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DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

advanced metering infrastructure↗

DOE OSTI2020

BEEAM (Building Electrical Efficiency Analysis Model) [SWR-20-107]

Modern high-performance buildings exhibit an increasing number of building loads that use direct current (DC) electricity internally, rather than alternating current (AC), due to the advent of low-cost computing and advanced power electronics. Powering DC devices from the AC grid requires AC/DC power conversion, which introduces energy losses and reduces efficiency. As DC loads proliferate, the cumulative wasted energy associated with hundreds of millions of AC/DC converters has become one of the broadest energy savings opportunities in buildings. DC power distribution systems have been proposed as an elegant and transformative solution to the problem of DC devices. In a DC distribution system, the building's wires carry DC electricity, rather than AC; and a few centralized, highly efficient AC/DC converters replace the many smaller, less efficient converters that serve individual DC loads. Unfortunately, the trade-offs associated with DC distribution systems are not well understood. Reported energy savings associated with DC power distribution differ widely and have not been well validated. The Building Electrical Efficiency Analysis Model (BEEAM) is a Modelica library that simulates the efficiency of building electrical distribution systems using harmonic power flow. BEEAM can model a wide variety of building distribution topologies, including three-phase AC, single-phase AC, unipolar DC, bipolar DC, and hybrid networks under both balanced and unbalanced load conditions. BEEAM accurately models power electronic converter losses, provides granular estimates of losses throughout the distribution system, and properly models efficiency at part load conditions. Users can package BEEAM within a functional mockup unit (FMU), enabling co-simulation with other modeling platform, such as EnergyPlus. In summary, BEEAM provides a tool suite for fair and accurate comparison of the efficiency of building electrical distribution systems, including AC, DC, and hybrid systems.

Frank, Stephen↗

DOE OSTI2026

New Directions in Focused Ion Beam Induced Deposition for the Nanoprinting of Functional 3D Heterostructures

The focused ion beam (FIB) microscope is well established as a high-resolution machining instrument capable of site-selectively removing material down to the nanoscale. Beyond subtractive processing, however, the FIB can also add material via a technique known as focused ion beam induced deposition (FIBID). Using FIBID, the FIB can thus be employed for the direct-write of complex nanostructures. This work explores new directions in three-dimensional FIBID nanoprinting, harnessing unique features of helium and neon FIBs. In particular, the superior spatial resolution of these novel FIBs is leveraged to fabricate precise multimaterial architectures, an isotope effect is used to create satellite deposits, and dose-controlled implantation of the gaseous ions is used to engineer internal voids. In the context of voids, the fabrication of hollow nanopillars by helium-FIBID due to concurrent milling (as shown previously by others) is revisited. Insight into the chemical and structural composition of the nanostructures is obtained using advanced electron microscopy, accurately revealing buried interfaces, crystallite distributions, chemical compositions, and material transformations. Next-generation devices and technologies that could be enabled by the novel heterostructures demonstrated here are discussed, setting the stage for the potential evolution of FIBID into a versatile platform for functional nanomaterials design and fabrication.

FIBID↗

DOE OSTI2023

Synthesis of Free-Standing Pd-Ni-P Metallic Glass Nanoparticles with Durable Medium-Range Ordered Structure for Enhanced Electrocatalytic Properties

Topologically disordered metallic glass nanoparticles (MGNPs) with highly active and tailorable surface chemistries have immense potential for functional uses. The synthesis of free-standing MGNPs is crucial and intensively pursued because their activity strongly depends on their exposed surfaces. Herein, a novel laser-evaporated inert-gas condensation method is designed and successfully developed for synthesizing free-standing MGNPs without substrates or capping agents, which is implemented via pulse laser-induced atomic vapor deposition under an inert helium atmosphere. In this way, the metallic atoms vaporized from the targets collide with helium atoms and then condense into short-range-order (SRO) clusters, which mutually assemble to form the MGNPs. Using this method, free-standing Pd 40 Ni 40 P 20 MGNPs with a spherical morphology are synthesized, which demonstrates satisfactory electrocatalytic activity and durability in oxygen reduction reactions. Moreover, local structure investigations using synchrotron pair distribution function techniques reveal the transformation of SRO cluster connection motifs of the MGNPs from face-sharing to edge-sharing modes during cyclic voltammetry cycles, which enhances the electrochemical stability by blocking crystallization. Further, this approach provides a general strategy for preparing free-standing MGNPs with high surface activities, which may have widespread functional applications

36 MATERIALS SCIENCE↗

DOE OSTI2024

Formally Verified ZTA Requirements for OT/ICS Environments with Isabelle/HOL

The clean energy transformation includes the integration of distributed energy resources with the power grid, which has led to a substantial increase in the complexity of power grids infrastructure and the underlying operational technology environment. Power grids infrastructure represents an operational technology environment that has become a system of systems, integrating heterogeneous devices which are both software-and hardware-intensive; as a result, there are increasing demands to exploit advances in the commodity of software-hardware infrastructures to improve energy systems requirements such as cybersecurity and resilience. In such a setting, system requirements at different levels mix, which leads to vulnerabilities and undesirable outcomes. The use of formal methods to characterize and prove system requirements removes ambiguity, increases automation, and provides high levels of assurance and reliability. In this paper, we contribute a methodology and a framework for the system-level verification of zero trust architecture requirements in operational technology environments. We define a formal specification for the core functionalities of operational technology environments, the corresponding invariants, and security proofs. Of particular note is our modular approach for the formal verification of asynchronous interactions in operational technology environments. The formal specification and the proofs have been mechanized using the interactive theorem proving environment Isabelle/HOL.

formal methods↗

DOE OSTI2023

Guest Editorial: Control interactions in power electronic converter dominated power systems

The modern power systems have undergone significant transformations at the generation, transmission, distribution, and utilization levels due to the remarkable advancements in power electronic converter technology. Power electronic converters are now prevalent in various applications, including wind turbine converters, photovoltaic inverters, flexible AC transmission systems (FACTS) and high-voltage DC (HVDC) converters, distributed generators, microgrids, and electric vehicles. The widespread adoption of power electronic converters has revolutionized the power system by providing fast and flexible controllability. However, their unique characteristics, such as fast response, multi-time scale dynamics, reconfigurable control, and varying sizes and capacities, have introduced new stability challenges, fundamentally altering the dynamics of modern power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Fourier-informed knot placement schemes for B-spline approximation

Fitting B-splines to scientific data is especially challenging when the given data contain noise, jumps, or corners. Here, we describe how periodic data sets with these features can be efficiently approximated with B-splines by analyzing the Fourier spectrum of the data. Our method uses a collection of spectral filters to compute high-order derivatives, smoothed versions of noisy data, and the locations of jump discontinuities. Further, these quantities are then combined to choose knots that capture the qualitative features of the data, leading to accurate B-spline approximations with few knots. The method we introduce is direct and does not require any intermediate B-spline fitting before choosing the final knot distribution. Aside from fast Fourier transforms to transfer to and from Fourier space, the method runs in linear time with very little communication. We assess performance on several test cases in one and two dimensions, including data sets with jump discontinuities and noise. These tests show the method fits discontinuous data without spurious oscillations and remains effective in the presence of noise.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Impact of baryonic feedback on HSC-Y1 weak lensing non-Gaussian statistics

Baryonic feedback is a major systematic in weak lensing cosmology. Its most studied effect is the suppression of the lensing power spectrum, a second-order statistic, on small scales. Motivated by the growing interest in statistics beyond the second order, we investigate the effect of baryons on lensing non-Gaussian statistics and the resulting biases in the matter clustering amplitude S 8 = σ 8 Ω m / 0.3 . We focus on the Subaru Hyper Suprime-Cam Year 1 (HSC-Y1) data which, with its high source number density, closely resembles those expected from the upcoming Euclid and Rubin Legacy Survey of Space and Time. We study four non-Gaussian statistics of convergence maps—peak counts, minimum counts, the probability distribution function, and the scattering transform coefficients—in addition to the usual power spectrum. We first estimate the biases in S 8 using mock observations built from the IllustrisTNG and BAHAMAS hydrodynamical simulations and theoretical models built from dark-matter-only simulations. We find up to 1 σ bias in S 8 when the smallest scales (2 arcmin) and the highest feedback level are considered. We then analyze the HSC-Y1 data and compare the S 8 obtained for each statistic with different smoothing scales or scale cuts. As we expect that baryons mostly affect the small scales, comparing the results obtained from including and excluding small scales can indicate the level of impact from baryons. With HSC data, we find only minor ( ≤ 0.5 σ ) differences in S 8 for all statistics, even when considering very small scales (2 arcmin). Our results suggest that the effect of baryons is insignificant at the level of HSC-Y1 down to 2 arcmin for all statistics examined here, or it is canceled by other scale-dependent systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Humming Trains in Seismology: An Opportune Source for Probing the Shallow Crust

Abstract Seismologists are eagerly seeking new and preferably low-cost ways to map and track changes in the complex structure of the top few kilometers of the crust. By understanding it better, they can build on what is known regarding important, practical issues. These include telling us whether imminent earthquakes and volcanic eruptions are generating telltale underground signs of hazard, about mitigation of induced seismicity such as from deep injection of wastewater, how the Earth and its atmosphere couple, and where accessible natural resources are. Passive seismic imaging usually relies on blind correlations within extended recordings of Earth’s ceaseless “hum” or coda of well-mixed, small vibrations. In this article, we propose a complementary approach. It is seismic interferometry using opportune sources—specifically ones not stationary in time and moving in a well-understood configuration. Its interpretation relies on an accurate understanding of how these sources radiate seismic waves, precise timing, careful placement of pairs of listening stations, and seismic phase differentiation (surface and body waves). Massive freight trains were only recently recognized as such a persistent, powerful cultural (human activity-caused) seismic source. One train passage may generate a tremor with an energy output of a magnitude 1 earthquake and be detectable for up to 100 km from the track. We discuss the source mechanisms of train tremors and review the basic theory on sources. Finally, we present case studies of body- and surface-wave retrieval as an aid to mineral exploration in Canada and to monitoring of a southern California fault zone. We believe noise recovery from this new signal source, together with dense data acquisition technologies such as nodes or distributed acoustic sensing, will deeply transform our ability to monitor activity in the shallow crust at sharpened resolution in time and space.

Geochemistry & Geophysics↗

DOE OSTI2021

Investigation of the background in coherent J/ψ production at the EIC

Understanding various fundamental properties of nucleons and nuclei is among the most important scientific goals at the upcoming Electron-Ion Collider (EIC). With the unprecedented opportunity provided by the next-generation machine, the EIC might provide definitive answers to many standing puzzles and open questions in modern nuclear physics. We investigate one of the golden measurements proposed at the EIC, which is to obtain the spatial gluon density distribution within a lead (Pb) nucleus. The proposed experimental process is the exclusive J/ψ vector-meson production off the Pb nucleus: e + Pb → e' + J/ψ + Pb'. The Fourier transformation of the momentum transfer |t| distribution of the coherent diffraction is the transverse gluon spatial distribution. In order to measure it, the experiment has to overcome an overwhelmingly large background arising from the incoherent diffractive production, where the nucleus Pb' mostly breaks up into fragments of particles in the far-forward direction close to the hadron-going beam rapidity. We systematically study the rejection of incoherent J/ψ production by vetoing products from these nuclear breakups—protons, neutrons, and photons—which is based on the BeAGLE event generator and the most up-to-date EIC Far-forward Interaction Region design. The achieved vetoing efficiency, the ratio between the numbers of vetoed events and total incoherent events, ranges from about 80% to 99% depending on |t|. Assuming a 5% smearing applied to the reconstructed |t| resolution in the Sar t re model, this vetoing efficiency can suppress the incoherent background to at least the first minimum of the coherent |t| distribution. Experimental and accelerator machine challenges as well as potential improvements are discussed herein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

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↗

DOE OSTI2022

Charged particle motion in spherically symmetric distributions of magnetic monopoles

The classical equations of motion of a charged particle in a spherically symmetric distribution of magnetic monopoles can be transformed into a system of linear equations, thereby providing a type of integrability. In the case of a single monopole, the solution was given long ago by Poincaré. In the case of a uniform distribution of monopoles, the solution can be expressed in terms of parabolic cylinder functions (essentially the eigenfunctions of an inverted harmonic oscillator). Further, this solution is relevant to recent studies of nonassociative star products, symplectic lifts of twisted Poisson structures, and fluids and plasmas of electric and magnetic charges.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Topographic hydro-conditioning to resolve surface depression storage and ponding in a fully distributed hydrologic model

Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here, in this study, we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Molecular-Weight-Dependent Infiltration and Adsorption of Polymers into Nanochannels

The mesoporous silica shell coating hydrogenolysis nano-catalysts alters the molecular weight distributions of cleaved polymer chains compared to catalysts without a shell. The shell, composed of radially aligned narrow cylindrical nanopores, reduces the formation of low-valued gaseous products and increases the median molecular weight of the product, thus enhancing the value of the products for polymer upcycling. Here, to understand the role of the mesoporous shell, we have studied the spatial distribution of polystyrene chains, used as a model polymer, in the nanochannels in both the melt phase and solution phase. In the melt, we observed from small-angle X-ray scattering experiments that the infiltration rate of the polymer into the nanochannels is inversely proportional to the molecular weight, which is consistent with theory. In theta solution experiments using UV–vis spectroscopy, we found that the shell significantly enhances polymer adsorption compared to nanoparticles without pores. In addition, the degree of polymer adsorption is not a monotonic function of molecular weight but initially increases with the molecular weight before eventually decreasing. The molecular weight for the peak adsorption increases with the pore diameter. This adsorption behavior is rationalized as resulting from a balance between the mixing entropy gain by surface adsorption and the conformational entropy penalty incurred by chains confined in the nanochannels. The spatial distribution of polymer chains in the nanochannels is visualized by energy-dispersive X-ray spectroscopy (EDX), and inverse Abel-transformed data reveals a less uniform polymer distribution along the primary pore axis for longer chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Surface Merging Technique to Design GA-Optimized Heat Sinks

High-power density inverters require efficient and small heat dissipation systems. For liquid cooling systems, an effective heat sink design is important to enable higher heat transfer and keep the pressure drop within reasonable limits. A heat sink geometry generated from an extruded, 1D fast Fourier transform can achieve more even temperature distribution and improved heat transfer than a conventional heat sink. Building on this concept, this paper proposes a new heat sink geometry creation algorithm. Instead of one plane profile being extruded, multiple plane profiles, each created from the fast Fourier transform method, are subsequently merged by smooth surfaces along the flow direction of the heat sink. Thus, a 3D heat sink geometry is created that enables 3D coolant flow and efficient heat transfer. The volume of this design was reduced by 50% compared with pin fin heat sinks. Compared with extruded 1D fast Fourier transform heat sinks, the proposed design showed an approximately 5% device temperature reduction and a 20% pressure drop reduction.

Barua, Himel↗

DOE OSTI2026

Least-cost Optimal Distribution Grid Expansion (LODGE): Utility Pilots

The Least-cost Optimal Distribution Grid Expansion (LODGE) model provides the optimal portfolio of distribution system upgrades—e.g., voltage regulators, feeder reconductoring, transformer upgrades and non-wires alternatives (NWA), such as strategic siting of storage and distributed generation—to interconnect distributed energy resources (DERs) and enable load growth. It can be used to assess grid infrastructure costs and explore policy and regulatory solutions for distribution planning and DER valuation.In 2025, Berkeley Lab conducted three pilot analyses to validate LODGE results with empirical utility data before the model’s first release in 2026. The pilots, done with utilities in Washington, Colorado, and New Mexico, provide examples that illustrate how the model works, what it can do, and the value of the analysis.

Heleno, Miguel↗

DOE OSTI2021

Insulation Coordination Design for Grid-Connected Solid-State Transformers

The deployment of solid-state transformers (SSTs) in medium-voltage (MV) distribution systems is facing various challenges, especially the challenge of insulation coordination design against grid-originated lightning impulses. In this article, two challenges in existing insulation coordination designs for grid-connected SSTs are identified. One challenge is the mismatch between metal-oxide varistor (MOV) protective levels and SST insulation strength, the other challenge is the incompatibility of standard impulse test on SST protective structures. To address the MOV selection challenge, a novel lightning protection scheme is designed to protect a single-stage SST where the semiconductor modules are directly exposed to external lightning impulses. Here, the in-lab lightning impulse tests are performed to verify the overvoltage attenuation performance of the prototyped lightning protection scheme. To address the impulse test challenge, the surge withstand capability of the protected SST is comprehensively evaluated with a complete set of insulation coordination design procedures beyond the basic insulation level (BIL) test. After these two challenges are addressed, a discussion is presented on substituting conventional transformers with the protected SSTs into insulation-coordinated distribution systems to facilitate the field deployment of SSTs.

solid-state transformer (SST)↗

DOE OSTI2022

A Medium Voltage Three-Stage Power Converter Topology for Distribution Grid Scale Energy Storage Systems

This work proposes a three-stage converter topology with a medium frequency isolation transformer for direct integration of energy storage systems into medium voltage distribution grids. The distributed architecture of the topology, using standard AC/DC converters, has been developed with the aim of plug-and-play capability and voltage and power scalability. The medium frequency transformer facilitates improved power density, while its 20:1 voltage translation capability enables the use of limited voltage level (500 Vdc) of the commercially available batteries. This paper presents the topology structure and details how it can be scaled to reach the full range of the distribution grid voltages and the required power levels. A transformer design for 500 Hz rated frequency is discussed. The control scheme of the converter equipped with voltage support, frequency support, and black start capabilities is also presented. Control hardware-in-the-loop results using OPAL-RT, with converter switching models connected to a weak IEEE-4 node test feeder modified for the aforementioned grid services, are provided to show the topology feasibility.

Chinthavali, Madhu Sudhan↗