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This dataset contains survey, navigation, and platform-design data supporting instrument deployment and positioning during the 2016-2017 RODEO field operations at Block Island. It includes an acoustic localization software toolkit and its Block Island position solutions, R/V Tioga ship navigation and underway data, CTD casts, ship-track charts, and engineering designs for the deployed moorings and arrays.
Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.
Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore, forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm's true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory's Intelligent Campus program.
The Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) was developed by the National Laboratory of the Rockies as a modular platform for advancing wave-powered desalination technologies. Designed to operate in either a hydraulic or electric configuration, the system enables direct comparison of mechanical-to-water and electrical-to-water conversion pathways using a single hardware architecture. Deployments in 2022 and 2024 at the Jennette's Pier test site in North Carolina demonstrated freshwater production between 60 and 300 gallons per day, including successful operation in wave heights as low as 0.25 m. A structured evaluation approach combining numerical modeling of hydrodynamic and power take-off response, controlled laboratory testing, and in-ocean field deployments has been used to characterize and refine system performance. Specifically, insights from these efforts are now informing the second-generation HERO WEC (V2) to increase robustness and overall survivability. Changes from the original prototype include improved drivetrain components, refined geometry, enhanced control systems, and design. The HERO WEC platform continues to serve as an open-access research asset supporting broader marine energy and desalination development.
Developing methods for rapid, large-scale deployment of carbon capture systems is critical for meeting climate change goals. Optimization-based decisions can be employed at the design and manufacturing phases to minimize costs of deployment and operation. Manufacturing standardization results in significant cost savings due to economies of numbers. Building off previous work, we present a process family design approach to design a set of carbon capture systems while explicitly including economies of numbers savings within the formulation. Our formulation optimizes both the number and characteristics of the common components in the platform and simultaneously designs the resulting set of carbon capture systems. Savings from economies of numbers are explicitly included in the formulation to determine the number of components in the platform. We show and discuss the savings we gain from economies of numbers.
Driven mostly by decarbonization goals, geothermal interest in the US power sector has grown considerably, gradually evolving from being considered a niche technology, to being recognized as a viable source of clean, baseload, grid-balancing power, and renewable electric power generation. Furthermore, recent technical advances that could greatly accelerate deployment in the near future, and US federal incentives for low-carbon generation technologies, including geothermal, can enable opportunities for integrating geothermal into utilities resource planning portfolios. However, in general, utilities do not have in-house expertise to evaluate geothermal technologies and its potential role in helping decarbonize the grid as well as help them achieve their individual decarbonization goals. To date, geothermal is rarely included in Capacity Expansion Models (CEM) which utilities use in their planning activities and resource/technologies prioritization. To this end, EPRI and NREL are working together in a DOE-GTO funded research project to improve geothermal understanding (opportunities, value, risks) among the power industry to help accelerate geothermal deployment. In the present paper we describe the approach and preliminary findings of this work, focused on two topics: 1) Expand the degree of understanding on the value, opportunity, and risk of geothermal technologies among utilities and related companies/groups, specifically around geothermal for power generation. 2) Improve representation of geothermal power technologies in capacity expansion models (CEM).
Most existing vacuum pump technologies remain stationary, limiting their effectiveness in a molecular environment. In low vacuum environments, molecules move in a particular path towards the source of pumping. As the mean-free-path increases, the system’s ability to capture molecules diminishes and pump-down time increases. To address this, the team explored various designs that would deploy the pump surface closer to the molecules. After several iterations, a “windmill” shape for the final design. This design employs titanium sublimation technology in a deployable configuration, effectively reducing the distance between residual molecules and the pump surfaces, thereby minimizing randomness and improving particle capture.
This presentation covers opportunities and challenges for accelerating nuclear deployment, specific application areas for artificial intelligence (AI) in nuclear power plant design, licensing, and deployment, some early R&D that has been conducted in this space at INL, and a look to the future.
Driven by the growth in digital services, cloud computing, AI, and manufacturing, data centers face rising energy demands that challenge traditional power sources and cooling efficiency. This study explores using nuclear power to meet these demands, focusing on accelerated reactor technology deployment and highlighting needs such as N+1/N+2 power supplies and integrated power-thermal management. A SWOT analysis addresses grid connectivity, reactors, and site selection, particularly DOE sites. Reactor technology demonstration and deployment could be accelerated by leveraging test facilities such as MARVEL, MAGNET, TED, FAS, DOME, LOTUS, ATR, Energy System Proving Grounds, and upcoming Energy Launch Pads, along with modeling and simulation tools such as RELAP5, MOOSE, VERA, RAVEN, and FORCE. The potential power and thermal management options, including various cooling technologies, waste-heat utilization, and an industrial-scale demonstration plan, aim to accelerate the integration of nuclear power and data centers in the USA, while emphasizing community and stakeholder engagement and synergistic efforts.
Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.
Digital twins that are automatically constructed from robot sensor data offer a promising pathway for scalable Real2Sim and Sim2Real transfer. However, it remains an open question whether photorealistic reconstruction alone is sufficient to support reliable deployment of vision-language-action (VLA) policies. We present a generative-AI-assisted Real2Sim pipeline that generates simulation-ready digital twins from real-world RGB observations with minimal manual intervention. The pipeline uses prompted segmentation to isolate scene components and a generative 3D model to directly produce simulation assets, eliminating the need for traditional multi-view reconstruction or manual 3D modeling.\r\nTo evaluate simulation fidelity, we deploy and compare policies from two VLA models in both the real robot and the reconstructed\r\nsimulation under identical tasks and initial conditions. We compare joint-level action trajectories and analyze how divergence evolves over time in closed-loop execution. Although the reconstructed environments are visually accurate, we observe increasing trajectory divergence during closedloop operation. These results indicate that photorealistic reconstruction alone is insufficient to preserve closed-loop control behavior\r\nin VLA policies, particularly in contact-rich manipulation settings where small perceptual errors compound over time.
This study proposes a framework for evaluating cloud computing deployment in the electric sector, focusing on the digital transition of energy systems. It assesses the implications of cloud technology adoption, particularly in terms of security, operational resilience, and efficiency. The paper introduces a method for consequence-driven risk analysis, enabling utilities to prioritize and mitigate potential threats effectively. It also discusses the shared responsibility model in cloud computing, highlighting the need for collaborative security efforts. The research aims to provide utilities with a strategic assessment tool for cloud adoption, emphasizing the importance of security culture in enhancing cloud computing's role in critical infrastructure.
This presentation is on Accelerated Materials Deployment and is intended to be presented at the 2024 NRC Standards Forum.
This document is designed to provide comprehensive considerations, best practices and guidance for deployment of Distributed Energy Resources (DERs) in Antigua and Barbuda. The document provides lists of various technology selection guidance including technical specifications, requirements and applicable key standards and code associated with key DERs and balance of plant equipment. The document also presents the key microgrid design considerations to integrate the DERs to achieve the economic, decarbonization and resiliency goals. Taking into account sensitive grid for such islands, for grid readiness and screening the potential DER interconnection, general approach is highlighted along with the potential technical interconnection requirements. Furthermore, this document shows the key stakeholders involved in such DER integration and/or microgrid project and how key responsible project team can be qualified. Finally, this document refers to key information about warranties and consumer rights and maintenance of the systems.
This presentation provides an overview of NLR's megawatt-scale controller-hardware-in-the-loop (CHIL) and power-hardware-in-the-loop (PHIL) platforms to evaluate distributed energy resource and microgrid controls for successful field deployment.
The community solar market analysis presented here is based primarily on data collected through Sharing the Sun, an initiative of the National Community Solar Partnership+ (NCSP+). Sharing the Sun data collection and analysis are conducted by the National Laboratory of the Rockies (NLR) as part of its support for implementation of NCSP+. NLR first released a dataset of community solar projects in 2018 and updates it biannually. The January 2026 dataset, data collection methodology, and all the previous datasets are available from NLR's Data Catalog: https://data.nlr.gov/submissions/244. The dataset presents project-level information including location, capacity, operating utility, and year of interconnection. The dataset is created from multiple data sources such as utility data, public utility commissions, project developer websites, media releases, primary data collection by NLR, and data provided by developers under nondisclosure agreements. This presentation builds on a previous analysis of the community solar project dataset, Sharing the Sun: Community Solar Deployment and Subscriptions (as of June 2024). Dr. Gabriel Chan and his team at the University of Minnesota contribute to this effort. NCSP+ is led and funded by U.S. Department of Energy's Integrated Energy Systems Office (IESO).
The U.S. Department of Energy (DOE) national laboratories represent a unique class of government‐owned, contractor‐operated research institutions dedicated to conducting research and development (R&D) related activities that address national priorities, supporting and advancing the DOE mission. They play a vital role in sustaining U.S. innovation capacity, stewarding the nation's technical base, and nurturing science and technologies. In this perspective, we highlight the processing science and scaleup capabilities of the Materials Engineering Research Facility (MERF) at DOE's Argonne National Laboratory to demonstrate how DOE National Laboratories bridge fundamental science and applied technology development to accelerate deployment. Case studies are presented on selective membranes for critical mineral recovery, sensors for per‐ and polyfluoroalkyl substances (PFAS) detection, surface functionalization via atomic layer deposition (ALD) and sequential infiltration synthesis (SIS), and lithium recovery from battery recycling waste streams using a novel electrodialysis process. These examples underscore MERF's role in translating innovative technologies into practical solutions for renewable water and critical resource recovery, which also leverage Argonne's analytical and computational capabilities. This perspective also outlines mechanisms for collaborating with the DOE national laboratories to strengthen partnerships across government, the national laboratories, academia, and industry.
Charge Coupled Devices (CCD) are being used for reactor neutrino experiments and have already demonstrated their potential in constraining new physics models. The prospect of a Skipper-CCD experiment looking for standard and beyond standard model (BSM) physics in a nuclear reactor has been evaluated for different benchmark scenarios. Here, we report the first installation of a 2-g Skipper-CCD inside the containment building of a 2 GW th nuclear power plant and analyze its performance throughout its first 18 months of operation. The sensor was successfully deployed at Atucha II, in Argentina, 12 meters away from the center of the reactor core. We discuss the challenges involved in the commissioning of the detector and present data acquired during reactor ON and reactor OFF periods, with the sensor functioning with a sub-electron readout noise of 0.17 e - . Based on an exposure of 56.8 g day reactor ON and two reactor OFF data sets with a total exposure of 118.1 g day we characterize the system and evaluate the sensitivity to CEvNS. We achieved a background rate of 33 kdru and a low threshold of 45 eV ee . The ongoing efforts to improve sensitivities to CEvNS and BSM interaction are also discussed.