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DOE OSTI2017

Beyond Californium—A Neutron Generator Alternative for Dosimetry and Instrument Calibration in the U.S.

Many evaluations of neutron survey instruments, area monitors and personal dosimeters rely on neutron reference radiations from 252Cf, a source of spontaneous fission neutrons that produces high dose rates from an almost point source geometry. Its characteristics are generally more favorable when compared to (a,n), photoneutron and reactor-based sources of neutrons. The source is typically used in two standardized configurations: unmoderated, to yield a fission energy spectrum, or placed within a heavy-water moderating sphere to produce a softened spectrum that is more appropriate for evaluating devices used in nuclear power plant work environments. The U.S. Department of Energy (DOE) 252Cf Loan/Lease Program was terminated in 2009, and the replacement of high-activity sources is increasingly cost prohibitive for laboratories that formerly benefited from that program, including the Pacific Northwest National Laboratory (PNNL) and National Institute of Standards and Technology (NIST). Neutron generators, based on the D-T and D-D fusion reactions, are becoming economically competitive with 252Cf and are recognized internationally as important calibration and test standards. Researchers from NIST and PNNLare jointly considering the practicality and technical challenges of implementing neutron generators as calibration standards in the U.S. This article reviews characteristics of isotope-based sources, leading to rationale behind increasing favor of electronically-generated neutron options. The evaluation of a D-T system at PNNL has revealed primary challenges in adapting generators to the task of calibration and testing in which neutron characteristics must be accurately determined. Concepts for modifying the generated neutron spectra to achieve particular targeted spectra, simulating 252Cf or workplace environments, are discussed.

Calibration, californium-252, Dosimeter Irradiatio↗

DOE OSTI2021

Providing Ancillary Services with Photovoltaic Generation in Multi- Timescale Grid Operation

With penetration levels of photovoltaic (PV) generation substantially increasing, electric power systems need more flexible resources that can provide ancillary services to mitigate the variability and uncertainty of the PV. On one hand, the increase in PV generation necessitates more flexible resources; on the other hand, because of its low operation cost, PV generation has been replacing conventional generation, which is currently the main flexible resource. Consequently, there is a trend to require renewable generation, including PV, to provide flexible ancillary services. This paper proposes a multi-timescale grid operation model considering the various control strategies of PV providing different ancillary services. Numerical case studies demonstrate that with PV providing both regulation reserve and primary frequency reserve, the system operating costs and PV curtailment will be reduced significantly. Results show that not only the system reliability can be improved but also PV profitability with PV providing more ancillary services.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid

A dynamic neural network (NN) based multi-class classifier is proposed for improving online prediction of coherent generator groups (CGGs), following the occurrences of various contingencies in the power grid. This is motivated by the increasing availability of the measurements from phasor measurement units (PMUs) and the number of grouping schemes is limited. The proposed method consists of three steps. First, by performing offline simulations, a library of system dynamic responses characterized by post-contingency rotor angles and speeds of individual generators is obtained. To generate sufficient data, up to N-2 contingencies and the uncertain parameters associated with the power grid including type and location of disturbance and fault clearing times are modeled. Secondly, the training data-set is produced by generating labels for individual contingencies using a hierarchical clustering method based on rotor angle and speed data. Lastly, the dynamic NN models are trained for online applications such as emergency controls and controlled islanding. The proposed method is tested on the standard 16-generator 68-bus system to demonstrate its performance. Furthermore, the impact of the sample data lengths on the CGG numbers is evaluated. It is interesting to observe that the time domain stability behaviors can be determined by examining the changes in the CGG numbers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Stochastic Generation Capacity Expansion Planning with Approximate Dynamic Programming

Failing to consider the many long-term uncertainties that affect the performance of a power generation portfolio can result in suboptimal generation expansion plans. Further, traditional deterministic approaches can omit flexible plans that are able to adapt to future events. In this paper, we explore the use of approximate dynamic programming (ADP) to create forward-looking generation expansion plans. A case study is included with three sequential decision periods; three generation technologies; and four uncertainties: demand growth, natural gas prices, renewable portfolio standards, and the adoption of customer-driven solar generation. The flexible plans found through ADP show a 3 % reduction in total expected cost when compared to myopic planning heuristics while circumventing the computational burdens that accompany high-dimensional dynamic programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

An Investigation into High Voltage Spiral Generators Utilizing Thyristor Input Switches

High voltage nanosecond pulses are widely used in scientific research, but their wider adoption in industry requires compact, cost effective and easy to use generators to be developed. This paper presents modelling and experimental investigations into one method of producing such pulses – a spiral generator with a solid-state, thyristor-based input switch. It includes how the pulses are formed within the spiral, why a high speed input switch is required, how the geometry of the spiral dictates its output characteristics, and the effects of different loads. Using thyristors, often connected in series to increase the operating voltage of the spiral, enables the spiral generators to have low jitter, high repetition rate, and long lifetime. Modelling of the circuit used a combination of telegraph equations to account for the wave propagation along the spiral and a lumped circuit exchanging charge between the spiral, the input switch, and the load. The model is verified by detailed experimental results with the relative error being < 10% in most cases. Here, the output voltage pulse was often observed to have an initial peak of much lower magnitude than the subsequent peak(s) – which can only be fully explained by considering wave propagation effects. Lower input switch inductance, shorter switching time, larger mean diameter of the spiral, and increasing the width of the copper tape that makes up the spiral can all increase the voltage multiplication efficiency. Though increasing the number of turns that make up the spiral can increase the output voltage, it can also lower the multiplication efficiency. By understanding the effects of different geometries the spiral can be optimized to drive different loads - three applications of such spiral generators are then presented: pulses with 10 kV amplitude and 10 kHz repetition rate for driving DBD plasma, pulses with amplitude of 10 kV and 10 kV/ns rising rate for triggering of advanced solid state switches, and pulses with -50 kV amplitude and 50 ns rising time for triggering high current gas switches through field distortion.1 Index Terms—spiral generator, nanosecond pulses, telegraph equations, series-connected thyristor module, pulse forming process

42 ENGINEERING↗

DOE OSTI2022

Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration

Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. Here, we designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

electricity distribution market↗

DOE OSTI2020

FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Furthermore, our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.

42 ENGINEERING↗

DOE OSTI2025

Concept Lens: Visual Comparison and Evaluation of Generative Model Manipulations

Generative models are becoming a transformative technology for the creation and editing of images. However, it remains challenging to harness these models for precise image manipulation. These challenges often manifest as inconsistency in the editing process, where both the type and amount of semantic change, depend on the image being manipulated. Moreover, there exist many methods for computing image manipulations, whose development is hindered by the matter of inconsistency. This paper aims to address these challenges by improving how we evaluate, compare, and explore the space of manipulations offered by a generative model. We present Concept Lens, a visual interface that is designed to aid users in understanding semantic concepts carried in image manipulations, and how these manipulations vary over generated images. Given the large space of possible images produced by a generative model, Concept Lens is designed to support the exploration of both generated images, and their manipulations, at multiple levels of detail. To this end, the layout of Concept Lens is informed by two hierarchies: a hierarchical organization of (1) original images, grouped by their similarities, and (2) image manipulations, where manipulations that induce similar changes are grouped together. This layout allows one to discover the types of images that consistently respond to a group of manipulations, and vice versa, manipulations that consistently respond to a group of codes. We show the benefits of this design across multiple use cases, specifically, studying the quality of manipulations for a single method, and offering a means of comparing different methods.

clustering↗

DOE OSTI2021

Generative Patch Priors for Practical Compressive Image Recovery

In this work, we propose the generative patch prior (GPP) that defines a generative prior for compressive image recovery, based on patch-manifold models. Unlike learned, image-level priors that are restricted to the range space of a pre-trained generator, GPP can recover a wide variety of natural images using a pre-trained patch generator. Additionally, GPP retains the benefits of generative priors like high reconstruction quality at extremely low sensing rates, while also being much more generally applicable. We show that GPP outperforms several unsupervised and supervised techniques on three different sensing models – linear compressive sensing with known, and unknown calibration settings, and the non-linear phase retrieval problem. Finally, we propose an alternating optimization strategy using GPP for joint calibration-and-reconstruction which performs favorably against several baselines on a real world, uncalibrated compressive sensing dataset. The code and models for GPP are available on github.

compressed sensing↗

DOE OSTI2020

Effect of frequency and applied voltage of an atmospheric-pressure dielectric-barrier discharge on breakdown and hydroxyl-radical generation with a liquid electrode

This work examines the effect of the frequency and peak applied voltage on hydroxyl-radical generation in a dielectric-barrier plasma discharge between a metallic needle electrode and one electrode covered with dielectric. The authors examine a system that can expose up to 96 liquid samples in an automated fashion without human intervention beyond setting the initial software configuration. Then, hydroxylradical concentration, measured through coumarin fluorescence, was measured for 5 s plasma exposures generated under different highvoltage conditions with frequencies from 2 to 16 kHz and amplitudes from 4 to 9 kV. Their results show that an increase in frequency and/or applied voltage, within the range prescribed above and the limits of the high-voltage power supply, can yield up to a 150% increase in fluorescence with an equivalent hydroxyl-radical increase. Applications using typical previous methods, such as the Fenton Reaction, are limited in that they continuously generate hydroxyl radicals over millisecond and longer intervals. These results establish the electrical parameters that can now be applied to polymers, like proteins, which show three dimensional structures that are flexible and fluctuate on a microsecond and nanosecond time scale, with hydroxyl-radical generation on this time scale using this device. Additionally, plasma exposures may be optimized for a great variety of proteins, devices and techniques, where hydroxyl-radical generation is of utmost importance, reducing exposure time and potential subjection of samples to harmful side effects.

42 ENGINEERING↗

DOE OSTI2017

3D-printed, integrated plug-and-play turbine-generator set for very-low-head hydro. Final Technical Report DOE-EMotors-15757-1

Hydro power has been used since the early days of electricity, but is generally limited to very large facilities. There remains a vast array of untapped locations, either rivers, tidal currents, or also man-made facilities where energy harvesting is technically feasible, but still too costly. E-Motors, in partnership with MTI, is developing an integrated design aimed at facilitating manufacturing, installation, and retrofitting. The integrated design includes a rim on the outside of the turbine runner to house the electrical generator rotor, a so-called rim drive. With this approach, a compact, in-line assembly can be produced and placed in straight pipes, without having to resort to elbows, shafts, and outside generators, thus improving efficiency and facilitating assembly. The long term goal of the project is to develop a realistic prototype for market development and start of manufacturing. During this Phase I grant, in collaboration with the University of Wisconsin-Milwaukee (UWM), the team pursued two objectives: First, design and test a small (3”) system in a laboratory at UWM, and second develop a prototype design at higher power between 50 and 100 kW. The tests showed the potential for integrating the generator rotor with the turbine runner. Output power is so far limited due to test conditions, but CFD calculations show good efficiency potential. With the larger system, a number of possibilities were explored, including: a generator design matching the turbine output, and capable of generating a peak of 86 kW from a flow of 136 cfs at 9’ of head; various intake tube geometries, showing that an elliptical shape is quite favorable; removing the hub, something made possible with a rim design, which can improve efficiency by about 10 percentage points.

13 HYDRO ENERGY↗

DOE OSTI2019

A Comparison of Fuel Choice for Backup Generators

The costs of a power outage to a business can be substantial, so there is a growing interest in generators for reliable backup power. Businesses are either considering installing backup generators or redundant backup systems for added resilience against grid outages. This report discusses the costs and benefits of backup generator configurations, and compares the relative merits of grid-connected backup systems that enable financial benefits when the grid is functioning, versus backup-only systems that only generate energy for critical services when the primary grid is down. This report discusses how to assign value to the reliability of each system and the revenue streams related to backup generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Hydrogen Generation Rates for Saltstone Related Samples with Tank 50 Waste Grout and Glycoate Using Sealed Reactors

This report documents the results of sealed reactor tests to measure the hydrogen generation rate (HGR) from Tank 50 waste, which may be on the order of 3 x 10 -9 (ft 3 /h)/gal. Because the estimated HGR limit of detection (LOD) for an open, continuous flow system is approximately 6 x 10 -8 (ft 3 /h)/gal a more sensitive method was needed. By accumulating generated gases in a sealed system without a continuous purge gas, the resulting concentrations can increase the sensitivity of measurement, which is based on duration of heating. Prior sealed-reactor testing demonstrated measurements down to 7 x 10 -10 (ft 3 /h)/gal. In 2017, Savannah River Remediation, LLC (SRR) declared a Potential Inadequacy in the Safety Analysis (PISA) for the Saltstone Production Facility (SPF), among other facilities. The research for this report concerns waste from Tank 50 which is processed through SPF to produce saltstone. Previous studies examined the thermolysis of Tank 50 waste mixed with small individual amounts of saltstone grout premix additives, as well as glycolate. In contrast, the current study evaluated the impact of Tank 50 waste containing glycolate and stabilized within saltstone, on the generation of flammable gases, specifically hydrogen. The task included holding at target temperatures Tank 50 samples of salt solution in grout and spiked with 350 mg/L of glycolate to determine if the glycolate decomposes into H 2 . The measured hydrogen generation rates for radioactive saltstone monoliths over 28 days are: 2.0 x 10 -7 (ft 3 /h)/gal at 64-68°C, 1.4 x 10 -7 (ft 3 /h)/gal at 80-82°C, and 4.5 x 10 -7 (ft 3 /h)/gal at 112°C. Testing also included a non-radioactive salt simulant of Tank 50 waste, which was spiked with a concentration of glycolate expected in Savannah River Site (SRS) low-level waste, after the waste is solidified in grout. Included in the simulant tests was using grout made with two different surface areas, a monolith form with only the upper surface exposed and a pellet, or cube, form to increase the exposed surface area, to demonstrate the importance of exposed area in H 2 generation.

08 HYDROGEN↗

DOE OSTI2026

Region-Specific Merchant Hydrogen Market Assessment and Techno-Economic Assessment of Electrolytic Hydrogen Generation: Cooperative Research and Development Final Report, CRADA Number CRD-18-00751

Utilities need to recover value from baseload and some renewable assets even when the electricity is curtailed. The declining cost of renewables (particularly solar), the persistently low-cost of natural gas, and the declining electricity demand, have all worked to lower the price of electricity. Furthermore, the baseload assets have a high turndown cost, which is not reflected in the low market electricity prices. One option for utilizing the curtailed electricity is hydrogen generation. One important aspect of this project will be to provide a “go/no-go” recommendation on the economic feasibility of pursuing hydrogen generation vs. other markets that could offtake curtailed electricity or nuclear steam. This project will specifically assess the opportunities for hydrogen production and use in the service territories of Southern Company Services (SCS), Exelon SBC, and Xcel Energy. Each utility partner represents a different mix of generation fleets: Exelon has significant nuclear assets, Xcel is primarily renewables (wind) with some nuclear, and SCS represents both a renewables fleet and a heavily baseload fleet, which includes significant nuclear. The Contractors will conduct the study of hydrogen market opportunities by first considering the approximate electricity demand and price profiles for the year, and power generation capacity that can be used to produce hydrogen. Therefore, a profile for hydrogen generation will also be developed to better understand the quantity and timing of hydrogen production. Once the hydrogen production schedule is estimated, hydrogen storage requirements can be determined. This effort will provide sufficient information to make a go/no-go decision on moving ahead with a detailed regional case study for hydrogen production in any of the three utility areas.

08 HYDROGEN↗

DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

A Methodology for Calculating EGS Electricity Generation Potential Based on the Gringarten Model for Heat Extraction From Fractured Rock

Existing methodologies for estimating the electricity generation potential of Enhanced Geothermal Systems (EGS) assume thermal recovery factors of 5% or less, resulting in relatively low volumetric electricity generation potentials for EGS reservoirs. This study proposes and develops a methodology for calculating EGS electricity generation potential based on the Gringarten conceptual model and analytical solution for heat extraction from fractured rock. The electricity generation potential of a cubic kilometer of rock as a function of temperature is calculated assuming limits on the allowed produced water temperature decline and reservoir lifetime based on surface power plant constraints. The resulting estimates of EGS electricity generation potential can be one to nearly two-orders of magnitude larger than those from existing methodologies. The flow per unit fracture surface area from the Gringarten solution is found to be a key term in describing the conceptual reservoir behavior. The methodology can be applied to aid in the design of EGS reservoirs by giving minimum reservoir volume, fracture spacing, number of fractures, and flow requirements for a target reservoir power output. Limitations of the idealized model compared to actual reservoir performance and the implications on reservoir design are discussed.

15 GEOTHERMAL ENERGY↗