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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 OSTI2021

Robust hierarchical dispatch for residential distribution network management considering home thermal flexibility and model predictive control

In the transactive energy (TE) paradigm, the devices of participative consumers, or prosumers, may be aggregated and employed to drive operational objectives at the network level. Home heating, ventilation and air-conditioning (HVAC) systems in particular are well-suited to modulate their behaviours based on both home thermal flexibility and requests from the utility grid. This paper develops a robust, hierarchical power dispatch scheme in the context of a residential distribution network. The formulation couples a unique, multiphase linear distribution optimal power flow (OPF) at the upper level with model predictive control (MPC)-based HVAC fleet controllers at the lower level. The proposed approach is tested on nearly 2000 homes with a three-phase distribution network in an intraday market setting, where two major applications are explored and analysed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Microgrid Assisted Design for Remote Areas

In this work, we present a three-stage multiobjective mixed-integer linear programming (MILP) for the optimal expansion planning and operation of isolated multienergy microgrids in remote areas. By selecting the optimal distributed generators (DGs) and energy storage systems (ESSs) mix selection, siting, sizing, and scheduling in the remote microgrid, the proposed model is targeted to minimize the annualized total cost of microgrids while enhancing the performance of the system, i.e., minimizing the voltage deviations and line power loss. To represent the electricity and heat flow between generation resources and various electrical, heating, and cooling loads in the isolated microgrid, linearized power flow, and heat flow constraints are employed in the proposed optimization model. The available capacity of DGs and ESSs are modeled as discrete constants instead of continuous variables for practical purpose. Numerical simulation results on a remote microgrid consisting of DGs, ESSs, and various loads validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Feedback Power Cost Optimization in Power Distribution Networks with Prosumers

Here, we propose a feedback control approach for solving optimal power flow (OPF) problems in power distribution networks (DNs) based on a projected gradient scheme, where the cost is given by the sum of functions of local power injections. This approach does not require detailed knowledge of the grid model and enables real-time tracking of the optimal generation configuration. Moreover, the communication burden is minimal: only a broadcast from a central entity, called the Network Supervisor (NS), to the agents in the network is needed. Hence, agents do not have to share local information that can be sensitive, e.g., their power injections or the value of the local costs. Our approach is then used to solve a particular OPF problem targeting DNs with high penetration of distributed energy resources (DERs) in which the energy cost is given by the remuneration that the DERs owners receive for the service they provide.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Opportunities and Challenges to Capturing Distributed Battery Value via Retail Utility Rates and Programs

Distributed battery deployment is increasing with advanced metering, control, and communication technologies, leaving electric utilities with an under-utilized, flexible grid resource in aggregate. Rates can reflect locational and temporal prices while utility incentive-based programs allow DERs to provide direct grid services. However, utilities must balance accurately reflecting dynamic grid conditions versus simple and feasible design that encourages customer participation. Currently, most rates and incentive-based programs are simple, but as penetration of DER and advanced controls increase, dynamic designs could become prevalent. Utilities could encourage providing multiple services to optimize distributed battery dispatch and value streams, however, challenges persist when stacking services across distribution and bulk systems. A DER committed to multiple discrete services concurrently necessitates coordination between operators and a clear hierarchy of commitments. One way to address this is to separate commitments by time or capacity. For services that follow cyclic, predictable patterns, or those that are peak driven with predictability, an operator could ensure sufficient state of charge for participation, leaving time where a distributed battery could otherwise provide different services by segmenting participation temporally. To provide continuous or unexpected services, a battery operator may use state of charge management to reserve some percentage of the battery and segment participation by capacity. Macroeconomic trends, load patterns, generation profiles, and grid configurations drive variation in value and the subsequent implications for utility offerings and how a customer might participate. As distributed battery adoption increases, both regulators and utilities will need to ensure no adverse grid impacts and encourage provision of societal value beyond the customer domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. Finally, the proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.

convergence↗
NASA NTRS2012

Applying Biomimetic Algorithms for Extra-Terrestrial Habitat Generation

The objective is to simulate and optimize distributed cooperation among a network of robots tasked with cooperative excavation on an extra-terrestrial surface. Additionally to examine the concept of directed Emergence among a group of limited artificially intelligent agents. Emergence is the concept of achieving complex results from very simple rules or interactions. For example, in a termite mound each individual termite does not carry a blueprint of how to make their home in a global sense, but their interactions based strictly on local desires create a complex superstructure. Leveraging this Emergence concept applied to a simulation of cooperative agents (robots) will allow an examination of the success of non-directed group strategy achieving specific results. Specifically the simulation will be a testbed to evaluate population based robotic exploration and cooperative strategies while leveraging the evolutionary teamwork approach in the face of uncertainty about the environment and partial loss of sensors. Checking against a cost function and 'social' constraints will optimize cooperation when excavating a simulated tunnel. Agents will act locally with non-local results. The rules by which the simulated robots interact will be optimized to the simplest possible for the desired result, leveraging Emergence. Sensor malfunction and line of sight issues will be incorporated into the simulation. This approach falls under Swarm Robotics, a subset of robot control concerned with finding ways to control large groups of robots. Swarm Robotics often contains biologically inspired approaches, research comes from social insect observation but also data from among groups of herding, schooling, and flocking animals. Biomimetic algorithms applied to manned space exploration is the method under consideration for further study.

Birge, Brian↗
DOE OSTI2022

Impact of cyber attacks on distributed compressive sensing based state estimation in power distribution grids

Modern power distribution grids suffer from multiple vulnerabilities due to the tight integration between the physical system and the cyber infrastructure. Sophisticated and malicious cyber attacks continue to adversely impact the grid operation leading to performance degradation, service interruption, and grid failure. State estimation plays an essential role in grid monitoring and advancing cyber-attack situational awareness. In this regard, this paper first proposes a distributed compressive sensing (CS) state estimation approach for an unobservable distribution grid. Further, the proposed distributed CS approach divides the distribution grid into sub-areas to perform local state estimation. Then an alternating direction method of multipliers (ADMM) based iterative information exchange among neighboring areas is employed to complete the estimation process. In this estimation process, the impact of loss of measurement data, false data injection (FDI), replay, and neighborhood cyber-attacks is analyzed. Extensive simulations are performed on the IEEE 37-bus and IEEE 123-bus standard networks to demonstrate the algorithm’s robustness to the aforementioned cyber-attacks. A quantitative analysis of computational complexity and simulation time of the distributed CS based approach is also presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Estimating energy consumption and GHG emissions in the U.S. food supply chain for net-zero

This work provides a database of the U.S. food system’s energy consumption and GHG emissions at the national and state levels by food supply chain (FSC) stage, fuel type, and food commodity. We estimate that the U.S. FSC consumed a total 4660 TBTU (4900 PJ) of site energy, 7130 TBTU (7500 PJ) of primary energy, and generated 970 MMT of GHG emissions in 2016. Among all the stages, on-farm production is the largest energy consumer (31% primary energy) and GHG emissions contributor (70%), largely due to raising animals. Optimizing distribution can reduce the stage’s energy consumption and GHG emissions and increase products’ shelf-life. Reducing food loss and waste is another good option, as it decreases the amount of food necessary to grow, thus impacting the overall FSC. The database can help stakeholders identify stage- and region-specific strategies and measures to curtail the environmental footprint of the U.S. food system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Networked Microgrids for Improved Resilient Operation: A Case Study in Adjuntas Puerto Rico

This paper presents a case study of a network microgrid orchestrator designed to allow coordinated operation of microgrids. Through Control Hardware in the Loop (CHIL), the proposed microgrid orchestrator was validated using, as case study two community-owned microgrids in Adjuntas, Puerto Rico. These two microgrids in Adjuntas were designed to provide affordable and reliable access to electricity to 14 businesses located in the town square. In its current design, these two microgrids operate independently from each other. This paper studies how networking them would increase their resiliency metrics while operating as an island. A distributed optimization is implemented in the microgrid orchestrator due to its superior scalability compared to centralized approach as well as to maintain data privacy. Two operational modes are considered in this study to showcase the advantages of networking microgrids: a) normal island, and b) degraded island. CHIL simulations are performed to validate the proposed microgrid orchestrator. CHIL results show meaningful resiliency improvements obtained by networking microgrids during contingencies such as loss of PV generation.

Ferrari Maglia, Max↗
DOE OSTI2020

A Robust Hierarchical Dispatch Scheme for Active Distribution Networks Considering Home Thermal Flexibility

Distribution networks are changing from passive absorbers of electric energy to active distribution networks (ADNs) capable of operating and participating in electricity markets. In the context of residential microgrids, which is a type of ADNs, aggregated home heating, ventilation and air-conditioning (HVAC) loads present a key opportunity to drive operational and economic objectives, facilitate high renewable energy penetration, and enhance both system resiliency and flexibility. A robust, hierarchical dispatch scheme is developed and presented in this paper, which connects an upper level multi-phase distribution optimal power flow (DOPF) to a lower level model predictive control (MPC)-based HVAC fleet controller. The approach is tested and verified on a modified IEEE 13 bus system in an intraday market application. The results demonstrate that the proposed hierarchical dispatch scheme is able to drive both economic and operational objectives for the ADN operator.

Rooks, Cody D.↗
DOE OSTI2021

The effects of allelochemical transfer on the dynamics of hosts, parasitoids, and competing hyperparasitoids

Allelochemicals produced by plants may be ingested by herbivorous insects and transferred to higher trophic levels with potentially deleterious effects. We develop a system of differential equations to investigate the effect of the transfer of allelochemicals, such as nicotine, on the population dynamics of a system of hosts, parasitoids, and two competing hyperparasitoids that attack different life stages of the parasitoids. We find both somewhat deleterious effects of nicotine on the larvae-attacking hyperparasitoids and increased attack rates for the pupae-attacking hyperparasitoids can promote coexistence. We also use an evolutionary game-theoretic approach to determine the optimal distribution of hyperparasitoid attacks among nicotine-producing and nicotine-free plants. With strong deleterious effects of nicotine and increased attack rates for the pupae-attacking hyperparasitoid, we find both species attack parasitoids on the nicotine-free plant but only pupae-attacking hyperparasitoids attack parasitoids on the nicotine-producing plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Interpreting Ionic Conductivity for Polymer Electrolyte Fuel Cell Catalyst Layers with Electrochemical Impedance Spectroscopy and Transmission Line Modeling

A cathode catalyst layer containing optimally distributed ionomer is critical to reduce the platinum loading and increase its utilization in polymer electrolyte fuel cells. Here, electrochemical impedance spectroscopy (EIS) was used to measure effective ionic conductivity of pseudo catalyst layers (PCLs) at a relative humidity (RH) range of 50%-120%. These results are compared to previous work using the hydrogen pump (HP) method. EIS effective ionic conductivity results reported here are higher than those from the HP because in the HP set-up ionic pathways must be effectively connected through the PCL to be counted, whereas in the EIS measurement, ionomer segments that are in contact with the membrane but are not effectively connected all the way through the PCL can be detected. Double layer capacitances and effective ionic conductivities of Pt/C catalyst layers with various supports and ionomer to carbon (I/C) ratios were studied. High surface area carbon support resulted in a lower effective ionic conductivity compared to the graphitized carbon support due to worse ionomer dispersion. Effective ionic conductivities of Pt/C layers were compared to that of PCLs. On average, effective ionic conductivities of Pt/C layers were higher than PCLs because of possible carbon agglomeratio n within the PCLs.

25 ENERGY STORAGE↗
DOE OSTI2020

Wattile: Probabilistic Deep Learning-based Forecasting of Building Energy Consumption [SWR-20-94]

Accurate energy forecasting is becoming critical due to many reasons: i ) optimal distributed energy resources operations and dispatch, ii) fault detection and diagnostics, and iii) meeting operational energy efficiency targets. Wattile uses deep learning (DL) for the building's short-term load forecasting application. Two specific types of neural networks called, Long Short Term Memory (LSTM) and Sequence-to-Sequence (S2S) models are used to make predictions. Forecasting models are trained using online historical weather and occupancy indicator data streams from the Intelligent Campus Program's data acquisition systems at the National Renewable Energy Laboratory (NREL) for main meters and sub-meters of multiple building types. These models use probabilistic methods to provide quantile-based forecasts in addition to nominal conditional median predictions of electricity consumption.

Frank, Stephen↗
DOE OSTI2023

Multi-injection investigation of a high-volatility diesel in advanced compression ignition combustion for NO x control

Traditional selective catalytic reduction aftertreatment technologies used to reduce [Formula: see text] are very limited at exhaust temperatures below [Formula: see text]. Therefore, under these low engine load conditions, having effective in-cylinder control of [Formula: see text] emissions is important. Previous work by the authors explored the effect of fuel physical properties on the ability to control [Formula: see text] in-cylinder. That work was limited to one direct injection near top dead center. Modern diesel high-pressure fuel systems have the capability of five or more injections in one engine cycle. A higher-volatility diesel fuel and high amounts of exhaust gas recirculation to delay ignition could provide an opportunity for reduction in engine-out [Formula: see text] through an increased level of fuel premixing. By appropriately timing multiple short injections, a more optimal distribution of fuel in-cylinder may be achieved, which could reduce [Formula: see text] while maintaining an efficient combustion phasing. A computational fluid dynamics model previously validated against experimental data was used to explore several injection strategies with increased levels of fuel premixing to assess the potential trade-offs between [Formula: see text] and CO/unburned hydrocarbon (UHC) emissions and thus reduce reliance on the aftertreatment system for [Formula: see text] control. The results show that the devised injection strategies resulted in an increased level of fuel premixing. However, none of the attempted injection strategies resulted in significant [Formula: see text] reductions, and all strategies showed a significant increase in CO and UHC emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
NASA NTRS1972

Conjugate gradient determination of optimal plane changes for a class of three-impulse transfers between noncoplanar circular orbits

A particular type of three-impulse transfer between two circular orbits is analyzed. The possibility of three plane changes is recognized, and the problem is to optimally distribute these plane changes to minimize the sum of the individual impulses. Numerical difficulties and their solution are discussed. Numerical results obtained from a conjugate gradient technique are presented for both the case where the individual plane changes are unconstrained and for the case where they are constrained. Possibly not unexpectedly, multiple minima are found. The techniques presented could be extended to the finite burn case, but primarily the contents are addressed to preliminary mission design and vehicle sizing.

Burrows, R. R.↗
NASA NTRS1974

Analysis of three-dimensional unsteady flow around oscillating wings

A method based on the Navier-Stokes equations was developed for determining analytically the three-dimensional unsteady flow patterns around oscillating wings. The Helmholz vorticity transport equations were discretized in three-dimensional finite element form from a variational formulation and integrated numerically. At each time step of the numerical integration the velocity field was calculated from the representation of the three-dimensional wing by a system of optimized distribution of vortices in space. During the numerical integration of the vorticity transport equations the time-dependent boundary conditions on the wing were specified as external constraint conditions. Examples of obtained results describing the three-dimensional unsteady flow around a wing were presented.

Bratanow, T.↗