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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 OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated reactor physics analysis framework of High Flux Isotope Reactor low-enriched uranium silicide dispersion fuel designs

The High Flux Isotope Reactor (HFIR) is a versatile research reactor that provides one of the highest steady-state neutron fluxes of any reactor in the world. The HFIR reactor physics team investigated the conversion of the current 93 wt% highly enriched uranium U 3 O 8 -Al dispersion fuel to a 19.75% low-enriched uranium (LEU) U 3 Si 2 -Al dispersion fuel. The team continuously develops a Python module to streamline the analysis steps required for an LEU core design to ensure reproducible and agile design iteration. The Python module automates the data processing between analysis steps and automates the input perturbation for branch calculations and design changes. The automated framework has proven to significantly increase the efficiency and reproducibility of the reactor physics team to design High Flux Isotope Reactor (HFIR) LEU cores and thoroughly analyze performance metrics, safety metrics, and thermal safety margins. Consequently, the team can now respond rapidly to fuel fabrication engineer and thermal-hydraulic-structural analyst requests. Numerous combinations of LEU fuel designs are explored, of which two LEU fuel designs are presented here in this paper: a low density silicide design, and a high-density silicide design. Results show that both designs meet or exceed safety and performance metrics with exception for minor differences caused by the hardened spectrum from LEU.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

The energy footprint of automotive electronic sensors

We discuss how Information and communication technologies (ICT) have emerged as one of the leading technologies to reduce global emissions, particularly in the mobility sector. Automotive electronics, such as sensors/actuators and microcontrollers (commonly known as electronic control units (ECU) which control one or more of the electrical systems or subsystems in a vehicle), play a key role in ICT. Sensors/actuators are key in electronic ICT devices, starting with the data collection and data communication with the internet. The latest two big trends of electrification and automation in vehicles, are projected to increase the use of worldwide automotive sensors from 7.5 billion units in 2017 to 11.0 billion units by the year 2024. A representative state-of-the art automotive sensor system, (i.e., an ultrasonic backup system), including the ECUs controlling the vehicle electrical systems/subsystems, has been considered to estimate the energy footprint in terms of manufacturing and operational energy of global automotive sensors use. A widely used life cycle energy assessment method (i.e., cumulative energy demand) was used as both direct and indirect (including the extraction, manufacturing, and disposal of the raw and auxiliary materials) energy use can be considered for the energy footprint estimation. The embodied manufacturing energy impacts of the system was estimated to be 559 MJ/system, compared to the 417 MJ/system for lifetime system power and additional gasoline use. The share of purchased energy to the embodied energy where the upstream energy isn't included in the former case, is less than 10% and ~ 85% for the component manufacturing and vehicle operation energy uses, respectively. As the purpose of this ultrasonic backup system is to prevent rear crashes, an estimated 1.0 MJ/system is avoided from reduced lifetime vehicular repairs (from an estimated 11% chance of requiring a rear bumper replacement). While all of this is small compared to the overall automotive manufacturing and use energy, the 11 billion automotive sensors expected to be produced in 2024 could require 1540 PJ for manufacturing and those sensors would require an additional 780–1150 PJ for lifetime energy use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Scientific machine learning benchmarks

Deep learning has transformed the use of machine learning technologies for the analysis of large experimental datasets. In science, such datasets are typically generated by large-scale experimental facilities, and machine learning focuses on the identification of patterns, trends and anomalies to extract meaningful scientific insights from the data. In upcoming experimental facilities, such as the Extreme Photonics Application Centre (EPAC) in the UK or the international Square Kilometre Array (SKA), the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. Furthermore, at present, identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is a challenge due to the potential applicability of many different machine learning frameworks, computer architectures and machine learning models. Historically, for modelling and simulation on high-performance computing systems, these issues have been addressed through benchmarking computer applications, algorithms and architectures. Extending such a benchmarking approach and identifying metrics for the application of machine learning methods to open, curated scientific datasets is a new challenge for both scientists and computer scientists. Here, we introduce the concept of machine learning benchmarks for science and review existing approaches. As an example, we describe the SciMLBench suite of scientific machine learning benchmarks.

42 ENGINEERING↗
DOE OSTI2023

The fast camera (Fastcam) imaging diagnostic systems on the DIII-D tokamak

Two camera systems are installed on the DIII-D tokamak at the toroidal positions of 90° (90° system) and 225° (225° system), respectively. The cameras have two types of relay optics, namely, a coherent optical fiber bundle and a periscope system. The periscope system provides absolute intensity calibration stability while sacrificing resolution (10 lp/mm), while the fiber system provides high resolution (16 lp/mm) while sacrificing calibration stability. The periscope is available only for the 90° system. The optics of the 225° system were designed for view stability, repeatability, and easy maintenance. The cameras are located inside optimized neutron, x ray and magnetic shielding in order to reduce electronics damage, reboots, and magnetic and neutron interference, increasing the overall system reliability. An automated filter wheel, providing remote filter change, allows for remote wavelength selection. A software suite automates camera acquisition and data storage, allowing for remote operation and reduced operator involvement. System metadata is used to streamline the data analysis workflow, particularly for intensity calibration. Here, the spatial calibration uses multiple observable wall features, resulting in a reconstruction accuracy ≤2 cm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators

Tensor algebra operations represent an important class of algorithms used across many applications, including machine learning, scientific computing, and data analytics. As a result, the efficient generation of custom accelerators for tensor operations has received increased attention. Previous efforts have produced automated tools enabling users to prototype and explore optimized accelerators. However, little effort has been focused on the host-accelerator interaction in these tools. Efficient use of hardware accelerators requires knowledge about the accelerator's capabilities (operations, data formats, and opcode support), the host CPU microarchitecture (e.g., memory hierarchy), the host-accelerator interface, and the application's features (which code regions should be mapped onto an accelerator). Manually rewriting the original applications to facilitate improved custom accelerator mapping is an error-prone and time-consuming endeavor. To cope with this, we propose AXI4MLIR, a new framework to automatically generate and optimize the communication between the host CPU and arbitrary accelerators that implement linear algebra algorithms. AXI4MLIR extends the MLIR compiler framework to automatically generate efficient host-accelerator driver code for accelerators with AXI-based interfaces. Our compiler extensions enable automatic driver code generation while carefully considering the host's memory hierarchy and target accelerator features. To demonstrate the flexibility and utility of AXI4MLIR, we test it with diverse use cases that include different types of accelerators, tiling scenarios, and dataflow schemes. We compare our experimental results to manual implementations of host-accelerator driver code and find that our approach can reduce CPU cache references by 56% and deliver up to a 1.65x speedup.

Bohm Agostini, Nicolas↗
DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Assessment of Flow-Enhanced Electrochemical Sensor Testing and Deployments for MSRs

This report serves as the deliverable for Milestone M3RS-23AN0401061 that is part of Work Package RS-23AN040106 (Flow Enhanced Sensors for MSRs – ANL). The goal of this milestone was to determine performance of the flow enhanced electrochemical sensor (FEES) and modular flow instrumentation testbed (MFIT) in safeguards relevant scenarios. Flow enhanced electrochemical sensors are a type of electroanalytical sensor that has been developed at Argonne National Laboratory to be installed directly into MSR flow conduits to make measurements of the salt composition. These sensors represent a significant improvement in capabilities compared to earlier electroanalytical sensors that instead can only be operated in quiescent conditions. Previous work has focused on testing of the FEES in flowing conditions provided by the MFIT to assess the accuracy and precision of the sensor measurements. To further improve this capability, in FY23 we undertook a campaign of safeguards relevant scenarios in molten salt containing a range of uranium chloride concentrations (0 to 3 wt%). All the testing carried out in FY23 was aided by a control system designed to automatically actuate flow conditions and collect data. This new automation system is estimated to have increased experimental throughput by a factor of four and enabled testing in a variety of complex conditions. The advancements in throughput and repeatability led to improved quantification of actinide concentrations using the in-flow sensors, with a reduction of the mean absolute relative error from 5.6% in FY22 to 3.1% in FY23. In addition to safeguards scenarios run in the MFIT, FY23 work included deployment of a FEES at a partner institution where it will be tested in a flowing salt loop. The FEES was successfully integrated into that loop and is being tested prior to loop startup. In FY23, work also continued on the smaller flow system that we have named the mini-MFIT. This smaller system is capable of rapid prototyping of new sensor designs prior to installation in the larger MFIT radiological flow system. Work was carried out to test this new system in non-radiological molten salts in a separate glovebox. This work is helping us to enhance the accuracy of our salt monitoring capabilities through the integration of multiple types of sensors. The high degree of accuracy required by 10 CFR 74 represents a significant challenge, and further design evolution and integration of the sensors into multimodal sensing frameworks will be needed to further push the measurement accuracy to the needed level.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Big Data Meets Geothermal Exploration (CRADA Final Report)

As part of the Cyclotron Road program, Zanskar Geothermal & Minerals, Inc. investigated the application of micro-earthquake and ambient noise seismology methods to imaging and characterizing the structural characteristics and hydrothermal flux of subsurface faults. Significant advances in what could be resolved were enabled by two major developments in seismology: 1) the availability of large-n arrays of low-cost seismometers, and 2) the availability of increased computational power and semi-automated data reduction algorithms. In tandem, these advances may improve the signal-to-noise ratio and spatial precision of the data collected and enable higher-resolution characterization of subsurface fracture systems and their spatio-temporal evolution. These tools supported efforts to reduce dry-hole risk and to improve wellfield productivity for geothermal resource development. In particular, two applications of these advances were evaluated: 1) fracture-seismic imaging, which was used to detect ambient emissions from fluid-filled fractures, and 2) reservoir tomography, which used information about travel paths, source locations, and source parameters of micro-earthquakes to identify areas of enhanced permeability. Integration of these methods provided guidance for siting wells and served as prior constraints for reservoir models, informing forecasts of power potential and production and injection strategies aimed at minimizing temperature decline and improving overall resource productivity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Producing High-fidelity Synthetic Population Ensembles at Scale

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the US via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. Our initial task involves creating ensembles for 17 US metropolitan areas, each consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system comprised of a research cloud, virtual containerization, GPU-enhanced functionality, and a dual API/CLI to interact with UrbanPop’s maturing Likeness Python ecosystem. We observe a reduction in theoretical execution time while maintaining high-fidelity approximations of residential totals by metropolitan area and the demographic characteristics of neighborhoods. We discuss expansion of our approach to produce synthetic population ensembles for the entire US, particularly plans to establish automated workflows for job orchestration to increase computational efficiency, as well as provide outlook for broadening applications of the ensembles.

Gaboardi, James [ORNL] (ORCID:0000000247766826)↗
DOE OSTI2023

Milone eTape Liquid Level Sensor Laboratory Calibration with a Commercial 12.2 cm HS-Flume and 3D Printed 4 cm Micro-HS-Flumes

The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Prior to field deployment, H-flume discharge and the corresponding Milone eTape Liquid Level Sensor (eTape) resistance values were measured in the laboratory so that discharge measurements from the NGEE Arctic Rainfall Simulator could be automated. eTape resistance was measured with increasing fluid heights for the full range of the eTape to calculate sensor detection limits and resolution. The eTape was calibrated to both a commercially available 12.2 cm HS-flume and a 3D printed 4 cm Micro-HS-flume. This data package contains two .csv files, one for the eTape calibration and the other for the flume calibrations, and two .stl files to 3D print the 4 cm Micro-HS-Flume design with a 1 cm x 3.6 cm stilling well opening. The .stl files can be opened in most 3D printing or CAD programs/software. Two .kml files of the laboratory location and broader area where testing was conducted are also included in this data package.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Can We Fix It Automatically? Development of Fault Auto-Correction Algorithms for HVAC and Lighting Systems

A fault detection and diagnostics (FDD) tool is a type of energy management and information system designed to continuously identify the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and application of automated analytics. Building owners and operators at the leading edge of technology adoption are using FDD tools to enable average whole-building portfolio savings of 8 percent. Although FDD tools can inform building operators of operational faults, currently a manual action is always required to correct faults and generate the associated energy savings. A subset of faults, however, such as biased sensors and manual override, can be addressed automatically, removing the need for operations and maintenance staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability, as well as the technical and economic performance, of FDD technologies. In this paper, we present the development of 10 innovative fault auto-correction algorithms for HVAC and lighting systems. When the auto-correction routine is triggered, it will overwrite the control setpoints or other variables (via BACnet or other protocol) to implement the intended changes. These algorithms are able to automatically correct the faults or improve the operation associated with an incorrectly programmed schedule, override manual control, sensor bias, control hunting, rogue zone, and less aggressive setpoints/setpoints setback. The paper will also discuss the implementation of the auto-correction algorithms in FDD software products.

Lin, Guanjing↗
DOE OSTI2021

Enhancing Responsiveness and Resilience with Distributed Applications in the Grid

A number of trends are increasing the variability in electric power systems resulting in a need for new approaches in control. The increased variability is originating from distributed energy resources and an increasing demand for personalized energy choice from customers grid-edge. Electricity distribution systems continue to incorporate increasing numbers of intelligent end devices, automated switchgear, and sensing and measurement devices which are providing more visibility and control at the grid-edge. Traditional distribution system planning and operations techniques must evolve to meet the new complexity while continuing to deliver safe, reliable, and cost-effective energy. New technologies are enabling new capabilities at the grid-edge to respond to these needs. Distributed applications can deploy intelligence across the distribution grid with peer-to-peer communications and a shared data context for their local environment. These applications can cooperate with each other to respond quickly to changing grid conditions. This enables a layered coordination framework spanning the centralized Advanced Distribution Management System functions with wide area visibility to these new distributed applications running at intelligent devices and meters throughout the distribution system. This paper describes an open-architecture, open-source system approach for commercial scale integration of devices and intelligence at the grid-edge.

Ogle, James P.↗
DOE OSTI2020

Irradiation Assessment of a Lightweight Industrial Robot - Towards Faster, Safer and Sooner Waste Decommissioning - 20317

Robotic systems have been successfully applied in the nuclear industry for several decades as a safe approach to minimize the exposure dose of human operators. As nuclear waste management and decommissioning gathers pace, there is an emerging interest integrating modern off-the-shelf industrial robots in nuclear robotic systems which make use of complex electronics and software to improve functionality over traditional machines. The use of the industrial robots will significantly increase the pace of development of automated waste management systems at a reduced cost, and although these off-the-shelf robots are proven robust in typical industrial environments, performance in radioactive environments is less clear. This paper investigates the performance degradation of a lightweight industrial robot (KUKA iiwa 7 LBR 800) in a controlled radiation field, aiming to simulate conditions in highly radioactive nuclear waste handling facilities. The degradation of the industrial robot's performance is identified while measuring the air kerma dose-tolerance of sensitive components, via a systematic experimental methodology. The experience from this experiment has demonstrated the significant capabilities of industrial robots, which tolerated a large gamma dose of 164 Gy before a system failure. Future tests are planned, aiming to enable faster, safer and sooner waste management and decommissioning using complex robotic systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2024

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗
DOE OSTI2020

Automated Control of Transactive HVACs in Energy Distribution Systems

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. Here, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user comfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Yeast Transformation on Hamilton Vantage (YT Vantage) v1

Our software program is designed for the Hamilton Vantage liquid handling robot, automating the Build step in the Design-Build-Test-Learn (DBTL) cycle for Saccharomyces cerevisiae. This program minimizes human intervention, enabling rapid identification of pathway bottlenecks and genes that enhance verazine production. The program takes competent yeast and plasmid DNA as input and generates an output library of engineered strains compatible with automated colony picking, high-throughput culturing, and chemical extraction for downstream LC-MS analysis. A user-friendly interface, developed using the Hamilton Method Editor software, allows for on-demand parameter customization. By automating this process, our program streamlines the construction of Saccharomyces cerevisiae, reducing manual labor and increasing efficiency. While the manual process is well-documented, integration with robotic automation is less common, making our program a valuable tool for researchers. With this software, we achieved 2-5 fold increases in verazine production, demonstrating its potential to accelerate research in this field.

Louie, Randy [Lawrence Berkeley National Laborator↗