Search NASA⌕ Search

SEARCH · Search NASA

Results for “Distributed generation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Landsat Data Continuity Mission

The Landsat Data Continuity Mission (LDCM) is currently under development and is on schedule to launch the 8th satellite in the Landsat series in December of 2012. LDCM is a joint project between the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS). NASA is responsible for developing and launching the flight hardware and on-orbit commissioning and USGS is responsible for developing the ground system and operating the system onorbit after commissioning. Key components of the flight hardware are the Operational Land Imager (OLI), nearing completion by Ball Aerospace & Technologies Corp in Boulder, CO, the Thermal Infrared Sensor (TIRS), being built by NASA's Goddard Space Flight Center and the spacecraft, undergoing integration at Orbital Sciences Corp in Gilbert, Arizona. The launch vehicle will be an Atlas-5 with launch services provided by NASA's Kennedy Space Center. Key ground systems elements are the Mission Operations Element, being developed by the Hammers Corporation, and the Collection Activity Planning Element, Ground Network Element, and Data Processing and Archive System, being developed internally by the USGS Earth Resources Observations and Science (EROS) Center. The primary measurement goal of LDCM is to continue the global coverage of moderate spatial resolution imagery providing continuity with the existing Landsat record. The science goal for this imagery is to monitor land use and land cover, particularly as it relates to global climate change. Together the OLI and TIRS instruments on LDCM replace the ETM+ instrument on Landsat-7 with significant enhancements. The OLI is a pushbroom design instrument where the scanning mechanism of the ETM+ is effectively replaced by a long line of detectors. The OLI has 9 spectral bands with similar spatial resolution to ETM+: 7 of them similar to the reflective spectral bands on ETM+ and two new bands. The two new bands cover (1) the shorter wavelength blue part of the spectrum to help with coastal studies and aerosol analyses/atmospheric correction and (2) an atmospheric water absorption band, where the Earth surface is generally not visible, but Cirrus clouds are, to aid in cloud detection and screening. The radiometry of OLI benefits from improved SNR, dynamic range and quantization. OLI is undergoing system testing with a delivery scheduled for Spring 2011. The TIRS is also a pushbroom design and used QWIPS detectors that require cooling to 43K using a cryocooler. It.has two spectral bands, effectively splitting the ETM+ band 6 in half, that can be used as a split window to aid in atmospheric correction. It has nominally 100 m spatial resolution as opposed to the 60 m of Landsat-7 ETM+: TIRS has commenced integration and test, with a delivery to the spacecraft vendor scheduled for Winter 2011-2012. The Orbital spacecraft currently being integrated for LDCM will have improved capabilities for pointing over previous missions. These capabilities will allow the OLI and TIRS instruments to point off-nadir the equivalent of one WRS-2 path to increase the chances of coverage for high priority targets, particularly in the event of natural disasters. Also, the pointing capability will allow the calibration of the OLI using the sun (roughly weekly), the moon (monthly), stars (during commissioning) and the Earth (at 90 deg from normal orientation, a.k.a., side slither) quarterly. The solar calibration will be used for OLI absolute and relative calibration, the moon for trending the stability of the OLI response, the stars will be used for Line of Sight determination and the side slither will be an alternate OLI and relative gain determination methodology. The spacecraft is scheduled to begin integration with the OLI instrument in Summer 2011. The LDCM data processing and archive system (DPAS), located at USGS EROS, generates the products for distribution to users. Like Landsat-7 this includes an image assessment system for characrizing instrument performance and updating calibration parameters. Products will be generated that include the spectral bands from both instruments, terrain corrected and registered to the geoid. Also, like Landsat-7, data products will be distributed at no charge to the user. The current status and plans of the space and ground segments of the LDCM project will be presented along with performance predictions as available. More detailed information on the two instruments is intended to be presented in separate papers.

Markham, Brian↗
NASA NTRS2004

A 100 kW-Class Technology Demonstrator for Space Solar Power

A first step in the development of solar power from space is the flight demonstration of critical technologies. These fundamental technologies include efficient solar power collection and generation, power management and distribution, and thermal management. In addition, the integration and utilization of these technologies into a viable satellite bus could provide an energy-rich platform for a portfolio of payload experiments such as wireless power transmission (WPT). This paper presents the preliminary design of a concept for a 100 kW-class free-flying platform suitable for flight demonstration of technology experiments. Recent space solar power (SSP) studies by NASA have taken a stepping stones approach that lead to the gigawatt systems necessary to cost-effectively deliver power from space. These steps start with a 100 kW-class satellite, leading to a 500 kW and then a 1 MW-class platform. Later steps develop a 100 MW bus that could eventually lead to a 1-2 GW pilot plant for SSP. Our studies have shown that a modular approach is cost effective. Modular designs include individual laser-power-beaming satellites that fly in constellations or that are autonomously assembled into larger structures at geosynchronous orbit (GEO). Microwave power-beamed approaches are also modularized into large numbers of identical units of solar arrays, power converters, or supporting structures for arrays and microwave transmitting antennas. A cost-effective approach to launching these modular units is to use existing Earth-to-orbit (ETO) launch systems, in which the modules are dropped into low Earth orbit (LEO) and then the modules perform their own orbit transfer to GEO using expendable solar arrays to power solar electric thrusters. At GEO, the modules either rendezvous and are assembled robotically into larger platforms, or are deployed into constellations of identical laser power-beaming satellites. Since solar electric propulsion by the modules is cost-effective for both self-transport of the modules from LEO to GEO, and for on-orbit stationkeeping and repositioning capability during the satellite’s lifetime, this technology is also critical in technology development for SSP. The 100 kW-class technology demonstrator will utilize advanced solar power collection and generation technologies, power management and distribution, advanced thermal management, and solar electric propulsion. State-of-the-art solar concentrators, highly efficient multi-junction solar cells, integrated thermal management on the arrays, and innovative deployable structure design and packaging make the 100 kW satellite feasible for launch on one existing launch vehicle. Early SSP studies showed that a major percentage of the on-orbit mass for power-beaming satellites was from massive power converters at the solar arrays, at the bus, at the power transmitter, or at combinations of these locations. Higher voltage mays and power management and distribution (PMAD) systems reduce or eliminate the need for many of these massive power converters, and could enable direct-drive of high-voltage solar electric thrusters. Lightweight, highly efficient thermal management systems are a critical technology that must be developed and flown for SSP feasibility. Large amounts of power on satellites imply that large amounts of waste heat will need to be managed. In addition, several of the more innovative lightweight configurations proposed for SSP satellites take advantage of solar concentrators that are intractable without advanced thermal management technologies for the solar arrays. These thermal management systems include efficient interfaces with the WPT systems or other high-power technology experiments, lightweight deployable radiators that can be easily integrated into satellite buses, and efficient reliable thermal distribution systems that can pipe heat from the technology experiments to the radiators. In addition to demonstrating the integration and use of these mission-critical technologies, the 100 kw-class satellite will provide a large experiment deck for a portfolio of technology experiments. Current plans for this technology demonstrator allow 2000 kg of payload capability and up to 100 kW of power. The technology experiments could include one or more wireless power transmission demonstrations, either to the Earth’s surface or to a suitable space-based receiver. Technology experiments to quantify the on-orbit performance of critical technologies for SSP or space exploration are welcomed. In addition, the technology experiments provide an opportunity for international cooperation, to advance technology readiness levels of SSP technologies that require flight demonstration. This paper will present the preliminary design for a 100 kW solar-powered satellite and a variety of technology experiments that may be suitable for flight demonstration. In addition, a space-to-Earth-surface WPT experiment will be discussed.

Connie Carrington↗
DOE OSTI2024

Efficient six-dimensional phase space reconstructions from experimental measurements using generative machine learning

Next-generation accelerator concepts, which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within six-dimensional position-momentum spaces. However, the characterization of intricate features within six-dimensional beam distributions using current diagnostic techniques necessitates a substantial number of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena and as a feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed six-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that this approach can be used to resolve six-dimensional phase space distributions from scratch, using basic beam manipulations and as few as 20 two-dimensional measurements of the beam profile. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75× faster than previous methods.

43 PARTICLE ACCELERATORS↗
NASA NTRS1979

A quasi-static model of global atmospheric electricity. I - The lower atmosphere

A quasi-steady model of global lower atmospheric electricity is presented. The model considers thunderstorms as dipole electric generators that can be randomly distributed in various regions and that are the only source of atmospheric electricity and includes the effects of orography and electrical coupling along geomagnetic field lines in the ionosphere and magnetosphere. The model is used to calculate the global distribution of electric potential and current for model conductivities and assumed spatial distributions of thunderstorms. Results indicate that large positive electric potentials are generated over thunderstorms and penetrate to ionospheric heights and into the conjugate hemisphere along magnetic field lines. The perturbation of the calculated electric potential and current distributions during solar flares and subsequent Forbush decreases is discussed, and future measurements of atmospheric electrical parameters and modifications of the model which would improve the agreement between calculations and measurements are suggested.

Hays, P. B.↗
DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗
DOE OSTI2024

Distributed Wind Guidebook: Agricultural Producers and Rural Small Business Owners

Distributed wind energy technologies generate clean, carbon-free power close to the point of consumption (i.e., close to people and their energy needs). Distributed wind energy can help individuals, farms, businesses, and communities meet their unique goals, such as reducing impacts on climate change, decreasing electricity bills, boosting energy independence or autonomy from the electric grid, and enhancing grid reliability and resilience. This guidebook is designed to support you in (1) deciding if distributed wind energy is right for you, (2) installing a proven wind turbine technology by working with a reputable installer, and (3) setting up your project for success through its lifetime. The information in this guidebook is tailored to rural small businesses and agricultural producers who are interested in exploring distributed wind energy to meet their electricity, resilience, financial, and environmental goals. You will find gray boxes with key topics, definitions, and considerations throughout the guidebook. The report has been adapted from the Distributed Wind Guidebook, which offers a comprehensive view on core aspects of deploying distributed wind energy technologies. In comparison, this edition of the guidebook is designed to offer a more succinct and tailored guidebook for rural small businesses and agricultural producers. For additional detail on any topic presented within this edition, readers are advised to reference the original version of the Distributed Wind Guidebook.

17 WIND ENERGY↗
NASA NTRS2018

Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services

This paper presents the development of ICAROUS-2 (Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services), the second generation of a software architecture that integrates several algorithms as distributed onboard services to enable robust autonomous UAS applications. In particular, the ICAROUS architecture defines a framework to perform detect and avoid, geofencing, path monitoring, path planning, and autonomous decision making to ensure safety and mission progress. Most of the core algorithms implemented in ICAROUS are formally verified using an interactive theorem prover. These algorithms are composed together using a plan execution engine, whose operational semantics is formally specified. A description of the integrated architecture, services currently available, and flight test results highlighting the capability of ICAROUS are presented.

Balachandran, Swee↗
NASA NTRS2018

Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services

This paper presents the development of ICAROUS-2 (Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services), the second generation of a software architecture that integrates several algorithms as distributed onboard services to enable robust autonomous UAS applications. In particular, the ICAROUS architecture defines a framework to perform detect and avoid, geofencing, path monitoring, path planning, and autonomous decision making to ensure safety and mission progress. Most of the core algorithms implemented in ICAROUS are formally verified using an interactive theorem prover. These algorithms are composed together using a plan execution engine, whose operational semantics is formally specified. A description of the integrated architecture, services currently available, and flight test results highlighting the capability of ICAROUS are presented.

Balachandran, Swee↗
NASA NTRS1993

Electrotastic wave excitation in planetary magnetospheres: Application to Neptune

Voyager 2 observations of electrostatic electron and ion harmonic waves in Neptune's magnetosphere are addressed. A model of electron Bernstein modes generated by a loss cone distribution of superthermal electrons is scaled to Neptune parameters and a comparison of theory with the observed electron flux shows good agreement. A model of proton Bernstein modes generated by a ring distribution of Tritonogenic nitrogen ions is also investigated and satisfactory agreement with the data are obtained compatible with known properties of the magnetosphere. The success of the model in accounting for electrostatic emission observed by Voyager over a wide range of sampled parameters recommends its general applicability to planetary magnetospheres.

Barbosa, D. D.↗
DOE OSTI2024

Distributed Wind Guidebook [Slides]

Distributed wind energy technologies generate clean, carbon-free power close to the point of electrical consumption (i.e., close to people and their energy needs). Distributed wind energy can help individuals and communities meet their unique goals, such as reducing impacts on climate change, decreasing electricity bills, boosting energy independence or autonomy from the electric grid, and enhancing grid reliability and resiliency. This guidebook is designed to support individuals and communities in deploying distributed wind energy technologies by providing fundamental information needed for success. Each section is framed around a key question in the journey to deployment and offers resources to help you answer it.

17 WIND ENERGY↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
NASA NTRS1976

The electron Boltzmann equation in a plasma generated by fission fragments

A Boltzmann equation formulation is presented for the determination of the electron distribution function in a plasma generated by fission fragments. The formulation takes into consideration ambipolar diffusion, elastic and inelastic collisions, recombination and ionization, and allows for the fact that the primary electrons are not monoenergetic. Calculations for He in a tube coated with fissionable material show that, over a wide pressure and neutron flux range, the distribution function is non-Maxwellian, but the electrons are essentially thermal. Moreover, about a third of the energy of the primary electrons is transferred into the inelastic levels of He. This fraction of energy transfer is almost independent of pressure and neutron flux but increases sharply in the presence of a sustainer electric field.

Hassan, H. A.↗
DOE OSTI2025

Regional Inertia Estimation Using Actual Event Measurements: Florida Case

As inverter-based resources’ integration in power grids increases, their uneven distribution across the electrical network leads to the formation of local regions that are weakly coupled to the larger interconnection. This signifies the necessity of regional frequency dynamics investigation and analyzing various inertia metrics. This paper presents a practical methodology for estimating regional rate-of-change of frequency (RoCoF) using actual event recordings, which is then used to evaluate regional inertia. Florida (FL) is selected as the region of interest due to its distinct regional frequency dynamics and the rising levels of solar generation. We identify and analyze confirmed events from 2017 to 2024 that occurred in FL. The results indicate that FL contributes about 14% to the total inertia of the US Eastern Interconnection, which approximately matches its share of generation capacity. Results also highlight seasonal fluctuations in energy generation, which play a significant role in influencing inertia and thus the RoCoF levels. This emphasizes the importance of estimating regional inertia to enhance grid operations for a future that focuses on distributed generation.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2025

Integrated Distribution Planning

The contemporary distribution planning landscape is comprised of an increasing number of factors that require integration into the engineering of the modern electric grid. Expectations for electric utilities to accommodate heightened awareness of stakeholders' interest in things like decarbonization, resilience and equity are growing. As these interests are formed into objectives, many jurisdictions will experience increasing levels of load modifying technologies like DER, building and industrial electrification and electric vehicles which prove not only to challenge the capabilities of the grid; but the processes by which planning for it is traditionally done. Other related factors that strain the conventional distribution planning mold are the swelling amount and sources of data associated with these technologies and the need it creates for improved capabilities in the processes and tools that manage it. As the complexity of the distribution system expands, so will the distribution system's effects on the transmission and generation systems that it is a part of. Forecasting distribution system load and DER are examples of areas where this complexity will manifest, and harmonizing distribution forecasting with transmission and generation forecasting requires higher amounts of intentionality as these typically separate processes become a solitary one. Of course, core activities do not cease as a utility begins to integrate these other factors, and in this webinar we explore specifics of how distribution planning can be expected to evolve as progress towards Integrated Distribution System Planning is made.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
DOE OSTI2025

An efficient surrogate model of secondary electron formation and evolution

This work extends the adjoint-deep learning framework for runaway electron (RE) evolution, developed by McDevitt et al. [Phys. Plasmas 32, 042503 (2025)], to account for large-angle collisions. By incorporating large-angle collisions, the framework allows the avalanche of REs to be captured, an essential component of RE dynamics. This extension is accomplished by using a Rosenbluth–Putvinski approximation to estimate the distribution of secondary electrons generated by large-angle collisions. By evolving both the primary and multiple generations of secondary electrons, the present formulation can capture both the detailed temporal evolution of a RE population beginning from an arbitrary initial momentum space distribution, along with providing approximations to the saturated growth and decay rates of the RE population. Predictions of the adjoint-deep learning framework are verified against a traditional RE solver, with good agreement present across a broad range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗