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DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

ACOPF↗

DOE OSTI2025

Impacts of year-to-year weather variability and inter-panel spacing on agrivoltaic crop yields in Massachusetts

Deployment of utility-scale solar power plants could lead to agricultural land-use changes. Agrivoltaics, a dual land use combining solar and agriculture on the same land, can provide multiple environmental benefits, including improving soil quality and water use efficiency. The body of agrivoltaic field data is still growing, and crop responses to different solar configurations under different local climates are highly varied. We investigate the impact of adding spacing between adjacent solar panels in a fixed-tilt system to improve light diffusion to crops. For four crops (broccoli, peppers, kale, Swiss chard) grown across 3 years in an agrivoltaic system in Massachusetts, we found that only kale had a linearly increasing trend as the inter-panel spacing increased from 0.6 m to 1.5 m (2 ft to 5 ft). However, there were significant year-to-year differences in the yield of agrivoltaic versus control fields. Agrivoltaic and full sun fields produced equivalent yields in a hot, dry year, whereas the full-sun control beds produced more salable yield for all four crops in a warm, wet year. This demonstrates variability of agricultural outcomes and the need for more multi-year studies to ensure agrivoltaic impacts are not under- or overestimated.

14 SOLAR ENERGY↗

DOE OSTI2024

2024 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2024 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and heavy-duty vehicle technologies; scenarios for conventional and alternative fuels. It details the assumptions used to calculate those costs, such as natural gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 Transportation ATB vehicle data are specifically for cars powered by gasoline, diesel, natural gas, gasoline hybrid, plug-in hybrid, battery electric, and fuel-cell powertrains and for trucks powered by diesel, diesel hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Fuels and blendstocks include gasoline, ethanol, blendstock for oxygenate blending, diesel, diesel from biomass, natural gas, electricity, hydrogen, aviation fuel, and marine fuel. At this time, the ATB does not include other vehicles such as 2- and 3-wheeled motorized vehicles, or non-road vehicles such as aircraft, vessels, locomotives, and those for industry and agriculture. See "Transportation ATB Website" resource below for more project information.

2024↗

DOE OSTI2025

STREAM: A technology planning and capacity expansion model for the industrial sector

The Strategic Technology Roadmapping and Energy, Environmental, and Economic Analysis Model—STREAM—is an optimization-based modeling tool and analysis framework to assist with strategic planning and technology investments of the industrial sector. This open-source framework is written in Julia using the JuMP package, which enables users to model future “pathways” for incumbent and future production technologies, costs, fuels and energy carriers, and energy and non-energy environmental impacts from industries as they transform in pursuit of a robust and competitive manufacturing sector. The model starts with an initial stock of industrial production technologies and assets at a facility level and then determines pathways that minimize cost, subject to an array of possible constraints on demand, market shares, environmental flows, and other exogenously specified operational considerations such as capacity utilization rates or regional energy costs. Key features of the framework include flexibility to model a wide range of industries and industrial technologies/processes at varying levels of granularity, ability to perform parametric sensitivity analyses, and ability to visualize model results using visualization objects.

capacity expansion↗

DOE OSTI2024

Investigating resource-efficient neutron/gamma classification ML models targeting eFPGAs

There has been considerable interest and resulting progress in implementing machine learning (ML) models in hardware over the last several years from the particle and nuclear physics communities. A big driver has been the release of the Python package, hls4ml, which has enabled porting models specified and trained using Python ML libraries to register transfer level (RTL) code. So far, the primary end targets have been commercial field-programmable gate arrays (FPGAs) or synthesized custom blocks on application specific integrated circuits (ASICs). However, recent developments in open-source embedded FPGA (eFPGA) frameworks now provide an alternate, more flexible pathway for implementing ML models in hardware. These customized eFPGA fabrics can be integrated as part of an overall chip design. In general, the decision between a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the details of the end-use application. In this work, we explored the parameter space for eFPGA implementations of fully-connected neural network (fcNN) and boosted decision tree (BDT) models using the task of neutron/gamma classification with a specific focus on resource efficiency. We used data collected using an AmBe sealed source incident on Stilbene, which was optically coupled to an OnSemi J-series silicon photomultiplier (SiPM) to generate training and test data for this study. We investigated relevant input features and the effects of bit-resolution and sampling rate as well as trade-offs in hyperparameters for both ML architectures while tracking total resource usage. The performance metric used to track model performance was the calculated neutron efficiency at a gamma leakage of 10 -3 . The results of the study will be used to aid the specification of an eFPGA fabric, which will be integrated as part of a test chip.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Community Solar Project Models: An Introduction to Basic Financial and Administrative Relationships [Slides]

This resource is a collection of visual aids and descriptions to help explain the basic financial and administrative relationships underpinning common community solar project models. The graphics in this resource depict the flow of electricity from generation to consumption and the financial exchanges between the community solar project owner, subscribers, and distribution utility. Community solar models vary based on ownership (e.g., third party, utility), customer type (e.g., multifamily, single-household), and solar array location (e.g., on-site, off-site).

14 SOLAR ENERGY↗

DOE OSTI2024

Rooting for survival: how plants tackle a challenging environment through a diversity of root forms and functions

Abstract The current climate crisis has global impacts and will affect the physiology of plants across every continent. Ensuring resilience of our agricultural and natural ecosystems to the environmental stresses imposed by climate change will require molecular insight into the adaptations employed by a diverse array of plants. However, most current studies continue to focus on a limited set of model species or crops. Root systems are particularly understudied even though their functions in water and nutrient uptake are likely pivotal for plant stress resilience and sustainable agriculture. In this review, we highlight anatomical adaptations in roots that enable plant survival in different ecological niches. We then present the current state of knowledge for the molecular underpinnings of these adaptations. Finally, we identify areas where future research using a biodiversity approach can fill knowledge gaps necessary for the development of climate-resilient crops of the future.

Plant Sciences↗

DOE OSTI2025

Practical Introduction to Benchmarking and Characterization of Quantum Computers

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.

open quantum systems & decoherence↗

DOE OSTI2025

Feasibility of an Active Interrogation System to Classify Waste with He-4 Neutron Spectroscopy

This work investigates a 4 He-detector active interrogation system that leverages neutron spectroscopy to classify nuclear waste streams. MCNP models tested the concept through the simulation of a D-D neutron generator, an array of 4 He detectors, and various waste compositions. The fast-neutron Differential Die-Away signature was augmented with a neutron-energy discrimination signature. This signature isolates induced fission neutrons, the energy of which is greater than that of the D-D monoenergetic spectrum. With the incorporation of this spectroscopic technique, the measurement time decreased by 3–9% (depending on the degree of neutron moderation and absorption presented by the sample), demonstrating how neutron spectroscopy can enhance active interrogation methods. The reduced measurement times would have significant financial and logistical benefits for facilities with large footprints of low-level waste production.

4He detectors↗

DOE OSTI2024

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

Machine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

PV Systems Modelling with Python

PVSC tutorial demonstrating how to download irradiance data and sun position for a specific location, determine the orientation of a single-axis tracker system, calculate Plane of Array irradiance, and estimate the power output of a PV system.

14 SOLAR ENERGY↗

DOE OSTI2024

2024 Standard Scenarios: A U.S. Electricity Sector Outlook

This data corresponds to the 2024 Standard Scenarios report, which contains a suite of forward-looking scenarios of the possible evolution of the U.S. electricity sector through 2050. These files contain modeled projections of the future. Although we strive to capture relevant phenomena as comprehensively as possible, the models used to create this data are unavoidably imperfect, and the future is highly uncertain. Consequentially, this data should not be the sole basis for making decisions. In addition to drawing from multiple scenarios within this set, we encourage analysts to also draw on projections from other sources, to benefit from diverse analytical frameworks and perspectives when forming their conclusions about the future of the power sector. For further discussions about the limitations of the models underlying this data, see section 1.4 of the "ReEDS Documentation" linked below. For scenario descriptions, input assumptions, and metric definitions for the data in these files, see the "2024 Standard Scenarios Report" linked below.

2050↗

DOE OSTI2026

Next Generation Ta-STJ Sensor Arrays for BSM Physics Searches

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment uses superconducting tunnel junction (STJ) sensors to search for physics beyond the standard model (BSM) with recoil spectroscopy of the 7 Be EC decay into 7 Li. A pulsed UV laser is used to calibrate the STJs throughout the experiment with ∼20 meV precision. Phase-III of the BeEST experiment revealed a systematic calibration discrepancy between STJs. We found these artifacts to be caused by resistive crosstalk and by intensity variations of the calibration laser. For phase-IV of the BeEST experiment, we have removed the crosstalk by designing the STJ array so that each pixel has its own ground wire. We now also use a more stable UV laser for calibration. The new STJ arrays were fabricated at STAR Cryoelectronics and tested at LLNL and FRIB. They have the same high energy resolution of ∼1-2~eV in the energy range of interest below 100~eV as before, and they no longer exhibit the earlier calibration artifacts. We discuss the design changes and the STJ array performance for the next phase of the BeEST experiment.

Laser Calibration↗

DOE OSTI2024

Multifrequency Analysis of Favored Models for the Messier 87* Accretion Flow

Abstract The polarized images of the supermassive black hole Messier 87* (M87*) produced by the Event Horizon Telescope (EHT) provide a direct view of the near-horizon emission from a black hole accretion and jet system. The EHT theoretical analysis of the polarized M87* images compared thousands of snapshots from numerical models with a variety of spins, magnetization states, viewing inclinations, and electron energy distributions, and found a small subset consistent with the observed image. In this article, we examine two models favored by EHT analyses: a magnetically arrested disk with moderate retrograde spin and a magnetically arrested disk with high prograde spin. Both have electron distribution functions that lead to strong depolarization by cold electrons. We ray trace five snapshots from each model at 22, 43, 86, 230, 345, and 690 GHz to forecast future very long baseline interferometry (VLBI) observations and examine limitations in numerical models. We find that even at low frequencies where optical and Faraday rotation depths are large, approximately rotationally symmetric polarization persists, suggesting that shallow depths dominate the polarization signal. However, morphology and spectra suggest that the assumed thermal electron distribution is not adequate to describe emission from the jet. We find 86 GHz images show a ringlike shape determined by a combination of plasma and spacetime imprints, smaller in diameter than recent results from the Global mm-VLBI Array. We find that the photon ring becomes more apparent with increasing frequency, and is more apparent in the retrograde model, leading to large differences between models in asymmetry and polarization structure.

Astronomy & Astrophysics↗

DOE OSTI2025

Nanomagnet shape effects on magnetic reversal in artificial spin ice

We use micromagnetic simulations to study the influence of the shape of elongated nanomagnets that vary from rectangular to elliptical shapes in an artificial spin ice array. For weakly interacting ellipselike islands, the magnetic hysteresis loops align with the Stoner-Wohlfarth model, demonstrating nearly uniform magnetization at remanence and nearly coherent rotation. As expected, nonelliptical weakly interacting islands show deviations from the model due to the curling and pinning of the magnetization at the island edge. We find that increasing interisland coupling leads to an increasingly complex magnetic reversal process due to an interplay between internal and external degrees of freedom as the shape of the islands varies. Furthermore, our results open a roadmap for tailoring systems with desired magnetic properties by choosing the appropriate nanomagnet shape.

Artificial spin ice↗

DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 2

The ARPA-E Grid Optimization (GO) Competition Challenge 2, from 2020 to 2021, expanded upon the problem posed in Challenge 1 by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment. Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. Specifically, the economic surplus, defined as the benefit of serving load minus the cost of generation, is being maximized. It was expected that the objective value of a given solution should be positive, representing economic gain, but negative objectives from poor solutions were possible. The two code submission feature of Challenge 1 was maintained. Additionally, Divisions 3 and 4 within the competition permitted on/off switching of transmission lines (Divisions 1 and 2 did not). After the initial release of the Problem Formulation on 7/20/2020, ARPA-E Director Lane Genatowski announced Challenge 2 on 9/12/2020. The final May 31, 2021, version of the Problem Formulation was 97 pages long with 299 equations. The Challenge proceeded with 2 non-prize Events and 2 prize Events. Teams receiving Challenge 1 FOA awards and prize money were required to use the prize money to fund their Challenge 2 efforts (Georgia Institute of Technology, Global Optimal Technology, Inc., Lawrence Livermore National Laboratory, Lehigh University, Northwestern University, Artelys, Columbia, Pearl Street Technologies, Pennsylvania State University, and University of Colorado Boulder). For more information on the competition and challenge 2 see the "GO Competition Challenge 2 Information" resource below. Challenge 1 and Challenge 3 information can be found in the resources linked below.

ACOPF↗

DOE OSTI2024

Raman Sideband Cooling of Molecules in an Optical Tweezer Array to the 3D Motional Ground State

Ultracold polar molecules are promising for quantum information processing and searches for physics beyond the standard model. Laser cooling to ultracold temperatures is an established technique for trapped diatomic and triatomic molecules. Further cooling of the molecules to near the motional ground state is crucial for reducing various dephasings in quantum and precision applications. In this work, we demonstrate Raman sideband cooling (RSC) of CaF molecules in optical tweezers to near their motional ground state, with average motional occupation quantum numbers of n ¯ x = 0.16 ( 12 ) , n ¯ y = 0.17 ( 17 ) (radial directions), and n ¯ z = 0.22 ( 16 ) (axial direction), and a 3-D motional-ground-state probability of 54 ± 18 % of the molecules that survive the RSC. This process paves the way to increase molecular coherence times in optical tweezers for robust quantum computation and simulation applications. Published by the American Physical Society 2024

Physics↗

DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗