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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Beneficial CHP – Is that a Thing? Considering CHP in the Context of Beneficial Electrification

For decades, combined heat and power (CHP) has been promoted and embraced as a cost-effective technology for meeting on-site thermal and electric needs more efficiently and with fewer emissions than separate procurement of those resources. But climate change concerns are leading to a reevaluation of CHP’s benefits. Significantly, natural gas, the most common fuel for CHP and until recently regarded as an environmentally preferable “bridge fuel” in the energy transition to renewables, is increasingly being reexamined amid calls for deep decarbonization and emerging clean energy policies that limit the use of natural gas. Given this trend, electrification has gained traction as a net zero carbon energy strategy. With natural gas being a preferred fuel for CHP, policymakers and others are beginning to question CHP’s role in a cleaner, more electrified future. However, CHP fueled by low carbon fuels such as renewable natural gas and hydrogen may be a more viable path to decarbonizing industrial processes that are difficult to electrify due to technology limitations or cost, and for applications where energy resilience is a critical requirement. This paper seeks to add clarity to a complex issue. It offers a framework for assessing industrial applications where natural gas CHP will provide significant GHG reductions in the near term and provide a more economic and practical path to deep decarbonization in the long term through a transition to low carbon fuels.

Hedman, Bruce↗

DOE OSTI2024

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. Here, this paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

25 ENERGY STORAGE↗

DOE OSTI2023

Internal consistency and diversity scenario development: A comparative framework to evaluate energy model scenarios

Energy modeling frameworks and scenario analysis help us explore the potential impact of our actions and uncertainties in future energy systems. Despite their importance, there is no systematic procedure for evaluating the scenario development process. In a literature review, we identify two core elements of the scenario development process: internal consistency and diversity which are oftentimes missing from scenarios. Here, to address this gap, we create the Internal consistency and Diversity Scenario Development (IDSD) comparative framework which aims to assess the feasibility and diversity of scenarios for a given energy model. With this framework, we review commonly used energy models and demonstrate our framework on their scenarios. The IDSD comparative framework can serve several purposes absent from previous scenario development work by aiding energy modelers and report writers in crafting high-quality scenarios. First, the IDSD is a reflective tool which can improve the quality of the scenario development process, enabling a comparative assessment of energy models and scenarios. Second, the IDSD can provide guidance to modeling frameworks with existing scenarios and those still in development; this feedback will enable modelers to improve the development and the communication of the limitations of their scenarios. Third, this study has highlighted areas for improvement in the scenario development of some commonly used energy model frameworks. Finally, there is a complete lack of explanation regarding the stakeholder selection process. Addressing these identified items could increase opportunities for advanced energy technology uptake and improve our options for achieving a more resilient energy system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Hydropower Cybersecurity Value-at-Risk Framework

Hydropower remains one of the strongest forms of renewable energy generation methods. It is crucial to address the increasing risks associated with the rapid digitization. The push towards decarbonization also factors in the need to ensure security and resilience for grid-connected renewable energy resources. This report summarizes the U.S. Department of Energy's Water Power Technologies Office's effort to develop a cybersecurity valuation methodology that assists hydropower stakeholders in assessing risks associated with plan operations and gathers valuation guidance through a web-based application. The Hydropower Cybersecurity Value-at-Risk Framework delivers a platform for industry members to perform self-assessments and make informed decisions on their cybersecurity investments.

13 HYDRO ENERGY↗

DOE OSTI2023

The Cybersecurity Value-at-Risk Framework

Hydropower remains one of the strongest forms of renewable energy generation methods. It is crucial to address the increasing risks associated with the rapid digitization. The push towards decarbonization also factors in the need to ensure security and resilience for grid-connected renewable energy resources. This report summarizes the U.S. Department of Energy's Water Power Technologies Office's effort to develop a cybersecurity valuation methodology that assists hydropower stakeholders in assessing risks associated with plan operations and gathers valuation guidance through a web-based applicaiton. The Hydropower Cybersecurity Value-at-Risk Framework delivers a platform for industry members to perform self assessments and make informed decisions on their cybersecurity investments.

CVF↗

DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗

DOE OSTI2021

The Value of Distributed Wind: A Valuation Framework

While there has been an increase in distributed energy resource (DER) adoption, particularly for solar photovoltaic (PV) DERs, distributed wind deployment is still relatively low. However, distributed wind has significant growth potential for behind-the-meter applications and can provide improved resource diversity and resilience in high-DER grid systems. In order to make optimized decisions about distributed wind deployment, it is important to identify and characterize the value streams of distributed wind in a way that will help policy makers, microgrid developers, utilities, project developers, and others understand the potential value of distributed wind. In our paper, we develop a framework to provide a consistent and comprehensive approach to finding the net benefits of distributed wind to various stakeholders in different scenarios. We do this by considering the main use cases for distributed wind, and then developing a framework which is flexible for these various use cases and the relevant stakeholders while maintaining the same overall structure for analysis. Ongoing work includes applying this framework to valuing two distributed wind power systems, one in a remote village in Alaska and one in a front-of-the-meter deployment in Iowa. Our analysis aims to understand the full value that distributed wind is providing in these systems, including the resilience benefits it is contributing. As a better understanding of distributed wind valuation is gained, it will be easier for stakeholders to make informed decisions in order to have resilient renewable energy systems.

17 WIND ENERGY↗

DOE OSTI2026

Analyzing Land Characteristics Associated with Historical Large-Scale Solar Photovoltaic Deployment in the United States

Large-scale solar (LSS) deployment has accelerated rapidly across the United States, yet comprehensive understanding of its siting patterns remains limited. Here, we present a national-scale geospatial assessment of land characteristics converted for LSS development and apply statistical analyses to evaluate whether installations are disproportionately concentrated within specific land types. We find that siting patterns are strongly correlated with high solar irradiance, underscoring the role of resource quality in shaping deployment decisions. LSS projects are disproportionately concentrated on agricultural lands and shrubland. Within agricultural areas, installations predominantly occur on croplands and are strongly associated with irrigated systems. Notably, more than 90% of projects are deployed on agricultural land with Productive, Versatile, and Resilient (PVR) index values of land quality exceeding the national median. These findings highlight the need for strategic siting frameworks that reconcile solar development with the conservation of agriculturally valuable land.

14 SOLAR ENERGY↗

DOE OSTI2025

Load Matching Potential of Urban Renewables during Extreme Heat in New York City

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on the matter by assessing the feasibility of using offshore wind and rooftop photovoltaic systems to meet electricity demand during a period of thermal stress in New York City—August 2019. A unified modelling framework, based on the Urbanized Weather Research and Forecasting model, is used to simulate climate, renewable resources and energy demand variables. Findings show significant energy load miss-match, ranging from 3 to 5 GW, between the demand and the composite renewable generation outcome. This study provides a transferable framework for evaluating renewable integration in dense urban regions and highlights the need for additional strategies to support grid resilience during extreme heat events, while clarifying the role of local resources in fully managing NYC’s load.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Nonlinearities in Magnetic Confinement, Ionospheric Physics, and Population Explosion Leading to Profile Resilience Нелінійності в магнетному утриманні, фізиці іоносфери та процесі демографічного вибуху, які приводять до стійкості профілю

Nonlinearities play an important role in many fields. In the field of thermonuclear fusion, they are involved in questions such as profile resilience and fluid closure. A nonlinear phenomenon common to both fusion and astrophysical planets is the generation of zonal flows. These flows play a significant role in determining the level of turbulence and fluid closure in fusion. The effects of resonance broadening and nonlinearities are investigated, specifically focusing on the case of nonlinear instability that has appeared in drift waves. Similarities and differences between our systems are discussed, with population explosion and the dynamics of nonlinear systems for drift waves by different states in profile resilience described with great precision. The aim of our study is to put our fluid model for drift waves in tokamaks within the wider framework of statistical physics principles. This reinforces our belief in the broad application of our drift wave model, which encompasses current tokamaks, ITER, and the fusion pilot plant.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Governance and resilience as entry points for transforming food systems in the countdown to 2030

Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change.

Schneider, Kate R. [Johns Hopkins Univ., Washingto↗

DOE OSTI2025

A conceptual framework for residential energy security in the context of clean energy transitions

Energy security is a crucial aspect of human well-being. As climate change impacts become more evident, countries are constructing equitable, resilient, and sustainable clean energy transition policies to reduce emissions while ensuring energy security. Climate policies globally highlight the importance of national energy security. Furthermore, adequate and affordable access to household energy is also critical to the continued prioritization of climate mitigation. However, past energy security discussions within the broader climate research and policymaking community primarily focused on national-level energy supply as a critical metric of energy security. Less research has explored the potential implications of energy transitions for residential energy security, often focusing on a single dimension of residential energy security. Thus, we conduct a review of journal articles and governmental plans to develop a conceptual framework of residential energy security and facilitate communication among researchers and policymakers. The framework is designed around four foundational pillars, five metrics measuring residential energy security, and seven drivers influencing the metrics. Additionally, we provide policy examples to show how this framework can be applied to inform decision-making. Thus, this paper makes important contributions to the literature by (a) creating a framework to better understand the concept of energy security at the household level for future research and policy-relevant communications, (b) identifying gaps in the current literature, and (c) highlighting instances where aspects of residential energy security are discussed in policies and governmental plans, which help serve as guiding examples for future applications of our framework in the policymaking processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Addressing Coldwater Temperature Impairment in a Changing Climate

Climate change is concerning for fisheries professionals because increased water temperatures threaten coldwater habitats, which are vital to recreational and commercial fisheries and keystone species. The Clean Water Act provides a legal framework to identify and address water quality impairments including those associated with temperature. However, although this framework provides a basic strategy to identify impairments, develop total maximum daily loads, and reduce pollution, it does not explicitly consider the challenges of a changing climate in addressing water quality impairments. Clean Water Act administrators and fisheries professionals can facilitate temperature restoration planning and implementation using the Clean Water Act framework to link stakeholders to the management of coldwater resources. Stakeholder engagement and collaboration, robust local monitoring, and incorporating climate resilience into restoration planning and implementation will promote comprehensive management of temperature impairments and the effects of climate change on fisheries.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Unified Modeling Architecture for Load Management in Extreme Heat: The New York City Case

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using coastal-urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modeling framework, based on the urbanized weather research and forecasting model, is used to simulate climate, renewable resources, and energy demand variables. Findings show significant energy load mismatch of approximately 1150 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 9% over the duration of the month. This study provides a transferable modeling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience during extreme heat events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Resilience of terrestrial and aquatic fauna to historical and future wildfire regimes in western North America

Wildfires in many western North American forests are becoming more frequent, larger, and severe, with changed seasonal patterns. In response, coniferous forest ecosystems will transition toward dominance by fire-adapted hardwoods, shrubs, meadows, and grasslands, which may benefit some faunal communities, but not others. We describe factors that limit and promote faunal resilience to shifting wildfire regimes for terrestrial and aquatic ecosystems. We highlight the potential value of interspersed nonforest patches to terrestrial wildlife. Similarly, we review watershed thresholds and factors that control the resilience of aquatic ecosystems to wildfire, mediated by thermal changes and chemical, debris, and sediment loadings. We present a 2-dimensional life history framework to describe temporal and spatial life history traits that species use to resist wildfire effects or to recover after wildfire disturbance at a metapopulation scale. The role of fire refuge is explored for metapopulations of species. In aquatic systems, recovery of assemblages postfire may be faster for smaller fires where unburned tributary basins or instream structures provide refuge from debris and sediment flows. We envision that more-frequent, lower-severity fires will favor opportunistic species and that less-frequent high-severity fires will favor better competitors. Along the spatial dimension, we hypothesize that fire regimes that are predictable and generate burned patches in close proximity to refuge will favor species that move to refuges and later recolonize, whereas fire regimes that tend to generate less-severely burned patches may favor species that shelter in place. Looking beyond the trees to forest fauna, we consider mitigation options to enhance resilience and buy time for species facing a no-analog future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Modeling Framework for Evaluating Grid Disturbances

Recent research in the literature proposed parameter-based functional forms to quantify the impact of disturbances on electric power grids. This modeling was based on a concept of generalized grid disturbances that harmonized reliability and resilience. Three main stages—avoid, react, and recover—were identified, and to support practicality, were intended to be independently planned. An objective of the grid disturbance model was to fairly analyze the potential impacts of novel mitigations like transactive energy systems (TES) on grid reliability and resilience. Quantitative evaluation of a common performance metric (e.g., area under the curve of percentage customers online before, during, and after an event) over time was shown as an effective tool for such analysis. Such quantification may also enable system planners to assess the impact of appropriate mitigative actions for improving grid performance against potential disturbances. This is the focus of this report, i.e., presenting an evaluation modeling framework that can be utilized to evaluate grid performance against disturbances, under a variety of existing and possible new mitigative actions.

24 POWER TRANSMISSION AND DISTRIBUTION↗