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DOE OSTI2021

Emerging Energy Market Analysis Initiative, Methodological Framework

Planning and operations of the electric power sector are undergoing radical changes. Climate change mitigation efforts have forced rapid changes to the technology mix. Technologies like wind and solar have experienced rapid growth, while investment in fossil sources has peaked or is declining. These foundational changes are forcing changes to energy systems. Demand-side adoption of electrified technologies, including electric vehicles, is changing load profiles and opening up new avenues for consumer participation in the power systems. The implications of an evolving power system pertain to more than environmental and technical dimensions. Changes to the generation mix and its consequent upstream and downstream impacts such as fuel production have significant and highly concentrated consequences on economies and employment. Shifts towards distributed (or decentralized) generating assets offer the potential to reshape economic and employment opportunities associated with the energy sector across space and socioeconomic groups. The Emerging Energy Market Analysis (EMA) initiative aims to identify sustainable, regionally acceptable, and high-value energy solutions that are secure and equitable. Unlike short-term, least-cost choices that can narrowly account for traditional options, EMA’s focus on emerging energy markets recognizes that new or adapted practices and technologies can alter the frontier of solutions and advance a community’s social, economic, and natural pathways. Such change requires a more comprehensive analysis of societal input, resources, capabilities, and infrastructure. These considerations lay the foundation for community decision-making models that are responsive to community values as well as the history and drivers. The result is a community-based decision and engagement model that will be valuable to decisionmakers and developers of advanced and emerging energy solutions, seeking a social license to operate prior to project development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

The evolution of the Human Systems and Simulation Laboratory in nuclear power research

The events at Three Mile Island in the United States brought about fundamental changes in the ways that simulation would be used in nuclear operations. The need for research simulators was identified to scientifically study human-centered risk and make recommendations for process control system designs. This paper documents the human factors research conducted at the Human Systems and Simulation Laboratory (HSSL) since its inception in 2010 at Idaho National Laboratory. The facility’s primary purposes are to provide support to utilities for system upgrades and to validate modernized control room concepts. In the last decade, however, as nuclear industry needs have evolved, so too have the purposes of the HSSL. Thus, beyond control room modernization, human factors researchers have evaluated the security of nuclear infrastructure from cyber adversaries and evaluated human-in-the-loop simulations for joint operations with an integrated hydrogen generation plant. Lastly, our review presents research using human reliability analysis techniques with data collected from HSSL-based studies and concludes with potential future directions for the HSSL, including severe accident management and advanced control room technologies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

An Integrated Feasibility Study of Reservoir Thermal Energy Storage (RTES) in Portland, OR, USA

In regions with long cold overcast winters and sunny summers, Deep Direct-Use (DDU) can be coupled with Reservoir Thermal Energy Storage (RTES) technology to take advantage of pre-existing subsurface permeability to save summer heat for later use during cold seasons. Many aquifers worldwide are underlain by permeable regions (reservoirs) containing brackish or saline groundwater that has limited beneficial use due to poor water quality. We investigate the utility of these relatively deep, slow flowing reservoirs for RTES by conducting an integrated feasibility study in the Portland Basin, Oregon, USA, developing methods and obtaining results that can be widely applied to RTES systems elsewhere. As a case study, we have conducted an economic and social cost-benefit analysis for the Oregon Health and Science University (OHSU), a teaching hospital that is recognized as critical infrastructure in the Portland Metropolitan Area. Our investigation covers key factors that influence feasibility including 1) the geologic framework, 2) heat and fluid flow modeling, 3) capital and maintenance costs, 4) the regulatory framework, and 5) operational risks. By pairing a model of building seasonal heat demand with an integrated model of RTES resource supply, we determine that the most important factors that influence RTES efficacy in the study area are operational schedule, well spacing, the amount of summer heat stored (in our model, a function of solar array size), and longevity of the system. Generally, heat recovery efficiency increases as the reservoir and surrounding rocks warm, making RTES more economical with time. Selecting a base-case scenario, we estimate a levelized cost of heat (LCOH) to compare with other sources of heating available to OHSU and find that it is comparable to unsubsidized solar and nuclear, but more expensive than natural gas. Additional benefits of RTES include energy resiliency in the event that conventional energy supplies are disrupted (e.g., natural disaster) and a reduction in fossil fuel consumption resulting in a smaller carbon footprint. Key risks include reservoir heterogeneity and a possible reduction in permeability through time due to scaling (mineral precipitation). Lastly, a map of thermal energy storage capacity for the Portland Basin yields a total of 43,400 GWh, suggesting tremendous potential for RTES in the Portland Metropolitan Area.

14 SOLAR ENERGY↗

DOE OSTI2026

Scalable and Secure Power Outage Data Reporting: A Hexagonal Geospatial Approach

Power outages disrupt critical infrastructure and cause billions of dollars in economic losses annually in the United States. Accurate and granular outage reporting is vital for effective restoration and mitigation. This paper examines the integration of the Hexagonal Hierarchical Geospatial Indexing System (H3) to enhance power outage reporting, leveraging its uniform grid structure, scalable resolutions, and support for privacy-preserving analysis. Using high-resolution LandScan Global population data and K-anonymization techniques, this work achieves a balance between data granularity and privacy. Results show that lower privacy thresholds (e.g., K-anonymity = 2) enable higher resolution, while stricter thresholds (e.g., >15 people per hex) reduce granularity, potentially affecting localized responses. State-and county-level resolution case studies demonstrate H3’s adaptability and the trade-offs between precision and privacy. The proposed H3-based framework offers a scalable and efficient solution for geospatial data integration within the energy sector, such as outage data, aiding utilities and regulators in improving resilience and response efforts, particularly in disaster-prone regions.

Ahmad, Nasir [ORNL] (ORCID:0000000150677368)↗

DOE OSTI2025

A Typology of Quantum-Classical Faults

This paper introduces an extended taxonomy of faults specific to hybrid quantum-classical systems, addressing the unique challenges that arise from integrating quantum accelerators into high-performance computing (HPC) infrastructures. Building on the foundational fault classification by Avizienis et al., we incorporate fault types unique to quantum computing-such as qubit decoherence, spontaneous gate errors, and photon loss-alongside traditional and human-induced faults including development errors, operational mistakes, and malicious attacks. Our taxonomy classifies faults by their origin (natural vs. human-made), intent (accidental, deliberate non-malicious, or malicious), system boundaries (internal vs. external), and persistence (transient to permanent). We also explore how different architectural integration patterns-ranging from tight coupling to loose on-premise and cloud-based configurations-shape the manifestation and propagation of faults. These scenarios are analyzed in terms of timing mismatches, interface inconsistencies, and security threats such as data tampering and denial-of-service attacks. Through this fault-centric lens, we aim to support the co-design of dependable quantum-classical systems and highlight the critical role that integration strategies play in ensuring reproducibility, resilience, and security across hybrid computing platforms.

Giusto, Edorado [University of Naples Federico II,↗

DOE OSTI2025

A Framework for Assessing Economic and Environmental Trade-offs of Internalized Emission Costs in ERCOT Grid Planning

The power grid is on the cusp of a massive transition driven by three major areas: 1) the growth in demand for electricity, 2) efforts to decarbonize the United States economy, and 3) a desire to mitigate social disparities from the impact of electricity generation on local populations. However, most studies of the electricity sector do not include equity impacts in their models. This study seeks to do so by developing a comprehensive and generalizable model tailored to the Electric Reliability Council of Texas (ERCOT) grid, designed to incorporate the equity impacts of electricity generation in a decarbonized and resilient framework. To integrate equity into our research, we incorporate environmental externalities into our capacity expansion model of ERCOT. Specifically, we factor in intermediate-level local marginal damages of precursor pollutants (NH3, NOx, primary PM2.5, SO2, and VOC) and global pollutant CO2 into the cost of generating electricity. We do this by taking into account county population, county ambient pollution concentration, and generator emission rates. Leveraging open-source modeling tools, such as PowerGenome, pyGRETA, and GenX we construct a county-level model to account for these costs. We integrate these marginal damages into the variable operations and maintenance costs of generators, for both existing and potential future builds. This study’s findings suggest that the value of a dynamic social cost of carbon (SSC) will cover criteria pollutant marginal damages within the ERCOT grid and solar and wind is expected to increase out to 2035. Key metrics evaluated within this research include fuel mix distribution across technologies, transmission and grid infrastructure costs, CO2 emissions, local pollutants marginal damages, and the variation in generation capacity built by the model. These results and framework can be used to support grid decisions that explicitly include distributional and procedural equity within a decarbonized and sustainable grid framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Modeling Electric Vehicle Charging Load Using Origin-Destination Data

The accelerating adoption of electric vehicles (EVs) poses challenges to the power grid, necessitating precise representation of mobility patterns for effective infrastructure upgrades. Traditional simulation-based charging demand estimation faces limitations in generating trip chains reflective of actual travel patterns without complex network modeling. Hence, an innovative agent-based trip chain generation model is introduced to overcome these challenges. Drawing from the National Household Travel Survey (NHTS) and the NextGen NHTS origin-destination add-on data for Clarke County, Georgia, this study proposes a simulation method capturing both temporal and spatial mobility patterns without relying on extensive network topology data. The resulting trip chains predict EV charging load at the Census Block Group level, validated with a 1.03 correlation to actual trip counts, affirming their reflective accuracy. Two charging scenarios, residential-only and charging-everywhere, reveal distinct demand profiles. The charging-everywhere scenario aligns closely with the trip profile, while the residential-only scenario exhibits an afternoon peak slightly surpassing the former. This study contributes a data-driven charging demand estimation methodology, offering critical insights for grid resiliency planning amid the evolving landscape of EV adoption.

Pan, Melrose↗

DOE OSTI2022

The Role of Innovation in the Electric Utility Sector

Innovation is essential for future power systems to be safe and secure, clean and sustainable, affordable and equitable, and reliable and resilient, according to a recent National Academies report. But state regulatory reforms are needed to encourage adoption of new technologies to support evolution of the nation’s power systems.1 Berkeley Lab's report, The Role of Innovation in the Electric Utility Sector, provides consumer, labor, utility, third-party provider, and clean technology consultant perspectives on this theme. To achieve state targets for clean energy and greenhouse gas emissions, some state regulatory utility commissions are exploring new approaches to spur innovation: -For utilities, regulatory and marketing flexibility, increased funding for demonstration projects, and performance-based ratemaking including multi-year rate plans -For third parties, ways to provide utility customers with innovative products and services directly Among the questions the report addresses: 1. How are consumer advocate views evolving with respect to innovative regulatory and ratemaking approaches? 2. How can utility decarbonization and grid modernization initiatives provide opportunities for local communities and workers to receive tangible benefits and facilitate community support for siting electricity infrastructure? 3. How are electric utilities partnering with technology companies to provide innovative energy management services and sustainable energy solutions for utility customers? 4. What regulatory innovations are public utility commissions exploring to enable third-party providers to participate in the transition to a modern electric system? 5. What regulatory changes are needed to enable innovative solutions from utilities and third parties at the necessary speed and scale to meet state decarbonization goals? The report is the 13th in the Future Electric Utility Regulation series, which taps leading thinkers to tackle complex regulatory issues for electricity. 1 National Academies of Sciences, Engineering, and Medicine. 2021. The Future of Electric Power in the United States. The National Academies Press.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

NASA Space Communications and Navigation: One Network Evolution

The NASA Space Communications and Navigation (SCaN) Program is responsible for providing the essential connectivity to robotic and human space explorers. The missions relying on SCaN range from suborbital and balloon missions to those traveling beyond the edge of the solar system. The demands for communications and navigation services enabled by SCaN (and its affiliated partners) are projected to increase and outpace the current network capacity. At the same time, the Agency finds itself surrounded by a burgeoning commercial space marketplace, technological advancement, and other government agencies that share common interests in space resiliency, robustness, and performance. As a result, SCaN has begun pivoting toward commercial services and collaborating with partners to close capacity and capability gaps. Given these growing demands of the Agency there is increasing need for multi-network solutions. Future mission concepts will rely on both government and commercial capabilities, both Near Space Network capacity and Deep Space Network capacity. Integrating these diverse support services together from a technical, programmatic and implementation standpoint will be key to meet the growing needs of the future. To accomplish this, a more substantive shift is required, and SCaN is reshaping itself to be a customer-centric, service-oriented, high-performance leader in the space communications community. This paper outlines the SCaN One Team, One Mission, One Network approach, and provides a vision for future mission community experience that includes streamlined mission commitment interfaces and clear processes, dynamic network scheduling and load balancing, and higher efficiency data transport and delivery through the integration of cloud infrastructure and services.

Near Space Network↗

DOE OSTI2026

State of Innovation 2025: Progress in Accelerating Next-Generation Cement and Concrete Technologies

The cement and concrete sectors are entering a decisive period as next-generation technologies advance from laboratory research to demonstration, early deployment, and first-of-a-kind commercial plants. Building on the 2024 State of Innovation report, the 2025 outlook highlights both the rapid acceleration of innovation and the urgent need for coordinated action across the value chain. Venture capital activity into the cement and concrete space stabilized following the record surge of 2022-2023, yet landmark financings, such as Sublime Systems' $200 million round and Terra CO2's $124 million Series B, signal continued investor confidence in companies approaching commercialization. Corporate procurement has become a powerful new catalyst, with Microsoft, Amazon, and CRH (Cement Roadstone Holdings) Ventures providing long-term commitments that underpin the first wave of next-generation cementitious products. The sector is shifting from early-stage experimentation toward the scaling of well-capitalized leaders capable of bridging the critical "capitalization gap." Early innovators continue to expand the toolkit through novel binders, electrochemical cements, biogenic limestone, and carbonate mineralization pathways. Going into 2026, cost competitiveness, durability validation, and scalability enabled by resilient supply chains remain the decisive factors for market adoption. At the 2025 Next Generation Cement and Concrete Critical Technologies Meeting, attendees emphasized dual-track funding strategies that integrate federal grants with private capital as key to enabling market breakthrough. State programs and corporate demand are sustaining momentum, while successful companies increasingly demonstrate both economic value and reduced dependence on imported materials. The National Concrete Pavement Technology Center and others underscored that broad integration of next-generation materials will hinge on standards compatibility, verified field performance, and workforce readiness. Colorado continues to serve as a proving ground through pilot programs that combine supplier training, phased implementation, and real-world data to de-risk innovation and provide replicable models for other regions. The 2025 Cement and Concrete Critical Technologies Workshop reinforced that scaling next-generation materials will require alignment among technology innovation, performance validation, and market demand. Stakeholders must move beyond siloed efforts toward collaborative frameworks that coordinate standards, funding, and infrastructure deployment. As a neutral convener and technical validator, the National Laboratory of the Rockies (NLR) plays a pivotal role in bridging innovation and market adoption through collaborative research, technology validation, and entrepreneurship programs. By uniting innovators, incumbents, policymakers, contractors, and investors, NLR and its partners are helping chart a credible pathway toward widespread commercialization in the decade ahead.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Considerations for Distributed Energy Resource Integration in Puerto Rico: DOE Multi-Lab Grid Modeling Support for Puerto Rico; Analytical Support for Interconnection and IEEE Std 1547-2018 National Renewable Energy Laboratory (Task 3.0)

To ensure the sustainable long-term recovery of Puerto Rico's electric power grid from hurricanes María and Irma and to build capacity to manage future potential natural disasters in the most secure and resilient way, the U.S. Department of Energy (DOE) convened experts from multiple national laboratories to develop a comprehensive set of data, models, analytic tools, and studies, considering inputs from a wide variety of stakeholder groups, to support technically sound recommendations for Puerto Rico's energy investment decisions. A resilient electric grid is vital to Puerto Rico's security, economy, and way of life, and it will provide the foundation for essential services that people and businesses on the island rely on every day. This report shows progress for a grid modeling task under the DOE-sponsored project that is a collaboration among the National Renewable Energy Laboratory (NREL) and other national laboratories. In Phase 1 of the multilab effort to support Puerto Rico's recovery, NREL provided the utility company Puerto Rico Electric Power Authority (PREPA) recommendations for a new framework of interconnection standards to accelerate the integration of utility-scale, transmission-connected renewable electrical generation and energy storage that ensure cross-technology compatibility and enable high deployment levels without compromising grid reliably, safety, or security. This Phase 2 report focuses on the interconnection of distributed energy resources (DERs) to the electric distribution system in Puerto Rico. This report is intended to familiarize the reader with Puerto Rico's distribution infrastructure and operational practices and procedures that are relevant to DER interconnection. The report also provides considerations for streamlining the interconnection process given the expected increase in deployments resulting from Puerto Rico's renewable portfolio standard (RPS) goal of 100% renewables by 2050. Accordingly, the report identifies considerations and concerns associated with the increase in intermittent generation, strategies for DER interconnection best practices, and the potential use of the latest technological solutions identified in the latest revision of the Institute of Electrical and Electronics Engineers (IEEE) 1547-2018 interconnection standard.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗

NASA NTRS2016

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

plant communities↗

DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗

DOE OSTI2023

A National Secure-by-Design Strategy

The US National Cybersecurity Strategy published March 2, 2023 uses plain language to communicate that the US is calling for a major change in how we prioritize the security of software systems used in critical infrastructure. It acknowledges that our current approach, which is essentially, “let the buyer beware,” leaves entities who are least able to assess or defend vulnerable software responsible for the impacts of designed-in weaknesses while the makers of the technology bear no liability. The strategy recommends a security-by-design approach, recommending that software vendors be held liable to uphold a “duty of care” to consumers and for systems to be designed to “fail safely and recover quickly” . For energy infrastructure, the strategy calls out the need to implement the National Cyber-Informed Engineering Strategy to achieve higher confidence security for energy infrastructures. The Idaho National Laboratory, a pioneer in cyber-informed engineering concepts, is at the forefront of organizations educating others in industry, academia, and government on how to apply these concepts to real-world challenges. In this brief, we'll outline some of the basic principles of security-by-design and offer examples of how, in a water sector context, they're being put into successful practice.

42 ENGINEERING↗

DOE OSTI2021

Geothermal Heat Recovery Complex: Large-Scale, Deep Direct-Use System in a Low-Temperature Sedimentary Basin (Final Report)

A feasibility study of using deep direct-use (DDU) geothermal energy to heat agricultural research facilities (ARFs) was conducted at the University of Illinois at Urbana-Champaign (U of IL) and its similar application to military facilities in the Illinois Basin (ILB). The geothermal energy system (GES) investigated utilizes low-temperature (30–90°C; 90–190°F) geothermal fluid (i.e., brine) from an extraction well that is part of a deep, two-well (doublet) system that extends to the bottom of the ILB. The geothermal reservoir modeled, the Mt. Simon Sandstone (MSS), is about 1,280 m (4,200 feet) deep and 457 m thick (1,500 feet) beneath the U of IL. The DDU GES surface infrastructure includes heat exchangers connected in-parallel to pipelines carrying the geothermal fluid and fresh cold and hot water. Analysis of the GES indicated that the MSS can provide a baseload of 2 MMBtu/hr to heat the ARFs by extracting 954 m³/d (6,000 barrels/day [bbl/d]) of geothermal fluid that has a temperature of 44–46 °C (111–115 °F). In addition to analyzing the levelized cost of heat (LCOH) and life cycle costs, the environmental effects of the DDU GES were evaluated, including reduced greenhouse gas (GHG) emissions and water consumption. Multiple system designs were evaluated and then ranked based on their maximum heating performance, energy efficiency, and cost recovery. This feasibility study identified the key components of the fully-integrated DDU technology that can be implemented, both technically and economically. The results and information from this study provides end-users and policy makers with guidance for additional research on the specific components of DDU technology such that its widespread use can provide an uninterruptible energy source, increase resilience from extreme weather conditions, reduce U.S. dependency on fossil fuels, and reduce greenhouse gas (GHG) emissions. The site-specific part of this study gives U of IL administrators a realistic and pragmatic assessment of the financial resources necessary to add a DDU GES in the MSS to the campus’ energy portfolio.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Semiconductor: Supply Chain Deep Dive Assessment

In February 2021, President Biden signed Executive Order (EO) 14017, “America’s Supply Chains,” directing seven executive agencies to evaluate the resilience and security of the nation’s critical supply chains and to craft strategies for six industrial bases that underpin America’s economic and national security. See Sec. 3(b) of E.O. 14017. As part of the one-year response to EO 14017, the U.S. Department of Energy (DOE), through the national laboratories, conducted evaluations of the supply chains that encompass the Energy Sector Industrial Base, with a particular focus on technologies required to decarbonize the U.S. by 2050. This report focuses on one of these technologies, semiconductors, both conventional and wide bandgap (WBG) power electronics (PE). As noted in the 100-Day report, conventional semiconductors are a keystone technology that are essential for the operation of nearly every electronic device, including those that are critical to decarbonization, such as electric vehicles (EVs), industrial and building applications, and electricity generation and end use. In addition, power electronics have been applied to industrial equipment to improve energy efficiency and enhance controllability. Power electronics have also been used to improve the reliability of data centers and critical infrastructure, including the stabilization of the electric grid while subjected to disturbances. Increasingly, power electronics are being used to integrate renewable energy and battery storage systems, enabling new grid services and the development of microgrids. It is estimated that 30% of the electricity used in the U.S. passes through power electronic devices, and studies suggest that this number could reach 80% as power electronics are deployed in more markets. Nearly all forms of electrified transportation will depend on power electronics. Power electronic systems utilize high-capacity semiconductor devices at their core. The continued growth in power electronics depends on sustained innovation in the semiconductor industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗