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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression

Abstract Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

2020 Transportation Research Board Workshop: Infrastructure Spatial Sensing at Intersections: The Potential to Enhance Safety and Enable Automation

The sensing technology that is enabling vehicle automation also can revolutionize traffic intersection safety, control, and efficiency. Infrastructure spatial sensing technology refers to the deployment of sensors that can detect and track all objects in the field of view - for example, mounting lidar sensors, radar, and video imaging at an intersection and fusing the data to produce a three-dimensional dynamic operational awareness. Combining such sensing capability with fail-safe vehicle communications can facilitate applications such as eco-approach and departure, optimizing efficient vehicle-signal coordination, and reducing traffic accidents through increasing safety. Furthermore, evidence is mounting suggesting that infrastructure-based sensing is needed in order to safely deploy automated public mobility alongside the normal vehicle and pedestrian traffic in our cities. Six speakers were invited to present their work from various aspects (safety, efficiency, and automation) addressing infrastructure-based sensing at the workshop on Infrastructure Spatial Sensing hosted in Washington DC as part of the Transportation Research Board Annual Meeting in January 12, 2019. A summary of the presentation are included herein.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources: Preprint

This paper presents a distributed peer-to-peer market control strategy to manage and to enable resource sharing of behind-the-meter distributed energy resources in a residential community. In the proposed strategy, each consumer or prosumer determines the flexibility of their point of connection to the power network such that the obtained flexibility is network-feasible. Based on the feasible flexibility, the consumers and the prosumers trade power among each other at each time instance to fulfill their preferred load requirements while maximizing their payoffs and helping to regulate node voltages inside the community. Because the problem to be solved is non-convex, a distributed particle swarm optimization algorithm is used to coordinate the consumers/prosumers in a fully autonomous manner without any centralized or hierarchical coordination. Numerical simulations performed on a community of 48 homes demonstrate the efficacy of the proposed approach.

behind-the-meter↗
DOE OSTI2023

A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources

This paper presents a distributed peer-to-peer market control strategy to manage and to enable resource sharing of behind-the-meter distributed energy resources in a residential community. In the proposed strategy, each consumer or prosumer determines the flexibility of their point of connection to the power network such that the obtained flexibility is network-feasible. Based on the feasible flexibility, the consumers and the prosumers trade power among each other at each time instance to fulfil their preferred load requirements while maximizing their payoffs and helping to regulate node voltages inside the community. Because the problem to be solved is non-convex, a distributed particle swarm optimization algorithm is used to coordinate the consumers/prosumers in a fully autonomous manner without any centralized or hierarchical coordination. Numerical simulations performed on a community of 48 homes demonstrate the efficacy of the proposed approach.

distributed energy resource↗
DOE OSTI2021

Market-based Co-optimization of Energy and Ancillary Services with Distributed Energy Resource Flexibilities

Energy storage systems and flexible loads have attracted significant interests due to their capabilities to provide various grid services. In this paper, the flexibilities are optimally allocated among multiple distributed energy resource aggregators with energy storage systems and flexible buildings by the utility coordinator through market-based co-optimization. An iterative market clearing algorithm is developed to determine the optimal energy and ancillary service prices, with consideration of both local dynamics and network constraints. Simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of co-optimizing the energy and ancillary service markets.

Ma, Ke↗
DOE OSTI2023

Controlling circuitry underlies the growth optimization of Saccharomyces cerevisiae

Microbial growth emerges from coordinated synthesis of various cellular components from limited resources. In Saccharomyces cerevisiae, cyclic AMP (cAMP)-mediated signaling is shown to orchestrate cellular metabolism; however, it remains unclear quantitatively how the controlling circuit drives resource partition and subsequently shapes biomass growth. Here we combined experiment with mathematical modeling to dissect the signaling-mediated growth optimization of S. cerevisiae. We showed that, through cAMP-mediated control, the organism achieves maximal or nearly maximal steady-state growth during the utilization of multiple tested substrates as well as under perturbations impairing glucose uptake. However, the optimal cAMP concentration varies across cases, suggesting that different modes of resource allocation are adopted for varied conditions. Under settings with nutrient alterations, S. cerevisiae tunes its cAMP level to dynamically reprogram itself to realize rapid adaptation. Moreover, to achieve growth maximization, cells employ additional regulatory systems such as the GCN2-mediated amino acid control. This study establishes a systematic understanding of global resource allocation in S. cerevisiae, providing insights into quantitative yeast physiology as well as metabolic strain engineering for biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Data for Controlling Circuitry Underlies the Growth Optimization of Saccharomyces cerevisiae

Microbial growth emerges from coordinated synthesis of various cellular components from limited resources. In Saccharomyces cerevisiae , cyclic AMP (cAMP)-mediated signaling is shown to orchestrate cellular metabolism; however, it remains unclear quantitatively how the controlling circuit drives resource partition and subsequently shapes biomass growth. Here we combined experiment with mathematical modeling to dissect the signaling-mediated growth optimization of S. cerevisiae . We showed that, through cAMP-mediated control, the organism achieves maximal or nearly maximal steady-state growth during the utilization of multiple tested substrates as well as under perturbations impairing glucose uptake. However, the optimal cAMP concentration varies across cases, suggesting that different modes of resource allocation are adopted for varied conditions. Under settings with nutrient alterations, S. cerevisiae tunes its cAMP level to dynamically reprogram itself to realize rapid adaptation. Moreover, to achieve growth maximization, cells employ additional regulatory systems such as the GCN2-mediated amino acid control. This study establishes a systematic understanding of global resource allocation in S. cerevisiae , providing insights into quantitative yeast physiology as well as metabolic strain engineering for biotechnological applications.

Conversion↗
DOE OSTI2020

COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility

Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL’s end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordi- nate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs’ local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.

demand response↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗
DOE OSTI2020

Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

active distribution networks↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

IDAES-PSE Software Tools for Optimizing Energy Systems and Market Interactions

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-eco-nomic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully generates multi-period price-taker models, facilitates model discrimination, and aids in analyzing various technologies for deployment in unique energy markets.

Laky, Daniel↗
DOE OSTI2023

Distributed Rules-Based Deconfliction of ADMS Applications: Part 1: Requirements & Decomposition

This work introduces a rules-based deconfliction methodology for resolving conflicting device control commands issued by advanced power applications considering a range of technical, economic, environmental, and social objectives. The methodology is designed to serve as one of multiple alternative implementations (along with application cooperation and global optimization) for the numerical component of the Deconfliction Pipeline. Development of the methodology is divided into two parts. This first document introduces the requirements, context, and methods for decomposing the deconfliction problem using the Laminar Coordination Framework and Variable Grid Structures. The deconfliction problem is decomposed into a distributed optimization problem based on the concept of quasi-static grid segments, which form independent distributed areas for control and coordination. Selection of the optimal number of decompositions of the deconfliction problem should be made based on a tradeoff analysis between computational speed and global optimality. This second document will define an initial set of technical, economic, and environmental criteria, as well as thirty specific qualitative rules that are used as part of the deconfliction methodology to eliminate non-viable setpoint alternatives. The deconfliction optimization problem is converted into a ranking of individual discrete setpoints, which are scored by the extent to which they satisfy specific decision criteria. The ranking is determined through the concepts of deconfliction exclusivity, priority, and preference. Several multi-criteria decision-making frameworks are examined with the simple multi-attribute rating technique exploiting ranks (SMARTER) recommended as a simple implementation alternative that aligns with the steps of the rules-based deconfliction methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evaluation of Aggregated EV Flexibility With TSO-DSO Coordination

Electric vehicles (EVs) are becoming a promising source of grid ancillary services due to the temporal and spatial charging flexibility, quick response and storage capability. Such advantages are increasing with government policy promotion and technology improvement. However, the exploration of EV flexibility requires the coordination of both transmission system operators (TSOs) and distribution system operators (DSOs), to ensure the safe and reliable operation of power network. In this paper, we propose a coordinated evaluation method that determines the optimal utilization of EV temporal flexibility without compromising EV owners’ usage. At the distribution level, DSOs first evaluate EV aggregators’ operational boundaries to exploit distribution level services. At the transmission level, TSOs then determine EV charging schedules and ancillary service capacity simultaneously, taking into account the requirement from DSOs. Here, we validate the model in a case study using the IEEE 123 node test feeder and EV charging sessions obtained from a transportation simulation tool that uses real-world data.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Metal–Nitrogen–Carbon Cluster–Decorated Titanium Carbide is a Durable and Inexpensive Oxygen Reduction Reaction Electrocatalyst

Clusters of nitrogen- and carbon-coordinated transition metals dispersed in a carbon matrix (e. g., Fe-N-C) have emerged as an inexpensive class of electrocatalysts for the oxygen reduction reaction (ORR). In this work, it was shown that optimizing the interaction between the nitrogen-coordinated transition metal clusters embedded in a more stable and corrosion-resistant carbide matrix yielded an ORR electrocatalyst with enhanced activity and stability compared to Fe-N-C catalysts. Utilizing first-principles calculations, an electrostatics-based descriptor of catalytic activity was identified, and nitrogen-coordinated iron (FeN 4 ) clusters embedded in a TiC matrix were predicted to be an efficient platinum-group metal (PGM)-free ORR electrocatalyst. Guided by theory, selected catalyst formulations were synthesized, and it was demonstrated that the experimentally observed trends in activity fell exactly in line with the descriptor-derived theoretical predictions. The Fe-N-TiC catalyst exhibited enhanced activity (20 %) and durability (3.5-fold improvement) compared to a traditional Fe-N-C catalyst. It was posited that the electrostatics-based descriptor provides a powerful platform for the design of active and stable PGM-free electrocatalysts and heterogenous single-atom catalysts for other electrochemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Advanced Laboratory and Field Arrays: Technical Report for Performance Enhancement for Marine Energy Converter (MEC) Arrays (Task 5)

Task 5 consisted of two parts, wave energy converter (WEC) arrays and current energy converter (CEC) arrays. The two parts of the project were executed independently, and this report covers only the work on WECs. In this part of the task, the team characterized the behavior of and developed control schemes for wave energy converter (WEC) arrays that improved performance (i.e., maximized aggregate power generation and reduced the levelized cost of energy) over baseline, non-coordinated control approaches. Specifically, for WEC arrays: the team developed optimal layouts of arrays that considered the effect of WEC placement within an array on coordinated array control with real-time estimation schemes. The numerical codes developed through this task were made available to users for array design. These tools utilized existing commercial software as well as software developed within the project team.

13 HYDRO ENERGY↗