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At least 1,225 records · Page 68

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DOE OSTI2024

Potential biological control agents of Geosmithia morbida restrict fungal pathogen growth via mycoparasitism and antibiosis

Abstract Thousand cankers disease of Juglans (walnut) and Pterocarya (wingnut) spp. (Fagales: Juglandaceae) is caused by the fungal pathogen Geosmithia morbida Kolarík, Freeland, Utley, and Tisserat (Hypocreales: Bionectriaceae) and bark beetle pest/vector, Pityophthorus juglandis Blackman (Coleoptera: Curculionidae). To further the development of biological management strategies for thousand cankers disease, we assessed the ability of 14 endophytic Trichoderma (Hypocreales: Hypocreaceae) isolates and the commercially available isolate T. afroharzianum strain KRL-AG2 to inhibit the in vitro growth of three different G. morbida isolates via mycoparasitism and antibiosis. To identify factors that may affect field success of candidate biological control agents, we quantified the growth responses of Trichoderma spp. and the commercially available entomopathogenic fungus, Beauveria bassiana (Bals.-Criv.) Vuill. (Hypocreales: Cordycipitaceae) strain GHA, to the plant secondary metabolite and antimicrobial compound, juglone in vitro. A total of 12 Trichoderma isolates (from six different Trichoderma species) demonstrated antagonistic activity towards G. morbida in dual-plate assays. Juglone consistently reduced the growth of B. bassiana strain GHA and 14 of the 15 screened Trichoderma isolates in vitro. Additionally, one metabolite-producing Trichoderma isolate, TN4-47, completely inhibited the growth of all three G. morbida isolates across all tested metabolite concentrations and had comparatively greater tolerance to juglone compared to other Trichoderma isolates. Future lines of research should focus on characterizing the active antagonistic compound present in the metabolite filtrates, determine the mode of action of the active component(s), and elucidate how abiotic and biotic factors may influence the growth, persistence, and antagonistic activity of candidate biological control agents in planta .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Droop Control-Based Dispatch of an Islanded Microgrid with Multiple Grid-Forming Sources

Before rotating, fossil fuel-based, synchronous generators (SGs) are phased out, in line with renewable generation goals, grid-forming (GFM) inverters are expected to parallel SGs. Primary droop control allows GFM inverters to share power without communication; however, it is necessary to dispatch GFM inverters and/or SGs with the desired output power for better energy management (e.g., one GFM inverter needs to charge the battery due to a low state of charge). Therefore, this paper develops an analytic approach to dispatching GFM inverters and SGs with the desired output power by shifting the droop intercept up/down while maintaining the same frequency operating point for improved transient stability. This concept is demonstrated through a pure hardware setup with two off-the-shelf inverters and one diesel generator under an islanded microgrid, and we provide insight on the real-world implementation of the proposed concept.

droop control↗

DOE OSTI2023

Droop Control-Based Dispatch of an Islanded Microgrid With Multiple Grid-Forming Sources

droop control↗

DOE OSTI2025

Tailored glycolysis of Nylon 6 to enable upcycling into high-strength adhesives

Nylon is a high-strength polyamide widely used in automotive, textiles, packaging, etc. However, its durability makes nylon waste difficult to manage, with recycling limited to mechanical grinding to make fillers. Here, we report a catalytic glycolysis approach to deconstruct Nylon 6 into controlled-length oligomers, enabling upcycling into value-added materials. A low-molecular-weight oligomer (M n = 1.8 kg/mol) was repolymerized with diepoxy-terminated poly(bisphenol A-co-epichlorohydrin) via mechanochemistry to create a high-performance adhesive. This copolymer achieves lap shear strengths over 22 MPa on steel and bonds steel to carbon fiber composites, nearly tripling the performance of commercial adhesives even at 90°C. Thermomechanical analyses show that the adhesive retains thermal stability similar to nylon 6 but melts at lower temperatures, allowing easier processing. The material can be reprocessed and reused without significant performance loss. This study demonstrates a strategy to convert nylon 6 waste into valuable materials, offering a sustainable path for difficult-to-recycle plastics.

Nylon waste↗

DOE OSTI2022

The global spectrum of plant form and function: enhanced species-level trait dataset

Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

SMARTER Rules-Based Distributed Deconfliction of ADMS Applications

A conceptual numerical methodology derived from Grid Architecture principles is introduced for deconflicting setpoints issued by multiple advanced distribution management system applications. The methodology applies technical, economic, environmental, and social rules to eliminate non-viable combinations. The concept of temporal equipment controls budgets is introduced to preserve the health of physical assets and avoid equipment damage through repeated controls cycling. The rules are combined with a multi-criteria decision-making framework to select a near-optimal set of deconflicted setpoints using a set of qualitative and quantitative decision criteria selected by the distribution system operator. Numerical results are demonstrated on the IEEE 123-bus test feeder for three competing applications. Three alternative distributed schemes are used to decompose the problem: by topological area, by phase, and fully decentralized. The fully decentralized implementation is shown to yield near-optimal deconfliction results with significantly reduced computational time.

Anderson, Alexander A.↗

DOE OSTI2021

Virtual Battery Aggregator (Virtual Battery Aggregator for Distributed Energy Resource Management Systems)

Distributed energy resource management systems (DERMS) are being deployed in feeders with high distributed energy resource (DER) penetration to provide visibility of DERs and enable DER control strategies for grid operators. DERMS can support centralized grid controls by aggregating individual DER information into a single virtual DER model. This codebase creates algorithms for a DERMS platform that are able to aggregate a set of DERs that can be modeled as batteries, create a virtual battery model for centralized grid controls, and dispatch power setpoints to the individual DERs based on a desired virtual power setpoint received from the centralized grid control. It integrates with existing co-simulation frameworks and models for buildings, DERs, and distribution systems. While the DERMS is designed to work with batteries, it can be used with any DER that can be defined by a generalized battery model.

Blonsky, Michael↗

DOE OSTI2021

Tier 2 Safety Basis Document: Weapons and Complex Integration - Building 225

This Safety Basis Document (SBD) covers operations in the Weapons and Complex Integration (WCI) Building 225 (B225) facility. B225 is located in the southwestern quadrant of Lawrence Livermore National Laboratory (LLNL) and is operated and owned by WCI. This SBD satisfies the requirements of LLNL ES&H Manual Document 3.1, Nonnuclear Safety Basis Program (Ref. 2). B225 is classified as a Low Hazard Facility based on the inventory of radiological materials. Other hazards in B225: chemicals, radiation generating devices, and industrial hazards are managed at the Light Science and Industry (LSI) level. Biological and explosive hazards are not authorized in B225. There are four general Administrative Controls (ACs) identified as the B225 Operational Safety Requirements (OSRs). These administrative controls are deemed sufficient to ensure safe operations with respect to co-located workers and the public. Other safety features and administrative requirements are required by the LLNL ES&H Manual, including those to protect the facility workers and the environment, but these have not been selected as credited controls to protect the co-located workers or the public, which are the target populations of concern for the SBD.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Synthetic Calcium Carbonate Production by Carbon Dioxide (CO 2 ) Mineralization of Industrial Waste Brines

The global scale of CO 2 emission has crossed 36 B tons, and the United State represents 14% of the total emissions. In light of the high cost associated with current CO 2 capture processes ($60-to-150 per ton of CO 2 ), carbon capture and utilization (CCU), wherein CO 2 is converted to beneficial products, provides a pragmatic path to overcome the economic barrier for CO 2 emissions control. In particular, CO 2 mineralization offers an attractive route as it produces high-value mineral carbonates that sequester CO 2 in a stable form. For instance, fine precipitated calcium carbonate, a valuable product with unit price in the range of $230-280/t and a global market projected to reach 99 M tons in 2020, can be produced by capturing CO 2 within aqueous Ca 2+ solution. However, two critical challenges need to be overcome—(i) the need for costly processes such as electrolysis or addition of alkali hydroxides to maintain alkalinity during mineralization, and (ii) the geographic availability of Ca-rich solutions (or brines) which often renders efficient integration with power plants impractical. To simultaneously address these challenges, this project developed two novel routes to enable carbonate production based on efficient CO 2 mineralization. In one variation, the project developed a new alkaline carbonation process to capture CO 2 and produce calcite precipitates from coal ashes. Herein, coal ash is carbonated first in a sodium carbonate solution. The carbonation reaction produces sodium hydroxide and raises the solution pH. This high-pH hydroxide solution is then used for CO 2 capture, which converts the sodium hydroxide solution back to sodium carbonate to repeat the carbonation cycle. The carbonated ash residue is refined with a CO 2 pressure swing step to produce high-purity precipitated calcium carbonate. In another variation, Ca-rich produced water brine serves as the Ca-source to mineralize CO 2 . An H + /Na + ion-exchange cycle was designed to provision alkalinity during mineralization and regenerate the ion-exchange reagent in brines with high salinity. Ca-depleted brines can then be treated within a centralized water treatment facility. Both variations beneficially utilize reject streams—such as coal ashes and brines from oil and gas extraction or CO 2 storage operations—that are available at substantial quantities in the vicinity of coal power plants within the U.S. In addition to technology development, this project included techno-economic and life-cycle analysis to identify technically and economically appropriate solutions for power plants in different geographic locations. Taken together, the project provides a unique route to integrate CO 2 emissions control and waste handling/treatment for coal power plants, while producing a high-value product to offset the economic burden associated with waste management. The developed processes utilize post-desulfurization flue gas from coal-fired power plants as is. The CO 2 conversion reactions are performed in alkaline brines at ambient pressure from the flue gas, thereby minimizing the energy burden. By beneficiation of industrial waste streams with low energy input, the processes offers significant technical advantages in energy and CO 2 footprint over the current paradigm of precipitated calcium carbonate production, which involves calcination of limestone at processing temperatures in excess of 800 °C. Overall, this project developed viable CO 2 mineralization processes while strategically maximizing the economic benefit.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Achieving Cyber-Resilience for Power Systems using a Learning, Model-Assisted Blockchain Framework

The secure integration and management of distributed energy resources (DER) and power aggregators in the electric grid requires secure communications and a physics-aware Command and Control (C2) strategy. A Blockchain (BC)-based overlay network was developed to provide a security layer for the existing power grid network that mitigates risks in current and legacy network and C2 protocols. By integrating a Model-Assisted Machine Learning (MAML) framework with a Secure Blockchain Overlay Network (SBON) a defense-in-depth strategy was achieved. In our approach, the MAML framework leveraged a smart contract framework to gather network data and learn the dynamics of DER to develop detection strategies for attacks targeting sensors and actuators used by DER. The MAML framework learned dynamical systems models for individual DERs to detect sensor attacks. For DER we utilized a Digital Twin (DT) to accelerate the learning process for a model resistant to stealthy attacks. The project created DT for PV inverters and BESS. The DTs were coupled with a model-assisted, data-driven learning of DER behavior. Specifically, we evaluated architectures for model-based learning with model-free fine-tuning. Additionally, differential privacy techniques were used to obfuscate data, while still allowing the computation of attack detection results based on obfuscated data. The SBON developed leverages a private permissioned blockchain network orchestrated with the Hyperledger Fabric framework. To connect the cyber world, which orchestrates the blockchain fabric, and the physical world where the power network resides, we developed a system implementation to enable the secure interaction of the physical world and the abstracted blockchain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗

DOE OSTI2023

A Novel Multi-Agent Deep Reinforcement Learning-enabled Distributed Power Allocation Scheme for mmWave Cellular Networks

We consider the power allocation problem over shared spectrum for millimeter-Wave (mmWave) cellular downlink. Existing approaches usually find sub-optimal solutions by solving a non-convex optimization which leads to scalability issues due to centralized control. Therefore, distributed and adaptive approaches are desirable. Recently, model-free Deep Reinforcement Learning (DRL) has achieved success in such wireless resource management tasks. By modeling the radio environment as a Markov Decision Process (MDP) with the base stations (BSs) being the agents, power allocation can be automated at the agent level with comparable throughput performance to conventional centralized schemes. The multi-agent setting presents new challenges as the radio environment is impacted by the joint actions of the agents and is no longer stationary from any individual agent’s perspective. Existing literature bypasses this non-stationarity violation by ignoring it which may cause performance degradation. To tackle this issue, we propose a distributed continuous power allocation scheme based on a modified version of multi-agent Deep Deterministic Policy Gradient (MADDPG) that is tailored for the distributed multiple-agent setting. The proposed scheme employs a centralized-training distributed-execution framework where Q-functions are trained over subsets of BSs while each BS determines its transmit power based only on its own local observation. It admits constant per-BS communication and computation complexity and is thus scalable to large networks. Numerical evaluation shows that the proposed scheme adapts well to a wide range of interference conditions and can achieve comparable or better performance than several state-of-the-art non-learning approaches.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Transitioning to Hybrid Power Plants

Hybrid power plants are power generation systems that combine two or more types of electricity generation or storage, creating systems where the optimal dispatch provides more benefits that the sum of the individual components can. These plants exemplify the interconnected challenges of grid modernization. On one hand, rapid increase in deployment and enhanced controllability of renewable energy assets is needed to meet clean energy targets, but on the other, increasing electrification makes it more difficult to manage load and growth in the number of endpoints and stakeholders involved with hybrid plants complicates asset management and cybersecurity. This talk will cover trends in hybrid power plant technologies and deployments, identify future prospects for hybrid systems, and discuss the challenges and research opportunities that exist to transition from the traditional power grids of today to smart, distributed, hybrid power grids we may see in the next few decades.

14 SOLAR ENERGY↗

DOE OSTI2023

Active Causal Machine Learning for Molecular Property Prediction

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

Fox, Zach↗

DOE OSTI2021

Real Time - Optimal Power Flow Based Distributed Energy Resources Management System (DERMS) (CRADA Number CRD-20-16909 Final Report)

The integration of behind-the-meter distributed energy resources (DERs) into distribution systems brings transformative changes to power systems. This requires operators and planners to find solutions to modernize electric grids and to effectively manage DERs for grid services. NREL developed novel DER management algorithms (referred to as Real-Time Optimal Power Flow, RT-OPF) through U.S. Department of Energy (DOE)-funded efforts, including Advanced Research Projects Agency–Energy (ARPA-E) Network Optimized Distributed Energy Systems (NODES) funding. This cutting-edge control technology aims to modernize distribution systems with large amounts of DER integration, which will help utilities solve issues brought by renewable integration and build resilient and renewable-based electric grids nationwide. Utilidata worked with NREL to investigate the commercialization opportunity of this RT-OPF-based distributed energy resource management system (DERMS). In this project, NREL performs the technology transfer of the RT-OPF to Utilidata to help them fully understand the RT-OPF solution, to identify potential engineering hurdles, and to assess the expected commercial value of various RT-OPF use cases and deployment. The technology transfer work includes two major tasks. First, NREL performs an in-depth knowledge transfer of the entire RT-OPF solution to Utilidata to help them gain an extensive and detailed understanding of the complete RT-OPF solution. In this task, NREL provides exhaustive information (e.g., documentation, code packages, laboratory and field trial data, performance results) while conducting in-depth training sessions to provide a thorough explanation of the entire solution. NREL hosts meetings to present different topics related to the RT-OPF solution, and question-and-answer sessions are included in each meeting to better explain the RT-OPF-related work. Second, the RT-OPF simulations are performed in a laboratory environment. The main objectives of this task are to walk through with Utilidata engineers how to set up a simulation of RT-OPF, identifying each RT-OPF code block/component in operation, learning how these components interact with each other, and eventually running RT-OPF simulations under various system conditions. This task helps Utilidata engineers understand performance limitations and constraints while also quantifying the commercial value of the RT-OPF for different use cases. Based on these two tasks, Utilidata engineers should be able to define and prioritize the next steps of RT-OPF implementation with an eye toward commercial success and scalability of the solution. The next steps are expected to be part of a new project following the conclusion of this project.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Automation of the Receipt Inspection of Criticality Control Overpacks (CCO)

This research work has been supported by the DOE-FIU Science & Technology Workforce Development Initiative, an innovative program developed by the U.S. Department of Energy’s Office of Environmental Management (DOE-EM) and Florida International University’s Applied Research Center (FIU-ARC). During the spring of 2022, a DOE Fellow intern, Philip Moore, spent 16 weeks doing a spring internship at Savannah River National Lab under the supervision and guidance of Corey Hopper. The intern’s project was initiated on January 24, 2022, and continued through April 2, 2022, with the objective of contributing to a robotic system for automation of CCO inspection.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Machine Vision-based Robot Manipulators for Nuclear Applications - 20370

Decommissioning and dismantling of nuclear facilities are major challenges facing the nuclear industry. Robot manipulators with capabilities of restoring nuclear structures and significantly prolonging nuclear power generation in addition to their applications in decommissioning and dismantling (D and D) would immensely ease the above-mentioned challenge. Ability to remotely sense/monitor and analyze dangerous and hazardous environments such as nuclear reactors as well as performing reparatory tasks in such environments using robot manipulators require additional information from vision sensors. Structural sample collection and inspection as well as restoration require object and position information. Computer vision is used to obtain precise 3D position information. The current work uses the Sawyer robot with the integrated Cognex camera. The research presents an approach to facilitate and improve operations in a nuclear reactor using robotic vision control. The vision control system is modeled with Augmented Image Space-based visual servoing approach. Results showing accurate robot control. Object recognition is used to recognize the image and calculate it poses. To transform coordinates of the object's pose from the camera frame to the robotic frame transformation matrices are employed. Future work will employ a 3D camera (with depth information) using Denso robot. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Optimized V1G and V2G Electric Vehicle Fleet Management and Grid Transaction at Marine Corps Air Station Miramar in San Diego, CA

The overall technical goal of the project was to demonstrate an all-electric bi-directional non-tactical fleet at Marine Corps Air Station (MCAS) Miramar that was integrated and controlled with other distributed energy resources (DERs) (i.e., PV, stationary battery, and building loads) to provide resilience to critical electric loads in the event of grid outages, to minimize charging costs, and to provide economic energy resources to electricity markets. In this project, the specific, technical objectives were: 1. Demonstrate that bi-directional electric vehicles can provide critical complementary services to fixed storage batteries in microgrid applications while performing function as non-tactical vehicles. 2. Demonstrate participation of bi-directional (V2G) and unidirectional (V1G) PEVs for demand management and minimization of charging costs. 3. Demonstrate integration of multiple DERs for grid service participation. US Marine Corps Air Station (MCAS) Miramar in San Diego was the site of this electric vehicle-to-microgrid-utility grid test and demonstration project. Existing microgrid assets in this study included (1) a public works building; (2) a 30-kW rooftop photovoltaic (PV) system and (3) a separate 250 kW carport PV system. In this project, six bi-directional V2G vans were located at the MCAS Miramar’s showcase building-scale microgrid to develop and test technical capabilities that V2G can provide in microgrid applications (e.g., cost reduction and resiliency). These resources provided aggregated demand management and simulated participation in current retail DR programs. The vehicles used in this demonstration were selected because they provided functionality that MCAS Miramar needed, 15 passenger transport and facilities work cargo carrying capacity, and bi-directional charging capability that the research project required. All vehicles in this study were manufactured and distributed by VIA Motors, Inc. There were six vehicles total and each was VIA’s VTRUX eREV V2G model, a modified General Motors Chevrolet 2500 2WD van. Three of the vans were configured as passenger vans and the other three were configured as cargo vans. Each van had an on-board bi-direcrtional inverter/charger, Bel Power Solutions model 350INVCHGT150-120-240-8G nominally rated at +/-15 kW. The VIA van’s charging connector follows the J1772 charging protocol. The bi-directional EVSEs demonstrated in this study were manufactured by Coritech, Inc. Each VGI-80-AC charging station enabled enhanced V2G charging capability to a Clipper Creek CS-100 charging module. The enhanced capabilities included ethernet communication following the SEP2.0 protocol with a distributed energy resource function set and an operator screen displaying real-time SOC, voltage, and current. The VGI-80-AC charging stations are classified as level 2 with a maximum current output of 80 A or effectively 19 kW. The VIA van’s onboard charger limited the charging and discharging power to 15 kW in each direction. A control computer was installed in the EWOC and connected to an existing monitor. The V2G control communication network was a completely stand-alone closed system that did not have any connection to any other networks on the base. A cybersecure remote communication connection was created with a cellular modem, firewall hardware, and a virtual private network configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗