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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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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparing event generator predictions and ab-initio calculations of $\nu$-$^{12}$C neutral current quasi-elastic scattering at 1 GeV

The measurement of neutrino oscillations and exotic physics searches are important parts of the physics program in the near future, with new state-of-the-art experiments planned within the next decade. Future and modern experiments in these fields will make use of nuclear targets. Event Generators (EGs) are software used in the analysis of neutrino oscillation experiments. EGs use to predict kinematic observables for a range of neutrino energies. These simulations may lack physics captured by more rigorous theoretical calculations. This work compares EG performance to nuclear theory calculations by comparing observables generated in the two frameworks. We provide a common set of definitions between theory and experiment and assess the physics contained in EG simulations. Neutral current quasi-elastic (NCQE) scattering events for neutrinos and anti-neutrinos on a $^{12}$C target are simulated with a specific EG, NEUT, used by the T2K experiment for its analysis. The simulated cross sections are compared to analytic calculations from nuclear theory within the factorization scheme. We compare the NEUT implementation of two different models on nuclear spectral functions: the Relativistic Fermi Gas (RFG) and the correlated basis spectral function (CBF) to analytic calculations of the same models in the factorization scheme. For both nuclear physics models, we compare the appearance of features in the distributions relevant to experimental analyses. Qualitatively, the shape of the simulated distribution is similar to the one obtained through theory calculations; however, there are some discrepancies between the theory calculations and the NEUT simulation. While the EG simulations and analytic calculations with the same model of nuclear dynamics show similar overall features, there are still differences between the two.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2002

Spatial distribution and yield of DNA double-strand breaks induced by 3-7 MeV helium ions in human fibroblasts

Accelerated helium ions with mean energies at the target location of 3-7 MeV were used to simulate alpha-particle radiation from radon daughters. The experimental setup and calibration procedure allowed determination of the helium-ion energy distribution and dose in the nuclei of irradiated cells. Using this system, the induction of DNA double-strand breaks and their spatial distributions along DNA were studied in irradiated human fibroblasts. It was found that the apparent number of double-strand breaks as measured by a standard pulsed-field gel assay (FAR assay) decreased with increasing LET in the range 67-120 keV/microm (corresponding to the energy of 7-3 MeV). On the other hand, the generation of small and intermediate-size DNA fragments (0.1-100 kbp) increased with LET, indicating an increased intratrack long-range clustering of breaks. The fragment size distribution was measured in several size classes down to the smallest class of 0.1-2 kbp. When the clustering was taken into account, the actual number of DNA double-strand breaks (separated by at least 0.1 kbp) could be calculated and was found to be in the range 0.010-0.012 breaks/Mbp Gy(-1). This is two- to threefold higher than the apparent yield obtained by the FAR assay. The measured yield of double-strand breaks as a function of LET is compared with theoretical Monte Carlo calculations that simulate the track structure of energy depositions from helium ions as they interact with the 30-nm chromatin fiber. When the calculation is performed to include fragments larger than 0.1 kbp (to correspond to the experimental measurements), there is good agreement between experiment and theory.

NASA Program Biomedical Research and Countermeasur↗
DOE OSTI2016

Tritium Decay Helium-3 Effects in Tungsten

A critical challenge for long-term operation of ITER and beyond to a Demonstration reactor (DEMO) and future fusion reactor will be the development of plasma-facing components (PFCs) that demonstrate erosion resistance to steady-state/transient heat fluxes and intense neutral/ion particle fluxes under the extreme fusion nuclear environment, while at the same time minimizing in-vessel tritium inventories and permeation fluxes into the PFC’s coolant. Tritium will diffuse in bulk tungsten at elevated temperatures, and can be trapped in radiation-induced trap site (up to 1 at. % T/W) in tungsten [1,2]. Tritium decay into helium-3 may also play a major role in microstructural evolution (e.g. helium embrittlement) in tungsten due to relatively low helium-4 production (e.g. He/dpa ratio of 0.4-0.7 appm [3]) in tungsten. Tritium-decay helium-3 effect on tungsten is hardly understood, and its database is very limited. Two tungsten samples (99.99 at. % purity from A.L.M.T. Co., Japan) were exposed to high flux (ion flux of 1.0x1022 m-2s-1 and ion fluence of 1.0x1026 m-2) 0.5%T2/D2 plasma at two different temperatures (200, and 500°C) in Tritium Plasma Experiment (TPE) at Idaho National Laboratory. Tritium implanted samples were stored at ambient temperature in air for more than 3 years to investigate tritium decay helium-3 effect in tungsten. The tritium distributions on plasma-exposed was monitored by a tritium imaging plate technique during storage period [4]. Thermal desorption spectroscopy was performed with a ramp rate of 10°C/min up to 900°C to outgas residual deuterium and tritium but keep helium-3 in tungsten. These helium-3 implanted samples were exposed to deuterium plasma in TPE to investigate helium-3 effect on deuterium behavior in tungsten. The results show that tritium surface concentration in 200°C sample decreased to 30 %, but tritium surface concentration in 500°C sample did not alter over the 3 years storage period, indicating possible tritium retention in helium-3 bubble. This paper reports the initial experimental observation of tritium-decay helium-3 in tungsten exposed to deuterium/tritium plasma along with electron microscope analysis and also discusses a Tritium Migration Analysis Program (TMAP) analysis of tritium-decay helium-3 effects on tritium retention in tungsten for DEMO and future fusion reactor. [1] Y. Hatano, et.al., Nucl. Fusion 53 (2013) 073006 [2] M. Shimada, et.al., Nucl. Fusion 55 (2015) 013008 [3] M. Sawan, Fus. Sci. Technol. 66 (2014) 272 [4] T. Otsuka, Fus. Sci. Technol. 60 (2011) 1539 This work was prepared for the U.S. Department of Energy, Office of Fusion Energy Sciences, under the DOE Idaho Field Office contract number DE-AC07-05ID14517.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗
DOE OSTI2025

Adaptive PID Gain Scheduling Control for Hydropower Turbine Using Neural CDE and Stochastic Distribution Shaping

This paper introduces a gain-scheduling PID controller design strategy for hydroturbine frequency control mode. This scheme first uses real data to learn the nonlinear dynamics of the hydroturbine using neural controlled differential equations and then perturbs the obtained nonlinear system at different equilibrium points, based on which a static output feedback adaptive dynamic programming algorithm is then used to optimize the PID gains for each equilibrium point. Moreover, a continuous-time version of stochastic distribution control is proposed to further fine-tune the optimized PID gains. Finally, the controller is obtained by implementing linear interpolation between the optimized PID control gains. The simulation results show that the proposed gain-scheduling PID controller can control a larger range of operation points compared with the given fixed PID controller and the baseline method. Compared with the given fixed PID controller, the proposed gain-scheduling PID controller can regulate hydroturbine frequency against disturbances induced by power-load variation with over 50% less overshoot for some operation points.

13 HYDRO ENERGY↗
DOE OSTI2025

Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications

Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗
NASA NTRSDate not supplied

Demonstration of Capability to Simulate Particle Irregular Shape and Poly-Disperse Mixtures Within Lunar Lander Plume-Surface Interaction

Plume-Surface Interaction (PSI) between lander engine plumes and regolith soil creates hazards in obscuration and contamination by particle clouds, high-energy ejecta streams, and landing area cratering damage. The MSFC Fluid Dynamics Branch is developing simulation tools to offer a predictive PSI capability to NASA customers such as the Human Lander System (HLS) and Commercial Lunar Payload Services (CLPS). The Gas-Granular Flow Solver (GGFS) is the main application tool for coupled gas-particle two-phase flow simulations to predict the range of PSI effects from onset of surface erosion to deep crater formation. GGFS features an Eulerian-Eulerian modeling approach, treating both gas and granular material as interacting continuum phases. Modeling the lunar regolith granular material fluidic characteristics poses special challenges due to complex particle shapes and mixture composition. The lunar regolith is poorly sorted with broad particle size distributions and large fines content. It has significant cohesion, due to interlocking jagged particle shapes. Eulerian granular material flow modeling requires closure formulations for the granular material constitutive models (stress, friction, collisional and kinetic energy dissipation, drag, etc.). While closure models for spherical particles are available from particle kinetic theory, closure models for realistic non-spherical particles must be extracted from unit physics Discrete Element Model (DEM) particle interaction simulations and provided in the form of tabular datasets. The effects of particle irregular shape (non-spherical shape factors, angular particle surface roughness, and interlocking features) are simulated by approximating the particle features in the form of grouped elemental spheres to form composite particles in the DEM simulations. The effects of the wide range of regolith mixture particle sizes and the strong effects of the presence of the small particle sizes results in high cohesion and low porosity of the regolith mixture. The range of particle sizes is simulated by binning the particle sizes into an appropriate finite number of particle-size species and solving the problem as a species mixture. Combining these two modeling approaches enables simulations to capture both, the contributions of the irregular particle shape and the particle size distribution. The integration and maturation of the DEM-based constitutive model database generation process and poly-disperse mixture binning approach into the GGFS simulation framework are proceeding under funding by the NASA Game Changing Development program. The status of current capabilities will be presented in comparisons of crater characteristics resulting for spherical and irregular shape particles, and for mono-, bi-, and tri-disperse mixture simulations of Apollo LM plume-surface interaction. The computational results confirm the significance of including the particle shape and mixture effects. Going forward plans for the full implementation of the general poly-disperse regolith modeling capability and maturation towards NASA project application readiness under the GCD program will be presented.

Peter A Liever↗
NASA NTRSDate not supplied

Demonstration of Capability to Simulate Particle Irregular Shape and Poly-Disperse Mixtures Within Lunar Lander Plume-Surface Interaction

Plume-Surface Interaction (PSI) between lander engine plumes and regolith soil creates hazards in obscuration and contamination by particle clouds, high-energy ejecta streams, and landing area cratering damage. The MSFC Fluid Dynamics Branch is developing simulation tools to offer a predictive PSI capability to NASA customers such as the Human Lander System (HLS) and Commercial Lunar Payload Services (CLPS). The Gas-Granular Flow Solver (GGFS) is the main application tool for coupled gas-particle two-phase flow simulations to predict the range of PSI effects from onset of surface erosion to deep crater formation. GGFS features an Eulerian-Eulerian modeling approach, treating both gas and granular material as interacting continuum phases. Modeling the lunar regolith granular material fluidic characteristics poses special challenges due to complex particle shapes and mixture composition. The lunar regolith is poorly sorted with broad particle size distributions and large fines content. It has significant cohesion, due to interlocking jagged particle shapes. Eulerian granular material flow modeling requires closure formulations for the granular material constitutive models (stress, friction, collisional and kinetic energy dissipation, drag, etc.). While closure models for spherical particles are available from particle kinetic theory, closure models for realistic non-spherical particles must be extracted from unit physics Discrete Element Model (DEM) particle interaction simulations and provided in the form of tabular datasets. The effects of particle irregular shape (non-spherical shape factors, angular particle surface roughness, and interlocking features) are simulated by approximating the particle features in the form of grouped elemental spheres to form composite particles in the DEM simulations. The effects of the wide range of regolith mixture particle sizes and the strong effects of the presence of the small particle sizes results in high cohesion and low porosity of the regolith mixture. The range of particle sizes is simulated by binning the particle sizes into an appropriate finite number of particle-size species and solving the problem as a species mixture. Combining these two modeling approaches enables simulations to capture both, the contributions of the irregular particle shape and the particle size distribution. The integration and maturation of the DEM-based constitutive model database generation process and poly-disperse mixture binning approach into the GGFS simulation framework are proceeding under funding by the NASA Game Changing Development program. The status of current capabilities will be presented in comparisons of crater characteristics resulting for spherical and irregular shape particles, and for mono-, bi-, and tri-disperse mixture simulations of Apollo LM plume-surface interaction. The computational results confirm the significance of including the particle shape and mixture effects. Going forward plans for the full implementation of the general poly-disperse regolith modeling capability and maturation towards NASA project application readiness under the GCD program will be presented.

Peter A. Liever↗
DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Building America Case Study: Multifamily Zero Energy Ready Home Analysis, Elmsford, New York

Two multifamily buildings planned in Climate Zone 4 were analyzed to determine the cost, energy and performance implications of redesigning them to comply with Zero Energy Ready Home, a recognition program of the U.S. Department of Energy. Energy modeling was conducted on one representative apartment in each building using BEopt. Construction costs were obtained from the developer and subcontractors to determine savings and cost increases over ENERGY STAR. It was found that seven items would be necessary to change to comply with ZERH criteria when starting from the original design which was compliant with ENERGY STAR version 3.0. Design changes were made to the exterior walls, domestic water heating system, duct protection, duct design, garage ventilation, and pest control to comply with ZERH requirements. Energy impacts of upgrading from the original design to ZERH resulted in 2 to 8 percent reduction in modeled source energy consumption, or 1.7 to 10.4 MMBtu per year, although the original design was already about 8 percent better than a design configured to minimum ENERGY STAR criteria. According to the BEopt analysis, annualized energy related costs of the ZERH design were slightly higher for the apartment and slightly lower for the townhome when compared to the original design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2004

Brayton Power Conversion System Study to Advance Technology Readiness for Nuclear Electric Propulsion

Recently, there has been significant interest within the aerospace community to develop space based nuclear power conversion technologies especially for exploring the outer planets of our solar system where the solar energy density is very low. To investigate these technologies NASA awarded several contracts under Project Prometheus, the Nuclear Systems Program. The studies described in this paper were performed under one of those contracts, which was to investigate the use of a nuclear power conversion system based on the closed Brayton cycle (CBC).The investigation performed included BPCS (Brayton Power Conversion System) trade studies to minimize system weight and radiator area and advance the state of the art of BPCS technology. The primary requirements for studies were a power level of 100 kWe (to the PPU), a low overall power system mass and a lifetime of 15 years (10 years full power). For the radiation environment, the system was to be capable of operation in the generic space environment and withstand the extreme environments surrounding Jupiter. The studies defined a BPCS design traceable to NEP (Nuclear Electric Propulsion) requirements and suitable for future missions with a sound technology plan for technology readiness level (TRL) advancement identified. The studies assumed a turbine inlet temperature approx. 100 C above the current the state of the art capabilities with materials issues and related development tasks identified. Analyses and evaluations of six different HRS (heat rejection system) designs and three primary power management and distribution (PMAD) configurations will be discussed in the paper.

Patrick E. Frye↗
DOE OSTI2015

SU-E-T-769: T-Test Based Prior Error Estimate and Stopping Criterion for Monte Carlo Dose Calculation in Proton Therapy

Purpose: The Monte Carlo (MC) method is a gold standard for dose calculation in radiotherapy. However, it is not a priori clear how many particles need to be simulated to achieve a given dose accuracy. Prior error estimate and stopping criterion are not well established for MC. This work aims to fill this gap. Methods: Due to the statistical nature of MC, our approach is based on one-sample t-test. We design the prior error estimate method based on the t-test, and then use this t-test based error estimate for developing a simulation stopping criterion. The three major components are as follows.First, the source particles are randomized in energy, space and angle, so that the dose deposition from a particle to the voxel is independent and identically distributed (i.i.d.).Second, a sample under consideration in the t-test is the mean value of dose deposition to the voxel by sufficiently large number of source particles. Then according to central limit theorem, the sample as the mean value of i.i.d. variables is normally distributed with the expectation equal to the true deposited dose.Third, the t-test is performed with the null hypothesis that the difference between sample expectation (the same as true deposited dose) and on-the-fly calculated mean sample dose from MC is larger than a given error threshold, in addition to which users have the freedom to specify confidence probability and region of interest in the t-test based stopping criterion. Results: The method is validated for proton dose calculation. The difference between the MC Result based on the t-test prior error estimate and the statistical Result by repeating numerous MC simulations is within 1%. Conclusion: The t-test based prior error estimate and stopping criterion are developed for MC and validated for proton dose calculation. Xiang Hong and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Zero Energy Ready Home Multifamily Case Study Analysis

Two multifamily buildings planned in Climate Zone 4 were analyzed to determine the cost, energy and performance implications of redesigning them to comply with Zero Energy Ready Home, a recognition program of the U.S. Department of Energy. Energy modeling was conducted on one representative apartment in each building using BEopt. Construction costs were obtained from the developer and subcontractors to determine savings and cost increases over ENERGY STAR. It was found that seven items would be necessary to change to comply with ZERH criteria when starting from the original design which was compliant with ENERGY STAR version 3.0. Design changes were made to the exterior walls, domestic water heating system, duct protection, duct design, garage ventilation, and pest control to comply with ZERH requirements. Energy impacts of upgrading from the original design to ZERH resulted in 2 to 8% reduction in modeled source energy consumption, or 1.7 to 10.4 MMBtu per year, although the original design was already about 8% better than a design configured to minimum ENERGY STAR criteria. According to the BEopt analysis, annualized energy related costs of the ZERH design were slightly higher for the apartment and slightly lower for the townhome when compared to the original design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Sensitivity-Uncertainty Based Nuclear Criticality Safety Validation

These are slides from a seminar given to the University of Mexico Nuclear Engineering Department. Whisper is a statistical analysis package developed to support nuclear criticality safety validation. It uses the sensitivity profile data for an application as computed by MCNP6 along with covariance files for the nuclear data to determine a baseline upper-subcritical-limit for the application. Whisper and its associated benchmark files are developed and maintained as part of MCNP6, and will be distributed with all future releases of MCNP6. Although sensitivity-uncertainty methods for NCS validation have been under development for 20 years, continuous-energy Monte Carlo codes such as MCNP could not determine the required adjoint-weighted tallies for sensitivity profiles. The recent introduction of the iterated fission probability method into MCNP led to the rapid development of sensitivity analysis capabilities for MCNP6 and the development of Whisper. Sensitivity-uncertainty based methods represent the future for NCS validation – making full use of today’s computer power to codify past approaches based largely on expert judgment. Validation results are defensible, auditable, and repeatable as needed with different assumptions and process models. The new methods can supplement, support, and extend traditional validation approaches.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

A GENERIC MICROGRID CONTROLLER

The main objective of this project was to develop and test a Generic Microgrid Controller (GMC) that provides (1) seamless islanding and reconnection of the microgrid, (2) efficient, reliable, and resilient operation of the microgrid with the required power quality, whether islanded or grid-connected, (3) existing and future ancillary services to the larger grid, (4) the capability for the microgrid to serve the resiliency needs of participating communities, (5) communication with the electric grid utility as a single controllable entity, and (6) increased reliability, efficiency and reduced emissions. The GMC developed under this project was designed to facilitate the deployment of microgrids, to be easily adapted to various microgrid configurations, and reduce up-front engineering costs associated with the design and development of microgrid controllers. Specifically, the GMC: 1. Provides a control structure amenable to accommodating an array of microgrid configurations and a portfolio of functional requirements; 2. Possesses a high level architecture that can readily be adopted by commercial suppliers; 3. Supports unlimited nesting of conforming microgrid control schemes; 4. Integrates into a full-featured Energy Management System (EMS) as a module. A select set of microgrids, operating a variety of microgrid configurations, served as “collaborating microgrid partners” in the project and assured thereby that the GMC developed under this program can readily be applied to microgrids of different sizes, and equipped with various resources, attributes, and equipment. Collaborating microgrid partners included the UCI Medical Center), the Port of Los Angeles, Port of Long Beach, and the Irvine Ranch Water District. The objectives of the project were achieved in two phases: (Phase I) Research, Development and Design (“Design”), and (Phase II) Testing, Evaluation, and Verification (“TEV”). In Phase I, specifications were developed for the GMC and a detailed test plan was established to test the functional requirements of the GMC. The GMC addresses two core functions, transition and dispatch, as well as several optional higher level functions such as economic dispatch, and renewable and load forecasting.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Bilevel Voltage Regulation Operation for Distribution Systems With Self-Operated Microgrids

The emerging of microgrids in distribution systems has significantly enhanced the resilience of power grids. However, the operators of a distribution system and microgrids therein can be different and have accessibility to different devices. To model the operation of such a grid, this work proposes a bilevel formulation and probes into the voltage regulation operation, considering the interaction between different systems. The proposed bilevel formulation considers the cooperation of active energy resources (AER), transformer tap-changers, and capacitor banks that are controlled by different operators. To facilitate the solution time of the target bilevel optimization, the lower-level problems with different objectives are modeled using deep neural networks (DNNs) which are then converted into a set of constraints. Hence, the bilevel problem can be reformed to a single-level problem. Lastly, the proposed solution procedures are validated using a customized joint system constructed by the IEEE 123-bus system and a real distribution system in Iowa. According to the numerical validation results, the solution time of the proposed nonlinear activation function based DNN model is 69 times faster than other methods in solving voltage regulation with a bilevel structure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAIC) Atmosphere Science Plan

Arctic climate change is amplified relative to global change, and is embodied by a dramatic decline in the perennial sea-ice pack. These cryospheric transitions carry significant implications for regional resource development, geopolitics, and global climate patterns. Indeed, the changing arctic cryosphere is considered a grand challenge for global climate research. Arctic change, and its linkages with the global system, must be understood. Additionally, there is a growing stakeholder community that requires improved sea-ice forecasting for many applications. To make progress on these important issues requires developing a detailed, process-level understanding of the coupled climate system that can help to address the numerous deficiencies in numerical model representations of the Arctic. Such an understanding is only possible by making targeted, interdisciplinary measurements within the central arctic sea-ice environment. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative has been developed in response to these great challenges. It comprises three parts: 1) An intensive, icebreaker-based observatory that will freeze in, and drift with, the arctic sea ice for a full annual cycle making interdisciplinary measurements in the atmosphere, sea ice, upper ocean, and biosphere; 2) A distributed network of targeted, autonomous measurements to characterize spatial variability on model grid-box scales; and 3) Coordinated, multiscale analysis and modeling activities. MOSAiC is a major, international effort that will involve participation of many different agencies and entities. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is uniquely positioned to play a critical role in this initiative by providing a comprehensive instrument suite to characterize the atmosphere and its interactions with the sea-ice surface. ARM will deploy its second Mobile Facility (AMF2) and Mobile Aerosol Observing System (MAOS) at the MOSAiC central observatory as it drifts through the central Arctic for a 13-month campaign starting in September 2019. Sea-ice is an integrator of energy fluxes in the coupled arctic climate system; thus, atmosphere-ice-ocean processes impacting the flow of energy through this system in all seasons are the primary target of MOSAiC. The broader, collaborative MOSAiC initiative will allow for many interdisciplinary studies along this theme. ARM’s involvement will target specific areas related to the atmosphere and atmosphere-surface interactions that are critically under-observed in the Arctic, are leading contributors to model uncertainties in the region, and are programmatically important to DOE research and modeling programs. The guiding science themes for this project include: (1) The surface energy budget of sea ice; (2) Clouds and precipitation; (3) Aerosols; and (4) The atmospheric boundary layer. Many fundamental issues concerning these themes are lacking in observational constraints, particularly in the arctic winter and through consecutive seasons. For example, little is known about the energy budget of first-year sea ice, the annual cycle of central arctic aerosol concentrations, the spatial organization of cloud-precipitation systems relative to sea-ice heterogeneity, the winter boundary-layer evolution, or cloud-surface coupling processes over sea ice. The proposed ARM observations will be groundbreaking in many ways, and will ultimately have a dramatic impact on the arctic research community and its ability to represent coupled arctic processes in numerical models.

North Slope of alaska, Arctic sea ice, Barrow, Arc↗