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DOE OSTI2026

Electrode Erosion and Prefire Studies Towards Fusion Scale Pulsed Power

This study presents a comprehensive investigation of electrode erosion and discharge behavior in spark gap switches over long switching cycle lifetimes. Brass, copper–tungsten (CuW), and stainless steel electrodes are tested under controlled conditions to quantify material degradation, debris accumulation, and changes in breakdown voltage. High-resolution imaging and statistical analysis of spark channel locations and gap breakdown voltages reveal how surface evolution influences long-term performance and reliability. These results provide essential data for lifetime modeling and inform design strategies for pulsed power systems in emerging applications such as private sector fusion energy and large-scale facilities like Sandia’s Z Machine and proposed ZX upgrades, where high repetition reliability and predictable behavior are critical.

electrical breakdown↗

DOE OSTI2022

Thermodynamic Model and Initial Experimental Investigation of Air Dehumidification through Electrically Charged Vapor Capturing Electrostatic Droplets

The removal of water vapor from the air to reduce relative humidity is a well known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation with the goal to reduce the energy consumption associated with dehumidification. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. In this thesis, a broad theoretical investigation to study the electrostatically enhanced condensation processes was carried out. These processes consist of the use of highly charged particles, preferably highly charged water droplets to attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. The electrical charge promotes the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation on curved surfaces and, consequently, the equilibrium between evaporation and condensation is shifted towards condensation. This investigation resulted in the development of a thermodynamics model, which was able to predict an effective size range of the charged droplets for optimal dehumidification, under ideal conditions. The range resulted in about 2 to 4 m in diameter, while the electrical charge was kept to the maximum limit predicted by the Rayleigh model. A sensitivity analysis on the variation of the size and the charge of the charged droplets was also performed. In terms of dehumidification rates, when six electrospray heads, i.e. the maximum number of heads tested in the experimental campaign, were considered, the model predicted very limited rates, way lower than the target of this research, i.e. a dehumidification rate in terms of relative humidity of 5 % with an air flow rate of 5 cfm. While ways to increase this rate existed, their implementation proved difficult for this initial investigation. According to the requirements predicted by the model, the use of electrosprays appeared the most suitable solution for the production of small but highly charged droplets. The electrospray features and operational modes were studied in detail and an electrospray assembly was designed to be tested in this initial experimental investigation. For this initial investigation, an experimental system was designed and built to validate the thermodynamics model and with the goal to achieve a dehumidification rate of 5 % in terms of relative humidity with an air flow rate of 5 cfm. The experimental system consisted of a wind tunnel test apparatus, dew point sensors, thermocouples for temperature measurements, pressure sensors and other devices. The experimental system was equipped with a computerized data acquisition and storage system. The system was suitable to evaluate the air water content differential before and after the test section where the electrospray heads were installed. The air water content differential was evaluated in terms of the ∆ω/ω_1 ratio, to eliminate the dependence on the dry bulb temperature along the test apparatus. This work presented initial experimental data for electrostatically enhanced dehumidification processes. Working parameters, such as air and water flow rates, high voltage potentials and polarity, deionized water types with different electrical conductivity, were varied to find the best combination for an improved dehumidification. With the set up used and the conditions considered, it was never possible to achieve a 5 % dehumidification rate with 5 cfm. In general, the dehumidification rate was always limited and lower than 1 % for all the air flow rates considered and mainly within the uncertainty of the dew point sensors. These results confirmed the already limited predictions of the thermodynamics model, for ideal conditions and the interesting and promising results obtained in some experimental investigations available in the open literature were not achievable in this thesis. The more probable reasons were the higher air flow rates considered and the complete absence of the use of cooling power, which was mainly implemented to facilitate the vapor condensation.

Morcelli, Stefano↗

DOE OSTI2024

Strain-Driven Surface Reactivity in Magnesium-Ion Battery Cathodes

Successful deployment of a Mg-ion battery requires cathodes that can achieve reversible Mg intercalation and high energy density. Recent theoretical and experimental studies indicated that the overall transport is likely limited by sluggish Mg transport at the cathode-electrolyte interface and not Mg diffusion through bulk. Here, in this work, we investigated the surface electrochemical activity of Mg ions by using a spinel-structured manganese oxide thin-film model system and in situ X-ray scattering. In combination with post-mortem microscopy analysis, we found that magnesium insertion was more favorable than subsequent extraction near the surface of the Mg x Mn 2 O 4 film, resulting in overmagnesiation, and eventually amorphization of the surface. This structural irreversibility and high overpotential required for Mg extraction could explain significant voltage hysteresis and Mg surface enrichment previously observed in bulk cathodes. Density functional theory calculations suggested that the tendency for the Mg surface enrichment could be associated with Mg diffusion kinetics, which varies with the strain state evolved due to constrained film volume change during Mg insertion and extraction. Particularly, out-of-plane Mg migration was predicted to be favorable in the tensile strain rather than in the compressive case.

25 ENERGY STORAGE↗

DOE OSTI2023

A Versatile Fiber Coating Process for Efficient Fabrication of Multifunctional Composites

The objective of this research is to demonstrate the versatility of a dip coating process for the efficient integration of piezoelectric barium titanate (BaTiO3) microparticles on a wide variety of fibers to design passive self-sensing composites. The microparticles were deposited on glass, aramid, and basalt fiber weaves through the proposed dip coating technique. A computational framework is established to predict the deposition thickness on the fiber surfaces from the given microparticle concentration, size, coating velocity, and coating fluid viscosity. The deposition quality assessment was performed through scanning electron microscope imaging and subsequent image analysis. BaTiO3-coated fibers were directly used in composite preparation. After fabrication, the BaTiO3-enhanced composites were subjected to high-voltage poling. Finally, their passive self-sensing properties were characterized through experimental studies. The results show the adaptability of the proposed coating process to integrate BaTiO3 microparticles within different types of fiber-reinforced composites enabling passive self-sensing to attain subsurface damage characterization.

Gupta, Sumit↗

DOE OSTI2022

Multi-Fidelity Learning for Distribution System Voltage Probabilistic Analysis with High Penetration of PVs

This paper proposes a multi-fidelity learning approach for distribution voltage probabilistic analysis with high penetration of PVs. Unlike the existing machine learning-based approaches that require a large number of high fidelity data to achieve satisfactory results, our approach strategically leverage massive low fidelity data from inaccurate model simulations and limited high fidelity historical data. The key idea is to use low-fidelity data to establish an initial model and then the high-fidelity data to calibrate and correct the constructed low-fidelity model. This allows us to fuse low- and high-fidelity data, yielding a high fidelity prediction model. Results obtained from a realistic feeder in US with 80% penetration of PVs show that the proposed approach can achieve a similar accuracy to the one with a large number of high fidelity data. This significantly highlights the advantages of the proposed method as compared to existing data-hungry machine learning methods. Different levels of fidelity data and their impacts are also investigated.

distribution system↗

DOE OSTI2025

Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys

Aqueous corrosion of metals is governed by formation and dissolution of a passivating, multi-component surface oxide. Unfortunately, a detailed atomistic description is challenging due to the compositional complexity and the need to consider multiple kinetic factors simultaneously. To this end, we combine experiments with a first-principles-derived, multiscale computational framework that transcends thermodynamic descriptions to explicitly simulate the kinetic evolution of surface oxides of Ni-Cr alloys as a function of composition, temperature, pH, and applied voltage. In the absence of pitting, we identify three distinct voltage regimes, which are kinetically dominated by oxide growth, dissolution, and competitive dissolution and reprecipitation. Evolving compositional gradients and oxide thickness are revealed, including a transition between a metastable Ni-Cr mixed oxide and a thick, porous Ni-dominated oxide. Beyond elucidating the underlying physics, we highlight the need for competing kinetics in models to properly predict the transition from passivation to corrosion. Our results provide a key step towards co-design of alloy composition alongside environmental conditions for sustainable use across a variety of critical energy and infrastructure applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

High-speed imaging of transition from fluid breakup to phase explosion in electric explosion of tungsten wires in air

High-speed visible imaging of sub-microsecond electric explosion of wires at the low specific energy deposition threshold reveals three distinct modes of wire failure as capacitor charge voltage and energy deposition are increased. For 100 micron diameter gold-plated tungsten wires of 2 cm length, deposited energies of 1.9 eV/atom produces a liquid column that undergoes hydrodynamic breakup into droplets with radii of order of wire diameter on timescales of 200 μs. Instability growth, column breakup, and droplet coalescence follow classic Rayleigh-Plateau predictions for instability of viscous fluid column. Above 3.2 eV/atom of deposited energy, wires are seen to abruptly transition to an expanding mixture of micron scale liquid-droplets and vapor within one frame (less than 3.33 μs), which has been termed ‘phase explosion’ in literature. Between these two limits, at 2.5 eV/atom of deposited energy, wire radius is unchanged for the first 10 μs before the onset of a rapid expansion and disintegration that resembles homogenous nucleation of mechanically unstable bubbles. Here, thermodynamic calculations are presented that separate cases by temperature obtained during heating: below boiling point, near boiling point, and exceeding boiling point.

42 ENGINEERING↗

DOE OSTI2020

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage–power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50 % reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50–250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75 % of N-2 contingency network structures, even without historical N-2 outage data. Furthermore, the proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Quantitative Prediction of Perovskite Stability Using Accelerated Testing and Machine Learning

PV technologies based on hybrid perovskites offer the potential for reducing solar cell costs, but they are particularly vulnerable to degradation by environmental factors such as moisture, oxygen, and illumination. Commercialization will require not only stable materials and device architectures but also accelerated testing protocols and models that can predict degradation from the accelerated testing data. Here, we report results from in situ photoluminescence (PL) and photoconductivity (PC) measurements during perovskite degradation with simultaneous optical transmittance (Tr) measurements or reflected dark field (DF) imaging. From PL, PC, Tr, and DF, we determine (respectively) the steady-state quasi-Fermi level splitting, the mean effective carrier diffusion length, the extent of conversion of perovskite to higher bandgap degradation products, and the extent of scattering from domains with different orientation or composition, all as a function of time during degradation. Simultaneous measurement of PL-PC-Tr or PL-PC-DF on perovskite absorbers in an environmental chamber over a wide range of humidity, oxygen, temperature, and illumination levels yields a rich data set. We use machine learning to develop a model that accurately predicts (within 10%) the time for the diffusion length to decrease to 85% of its initial value. The model takes the environmental conditions and the first few measurements from the PL-PC-Tr or PL-PC-DF experiment as input. Thus, the model provides a framework to interpret the results of accelerated testing of absorber materials. One of the dominant features in the model of degradation for CH 3 NH 3 PbI 3 is the initial rate at which transmittance increase. For devices with opaque contacts, the pixel-averaged rate of change of the intensity in dark-field images can be used in place of transmittance. Results are also presented on full PV devices with in-situ current-voltage (JV) measurements. Here, the data from simultaneous PL-DF-JV under environmental stresses reveal intimate connection between degradation and shunts and provide a framework to extend accelerated testing to devices. Further, the presentation reveals underlying universal behavior in degradation pathways over a broad range of environmental stresses and perovskite compositions.

14 SOLAR ENERGY↗

DOE OSTI2025

A high-temperature heat flux sensor using the transverse Seebeck effect in elemental rhenium

Heat flux sensors compatible with hot environments are critical to advance aerospace, materials, and energy generation technologies that cope with extreme thermal conditions. In this work, we report on the development and characterization of a high-temperature heat flux sensor using the transverse Seebeck effect in rhenium single crystals. The sensor leverages refractory alloys and ceramics compatible with temperatures exceeding 1000 °C. The heat flux sensor was characterized from room temperature to 500 °C using a temperature-controlled calibration facility. At constant temperature, the sensor’s voltage output is linear with respect to the absorbed heat flux. The responsivity of the sensor varies with temperature, from 1.3 μV/(W/cm 2 ) at room temperature to −3.2 μV/(W/cm 2 ) at 500 °C, increasing monotonically in magnitude after changing sign from positive to negative at approximately 300 °C. The experimental results are in good agreement with analytical predictions of the sensor’s temperature-dependent responsivity, which suggest a further increase in magnitude up to −7.4 μV/(W/cm 2 ) at 1000 °C. These results highlight the unique characteristics of rhenium as a TSE transducer. The design offers compatibility with a wide range of operating temperatures and yields a measurement sensitivity that increases as the environmental conditions become more challenging.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

Multiscale Electrothermal Design of a Modular Multilevel Converter for Grid-Tied Applications

As a key feature of modular multilevel converters (MMCs), a large number of semiconductor devices are employed in the converter and distributed over a stack of submodules. Each submodule has a strict temperature limit that imposes constraints on the operating range of the converter. Different loading conditions/losses in the submodules lead to unavoidable temperature variations inside the MMC, which consequently affect the system-level performance and reliability. This paper is focused on the electrothermal analysis and design of a medium-voltage silicon carbide (SiC)-based MMC system, from submodule power semiconductors to the overall MMC system integration of multiple submodules. Loss calculations are performed to estimate the cooling requirements and aid thermal design at different levels. The performance of forced-air cooling approach is analyzed within a numerical modeling framework. Maximum temperature of the SiC power modules is predicted using numerical tools. Thermal design of the MMC cabinet with various arrangement of air inlet(s) and outlet(s) is investigated and compared from a cooling performance perspective.

CFD↗

DOE OSTI2021

Effect of microphase separation on the limiting current density in hybrid organic-inorganic copolymer electrolytes

Hybrid organic-inorganic block copolymer electrolytes are of interest to enable batteries containing lithium metal anodes. The conductive block is a standard polymer electrolyte of poly(ethylene oxide) and the mechanically rigid block is an inorganic poly(acryloisobutyl polyhedral oligomeric silsesquioxane) polymer. Here, in this paper, we compare a poly(acryloisobutyl polyhedral oligomeric silsesquioxane)-b-poly(ethylene oxide)-b-poly(acryloisobutyl polyhedral oligomeric silsesquioxane) (POSS-PEO-POSS) triblock copolymer and a poly(ethylene oxide)-b- poly(acryloisobutyl polyhedral oligomeric silsesquioxane) (PEO-POSS) diblock copolymer mixed with lithium bis(trifluoromethanesulfonyl)imide salt. We have experimentally measured the limiting current density in lithium symmetric cells containing hybrid organic-inorganic electrolytes at 90 °C. The cells were polarized at a large range of applied current density. The diblock copolymer electrolyte exhibited a clear plateau in cell potential at all current densities below the limiting current density. At low applied current density, the triblock copolymer electrolyte also exhibited a clear plateau in cell potential. At currents approaching the limiting current density, the triblock copolymer electrolyte exhibited an underdamped potential profile. The cell potential did not reach a plateau at current densities above the limiting current in both systems. The diblock and triblock copolymer electrolytes were fully characterized using electrochemical methods to determine the ionic conductivity, cation current fraction, salt diffusion coefficient, and open circuit voltage as a function of salt concentration. Cell potential and salt concentration as functions of position in the cell at various current densities were calculated using Newman's concentrated solution theory. The theoretical limiting current density was calculated to be the current density at which salt is depleted at the cathode. We see quantitative agreement between experimental measurements and theoretical predictions for the limiting current density in the diblock copolymer electrolyte which has an ordered structure at all salt concentrations, while the experimental limiting current density is lower than the theoretical prediction for the triblock copolymer electrolyte, which exhibits a disordered morphology at high salt concentrations.

25 ENERGY STORAGE↗

DOE OSTI2023

Insulator-to-metal phase transition in a few-layered MoSe 2 field effect transistor

The metal-to-insulator phase transition (MIT) in low-dimensional materials and particularly two-dimensional layered semiconductors is exciting to explore due to the fact that it challenges the prediction that a two-dimensional system must be insulating at low temperatures. Thus, the exploration of MITs in 2D layered semiconductors expands the understanding of the underlying physics. Here we report the MIT of a few-layered MoSe 2 field effect transistor under a gate bias (electric field) applied perpendicular to the MoSe 2 layers. With low applied gate voltage, the conductivity as a function of temperature from 150 K to 4 K shows typical semiconducting to insulating character. Above a critical applied gate voltage, V c , the conductivity becomes metallic (i.e., the conductivity increases continuously as a function of decreasing temperature). Evidence of a metallic state was observed using an applied gate voltage or, equivalently, increasing the density of charge carriers within the 2D channel. We analyzed the nature of the phase transition using percolation theory, where conductivity scales with the density of charge carriers as σ ∝ (n - n c ) δ . The critical exponent for a percolative phase transition, δ(T), has values ranging from 1.34 (at T = 150 K) to 2 (T = 20 K), which is close to the theoretical value of 1.33 for percolation to occur. Thus we conclude that the MIT in few-layered MoSe 2 is driven by charge carrier percolation. Finally, the conductivity does not scale with temperature, which is a hallmark of a quantum critical phase transition.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Intelliquench: An Adaptive Machine Learning System for Detection of Superconducting Magnet Quenches

In superconducting magnets, the irreversible transition of a portion of the conductor to resistive state is called a “quench.” Having large stored energy, magnets can be damaged by quenches due to localized heating, high voltage, or large force transients. Unfortunately, current quench protection systems can only detect a quench after it happens, and mitigating risks in Low Temperature Superconducting (LTS) accelerator magnets often requires fast response (down to ms). Additionally, protection of High Temperature Superconducting (HTS) magnets is still suffering from prohibitively slow quench detection. In this study, we lay the groundwork for a quench prediction system using an auto-encoder fully-connected deep neural network. After dynamically trained with data features extracted from acoustic sensors around the magnet, the system detects anomalous events seconds before the quench in most of our data. While the exact nature of the events is under investigation, we show that the system can “forecast” a quench before it happens under magnet training conditions through a randomized experiment. This opens up the way of integrated data processing, potentially leading to faster and better diagnostics and detection of magnet quenches

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Product ion distributions using H 3 O + proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS): mechanisms, transmission effects, and instrument-to-instrument variability

Abstract. Proton-transfer-reaction mass spectrometry (PTR-MS) using hydronium ion (H3O+) ionization is widely used for the measurement of volatile organic compounds (VOCs) both indoors and outdoors. H3O+ ionization, as well as the associated chemistry in an ion–molecule reactor, is known to generate product ion distributions (PIDs) that include other product ions besides the proton-transfer product. We present a method, using gas-chromatography pre-separation, for quantifying PIDs from PTR-MS measurements of nearly 100 VOCs of different functional types including alcohols, ketones, aldehydes, acids, aromatics, organohalides, and alkenes. We characterize instrument configuration effects on PIDs and find that reactor reduced electric field strength (E/N), ion optic voltage gradients, and quadrupole settings have the strongest impact on measured PIDs. Through an interlaboratory comparison of PIDs measured from calibration cylinders, we characterized the variability of PID production from the same model of PTR-MS across seven participating laboratories. Product ion variability was generally smaller (e.g., < 20 %) for ions with larger contributions to the PIDs (e.g., > 0.30) but less predictable for product ions formed through O2+ and NO+ reactions. We present a publicly available library of H3O+ PTR-MS PIDs that will be updated periodically with user-provided data for the continued investigation into instrument-to-instrument variability of PIDs.

Link, Michael F. (ORCID:0000000218412455)↗

DOE OSTI2025

Synchronous Machine Governor Upgrade

Conventional generation sources play a critical role in the stability and reliability of the electrical grid, particularly as we transition towards more renewable energy sources. To understand and accurately emulate their behavior for optimizing grid operations and ensuring seamless integration with renewable technologies, it is essential to better emulate the grid- and plant-level impacts of conventional generation sources, such as natural gas (NG) driven heat recovery steam generators (HRSGs) and combustion turbines (CTs). Therefore, a governor model is developed in a programmable logic controller (PLC) to investigate the performance of the conventional generator under various dynamic operating conditions and to identify the impact on grid stability in a controlled environment. The governor model aims to enable the hardware-in-the-loop (HIL) based emulation of these conventional generation sources using the existing 2 MVA synchronous machine/generator that is driven by a flexible 2.5 MW variable speed drive. This setup will allow us to replicate the dynamic characteristics and response behaviors of NG-driven HRSGs and CTs. The controls for the emulated conventional plants follow the industry standard and are adjustable, ensuring they accurately reflect the operational capabilities and limitations of real-world systems. These controls include load-following capabilities, ramp rates, startup and shutdown sequences, and emissions characteristics. By incorporating these adjustable controls, we aim to capture the nuanced impacts of conventional generation, such as their ability to provide ancillary services like frequency regulation, voltage support, and spinning reserve. In this report, we simulate two types of dynamic operations: grid-connected and islanding. For each dynamic operation, representative starting sequences are tested, including turbine purge, ignition, speed ramping up, generator excitation and synchronizing, and breaker close. The HIL based tests provides insights for field deployment, specifically the high-fidelity governor model provides results to predict the potential stability and reliability risk and suggest possible integration measures (e.g., generation and load balancing, tuning of governor control parameters). Ultimately, this enhanced emulation capability will be integrated into our Advanced Research on Integrated Energy Systems (ARIES), enabling us to conduct comprehensive studies on the interactions between conventional and renewable energy sources. By better understanding these interactions, we can develop strategies to optimize the overall performance and reliability of the grid. This will support the deployment of advanced grid management techniques, such as demand response, grid-forming inverters, and energy storage systems. The main contributions are summarized as follows: (1) This report introduces a PLC-based governor model for gas turbines. This model accurately simulates the dynamic behavior of conventional generation sources under various operational scenarios; (2) The model is integrated with an HIL testbed that includes a 2.5 MW variable speed drive and a 2 MVA synchronous machine. This setup enables realistic, real-time emulation of conventional power plants, particularly NG driven HRSGs and CTs; (3) The developed model is adaptable to various gas turbine configurations and allows for precise control over parameters such as MW ramp rates. This flexibility makes it a valuable tool for future research and industry collaboration; and (4) By incorporating the model into the National Renewable Energy Laboratory's Advanced Research on Integrated Energy Systems, the report lays the groundwork for future studies on interactions between conventional and renewable energy sources, enhancing the ability to develop advanced grid management strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗