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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Prototype Software for Future Spaceflight Tested at Mars Desert Research Station

NASA scientists in MDRS Crew 49 (April 23-May 7, 2006) field tested and significantly extended a prototype monitoring and advising system that integrates power system telemetry with a voice commanding interface. A distributed, wireless network of functionally specialized agents interacted with the crew to provide alerts (e.g., impending shut-down of inverter due to low battery voltage), access md interpret historical data, and display troubleshooting procedures. In practical application during two weeks, the system generated speech over loudspeakers and headsets lo alert the crew about the need to investigate power system problems. The prototype system adapts the Brahms/Mobile Agents toolkit to receive data from the OneMeter (Brand Electronics) electric metering system deployed by Crew 47. A computer on the upper deck was connected to loudspeakers, four others were paired with wireless (Bluetooth) headsets that enabled crew members to interact with their personal agents from anywhere in the hab. Voice commands and inquiries included: 1. What is the {battery | generator} {volts | amps | volts and amps}? 2. What is the status of the {generator | inverter | battery | solar panel}? 3. What is the hab{itat} {power usage | volts | voltage | amps | volts and amps}? 4. What was the average hab{itat} {amps | volts | voltage} since <#> {AM | PM)? 5. When did the {generator | batteries} change status? 6. Tell {me I | everyone} when{ ever} the generator goes offline. 7. Tell {me | | everyone} when the hab{itat} {amps | volts | voltage} {exceeds | drops brelow} <#>. 8. {Send | Take | Record} {a} voice note {(for | to} } {at }. This research demonstrates the principles of design in the context of use, investigating requirements through experimental use of prototype systems in an analog setting, and use of MDRS as a research facility for designing and implementing new systems.

Clancey, William J.↗
DOE OSTI2021

Sustainable Campus with PEV and Microgrid

Market penetration of electric vehicles (EVs) is gaining momentum, as is the move towards increasingly distributed, clean and renewable electricity sources. EV charging shifts a significant portion of transportation energy use onto building electricity meters. Hence, integration strategies for energy-efficiency in buildings and transport sectors are of increasing importance. This paper focuses on a portion of that integration: the analysis of an optimal interaction of EVs with a building-serving transformer, and coupling it to a microgrid that includes PV, a fuel cell and a natural gas micro-turbine. The test-case is the Nanyang Technological University (NTU), Singapore campus. The system under study is the Laboratory of Clean Energy Research (LaCER) Lab that houses the award winning Microgrid Energy Management System (MG-EMS) project. The paper analyses three different case scenarios to estimate the number of EVs that can be supported by the building transformer serving LaCER. An approximation of the actual load data collected for the building into different time intervals is performed for a transformer loss of life (LOL) calculation. The additional EV loads that can be supported by the transformer with and without the microgrid are analyzed. The numbers of possible EVs that can be charged at any given time under the three scenarios are also determined. The possibility of using EV fleet at NTU campus to achieve demand response capability and intermittent PV output leveling through vehicle to grid (V2G) technology and building energy management systems is also explored.

Singh, Reshma↗
DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Distribution Grid Impact Study in Highland Park, Michigan: Understanding Rooftop Solar, Behind-the-Meter Energy Storage, Electric Vehicle Charging, and Building Electrification [Slides]

Through the Communities LEAP (Local Energy Action Program) Pilot, the National Renewable Energy Laboratory (NREL) engaged the Highland Park Stakeholder Coalition to scope four technical assistance work areas to address their energy needs and goals. This slide deck addresses the highlighted tasks under work area "B" related to policy analysis, due diligence, and case studies for removing barriers and providing best practices for local clean energy development. Highland Park community members face frequent, long-duration power interruptions due largely to the aging distribution system serving the area and the legacy design standards used in its construction. While degrading physical infrastructure such as poles, crossarms, and transformers can result in this substandard reliability, another notable characteristic of this legacy system is the lower, 4.8 kV, voltage class. This is a legacy construction standard which many utilities, DTE included, are phasing out in favor of higher, 15 or 25 kV, voltage classes instead. The existing 4.8 kV distribution system serving Highland Park may limit significant adoptions of clean-energy technologies like high percentages of building electrification or electric vehicle adoption. The following analysis seeks to quantify these limitations under a variety of clean-energy technology adoption scenarios. It compares the overall system risks of the present system to those of a hypothetical, upgraded 13.2 kV system, using NREL-developed risk metrics and offers upgrade cost considerations. The legacy 4.8 kV voltage class serving Highland park is not, as we have modeled it, a major limitation to the widespread adoption of cost-optimal rooftop solar and/or behind-the-meter energy storage. Within our modeling framework, these technologies namely impact secondary, low-voltage assets, which may be remedied without the need for a system-wide upgrade to a 13.2 kV voltage class. Our model of the current 4.8kV system indicates it is not capable of supporting community-wide electrification efforts. Widespread building electrification, and the resulting large increase in wintertime load, dramatically increases the prevalence of voltage violations and thermal overloading on the current 4.8 kV distribution system. These impacts illustrate the need for system-wide upgrades to a 13.2 kV voltage class to accommodate these technologies. Low to Moderate DER adoption does not adversely impact the grid but does improve undervoltage and asset overloading issues. However, these benefits are insufficient to defer grid upgrades. Higher DER penetration is shown to increase overvoltage and asset overloading in future electrification scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Smart Meter Pinging and Reading Through AMI Two-Way Communication Networks to Monitor Grid Edge Devices and DERs

Today’s power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with two-way communications presents great potential. At present, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This article discusses the AMI two-way communication and its recent industrial practice in the U.S., especially for applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then develops the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Co-Investigator Proposal for Enstrophy - - Filamentation of Auroral Currents

Cornell University provided three instruments for the Enstrophy experiment: an electric field meter, a plasma wave receiver, and a magnetometer for measuring FAC. The electric field meter consisted of a 6 m Weitzmann boom system with analog signal processing and 12 bit ADC, which yielded one electric field component instantaneously and a two dimensional electric field every half spin. The plasma wave receiver used the same sensing system with the addition of pre-amplifiers in the spheres to sense plasma waves up to and including the electron Langmuir frequency. Signal processing employed a variety of continuous and snap shot techniques depending on the frequency range and band width. The science magnetometer provided by Cornell University was a Billingsly design fluxgate previously used on spacecraft missions but without radiation hardening. The magnetometer was mounted on a one meter, stiff aluminum "flop-down" boom. The Enstrophy payload was launched on february 11, 1999. Because of a design flaw in the event timers, the magnetometer boom was deployed before the payload despun. As a result the magnetometer separated mechanically from the boom but maintained electrical connection. This was confirmed by the calculation of the scalar magnetic field from all three vector components of the magnetic field. However, the individual vector values had no scientific value. The electric field and plasma wave instrumentation worked as designed. The data from these instruments was provided to the University of New Hampshire and to the Principal Investigator, as proposed.

Kintner, Paul M.↗
DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2020

SAM™ (System Advisor Model™) [SWR-16-02, SWR-10-13]

See the SAM™ website to build a desktop version of the National Laboratory of the Rockies' (NLR's) System Advisor Model™ (SAM). https://sam.nlr.gov/ The System Advisor Model™ (SAM™) is a free techno-economic software model that facilitates decision-making for people in the renewable energy industry: -Project managers and engineers -Policy analysts -Technology developers -Researchers SAM can model many types of renewable energy systems: -Photovoltaic systems, from small residential rooftop to large utility-scale systems -Battery storage with Lithium ion, lead acid, or flow batteries for front-of-meter or behind-the-meter applications -Concentrating Solar Power systems for electric power generation, including parabolic trough, power tower, and linear Fresnel -Industrial process heat from parabolic trough and linear Fresnel systems -Wind power, from individual turbines to large wind farms -Marine energy wave and tidal systems -Solar water heating -Fuel cells -Geothermal power generation -Biomass combustion for power generation -High concentration photovoltaic systems SAM's financial models are for the following types of projects: -Residential and commercial projects where the renewable energy system is on the customer side of the electric utility meter (behind the meter), and power from the system is used to reduce the customer's electricity bill. -Power purchase agreement (PPA) projects where the system is connected to the grid at an interconnection point, and the project earns revenue through power sales. The project may be owned and operated by a single owner or by a partnership involving a flip or leaseback arrangement. -Third party ownership where the system is installed on the customer's (host) property and owned by a separate entity (developer), and the host is compensated for power generated by the system through either a PPA or lease agreement. For a more detailed description of SAM, see Blair et al. (2018), System Advisor Model (SAM) General Description (Version 2017.9.5), NREL/TP-6A20-70414. https://www.nrel.gov/docs/fy18osti/70414.pdf

Ryberg, David↗
DOE OSTI2023

Assessment and Commissioning of Electrical Substation Grid Testbed with a Real-Time Simulator and Protective Relays/Power Meters in the Loop

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches. Substation engineers commission these IEDs to assess the appropriate measurements for monitoring, control, power system protection, and communication applications. Like real electrical utility substations, complex electrical substation grid testbeds (ESGTs) need to be assessed for measuring current and voltage signals in monitoring, power system protection, control (synchro check), and communication applications that are limited by small measurement percentage errors. In the process of setting an ESGT with real-time simulators and IEDs in the loop, protective relays, power meters, and communication devices must be commissioned before running experiments. In this study, an ESGT with IEDs and distributed ledger technology was developed. The ESGT with a real-time simulator and IEDs in the loop was satisfactorily assessed and commissioned. The commissioning and problem-solving tasks of the testbed are described to define a method with flowcharts to assess possible trouble-shooting in ESGTs. This method was based on comparing the simulations versus IED measurements for the phase current and voltage magnitudes, three-phase phasor diagrams, breaker states, protective relay times with selectivity coordination at electrical faults, communication data points, and time-stamp sources.

Piesciorovsky, Emilio↗
NASA NTRS1993

Aircraft measurement of electric field - Self-calibration

Aircraft measurement of electric fields is difficult as the electrically conducting surface of the aircraft distorts the electric field. Calibration requires determining the relations between the undistorted electric field in the absence of the vehicle and the signals from electric field meters that sense the local distorted fields in their immediate vicinity. This paper describes a generalization of a calibration method which uses pitch and roll maneuvers. The technique determines both the calibration coefficients and the direction of the electric vector. The calibration of individual electric field meters and the elimination of the aircraft's self-charge are described. Linear combinations of field mill signals are examined and absolute calibration and error analysis are discussed. The calibration method was applied to data obtained during a flight near thunderstorms.

Winn, W. P.↗
NASA NTRS1991

Airborne observations of electric fields around growing and decaying cumulus clouds

Airborne electric field data were gathered in an atmospheric electrification study near Cape Canaveral, FL. A Learjet 36A was instrumented with eight electric field meters (mills) and five different particle probes. The local electric field enhancements at each field mill site were determined under lab conditions and verified using in-flight data. The overdetermined system of eight equations (one for each field mill) was solved using a weighted least squares algorithm to compute the magnitude and direction of the ambient electric field. The signal processing system allowed the measured data to be expressed in terms of earth coordinates, regardless of the attitude of the aircraft. Thus, it was possible to take maximum advantage of the Learjet's speed and maneuverability in studying the electric field structure in the vicinity of the clouds. Data gathered while circling just outside the boundary of a growing cumulus cloud show a nonsymmetric pattern of electric field strength. Field intensity grew rapidly over a period of less than 10 minutes. The observed direction of the ambient electric field vector can be explained by an ascending motion of the charge centers of a classic tripole model of a thunderstorm.

Giori, K. L.↗
NASA NTRS1991

Electrical emissions of airplanes flying in electrified clouds and their effect on airplane measurements of cloud electric fields

The signature of the cloud electric field components deduced from measurements made with electric field meters carried on airplane penetrating electrified clouds is often complex, especially when the airplane experiences strong electrical charging. However, simple electric field variations were obtained for penetrations involving severe charging of the airplane on flights over KSC on 19 Aug. 1989. During these episodes of severe electrical charging, the airplane typically became negatively charged as it approached a region of negative cloud charge and then became positively charged as it receded from the cloud charge. The charge acquired by the airplane within the cloud was so large that the electric fields at the faces of the mills mounted on the fuselage were as large as for an ambient electric field of 60 to 80 kV/m. However, the deduced electric field components perpendicular to the direction of flight, to which these mills respond, were only about 5 to 10 kV/m. The variation of the deduced ambient field component in the direction of flight was antisymmetric about the charge region for these penetrations. Analysis of these results suggest that intense plumes of electric charge were emitted from the airplane and that the electric field associated with these plumes overcame the electric field due to the cloud charge at the tail-mounted field mill. As a consequence, the deduced component of the ambient electric field in the direction of flight was severely distorted. These findings emphasize the need for careful evaluation of airplane electric field measurements and of the need for further work on techniques for improving the measurements.

Jones, James J.↗
DOE OSTI2020

Virtual Metering for Monitoring Building Energy Consumption

The United States Department of Energy (DOE) has standard metering requirements of commercial buildings for optimizing energy performance. The guiding principles are to continuously track and optimize energy performance and install building-level meters for electricity, natural gas, and steam. Some buildings at Los Alamos National Laboratory (LANL) have physical submeters monitoring their energy consumption, but these meters have proven to be unreliable. And, in most cases, replacing them has proven to be a slow process. Installing new submeters also requires a temporary lockout of the circuit on which they are being installed. Many buildings at LANL contain laboratories with ongoing experiments or data centers, which makes an equipment power outage nearly impossible to plan. This inability to plan power outages results in long-term submeter failures. Although most submeters are eventually replaced, failures lead to missing consumption data for some unpredictable, extended time. A building automation system (BAS) is a system that provides control and monitoring on a building to maintain the operational performance of the building and occupancy comfort. Many buildings at LANL currently have a BAS, and all new renovations and installs will include installing a BAS if one does not already exist. The intended purpose for a BAS is primarily to monitor the health and efficiency of a building; however, it is also possible to calculate equipment power and energy consumption using BAS information. This project aims to use virtual meters to monitor building energy consumption as a cost-effective and minimally labor-intensive alternative to installing physical submeters. The fault detection and diagnostics tool, SkySpark, provides a centralized database for all the data from the various BAS that are active at LANL. This data includes the information that is needed to create virtual meters for heating, ventilating, and air conditioning (HVAC) systems in most buildings, including heating and cooling loads. 9 This report begins with a detailed summary of the project, including the reasoning, procedure, and results. The specific processes of creating the various virtual meters are then identified. Then the limitations are discussed. And, lastly, the results and future potential are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1965

Investigation of ion beam diagnostics final report

Ion beam diagnostics - plasma wind tunnel stream generation and characteristics, mercury optical resonance probe, environment effects on ion stream neutralization, and electric field meters

PLASMA GENERATION↗
DOE OSTI2024

Electric Field Data from the North Slope of Alaska

File contains raw as well as calibrated electric field values (V/m) from two CS110 electric field meters. The file also contains raw 2-m U and V winds at the same site. The sampling rate is 1Hz. All files contain raw electric field data, as well as calibrated values to a one week ground-based calibration setup. During this week, simultaneous upward-facing ground based electric field measurements were taken alongside the two downward facing instruments on the pole (CS110-3 at 2m and CS110-2 at 5m). Slopes and intercepts were calculated and applied to the values influenced by the pole to convert the raw data to calibrated values. These calibrated values are absolute electric field measurements. There are no missing data codes. The missong data is not included in the files, and thus any files with missing data will be shorter than the normal 86,400 values. In July 2023, the CS110-3 was replaced with an identical instrument named CS110-8, and the CS110-2 was replaced with an identical instrument called CS110-9. All the variable names remained consistent and the same.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Mobile intercept of storms

The primary goal was to acquire lightning data to serve as ground truth for U2 overflights. Researchers were successful in instrumenting the Univ. of Mississippi/National Severe Storms Lab. (UM/NSSL) mobile laboratory and in coordinating storm intercept through communication to the U2 provided by airplane guidance at NSSL and through direct communication with the U2 pilot from a portable transceiver in the mobile lab. A demonstration showed that a mobile laboratory can be directed within a large geographical area and used to collect ground truth data for comparison with airborne data on a routine basis with proper utilization of forecasts, nowcasts, and communication among all participants. After the U2 flights, researchers turned their attention solely to intercepting severe storms within the area of Oklahoma with good Doppler radar coverage. They incorporated a second vehicle, which followed the mobile lab and from which they released instrumented balloons. This project utilized a standard meteorological rawinsonde and a balloon-borne electric field meter. They were successful in flying, tracking, and receiving data from mobily launched balloons on several days. Researchers believe that they have demonstrated the ability to obtain meteorological and electrical data in severe storms using instrumented balloons. This also includes the capability to launch into the mesocyclone region and for multiple launches in the same storm.

Arnold, R. T.↗