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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Topical Report on Water Balance Model Development and Initial Application

This topical report summarizes results from developing a generic dynamic water balance tool which can be used by coal-fired power plant operators to evaluate FGD water management strategies. EPRI is using a statistical software model created on the GoldSim platform and data from coal-fired energy plants to determine the fraction of power plant water needs that can be satisfied by electrodialysis reversal treatment and, correspondingly, the discharge volume and/or byproducts that still need to be managed. The generic model is designed to accept a number of easily obtainable process variables and generate probabilities of resultant water characteristics. The model is also constructed as a universal process flow; one that can be adjusted to accommodate most major components of an FGD blowdown handling system. The generic model is dynamic and utilizes user-inputted operational variation in water quality and quantity to best provide a probabilistic representation of potential performance outcomes. In order to account for complexities of water composition and treatment, a separate chemical modeling software is used to evaluate water chemistry and unit operation performance. The thermodynamic modeling software, OLI Studio, is employed to convert ion data from laboratory analyses into balanced molecular concentrations that are classified as dissolved and suspended solids. The dynamic water model is designed to import a total of 11 species commonly found in FGD blowdown water as molecular concentrations.Water management unit operations are used in the model and categorized by either physical unit operation blocks or chemical unit operations blocks. As the name implies, the physical water treatment blocks are unit operations that are exclusively physical changes to the system (e.g., separations and volume changes). The chemical water treatment blocks are unit operations where chemical changes occur and require the use of OLI Studio to characterize subsequent streams. Water and material balances from three unique coal-fired power plants were obtained and preliminary modeling on the dynamic water balance tool was performed using the extracted data from these power plants. These three power plants were chosen due to their unique configurations and water profiles that represented a variety of operational changes. Results from using the dynamic tool with the three plant configurations are presented and discussed in the context of the existing tool and planned future capabilities.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An analysis of physics limited dispatch of nuclear renewable integrated energy systems using deep reinforcement learning and dynamic modeling

Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. Furthermore, this work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

14 - SOLAR ENERGY↗
DOE OSTI2024

The Grid Value of Ocean Current Energy in Florida: Preprint

Ocean current technology has been proposed as a potential contributor to Florida's energy portfolio. There has been limited investigation of how this energy would be valued when integrated into the Florida electrical grid. This study assesses three future grid scenarios to evaluate the impact of adding ocean current to each. NREL's capacity expansion model, Resource Planning Model, is used to identify the least-cost generation mix through 2050, with and without ocean current. The first scenario, Business as Usual, Base case assuming current policies, ocean current does not replace fossil-based technologies. In the second scenario, we allow solar and storage to have lower costs than the first scenario which allows ocean current to retire gas earlier and more variable generation technologies to be deployed. In the third scenario, the Florida carbon constraint 95 by 2050 from 2020 levels case, ocean current can play a bigger role in decarbonization than the two other cases when coupled with other technologies.

capacity expansion model↗
DOE OSTI2020

Wind ramp events validation in NWP forecast models during the second Wind Forecast Improvement Project (WFIP2) using the Ramp Tool and Metric (RT&M)

The second Wind Forecast Improvement Project (WFIP2) is a multi-agency field campaign held in the Columbia Gorge area (October 2015 - March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well-known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011 - August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and 3 lidars, and to forecasts from the High Resolution Rapid Refresh (HRRR), 3-km, and from the High Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

Djalalova, Irina V.↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Why is it still too warm or cold in my house? Examining the relationships between energy efficient capital and household energy insecurity

Here, this paper examines the relationships between energy efficient (EE) capital technology and household energy insecurity in the United States. The theoretical model of these relationships employs household production theory to capture the demand for and production of household energy services, and a stochastic production frontier approach to describe how having access to and the usage intensity of EE capital technology could help alleviate inefficiency in the production of household energy services. A working hypothesis formulated from the theoretical model posits that having EE capital technology in the home will reduce the level of household energy insecurity experienced. The extent of energy insecurity experienced is inferred from an energy insecurity index value assigned to each household, generated via the application of a dichotomous Rasch model to questions contained in the 2015 Residential Energy Consumption Survey. Noting the potential simultaneous relationship that exists between a household having access to and the usage intensity of EE capital technology and the experience of being energy insecure, an instrumental variables approach was employed to estimate a series of ordered logit models. Results suggest access to EE capital technology in the form of Energy Star® appliances, Energy Star® windows, or a SMART thermostat does not reduce the probability of experiencing a greater level of energy insecurity. Nor does the usage intensity of EE capital. Thus, policy instruments designed to alleviate household energy insecurity may need to go beyond simply helping households obtain EE capital technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Time and Frequency Analysis of Load Profile Data

Technology advancements and integration of modern advanced metering systems can monitor, forecast, inform, control, and operate the building's mechanical, electrical, and plumbing (MEP) systems. They offer a higher level of information, which can contribute to making smart buildings more energy efficient and to making them closer to becoming grid-interactive energy efficient buildings (GEB). This paper builds on the ongoing research on variability analysis of a case study building with a 1-minute load profile and examines the Discrete Wavelet Transform (DWT) process in the frequency domain to quantify the signal's energy in each bandwidth, with respect to each end-use category. Moreover, the amount of variability in the total variability is not similar among the end-use categories. This information is needed to understand the behavior of the variability in the frequency domain for future applications, such as generating synthetic load profiles with a similar frequency spectrum as the measured signal.

decomposition↗
DOE OSTI2026

Challenges and Opportunities for Electric Utility Modeling and Asset Valuation Frameworks: Case Study on Valuing New Pumped Storage Hydropower

Asset valuation by electric utilities is becoming increasingly difficult in the rapidly changing electric sector. Rapid deployment of variable generation and inverter-based storage systems along with uncertain demand growth, climate, policies, and other factors create a challenging environment for understanding the value proposition of a new potential asset. This report describes an effort between the Tennessee Valley Authority (TVA) and three U.S. Department of Energy laboratories to perform a detailed review of utility modeling and analysis practices for asset valuation and identify challenges and opportunities for advancing its methods into the future. It focuses on a case study of new potential pumped storage hydropower (PSH) because of growing interest in new PSH capacity to provide energy balancing, firm capacity, and a range of ancillary services. Staff from the DOE labs conducted systematic interviews about current practices in capacity expansion modeling, production-cost modeling, hydrological modeling, and transmission stability modeling while also discussing how scenario analysis is conducted and how models and data are integrated. The effort resulted in a set of model, integration, and scenario recommendations that could be valuable to TVA, other utilities, system operators, and other stakeholders conducting integrated grid analysis. Individual model recommendations suggest exploring computational tradeoffs with detail and resolution across spatiotemporal structure, supply- and demand-side details, transmission overlays, market interactions, and ancillary services. Automated processes to pass data between models and conduct larger scenario suites could also enhance valuation practices by enabling a more consistent study of asset value across a broader range of uncertain future grid conditions where PSH could be particularly valuable. TVA and other industry stakeholders can learn from and adapt applied research-grade methods developed by DOE laboratories and other research institutions to improve decision making and accelerate progress towards a reliable, economic, sustainable energy system.

13 HYDRO ENERGY↗
DOE OSTI2024

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

17 WIND ENERGY↗
DOE OSTI2020

Factors Impacting Nuclear Energy Share in U.S. Energy Markets

The purpose of this report is to collate information and findings from recent studies conducted by national and international bodies/institutes to identify approaches for maintaining/enhancing the role of nuclear energy in the current and future energy mix of the United States. This report shows how nuclear generation grew quickly to provide 20% of U.S. electricity, sustaining that level for three decades without the benefit of new construction, but is now projected to decline going forward. Nuclear construction costs in the U.S. spiraled out of control, ending construction for two decades and the recent resumption of construction has continued the pattern of schedule delays and cost overruns. However, nuclear operations exhibited strong learning, achieving and sustaining the highest capacity factor of any electricity generation technology and license extensions and uprates have sustained nuclear market share. The share of nuclear energy in the U.S. electricity market is projected to decline by ~1/3rd over the next 30 years The report provides an overview of how the markets work in theory and in practice. It indicates how market deregulation and clean energy policies have created conditions where nuclear plants are being retired for economic rather than technical reasons. The report also shows how many of the markets do not in practice have free competition but instead have outcomes that are being determined more and more by policy instead of market forces. While electricity costs from existing nuclear plants are low, electricity from new builds is projected to be too expensive to be competitive head-to-head with natural gas, even for nth-of-a-kind costs. Wind and solar energy have enjoyed an extended period of sustained subsidy. This protected environment has resulted in a sustained reduction in plant level costs to the point that some of these Variable Renewable Energy (VRE) technologies are becoming competitive even if their direct subsidies are removed. But plant level costs underestimate total VRE costs which include a number of system-level externalities. The incremental system value of additional VRE capacity was shown to decline as market share increases, with solar value declining more quickly than wind. The report closes with examination of a possible future for nuclear generation as part of deep decarbonization of the electricity sector. This approach avoids direct competition with natural gas. The two options for achieving 100% decarbonization are to use only renewables or to use all zero emissions technologies, and the report shows the second approach is much less expensive than the first.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Pangenomics reveals alternative environmental lifestyles among chlamydiae

Chlamydiae are highly successful strictly intracellular bacteria associated with diverse eukaryotic hosts. Here we analyzed metagenome-assembled genomes of the “Genomes from Earth’s Microbiomes” initiative from diverse environmental samples, which almost double the known phylogenetic diversity of the phylum and facilitate a highly resolved view at the chlamydial pangenome. Chlamydiae are defined by a relatively large core genome indicative of an intracellular lifestyle, and a highly dynamic accessory genome of environmental lineages. We observe chlamydial lineages that encode enzymes of the reductive tricarboxylic acid cycle and for light-driven ATP synthesis. We show a widespread potential for anaerobic energy generation through pyruvate fermentation or the arginine deiminase pathway, and we add lineages capable of molecular hydrogen production. Genome-informed analysis of environmental distribution revealed lineage-specific niches and a high abundance of chlamydiae in some habitats. Together, our data provide an extended perspective of the variability of chlamydial biology and the ecology of this phylum of intracellular microbes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Redefining Resource Adequacy for Modern Power Systems: A Report of the Redefining Resource Adequacy Task Force

Today's rapidly increasing levels of wind, solar, storage, and load flexibility require the industry to rethink reliability planning and resource adequacy methods for modern power systems. Periods with a risk of shortfall often no longer coincide with peak demand - reliability risks are less about peak load and more about the daily setting of the sun, extended cloud cover, wind speeds, cold snaps, and heat waves. In addition, demand is increasingly flexible. Key resources are time-sensitive, as batteries need time to recharge and electricity customers can only be asked to provide demand response for just so long. And reliability failures are often correlated - with one another and with the weather. Two driving factors require the industry to reconsider its analytical approach for resource adequacy: (1) Chronological grid operations: The increasing importance of variable renewable resources (such as wind and solar) and of energy-limited resources (storage and demand response) make it essential to understand the full year of chronological operation of the grid. Specific attention must be paid to hourly, seasonal, and inter-annual resource variability. The sequence of the variability is key, as energy-limited resources such as batteries or demand response require either a preceding period or subsequent period of high production to be useful for grid reliability. (2) Correlated events: Historically, resource adequacy analysis focused on shortfalls caused by random, discrete mechanical failures of large generating units. In contrast, shortfalls today are often caused by multiple, correlated events caused by common weather patterns. Resource adequacy analysis must increasingly shift its focus to these correlated events. The redesign of resource adequacy methods will benefit from a set of guiding principles to better allow for sharing of insights and best practices, interregional resource coordination, and a smoother regulatory process for resource procurement. The objective of this report is to move this redesign forward. It provides an overview of key drivers changing the way resource adequacy needs to be evaluated, identifies shortcomings of conventional approaches, and outlines first principles for practitioners to consider as they adapt their approaches. The central message is: what got us here won't get us there.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An overview of the E3SM version 2 large ensemble and comparison to other E3SM and CESM large ensembles

Abstract. This work assesses a recently produced 21-member climate model large ensemble (LE) based on the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 2 (E3SM2). The ensemble spans the historical era (1850 to 2014) and 21st century (2015 to 2100), using the SSP370 pathway, allowing for an evaluation of the model's forced response. A companion 500-year preindustrial control simulation is used to initialize the ensemble and estimate drift. Characteristics of the LE are documented and compared against other recently produced ensembles using the E3SM version 1 (E3SM1) and Community Earth System Model (CESM) versions 1 and 2. Simulation drift is found to be smaller, and model agreement with observations is higher in versions 2 of E3SM and CESM versus their version 1 counterparts. Shortcomings in E3SM2 include a lack of warming from the mid to late 20th century, likely due to excessive cooling influence of anthropogenic sulfate aerosols, an issue also evident in E3SM1. Associated impacts on the water cycle and energy budgets are also identified. Considerable model dependence in the response to both aerosols and greenhouse gases is documented and E3SM2's sensitivity to variable prescribed biomass burning emissions is demonstrated. Various E3SM2 and CESM2 model benchmarks are found to be on par with the highest-performing recent generation of climate models, establishing the E3SM2 LE as an important resource for estimating climate variability and responses, though with various caveats as discussed herein. As an illustration of the usefulness of LEs in estimating the potential influence of internal variability, the observed CERES-era trend in net top-of-atmosphere flux is compared to simulated trends and found to be much larger than the forced response in all LEs, with only a few members exhibiting trends as large as observed, thus motivating further study.

58 GEOSCIENCES↗
DOE OSTI2024

iButton and Tinytag snow temperature measurements at Teller 27 and Kougarok 64, Seward Peninsula, Alaska, 2021-2022

Snow temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula. Data were collected from October 1, 2021 to August 16, 2022 using iButton Link DS1926-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours beginning on October 1, 2021. Tinytag data collection began between October 9 and October 12, 2021 depending on sensor installation date. Tinytag data were collected every 30 minutes. In total, data were collected from 236 iButtons and 30 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and four *.shp files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A National Roadmap for Grid-Interactive Efficient Buildings

The way electricity is generated and consumed in the US is quickly changing, including in terms of the rapid growth in variable power generation resources and the need for large-scale investments to replace aging infrastructure and modernize the grid. Buildings that coordinate electricity use with grid conditions are a flexible and cost-effective resource to address the evolving power system challenges. Outfitted with smart technologies, GEBs are energy-efficient buildings with smart technologies characterized by the active use of distributed energy resources to optimize energy use for grid services, occupant needs and preferences, and cost reductions in a continuous and integrated way. In doing so, GEBs can play a key role in promoting greater affordability, resilience, environmental performance, and reliability. The report finds that, over the next two decades, GEBs could deliver between $100 and $200 billion in savings to the US power system and cut CO 2 emissions by 80 million tons per year by 2030, or 6% of total power sector CO 2 emissions. The report also provides 14 recommendations for addressing the top barriers to overcome barriers to GEB adoption and deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Governing in Time: Temporal Capacity and the Feasibility of Energy Transitions

Energy systems function as both technological systems and temporal institutions that shape how societies coordinate, justify, and support collective choices over time. This paper introduces the concept of governance horizons to explain why energy transitions can remain morally supported yet become institutionally weak under increasing pressure. We argue that governability depends on institutions' capacity to synchronize across multiple timeframes - aligning short-term decisions with intermediate coordination and long-term commitments. When this synchronization fails, transitions struggle not because their goals are dismissed, but because governance lacks sufficient time to justify, coordinate, and uphold decisions. Comparative analysis of San Antonio, Texas, and Interior Alaska reveals how energy system pressures generate distinct temporal configurations: San Antonio exhibits governance horizon stretching, where institutions must simultaneously meet near-term reliability demands and long-term transformation goals, while Interior Alaska exhibits horizon compression, where extreme environmental constraints force decision-making into short stabilization cycles. In both contexts, public support for sustainability goals coexists with institutional strain because evaluative judgments are unevenly distributed over time. A temporal configuration analysis is introduced as a diagnostic analytic stance for identifying these patterns. By treating temporal alignment as an explanatory variable rather than a background condition, this approach clarifies how feasibility, sequencing, and legitimacy are shaped by constraints on institutional time. The analysis demonstrates that successful energy transitions depend not only on technological innovation or institutional support, but on governance systems’ ability to sustain credible coordination across multiple time horizons.

Comparative case study↗
DOE OSTI2025

Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking

Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationships among these models remain largely unexplored, a gap that hinders the development of a unified theory of AI learning. In this work, we address two central challenges: clarifying the connections between different deep generative models and deepening our understanding of their learning mechanisms. We focus on Restricted Boltzmann Machines (RBMs), a class of generative models known for their universal approximation capabilities for discrete distributions. By introducing a reciprocal space formulation for RBMs, we reveal a connection between these models, diffusion processes, and systems of coupled bosons. Our analysis shows that at initialization, the RBM operates at a saddle point, where the local curvature is determined by the singular values of the weight matrix, whose distribution follows the Marc̆enko-Pastur law and exhibits rotational symmetry. During training, this rotational symmetry is broken due to hierarchical learning, where different degrees of freedom progressively capture features at multiple levels of abstraction. This leads to a symmetry breaking in the energy landscape, reminiscent of Landau’s theory. This symmetry breaking in the energy landscape is characterized by the singular values and the weight matrix eigenvector matrix. We derive the corresponding free energy in a mean-field approximation. We show that in the limit of infinite size RBM, the reciprocal variables are Gaussian distributed. Our findings indicate that in this regime, there will be some modes for which the diffusion process will not converge to the Boltzmann distribution. To illustrate our results, we trained replicas of RBMs with different hidden layer sizes using the MNIST dataset. Our findings not only bridge the gap between disparate generative frameworks but also shed light on the fundamental processes underpinning learning in deep generative models.

97 MATHEMATICS AND COMPUTING↗