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DOE OSTI2021

iVirus 2.0: Cyberinfrastructure-supported tools and data to power DNA virus ecology

Microbes drive myriad ecosystem processes, but under strong influence from viruses. Because studying viruses in complex systems requires different tools than those for microbes, they remain underexplored. To combat this, we previously aggregated double-stranded DNA (dsDNA) virus analysis capabilities and resources into ‘iVirus’ on the CyVerse collaborative cyberinfrastructure. Here we substantially expand iVirus’s functionality and accessibility, to iVirus 2.0, as follows. First, core iVirus apps were integrated into the Department of Energy’s Systems Biology KnowledgeBase (KBase) to provide an additional analytical platform. Second, at CyVerse, 20 software tools (apps) were upgraded or added as new tools and capabilities. Third, nearly 20-fold more sequence reads were aggregated to capture new data and environments. Finally, documentation, as “live” protocols, was updated to maximize user interaction with and contribution to infrastructure development. Together, iVirus 2.0 serves as a uniquely central and accessible analytical platform for studying how viruses, particularly dsDNA viruses, impact diverse microbial ecosystems.

Bolduc, Benjamin (ORCID:0000000324200755)↗

DOE OSTI2021

Modeling and Optimizing Pumped Storage in a Multi-stage Large Scale Electricity Market under Portfolio Evolution

To leverage the fast-ramping capability of resources to provide great value to the grid, electricity system operators such as the Midcontinent Independent System Operator (MISO) continue to evolve their approaches for integrating energy storage resources, including pumpedstorage hydro (PSH), into the electricity markets. However, new challenges arise in modeling and optimizing these energy-limited resources across multiple market clearing processes and planning studies with uncertainties and imperfect information. For instance, current market practices of PSH owners specifying pumping/generating hours can result in sub-optimal generation dispatch. Letting grid operators optimize PSH with the consideration of multiple operating modes and energy limitation constraints can potentially bring economic benefits to both the system and the PSH owners. However, in multi-stage clearing process of electricity markets, utilizing the PSH flexibility to deal with realized uncertainties can cause deviation in the multi-stage scheduling processes. The resulting financial risks from the schedule deviation may not be acceptable to PSH owners. In addition, to effectively utilize this energy limited resource, the state of charge (SOC) constraints of PSH needs to be continuously optimized and the marginal cost of deviation need to reflect the expected cost to purchase or sell energy at future times to compensate for deviations. This project aims to develop a prototype enhanced PSH model and improved price signals in the multi-stage market clearing process with proper consideration of the unique characteristics of PSH, in order to better align underlying PSH capabilities with evolving grid needs, particularly including the needs for more frequent and larger cycling to manage variability and uncertainty from renewables.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Technologist in Residence for Advanced Manufacturing and Materials in Unconventional Oil and Gas Equipment and Infrastructure

The DOE Technologist in Residence (TIR) program pairs a senior technical staff member from a National Laboratory (Lab Technologist), and a senior technical staff member(s) from a clean energy manufacturing company or consortium of companies (Industry Technologist). Each technologist may represent single or multiple national laboratories, or single or multiple companies. These pairs of Lab and industry technologists work together for a period of 18 to 24 months to accomplish several goals: 1) Identify the participating company’s (or companies’) technical priorities and challenges, and the resources and capabilities in DOE’s National Laboratories that may be highly suitable to address them; 2) Propose collaborative R&D efforts to develop science-based solutions to the company’s (or companies’) most strategic scientific, technological, and business issues; and 3) Develop a general framework agreement and begin developing specific scopes of work for the proposed collaborative R&D efforts. The proposed R&D will then take place outside of the program and will not use TIR program funds. Oak Ridge National Laboratory (ORNL) and Pioneer Natural Resources were awarded a TIR project by DOE in late 2016 and the CRADA component of the project was initiated in 2017. This report describes work performed under the CRADA agreement between 2017 and 2020.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

A computationally efficient algorithm for computing convex hull prices

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may be required to execute side payments to participants whose costs are not covered through energy sales as determined via traditional locational marginal pricing schemes. Convex hull pricing minimizes this and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. Computing convex hull prices involves solving either a large-scale linear program or the Lagrangian dual of the corresponding non-convex scheduling problem. Further, the former approach requires explicit descriptions of market participants' convex hulls. While linear programs for computing convex hull prices are large, their structure is naturally decomposable by generators. Here, in this work, we propose and empirically analyze a Benders decomposition approach to computing convex hull prices that leverages recent advances in convex hull formulations for thermal generating units. We demonstrate across a large set of test instances that our decomposition approach only requires modest computational effort, obtaining solutions at least an order of magnitude faster than the equivalent large-scale linear programming approach. Overall, we provide a computationally feasible method for computing convex hull prices for industrial scale market clearing problems, enabling the possibility of practical adoption of this advanced pricing mechanism.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps

Abstract Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Seismic moment tensor classification using elliptical distribution functions on the hypersphere

Discrimination of underground explosions from naturally occurring earthquakes and other anthropogenic sources is one of the fundamental challenges of nuclear explosion monitoring. In an operational setting, the number of events that can be thoroughly investigated by analysts is limited by available resources. The capability to rapidly screen out events that can be robustly identified as not being explosions is, therefore, of great potential benefit. Nevertheless, possible mis-classification of explosions as earthquakes currently limits the use of screening methods for verification of test-ban treaties. Moment tensors provide a physics-based classification tool for the characterization of different seismic sources and have enabled the advent of new techniques for discriminating between earthquakes and explosions. Following normalization and projection of their six-degree vectors onto the hypersphere, existing screening approaches use spherically symmetric metrics to determine whether any new moment tensor may have been an explosion. Here, we show that populations of moment tensors for both earthquakes and explosions are anisotropically distributed on the hypersphere. Distributions possessing elliptical symmetry, such as the scaled von Mises–Fisher distribution, therefore provide a better description of these populations than the existing spherically symmetric models. We describe a method that uses these elliptical distributions in combination with a Bayesian classifier to achieve successful classification rates of 99 per cent for explosions and 98 per cent for earthquakes using existing catalogues of events from the western United States. The 1983 May 5 Crowdie underground nuclear test and 2018 July 20 DAG-1 deep-borehole chemical explosion are the only two explosions out of 140 that are incorrectly classified. Application of the method to the 2006–2017 nuclear tests in the Democratic People’s Republic of Korea yields 100 per cent identification rates and we provide a simple routine MTid for general usage. The approach provides a means to rapidly assess the likelihood of an event being an explosion and can be built into monitoring workflows that rely on simultaneously assessing multiple different discrimination metrics.

58 GEOSCIENCES↗

DOE OSTI2024

Overexpression of RuBisCO form I and II genes in Rhodopseudomonas palustris TIE-1 augments polyhydroxyalkanoate production heterotrophically and autotrophically

ABSTRACT With the rising demand for sustainable renewable resources, microorganisms capable of producing bioproducts such as bioplastics are attractive. While many bioproduction systems are well-studied in model organisms, investigating non-model organisms is essential to expand the field and utilize metabolically versatile strains. This investigation centers on Rhodopseudomonas palustris TIE-1, a purple non-sulfur bacterium capable of producing bioplastics. To increase bioplastic production, genes encoding the putative regulatory protein PhaR and the depolymerase PhaZ of the polyhydroxyalkanoate (PHA) biosynthesis pathway were deleted. Genes associated with pathways that might compete with PHA production, specifically those linked to glycogen production and nitrogen fixation, were deleted. Additionally, RuBisCO form I and II genes were integrated into TIE-1’s genome by a phage integration system, developed in this study. Our results show that deletion of phaR increases PHA production when TIE-1 is grown photoheterotrophically with butyrate and ammonium chloride (NH 4 Cl). Mutants unable to produce glycogen or fix nitrogen show increased PHA production under photoautotrophic growth with hydrogen and NH 4 Cl. The most significant increase in PHA production was observed when RuBisCO form I and form I & II genes were overexpressed, five times under photoheterotrophy with butyrate, two times with hydrogen and NH 4 Cl, and two times under photoelectrotrophic growth with N 2 . In summary, inserting copies of RuBisCO genes into the TIE-1 genome is a more effective strategy than deleting competing pathways to increase PHA production in TIE-1. The successful use of the phage integration system opens numerous opportunities for synthetic biology in TIE-1. IMPORTANCE Our planet has been burdened by pollution resulting from the extensive use of petroleum-derived plastics for the last few decades. Since the discovery of biodegradable plastic alternatives, concerted efforts have been made to enhance their bioproduction. The versatile microorganism Rhodopseudomonas palustris TIE-1 (TIE-1) stands out as a promising candidate for bioplastic synthesis, owing to its ability to use multiple electron sources, fix the greenhouse gas CO 2 , and use light as an energy source. Two categories of strains were meticulously designed from the TIE-1 wild-type to augment the production of polyhydroxyalkanoate (PHA), one such bioplastic produced. The first group includes mutants carrying a deletion of the phaR or phaZ genes in the PHA pathway, and those lacking potential competitive carbon and energy sinks to the PHA pathway (namely, glycogen biosynthesis and nitrogen fixation). The second group comprises TIE-1 strains that overexpress RuBisCO form I or form I & II genes inserted via a phage integration system. By studying numerous metabolic mutants and overexpression strains, we conclude that genetic modifications in the environmental microbe TIE-1 can improve PHA production. When combined with other approaches (such as reactor design, use of microbial consortia, and different feedstocks), genetic and metabolic manipulations of purple nonsulfur bacteria like TIE-1 are essential for replacing petroleum-derived plastics with biodegradable plastics like PHA.

Ranaivoarisoa, Tahina Onina↗

DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Assessment and Planning of Decarbonization Research and Training at UTRGV

During the one-year project, we identified research areas that align with Fossil Energy and Carbon Management (FECM) mission goals and assessed our university's current research capability and resources. In addition, we determined the resources needed to support FECM-related research and development (R&D) at our minority-serving institution, University of Texas Rio Grande Valley (UTRGV) in to increase our competitiveness for future funding opportunities in this area. We also gathered information on the current academic courses, programs and curriculum at UTRGV that aligns with FECM goals and described additional needs pertaining to student training.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A Computationally Efficient Algorithm for Computing Convex Hull Prices

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may execute side payments when a participant’s cost is not covered through energy sale settlements from locational marginal pricing schemes, or when a participant incurs lost opportunity costs to follow the dispatch signal. Convex hull pricing minimizes these and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. However, computing convex hull prices involves solving either a large-scale linear program - which in turn requires explicit descriptions of market participants’ convex hulls -or the Lagrangian dual of the corresponding non-convex scheduling problem. Here, we propose a computationally feasible and industrially scalable Benders decomposition approach to computing convex hull prices at least an order of magnitude faster than the current state-of-the-art while leveraging recent advances in convex hull formulations for thermal generating units.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

DOE OSTI2022

28 NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning: Preprint

AI↗

DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗

DOE OSTI2026

Potential of Data Center Controls in Grid Services

The rapid proliferation of large data centers brings both challenges and opportunities for grid reliability. The data center resources and their potential flexibility have the potential to contribute resources to grid operations. Through capabilities like energy shifting and resource coordination, data centers can help reduce their net demand on the transmission network, as well as provide additional grid services to support reliable operation on the grid. While transient and long-term grid planning and operations are the scenarios that draw most attention, the quasi-steady state timeseries (QSTS) operation of data centers and grid bring interesting scenarios that can help evaluate the data center controls to aid grid services. This work is focused on modeling data centers for QSTS applications – incorporating the AI data center load profiles and building on the PNNL digital twin model for the thermal management loads to enable simulation studies to reveal the impact of data center controls on grid performance. This includes the integration of a QSTS battery and natural gas generator model to incorporate local resource impacts to the system. The simulation study is performed with a modified IEEE 24-Bus transmission system. Scenarios are focused on evaluating the data center load impacts on the transmission system and leveraging both data center and local generation controls to mitigate those impacts and provide additional grid services. The data center controls revealed the ability to contribute to two main kinds of grid services: preventing congestion on a weak grid by coordinating the data center resources with the collocated BESS and onsite generation; and the ability to help the grid operations during stressed times of operation like during a contingency. Leveraging these and other capabilities has the potential to help data centers become grid responsive assets, aiding in both their integration into the power system and grid reliability.

power grid simulation↗

DOE OSTI2024

Exploring Dynamic P-Q Capability and Abnormal Operations of Inverter-Based Resources

Nowadays, inverter-based resources are rapidly growing in electric power systems. On the other side, abnormal inverter operations are also occurring more often. Traditionally, inverter PQ capability charts are employed by electric utility companies to ensure safe and reliable operations of inverter-based resources. However, it is found in this paper that existing inverter PQ capability charts do not address the operating characteristics of an inverter correctly, which can result in irregular and unstable inverter operations. Here in this paper, a novel study is conducted to investigate inverter PQ capability charts, where constraints that are specific to an inverter are considered. It is found in this paper that the inverter PQ capability charts exhibit a unique dynamic nature under uncertain grid conditions. This dynamic nature has not been considered by the industry in managing, designing, and controlling an inverter as well as in developing international standards for inverter-based resources. Via both electromagnetic transient simulation and hardware experimental investigations, it is found that the dynamic PQ capability nature, together with the limitations of the existing control methods, are the underlying causes of many unusual inverter operations that have been reported in the literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Advanced Simulation and Computing: ASC FY24 Implementation Plan

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, security, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including previous nuclear tests, stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent. The ASC program is a cornerstone of the SSP, providing simulation capabilities and computational resources to support the annual stockpile assessment and certification process, study advanced nuclear weapons design and manufacturing processes, analyze accident scenarios and weapons aging, and provide the tools to enable stockpile Life Extension Programs (LEPs) and the resolution of Significant Finding Investigations (SFIs). This work requires a balance of resources, including technical staff, hardware, simulation software, and computer science solutions. The ASC program focuses on increasing the predictive capabilities in a three-dimensional (3D) simulation environment while maintaining support to the SSP. The Program continues to improve its unique tools for understanding and solving progressively more difficult stockpile problems (sufficient resolution, dimensionality, and scientific details), and quantifying critical margins and uncertainties. Resolving each issue requires increasingly difficult analyses because the aging process has progressively moved the stockpile further from the original test base. While the focus remains on the U.S. nuclear weapons program, where possible, the Program also enables the use of high-performance computing (HPC) and simulation tools to address broader national security needs, such as foreign nuclear weapon assessments and nuclear counterterrorism. The 2022 Nuclear Posture Review (NPR) calls for NNSA to “deliver a modern, adaptive nuclear security enterprise based on an integrated strategy for risk management, production-based resilience, science and technology innovation, and workforce initiatives.” Furthermore, “NNSA will establish a Science and Technology Innovation Initiative to accelerate the integration of science and technology (S&T) throughout its activities.” Executing this strategy necessitates the continued emphasis on developing and sustaining high-quality scientific and engineering staff, as well as supporting computational and experimental capabilities. These components constitute the foundation of the nuclear weapons program. The continued success of the SSP and LEPs is predicated upon the ability to credibly certify the stockpile, without a return to underground nuclear tests (UGTs). Shortly after the nuclear test moratorium entered into force in 1992, the Accelerated Strategic Computing Initiative (ASCI) was established to provide an extensive simulation capability to underpin stockpile certification. While computing and simulation have always been essential to the success of the nuclear weapons program, the program goal of ASCI was to execute NNSA’s vision of using these tools in support of the stockpile stewardship mission. The ASCI program was essential to the successful demonstration of the SSP, providing critical nuclear weapons simulation and modeling capabilities. ASCI officially evolved into the ASC program in fiscal year (FY) 2005, but the mission remains essentially the same: provide the simulation and computational capabilities that underpin the ability to maintain a safe, secure, effective nuclear weapon stockpile, without returning to underground nuclear testing. The capabilities that the ASC program provides at the national laboratories play a vital role in the nuclear security enterprise and are necessary for fulfilling the stockpile stewardship and life extension requirements outlined for NNSA. The Program develops modern simulation tools that provide insights into stockpile aging issues, provide the computational and simulation tools that enable designers and analysts to certify the current stockpile and life-extended nuclear weapons, and inform the decision-making process when any modifications in nuclear warheads or the associated manufacturing processes are deemed necessary. Furthermore, ASC is enhancing the predictive simulation capabilities that are essential to evaluate weapons effects, design experiments, and ensure test readiness. The ASC program continues to improve its unique tools to solve stockpile problems— with a focus on sufficient resolution, dimensionality, and scientific detail—to enable Quantification of Margins and Uncertainties (QMU) and to resolve the increasingly difficult analyses needed for stockpile stewardship. The needs of the Stockpile Management and Production Modernization programs (formerly Directed Stockpile Work) also drive the requirements for simulation and computational resources. These requirements include planned LEPs, stockpile support activities, and mitigation efforts against the potential for technical surprise. All of the weapons within the current stockpile are in some stage of the life extension process. The simulation and computational capabilities are crucial for successful execution of these life extensions and for ensuring NNSA can certify these life-extended weapons without conducting a UGT.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Inverter Design with High Short-Circuit Fault Current Contribution to Enable Legacy Overcurrent Protection for Islanded Microgrids

The resiliency offered by a microgrid may be lost if the microgrid is not properly protected during short-circuit faults inside its boundaries. Many studies conclude that protecting microgrids in islanded mode is very challenging due to the limited short-circuit capability of distributed energy resources (DERs). The limited short-circuit capability of DERs typically inhibits the use of reliable and affordable overcurrent protective devices in microgrids. Although extensive research on microgrid protection is available in the literature, to date this research has not led to a cost-effective, commercially available relay that effectively tackles the challenges of microgrid protection. This work proposes hardware modifications to enhance the current contribution of an energy storage inverter with the objective of enabling the use of legacy overcurrent protection for islanded microgrids. This paper demonstrates through experimental results that few modifications are required in the inverter to significantly enhance its current contribution. In this study, a three-phase energy storage inverter was modified to provide three times its rated current during three-phase faults, which proved sufficient current for enough time to enable fuse-relay, and relay-to-relay coordination. The proposed modifications effectively increase the current contribution of the inverter, which is a promising advancement to allow the adoption of overcurrent protective devices for protecting microgrids.

Ferrari Maglia, Max↗