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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗
DOE OSTI2023

Potential impacts of Fukushima nuclear wastewater discharge on nutrient supply and greenhouse gas emissions of food systems

Seafood plays a critical role in promoting a nutritious and climate-friendly food system but faces challenges from marine contamination that pose food safety risks. Here, to address these challenges, we developed a Marine Pollution-induced Food System Risk Assessment and Response (MP-FSRAR) model to assess the impact of consumer perceptions of marine pollution risk events on nutrient supply and greenhouse gas (GHG) emissions. Applying this model to the proposed Fukushima Nuclear Wastewater Discharge (FNWD) into the Pacific Ocean, we identified the potential variation in nutrient supply and GHG emissions resulting from changes in Chinese consumers' willingness to consume seafood under 12 scenarios. Our analysis revealed nutrient supply gaps in daily protein, minerals, vitamins, and fatty acids, ranging from 1.5 to 2.7 g/person, 2.5–4.5 mg/person, 5.6–10.3 mcg/person, and 0.08–0.14 g/person, respectively. Closing the protein gap in China would require an additional 3.2–15.8 billion kg yr -1 protein-rich foods, including aquatic, livestock, dairy, and pulses. Additional food supply would increase extra GHG emissions by 1–79 billion kg CO 2 e yr -1 , resulting in potential GHG emissions being transmitted to other countries through the trade chain. Furthermore, we found that the livestock-based food substitution pathway had 40 times higher GHG emissions than the plant-based one. Our MP-FSRAR model provides global stakeholders with new perspectives on the potential consequences of consumer perceptions of marine pollution risks and offers valuable insights for developing countermeasures to build a more resilient and low-carbon food system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Automatic Generation of Event Trees and Fault Trees: A Model-Based Approach

In the past few decades, the increasing complexity of modern engineering systems has been driven by the integration of a large number of components whose operations may involve many disciplines (e.g., thermal hydraulics, plant operations, cybersecurity). Most computational tools used by industry and regulators for system safety and reliability assessments are still based on the traditional fault tree (FT) and event tree (ET) approach, which may not be able to capture complex interactions among system constituents. The use of simulation tools has widely increased in the past few decades to improve the fidelity of the reliability and safety analyses. However, the direct use of simulation tools as part of dynamic probabilistic risk assessment (DPRA) methods is not getting traction since (1) modeling the whole system under consideration with DPRA methods may be computationally expensive and unnecessary, and (2) the manual integration of DPRA models into existing state-of-practice probabilistic risk assessment models (i.e., based on FTs and ETs) can be time consuming and prone to errors. Here, in this paper we propose a procedure to overcome this limitation by presenting several algorithms designed to automatically construct subsystem ETs and FTs from DPRA methods for integration into an existing ET/FT system model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Quantifying the impact of climate change on erosion - 20397

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha (3,338-ac) site located approximately 48 km (30 mi) south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (metric tons, or 705 short tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. One of the key processes governing erosion is extreme precipitation and it is critical that trends in the distribution of extreme precipitation events through time be represented. The evidence of climate change is increasingly well documented and projected impacts on extreme precipitation events should be incorporated in performance assessment studies when relevant. Not evaluating future climate states in a performance assessment is contradictory to good modeling practice. Specifically, excluding climate change limits development of modeling information that could aid in effective decision making. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. In this work, this information was used to assess the impacts of potential changes in climate on erosion processes. The goal was to understand how projections of future climate relate to the performance of the WNYNSC through time. In the first stage of this work multi-temporal historical aerial images were analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. The information from these analyses was then used in conjunction with simulated data from the West Valley Erosion Working Group (EWG). Analysis of EWG simulations provides estimates for the change in erosion rates through time that is driven by changes in climate. The time-varying rates of erosion change were applied to the historical aerial imagery data in order to inform time-varying rates of erosion that are driven by changes in climate. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. The analyses from the EWG were used to characterize the relative impact of climate change on the erosion rates. This relative impact was quantified by comparing the LEM based estimates of erosion that were derived using historical climate data with LEM based estimates of erosion that were derived using projections of future climate. The relative increase in the erosion rates was then applied to the historical estimates of erosion derived from the analysis of the historical aerial imagery. This approach was used since the LEM-based estimated of the historical erosion rates from the past century have a bias towards underestimating erosion. This underestimation is hypothesized to be a consequence of an inability of the LEMs to account for the impacts of land-use change (i.e. deforestation) which had been shown to significantly increase erosion in similar Northern hardwood forest ecosystems. In summary, gully head retreat rate and gully widening rate were characterized using statistical probability distributions from the historical aerial image analysis. Simulated data from the EWG was used to estimate the climatically-driven changes in erosion rates through time. The changes through time from the EWG were applied to the gully head retreat rate and gully widening rate characterized using the historical aerial image analysis. This information can be used to inform PPA models to project future risks from a given site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Quantifying conditional probabilities of fish-turbine encounters and impacts

Tidal turbines are one source of marine renewable energy but development of tidal power is hampered by uncertainties in fish-turbine interaction impacts. Current knowledge gaps exist in efforts to quantify risks, as empirical data and modeling studies have characterized components of fish approach and interaction with turbines, but a comprehensive model that quantifies conditional occurrence probabilities of fish approaching and then interacting with a turbine in sequential steps is lacking. We combined empirical acoustic density measurements of Pacific herring ( Clupea pallasii ) and when data limited, published probabilities in an impact probability model that includes approach, entrainment, interactions, and avoidance of fish with axial or cross-flow tidal turbines. Interaction impacts include fish collisions with stationary turbine components, blade strikes by rotating blades, and/or a collision followed by a blade strike. Impact probabilities for collision followed by a blade strike were lowest with estimates ranging from 0.0000242 to 0.0678, and highest for blade strike ranging from 0.000261 to 0.40. Maximum probabilities occurred for a cross-flow turbine at night with no active or passive avoidance. Estimates were lowest when probabilities were conditional on sequential events, and when active and passive avoidance was included for an axial-flow turbine during the day. As expected, conditional probabilities were typically lower than analogous independent events and literature values. Estimating impact probabilities for Pacific herring in Admiralty Inlet, Washington, United States for two device types illustrates utilization of existing data and simultaneously identifies data gaps needed to fully calculate empirical-based probabilities for any site-species combination.

collision risk↗
DOE OSTI2021

Sea Ice Collision Risk Assessment for Tidal Turbine Siting in Cook Inlet, Alaska

Cook Inlet has great potential for tidal stream energy development. However, the presence of drifting sea ice could create hazardous collision risks for tidal turbine farms. Before turbines can be installed in Cook Inlet, sites must be surveyed to determine how often sea ice is present, how fast it will be moved by the current, and where the trajectories of drifting sea ice will be concentrated. In this study, we use remotely sensed data to characterize the seasonal sea ice conditions in Cook Inlet, a hydrodynamic model to map the water velocities, and a particle trajectory tracking model to calculate collision risks. The resulting sea ice coverage and collision risk maps will enable tidal energy developers to choose the best locations for the deployment of tidal turbines and other offshore platforms in Cook Inlet.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Rapidly evolving aerosol emissions are a dangerous omission from near-term climate risk assessments

Abstract Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, driving strong, spatially complex trends in temperature, hydroclimate, and extreme events both near and far from emission sources. Under-resourced, highly populated regions often bear the brunt of aerosols’ climate and air quality effects, amplifying risk through heightened exposure and vulnerability. However, many policy-facing evaluations of near-term climate risk, including those in the latest Intergovernmental Panel on Climate Change assessment report, underrepresent aerosols’ complex and regionally diverse climate effects, reducing them to a globally averaged offset to greenhouse gas warming. We argue that this constitutes a major missing element in society’s ability to prepare for future climate change. We outline a pathway towards progress and call for greater interaction between the aerosol research, impact modeling, scenario development, and risk assessment communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Land-use and atmospheric shifts jointly amplify U.S. drought-driven crop losses

Agricultural drought (AD) poses a major threat to food security, yet its future risk remains uncertain under co-evolving atmospheric conditions and land-use trajectories. Using an integrated, multi-sector modeling framework, we project AD risks for major crops across the contiguous United States (CONUS) through 2055 under a range of plausible futures that capture thermodynamic changes and land-use and land-cover change (LULCC) trajectories. Model projections reveal that drought-driven crop production losses increase sharply by nearly 60% for corn, 250% for wheat, and 135% for soybean relative to historical levels. The primary drivers of these increases, which include atmospheric shifts and LULCC, vary by region and crop type. LULCC acting as an important risk amplifier in regions experiencing cropland expansion into drought-prone regions, such as the Great Plains and northwestern U.S. Wheat exhibits the largest projected loss increases, a result that remains robust across scenarios. These findings highlight that interactions between atmospheric conditions and land-use trajectories shape future agricultural drought risk and should be jointly considered to support effective adaptation and food-system planning.

Yao, Lili↗
DOE OSTI2023

Modeling-Based Assessment of Deep Seismic Potential Induced by Geologic Carbon Storage

Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO 2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. In conclusion, this study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.

58 GEOSCIENCES↗
DOE OSTI2020

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Mechanisms and Impacts of Earth System Tipping Elements

Tipping elements are components of the Earth system which may respond nonlinearly to anthropogenic climate change by transitioning toward substantially different long-term states upon passing key thresholds or “tipping points.” In some cases, such changes could produce additional greenhouse gas emissions or radiative forcing that could compound global warming. Improved understanding of tipping elements is important for predicting future climate risks and their impacts. Here we review mechanisms, predictions, impacts, and knowledge gaps associated with 10 notable Earth system components proposed to be tipping elements. We evaluate which tipping elements are approaching critical thresholds and whether shifts may manifest rapidly or over longer timescales. Some tipping elements have a higher risk of crossing tipping points under middle-of-the-road emissions pathways and will possibly affect major ecosystems, climate patterns, and/or carbon cycling within the 21st century. However, literature assessing different emissions scenarios indicates a strong potential to reduce impacts associated with many tipping elements through climate change mitigation. The studies synthesized in our review suggest most tipping elements do not possess the potential for abrupt future change within years, and some proposed tipping elements may not exhibit tipping behavior, rather responding more predictably and directly to the magnitude of forcing. Nevertheless, uncertainties remain associated with many tipping elements, highlighting an acute need for further research and modeling to better constrain risks.

58 GEOSCIENCES↗
DOE OSTI2023

Uncertainty analysis for techno-economic and life-cycle assessment of wet waste hydrothermal liquefaction with centralized upgrading to produce fuel blendstocks

Wet waste hydrothermal liquefaction is a promising technology for producing transportation fuels with much lower greenhouse gases emissions than petroleum-based fuels. However, its techno-economic and life cycle assessment are primarily based on laboratory scale testing data, subject to considerable uncertainties, and even bias, due to knowledge gaps. Here, a preliminary uncertainty analysis of key economic measures was conducted based on the 2019 state-of-technology model for biocrude production. Building on the preliminary analysis, this work presents a comprehensive uncertainty analysis in both economic and environmental measures of the entire supply chain of wet waste hydrothermal liquefaction to fuel blendstocks including biocrude upgrading based on the 2021 state-of-technology model. The analysis includes the most recent developments in hydrothermal liquefaction and biocrude upgrading technologies and Monte Carlo simulation based on an integrated model system including an improved reactor yield model, reduced-order process model, discounted cash flow economic model and simplified life-cycle assessment model. The estimated biocrude yield ranges from 42.2% to 52.4% with a median of 47.3%. The estimated fuel yield ranges from 34.7% to 42.7% with a median of 38.7%. The estimated minimum fuel selling price ranges from $\$ $2.28/gge to $\$ $3.45/gge with a median of $\$ $2.80/gge. Relative to petroleum-derived diesel, the estimated reduction in supply chain greenhouse gas emissions ranges from 73.4% to 81.8% with a median of 77.7%. Compared to the 2019 state-of-technology analysis, a significant improvement in biocrude selectivity and economic measures and reduction in uncertainties were achieved due to the incorporation of additional continuous experimental data sets, technology development and de-risking, and improvement in model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Site 300 B-851 Permit: Radiological Assumptions and Source Term - Air Dispersion Modeling for Radiological Dose Assessment

The following modeling assumptions and source term that is provided in the attachments to this document are meant to serve as a guide to the presentation delivered on April 23rd, 2020, to the San Joaquin Air District Permit Services slides. This analysis is in response to the District’s “Project Incompleteness Letter” (Facility Number: N-472 and Project Number: N-1173492) regarding the potential for depleted uranium (DU) to be resuspended from areas of contaminated soil at Site 300 as a result of high explosive (HE) experiments increased to a requested permitted maximum of 1,000 pound detonations with an annual HE throughput not to exceed 7,500 pounds per year. LLNL has taken every step in the radiological health risk concerns in the modeling assumptions and final source term to be at a level of maximum conservativeness. As an example, forty soil samples were taken in the B-851 area (LLNL-AR-738708 – 2018) and assayed for uranium-234, uranium-235, and uranium-238 concentrations. The maximum uranium soil concentrations were used in the assumptions (potential for resuspended dust occurring from detonation experiments) and not the use of median uranium soil concentrations – a more realistic outcome. Additionally, these uranium soil concentrations were considered to be at 100% uptake in the vegetation for prescribed burns; vegetation uptake factors were not used as that would lower the final source term. This level of conservativeness was maintained throughout the modeling assumptions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Impacts of Different Operation Conditions and Geological Formation Characteristics on CO2 Sequestration in Citronelle Dome, Alabama

Major concerns of carbon dioxide (CO2) sequestration in subsurface formations are knowledge of the well injectivity and gas storage capacity of the formation, the CO2 pressure and saturation plume extensions during and after injection, and the risks associated with CO2 leakage and fault reactivation. Saline reservoirs are considered as one of the target formations for CO2 sequestration through structural, residual, dissolution, and mineral trapping mechanisms. The boundary condition of the saline reservoir dictates the pressure and saturation plume extension of the injected supercritical CO2 that could expand over large distances. This can lead to sources of risk, e.g., leakage and/or fault reactivation due to presence of wells, thief zones, and geological discontinuities. Therefore, there is a critical need to develop a model that describes how risk-related performance metrics (i.e., the CO2 saturation plume size, the pressure differential plume area, and the pressure differential at specific locations) vary as a function of the size of injection, time following injection, injection operations, and geologic environment. In this study, a systematic reservoir modeling studies of anthropogenic CO2 sequestration in Citronelle dome, Alabama, was performed where all relevant scenarios and conditions to address the questions of the saturation and pressure plume size in the area of review (AoR) and post-injection site care (PISC) are considered. The objective for this study was firstly to systematically simulate CO2 sequestration, i.e., saturation dynamics, and pressure behavior over a range of operational and geological conditions and to derive conclusions about the factors influencing saturation and pressure plume size, post-injection behavior, and the risk associated with them, by developing third-generation reduced order models (ROMs) for reservoir behavior. Finally, to assess the uncertainty associated with our studies, Latin Hypercube Sampling (LHS) together with an experimental design technique, i.e., Plackett–Burman design, was used. Application of Pareto charts and respond surfaces enabled us to determine the most important parameters impacting saturation and pressure plume sizes and to quantify the auto- and cross-correlation among different parameters in both history-matched and upscaled models.

58 GEOSCIENCES↗
DOE OSTI2024

Adaptive Protection and Validated Models to Enable Deployment of High Penetrations of Solar PV (PV-MOD)

The availability and validation of various PV models in commercial tools differ, with some models not yet thoroughly validated for advanced inverter functionalities and reliable performance under weak system conditions. Many existing models do not fully incorporate new inverter control functions, which can affect system stability. The increasing deployment of solar PV and other inverter-based resources (IBRs), including distributed energy resources (DERs), is influencing the reliable operation of protection schemes in distribution systems and microgrids. Emerging adaptive protection schemes (APS) offer new opportunities for protecting these systems during varying configurations and DER operating conditions, though their demonstration and validation remain limited. Adaptive protection schemes face similar challenges, as they are typically designed for specific configurations. There is a growing need for tools and methodologies to streamline the deployment of adaptive protection for safe and reliable DER integration. The project main objective was to develop and validate high-fidelity generic models of solar PV facilities for stability, protection, EMT, and QSTS analyses. This objective was achieved, and these models can now be integrated into commercial software tools, enabling utilities, vendors, and developers to study high-penetration PV systems more confidently. The project also demonstrated advanced applications of these models, including the design and deployment of adaptive protection schemes in high-penetration field applications and microgrids, supporting grid safety and reliability. Several milestones were reached by the end of the project. A sophisticated inverter test plan was developed, and inverters representative of the North American marketplace were selected. EPRI and NREL tested various inverters, conforming to IEEE standards. Improvements were made to existing generic models of IBR units, IBR plants, and aggregated feeders for various analyses. The first generic electromagnetic transient (EMT) model for a solar PV plant was developed, conforming to IEEE Std 2800™-2022 and validated against laboratory measurements of a 2.2 MVA large-scale battery energy storage system (BESS) inverter. That model was then used to produce reference responses illustrating examples of validated and verified IBR plant models that pass or fail tests for technical minimum capability and performance as specified in the IEEE standard. The developed, tested, and validated generic models can be used for transmission planning, stability assessments, expansion planning, and evaluating potential future IBR interconnection requirements. They can also support interconnection screens and conformity assessments of IBR plants, including solar PV. The project significantly contributed to the ongoing standardization and model-based representation and verification of IBR responses. The project further addressed challenges of common distribution protection schemes with increasing deployment of DER by developing, validating, and demonstrating adaptive protection schemes (APS) that can improve the reliable and safe integration of DER into distribution systems. New APS were designed using improved DER models for three common distribution systems: a radial feeder, a meshed network, and a microgrid. Modeling and hardware-in-the-loop (HIL) testing of the APS were conducted, successfully showing their effectiveness and selectivity. Proof-of-concept field demonstration was achieved for two APS, i.e., one on a radial feeder and another one in a microgrid. Field demonstration could not be achieved for the APS on a meshed network, primarily due apprehension of one utility partner and also due to limited access to the protective algorithms in the network protectors. Guidelines developed from the lessons learned in the project lay out the general process followed in the design, installation, and commissioning of APS for various distribution systems. Distribution utility partners’ apprehension about field demonstration of the new APS were addressed—with varying success—by taking a stepped risk-management approach of modeling of a wide range of sensitivities first, performing in-depth proof-of-concept testing in the laboratory including HIL next, and finally deliberately implementing and commissioning the actual protection equipment and algorithms into parts of—or in parallel operation to—the three real distribution systems. Future work should include pilot projects that further show the acceptable performance of the developed APS before these schemes be rolled out more widely. Inclusion of both utility and original equipment manufacturers (OEMs) in future projects could increase chances of successful field demonstration. Despite challenges in achieving the field demonstration goal of the project for all three APS, the research significantly contributed to the innovation of adaptive protection solutions for scalable and reliable DER integration into distribution systems. This project significantly enhances the understanding of the impact of using appropriate inverter models on distribution and transmission (T&D) systems. By addressing the limitations of existing generic models, the project introduces high-fidelity models for stability, protection, electromagnetic transient (EMT), and quasi-static time series (QSTS) analyses. These models, integrated into commercial software tools, enable utilities, vendors, and developers to confidently study high-penetration PV systems. The project also demonstrates advanced applications, including adaptive protection schemes (APS) for distribution systems and microgrids, ensuring grid safety and reliability. The technical effectiveness and economic feasibility of the methods are evident through the development and validation of sophisticated inverter test plans and the selection of representative inverters. Testing by EPRI and NREL on retail, commercial, and utility-scale inverters, conforming to IEEE standards, underscores the robustness of the models. Improvements to existing generic models for various analyses further enhance their validity and applicability. The project also identifies gaps in common distribution protection schemes and designed new APS using improved DER models, demonstrating their effectiveness through modeling and hardware-in-the-loop (HIL) testing. The project’s benefits to the public are manifold. By advancing the standardization and model-based representation of IBR response, it supports transmission planning, stability assessments, and future IBR interconnection requirements. The generic models can facilitate better communication between transmission planners and developers, supporting expected IBR plant capability and performance. Additionally, the development of APS for radial feeders, meshed networks, and microgrids supports the integration of distributed energy resources (DERs) into distribution systems, enhancing grid reliability and safety. The project’s emphasis on thorough testing and simplicity in design ensures practical and scalable solutions for DER integration.

14 SOLAR ENERGY↗
DOE OSTI2022

Compressed Natural Gas Component Leak Frequency Estimation

The frequency of unintended releases in a compressed natural gas system is an important aspect of the system quantitative risk assessment. The frequencies for possible release scenarios, along with engineering models, are utilized to quantify the risks for compressed natural gas facilities. This report documents component leakage frequencies representative of compressed natural gas components that were estimated as a function of the normalized leak size. A Bayesian statistical method was used which results in leak frequency distributions for each component which represent variation and uncertainty in the leak frequency. The analysis shows that there is high uncertainty in the estimated leak frequencies due to sparsity in compressed natural gas data. These leak frequencies may still be useful in compressed natural gas system risk assessments, as long as this high uncertainty is acknowledged and considered appropriately.

03 NATURAL GAS↗
DOE OSTI2023

Understanding the compound flood risk along the coast of the contiguous United States

Abstract. Compound flooding is a type of flood event caused by multiple flood drivers. The associated risk has usually been assessed using statistics-based analyses or hydrodynamics-based numerical models. This study proposes a compound flood (CF) risk assessment (CFRA) framework for coastal regions in the contiguous United States (CONUS). In this framework, a large-scale river model is coupled with a global ocean reanalysis dataset to (a) evaluate the CF exposure related to the coastal backwater effects on river basins, and (b) generate spatially distributed data for analyzing the CF hazard using a bivariate statistical model of river discharge and storm surge. The two kinds of risk are also combined to achieve a holistic understanding of the continental-scale CF risk. The estimated CF risk shows remarkable inter- and intra-basin variabilities along the CONUS coast with more variabilities in the CF hazard over the US west and Gulf coastal basins. Different risk assessment methods present significantly different patterns in a few key regions such as the San Francisco Bay area, the lower Mississippi River, and Puget Sound. Our results highlight the need to weigh different CF risk measures and avoid using single statistics-based or hydrodynamics-based CFRAs. Uncertainty sources in these CFRAs include the use of gauge observations, which cannot account for the flow physics or resolve the spatial variability of risks, and underestimations of the flood extremes and the dependence of CF drivers in large-scale models, highlighting the importance of understanding the CF risks for developing a more robust CFRA.

54 ENVIRONMENTAL SCIENCES↗