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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1977

A survey on initial results of the Helios plasma experiment

The reported results are mainly based on data obtained during the first part of the Helios-1 mission. The sun was very quiet during the considered period. The data are, therefore, representative for comparatively stationary conditions in the solar corona. Data obtained concerning the protons were evaluated by means of a special computer routine. The significance of the data is discussed, taking into account the status of the corona and the interplanetary plasma during the considered part of the Helios-1 mission, fast stream structures in the region between 0.3 and 1 AU, radial gradients of fast and slow solar wind, and the separation of proton double streams and alpha-particles. Attention is also given to the 'strahl' in the electron distribution, differences between fast streams and slow plasma on the basis of the observed electron distributions, and radial gradients in the case of solar wind electron parameters.

Rosenbauer, H.↗
DOE OSTI2017

Active Longitude and Coronal Mass Ejection Occurrences

The spatial inhomogeneity of the distribution of coronal mass ejection (CME) occurrences in the solar atmosphere could provide a tool to estimate the longitudinal position of the most probable CME-capable active regions in the Sun. The anomaly in the longitudinal distribution of active regions themselves is often referred to as active longitude (AL). In order to reveal the connection between the AL and CME spatial occurrences, here we investigate the morphological properties of active regions. The first morphological property studied is the separateness parameter, which is able to characterize the probability of the occurrence of an energetic event, such as a solar flare or CME. The second morphological property is the sunspot tilt angle. The tilt angle of sunspot groups allows us to estimate the helicity of active regions. The increased helicity leads to a more complex buildup of the magnetic structure and also can cause CME eruption. We found that the most complex active regions appear near the AL and that the AL itself is associated with the most tilted active regions. Therefore, the number of CME occurrences is higher within the AL. The origin of the fast CMEs is also found to be associated with this region. We concluded that the source of the most probably CME-capable active regions is at the AL. By applying this method, we can potentially forecast a flare and/or CME source several Carrington rotations in advance. This finding also provides new information for solar dynamo modeling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Electric Grid, Distributed Generation, and Grid Interconnection

This fact sheet is part of the Community Planning for Solar toolkit designed to help Massachusetts municipalities and others proactively plan for solar development in their communities. This fact sheet will walk you through the electricity system, and help you understand how the grid is changing as distributed generation (DG) electricity sources become more common.

14 SOLAR ENERGY↗
NASA NTRS1988

Excitations of low-frequency hydromagnetic waves by freshly created ions in the solar wind

Low-frequency hydromagnetic waves excited by newborn ions in the solar wind plasma are studied. The freshly created ions appear in the solar wind frame with a ring beam distribution. Both Alfven and fast magnetosonic waves are made unstable by the presence of the newborn ions. The dependence of the growth rate of both waves on the newborn ion density, the angle between the interplanetary magnetic field (IMF) and solar wind flow, and the angle of wave propagation relative to the IMF is investigated. Analytic approximations for the growth rates are presented, and numerical solutions of the dispersion equation are shown. The approximations are quite close to the numerically determined growth rates. It is found that the waves grow preferentially in the direction parallel to the IMF, and that the growth rates increase with both newborn ion density and the angle between the IMF and the solar wind flow.

Price, C. P.↗
NASA NTRS1998

Power Management and Distribution System Developed for Thermionic Power Converters

A spacecraft solar, bimodal system combines propulsion and power generation into a single integrated system. An Integrated Solar Upper Stage (ISUS) provides orbital transfer capabilities, power generation for payloads, and onboard propulsion to the spacecraft. A key benefit of a bimodal system is a greater payload-to-spacecraft mass ratio resulting in lower launch vehicle requirements. Scaling down to smaller launch vehicles increases space access by reducing overall mission cost. NASA has joined efforts with the Air Force Phillips Laboratory to develop enabling technologies for such a system. The NASA/Air Force bimodal concept uses solar concentrators to focus energy into an integrated power plant. This power plant consists of a graphite core that stores thermal energy within a cavity. An array of thermionic converters encircles the graphite cavity and provides electrical energy conversion functions. During the power generation phase of the bimodal system, the thermionic converters are exposed to the heated cavity and convert the thermal energy to electricity. Near-term efforts of the ISUS bimodal program are focused on a ground demonstration of key technologies in order to proceed to a full space flight test. Thermionic power generation is one key technology of the bimodal concept. Thermionic power converters impose unique operating requirements upon a power management and distribution (PMAD) system design. Single thermionic converters supply large currents at very low voltages. Operating voltages can vary over a range of up to 3 to 1 as a function of operating temperature. Most spacecraft loads require regulated 28-volts direct-current (Vdc) power. A combination of series-connected converters and powerprocessing boosters is required to deliver power to the spacecraft's payloads at this level.

Baez, Anastacio N.↗
NASA NTRS1995

Solar wind ion composition and charge states

The solar wind, a highly tenuous plasma streaming from the Sun into interplanetary space at supersonic speed, is roughly composed of 95% hydrogen and 5% helium by number. All other, heavy elements contribute less than 0.1% by number and thus are truly test particles Nevertheless, these particles provide valuable information not present in the main components. We first discuss the importance of the heavy ions as tracers for processes in the solar atmosphere. Specifically, their relative abundances are found to be different in the solar wind as compared to the photosphere. This fractionation, which is best organized as a function of the first ionization time (FIT) of the elements under solar surface conditions, provides information on the structure of the chromosphere. where it is imparted on the partially ionized material by an atom-ion separation mechanism. Moreover, the charge states of the heavy ions can be used to infer the coronal temperature, since they are frozen-in near the altitude where the expansion time scale overcomes the ionization/recombination time scales. Next, we review the published values of ion abundances in the solar wind, concentrating on the recent results of the SWICS instrument on Ulysses. About 8 elements and more than 20 charge states can be routinely analyzed by this sensor. There is clear evidence that both the composition and the charge state distribution is significantly different in the fast solar wind from the south polar coronal hole, traversed by Ulysses in 1993/94, as compared to the solar wind normally encountered near the ecliptic plane. The fractionation between low- and high-FIT elements is reduced, and the charge states indicate a lower, more uniform coronal temperature in the hole. Finally, we discuss these results in the framework of existing theoretical models of the chromosphere and corona, attempting to identify differences between the low- and high-latitude regions of the solar atmosphere.

vonSteiger, R.↗
DOE OSTI2017

ANALYZING METEOROID FLIGHTS USING PARTICLE FILTERS

Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modeling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used, and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball, observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modeling. The final mass is determined to be 2.16±1.33 kg with a final velocity of 6030±216 m s{sup −1}, similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4 μm is 57% of that for the particles with the diameter of 202.8 μm. The temperatures of these two particle components increase from the initial ambient values by factors of 2 and 1.2, respectively, during the simulation time. In conclusion, the model developed allows for increased fidelity of particle–gas flow simulations with significant radiative effects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Probabilistic analysis of masked loads with aggregated photovoltaic production

In this paper we present a probabilistic analysis framework to estimate behind-the-meter photovoltaic generation in real time. We develop a forward model consisting of a spatiotemporal stochastic process that represents the photovoltaic generation and a stochastic differential equation with jumps that represents the demand. Here, we employ this model to disaggregate the behind-the-meter photovoltaic generation using net load and irradiance measurements.

14 SOLAR ENERGY↗
DOE OSTI2022

Turn Your Half-Cut Cells for a Stronger Module

Here, we employ an existing model of crystalline silicon photovoltaic module cell fracture to explore the effect of cell size and orientation on their probability of fracture under module uniform loading. In addition, we also apply continuum damage mechanics to modify this model to consider the effect of cell fracture on the probability of subsequent cell fracture. We elucidate that cell fracture increases the probability of subsequent cell fracture and that rectangular modules containing half-cells aligned with the module's long orientation are much more robust against cell fracture than full-cell and half-cell short orientation modules.

14 SOLAR ENERGY↗
DOE OSTI2018

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern is determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.

14 SOLAR ENERGY↗
DOE OSTI2018

Coordinative Voltage Control Strategy with Multiple Resources for Distribution Systems of High PV Penetration

This paper presents an optimal voltage control methodology with coordination among different voltage-regulating resources, including controllable loads, distributed energy resources such as energy storage and photovoltaics (PV), and utility voltage-regulating devices such as voltage regulators and capacitors. The proposed methodology could effectively tackle the overvoltage and voltage regulation device distortion problems brought by high penetrations of PV to improve grid operation reliability. A voltage-load sensitivity matrix and voltage-regulator sensitivity matrix are used to deploy the resources along the feeder to achieve the control objectives. Mixed-integer nonlinear programming is used to solve the formulated optimization control problem. The methodology has been tested on the IEEE 123-feeder test system, and the results demonstrate that the proposed approach could improve the voltage profile of distribution system operation.

14 SOLAR ENERGY↗
DOE OSTI2022

PV Hosting Capacity Estimation: Experiences with Scalable Framework

Hosting capacity is an indication of the amount of solar photovoltaics (PV) that can be hosted in a distribution system without additional changes to infrastructure or oper-ations. This paper presents a framework for estimating the PV hosting capacity at scale. First, we analyze computational, modeling and other key challenges of performing relevant, large-scale simulations, provided along with the experiences and lessons learned. Then, we develop two open-source Python-based software tools to conduct repeatable distribution analyses: the Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations and the Job Automation and Deployment Engine (JADE) for parallelizing jobs on high-performance computing clusters. A case study of hosting capacity estimation for the SMART-DS San Francisco (SFO) 2000+ synthetic feeders, is used to demonstrate the capability of the developed DISCO+JADE framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, which can help them better plan for the overall upgrade costs to integrate more PV in the future. The experiences are shared to aid the tool users and researchers to conduct relevant studies and research.

distributed energy resources↗
DOE OSTI2021

Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to addresses this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4) numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.

batteries↗
DOE OSTI2018

Metal Hydride Compression

Conventional hydrogen compressors often contribute over half of the cost of hydrogen stations, have poor reliability, and have insufficient flow rates for a mature fuel cell vehicle market. Fatigue associated with their moving parts including cracking of diaphragms and failure of seals leads to failure in conventional compressors, which is exacerbated by the repeated starts and stops expected at fueling stations. Furthermore, the conventional lubrication of these compressors with oil is generally unacceptable at fueling stations due to potential fuel contamination. MH technology offers a very good alternative to both conventional (mechanical) and newly developed (electrochemical, ionic liquid pistons) methods of hydrogen compression. Advantages of MH compression include simplicity in design and operation, absence of moving parts, compactness, safety and reliability, and the possibility to utilize waste industrial heat to power the compressor. Beyond conventional H 2 supply via pipelines or tanker trucks, another attractive scenario is the on-site generation and delivery of pure H 2 at pressure (> 875 bar) for refueling vehicles at electrolysis, wind, or solar H 2 production facilities in distributed locations that are too remote or widely distributed for cost effective bulk transport. MH hydrogen compression utilizes a reversible heat-driven interaction of a hydride-forming metal alloy with hydrogen gas to form the MH phase and is a promising process for hydrogen energy applications. To deliver hydrogen continuously, each stage of the compressor must consist of multiple MH beds with synchronized hydrogenation & dehydrogenation cycles. Multistage pressurization allows achievement of greater compression ratios using reduced temperature swings compared to single stage compressors. The objectives of this project are to investigate and demonstrate on a laboratory scale a twostage MH hydrogen gas compressor with a feed pressure of >100 bar and a delivery pressure > 875 bar of high purity H 2 gas using the scheme shown in Figure 1. Progress to date includes the selection of metal hydrides for each compressor stage based on experimental characterization of their thermodynamics, kinetics, and hydrogen capacities for optimal performance with respect to energy requirements and efficiency. Additionally, final bed designs have been completed based on trade studies and all components have been ordered. The prototype two-stage compressor will be fabricated, assembled, and experimentally evaluated in FY19.

08 HYDROGEN↗
DOE OSTI2022

Evaluation and Suitability of Using SERI QC Software for Estimating Measurement Uncertainty

SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report (the first of six in the Data Quality and Uncertainty Integration project) reviews and evaluates the software to determine its suitability for determining the estimated expanded measurement uncertainty of solar measurement data by including operational factors. Using redundancy incorporated in three-component (global, direct, diffuse) solar measurements, SERI QC can quantify the standard uncertainty estimates for estimating uncertainty. With minor modifications, SERI QC will provide an operational uncertainty to be used in conjunction with the National Renewable Energy Laboratory method for instrument uncertainty to provide an integrated estimate of solar measurement data sets. These modifications will capitalize on SERI QC's evaluation of data quality, including data input validation and a variety of built-in solar routines, and will further develop the utility of the software.

14 SOLAR ENERGY↗