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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Robotics for Energy Infrastructure Construction on Earth and Beyond: Pilot Study Exploring Level 3-4 Semi-Autonomous Installation of a Reduced-Scale Photovoltaic Power Plant Prototype: Preprint

There is a growing need to build energy infrastructure at massive scale on both Earth and in space. On Earth, increasing electricity demand from applications such as AI data centers is driving rapid expansion of power systems, while in space, long-duration missions such as lunar operations require reliable and resilient energy infrastructure that can be deployed quickly. These needs outpace the capabilities of conventional construction methods. Robotic construction offers a path to faster, lower-cost, and more consistent deployment, yet most existing approaches focus on automating individual tasks rather than end-to-end assembly. The Industrialized Construction Innovation (ICI) team at the National Laboratory of the Rockies (NLR), in collaboration with the Colorado School of Mines, has been investigating robotics and automation strategies for rapid end-to-end energy infrastructure construction. This paper presents a pilot study demonstrating level 3-4 semi-autonomous, end-to-end robotic assembly of a reduced-scale photovoltaic power plant prototype under terrestrial conditions. The approach combines hardware redesign of photovoltaic components for robotic compatibility, discrete-event and physics-based robotic simulations in synthetic environments, and sim-to-real deployment to execute end-to-end robotic assembly of[NM1.1][NM1.2] tracking components, structural members, photovoltaic modules, and embedded electrical connections. The pilot identifies key limitations like tolerance management, perception and localization, end effector capability, fastening methods, and multi-physics simulation fidelity, that must be addressed to enable end-to-end robotic assembly of full-scale energy infrastructure. The study highlights how these insights inform scaling strategies for terrestrial energy infrastructure and adaptation to unstructured and extreme environments, including off-world scenarios such as robotic assembly of vertical tall lunar towers.

14 SOLAR ENERGY↗
DOE OSTI2025

Cybersecurity for Digital Energy Infrastructure : Trends, Threats, and Cyber-Informed Engineering

This presentation discusses the rise in digital energy infrastructure and associated threats. It covers an in-depth risk assessment of digital energy equipment and provides real world examples of threats, vulnerabilities, and known incidents. Research conducted by INL assessing and addressing these risks follows, concluding with a dive into Cyber-Informed Engineering (CIE) and it's application to digital energy infrastructure, using battery energy storage systems (BESS) as an example.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2026

New Energy Infrastructure Outlook [Slides]

This report provides a perspective on energy infrastructure under development in the continental U.S. as of the end of 2025, focusing on those making significant progress toward achieving commercial operation. Infrastructures covered in this report include power plants, electric transmission, natural gas pipelines, liquefied natural gas terminals, and datacenters. Additionally, this report includes a section on stockpiled volumes of coal, natural gas, and petroleum.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Assessing Energy Infrastructure Devices for Vulnerabilities

Industrial control systems prove to be vital to the health and security of the nation in our critical infrastructure. Critical infrastructure includes the most foundational systems to support modern civilization which includes water and wastewater systems, communications, and the electricity we use to name a few sectors. However, these devices' overall composition remains largely unknown and are untested from a cyber security perspective. As part of the Cyber Testing for Resilient Industrial Control Systems (CyTRICS) program, I analyzed one such energy infrastructure device to better understand how it functions, what hardware and software components are present within it, and assess it for security vulnerabilities. To achieve this, I reverse engineered binary files using Ghidra to understand system functionality and learned more about how to collaborate with other researchers on a shared Ghidra project. I learned more about how web sockets function and how to interact with them through Python to test if they are secure or not. This work led me to assess possible vulnerabilities in this device and provide a better understanding of its composition and function, which are essential to INL's mission of securing our nation's energy infrastructure.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Critical Energy Infrastructure Cybersecurity: Enhanced Cyber Resilience for Federal Energy Systems

This presentation is an overview of FEMP Resilient and Secure Infrastructure and Facilities. An educational and interactive workshop centered on resilient and secure federal infrastructure and facilities, with a focus on inverter-based resources at Federal sites, building automation systems, and Federal supply chains. This workshop will illustrate an all-hazards scenario and discuss how Federal agencies can be positioned to resist these real-world scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

New Energy Infrastructure Outlook: Data as of December 31, 2024 [Slides]

This report provides a perspective on energy infrastructure under development in the continental U.S. as of the end of 2024, focusing on those making significant progress toward achieving commercial operation. Infrastructures covered in this report include power plants, electric transmission, natural gas pipelines and liquefied natural gas facilities. Additionally, this report includes a section on stockpiled volumes of coal, natural gas, and petroleum.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Organizational Influence on Supply Chain for Digital Energy Infrastructure: Business Models, and Policy Landscape

Significant investment is driving the essential modernization and digitization of U.S. energy infrastructure, but the United States faces a key challenge in this grid transformation: our renewable and clean energy supply chains have limited capacity to source necessary digital assets through U.S. or allied sources. Batteries and their associated power electronic interfaces are key components to delivering clean and more resilient energy delivery, providing much-needed fast ramping, emergency discharge, generation, and operations support to the electric grid. These services have grown to be invaluable over the past 10 years and will soon be an irreplaceable component of energy delivery. While there have been significant strides to move supply chains for raw and critical materials to U.S. and allied nations, the control and power electronic industry has lagged, in part because of lower cost margins. For example, the United States now has growing capacity to manufacture solar photovoltaic (PV) panels, but 90% of the inverters—which are essential to the conversion of DC to AC for grid connection and controls—are made in or source parts from the People’s Republic of China (PRC). While a global supply chain is beneficial for many economic and supply diversification reasons, presence of foreign entities of concern (FEOC) in a dominant role in this supply chain brings additional concerns for national security of infrastructure. The BESS relationship and new clean energy have features that warrant discussions and additional focus on impact for technology security, given their critical role. This review considers the impact and solutions for complex business and policy landscapes in clean energy supply chain, and the benefit trade-offs, focusing on the Battery Energy Storage System case study, and its complex organizational relationships across both the US, Allied countries and Foreign Entities of Concern. While many examples in this paper are presented on PRC focused manufacture, this could apply to any potentially adversarial relationship

25 - ENERGY STORAGE↗
DOE OSTI2024

Multiparameter optical fiber sensing for energy infrastructure through nanoscale light–matter interactions: From hardware to software, science to commercial opportunities

Monitoring of energy infrastructure through robust yet economical sensing platforms is becoming an area of increased importance, with ubiquitous applications including the electrical grid, natural gas and oil transportation pipelines, H2 infrastructure (storage and transportation), carbon storage, power generation, and subsurface environments. Plasmonic and functional nanomaterial enabled fiber optic sensors show excellent promise for a wide range of sensing applications due to their versatility to be engineered for specific analytes of interest while retaining inherent advantages of the optical fiber sensor platform. Through the design of novel sensing layers, the optical transduction mechanism and wavelength dependence can also be tailored for ease of integration with low-cost interrogation systems enabling an inexpensive yet highly functional optical fiber sensing platform. In addition, recent advances in artificial intelligence and machine learning theoretical methods have been leveraged to simultaneously extract multiple parameters through multi-wavelength interrogation such that unique wavelengths can also serve as unique sensing elements, analogous to electronic nose sensor technologies. The concept of an optical fiber based “photonic nose” via multiple interrogation wavelengths and/or sensor nodes offers a compelling platform technology to realize multiparameter speciation of chemical analytes within complex gas mixtures. In this Perspective, we further generalize the notion of multiparameter sensing through the novel “photonic nervous system” concept based upon low-cost, functionalized optical fiber sensor probes monitoring a variety of distinct analyte classes (physical, chemical, electromagnetic, etc.) simultaneously to provide broad situational awareness via integrated sensors.

Su, Yang-Duan (ORCID:0000000214820902)↗
DOE OSTI2026

The Manufacture of Advanced Key Energy Infrastructure Technologies (MAKE IT) Prize (Final Technical Report)

This is a final technical report for the American-Made Manufacture of Advanced Key Energy Infrastructure Technologies (MAKE IT) Prize, developed by the U.S. Department of Energy (DOE) Office of Technology Commercialization (OTC) in collaboration with the Office of Clean Energy Demonstrations (OCED) and the Office of Energy Efficiency and Renewable Energy (EERE) with support from the National Laboratory of the Rockies (NLR). The MAKE IT Prize aims to catalyze domestic manufacturing of critical energy technology components, moving manufacturing facilities from planning to shovel-ready and enabling strategies for vibrant manufacturing activities in communities. This prize was launched with a prize pool of approximately $30 million spread across two tracks: Facilities and Strategies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Solutions for Lasting, Viable Energy Infrastructure Technologies (SOLVE IT) Prize Final Technical Report

This is a final technical report for the American-Made The Solutions for Lasting, Viable Energy Infrastructure Technologies (SOLVE IT) Prize, funded by the Infrastructure Investments and Jobs Act through the Technology Commercialization Fund, administered by the U.S. Department of Energy (DOE) Office of Technology Commercialization (OTC) in collaboration with the Office of Clean Energy Demonstrations (OCED) and the Office of Energy Efficiency and Renewable Energy (EERE) with support from the National Laboratory of the Rockies (NLR). The SOLVE IT Prize aimed to enable local organizations to identify and implement innovative energy solutions in a way that works for their unique needs and challenges. The competition awarded $3,740,000 to winning teams across two phases. The prize was designed to support local stakeholders and organizations as they identified and implemented innovative energy solutions. In doing so, the SOLVE IT Prize looked to promote the commercialization of promising energy technologies that will lead to reliable, affordable energy across the U.S.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

High-resolution climate model datasets for energy infrastructure planning in a renewable-dependent future

Electrification and renewables deployment efforts are amplifying the interdependence of the climate and energy systems. Increases in climate model resolution, which is now approaching that of reanalysis datasets and operational weather forecast models, present a unique opportunity to use future climate projections for energy infrastructure planning. In this Perspective, we review recent developments in high-resolution climate modeling, which have been driven by increased computing power and advanced software tools. We then look ahead to discuss how high-resolution climate data can be used to plan for a renewable-dependent future, and envision a unified climate-energy model framework that captures the two-way feedbacks between these interdependent systems.

climate change↗
DOE OSTI2026

Elevating Engagement: Insights for Energy Infrastructure Siting from Oregon Literature

Our study conducts a meta-synthesis of existing Oregon-based literature on stakeholder and community engagement methods across a variety of contexts to identify best practices and lessons learned to inform future engagement processes for Oregon’s energy siting. We analyze and synthesize these strategies to understand what has worked well across engagement practices and how we can integrate and learn from a variety of methods to develop more effective practices for engagement moving forward. We look at the successes and challenges for each method, applying lessons learned to the pre-permitting phase of energy development and infrastructure planning in Oregon. Our results synthesize key recommendations for engagement in the pre-permitting phase for energy infrastructure siting in Oregon, such as consensus and relationship building, local partnerships, and background research to understand community context.

energy infrastructure↗
DOE OSTI2026

Characterizing Uncertainty in Synthetic Tropical Cyclone Hazard Models for U.S. Energy Infrastructure Resilience

Tropical Cyclones (TCs) are intense storms that pose a persistent and considerable risk to coastal communities and infrastructure in the global tropics and subtropics, including the United States (US). With known limitations associated with observations and high-resolution earth system models, synthetic TC models that capture a wide spectrum of storm possibilities have been developed to robustly quantify TC risk. Here we examine the simulation of various TC features in the North Atlantic relevant for US coastal risk in three synthetic TC models forced with ERA5 reanalysis: MIT, CHAZ and RAFT. While there is a broad agreement among these models in terms of their representation of salient TC characteristics, certain differences do exist. To connect these modeling uncertainties with energy infrastructure resilience, we apply fragility curves that link simulated TC intensities to damage probabilities, demonstrating how uncertainty in storm states may translate into that in coastal impacts. Our study indicates that acknowledging and accounting for inter-model uncertainty leads to more reliable risk assessments, strengthening science-to-action pathways for managing risks associated with TCs.

Baby John, Effy↗
NASA NTRS2024

Baltimore Energy & Infrastructure: Assessing Urban Heat Vulnerability in Baltimore Neighborhoods to Inform Transportation Resiliency Planning Efforts

Across the world, climate change is altering cities and their local climate systems. Following global trends of rising mean temperatures, Baltimore, Maryland is projected to experience more frequent extreme heat events. Exacerbated by Urban Heat Island (UHI) effects, residents of the city face high temperatures from limited tree canopy, highly paved and impervious surfaces, limited air flow, and high concentrations of localized emissions. Urban heat is dispersed asymmetrically, with historically marginalized populations experiencing disproportionately severe heat. Recognizing the impacts of extreme urban heat, the Maryland Transit Administration (MTA) collaborated with us, the Spring 2024 Goddard Energy and Infrastructure NASA DEVELOP team to assess urban impacts of MTA users and assets and explore opportunities to enhance resiliency planning. We evaluated the feasibility of NASA Earth observations (EOs) in visualizing heat vulnerability in Baltimore City and County. We mapped UHI and extreme heat using remote sensing observations, tailored a Heat Priority Score (HPS) to analyze heat data in tandem with socioeconomic data from American Community Survey (ACS), and utilized SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model to estimate MTA riders’ thermal comfort. NASA EOs served to quantify the distribution and severity of extreme heat at the block group level, which was found to correlate with a lack of vegetation and presence of densely built-up surroundings. The HPS revealed that extreme heat events most negatively impact communities that exhibit social vulnerability through indicators such as age, race, and income. These findings will inform neighborhood resiliency plans for the MTA that can be incorporated into their Adaptation Resiliency Toolbox (ART).

Baltimore,↗
DOE OSTI2024

Advanced Computational Techniques for Improving Resilience of Critical Energy Infrastructure under Cyber-Physical Attacks

In this chapter, we present recent advances in improving the resilience of cyber-physical systems, especially with regards to energy systems. We provide discussions around various types of cyber-physical events that can cause disruptions and new advances in optimization, control, and reinforcement learning (RL) to deal with the challenges posed by such cyber-physical events. The presented methods range from distributed robust optimization, autonomous and coordinated control, reinforcement learning based resilient control and topology reconfiguration in Inter-System resilient control.

Nazir, Mohammad Nawaf [BATTELLE (PACIFIC NW LAB)]↗