuclear materials but also supports broader goals in nuclear safety, performance, and nuclear energy innovation.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Theory of resonant x-ray scattering with ultrafast intense pulses

Here, we present a time-dependent Schrödinger equation approach within a nonrelativistic quantum electrodynamics framework to investigate resonant x-ray scattering driven by intense x-ray pulses. This method enables us to explore how coherent x-ray electron dynamics influence scattering signals from Ne + . We account for both resonance fluorescence and elastic scattering channels, while also considering competing photoionization and inner-shell decay processes. By computing the angular distribution and energy spectrum of scattered photons, we uncover interference effects between elastic scattering and resonance fluorescence pathways. Notably, this interference results in a small asymmetry in the energy spectrum. We discuss the experimental potential for detecting signatures of interference. Our findings demonstrate that the x-ray Rabi dynamics can be used to control scattering responses and provide insights into interference mechanisms and scattering efficiency in high-intensity x-ray regimes.

Venkatesh, Akilesh [Argonne National Laboratory (A↗

DOE OSTI2025

SAFARI - Secure Automation For Advanced Reactor Innovation (Final Technical Report)

The Secure Automation For Advanced Reactor Innovation (SAFARI) project was a pioneering initiative aimed at fundamentally changing how nuclear power plants are operated and maintained. Recognizing that current nuclear plants often rely on extensive manual procedures and large staffs, leading to higher costs compared to other energy sources like natural gas, SAFARI sought to introduce smart, automated technologies to make nuclear energy more efficient, cost-effective, and safer. This report details the research and development efforts of the SAFARI project, bringing us closer to a future where advanced nuclear reactors can operate more autonomously, adapt flexibly to energy demands, and predict their maintenance needs before issues arise. One of the key achievements of the SAFARI project is its contribution to our understanding of how Artificial Intelligence (AI) and sophisticated computer models, known as Digital Twins, can be effectively integrated with the complex physics of nuclear reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

BISON: A Finite Element-Based Nuclear Fuel Performance Code

BISON is a finite element-based nuclear fuel performance code applicable to a variety of fuel forms including light water reactor fuel rods, TRISO particle fuel, and metallic rod and plate fuel. It is a multiphysics fuel analysis tool that solves fully-coupled thermomechanical problems. BISON is based on MOOSE and can efficiently solve problems using standard workstations or very large high-performance computers in a variety of different dimensions, including full 3D, 2D-RZ axisymmetric, layered axisymmetric 1D, and spherically symmetric 1D systems. It is developed by a team of scientists and engineers at Idaho National Laboratory and by collaborators. The development of BISON is supported by various funding agencies, principally the United States Department of Energy.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Advanced Computational Modeling of High-Level Waste Vitrification at the Hanford Site

The U.S. Department of Energy (DOE) has selected vitrification for stabilizing legacy tank waste at the Hanford site, where radioactive waste from plutonium production was historically stored in underground tanks. This waste will be separated into low-activity waste (LAW) and high-level waste (HLW) fractions and processed at the Waste Treatment and Immobilization Plant (WTP). At WTP, glass melters are used for the vitrification of radioactive tank waste, transforming it into a stable borosilicate glass form for safe long-term storage. The melter vessel is constructed from highly durable and heat-resistant materials, where the vitrification process occurs. The main regions that are modeled are the melt pool, plenum, cold cap, riser/discharge chamber, and surrounding structure with insulation layers. Forced convection induced by air bubblers at the base of the melter ensure uniform temperature distribution and provide heat to the cold cap layer. The cold cap is a region of reacting batch feed that floats on top of the molten glass and is where the batch-to-glass reactions occur. Joule heating provided by electrodes mounted along the vertical walls of the melter and immersed directly in the glass, generates the necessary heat for the net endothermic conversion processes that occur in the cold cap. The high temperatures, radioactivity, and opaque nature of the glass prevent direct observation inside the melters. Therefore, computational models are essential for providing insight into factors that affect melter throughput. Thermocouples in the plenum provide operators with plenum temperature measurements. Operational adjustments include bubbling rate, voltage supplied to the electrodes, feed adjustments, and glass removal rate. Different computational fluid dynamics (CFD) models have been developed, each serving a specific purpose. There are CFD models of different scale melters, as well as models that capture the two-phase flow interfaces of rising bubbles in the molten glass or models with a simplified molten glass region so that the surrounding structure and plenum can be feasibly incorporated. Pilot-scale melter models have been developed to serve as validation of the methods employed in the simulation of the full-scale WTP melters. Models incorporating resolved bubbling are used to develop momentum source terms to implement into a single phase, multi-region, steady-state flow model that is being validated by measured process parameters such as glass production rate, voltage, input power, plenum temperatures, etc. The resolved bubbling model uses the multiphase volume of fluid approach to model the system with a high-resolution interface capturing scheme to maintain sharp interfaces between the molten glass and the air phase. The suite of CFD models is continually being improved to incorporate more realistic physics and achieve faster turnaround time. For example, an incremental controller is implemented to automatically adjust electrode voltage within the simulation to a molten glass set point temperature of 1150°C. Newer models feature improved meshes to ensure conformal meshes between regions and eliminate unnecessary mesh refinement in areas that are not of interest (such as boundary layers in offgas ports). Instead of explicitly modeling the structural, refractory, and insulation layers of the melter, a thermal resistance approach is used with published correlations used for boundary conditions. The development of robust and efficient CFD models will be instrumental in enabling the WTP to successfully fulfill its mission of safely stabilizing legacy nuclear waste.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗

DOE OSTI2025

Evolution and Degradation Patterns of Electrochemical Cells Based on the Analysis of Interfacial Phenomena at Li Metal Anode/Electrolyte Interfaces

In this work, we report the results of a theoretical–computational analysis of the solid electrolyte interphase (SEI) growth and degradation dynamics occurring in lithium metal batteries during cycling. We use ab initio-kinetic Monte Carlo simulations to generate a synthetic data set, which is analyzed by machine learning methods. We aim to determine: (i) how modifications in interfacial interaction energies between solid electrolyte interphase (SEI) blocks and between Li ions and SEI facets impact the Coulombic efficiency (CE) of the battery and (ii) what factors, including reactions, microscopic transport, and other interfacial events, may lead to cell performance “failure” during prolonged charge and discharge cycles, signaled as a sharp decay in the CE over cycling. The demonstration of our approach is done on a cell including a Li metal surface interfacing with a previously introduced state-of-the-art electrolyte, and the idea can be applied to any electrochemical system. Outcomes include the identification of the leading chemical, physical, and structural variables causing cell failure and relating them to the electrolyte formulation, thus paving the way to future more refined analysis and electrolyte design.

batteries↗

DOE OSTI2026

Machine Learning an Ab-Initio Based Bond-Order Potential for Bismuthene

Bismuthene is a heavy 2D material whose strong spin–orbit coupling and recently observed single-element ferroelectricity have intensified interest in its structural, vibrational, and transport properties. Accurate modeling of these behaviors requires a short-range interatomic potential that can reproduce the underlying bonding physics at a fraction of the computational cost of first-principles methods. However, such a potential is currently unavailable. Here, in this work, we construct a Tersoff bond-order potential for β-bismuthene using a reinforcement-learning framework that integrates a continuous Monte Carlo Tree Search with a simplex-based local optimizer. The optimized parameter sets reproduce first-principles lattice constants, cohesive energy, the equation of state, elastic constants, and phonon dispersion. We validate the models by performing thermal-conductivity calculations and uniaxial fracture simulations our findings confirm the reliability of the resulting models across multiple thermomechanical regimes. Comparison of the three best solutions reveals how differences in pairwise interactions, angular terms, and bond-order behavior govern phonon features and mechanical responses. We demonstrate an interpretable and computationally efficient potential for bismuthene and demonstrate a general reinforcement-learning strategy for developing bond-order models in emerging 2D materials.

deformation↗

DOE OSTI2024

Force-free Wave Interaction in Magnetar Magnetospheres: Computational Modeling in Axisymmetry

Abstract Crustal quakes of highly magnetized neutron stars can disrupt their magnetospheres, triggering energetic phenomena like X-ray and fast radio bursts. Understanding plasma wave dynamics in these extreme environments is vital for predicting energy transport across scales to the radiation length. This study models relativistic plasma wave interaction in magnetar magnetospheres with force-free electrodynamics simulations. For propagation along curved magnetic field lines, we observe the continuous conversion of Alfvén waves to fast magnetosonic (FMS) waves. The conversion efficiency can be up to three times higher when counter-propagating Alfvén waves interact in the equatorial region. Alfvén waves generate FMS waves of twice their frequency during their first crossing of the magnetosphere. After the initial transient burst of FMS waves, Alfvén waves convert to FMS waves periodically, generating variations on timescales of the magnetospheric Alfvén wave crossing time. This decaying FMS wave tail carries a significant portion (half) of the total energy emitted. Plastic damping of “bouncing” Alfvén waves by the magnetar crust has minimal impact on the FMS efficiency. We discuss the implications of the identified wave phenomena for magnetar observations. Outgoing FMS waves can develop electric zones, potential sources of coherent radiation. Long wavelength FMS waves could generate FRBs through reconnection beyond the light cylinder.

Astronomy & Astrophysics↗

DOE OSTI2024

To Exascale and Beyond—The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud-Resolving Scales

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real-world climate via a set of four 40 day simulations covering all 4 seasons of the year.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Prototype Modeling for a Light-Trapping Planar-Cavity Enclosed Particle Solar Receiver

Concentrating solar thermal (CST) systems present a promising avenue for affordable and reliable energy production. Solar receivers are key components that determine the efficiency and longevity of these systems. Particle-based solar receivers have emerged as a compelling alternative to traditional technologies, offering several advantages that address limitations in current CST systems. This is especially true as next-generation CST technologies target applications including electricity generation, thermochemical processes, and industrial process heat, many of which necessitate higher operating temperatures than current commercial molten salt systems. Molten-salt thermal energy storage (TES) systems, commonly used in CSP, face challenges related to freezing and corrosion. Particle-based TES systems, in contrast, do not experience these issues, as particles are stable at high temperatures, exceeding 1000 degrees Celsius. This capability allows for a wider range of applications, including those requiring higher temperatures for industrial processes and efficient electricity generation. A novel innovation in particle-based solar receiver technology is the light-trapping planar cavity receiver (LTPCR) configuration developed by NREL. The LTPCR design consists of small cavity-like structures using opaque planar surfaces, enabling efficient capture and absorption of solar energy. A high incident flux concentration at the cavity aperture is absorbed on the receiver walls, and subsequently transferred to particles on the inside of cavities. The particles flow through the system, forming a fluidized bed inside of the receiver panels, effectively capturing the absorbed solar heat. Air is used as a fluidizing medium in this process to enhance particle heat transfer and mixing. The effectiveness of this design lies in its ability to manage solar flux conditions and ensure high solar-to-thermal receiver efficiency. A 100-kW prototype is currently being tested at the King Saud University in Saudi Arabia to assess the receiver performance. A range of modeling analyses for the optical, thermal, and mechanical effects were conducted to assess the performance of the receiver under on-sun conditions. The solar flux resulting from the KSU heliostat field was modeled using NREL SolTrace software and produced up to 600 kW/m2 at the receiver aperture. The solar flux absorbed on the receiver walls was then used within a computational fluid dynamics (CFD) model to predict wall temperature distributions along with radiation and convection loss. A two-phase CFD model was developed for the fluidized bed of silica sand inside the receiver panels to predict local wall-to-particle heat transfer coefficients, particle temperature distributions, and outlet temperature of the particles. We have also conducted analyses to understand the thermomechanical behavior of these innovative enclosed light-trapping solar receivers optimized for particle heating. We used finite element analysis (FEA) to predict the receiver's performance using temperature distributions obtained from CFD and based on the resulting stress profiles, evaluated creep-fatigue damage with a goal of achieving a 30-year service life. Analysis showed a significant impact of the particle-to-wall heat transfer coefficients (HTCs) on receiver performance, with higher HTCs resulting in reduced stress and increased lifespan. For instance, when using Inconel 740H, increasing the HTC from 800 W/m2 K to 1400 W/m2 K increased the creep life from 4,000 hours to over 100,000 hours. This highlights the importance of understanding and optimizing heat transfer in the design of high-efficiency receivers.

14 SOLAR ENERGY↗

DOE OSTI2024

Performance Results on CPU/GPU Exascale Architectures for OMEGA: The Ocean Model for E3SM Global Applications

The US Department of Energy (DOE) conducts climate simulations on some of the world’s largest supercomputers. These exascale machines use heterogeneous architectures with both CPUs and GPUs, and scientific codes must adapt to make full use of this computing power. Los Alamos National Lab is developing Omega: The Ocean Model for E3SM Global Applications, which is specifically designed for modern exascale computers. It uses external libraries that have been optimized for a variety of architectures to run on different supercomputers. Omega is an unstructured-mesh ocean model based on TRiSK numerical methods. It will be the new ocean component of the DOE’s Energy Exascale Earth System Model (E3SM). The algorithms in Omega follow those of the current ocean component, MPAS-Ocean, but it will be written in C++ rather than Fortran to take advantage of the Kokkos performance portability library. Omega spatial operators are written as Kokkos kernels to run efficiently on both CPUs and GPUs. Work on Omega began in 2023 with a new C++ framework for unstructured mesh partitioning, halo exchanges, parallel IO, and Kokkos interfaces. The current version, Omega-0, is being developed to solve the shallow water equations and at present includes all of the tendency terms but not time stepping. Here we share the results of Omega-0 verification and performance testing. Verification includes unit tests implemented with CTest as well as convergence tests in Polaris, an in-house python package with a large suite of test problems. Performance tests compare simulations conducted on CPUs versus GPUs and across different architectures: tests are run on Frontier, which has AMD “Optimized 3rd Gen EPYC” CPUs and AMD MI250X GPUs, as well as Perlmutter, which is composed of AMD EPYC 7763 CPUs and NVIDIA A100 GPUs.

58 GEOSCIENCES↗

DOE OSTI2026

The remarkable inefficiency of stratocumulus

Marine stratocumulus clouds play a central role in Earth's climate system by reflecting incoming solar radiation and exerting a strong cooling effect. Their organization into open and closed mesoscale cellular morphologies can be thought of as an example of bistable dynamics driven by aerosol–cloud interactions and mesoscale processes. From the perspective of non-equilibrium thermodynamics, these structures are an example of a far-from-equilibrium open system that continuously produces and exports entropy. While entropy production has been studied in idealized deep convective systems, it has not yet been quantified for shallow clouds. Here, we compute and decompose the internal entropy production of open- and closed-cell stratocumulus using an ensemble of large-eddy simulations. We show that the overall entropy production of stratocumulus is low, reflecting the limited vertical extent and corresponding reduced ability to utilize the energy fluxes at the system's boundaries. Moist processes dominate the overall irreversibility, which, combined with their low entropy production, leads to a mechanical efficiency about an order of magnitude smaller than in deep convective systems. Although the dominant irreversible processes differ between open- and closed-cell regimes, the distributions of total entropy production largely overlap across the ensemble, limiting the ability to distinguish the dynamics of individual cases based solely on total entropy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A Computational Review of Privacy-Preserving Mechanisms for the Smart Grid

Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Integrated Reliability and Economic Modeling for Transmission Across Large Regions: A Space Odyssey

Power flow modeling and stability analysis are needed to more-comprehensively assess system reliability but the development of the system portfolios and conditions require use of economic models (e.g., production cost). What are the state of art methods for efficiently linking economic and reliability models to enable examination of multiple snapshots and perform detailed nodal analyses?

24 POWER TRANSMISSION AND DISTRIBUTION↗