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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An Adaptive Virtual Oscillator Control Structure for Grid-Forming Inverters

The electrical grid is facing unprecedented challenges due to the increasing penetration of inverter-based resources. Grid forming inverters (GFMIs) are a promising technology to address these challenges. Recently, the virtual oscillator based GFMI control is attracting more attention due to its superior performance over other control strategies. In this article, an adaptive control strategy is proposed to provide flexible operation and transition between grid-connected and islanded modes. The controller adapts the virtual oscillator's parameter values depending on the operation mode. It also provides a feedback signal using a measured frequency to account for any steady-state errors and to allow a seamless transition from grid-connected to islanded mode. Finally, to show the feasibility of the proposed controller, this article discusses the simulation results from the implementation of the controller on a single inverter system and on a group of inverters on a large practical utility feeder, the IEEE 13 node feeder, using the DIgSILENT simulation environment.

adaptive control↗
DOE OSTI2023

Adaptive Load Shedding as Part of Primary Frequency Response To Support Networked Microgrid Operations

Global changes in the deployment of distributed energy resources, control and communications technologies, business models, and regulatory policy are increasing the operational options for future distribution systems. One such option is the coordinated operation of distributed resources to form microgrids and networks of microgrids to support traditional bulk power systems during normal operations and critical end-use loads during outages. While individual standalone microgrids have been extensively studied and deployed, the coordinated operation of networked microgrids is operationally more challenging due to the dynamic boundaries and changing mix of generation resources. Here, this paper presents a method of using a distributed control architecture to support primary frequency response in networked microgrids operations. The support of primary frequency response is accomplished using the Open Field Message Bus reference architecture and Grid Friendly Appliance controllers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions

Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Multi-Site Networked Hardware-in-Loop Platform for Evaluation of Interoperability and Distributed Intelligence at Grid-Edge

Electric power systems have experienced large increases in the number of intelligent, connected and controllable devices being deployed, leading to a high degree of distributed intelligence at the grid-edge. These devices, both utility-owned and consumer-owned, include but are not limited to: renewable generation sources, energy storage, remote switches, voltage regulators, and smart controllable loads such as electric vehicles. These new devices provide significant potential for increased operational flexibility that can be leveraged to achieve system reconfiguration, resiliency improvements, power quality improvements, and distribution system automation. However, there are two significant challenges that must be addressed before these assets can be leveraged for operations: interoperability and system level validation prior to deployment. Because of the complexity of distributed control systems, and their interactions with legacy centralized controls, a purely simulations-based approach for pre-deployment validation is not sufficient. It requires hardware-in-loop testing to emulate the operational hardware devices and evaluate their performance. Additionally, securely integrating multiple test facilities at utility operators and vendors might enable rapid scale-up of evaluation platforms, and remove the need for multiple expensive standalone installations. Presented in this paper, is the development of a multi-site evaluation platform that employs Advanced Distribution Management Systems (ADMS), distributed control devices, real-time hardware-in-loop assets, secure communication links, and protocol adapters. This platform uses standards-based approaches and open-source tools, and hence can serve as a template for other researchers and institutions to implement their multi-site evaluation frameworks for pre-deployment testing.

Essakiappan, Somasundaram↗
DOE OSTI2022

Market mechanism to enable grid-aware dispatch of Aggregators in radial distribution networks

This paper presents a market-based optimization framework wherein Aggregators can compete for nodal capacity across a distribution feeder and guarantee that allocated flexible capacity cannot cause overloads or congestion. This mechanism, thus, allows Aggregators with allocated capacity to pursue a number of services at the whole-sale market level to maximize revenue of flexible resources. Based on Aggregator bids of capacity (MW) and network access price ($/MW), the distribution system operator (DSO) formulates an optimization problem that prioritizes capacity to the different Aggregators across the network while implicitly considering AC network constraints. This grid-aware allocation is obtained by incorporating a convex inner approximation into the optimization framework that prioritizes hosting capacity to different Aggregators. We adapt concepts from transmission-level capacity market clearing, utility demand charges, and Internet-like bandwidth allocation rules to distribution system operations by incorporating nodal voltage and transformer constraints into the optimization framework. Simulation based results on IEEE distribution networks showcase the effectiveness of the approach.

Nazir, Mohammad Nawaf↗
DOE OSTI2020

Application of Chebyshev’s Inequality in Online Anomaly Detection Driven by Streaming PMU Data

The day-to-day operation of modern power systems is highly reliant on prompt and adequate situational-awareness. This can be achieved via various system monitoring functions such as anomaly detection, in which static thresholds are commonly utilized to distinguish the normal and the abnormal system states. However, a predetermined static threshold usually lacks the flexibility to adapt to unobserved scenarios. In this paper, we propose two self-adaptive synchrophasor data driven anomaly detection approaches based on Chebyshev’s Inequality. The proposed approaches have been evaluated with Kundur’s 2area system and Mini-WECC system. Experimental results verify that the proposed approaches can dynamically adapt to unprecedented scenarios, and detect anomalous events with lower false alarm rate compared to static threshold based detection.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Whole-genome sequencing distinguishes the two most common giant kelp ecomorphs

Abstract Giant kelp, Macrocystis pyrifera, exists as distinct morphological variants—or “ecomorphs”—in different populations, yet the mechanism for this variation is uncertain, and environmental drivers for either adaptive or plastic phenotypes have not been identified. The ecomorphs Macrocystis “pyrifera” and M. “integrifolia” are distributed throughout temperate waters of North and South America with almost no geographic overlap and exhibit an incongruous, non-mirrored, distribution across the equator. This study evaluates the degree of genetic divergence between M. “pyrifera” and M. “integrifolia” across 18 populations in Chile and California using whole-genome sequencing and single-nucleotide polymorphism markers. Our results based on a principal component analysis, admixture clustering by genetic similarity, and phylogenetic inference demonstrate that M. “pyrifera” and M. “integrifolia” are genetically distinguishable. Analyses reveal separation by Northern and Southern Hemispheres and between morphs within hemispheres, suggesting that the convergent “integrifolia” morphology arose separately in each hemisphere. This is the first study to use whole-genome sequencing to understand genetic divergence in giant kelp ecomorphs, identifying 83 potential genes under selection and providing novel insights about Macrocystis evolution that were not evident with previous genetic techniques. Future studies are needed to uncover the environmental forces driving local adaptation and presumed convergent evolution of these morphs.

Environmental Sciences & Ecology↗
DOE OSTI2024

Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case.

data-driven model predictive control↗
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↗
DOE OSTI2021

Implementing Cybersecurity for Distributed Wind: An Exercise in ICS Security Application

Distributed wind is unique and fast-growing component of the energy production mix, but cybersecurity for distributed wind is not well developed. In this presentation, we will introduce distributed wind as an example of difficult-to-secure critical infrastructure. We will first provide background on distributed wind architectures, applications, and stakeholder roles. We will explain the need and challenges for cybersecurity for distributed wind, pointing to both academic studies and real-world events. The key component of the presentation will discuss recommendations for different aspects of security, such as risk assessments, communications, and access control, that specifically call out considerations that apply uniquely to distributed wind. We will discuss what sets distributed wind apart from other ICS and energy applications, and how to account for these features in a comprehensive security plan. This process is demonstrative of the type of analysis needed to adapt traditional security guidelines and standards to narrow and focused applications.

17 WIND ENERGY↗
DOE OSTI2024

Reliability and resilience of environmental flows under uncertainty: reconsidering water year types and inconsistent flow requirements in California

Abstract Environmental water allocation in California is a complex legal process involving various government agencies and stakeholders. E-flow requirements can be based on annual runoff typologies called water year types (WYTs), which dictate water volume, timing, and duration. In this study, we examined hydropower licensing documents of the major water and power projects in the Central Sierra Nevada to catalog e-flow requirements by WYT. In this study case, we identify how WYT classification systems and categories vary across and within different basins. Additionally, we assessed the impacts of climate change on hydrology, the frequency of WYTs identified, and the reliability and resilience of e-flows using future projections (2031–2060) of 10 Global Circulation Models (GCMs). We then propose a potential adaptation strategy using a 30 year moving percentiles approach to recalculate WYTs. We identified eight WYT classifications systems were identified, and their WYT distributions statistically significantly changes across all GCMs, even though most GCMs indicate no statistically significant change in hydrology. Disparities in future impacts are observed among and within hydropower projects, with some river reaches showing negative impacts on reliability and resilience. The adaptation strategy can generally boost resilience and improve reliability, but simply updating existing WYT thresholds without flexible regulatory frameworks reconsidering WYTs and e-flows thresholds, may not yield substantial improvements. Challenges in managing e-flows in California within regulatory and hydroclimatic contexts are intricate due to the lack of standardized approaches, leading to inconsistencies and potential conflicts among stakeholders, that will likely be exacerbated by climate change. Thus, we emphasize that targeted, site-specific, and adaptive management strategies are crucial, besides the need for a harmonized and consistent approach to defining and applying WYT categories and methods and/or e-flow assessments.

Environmental Sciences & Ecology↗
DOE OSTI2021

Macroecological distributions of gene variants highlight the functional organization of soil microbial systems

Abstract The recent application of macroecological tools and concepts has made it possible to identify consistent patterns in the distribution of microbial biodiversity, which greatly improved our understanding of the microbial world at large scales. However, the distribution of microbial functions remains largely uncharted from the macroecological point of view. Here, we used macroecological models to examine how the genes encoding the functional capabilities of microorganisms are distributed within and across soil systems. Models built using functional gene array data from 818 soil microbial communities showed that the occupancy-frequency distributions of genes were bimodal in every studied site, and that their rank-abundance distributions were best described by a lognormal model. In addition, the relationships between gene occupancy and abundance were positive in all sites. This allowed us to identify genes with high abundance and ubiquitous distribution (core) and genes with low abundance and limited spatial distribution (satellites), and to show that they encode different sets of microbial traits. Common genes encode microbial traits related to the main biogeochemical cycles (C, N, P and S) while rare genes encode traits related to adaptation to environmental stresses, such as nutrient limitation, resistance to heavy metals and degradation of xenobiotics. Overall, this study characterized for the first time the distribution of microbial functional genes within soil systems, and highlight the interest of macroecological models for understanding the functional organization of microbial systems across spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
DOE OSTI2022

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

17 WIND ENERGY↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Analysis of the Stable Interphase Responsible for the Excellent Electrochemical Performance of Graphite Electrodes in Sodium-Ion Batteries

Considerable efforts have been exerted to understand the formation and properties of the solid electrolyte interphase (SEI) in sodium ion batteries. However, the puzzling existence and role of SEI behind the huge volume changes of the graphite electrodes need to be answered. Herein, the reason of how ether-derived SEI maintains excellent reversibility despite the huge volume changes during cycling is unraveled. Theoretical simulations and Fourier-transform infrared spectroscopy demonstrate the formation mechanism of an SEI between the graphite anode and electrolyte. Furthermore, the high mechanical tolerance of the ether-derived SEI is confirmed in atomic force microscopy. A depth profile of X-ray photoelectron spectroscopy points to a multilayer structure of the ether-derived SEI. The outer layer comprises organics (sodium alkoxide), while the inorganics (Na 2 CO 3 , NaF) in interior region are mixed with some organics. Notably, the presence of organics ensures the adaptability of the SEI to the volume expansion of graphite during cycling, and the concentrated distribution of inorganics improves the Young's modulus (resistance to deformation). Therefore, the graphite anode exhibits high cycle stability (96.6% capacity retention ratio at 1 A g –1 over 860 cycles) and efficiency (≈99.5%).

25 ENERGY STORAGE↗
DOE OSTI2023

Permissiveness and competition within and between Neurospora crassa syncytia

A multinucleate syncytium is a common growth form in filamentous fungi. Comprehensive functions of the syncytial state remain unknown, but it likely allows for a wide range of adaptations to enable filamentous fungi to coordinate growth, reproduction, responses to the environment, and to distribute nuclear and cytoplasmic elements across a colony. Indeed, the underlying mechanistic details of how syncytia regulate cellular and molecular processes spatiotemporally across a colony are largely unexplored. Here, we implemented a strategy to analyze the relative fitness of different nuclear populations in syncytia of Neurospora crassa , including nuclei with loss-of-function mutations in essential genes, based on production of multinucleate asexual spores using flow cytometry of pairings between strains with differentially fluorescently tagged nuclear histones. The distribution of homokaryotic and heterokaryotic asexual spores in pairings was assessed between different auxotrophic and morphological mutants, as well as with strains that were defective in somatic cell fusion or were heterokaryon incompatible. Mutant nuclei were compartmentalized into both homokaryotic and heterokaryotic asexual spores, a type of bet hedging for maintenance and evolution of mutational events, despite disadvantages to the syncytium. However, in pairings between strains that were blocked in somatic cell fusion or were heterokaryon incompatible, we observed a “winner-takes-all” phenotype, where asexual spores originating from paired strains were predominantly one genotype. These data indicate that syncytial fungal cells are permissive and tolerate a wide array of nuclear functionality, but that cells/colonies that are unable to cooperate via syncytia formation actively compete for resources.

59 BASIC BIOLOGICAL SCIENCES↗