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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Accelerating Optimal Integration of Energy Efficiency Strategies with Industrialized Modular Construction: Preprint

The National Renewable Energy Laboratory's (NREL's) Industrialized Construction Innovation team first introduced the Industrialized Construction Assessment Framework to achieve affordable, net-zero energy (NZE) modular multifamily buildings in the 2020 ACEEE paper "Integrating Energy Efficiency Strategies with Industrialized Construction for our Clean Energy Future." Since then, NREL has continued to drive the ambitious plan to accelerate optimal integration of energy efficiency strategies during industrialized construction with little or no additional cost, labor, and production time. This follow-on paper introduces the Energy in Modular (EMOD) buildings method and presents NREL's research efforts over the last two years in collaboration with industry, including affordable housing partners. NREL has developed an idealized NZE modular multifamily building design that incorporates five energy efficiency strategies well suited for industrialized construction in factories: (1) envelope thermal control, (2) envelope infiltration control, (3) mechanical, electrical, and plumbing systems, (4) smart controls, and (5) solar plus storage. This paper highlights results from leveraging design for manufacturing and assembly principles, testing, and validation pilots with factory partners; demonstrating pod prototypes in test stand at NREL; and performing simulations. Overall, these research efforts address barriers to whole-building system integration, such as poor installation quality of thermal and air barriers; lack of unitized systems for space conditioning, energy recovery and ventilation, and water heating; problematic on-site installation, commissioning, and configuration of controls; and lack of cost-effective integration for grid-friendly design and emerging technologies. Conclusively, the paper delineates next steps for future work with NREL's partners toward developing a transformational pathway for our clean energy future.

affordable housing↗
DOE OSTI2021

A Bayesian and HRA-Aided Method for the Novel Reliability Analysis of Software

Technological advancements and nuclear power plant modernization has inspired considerable research in the areas of safety and reliability, yet there remains a lack of consensus for the reliability assessment of digital instrumentation and control (I&C) systems. Motivated by the lack of consensus for reliability analysis methods, this work employs a novel framework that incorporates Bayesian, human reliability, and common-cause failure (CCF) modeling techniques. The novel framework allows the use of state-of-the-art or classical modeling techniques when accounting for human and CCF effects on system reliability. The Bayesian and HRA-Aided Method for the Reliability Analysis of Software (BAHAMAS) is demonstrated by a case study for the quantification of software hazards found in a previous analysis of a digital reactor trip system. The results demonstrate the ability of BAHAMAS to account for human activities during the software development life cycle and their influence on software reliability. BAHAMAS is a flexible tool for extending the coverage of conventional probabilistic risk assessments to include modernized digital I&C systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Estimating List-Mode Data Sensitivities to Nuclear Data with MCNP6

Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Controlled-Environment Agriculture and the Geography of Food, Energy and Water Resilience in the United States

USDA guidelines call for an increase in fruit and vegetable consumption in U.S. households to promote healthier diets. Access to fresh fruits and vegetables are especially lacking in food deserts and food swamps (characterized by a prevalence of food that is highly processed and lacking in nutritional value). Adoption of the recommended healthy diet, which more than doubles the consumption of fruits and vegetables, would have a wide variety of health benefits and would reduce the land footprint of U.S diets. However, adopting a healthy diet would increase phosphate and nitrogen impacts associated with fertilizers and pesticides and would significantly increase freshwater consumption and energy use because of the resource intensity of field cultivation of fruits and vegetables. The majority of these crops are grown in just a few states, including California and Arizona, which are increasingly impacted by climate change. In addition, rural communities are rapidly becoming food deserts, while food produced in these areas is transported long distances to market. In recent years, highly intensified controlled environment (CE) agricultural systems, (i.e., vertical farming) have been developed to provide fresh food closer to consumers. Fruits and vegetables, many of which are amenable to CE culture, occupy a uniquely impactful segment of the food supply-chain, including their value in improving nutrition for vulnerable communities. The objective of this work is to elucidate the location dependency of the energy and water impacts of adoption of distributed CE farming for an important portion of the food system.

controlled environment agriculture↗
DOE OSTI2023

Cell Free and Immobilization Technologies (CFIT)

Today, several key factors negatively impact the production of fuels and chemicals from renewable sources. Common hindrances in the biological production of biochemicals are: (1) end-product or intermediate toxicity to the microbial biocatalyst, (2) the diversion of carbon to biomass formation, and (3) co-production of undesired byproducts. A particularly attractive alternative is to eliminate the biocatalyst entirely and instead operate the desired metabolic pathways in isolation, thus circumventing the roadblocks of biological toxicity, lower yields, and lack of specificity. However, cell-free enzyme systems still suffer from low productivities owing in part to the effects of free diffusion of intermediates, lack of long term enzyme stability, cofactor cost or inefficient recycling rates, and finally, the cost of enzyme production/purification. This project represents a new effort to propose innovative and cost competitive routes to producing biochemicals from a variety of feedstocks using cell free approaches. These routes will help reduce the current risk and cost associated with classical cell free production. Cell free technologies show promise for application to the production of toxic/inhibitory products or products difficult to separate from microbial growth media and can help reduce the production barriers in multiple areas of biological conversion of feedstocks to biochemicals. More specifically, we are developing new technologies and routes that could be used to produce high value biochemicals such as 2,3 BDO and terpenes (among many others) from biomass derived C5/C6 sugars or lignin but also from waste byproducts such as glycerol. This project will lead to significant innovation and also lead to new concepts and rational design of pathways and enzymes. Within this project, we will develop new metabolic enzyme cascades that will represent natural or artificial combinations of enzymes to produce the desired biochemicals from a variety of feedstocks. We are also developing basic design principals for constructing synthetic metabolons, using fusion proteins and synthetic protein scaffolds, to promote substrate channeling and stability while conserving peak activity. Additionally, our efforts include a techno economic analysis (TEA) of cell free approaches to provide the sensitivities of the process to enzyme loading, activity, pH, reactor volumes, cofactor recycling rates. Finally, we are focusing on further increasing stability, operating lifetime and efficiency of the pathway enzymes by immobilization on support surfaces. We are also focus on immobilizing pathway enzymes or combinations of enzymes on several different conducting polymers and evaluate the effect on stability and operating lifetime. As more combinations become available we will conduct a more systematic study of the means of immobilizing these enzymes. This preliminary work will enable the in-depth study of cofactor recycling at these interfaces using mediators for electron transfer. Taken together, these approaches will enable process intensification, continuous operation, lower capital and separations costs and end-product flexibility, and thus has the potential to contribute significantly to BETO's goals of cost competitive biofuels and bioproducts. To date we have demonstrated 1) the conversion of pyruvate to 2,3 BDO (4 enzymes with cofactor recycling, no additional cofactors needed) at >100g/L (>3g/L/h) using our enzyme tethering approach, 2) Generated mutants of a key redox enzyme with >180- fold improvement in NADH utilization over WT to >85% of NADPH utilization. 3) Produced mevalonate at >10g/L from glucose, limonene at >10g/L from mevalonate and more than >5g/L from glucose with crude enzyme preparations and complete cofactor recycling. 4) successfully generated fully active cross-linked aggregates (CLEAs) of NOX, a cofactor-regenerating water-forming oxidase that is key to our process with increased stability 5) Engineered and identified enzymes able to use the biomimetic cofactor NMN and synthesized new synthetic cofactors.

biocatalysis↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Closing the Gap between FAIR Data Repositories and Hierarchical Data Formats

Many in the scientific community, particularly in publicly funded research, are pushing to adhere to more accessible data standards to maximize the findability, accessibility, interoperability, and reusability (FAIR) of scientific data, especially with the growing prevalence of machine learning augmented research. Online FAIR data repositories, such as the Open Science Framework (OSF), help facilitate the adoption of these standards by providing frameworks for storage, access, search, APIs, and other features that create organized hubs of scientific data. However, the wider acceptance of such repositories is hindered by the lack of support of hierarchical data formats, such as Technical Data Management Streaming (TDMS) and Hierarchical Data Format 5 (HDF5), that many researchers rely on to organize their datasets. Various tools and strategies should be used to allow hierarchical data formats, FAIR data repositories, and scientific organizations to work more seamlessly together. A pilot project at Los Alamos National Laboratory (LANL) addresses the disconnect between them by integrating the OSF FAIR data repository with hierarchical data renderers, extending support for additional file types in their framework. The multifaceted interactive renderer displays a tree of metadata alongside a table and plot of the data channels in the file. This allows users to quickly and efficiently load large and complex data files directly in the OSF webapp. Users who are browsing files can quickly and intuitively see the files in the way they or their colleagues structured the hierarchical form and immediately grasp their contents. This solution helps bridge the gap between hierarchical data storage techniques and FAIR data repositories, making both of them more viable options for scientific institutions like LANL which have been put off by the lack of integration between them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Heliostat Consortium: Update on Resource, Training, and Education Development and Women+ in Concentrating Solar

One of the major barriers to Concentrating Solar Power (CSP) industry growth in the U.S. is the lack of accessibility of expert knowledge, a lack of a stable workforce pipeline, and little public awareness of the technology area. Improved Resource, Training, and Education (RTE) is needed to address these gaps, to draw workers into the field from diverse technology areas and backgrounds, and to reduce the learning curves of newcomers into the industry. As part of the RTE topic under the NREL-led Heliostat Consortium (HelioCon), a public online resource database has been created to support information sharing across the heliostat and CSP community. Here, information will be presented on the existing and planned database resources, including introductory information about CSP and heliostats, a reference library of texts and publications, educational videos, a CSP power tower plant database and case studies, a contact list of component suppliers and developers, and a list of available software/metrology tools. Women+ in Concentrating Solar is a newly created organization to promote the education, professional development, and advancement of underrepresented genders in the Concentrating Solar Power (CSP) community in a way that includes diverse backgrounds, attitudes, and perspectives. The main objectives of the groups are to provide the following opportunities for underrepresented genders: 1) organize events to provide networking opportunities and community building, 2) boost partnerships and scientific collaboration, 3) create resources to facilitate knowledge and experience sharing, 4) provide mentorship programs to support those new to the industry, 5) bring visibility to underrepresented genders and attract more to the field, and 6) promote Diversity, Equity, and Inclusion (DEI) and engage minorities and underrepresented groups. Initial activities being pursued by the group include creating an expert database to support event organizers in recruiting speakers with representation of all gender identities, hosting online networking and webinar events, organizing conference events including seminars and networking/social events, and establishing a mentorship program to connect members in the CSP community across the globe. The group is open to all genders and has grown to over 60 members in its first few months.

concentrating solar↗
DOE OSTI2020

Low Count and Background Radionuclides Analysis - 20488

The US Department of Energy (DOE) is often faced with the need to evaluate radionuclides at low concentrations. When site sample data are likely to be close to threshold activity concentrations of interest, then the means by which the radiochemical analysis is performed and reported is critical. This situation can occur when differentiating from zero (presence/absence) for radionuclides that do not occur naturally, close comparison with environmental background for naturally occurring radionuclides, close comparison with a risk- or dose-based threshold concentrations of interest, or even comparisons across studies. There are several analytical issues that are of concern, but the two that appear to cause incorrect decisions to be made most often involve establishing detection limits and subtracting ambient background conditions in the laboratory. These issues are not critical when radionuclide activity concentrations are large relative to thresholds of concern, but they seem to be poorly understood when it matters. When the comparisons are important and are likely to be close to a threshold of interest, then the general contract with the analytical laboratories needs to be changed so that the right or appropriate data are obtained. The concern is that important decisions are made incorrectly more often as greater scrutiny is placed on DoE's radionuclide cleanup or monitoring decisions by the public and other stakeholders. Examples are presented of problems that have been observed for different projects, both within and outside the realm of DOE and NRC remediation and radioactive waste disposal problems, and solutions are offered that should lead to better data from which important decisions need to be made. The first example is from Los Alamos National Laboratory (LANL) and involves radionuclide concentrations in soil and rock beneath LANL's Material Disposal Area (MDA) G. An initial review of the data led to a conclusion that americium and plutonium are a long way present beneath MDA G. A more thorough review of the data that accounted properly for ambient background and the detection limits that had been established led to the opposite conclusion. Another example is from the Nevada National Security Site where tritium results from one of the wells were unexpectedly high. Proper understanding and analysis of ambient background led to the conclusion that the increased concentrations were not so obvious, and that a different contract with the analytical laboratory was needed to provide more appropriate data to support a better determination. Other examples are used from regulatory review of projects in Nevada, where background levels and secular equilibrium for naturally occurring radionuclides are not established correctly because of analytical issues. The same basic issues have also been found to create difficulties analyzing historical data from the West Valley Demonstration Project. There is evidence in the data that the apparent lack of secular equilibrium where it is expected to exist is related to ambient background subtraction or other analytical issues. A final example is presented for analysis of Tc-99 in samples of depleted uranium. In this case, two different studies that were performed only three months apart provide quite different results. The US Environmental Protection Agency (EPA) established the data quality objectives (DQO) process in the mid-1980's to establish decision performance criteria for data collection. EPA guidance (EPA G-4, for example) clearly distinguishes between DQOs and measurement performance objectives (MQOs) that should be addressed for laboratory analysis of samples. The language of DQOs and MQOs has become confused over time it seems, and the subsequent effects seem to include a lack of attention to decision performance and a routine approach to measurement quality. In order to better address radionuclide sample analysis when the concentrations are close to thresholds of concern, which might be zero for some radionuclides, background for others, and risk-based thresholds for yet others, it is important that routine laboratory analysis methods are adjusted, and that the project team and the laboratory work closely together to ensure that the data meets the MQO requirements of laboratory analysis and reporting of results, and that the MQOs effectively support project-specific DQOs. This basic approach will be applied in Los Alamos in the coming year to the collection of moisture data from underneath MDA T that will be analyzed for americium and neptunium isotopes. Proper understanding of the radiochemistry methods and reporting, and of appropriate statistical methods is critical to the success of such projects, ensuring that the right decisions are made. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

High- and Mid-Fidelity Modeling Comparison for a Floating Marine Turbine System: Preprint

There is a lack of suitable numerical tools, particularly open-source tools, that can be used for designing and optimizing marine turbine systems. The National Renewable Energy Laboratory (NREL) has added features to their widely used mid-fidelity wind turbine modeling code, OpenFAST, to enable modeling of axial flow marine turbines. This necessitated the addition of several physical effects relevant to marine turbines that can be neglected for wind turbines. These include buoyant loads, added mass and inertia loads, wave-current superposition, and changes to the coordinate systems. This updated version of OpenFAST allows for the modeling of both fixed and floating marine turbine systems at a speed comparable to real time. While efficient for long simulations, large sets of load cases, and design studies, mid-fidelity codes cannot capture all of the potentially important physical phenomenon impacting marine turbine systems. High-fidelity computational fluid dynamics (CFD) simulations can capture more flow effects with fewer assumptions and provide detailed body pressure mapping and flow-field information. It is important to compare predictions between mid-fidelity and high-fidelity codes, both to verify the models and to understand the limitations. A floating marine turbine system designed by NREL was modeled both with OpenFAST and with the commercial CFD code, STARCCM+. The CFD model used a 3-D unsteady Reynolds-averaged Navier-Stokes solver for a volume-of-fluid numerical wave and current tank. The blade-resolved simulations used the sliding-interface technique for the spinning rotor and an overset grid to accommodate the rigid-body motion of the floating system. The mooring system was modelled with a custom coupling of the CFD solver with the open-source code, MoorDyn. This improves upon the existing quasi-static catenary solver in STARCCM+, which lacks seabed contact or line-to-line connections. Spatial and temporal convergence studies were conducted. The simulation results for a combined current and wave condition are compared between OpenFAST and CFD, highlighting the capabilities of the mid-fidelity code and identifying the areas where a high-fidelity approach is needed.

CFD↗
DOE OSTI2024

A High-Fidelity Electromagnetic Transient Model of Inverter-based Resources Integrated to an IEEE-9 Bus System for Benchmarking Studies

An inverter-based resource (IBR) is a source of electricity that is asynchronously connected to the electrical grid via an electronic power converter. These power sources lack the intrinsic behaviors of the standard power plants, presenting specific challenges to system stability. These power sources lack the intrinsic behaviors of the standards power plants and their features are almost entirely defined by the control strategy used on them, presenting specific challenges to system stability as their penetration increases into the already fragile Bulk Power System (BPS). Utilizing renewable energy has a great upside but the electrical industry needs to understand the requirements like performing electromagnetic transient (EMT) simulation studies in planning and/or in interconnection studies. This paper presents the development of a high-fidelity EMT model based on commercial equipment and field parameters, which at the same time aims to study the increasing penetration of IBRs and their impact on the actual BPS. The open-source EMT model developed will be a benchmarking example that can be used to evaluate algorithms in the power grid (including evaluating grid-forming controls, power system monitoring and operation, etc.).

Martinez Montejano, Misael↗
DOE OSTI2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗
DOE OSTI2024

High Penetration Microgrids Providing Grid Stability Using Frequency-Watt Control Presentation

The U.S. grid is rapidly transitioning towards utilizing inverter-based renewable energy resources such as solar, wind, and batteries, reducing the carbon emission footprint.Inverter-based microgrid control architectures remain a critical focus to address power system stability issues in future high penetration markets lacking spinning generation assets. Idaho National Laboratory (INL) is researching an active layered inverter based frequency-Watt control scheme that provides distribution level stability in high-penetration markets where grid inertia is lacking. Hardware in the loop case study was implemented using INL’s Microgrid Testbed to combat scalable frequency deviations ranging from 60 Hz down to 50 Hz initialized by a hydropower model implementing step loads using a 540-kW grid emulator. Our research findings demonstrate the importance of distribution level, inverter-based active frequency-Watt controls utilizing a battery energy storage system (BESS) to provide adequate frequency support at the point of common coupling without major power infrastructure upgrades.

13 HYDRO ENERGY↗
DOE OSTI2024

Dynamically Learning Incentives for Load Control

As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.

demand response↗
DOE OSTI2025

A Simple Data-Centric Methodology for Producible Geothermal Well Determinations: Preprint

The Bureau of Land Management (BLM) has traditionally lacked a standardized methodology for determining if a newly drilled geothermal well is "producible," a designation essential for deciding whether a lease should be "held by production." This is a straightforward problem to solve in oil and gas: Demonstrate that a well is economically viable, meaning it produces sufficient oil or gas to exceed direct operating costs and lease-related expenses, such as rentals or minimum royalties. In geothermal, the problem is more complex: Geothermal wells are tightly coupled with the downstream infrastructure - specifically, the power plant, which is often not designed until well after a lease is deemed as "held by production." Although this designation is critical for advancing geothermal power plant development on BLM-managed lands, current geothermal well assessments often rely on ad hoc approaches that can be complex, operator-biased, and heavy in assumptions related to economic viability. To address this, we have developed two complementary methodologies: a minimum power requirement-based approach and a productivity index (PI)-based approach. These methods leverage key flow test data - pressure, temperature, flow rate, and specific enthalpy - to provide reliable and standardized producible well determinations. The minimum power requirement-based approach evaluates wells against specific power output thresholds informed by reservoir experts and the associated temperature requirements. The PI-based approach assesses well productivity using widely accepted reservoir engineering metrics, proposing a threshold of 2.5 kg/s/bar. Both methods are data-driven and grounded in empirical production data from operational geothermal wells, avoiding uncertain economic assumptions while maintaining decision-making accuracy. Wells falling below key performance thresholds (i.e., PI, specific power) are deemed non-producible. These methodologies aim to streamline BLM's decision-making process, reduce nontechnical barriers to geothermal energy adoption, and enable regulatory expansion into states lacking geothermal expertise. Preliminary results indicate clear trends and thresholds in production data that provide actionable insights for evaluating well producibility. Validation using well completion report (WCR) data is ongoing, with promising results demonstrating the potential for these standardized methodologies to impact geothermal development significantly.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Evaluating Chemical Kinetics Predictions for Propane Using 3-D and 0-D Models in a Boosted Spark-Ignited Engine

Propane has been shown to be a promising alternative fuel to reduce emissions while simultaneously achieving high efficiencies in medium- and heavy-duty engines. These high-power density applications require boosted engines which, combined with high compression ratio, can lead to auto-ignition and knock. While three-dimensional (3-D) computational fluid dynamics (CFD) models are often used for resolving the complex fluid flow in engines, these models can become computationally expensive when simulating detailed chemical kinetics. Likewise, zero-dimensional (0-D) models are computationally concise enough for kinetics development, but lack any flow-field information which governs the flame propagation processes in spark ignition (SI) engines. This work presents a comprehensive comparison between 3-D and 0-D closed cycle simulations at knocking conditions in a high compression ratio high stroke-to-bore ratio propane engine. In order to initialize the flow-field for the 3-D closed cycle (intake valve closing, (IVC) to exhaust valve opening, (EVO)) simulation, a motored multi-cycle 3-D model was run using Converge to create a map at IVC, reducing the computational time. The map allowed a non-homogeneous 3-D closed cycle simulation to be satisfactorily validated against experiments, while a homogeneous case using only the turbulence field mapping was also simulated, mimicking 0-D modeling. The 3-D simulations were used to prescribe the initial conditions (e.g., IVC thermodynamics, speciation, burn-rate profile) for a 2-zone 0-D SI engine model in Chemkin Pro for both cases. It was found that 2-zone 0-D modeling underpredicted the knock onset timing, likely due to the lack of thermal stratification in the unburned gas region. Future work will carry multi-zone 0-D modeling to capture the fuel auto-ignition in the unburned region.

Douvry-Rabjeau, Julien [Oakland University, Roches↗
DOE OSTI2026

Quantifying System Strength From Grid-Forming Resources Using Frequency Scan Approach: Preprint

Current industry practices for quantifying the system strength contribution from grid-forming (GFM) resources to ensure stability of power systems dominated by inverter-based resources (IBRs) are primarily based on iterative electromagnetic transient (EMT) time-domain simulation studies. While feasible, these approaches are resource-intensive, lack scalability and intuition, and might not evaluate the system strength contribution over the entire frequency range of interest. This paper introduces a novel, frequency-domain approach to quantify system strength support provided by a GFM resource using frequency scans. The proposed method uses transfer functions from the grid voltage magnitude (V) and phase (?), respectively, to the reactive (Q) and active power (P) output of a GFM resource for quantifying its contribution to system strength. These transfer functions provide a direct measure of the ability of a GFM resource to behave as a stiff voltage source behind a reactance over a specified frequency range, enabling robust quantification of its system strength contribution. The key innovation of this work is the development of a frequency domain system strength metric called the dynamic short-circuit ratio (dSCR) that is suitable for IBR-dominated power systems and is directly related with the familiar short circuit ratio (SCR) metric. The new metric, dSCR, enables the assessment of system strength contributions from both synchronous machines and converter-based GFM resources using a unified benchmark, which is not possible with the traditional SCR metric. The paper also demonstrates how impedance scans could identify if an unstable condition observed during weak grid conditions is a result of the lack active or reactive power support or both. By leveraging the proposed frequency-domain dSCR metric for quantifying system strength contribution from GFM IBRs, the paper demonstrates targeted mitigation strategies for weak grid instabilities without resorting to repeated, time-consuming time-domain simulations. The result is a scalable and efficient approach to remediate stability challenges in power systems with high shares of IBRs and accelerating the integration of GFM technologies for system strength support in power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Association Between Changes in Timing of Spring Onset and Asthma Hospitalization in Maryland

The proposed association between climate change and increased burden of allergic diseases are based on three different thematic observational studies that have linked (i) increased CO 2 concentration with higher pollen production, (ii) warmer air/surface temperatures with early spring onset and longer pollen season, and (iii) higher pollen exposure with increased risk of asthma hospitalizations. Yet empirical evidence collectively linking climate change with pollen season and asthma exacerbation is still lacking. Using satellite observations, in situ pollen monitoring data, and hospitalization records (2001-2012), here we show that changes in the timing of spring onset resulting from ongoing climate variability and change is directly related to a longer tree pollen season and increased risk of asthma hospitalization. More specifically, we observed that very early start of season (SOS) to be associated with 10% increase in risk of asthma hospitalization (Incident Rate Ratio (IRR): 1.10, 95% Confidence Interval (CI): 1.02- 1.20) while late onset of SOS to be associated with 3% increase in risk of asthma hospitalization in Maryland (IRR: 1.03, 95% CI: 0.97-1.11), however the risk in the latter case was not statistically significant. Our results serve as a wake-up call to public health and medical communities regarding the need to anticipate and adapt to the ongoing changes in the timing and severity of the spring allergy season.

54 ENVIRONMENTAL SCIENCES↗