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At least 775 records · Page 43

A dynamic pricing method to manage the impact of EV charging on the grid using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

Summary Report of the Reactive CO 2 Capture: Process Integration for the New Carbon Economy Workshop

Decarbonization of our global economy is required to limit planetary warming to +1.5⁰C above pre-industrial levels, an ambitious goal set into motion by the Paris agreement. Given the scale and urgency, the solution demands international, cross-sector advancements spanning policy, social responsibility, and technology, with emphasis on step changes over incremental changes. To that end, technological revolutions that disrupt the status quo need to be envisioned and enacted. One potential technological revolution is the production of fuels, chemicals, and materials from carbon dioxide (CO 2 ) as the starting feedstock, leveraging renewable energy as the driving force. Over the past decades, significant research, development, and deployment has occurred on technologies for capturing CO 2 from point sources or the air and utilizing this CO 2 as a working fluid or as a chemical reactant; however, most of this work has been siloed in these two categories. Recently, an emerging field has started to explore the direct integration of CO 2 capture and conversion technologies as a means to reduce overall energy demand (i.e., avoid energy penalty of CO 2 desorption/regeneration of capture media) and capital expense through process intensification. This strategy represents an opportunity to leapfrog forward this technological revolution. However, the field is in its infancy and the technologies are at an early stage of development, thus it is critically important to define and assess the value proposition of this strategy relative to alternatives (e.g., separated capture and conversion technologies, fuels and chemicals derived from renewable feedstocks like biomass, and industrial electrification) to chart a path forward. To identify next steps, we organized a workshop titled “Reactive CO 2 Capture: Process Integration for the New Carbon Economy” which was held in Golden, Colorado, February 18–19, 2020. The focus of this workshop was to discuss approaches for merging CO 2 capture and CO 2 conversion/utilization systems into what we denoted as an integrated "reactive capture" strategy. By our definition, reactive capture of CO 2 is the coupled process of capturing CO 2 from a mixed gas stream and converting it into a valuable product without going through a purified CO 2 intermediate (see full definition in the Introduction section). This report seeks to summarize feedback from the approximately 125 participants and subject matter experts in attendance from academia, industry, U.S. Department of Energy (DOE), and DOE national laboratories. The workshop agenda is included in Appendix A and the full list of attendees can be found in Appendix B. To elucidate a path forward, we first asked the attendees to define what success would look like for reactive capture in the short term (0–5 years), midterm (5–10 years), and long term (10+ years) and then asked them to answer four questions related to how we could achieve that success: (1) What are the key barriers and challenges to success? (2) What are needed activities to overcome barriers and challenges? (3) What opportunities will arise from these activities? (4) What is a target outcome and what metrics need to be met?

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. Furthermore, the findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Ocean Deacidification Technologies for Marine Aquaculture

The increase in partial pressure of CO 2 in the oceans directly affects the productivity and survival of coastal industries and ecosystems. For marine aquaculture, the decreased alkalinity of seawater results in reduced availability of carbonates for marine organisms to build their shells, leading to decreased aquaculture quality and productivity. The industry has been implementing recirculating aquaculture systems (RASs) to reduce CO 2 in feedwaters, but recent interest in ocean-based CO 2 capture has led to additional strategies that may be relevant. The new methods in addition to CO 2 removal offer capture options for enhanced aquaculture sustainability. Here, we review and compare early-stage and commercially available technologies for deacidification of seawater and their suitability for aquaculture. Most methods considered rely on a voltage-induced pH swing to shift the carbonate/bicarbonate equilibrium toward the release of CO 2 , with subsequent capture of the released CO 2 as a gas or as solid mineral carbonates. The modular design and distributed deployment potential of these systems offers promise, but current demonstrations are limited to bench scale, highlighting the need for sustained research and development before they can be implemented for marine aquaculture.

42 ENGINEERING↗

Optimizing DER Deployment for Climate, Health, Resilience, and Energy Bill Benefits Using the REopt Model

Climate change, public health, and resilience to power outages are of critical concern to local governments, federal agencies, and the private sector, and are increasingly motivating investments in distributed energy resources (DERs). However, designing a solar-plus-storage system to co-optimize for climate, health, resilience, and energy bill benefits requires complex trade-offs. To address this need, the National Renewable Energy Laboratory has integrated climate and health impacts of grid-purchased electricity and on-site fuel consumption into the publicly-available REopt web tool and API - a techno-economic model that determines the cost-optimal DER system sizes and dispatch strategy. This presentation will provide an overview of the tool's methodology, datasets, and capabilities, including costing emissions into system sizing and dispatch and setting emissions reductions targets. Results of previous work will demonstrate the use of new emissions accounting capabilities by quantifying the impact of including climate and health costs on the optimal resilient microgrid configurations for a hospital, school, and warehouse across 14 U.S. cities.

climate↗

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM 2.5 ), a significant portion of ambient PM 2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM 2.5 , are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM 2.5 both temporally and spatially. Monthly network-average BC was 3×higher in winter than late spring, while PM 2.5 was only 10% lower. In both seasons, average PM 2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM 2.5 , and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM 2.5 . Thus, including BC in addition to PM 2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

54 ENVIRONMENTAL SCIENCES↗

funcX: Federated Function as a Service for Science

Here, funcX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, funcX decouples the cloud-hosted management functionality from the edge-hosted execution functionality. funcX's endpoint software can be deployed, by users or administrators, on arbitrary laptops, clouds, clusters, and supercomputers, in effect turning them into function serving systems. funcX's cloud-hosted service provides a single location for registering, sharing, and managing both functions and endpoints. It allows for transparent, secure, and reliable function execution across the federated ecosystem of endpoints-enabling users to route functions to endpoints based on specific needs. funcX uses containers (e.g., Docker, Singularity, and Shifter) to provide common execution environments across endpoints. funcX implements various container management strategies to execute functions with high performance and efficiency on diverse funcX endpoints. funcX also integrates with an in-memory data store and Globus for managing data that may span endpoints. We motivate the need for funcX, present our prototype design and implementation, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than 130000 concurrent workers. We show that funcX's container warming-aware routing algorithm can reduce the completion time for 3,000 functions by up to 61% compared to a randomized algorithm and the in-memory data store can speed up data transfers by up to 3x compared to a shared file system.

97 MATHEMATICS AND COMPUTING↗

Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

Preparing for the deployment of large scientific and engineering codes on upcoming exascale systems with GPU-dense nodes is made challenging by the unprecedented diversity of device architectures and heterogeneous programming models. In this work, we evaluate the process of porting a massively parallel, fluid dynamics code written in CUDA to SYCL, HIP, and Kokkos with a range of backends, using a combination of automated tools and manual tuning. We use a proxy application along with a custom performance model to inform the results and identify additional optimization strategies. At scale performance of the programming model implementations are evaluated on pre-production GPU node architectures for Frontier and Aurora, as well as on current NVIDIA device-based systems Summit and Polaris. Real-world workloads representing 3D blood flow calculations in complex vasculature are assessed. Our analysis highlights critical trade-offs between code performance, portability, and development time.

Martin, Aristotle↗

Establishment of an Intertribal Technical Energy Providers Network (Final Technical Report)

The Midwest Tribal Energy Resources Association (MTERA) embarked on a 5-year (extended to 7-year) initiative aimed at advancing Tribal energy resilience and sustainability through intertribal collaboration. By bringing together Tribes in Minnesota, Wisconsin, and Michigan (EPA Region 5 Tribes), the project's scope encompassed organizational growth, governance and management formalization and enhancement, and the planning and deployment of energy-related services to Member Tribes. MTERA strategically pursued four key objectives to fulfill its mission: 1. Clearinghouse Development: Creation of a centralized hub offering valuable information, education, and outreach for Tribal communities and their neighbors. 2. Policy Liaison: Serving as a vital link between federal and state agencies to contribute to the formulation of inclusive energy policies that ensure both Tribal and regional energy success. 3. Energy Planning and Research: Providing essential support for energy planning, researching financing opportunities, and exploring effective procurement strategies. 4. Training Support: Facilitating training and apprenticeship initiatives to empower the Tribal workforce and enhance community capabilities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Power Generation Enhancement through Latching Control for a Sliding Magnet-Based Wave Energy Converter

A Surface-Riding Wave Energy Converter (SR-WEC) featuring a sliding magnet inside a pitching cylindrical hull is investigated as an easily deployable small power device to support small-scale marine operations. This study extends the earlier development of the system by authors to enhance power performance through the application of end spring and latching control. The inclusion of springs at the tube’s end enhances the magnet release and travel speeds as well as the average power output compared to systems without them. Further improvement of power output can also be achieved by employing optimal latching control. We introduced constant-angle and variable-angle unlatching strategies to determine optimal parameters in combination with passive and reactive power take-off (PTO) controls to assess their effectiveness. The optimized latching control and end spring can increase 60–80% more power output compared with the case without them under certain PTO damping. Additionally, we discussed the effects of limiting peak powers and associated energy leaks with latching.

Lee, Yongseok↗

SCALE 6.3 Modeling Strategies for Reactivity, Nuclide Inventory, and Decay Heat of Non-LWRs

To assess modeling and simulation capabilities for thermal hydraulics, accident progression, source term, and consequence analysis for non–light-water reactor (LWR) technologies, the US Nuclear Regulatory Commission (NRC) initiated a collaborative project between Oak Ridge National Laboratory (ORNL) and Sandia National Laboratories (SNL) in FY20, which is detailed in “Volume 3: Computer Code Development Plans for Severe Accident Progression, Source Term, and Consequence Analysis.” This project demonstrated the capabilities of the MELCOR and SCALE codes to calculate accident scenarios during operation of relevant non-LWRs. The following five non-LWR concepts were selected for capability demonstration based on recently renewed industry interest in the United States to develop and deploy such reactor technologies: pebble-bed high temperature gas-cooled reactors (HTGRs), pebble-bed fluoride salt-cooled reactors (FHRs), molten salt–fueled reactors (MSRs), heat pipe reactors (HPRs), and sodium-cooled fast reactors (SFRs).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A Case Study in NREL DERMS Asset Management Implementation

The National Renewable Energy Laboratory (NREL) and Smarter Grid Solutions (SGS) plan to present a case study on their implementation of SGS' Strata Grid Distributed Energy Resource Management System at NREL's Energy Systems Integration Facility (ESIF). The ESIF is one of the nation's premier energy systems integration laboratory facilities focused on development and deployment of clean energy technologies and resilient delivery systems. NREL looked for a DERMS to install at the ESIF that is capable of replicating real world scenarios to control and monitor distributed devices from small residential scale to the grid substation level. The DERMS system demonstrates the use cases of beneficial operation and coordination of modern grid devices, leverage DERs for improved grid planning and operation, demand-side management and customer engagement through bidirectional communication with utilities and energy market operations. The presentation will cover the delivery strategy of NREL and SGS, how the use cases help NREL's research, and NREL and SGS plan to expand the system and use cases.

ARIES↗

Cyber-Informed Engineering Guidance—Implementing CIE in Early Systems Engineering Lifecycle Stages

Traditionally, cybersecurity is not considered in the design process. Design engineers typically focus on building safety and reliability into their products and applications. Security against malicious cyber incidents is often an afterthought, resulting in deployment of security solutions during installation or operation. Unfortunately, waiting to consider cybersecurity until later in the systems engineering lifecycle often results in less effective and more expense security. Idaho National Laboratory (INL) developed the concept of Cyber-Informed Engineering (CIE) in 2015 to provide a framework that enables cybersecurity to be built into systems beginning at the conceptual design stage. In addition to ongoing research by INL, the U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response has recently developed a National CIE Strategy document for incorporating CIE into the design and operation of infrastructure systems reliant on digital monitoring or controls. This paper provides a brief review of this National CIE Strategy as well as a roadmap to historical, current, and future CIE research by INL through the U.S. DOE Office of Nuclear Energy (NE) Cybersecurity Crosscutting Technology Development Program. A near-term focus of the DOE-NE’s research and development is to extend the foundational CIE work into detailed guidance for implementation during initial systems engineering stages in nuclear digital instrumentation and control projects and to demonstrate use of the guidance in an integrated energy systems project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Diamond surface functionalization via visible light–driven C–H activation for nanoscale quantum sensing

Nitrogen-vacancy (NV) centers in diamond are a promising platform for nanoscale NMR sensing. Despite significant progress toward using NV centers to detect and localize nuclear spins down to the single spin level, NV-based spectroscopy of individual, intact, arbitrary target molecules remains elusive. Such sensing requires that target molecules are immobilized within nanometers of NV centers with long spin coherence. The inert nature of diamond typically requires harsh functionalization techniques such as thermal annealing or plasma processing, limiting the scope of functional groups that can be attached to the surface. Solution-phase chemical methods can be readily generalized to install diverse functional groups, but they have not been widely explored for single-crystal diamond surfaces. Moreover, realizing shallow NV centers with long spin coherence times requires highly ordered single-crystal surfaces, and solution-phase functionalization has not yet been shown with such demanding conditions. In this work, we report a versatile strategy to directly functionalize C–H bonds on single-crystal diamond surfaces under ambient conditions using visible light, forming C–F, C–Cl, C–S, and C–N bonds at the surface. This method is compatible with NV centers within 10 nm of the surface with spin coherence times comparable to the state of the art. As a proof-of-principle demonstration, we use shallow ensembles of NV centers to detect nuclear spins from surface-bound functional groups. Our approach to surface functionalization opens the door to deploying NV centers as a tool for chemical sensing and single-molecule spectroscopy.

Science & Technology - Other Topics↗

Federal Best Practices: Core Principles of Sustainable Fleet Management

This report helps agency fleet managers understand and implement optimal petroleum reduction strategies for fleet locations by evaluating the most appropriate combination of the four core principles of sustainable fleet management: right-sizing the fleet to agency mission by implementing a vehicle allocation methodology study; minimizing vehicle miles traveled; increasing fleet fuel efficiency by replacing inefficient vehicles with more fuel-efficient vehicles, maintaining vehicles, driving more efficiently, and avoiding excessive idling; and optimizing cost-effective alternative fuel use, including maximizing utilization of existing alternative fuel infrastructure, installing alternative fuel infrastructure where practical (including electric vehicle charging stations), and aligning deployment of alternative fuel vehicles with fueling infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Federal Best Practices: Core Principles of Sustainable Fleet Management

This document is designed to help agency fleet managers understand and implement optimal petroleum reduction strategies for each fleet location by evaluating the most appropriate combination of the four core principles of sustainable fleet management: Right-sizing the fleet to agency mission by implementing a vehicle allocation methodology study; Minimizing vehicle miles traveled; Increasing fleet fuel efficiency by replacing inefficient vehicles with more fuel-efficient vehicles, maintaining vehicles, driving more efficiently, and avoiding excessive idling; Optimizing cost-effective alternative fuel use, including maximizing utilization of existing alternative fuel infrastructure, installing alternative fuel infrastructure where practical (including electric vehicle charging stations), and aligning deployment of alternative fuel vehicles with fueling infrastructure.

30 DIRECT ENERGY CONVERSION↗

Detecting Low-level Radiation Sources Using Border Monitoring Gamma Sensors

We consider a problem of detecting a low-level radiation source using a network of Gamma spectral sensors placed on the periphery of a monitored region. We propose a computationally light-weight, correlation-based method which is primarily intended for systems with limited computing capacity. Sensor measurements are combined at the fusion by first generating decisions at each time step and then taking their majority vote within a time widow. At each time step, decisions are generated using two strategies: (i) SUM method based on a threshold decision on a correlation statistic derived from measurements from all sensors, and (ii) OR method based on logical-OR of threshold decisions based on correlations statistics of individual sensor measurements. We derive analytical performance bounds for false alarm rates of SUM and OR methods, and show that their performance is enhanced by the temporal smoothing of majority vote within a time window. Using measurements from a test campaign, we generate a border monitoring scenario with twelve 2"x2" NaI Gamma sensors deployed on the periphery of 42m x 42m outdoor region. A Cs-137 source is moved in a straight-line across this region, starting several meters outside and finally moving away from it. We illustrate the performance of both correlation-based detection methods, and compare their performances with each other and with a particle filter method. Overall, under small false-alarm conditions, the OR fusion is found to produce better detection performance.

Sen, Satyabrata↗

A framework for strategic discovery of credible neural network surrogate models under uncertainty

The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. Here, this study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for ablation of solid fuels within hybrid rocket motors.

42 ENGINEERING↗