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At least 253 records · Page 14

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DOE OSTI2024

Voltage-induced modulation of interfacial ionic liquids measured using surface plasmon resonant grating nanostructures

We have used surface plasmon resonant metal gratings to induce and probe the dielectric response (i.e., electro-optic modulation) of ionic liquids (ILs) at electrode interfaces. Here, the cross-plane electric field at the electrode surface modulates the refractive index of the IL due to the Pockels effect. This is observed as a shift in the resonant angle of the grating (i.e., Δϕ), which can be related to the change in the local index of refraction of the electrolyte (i.e., Δnlocal). The reflection modulation of the IL is compared against a polar (D2O) and a non-polar solvent (benzene) to confirm the electro-optic origin of resonance shift. The electrostatic accumulation of ions from the IL induces local index changes to the gratings over the extent of electrical double layer (EDL) thickness. Finite difference time domain simulations are used to relate the observed shifts in the plasmon resonance and change in reflection to the change in the local index of refraction of the electrolyte and the thickness of the EDL. Simultaneously using the wavelength and intensity shift of the resonance enables us to determine both the effective thickness and Δn of the double layer. We believe that this technique can be used more broadly, allowing the dynamics associated with the potential-induced ordering and rearrangement of ionic species in electrode–solution interfaces.

Chemistry↗

DOE OSTI2022

A Closed-Form Mathematical Model and Method for Fast Fault Location on a Low Voltage DC Feeder Using Single-Ended Measurements

Not Available

Bin Gani, Munim↗

DOE OSTI2025

Lower Snake River Dams Contribution to Grid Services

Hydroelectric generation and water storage have long been components of the clean energy mix, providing both reliable steady output and operational flexibility. As variable renewable energy sources such as wind and solar increasingly replace traditional generation, the role of all flexible resources—including hydropower—in balancing supply and demand continues to evolve. This study examined the contribution of the Lower Snake River (LSR) Dam plants to Bonneville Power Administration grid services in maintaining power system reliability within the Western Interconnection. By analyzing publicly available data, the study evaluated various reliability services through performance metrics including energy capacity, balancing and ramping, voltage and reactive power support, frequency response, and transmission impact. Results indicated that the LSR plants deliver services as expected based on their size, contributing to the balancing process, ramping capabilities, and operational reserves, particularly during peak load conditions and weather events, while also providing measurable frequency and voltage support to the grid.

13 HYDRO ENERGY↗

DOE OSTI2025

Detecting isolated resonance curves using fixed frequency voltage control tests

Isolated resonance curves, or isolas, are resonance branches of the harmonically forced system that exist separately from the main nonlinear forced response curve, leading to excessive vibrations. Traditional stepped or swept sine simulations and tests rely on continuation along the frequency parameter, typically resulting in a jump phenomenon along the primary resonance branch, prior to the disconnected isola. The main objective of this research is to propose an approach to identify isolated resonance curves by performing continuation along the input amplitude that initializes the response from a low-amplitude solution in the linear regime. Furthermore, this is achieved with the open-loop fixed frequency voltage control method that continues along the shaker voltage parameter and measures the so-called S-curves, which are theoretically a continuous solution branch that connect to the isola. The methodology is demonstrated on a fixture-wing-pylon assembly with a vibro-impact nonlinearity localized in a pylon subcomponent attachment. Multi-harmonic balance simulations are deployed to compute both the nonlinear forced response curves and S-curves to demonstrate the isola detection strategy on a reduced-order finite element model of the nonlinear system. Swept sine and fixed frequency voltage control tests are then conducted on the physical structure to demonstrate the isola detection experimentally, revealing the existence of the large amplitude vibrations that are undetected in the forces levels and frequencies measured with traditional frequency sweeping.

Characterization and Analytical Technique↗

DOE OSTI2025

Low-Field Regime of Magnon Transport in PLD-Grown YIG Films

The diffusive propagation of magnons in the archetypal magnetic insulator yttrium iron garnet (YIG) is being actively explored for low-power and low-loss data communication. However, operation under external magnetic fields reduces the magnon diffusion length and attenuates the voltage amplitude at measurement terminals of magnonic devices. Here, we explore the low-field and field-free regime of diffusive magnon transport in YIG films, demonstrating that the field-induced suppression of magnon diffusion length can be fully inhibited only at the zero-field limit. Even a modest field of 10 mT attenuates the nonlocal spin voltage by ~20% in an ~1 μm long transport channel. We further identify the often overlooked in-plane uniaxial magnetic anisotropy as the dominant factor governing magnon transport in the low-field regime. Using Stoner-Wohlfarth macrospin simulations, we quantify the anisotropy parameters and reveal a 10-fold enhancement at low temperatures, a key finding for field-free operation of magnonic devices under cryogenic conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Structural evolution at the oxidative and reductive limits in the first electrochemical cycle of Li 1.2 Ni 0.13 Mn 0.54 Co 0.13 O 2

High-energy-density lithium-rich materials are of significant interest for advanced lithium-ion batteries, provided that several roadblocks, such as voltage fade and poor energy efficiency are removed. However, this remains challenging as their functioning mechanisms during first cycle are not fully understood. Here we enlarge the cycling potential window for Li 1.2 Ni 0.13 Mn 0.54 Co 0.13 O 2 electrode, identifying novel structural evolution mechanism involving a structurally-densified single-phase A’ formed under harsh oxidizing conditions throughout the crystallites and not only at the surface, in contrast to previous beliefs. We also recover a majority of first-cycle capacity loss by applying a constant-voltage step on discharge. Using highly reducing conditions we obtain additional capacity via a new low-potential P” phase, which is involved into triggering oxygen redox on charge. Altogether, these results provide deeper insights into the structural-composition evolution of Li 1.2 Ni 0.13 Mn 0.54 Co 0.13 O 2 and will help to find measures to cure voltage fade and improve energy efficiency in this class of material.

25 ENERGY STORAGE↗

DOE OSTI2025

H 2 O absorption spectroscopy via focused laser differential interferometry

A modification of focused laser differential interferometry (FLDI) is demonstrated with an infrared tunable diode laser (TDL) to achieve simultaneous absorption spectroscopy (AS) measurements. Measurements from this absorbing-FLDI (A-FLDI) are shown for a Hencken burner plume. Initial comparison measurements are recorded using TDLAS and an electrical hygrometer. Raw voltage and estimated absorbance measurements illustrate that the technique detects five distinct absorbance peaks of the methane–air flame while retaining the expected behavior of typical FLDI. This modification furthers efforts to enable FLDI to conduct analysis of the pressures, temperatures, and molecular densities of flows. It also explores the potential for reducing path-integration (PI) effects in absorption spectroscopy and potentially enabling spatially and temporally resolved local flow measurements. Reductions in PI length as high as 82%–84% are observed.

Holladay, Seth (ORCID:0009000650875421)↗

DOE OSTI2024

An octahedral Mach B-dot probe for 3D flows and magnetic fields in the edge of reversed field pinches

Measurements and simulations show that plasma relaxation processes in the reversed field pinch drive and redistribute both magnetic flux and momentum. To examine this relaxation process, a new 3D Mach B-dot probe has been constructed. This probe collects ion saturation currents through six molybdenum electrodes arranged on the flattened vertices of an octahedron made of boron nitride (BN). The ion saturation current flows through configurable voltage dividers for measurement and returns through one of six selectable return electrodes equally spaced along the 12 cm BN probe arm. In addition, the probe arm houses three B-dot magnetic pickup coils in the BN stalk immediately below to the octahedron, to measure the local magnetic field. Inserted in the Madison Symmetric Torus (MST) during deuterium discharges with 220 kA plasma current, density of 0.8 × 10 13 cm –3 , the probe collects ion saturation currents with sawtooth-like peaks correlated with relaxation events. This compact octahedral design fitting six Mach electrode surfaces within a 1 cm3 cube will enable future multi-point, multi-field probes compatible with the 1.5 in. ports of MST. Such probes will allow for flow circulation, current, and canonical vorticity to be calculated in the center of the finite difference stencil formed by the measurement locations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis

To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.

42 ENGINEERING↗

DOE OSTI2021

Design concept of co-ionic conducting solid oxide electrolyte for stable operation in a cell-imbalanced fuel cell stack

A bilayer composite electrolyte comprising BaCe 0.85 Y 0.15 O 3-d (BCY15) – Gd 0.2 Ce 0.8 O 2-d (GDC20) at the anode side and BaZr 0.85 Y 0.15 O 3-d (BZY15) – Nd 0.1 Ce 0.9 O 2-d (NDC10) at the cathode side is designed for improving the stability of co-ionic conducting solid oxide fuel cells (SOFCs) in a cell imbalanced stack. Contrary to the single layer structured SOFCs, the bi-layer cell stably operates without electrode delamination under negative voltage conditions. We measure local internal p O 2 values using embedded Pt probes. Based on these values, local electronic conduction is estimated in combination with four probe DC conductivity measurements. It is found that n-type conductivity (~10 –3 Scm –1 ) and p-type conductivity (10 –5 –10 –4 Scm –1 ) are developed in the BCY15-GDC20 near the anode side and in the BZY15-NDC10 near the cathode side, respectively. Finally, the results indicate that local electronic conduction in electrolyte regions near both the anode and cathode interfaces is a crucial factor for the durability of co-ionic SOFCs under negative voltage operation. We therefore suggest the bi-layer configuration as a practical solution to protect co-ionic SOFCs in a cell-imbalanced stack.

25 ENERGY STORAGE↗

DOE OSTI2022

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Retrofitting stoves with forced jets of primary air improves speed, emissions, and efficiency: Evidence from six types of biomass cookstoves

Incorporating jets of forced air into biomass cookstove combustion has been shown to potentially decrease harmful emissions, leading to a variety of designs in recent years. These have shown mixed success in terms of real-world performance, usability, and durability. The Jet-Flame forced draft retrofit accessory was recently developed to implement forced jets of primary air at a low cost into a wide range of types of cookstoves using a small 1.5-W fan housed in a low-cost cast iron body to be inserted beneath the fuel bed of a biomass cooking fire. This research sought to quantify the potential efficiency and emissions performance impacts of the Jet-Flame when installed in six different types of biomass cookstoves (three open or shielded fires and three rocket stoves) versus the natural draft performance of each. The effect of the operating fan voltage was also measured. A series of tests following a modified ISO 19867-1:2018 protocol were performed in the laboratory using the Aprovecho Laboratory Emissions Measurement System (LEMS) equipped with additional oxygen and temperature sensors. Results for each stove carefully tended with a single layer of sticks showed that the global average PM 2.5 reduction with the Jet-Flame was 89 % relative to the natural draft cases, with larger relative improvements seen in the most rudimentary stoves. CO was reduced by a global average of 74 %, reaching tier 4 or 5 for all stoves. Thermal efficiency was also improved by 34 % when calculated without taking into account the energy content of the remaining char (or 21 % with char), illustrating the value of burning char to provide cooking energy rather than leaving it unburned in the combustion chamber as is common in many natural draft stoves. Time to boil was also reduced by 8 %. In addition, adjusting the voltage of the jet-flame assisted in modulating firepower, improving the usability of the stove. These results indicate a strong potential for the Jet-Flame to help reduce emissions and fuel consumption in a wide range of cookstove designs at a relatively low cost or need for changes in behavior, fuel, or cooking device. The unit also provides enhanced usability in terms of ease of startup, cooking speed, low PM emissions at high power, and burning char. Additional studies are needed to measure performance in the field and under a variety of operational conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

A study on sub-bandgap photoexcitation in nitrogen- and boron-doped diamond with interdigitated device structure

Diamond is an ultrawide-bandgap semiconductor suitable for high power devices that require high current carrying capacity, high blocking voltages, and smaller form factors. We investigated various diamond structures for extrinsic photoconductive semiconductor switches, including an insulating high-pressure high-temperature type Ib (highly nitrogen-doped) substrate, a chemical vapor deposited (CVD) type IIa (unintentionally doped) substrate, a CVD grown semiconducting boron-doped epilayer on a type IIa substrate, and boron-implanted type Ib and IIa substrates. Using these samples, we fabricated and characterized planar interdigitated photoconductive switches with 30 μm, electrode gaps. 532 and 1064 nm Nd:YAG laser pulses with energies up to 3.5 mJ/pulse were used to trigger the switches. Photoresponses were measured at bias voltages ranging from 10 to 100 V, corresponding to electric fields of 3.3–33 kV/cm. In this field range, the type Ib device exhibited the highest average on/off-state current ratio, on the order of 10 11 , when triggered with 0.8 mJ/pulse, 532 nm laser pulses. However, only the CVD grown boron-doped epilayer and boron implanted IIa devices showed decent sensitivity to 1064 nm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

DOE OSTI2022

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike standard RL method, the safe RL method integrates a Barrier function into the reward function. Consequently, the optimal control policy can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Vu, Thanh Long↗