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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Application of multidimensional structural characterization to detect and describe moderate forest disturbance

The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Extending the Near-Infrared Emission Range of Indium Phosphide Quantum Dots for Multiplexed In Vivo Imaging

This report of the reddest emitting indium phosphide quantum dots (InP QDs) to date demonstrates tunable, near-infrared (NIR) photoluminescence (PL) as well as PL multiplexing in the first optical tissue window while avoiding toxic constituents. This synthesis overcomes the InP “growth bottleneck” and extends the emission peak of InP QDs deeper into the first optical tissue window using an inverted QD heterostructure, specifically ZnSe/InP/ZnS core/shell/shell nanoparticles. The QDs exhibit InP shell thickness-dependent tunable emission with peaks ranging from 515–845 nm. The high absorptivity of InP yields effective photoexcitation of the QDs with UV, visible, and NIR wavelengths. These nanoparticles extend the range of tunable direct-bandgap emission from InP-based nanostructures, effectively overcoming a synthetic barrier that has prevented InPbased QDs from reaching their full potential as NIR imaging agents. Further, multiplexed lymph node imaging in a mouse model demonstrates the potential of the NIR-emitting InP particles for in vivo imaging.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Role of Cation Ordering on Device Performance in (Ag,Cu)InSe 2 Solar Cells with KF Post-Deposition Treatment

CuInSe 2 (CIS) has been proposed as an attractive bottom cell candidate in tandem solar cells. However, to justify the coupling with high-performance top cells (e.g., perovskites, GaAs), significant work on improving the efficiency is required. To this extent, several authors have demonstrated the benefits of alkali post-deposition treatments (PDT) to increase device open-circuit voltage (V oc ) in CIS and how Ag alloying - (Ag,Cu)InSe 2 (ACIS) - reduces defect density and enhances current collection in devices. Herein, we present a detailed study of the role that KF-PDT plays on CIS and ACIS absorber composition and structure, and propose an explanation for the decreased V oc observed when silver and potassium coexist in the system (ACIS + KF). Through a suite of synchrotron-based techniques, we investigate the nanoscale chemical distribution of the films and the formation of secondary phases. Through photoluminescence imaging, we observed a high degree of passivation with the addition of KF, and synchrotron-based X-ray diffraction confirmed the absence of a KInSe 2 surface layer usually considered to be a passivating agent. Raman spectroscopy and synchrotron X-ray fluorescence show the increased presence of Cu- and Se-poor clusters in ACIS + KF, which are correlated to significantly reduced X-ray beam-induced current (XBIC). An increase in the intensity of the E/B 2 stretching mode of CIS is attributed to cation ordering near the junction and is found to track inversely to bulk V oc measurements. The cation ordering is hypothesized to arise from the formation and redistribution of defects that normally occur near the surfaces of CIS as a consequence of its polar character. Here, these defects compensate each other, and the overall inhomogeneity of the charge distribution generates electrostatic potential fluctuations that greatly increase the saturation current and hence reduce the open-circuit voltage of the device.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methanobactin from methanotrophs: genetics, structure, function and potential applications

ABSTRACT Aerobic methane-oxidizing bacteria of the Alphaproteobacteria have been found to express a novel ribosomally synthesized post-translationally modified polypeptide (RiPP) termed methanobactin (MB). The primary function of MB in these microbes appears to be for copper uptake, but MB has been shown to have multiple capabilities, including oxidase, superoxide dismutase and hydrogen peroxide reductase activities, the ability to detoxify mercury species, as well as acting as an antimicrobial agent. Herein, we describe the diversity of known MBs as well as the genetics underlying MB biosynthesis. We further propose based on bioinformatics analyses that some methanotrophs may produce novel forms of MB that have yet to be characterized. We also discuss recent findings documenting that MBs play an important role in controlling copper availability to the broader microbial community, and as a result can strongly affect the activity of microbes that require copper for important enzymatic transformations, e.g. conversion of nitrous oxide to dinitrogen. Finally, we describe procedures for the detection/purification of MB, as well as potential medical and industrial applications of this intriguing RiPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Load Shedding for Voltage Regulation With Probabilistic Agent Compliance

With the increased observability and controllability of distribution systems, the share of behind-the-meter systems is trending upwards rapidly. As a consequence, the impact of human behaviors on system performance can no longer be ignored and should be reflected in the energy management system models. In this paper, we discuss the problem of distribution system voltage control by active power curtailment where the agent compliance of the load curtailment signal is probabilistic. We discuss the modeling of the optimal voltage control problem with probabilistic agent compliance as a chance-constrained optimization problem, its tractable safe approximation using convex restriction, and a scenario-based mixed-integer reformulation as well as the associated solution method based on augmented Lagrangian method. The numerical simulation on IEEE test system validates the effectiveness of the proposed approach in obtaining high-quality feasible load curtailment signal with low computational cost, which makes it a viable tool for real time decision making.

augmented Lagrangian method↗
DOE OSTI2021

Hemicellulose and starch citrate chitosan foam adsorbents for removal of arsenic and other heavy metals from contaminated water

Arsenic and other heavy metal contaminants in water are a significant global health threat. In this study, low-cost, sulfur-free, sustainable, water-insoluble materials with heavy metal remediation properties were produced from renewable resources such as starch, xylan, citric acid, and chitosan. Synthesized starch citrate-chitosan (SCC) foam and xylan citrate-chitosan (XCC) foam were flexible, porous, and elastic. The foams’ arsenic uptake in water was significantly greater than five different commercial metal remediating agents. The mercury and lead uptakes with the synthesized foams were similar to the performance of a commercial sulfur-based product, SorbaTech 450 (ST450). However, the cadmium and selenium uptakes were comparatively lower. The complexation of arsenic with oxygen and nitrogen of the SCC foam was shown with time-of-flight secondary ion mass spectrometry (TOF-SIMS). The XCC foam was also shown to adsorb potassium iodide (KI) at a similar rate to sodium chloride. This may be used to remediate water contaminated with radioactive materials, such as iodine 131.

Materials Science↗
DOE OSTI2020

Resource for the Development of Biomedical Accelerator Mass Spectrometry (AMS) Final Report

The NIH Research Resource for Biomedical AMS was originally funded at Lawrence Livermore National Laboratory in 1999 to develop and apply the technology of accelerator mass spectrometry (AMS) in broad- based biomedical research. The Resource’s niche is to fill needs for ultra high sensitivity quantitation when isotope-labeled agents are used. The Research Resource’s Technology Research and Development (TR&D) efforts will focus on the needs of the biomedical research community in the context of seven Driving Biomedical Projects (DBPs) that will drive the Center’s technical capabilities through three core TR&Ds. We will expand our present capabilities by developing a fully integrated HPLC AMS to increase our capabilities for metabolic measurements, we will develop methods to understand cellular processes and we will develop and validate methods for the application of AMS in human studies, which is a growing area of demand by collaborators and service users. In addition, we will continue to support new and ongoing collaborative and service projects that require the capabilities of the Resource. The Center will continue to train researchers in the use of the AMS capabilities being developed, and the results of all efforts will be widely disseminated to advance progress in biomedical research. Towards these goals, our specific aims are to:1.) Increase the value and information content of AMS measurements by combining molecular speciation with quantitation of defined macromolecular isolates. Specifically, develop and validate methods for macromolecule labeling, characterization and quantitation.2.) Develop and validate methods and strategies to enable AMS to become more broadly used in human studies. Specifically, demonstrate robust methods for conducting pharmacokinetic/pharmacodynamics studies in humans and model systems.3.) Increase the accessibility of AMS to the Biomedical research community and the throughput of AMS through direct coupling to separatory instruments.4.) Provide high throughput 14C BioAMS analysis for collaborative and service clients.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

CB10412: Bulk CWA Destruction

The objective of this project was to eliminate and/or render bulk agent unusable by a threat entity via neutralization and/or polymerization of the bulk agent using minimal quantities of additives. We proposed the in situ neutralization and polymerization of bulk chemical agents (CAs) by performing reactions in the existing CA storage container via wet chemical approaches using minimal quantities of chemical based materials. This approach does not require sophisticated equipment, fuel to power generators, electricity to power equipment, or large quantities of decontaminating materials. By utilizing the CA storage container as the batch reactor, the amount of logistical resources can be significantly reduced. Fewer personnel are required since no sophisticated equipment needs to be set up, configured, or operated. Employing the CA storage container as the batch reactor enables the capability to add materials to multiple containers in a short period of time as opposed to processing one container at a time for typical batch reactor approaches. In scenarios where a quick response is required, the material can be added to all the CA containers and left to react on its own without intervention. Any attempt to filter the CA plus material solution will increase the rate of reaction due to increased agitation of the solution.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Improved method of solar-cell assembly

Method bonds solar-cell modules between rigid or flexible base and plastic protective cover. Method relies on using one of several commercially-available, transparent, silicone adhesives as bonding agent. Should it ever be necessary to repair or replace some part of assembly, it may be possible to remove cover without destroying package since adhesive remains flexible.

Broder, J. D.↗
NASA NTRS1982

Formation of pyrophosphate on hydroxyapatite with thioesters as condensing agents

'Energy-rich' thioesters are shown to act as condensing agents in the formation of pyrophosphate on hydroxyapatite in the presence of water at ambient temperature. The yield of pyrophosphate based on thioester ranges from 2.5% to 11.4% and depends upon the pH and concentration of reactants. Reaction of 0.130 M hydroxyapatite suspended in a solution of 0.08 M sodium phosphate and 0.20 M imidazole hydrochloride (pH 7.0) with 0.10 M N,S-diacetylcysteamine for 6 days gives the highest yield of pyrophosphate (11.4%). Pyrophosphate formation requires the presence of hydroxyapatite, sodium phosphate and the thioester, N,S-diacetylcysteamine. The related thioester, N,S-diacetylcysteine, also yields pyrophosphate in reactions on hydroxyapatite.

Weber, A. L.↗
NASA NTRS2001

Microscale Particulate Classifiers (MiPAC) Being Developed

The NASA Glenn Research Center is developing microscale sensors to characterize atmospheric-borne particulates. The devices are fabricated using MEMS (microelectromechanical systems) technologies. These technologies are derived from those originally developed in support of the semiconductor processing industry. The resulting microsensors can characterize a wide range of particles and are, therefore, suitable to a broad range of applications. This project is supported under a collaborative program called the Glennan Microsystems Initiative. The initiative comprises members of NASA Glenn Research Center, various university affiliates from the State of Ohio, and a number of participating industrial partners. Funding is jointly provided by NASA, the State of Ohio, and industrial members. The work described here is a collaborative arrangement between researchers at Glenn, the University of Minnesota, The National Institute of Standards and Technology (NIST), and the Cleveland State University. Actual device fabrication is conducted at Glenn and at the laboratories of Case Western Reserve University. Case Western is also located in Cleveland, Ohio, and is a participating member of the initiative. The principal investigator for this project is Paul S. Greenberg of Glenn. Two basic types of devices are being developed, and target different ranges of particle sizes. The first class of devices, which is used to measure nanoparticles (i.e., particles in the range of 0.002 to 1 mm), is based on the technique of Electrical Mobility Classification. This technique also affords the valuable ability of measuring the electrical charge state of the particles. Such information is important in the understanding of agglomeration mechanisms and is useful in the development of methods for particle repulsion. The second type of device being developed, which utilizes optical scattering, is suitable for particles larger than 1 mm. This technique also provides information on particle shape and composition. Applications for these sensors include fundamental planetary climatology, monitoring and filtration in spacecraft, human habitation modules and related systems, characterization of particulate emissions from propulsion and power systems, and as early warning sensors for both space-based and ter-restrial fire detection. These devices are also suitable for characterizing biological compounds such as allergens, infectious agents, and biotoxic agents.

Greenberg, Paul S.↗
DOE OSTI2021

Stopping criteria for ending autonomous, single detector radiological source searches

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1984

Removing Biostatic Agents From Fermentation Solutions

Liquid carbon dioxide inexpensive solvent. Inexpensive process proposed for removing such poisons as furfural and related compounds from fermentation baths of biomass hydrolysates. New process based on use of liquid carbon dioxide as extraction solvent. Liquid CO2 preferable to such other liquid solvents as ether or methylene chloride.

Du Fresne, E. R.↗
NASA NTRS2010

DF-1, A Nontoxic Carbon Fullerene Based Antioxidant, is Effective as a Biomedical Countermeasure Against Radiation

A long-term goal of radiation research is the mitigation of inherent risks of radiation exposure. Thus the study and development of safe agents, whether biomedical or dietary, that act as effective radioprotectors is an important step in accomplishing this long-term goal. Some of the most effective agents to date have been aminothiols and their derivatives. Unfortunately, most of these agents have side effects such as nausea, vomiting, hypotension, weakness, and fatigability. For example, nausea and emesis occur in most patients treated with WR-2721 (Amifostine), requiring the use of effective antiemetics, with hypotension being the dose-limiting side effect in patients treated. Clearly, the need for a radioprotector that is both effective and safe still exists. Development of biocompatible nano-materials for radioprotection is a promising emerging technology that could be exploited to address the need to minimize biological effects when exposure is unavoidable. Testing free radical scavenging nanoparticles for potential use in radioprotection is exciting and highly relevant. Initial investigations presented here demonstrate the ability of a particular functionalized carbon fullerene nanoparticle, (DF-1), to act as an effective radioprotector. DF-1 was first identified as the most promising candidate in a screen of several functionalized carbon fullerenes based on lack of toxicity and antioxidant therapeutic potential against oxidative injuries (i.e. organ reperfusion and ionizing radiation). Subsequently, DF-1 has been shown to reduce chromosome aberration yield and cell death, as well as overall ROS levels in human lymphocytes and fibroblasts after exposure to gamma radiation and energetic protons while demonstrating no associated toxicity. The dose-reducing factor of DF-1 at LD50 is nearly 2.0 for gamma radiation. In addition, DF-1 treatment also significantly prevented cell cycle arrest after exposure. Finally, DF-1 markedly attenuated COX2 upregulation in cell culture after irradiation thus preventing an inflammatory response to irradiation. Taken together, these results suggest that DF-1 provides potent protection against several deleterious cellular consequences of irradiation in mammalian systems including oxidative stress, DNA damage, inflammation and cell death.

Theriot, Corey A.↗
NASA NTRS2013

Dynamics and Control of a Disordered System in Space

In this paper, we present some ideas regarding the modeling, dynamics and control aspects of granular spacecraft. Granular spacecraft are complex multibody systems composed of a spatially disordered distribution of a large number of elements, for instance a cloud of N grains in orbit, with N greater than 10(exp 3). These grains can be large (Cubesat-size) or small (mm-size), and can be active, i.e., a fully equipped vehicle capable sensing their own position and attitude, and enabled with propulsion means, or entirely passive. The ultimate objective would be to study the behavior of the single grains and of large ensembles of grains in orbit and to identify ways to guide and control the shape of a cloud composed of these grains so that it can perform a useful function in space, for instance, as an element of an optical imaging system for astrophysical applications. This concept, in which the aperture does not need to be continuous and monolithic, would increase the aperture size several times compared to large NASA observatories such as ATLAST, allowing for a true Terrestrial Planet Imager that would be able to resolve exo-planet details and do meaningful spectroscopy on distant world. In the paper, we address the modeling and autonomous operation of a distributed assembly (the cloud) of large numbers of highly miniaturized space-borne elements (the grains). A multi-scale, multi-physics model is proposed of the dynamics of the cloud in orbit, as well as a control law for cloud shape maintenance, and preliminary simulation studies yield an estimate of the computational effort, indicating a scale factor of approximately N(exp 1.4) as a function of the number of grains. A granular spacecraft can be defined as a collection of a large number of space-borne elements (in the 1000s) designed and controlled such that a desirable collective behavior emerges, either from the interactions among neighboring grains, and/or between the grains and the environment. In this paper, each grain is considered to be a highly miniaturized spacecraft which has limited size and mass, hence it has limited actuation, limited propulsive capability, limited power, limited sensing, limited communication, limited computational resources, limited range of motion, limited lifetime, and may be expendable. The modeling and dynamics of clouds of vehicles is more challenging than with conventional vehicles because we are faced with a probabilistic vehicle composed of a large number of physically disconnected vehicles. First, different scales of motion occur simultaneously in a cloud: translations and rotations of the cloud as a whole (macro-dynamics), relative rotation and translation of one cloud member with respect to another (meso-dynamics), and individual cloud member dynamics (micro-dynamics). Second, the control design needs to be tolerant of the system complexity, of the system architecture (centralized vs. decentralized large scale system control) as well as robust to un-modeled dynamics and noise sources. Figure 1, top left, shows the kinematic parameters of a 1000 element cloud in orbit. The motion of the system is described with respect to a local vertical-local horizontal (LV-LH) orbiting reference frame (x,y,z)=F(sub ORF) of origin O(sub ORF) which rotates with mean motion omega and orbital semi-major axis R(sub 0). The orbital geometry at the initial time is defined in terms of its six orbital elements, and the orbital dynamics equation for point O(sub ORF) is propagated forward in time under the influence of the gravitational field of the primary and other external perturbations, described below. The origin of this frame coincides with the initial position of the center of mass of the system, and the coordinate axes are z along the local vertical, x toward the flight direction, and y in the orbit normal direction. The assumptions we used to model the dynamics are as follows: 1) The inertial frame is fixed at Earth's center. 2) The orbiting Frame ORF follows Keplerian orbit. 3) the cloud system dynamics is referred to ORF. 4) the attitude of each grain uses the principal body frame as body fixed frame. 5) the atmosphere is assumed to be rigidly rotating with the Earth. Regarding the grains forming the cloud: 1) each grain is modeled as a rigid body; 2) a simple attitude estimator provides attitude estimates, 3) a simple guidance logic commands the position and attitude of each grain, 4) a simple local feedback controller based on PD control of local states is used to stabilize the attitude of the vehicle. Regarding the cloud: 1) the cloud as a whole is modeled as an equivalent rigid body in orbit, and 2) an associated graph establishes agent connectivity and enables coupling between modes of motion at the micro and macro scales; 3) a simple guidance and estimation logic is modeled to estimate and command the attitude of this equivalent rigid body; 4) a cloud shape maintenance controller is based on the dynamics of a stable virtual truss in the orbiting frame. Regarding the environmental perturbations acting on the cloud: 1) a non-spherical gravity field including JO (Earth's spherical field) zonal component, J2 (Earth's oblateness) and J3 zonal components is implemented; 2) atmospheric drag is modeled with an exponential model; 3) solar pressure is modeled assuming the Sun is inertially fixed; and 4) the Earth's magnetic field is model using an equivalent dipole model. The equations of motion are written in a referential system with respect to the origin of the orbiting frame and the state is propagated forward in time using an incremental predictor-corrector scheme. A representative cloud with varying number of grains is simulated to identify the limitations in computation time as the number of grains grows. We derive a control law to track a desired surface in the ORF (equivalently to maintain a reference cloud shape) by defining an error from a desired surface shape, and designing a control law that is exponentially stable and reduces the tracking error to zero. Figure 1 (top right) shows a comparison of various requirements for simulation of single spacecraft vs. granular spacecraft, indicating the high degree of complexity that needs to be taken into consideration. The ORF components of control force required by one of the grains is, for this particular case, in the micro-Newton range. However, no attempt has been made yet to reconfigure (or re-orient) the cloud configuration internally, for which forces in the milli-Newton level are expected, depending on the time required to do the reconfiguration. Figure 1, bottom, shows the computation time as a function of the number of grains, indicating an order N(exp 1.43) scaling on a 8 Gb, 1067 MHz RAM MacOSX computer with a 3.06 GHz Intel Core 2 Duo processor. With this metric, the same simulation for a system of N=1000 grains would take 5.4 hours, and 146 hours (i.e., 6 days) for a system with N=10,000 grains. Therefore, efficient ways to simulate this complex system, where not only the time scales of natural system dynamics, but also the sampling times of the Guidance, Navigation, and Control are included, remain to be explored. Additional details on the cloud modeling, dynamics, and control will be described in the paper.

simulation↗
DOE OSTI2020

Sn-Based Alloys Synthesized in an Ionic Liquid at Room Temperature: Cu6Sn5 as a Case Study

Sn-based alloys are increasingly investigated owing to possible electronic/structural modulations of interest for electrocatalysis and energy storage applications. Here, we report on the use of a chemical system consisting of an ionic liquid (1-ethyl-3- methylimidazolium bis(trifluoromethanesulfonyl)imide: [EMIm(+)][TFSI-]) and Sn-based precursor Sn(TFSI)(2) both featuring similar anionic groups. This strategy increases the solubility of the cationic precursor in the IL and avoids the formation of side-products during the precipitation of Sn-nanoparticles formed upon reaction with a reducing agent (NaBH4). Using NMR relaxometry, we further established that these nanoparticles are stabilized by specific interactions with the cationic group of the IL. Targeting the composition Cu6Sn5, we further demonstrated that this approach can be used to prepare Sn-based alloys which could not be prepared using conventional chloride-based precursors.

cyclic voltammetry↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Reinforcement Learning‐Based Adaptation of Grid Following Inverter's Internal Controller to Networked Microgrids' Strengths

The varying topological configurations, generator commitments and dispatches, and dynamic load demand lead to changing system's strengths during the operations of networked microgrids. When the system's strengths significantly change, the fixed control gains at large devices may result in unsatisfactory system performance; this necessitates the tuning of the control gains at large devices to adapt to the changing system's strengths. In this paper, observer-based reinforcement learning (RL) is utilised to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing strengths of microgrids and networked microgrids. The RL agent in this framework augments an observer predicting system's strengths, from which the RL control policy will adjust accordingly to tune the PLL controller's gains towards the system's strengths. Also, to enhance the control performance, the recently introduced Barrier function-based RL framework is leveraged for the design of reward function to prevent the high frequency nadir. An operational 26 kV electric distribution system, which is modelled as networked microgrids, is used to illustrate the need and effectiveness of the proposed RL-tuned control.

frequency response↗