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DOE OSTI2023

Grid-Scale Electricity Storage at Lowest Possible Cost Enabled by Pumped Heat Electricity Storage (Final Report)

Grid-scale energy storage technologies that can provide greater than 10 hours of electricity at full power address a critical need for full penetration of renewables on electricity grids worldwide, minimize renewable curtailment, and maximize grid security and reliability. Pumped Heat Energy Storage (PHES) systems can store energy thermally in any location at the low costs necessary for displacing fossil fuels, reducing foreign fuel imports, and reducing emissions to maintain the energy security of the United States. The award allowed Southwest Research Institute® (SwRI®) and Malta, Inc. (Malta) to advance the PHES concept to a proof-of-concept demonstration for a kW-scale system. The project focused on system operation and integration. The main project objectives were: 1. Address first implementation challenges 2. Explore system control strategies and operation 3. Translate lessons learned to the full-scale system Through these objectives the project completes a first demonstration of this technology and reduces the technical risk associated with a full-scale system.

25 ENERGY STORAGE↗

DOE OSTI2024

Testing- and Model- Based Optimization of Coal-fired Primary Heater Design for Indirect Supercritical CO 2 Power Cycles (Final Scientific and Technical Report)

The overall objective of this project was to perform the R&D necessary to mitigate the risk associated with the design of a primary heat exchanger for a solid-fired combustion system coupled with an indirect-fired closed-loop Brayton Cycle utilizing supercritical CO 2 . The key technological hurdle was the coupling of a solid-fuel firing system with the primary heater, which poses a singular challenge, which is the management of burner performance and operational conditions in a way to manage heat exchanger tube metal temperatures and temperature ramp rates in the absence of fluid phase change on the inside of the tubes. We designed and built the first ever pseudo power system employing a simple recuperated supercritical CO 2 closed-loop Brayton Cycle coupled to a solid-fuel fired system. Advanced coupled CFD and process modeling were used to design the primary heat exchanger (PHX), which consisted of both radiative and convective sections, to limit tube metal temperatures resulting from the heat release profile of the solid fuel flame near the radiative tubes. The heat exchanger was designed to produce finished CO 2 temperatures of 600 °C a pressure of 20.7 MPa and CO 2 flow of 5.5 kg/s. The constructed PHX was capable of 1.2 MWth heat uptake. During design of the PHX, the modeling showed that most variables influencing flame shape (burner stoichiometric ratio and register velocities and swirl) were not suitable to manage heat flux to the metal surfaces. This is because they substantially increased adiabatic flame temperature through the influence of localized stoichiometric ratio. Excess air and firing rate were the two most powerful variables that could be used to control tube surface temperatures. The coupled system was operated for a total of 407 hours, with the longest continuous run of 248 hours. For 62% of the operational time, the unit was unmanned and in automatic control. The fuels used for the testing included natural gas, two Utah Bituminous coals, woody biomass, and bagasse. During the testing we were able to verify the 1.2 MWth heat uptake and we operated at a finished CO 2 temperature of 607 °C and a pressure of 20.3 MPa simultaneously. The real-time corrosion rate of the Super 304H tube CO 2 surface in the region of the radiative section of the PHX were measured, at an approximate temperature of 550 °C. The two key variables related to corrosion rate are the pressure and flow rate of the CO 2 . A technoeconomic analysis was performed at a scale of 120 MWE. The updated analysis showed that the efficiency of an sCO 2 power producing plant will be related to the pressure drop of the PHX.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Data-Driven Scalable Emulation of Hydropower Using Real-Time Hardware-in-the-Loop

This presentation covers Motivation: (1) With the increased grid integration of inverter-based resources, hydropower plays a crucial role in maintaining the bulk power system reliability and resilience; and (2) A more dynamic response and new control designs are required to meet the grid requirements. To evaluate any modification, control-prototyping, performance validation and de-risking grid integration of hydropower, a high-fidelity environment is required. Also covers Objectives: (1) To develop data-driven emulation of hydropower using hardware-in-the-loop for different size, types of hydro plants; and (2) To characterize hardware and obtain accurate dynamic response for shaft speed, torque, and power. Provide a mechanical power interface with emulated dynamics of a hydro-turbine shaft that can be coupled to electrical generators for mechanical and electrical PHIL.

electrical↗

DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Exploring Geothermal Potential of Great Basin Sub-Regions

exploration↗

DOE OSTI2024

Use Case-Informed Framework for Utility Cloud Migration

This white paper presents a comprehensive methodology for assessing utilities’ cloud postures and frameworks. It aims to produce a roadmap and strategy for a cloud-enabled grid future, providing guidance for integrators, asset owners, and operators. Instead of offering a yes or no answer for cloud implementation, this framework offers strategic guidance on responsibly preparing for and deploying cloud solutions. This paper delves into cloud-service models pertinent to the electric sector, dissecting the shared responsibility model and elucidating what on-premise infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) entail. A pivotal consideration within the context of the shared responsibility model is the allocation of responsibility for foundational security aspects—a decision that will be informed by a comprehensive risk assessment. The ensuing discussion will present a checklist of certifications necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and with a strategic roadmap. Furthermore, the paper outlines gaps in understanding the U.S. government’s role in shaping technology development and responsible use. Its purpose is to aid decision-making by offering support for risk-informed solutions that benefit those managing assets and operating in the cloud environment. The primary objective is to enhance the resilience and future readiness of a decarbonized electric grid, with cloud solutions as one viable option. The paper synthesizes information on current and future grid architectures and applications, considering both conservative and progressive energy transitions, along with scalable and distributed computing considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Potential Benefits in Remapping the Special Flood Hazard Area: Evidence from the U.S. Housing Market

A typical U.S. homebuyer's understanding of whether a property faces flood risk is based on whether the property is located inside the National Flood Insurance Program's (NFIP) Special Flood Hazard Area (SFHA). The SFHA boundary, however, may bias homebuyers’ perceptions of flood risk relative to unobserved true risk because the SFHA is an incomplete, and sometimes inaccurate, representation of flood hazards. Using a national dataset of property transactions, flood hazard data from the First Street Foundation, state-level measures of flood disclosure laws, and a spatially restricted triple-difference design, we distinguish capitalization effects of policy-driven (SFHA) versus quasi-objective (First Street) indicators of flood hazard. We identify these effects by assessing thousands of local spatial interactions between property SFHA designations, measures of quasi-objective flood hazard, and being in a state that mandates flood history disclosures. Being inside the SFHA generates a risk discount, but the signal is muted relative to underlying hazard exposure. Further, the SFHA signal can result in inefficient discounts for properties erroneously mapped in the SFHA. Disclosure requirements about flood history accentuate price effects for hazardous properties and result in more adequate risk internalization. In the absence of disclosures, however, we find either no or a weak risk signal for houses outside the SFHA that face flood hazard. Our results highlight potential benefits of updating SFHA boundaries to include all houses that may experience flooding, and argue in favor of requiring flood-related disclosures from sellers to improve the market's ability to internalize flood risk.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

BETO 2021 Peer Review - Continuous Enzymatic Hydrolysis Development (CEHD) WBS 2.4.1.101

The Continuous Enzymatic Hydrolysis Development (CEHD) project aims to reduce the cost and commercialization scale-up risks of biorefinery sugar-lignin production through development of a deployable continuous enzymatic hydrolysis (CEH) process. Through the use of external cross flow membrane filtration loops coupled to enzymatic hydrolysis (EH) reactors, pretreated biomass solids and enzymes are retained for reaction while solubilized product sugars are removed in situ, with high extents of conversion achieved through a series of reactor-membrane unit stages. The CEHD project is focused on advancing CEH as a transformational, process-intensified, lower-cost method for producing soluble clarified biomass sugars and insoluble lignin-rich streams than traditional batch enzymatic hydrolysis (BEH). The project's primary objective is to reduce the cost of CEH to be compellingly lower than conventional BEH, 10% lower in year 1 and 20% lower in year 3 (end of project). A related objective is to expand CEH's operating envelope to increase process efficiency. Through more thorough de-risking and demonstration of CEH, in conjunction with building a suite of modeling and optimization tools that allow for more facile rigorous in silico evaluation of novel CEH designs and modalities, this project intends to elevate industry interest in adopting CEH as an improvement over conventional batch processing. This project was merit reviewed in FY20. It's now in its first year of a new 3-year plan spanning FY21-FY23.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

A Pathway for the Development of Advanced Reactor Mechanistic Source Term Modeling and Simulation Capabilities

Source term analysis, or the estimation of the potential radionuclide release to the environment during reactor events, is a central focus of the reactor licensing process and a vital part of risk-informed reactor design approaches. A mechanistic source term (MST) analysis is designed to realistically model the release and transport of radionuclides from the source to the environment for specific scenarios, while accounting for retention or transmutation phenomena and associated uncertainties. The objective of MST analyses, in comparison to bounding or conservative source term assessments, is to provide a non-biased representation of reactor risk and improve the information available for siting, emergency planning, and reactor design decisions. In support of the advanced reactor community in its pursuit of MST analysis capabilities, this work outlines a recommended research pathway for the development of MST modeling and simulation (mod/sim) tools.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Progress on Near-Term Tasks for the Development of Advanced Reactor Mechanistic Source Term Modeling and Simulation Tools

To assist both the advanced reactor industry and U.S. Nuclear Regulatory Commission (NRC) in the pursuit of reactor design and licensing, the U.S. Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has established a mechanistic source term (MST) research project under the auspices of the Multiphysics Applications technical area. MST analysis is central to the reactor licensing process and a focus of the NRC regarding their mission to provide reasonable assurance of adequate protection of public health and safety and environment. The MST research project is a collaboration between Argonne National Laboratory (Argonne) and Sandia National Laboratories (SNL) with a high-level objective to coordinate the development of comprehensive advanced reactor MST mod/sim capabilities to support risk-informed design and licensing decisions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Advanced Laboratory and Field Arrays: Debris Modeling, Detection,& Mitigation (Task 1)

The statement of project objectives for this task was: develop tools, methods and models to assess, and mitigate the risk of damage to MHK infrastructure from woody debris. Develop the capability to detect woody debris using sonar and/or physical methods for purposes of characterizing debris statistics in river (at UAF’s Tanana River Test Site) and near-shore wave (at Yakutat, AK) environments and to activate debris mitigation measures. Develop debris impact risk maps and tables using statistics on debris size, geometry, type, prevalence, mobility and location. Improve and apply the COUPi discrete element method (DEM) to develop models of debris movement and impact on MHK infrastructure to evaluate risk of damage, and interference, to operations from debris. The proposed final deliverable for the task was a set of tools or techniques for providing estimates of the probability of debris impact, and resulting impact forces, on MHK infrastructure as a function of debris size, type, wave regime, and current velocity. Such estimates are required to assess damage risks to operational MHK infrastructure.

13 HYDRO ENERGY↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

1.2.4.403 - Pumped Storage Hydropower FAST Commissioning Prize

The Pumped Storage Hydropower (PSH) FAST Commissioning Prize aimed to reduce the time, cost, and risk required to commission PSH projects by crowdsourcing ideas via a three-stage prize. Objectives were to reduce the time to commission PSH projects from 10+ years to less than 5 years and to develop a baseline analysis for PSH.

HYDRO ENERGY↗

DOE OSTI2021

The Safe Urban Harvests Study: A Community-Driven Cross-Sectional Assessment of Metals in Soil, Irrigation Water, and Produce from Urban Farms and Gardens in Baltimore, Maryland

BACKGROUND: Emerging evidence suggests social, health, environmental, and economic benefits of urban agriculture (UA). However, limited work has characterized the risks from metal contaminant exposures faced by urban growers and consumers of urban-grown produce. OBJECTIVES: We aimed to answer community-driven questions about the safety of UA and the consumption of urban-grown produce by measuring concentrations of nine metals in the soil, irrigation water, and urban-grown produce across urban farms and gardens in Baltimore, Maryland. METHODS: We measured concentrations of 6 nonessential [arsenic (As), barium (Ba), cadmium (Cd), chromium (Cr), lead (Pb), nickel (Ni)] and three essential [copper (Cu), manganese (Mn), zinc (Zn)] metals in soil, irrigation water, and 13 types of urban-grown produce collected from 104 UA sites. We compared measured concentrations to existing public health guidelines and analyzed relationships between urban soil and produce concentrations. In the absence of guidelines for metals in produce, we compared metals concentrations in urban-grown produce with those in produce purchased from farmers markets and grocery stores (both conventionally grown and U.S. Department of Agriculture–certified organic). RESULTS: Mean concentrations of all measured metals in irrigation water were below public health guidelines. Mean concentrations of nonessential metals in growing area soils were below public health guidelines for Ba, Cd, Pb, and Ni and at or below background for As and Cr. Though we observed a few statistically significant differences in concentrations between urban and nonurban produce items for some combinations, no consistent or discernable patterns emerged. DISCUSSION: Screening soils for heavy metals is a critical best practice for urban growers. Given limitations in existing public health guidelines for metals in soil, irrigation water, and produce, additional exposure assessment is necessary to quantify potential human health risks associated with exposure to nonessential metals when engaging in UA and consuming urban-grown produce. Conversely, the potential health benefits of consuming essential metals in urban-grown produce also merit further research. https://doi.org/10.1289/EHP9431

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Sources of Conservatism in Making Corrective Action Decisions at the NNSS - 20477

During the course of closing nuclear testing release sites at the Nevada National Security Site (NNSS) under the Federal Facility Agreement and Consent Order (FFACO) using the Risk-Based Corrective Action (RBCA) evaluation process, conservative assumptions and estimates were used in many phases of the work. These assumptions and estimates were agreed to during Data Quality Objective (DQO) meetings and published in the FFACO documents. However, the potential cumulative effect of these multiple sources of conservatism was not evaluated. The RBCA process ensures that corrective action decisions minimize the risk of making Type I errors (not applying corrective action to areas that exceed action levels) at the expense of making Type II errors (applying corrective action to areas that do not exceed action levels). Rather than fully characterizing the spatial distribution of contaminants throughout each site, the RBCA process uses a bounding methodology that conservatively estimates spatial boundaries beyond which contamination is not present that exceed corrective action levels. This process accepts that these boundaries may encompass areas that do not exceed corrective action levels and, therefore, may be somewhat larger than for fully characterized sites. This is justified at sites where the reduction in investigation costs are more than any potential increase in corrective action costs. To minimize the risk of making Type I errors, conservatism was used throughout the process. This study documents the many sources of conservatism used in the RBCA process. Conservatism was used in the RBCA process in determining potential receptors, determining action levels, collecting samples, converting sample results into receptor dose, and accounting for uncertainty. This provides decision-makers with a high level of confidence that unacceptable risk to receptors will not result from exposure to residual contamination outside the corrective action boundaries. However, it does not provide an estimate of the actual extent of areas exceeding action levels. This study provides an evaluation of the effects of compounding conservatism on RBCA process decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin - Task 2 Topical Report

This attachment A is a detailed Topical Report for Task 2 (Advanced Field Characterization and Machine Learning Based Data Integration) under the project "Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin." The overall project activity and finding, including the field pilot testing of CO 2 injection are summarized in the companion Final Technical Report The Advanced Field Characterization and Machine Learning Based Data Integration task (Task 2) involved a systematic geologic characterization of the Trenton-Black River (TBR) play in the SMB which included the development of comprehensive datasets, advanced data analytics, risk assessment, and piggyback field characterization. These activities aimed to address the complex carbonate systems by evaluating the extent of fractures, dolomitization, facies, and reservoir properties with the primary objective of informing the static and dynamic modeling, field injection test, and providing input into the development strategy plan. The task was divided into four subtasks: • Subtask 2.1 – Data compilation, review, and analysis • Subtask 2.2 – Risk Assessment • Subtask 2.3 – Advanced Field Characterization • Subtask 2.4 – Integrated Physics-Based Machine Learning and Advanced Data Analytics

02 PETROLEUM↗

DOE OSTI2024

Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone Distribution

Importance Opioid-related overdose accounts for almost 80 000 deaths annually across the US. People who use drugs leaving jails are at particularly high risk for opioid-related overdose and may benefit from take-home naloxone (THN) distribution. Objective To estimate the population impact of THN distribution at jail release to reverse opioid-related overdose among people with opioid use disorders. Design, Setting, and Participants This study developed the agent-based Justice-Community Circulation Model (JCCM) to model a synthetic population of individuals with and without a history of opioid use. Epidemiological data from 2014 to 2020 for Cook County, Illinois, were used to identify parameters pertinent to the synthetic population. Twenty-seven experimental scenarios were examined to capture diverse strategies of THN distribution and use. Sensitivity analysis was performed to identify critical mediating and moderating variables associated with population impact and a proxy metric for cost-effectiveness (ie, the direct costs of THN kits distributed per death averted). Data were analyzed between February 2022 and March 2024. Intervention Modeled interventions included 3 THN distribution channels: community facilities and practitioners; jail, at release; and social network or peers of persons released from jail. Main Outcomes and Measures The primary outcome was the percentage of opioid-related overdose deaths averted with THN in the modeled population relative to a baseline scenario with no intervention. Results Take-home naloxone distribution at jail release had the highest median (IQR) percentage of averted deaths at 11.70% (6.57%-15.75%). The probability of bystander presence at an opioid overdose showed the greatest proportional contribution (27.15%) to the variance in deaths averted in persons released from jail. The estimated costs of distributed THN kits were less than $\$$15 000 per averted death in all 27 scenarios. Conclusions and Relevance This study found that THN distribution at jail release is an economical and feasible approach to substantially reducing opioid-related overdose mortality. Training and preparation of proficient and willing bystanders are central factors in reaching the full potential of this intervention.

Tatara, Eric [Argonne National Laboratory (ANL), A↗

DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗