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At least 1,225 records · Page 68

Software Control Program For Transportable Microgrid State-of-charge Balancing And Frequency Stability Controls

A deterministic state-of-charge (SOC) balancing approach software control code is introduced as an integral secondary management to primary control layer of an islanded small microgrid or nanogrid system made up of multiple grid-forming inverter/battery/solar combination systems, where each set of batteries with each inverter are on independent DC buses (i.e. non-paralleled on the DC sides). A DERMS-level control approach, algorithm and automation controller program was developed to improve coordination and enable microgrid asset compliance and SOC balancing, enabling provision of a system-level power stability support architecture, load support, and asset scalability. The architecture is configured to treat each unit or micro/nano-grid as a node in a microgrid network, allowing for autonomous DERMS control regarding load and SOC balancing and power stability. As the network grows with the addition of units, greater coordination efforts may be required. The ideal small network microgrid ranges from 2-10 inverter/battery units before additional control parameters must be considered in the existing architecture. The control approach focuses on a deterministic state-of-charge analysis as the primary level control process followed by a secondary control loop using a forced frequency-watt droop strategy to conform off-the-shelf components into behaving under a leader-follower configuration. Adopting this control scheme has been shown to allow for a balanced, unit-coordinated microgrid network, enabling stable power flow. The deterministic state-of-charge approach is introduced as an integral primary control layer of an islanded small network microgrid. A standard strategy for SOC balancing is implementing a battery management system (BMS) to control SOC on the DC side. An alternative approach is to determine how to coordinate sending and receiving power on the AC side with multiple units. The latter approach assesses all the integrated units in the microgrid network. Once the individual units are identified, further system data is required to calculate each unit's total kWh, provided information about its capability to supply or consume kWh and availability. The secondary control layer in the multi-layered small network microgrid methodology uses the primary layer’s decision to initiate frequency setpoint changes, initializing the SOC balancing. The secondary control layer considers numerous system-dependent variables to enable a charging and discharging profile based on adjustable frequency setpoints. The combined architecture will result in stable, coordinated power flow enhancing an AC microgrid's functionalities.

Myers, KurtS [Idaho National Laboratory (INL), Ida↗

OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms

OpenOA is an open source framework for operational data analysis of wind energy plants, implemented in the Python programming language. OpenOA provides a common data model, high level analysis workflows, and low-level convenience functions that engineers, analysts, and researchers in the wind energy industry can use to facilitate analytics workflows on operational data sets. OpenOA contains documentation, worked out examples in Jupyter notebooks, and a corresponding example dataset from the Engie Renewable’s La Haute Borne Dataset.

17 WIND ENERGY↗

The LANSCE experimental program: accelerator requirements

The LANSCE accelerator complex exploits a flexible, high power 800 MeV proton linear accelerator to enable a broad swath of experimental work supporting multiple scientific programs. The beam requirements for these programs are diverse and change over time. The purpose of this document is to record, at a high level, the experimental requirements driving the beam delivery requirements for each experimental facility at LANSCE. Revision 0 serves to baseline the current set of experiments, and we expect to revise the document on an as-needed basis when either the experimental requirements change or are found to require a more complete definition.

43 PARTICLE ACCELERATORS↗

LLNL IPD Tasks: ICSBEP and Pulsed Spheres Benchmark [Slides]

This lecture presented benchmark of the blank and polyethylene spheres at SINBAD in April 2023. In responding to comments about the building, we uncovered a large amount of additional detailed information about the building and experimental set up. We incorporated this new information into an even higher fidelity model and are working on uncertainty analysis at a low level. Scheduled to present at SINBAD meeting in April 2024.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

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↗

Research and Development on Advanced Electrochemical Energy Storage Devices Enabling a Spectrum of Electrified Vehicles

The USABC Advanced Battery development plan had the following three focus areas: 1. Existing technology validation, implementation, and cost reduction 2. Identification of the next viable technology with emphasis on the potential to meet USABC cost and energy density goals. 3. Support high-risk, high-reward battery technology R&D The primary aim of this project was focused on the barriers that most impede wider public adoption of electrification in vehicles-cost, energy density, low-temp performance, calendar life, and improvements in abuse tolerance. The specific objectives are as follows: Cost Reduction: drive to significantly reduce battery cost to be in-line with the $\$$100/kWh or less (by the end of calendar year 2020) DOE cost goals. Additionally, goals of $\$$75/KWh were set for 2023 aligned with fast charge and some offset of energy density. Energy Density Increase: focus on improving the cell-level specific energy and energy densities to >350 watt hours per kilogram (Wh/kg) and >750Wh/L. Low Temperature Performance Increase: strive toward developments that improve the discharge power and eliminate or dramatically reduce the life limiting lithium plating associated with regenerative braking at low temperatures, during the program. Calendar Life Increase: drive to achieve a 15-year calendar life. Abuse Tolerance Improvement: strive to develop improvements in Li-ion abuse tolerance and/or development of electrochemical energy storage technologies with inherently better response to abuse circumstances. Emerging Areas: initiate new programs that address emerging technologies that arise during the contract period.

25 ENERGY STORAGE↗

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

Emad-Ud-Din, Muhammad (ORCID:0000000279515538)↗

DEPRECATED - Sharing the Sun Community Solar Project Data (Dec 2020, Revision)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top. The revision dataset included projects with a total installed capacity in 2020 under the state-level program but missing the project level details.

14 SOLAR ENERGY↗

PV Validation Hub

The Validation Hub will be a clearinghouse for the transfer of novel algorithms and software from the PV research community to industry. Potential algorithms tested in the Hub could include the estimation of various PV loss factors and the detection of various operational issues. The primary function of the Hub will be for developers to submit executable code which will run on hosted data sets. Developers will receive private reports on the accuracy and performance (e.g., run-time) of the submitted algorithms, and public high level summaries will be hosted. These summaries will indicate the organization who submitted the algorithm (e.g., links to GitHub pages, documentation websites, etc.), high-level accuracy metrics, and standardized performance metrics. These results will be stored in a publicly available database, accessible through the Hub, with the ability for users to sort and filter the results. In short, the Hub will be presented to public users as a collection of interactive leaderboards, organized around specific analysis tasks pertinent to the PV data science community. These tasks include things such as the estimation of various PV loss factors and the detection of various operational issues. We will present progress on the development of this hub, including preliminary results of comparative validation of PV data science algorithms and progress towards building the platform itself.

algorithm↗

Criticality Accident Alarm System Modeling for the Uranium Processing Facility

The Criticality Accident Alarm System (CAAS) for the Uranium Processing Facility (UPF) provides coverage for two primary processing facilities. For UPF, detailed documentation and drawings of the design are readily available. While this design information allows for some CAAS modeling features to match the actual design with a high level of accuracy, assumptions and simplifications are necessary as it is unrealistic and unnecessary to replicate many aspects of the design. A set of modeling guidelines and techniques were used to develop consistency between the MCNP6 models. While ensuring conservatism, overly conservative choices must be avoided to demonstrate the minimum accident of concern can be detected. To ensure proper modeling choices were made for the final models, a group of parametric studies were performed to evaluate the impact on the dose rates detected at the individual detectors. Some of these modeling choices and/or associated parametric studies include: - Modeling the grid steel area above the detectors as homogeneous stainless steel and determining the impact on detectability with an increasing density. - Modeling the presence of equipment in close proximity to the CAAS detectors. - Determining an appropriate material density to represent miscellaneous items not specifically modeled. - Evaluating the impact of sprinkler activation. The results of UPF CAAS analyses show that the modeling assumptions made based on the detailed design and parametric studies are appropriate and demonstrate accurate yet conservative models have been developed for detecting minimum accident of concern within the UPF.

61 RADIATION PROTECTION AND DOSIMETRY↗

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Random Quantum Circuits

Quantum circuits—built from local unitary gates and local measurements—are a new playground for quantum many-body physics and a tractable setting to explore universal collective phenomena far from equilibrium. These models have shed light on longstanding questions about thermalization and chaos, and on the underlying universal dynamics of quantum information and entanglement. In addition, such models generate new sets of questions and give rise to phenomena with no traditional analog, such as dynamical phase transitions in quantum systems that are monitored by an external observer. Quantum circuit dynamics is also topical in view of experimental progress in building digital quantum simulators that allow control of precisely these ingredients. Randomness in the circuit elements allows a high level of theoretical control, with a key theme being mappings between real-time quantum dynamics and effective classical lattice models or dynamical processes. Many of the universal phenomena that can be identified in this tractable setting apply to much wider classes of more structured many-body dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Search for Higgs boson pairs decaying to WW*WW*, WW* ττ , and ττττ in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

The results of a search for Higgs boson pair (HH) production in the WW*WW*, WW*$ττ$, and $ττττ$ decay modes are presented. The search uses 138 fb –1 of proton-proton collision data recorded by the CMS experiment at the LHC at a center-of-mass energy of 13 TeV from 2016 to 2018. Analyzed events contain two, three, or four reconstructed leptons, including electrons, muons, and hadronically decaying tau leptons. No evidence for a signal is found in the data. Upper limits are set on the cross section for nonresonant HH production, as well as resonant production in which a new heavy particle decays to a pair of Higgs bosons. For nonresonant production, the observed (expected) upper limit on the cross section at 95% confidence level (CL) is 21.3 (19.4) times the standard model (SM) prediction. The observed (expected) ratio of the trilinear Higgs boson self-coupling to its value in the SM is constrained to be within the interval –6.9 to 11.1 (–6.9 to 11.7) at 95% CL, and limits are set on a variety of new-physics models using an effective field theory approach. The observed (expected) limits on the cross section for resonant HH production range from 0.18 to 0.90 (0.08 to 1.06) pb at 95% CL for new heavy-particle masses in the range 250–1000 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Planck intermediate results

We present the NPIPE processing pipeline, which produces calibrated frequency maps in temperature and polarization from data from the Planck Low Frequency Instrument (LFI) and High Frequency Instrument (HFI) using high-performance computers. NPIPE represents a natural evolution of previous Planck analysis efforts, and combines some of the most powerful features of the separate LFI and HFI analysis pipelines. For example, following the LFI 2018 processing procedure, NPIPE uses foreground polarization priors during the calibration stage in order to break scanning-induced degeneracies. Similarly, NPIPE employs the HFI 2018 time-domain processing methodology to correct for bandpass mismatch at all frequencies. In addition, NPIPE introduces several improvements, including, but not limited to: inclusion of the 8% of data collected during repointing manoeuvres; smoothing of the LFI reference load data streams; in-flight estimation of detector polarization parameters; and construction of maximally independent detector-set split maps. For component-separation purposes, important improvements include: maps that retain the CMB Solar dipole, allowing for high-precision relative calibration in higher-level analyses; well-defined single-detector maps, allowing for robust CO extraction; and HFI temperature maps between 217 and 857 GHz that are binned into 0'.9 pixels (Nside = 4096), ensuring that the full angular information in the data is represented in the maps even at the highest Planck resolutions. The net effect of these improvements is lower levels of noise and systematics in both frequency and component maps at essentially all angular scales, as well as notably improved internal consistency between the various frequency channels. Based on the NPIPE maps, we present the first estimate of the Solar dipole determined through component separation across all nine Planck frequencies. The amplitude is (3366.6 ± 2.7) μK, consistent with, albeit slightly higher than, earlier estimates. From the large-scale polarization data, we derive an updated estimate of the optical depth of reionization of τ = 0.051 ± 0.006, which appears robust with respect to data and sky cuts. There are 600 complete signal, noise and systematics simulations of the full-frequency and detector-set maps. As a Planck first, these simulations include full time-domain processing of the beam-convolved CMB anisotropies. The release of NPIPE maps and simulations is accompanied with a complete suite of raw and processed time-ordered data and the software, scripts, auxiliary data, and parameter files needed to improve further on the analysis and to run matching simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

Which Role Plays 2-Hydroxybutyric Acid on Insulin Resistance?

Type 2 Diabetes Mellitus (T2D) is defined as a chronic condition caused by beta cell loss and/or dysfunction and insulin resistance (IR). The discovering of novel biomarkers capable of identifying T2D and other metabolic disorders associated with IR in a timely and accurate way is critical. In this review, 2-hydroxybutyric acid (2HB) is presented as that upheaval biomarker with an unexplored potential ahead. Due to the activation of other metabolic pathways during IR, 2HB is synthesized as a coproduct of protein metabolism, being the progression of IR intrinsically related to the increasing of 2HB levels. Hence, the focus of this review will be on the 2HB metabolite and its involvement in glucose homeostasis. A literature review was conducted, which comprised an examination of publications from different databases that had been published over the previous ten years. A total of 19 articles fulfilled the intended set of criteria. The use of 2HB as an early indicator of IR was separated into subjects based on the number of analytes examined simultaneously. In terms of the association between 2HB and IR, it has been established that increasing 2HB levels can predict the development of IR. Thus, 2HB has demonstrated considerable promise as a clinical monitoring molecule, not only as an IR biomarker, but also for disease follow-up throughout IR treatment.

Sousa, André P. (ORCID:0000000314605361)↗

Distributed Load Shedding Application Architecture and Bi-Level Predictive Estimator Algorithm

Increasing penetrations of distributed renewables are decreasing the effectiveness of traditional decentralized under-frequency load shedding (UFLS) schemes. As more distribution circuits begin to back-feed the transmission system, operation of traditional UFLS may exacerbate frequency instability. This paper presents the conceptual framework for a data-rich environment to coordinate UFLS across multiple distribution providers based on the laminar coordination framework in order to ensure optimal adaptive setting of UFLS relays. Communication and control are enabled through a distributed implementation of the IEC 61968-1 Common Information Model message bus structure. In addition to the proposed architecture, a novel adaptive UFLS scheme informed by a bi-level state estimator to create optimal relay setpoints is introduced. Initial simulation results are presented for the IEEE 14-bus test system on scenarios leading to mis-operation of traditional UFLS.

Anderson, Alexander A.↗

Project 1: COLLECTS Project and Project 2: M&V for Saputo Cheese Project: Cooperative Research and Development (Final Report)

Project 1: Combined Power's Hyperlight Energy and NLR will collaborate on the DOE Concentrating Optics for Lower Levelized Energy CosTS (COLLECTS) project, DE-FOA-0001268. COLLECTS funding opportunity announcement (FOA) seeks to further concentrated solar power (CSP) system technologies by soliciting disruptive, transformative projects for the concentrating solar collectors in the CSP plant. These innovative projects will seek to surpass the targets set out in the SunShot Vision Study, enabling CSP to be cost-competitive with conventional forms of electric power generation. Projects will target the design and manufacturing of novel solar collectors with the ability to significantly reduce the solar field contribution to the overall levelized cost of energy (LCOE) of the CSP plant, and which have not been previously specifically targeted by CSP FOAs. Project 2: Hyperlight will perform measurement and verification (M&V) of Green House Gases and NLR will evaluate energy consumption reduction.

14 SOLAR ENERGY↗

Accurate Atomic Correlation and Total Energies for Correlation Consistent Effective Core Potentials

Very recently, we introduced a set of correlation consistent effective core potentials (ccECPs) constructed within full many-body approaches. By employing significantly more accurate correlated approaches, we were able to reach a new level of accuracy for the resulting effective core Hamiltonians. We also strived for simplicity of use and easy transferability into a variety of electronic structure methods in quantum chemistry and condensed matter physics. Here, as a reference for future use, we present exact or nearly exact total energy calculations for these ccECPs. The calculations cover H–Kr elements and are based on the state-of-the-art configuration interaction (CI), coupled-cluster (CC), and quantum Monte Carlo (QMC) calculations with systematically eliminated/improved errors. In particular, we carry out full CI/CCSD(T)/CCSDT(Q) calculations with cc-pVnZ with up to n = 6 basis sets and we estimate the complete basis set limits. Using combinations of these approaches, we achieved an accuracy of ≈1–10 mHa for K–Zn atoms and ≈0.1–0.3 mHa for all other elements—within about 1% or better of the ccECP total correlation energies. We also estimate the corresponding kinetic energies within the feasible limit of full CI calculations. In order to provide data for QMC calculations, we include fixed-node diffusion Monte Carlo energies for each element that give quantitative insights into the fixed-node biases for single-reference trial wave functions. The results offer a clear benchmark for future high-accuracy calculations in a broad variety of correlated wave function methods such as CI and CC as well is in stochastic approaches such as real space sampling QMC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗