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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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Strategy support to determine the best approach to deploy solar technologies and commercially viable heat pump projects across their existing Rural Energy Partners solar project development support channel.
The datasets include the capacity, energy, and ancillary service values of a marginal kilowatt-hour (kWh) of generic, daily, shiftable building flexibility as a presumed market entrant in the 2030 U.S. power systems. The results should be interpreted along with the caveats listed in "Valuation of Building Flexibility: Grid Service Value for Initial Market Entrants in Projected 2030 United States Power Systems", which also documents the methods used for the study. The factors examined in the study include: grid scenario, region, original usage hour in the day (local time), and building flexibility parameters - efficiency, dissipation, and shifting window include max pre-shift and max post-shift. Filenames in the datasets: The filenames contain information on the grid scenario, and the building flexibility efficiency and dissipation. For example: MidCase_2030_efficiency1.25_dissipation0.05_value.csv contains all results under Mid RE 2020, efficiency = 1.25, and dissipation = 0.05. * MidCase = Mid RE Each .csv file contains: region: The location of the building flexibility, aligned with U.S. Energy Information Administration (EIA) National Energy Modeling System (NEMS) Electricity Market Module regions. max_pre_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift earlier. max_post_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift later. local_datetime: Original datetime of 1kWh of building energy consumption. local_orig_h: Original usage hour (1-24) of building energy consumption, ignores daylight saving. local_shift_to: The datetime to when building energy consumption shifts, if shifting happens. If no shifting occurs, this cell is left blank. energy: Net energy value of energy shifting. capacity: Net capacity value of energy shifting. shifting_value: Net energy plus capacity value of energy shifting. spin: Spin reserve value at the original datetime of consumption. flex: Flexible reserve value at the original datetime of consumption. reg: Regulation reserve value at the original datetime of consumption. total_profit: Assuming the building flexibility is capable of providing energy, capacity, and ancillary services, total profit is the maximum value of energy shifting value, spin reserve value, flexible reserve value, and regulation reserve value - one of the four choices at any given hour - because we do not allow its value to be double-counted.
The merits of the RPRV (remotely piloted research vehicle) concept are discussed, along with its historical background and development culmination in the 3/8-scale F-15. The use of RPRVs is shown to be especially attractive when testing must be done at low cost, or in quick response to demand, or when hazardous testing must assure the safety of proceeding to manned vehicles.
MSG is a third generation glovebox for Microgravity Science investigations: SpaceLab Glovebox (GBX); Middeck/MIR Gloveboxes (M/MGBX); and GBX and M/MGBX developed by Bradford Engineering (NL). Previous flights have demonstrated utility of glovebox facilities: Contained environment enables broader range of science experiments; Affords better control of video and photographic imaging (a prime data source); Provides better environmental control than cabin atmosphere; and Useful for contingency operations. MSG developed in response to demands for increased work volume, increased capabilities and additional resources. MSG is multi-user facility to support a wide range of small science and technology investigations: Fluid physics; Combustion science; Material science; Biotechnology (cell culturing and protein crystal growth); Space processing; Fundamental physics; and Technology demonstrations. Topics included in this viewgraph are: MSG capabilities; MSG hardware items; MSG, GSE, and OSE items; MSG development approach; and Science utilization.
Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.
We introduce a new capability of the Neil Gehrels Swift Observatory to provide event-level data from the Burst Alert Telescope (BAT) on demand in response to transients detected by other instruments. We show that the availability of these data can effectively increase the rate of detections and arcminute localizations of gamma-ray bursts (GRB) like GRB 170817 by >400%. We describe an autonomous spacecraft-commanding pipeline purpose built to enable this science; to our knowledge, this is the first fully autonomous extremely low-latency commanding of a space telescope for scientific purposes. This pipeline has been successfully run in its complete form since 2020 January, and has resulted in the recovery of BAT event data for >800 externally triggered events to date (gravitational waves, GWs; neutrinos; GRBs triggered by other facilities; fast radio bursts; and very high-energy detections), now running with a success rate of ∼90%. We exemplify the utility of this new capability by using the resultant data to (1) set the most sensitive upper limits on prompt 1 s duration short GRB-like emission within ±15 s around the unmodeled GW burst candidate S200114f, and (2) provide an arcminute localization for short GRB 200325A and other bursts. We also show that using data from GUANO to localize GRBs discovered by other instruments, we can increase the net rate of arcminute-localized GRBs by 10%–20% per year. Along with the scientific yield of more sensitive searches for subthreshold GRBs, the new capabilities designed for this project will serve as the foundation for further automation and rapid target of opportunity capabilities for the Swift mission, and have implications for the design of future rapid-response space telescopes.
Heat pump water heaters (HPWH) can achieve energy savings of 60-70% compared to conventional electric-resistance water heaters. However, even with a favorable simple payback period within the typical product lifetime, HPWHs make up only 1% of all electric water heaters sold in the residential sector. Market adoption is challenged, in part, by the lack of effective energy efficiency programs in the U.S. region with the greatest amount of residential electric water heaters. The recent integration of connected functionality into HPWHs offers the capability to shift load without undue negative impact on customers. This capability can provide value to utilities with peak load constraints, motivating them to promote HPWHs for the first time. This paper will present a methodology to forecast connected HPWH energy use and extrapolate load shifting potential for the Southeast U.S. region. The methodology leverages data from a robust connected HPWH field study conducted in the Pacific Northwest. A coefficient of performance (COP) relationship for HPWHs was developed using ambient and inlet water temperature data to forecast HPWH performance on a month-by-month basis. Using the forecasted COP, HPWH operating hours were calculated for meeting load, which serve as the proxy to extrapolate load shifting potential from the Pacific Northwest to the Southeast. As a use case, analytical results are presented for a specific utility. This methodology, in combination with market analysis, facilitates the development of customized energy savings and load shifting forecasts to understand the potential impacts of launching connected HPWH programs in specific utility service territories.
The Electrification Futures Study (EFS) is a multi-year study designed to analyze the impacts of widespread electrification in the U.S. energy system. In this webinar we present the detailed power systems operational analysis from the final report in the EFS series. For this analysis, we simulate multiple 2050 power systems for the conterminous United States to assess how variations in the magnitude and shape of electricity demand driven by electrification, and the extent of load participation to more-actively provide grid services, might impact the hourly operation, operational costs, and emissions of various power systems in 2050. The impacts of electrification and demand flexibility are overlayed across systems with significantly greater penetrations of variable renewable energy than today. Overall, we find that the high electrification scenarios envisioned in the EFS with significant VRE penetration (66% of annual national generation) can be operated to meet future increased levels of electrified demand. We also find that demand-side flexibility can enhance operational efficiency and reduce overall annual production costs by $5-$10 billion. The complementary relationship between flexible electric vehicle charging loads and solar generation is particularly pronounced, but in the absence of demand-side flexibility electrification can lead to increased wind and solar curtailment. By helping to reduce renewable curtailment, flexibility can also reduce power sector CO2 emissions. The analysis highlights the value of increased integration and coordination of demand- and supply-side resources in future electric system planning and operations—particularly under high electrification futures.
Rapid deployment of residential photovoltaic (PV) systems helps decarbonize our electricity supplies, but under certain circumstances, high-penetration PV may pose challenges to the electrical distribution grid. In a project funded by the U.S. Department of Energy's Solar Energy Technologies Office and Building Technologies Office, the National Renewable Energy Laboratory and its partners studied how flexible building loads and battery storage, when coordinated at home-level and community-level scales, can be used to address those challenges and enhance grid resilience. In this webinar, we will discuss the methodology, simulation and field pilot results, insights from partners, and lessons learned from the project.
This presentation discusses the impact of and recovery from Hurricanes Irma and Maria in Puerto Rico. The hurricanes hit two weeks apart in 2017, resulting in damage to all elements (generation, distribution, and transmission) of Puerto Rico's already fragile energy system.
Residences with smart thermostats can use advanced control strategies to manage their cooling/heating demand, but it is difficult to evaluate optimal control strategies for flexible heating, ventilation, and air conditioning (HVAC) systems in a traditional laboratory setting. The HVAC hardware-in-the-loop (HIL) system combines physical HVAC equipment and a physical thermostat with a simulated house to enable realistic operation of the hardware in any climate. This HIL platform allows researchers to evaluate advanced control strategies for homes with different construction or vintage types, as well as different climates and occupancy schedules. To demonstrate the capabilities of the HVAC HIL system, experimental results with a SEER 16, HSPF 9.5, 3 ton single-speed air source heat pump are validated against past field data collected from a heavily instrumented, unoccupied, retrofit house located in Sacramento, California. Three different cooling strategies are recreated in the HVAC HIL platform, including two different pre-cooling schedules that were designed to shift energy use away from the evening peak. The room temperatures, heat pump energy use, and run time show good agreement between the field data and HIL experimental results for three strategies.
In this work, we present the proposed two-stage reinforcement learning approach for training building controllers to help buildings to participate in DR events. The original conference paper for this research idea can be found at https://www.nrel.gov/docs/fy21osti/78000.pdf.
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Virtual power plants (VPPs) can provide grid services by aggregating and controlling many distributed energy resources. However, their design, operation, level of participation, and outcomes depend on the regulatory and market environments they operate in, DER owner preferences, grid service value, and technical capabilities (e.g., communications, forecasting, controls, user interfaces). Although VPPs are inherently complex, they could enable high shares of behind-the-meter solar plus storage and reduce the cost of electrifying, e.g., personal vehicles and space heating.
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Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.
Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.
Observed Madden-Julian Oscillation (MJO) events are examined with the aid of regional model simulations to understand the role of cloud radiative effects in the MJO development. The importance of this role is demonstrated by the absence of MJO in the model simulations that contain no cloud radiative effects. Comparisons of model simulations with and without the cloud radiative effects and observation help identify the major processes arising from those effects. Those processes develop essentially from heating in the upper-troposphere due to shortwave absorption within anvil clouds in the upper troposphere and the convergence of longwave radiation in the middle-upper troposphere, with a peak at 300-hPa, during deep convection. First, that heating adds extra buoyancy and accelerates the rising motion in the upper troposphere in deep convection. The vertical acceleration in the upper troposphere creates a vacuum effect and demands for more deep convection to develop. Second, in response to that demand and required by mass balance arises the large-scale horizontal and vertical mass, moisture, and energy convergence. It strengthens deep convection and, with the feedback from continuing cloud radiative effect, creates conditions that can perpetuate deep convection and MJO development. That perpetuation does not occur however because those processes arising from the cloud radiative heating in the upper troposphere stabilize the troposphere till it supports no further deep convection. Weakening deep convection reduces cloud radiative effects. The subsequent reduction of the vacuum effect in the upper troposphere diminishes deep convection completing an MJO cycle. These results advance our understanding of the development of the MJO in the radiative-convective system over warm waters in the tropics. They show that while the embryo of intraseasonal oscillation may exist in the system its growth/development is largely dependent on cloud radiative effects and feedbacks.