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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads

Protection equipment are used to prevent damages to induction motor loads by isolating those from the power network in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's response moments after a fault has occurred. This article proposes an optimization based framework to generate composite protection models for commercial building motor loads. Introducing a mathematical abstraction, the task of finding a suitable (simplified) model of the composite protection scheme is formulated as a nonlinear regression problem. Numerical examples are provided to illustrate the application of the framework.

Kundu, Soumya↗
DOE OSTI2024

Towards Polymer-Free, Femto-Second Laser-Welded Glass/Glass Solar Modules

This project explores the use of femto-second (fs) lasers to form glass-to-glass welds for hermetically sealed, polymer-free solar modules. Low iron solar glass coupons were welded together without the use of glass filler using a fs laser with dedicated optics to elongate the focal plane parallel to the incident beam. The resulting welds were then stress tested to failure to reveal the critical stress intensity factor, KIc. These values were used in a structural mechanics model of a 1 m by 2 m glass/glass module under a simulated static load test. The results show that the fs laser welds are strong enough for a suitably framed module to pass the IEC 61215 static load test with a load of 5400 Pa. Key to this finding is that the module must be framed and braced, and the glass must be ribbed to allow pockets for the cells and welds inside the border of the module. The result is a module design that is completely polymer-free, hermetically sealed, has improved thermal properties, and is easily recycled.

femto-second laser welded↗
DOE OSTI2023

Picosecond Synchronization System for the Distribution of Photon Pairs Through a Fiber Link Between Fermilab and Argonne National Laboratories

Here we demonstrate a three-node quantum network for C-band photon pairs using 2 pairs of 59 km of deployed fiber between Fermi and Argonne National Laboratories. The C-band pairs are directed to nodes using a standard telecommunication switch and the detection system is synchronized to picosecond scale timing resolution using a coexisting O-or L-band optical clock distribution system. We measure a reduction of coincidence to-accidental ratio (CAR) of the C-band pairs from 51 ± 2 to 5.3 ± 0.4 due to Raman scattering of the O-band clock pulses. Despite this reduction, the CAR is nevertheless suitable for quantum networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Control and Simulation of a Grid-Forming Inverter for Hybrid PV-Battery Plants in Power System Black Start

Power system restoration is an important part of system planning. Power utilities are required to maintain black start capable generators that can energize the transmission system and provide cranking power to non-blackstart capable generators. Traditionally, hydro and diesel units are used as black start capable generators. With the increased penetration of bulk size solar farms, inverter based generation can play an important role in faster and parallel black start thus ensuring system can be brought back into service without the conventional delays that can be expected with limited black start generators. Inverter-based photovoltaic (PV) power plants have advantages that are suitable for black start. This paper proposes the modeling, control, and simulation of a grid-forming inverter-based PV-battery power plant that can be used as a black start unit. The inverter control includes both primary and secondary control loops to imitate the control of a conventional synchronous machine. The proposed approach is verified using a test system modified from the IEEE 9-bus system in the time-domain electromagnetic transient simulation tool PSCAD. The simulation results shows voltage and frequency stability during a multi-step black-start and network energization process.

Nguyen, Quan H.↗
DOE OSTI2024

Online Detection of Power Grid Anomalies via Federated Learning

Data from sensors is critical for advanced applica- tions that support efficient, reliable, and resilient electric grid operations. Historically, data from phasor measurement units (PMU) has been utilized to develop a wide variety of wide area control and protection applications suitable for power grid control centers. However, until now, most of these could not be deployed for automated operations due to a set of data corruption challenges and uncertainty in the incoming data pipeline. In this paper, we address the problem of detecting different variety of anomalies that are evident in different high- speed power grid measurements. The paper discusses a workflow for handling problems with data acquisition and highlights some of the key findings suitable for anomaly detection in a centralized and distributed environment. The effectiveness of the proposed method was demonstrated with results utilizing realistic PMU datasets

Shinkle, Matthew W.↗
DOE OSTI2023

Hydropower Evaluation Framework for Wildfire Resilient Microgrids

This paper presents a framework for characterizing hydropower plants based on plant and site attributes, assessing their feasibility to function within microgrids during periods of wildfire-induced outages. A set of nine metrics is developed to assign scores to hydropower plants, considering their suitability for forming wildfire-resilient microgrids, their capability to operate within such microgrids, and their simulated performance during wildfire events. A comprehensive database is constructed, comparing hydropower plants according to their suitability and capability metrics. To evaluate the performance score of a specific hydropower plant, namely the Hills Creek plant situated in a wildfire-prone region of Oregon, a case study is conducted, simulating a wildfire scenario. The results of steady-state and dynamic simulations demonstrate that the Hills Creek hydropower plant can effectively provide crucial microgrid services and power nearby communities during a prolonged wildfire-related outages.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A Generic and Multifunctional Electromagnetic Transient Model for Grid-Following Inverters

This paper presents a generic and multifunctional electromagnetic transient (EMT) dynamic model of grid-following (GFL) inverter-based resources (IBRs) using the PSCAD software platform. The features of the model include flexibility in selecting various types and combinations of DC sources covering photovoltaic modules, battery modules, and ideal DC-source modules as well as flexibility in selecting either switching or averaged models of the inverter. This model also covers exhaustive lists of controller algorithm, including open-loop/closed-loop PQ dispatch control, DC voltage and AC terminal voltage control, and conventional current control designed in the dq-domain, the ..alpha....beta.. -domain, and the positive-/negative-sequence domain. Moreover, this model is equipped with flexibility in selecting various types of current-limiting schemes, including saturation-based and latching-based current limiters, and anti-windup protection. Also, the EMT model is agnostic to the MVA rating and is suitable for interfacing transmission systems by complying with IEEE Std. 2800. The generality in the power circuits and the multifunctional options in the operation and control of the developed EMT model make it suitable for both academia and industry to study various power system aspects, including, but not limited to, the fault behavior of GFL IBRs, the impacts on the protection system, and the transient stability of a system interfaced with large numbers of GFL IBRs.

integrated circuit modeling↗
DOE OSTI2023

Towards 5G-Enabled Operational Technology for Process Monitoring and Network Slicing

Cyber-Physical Systems (CPS) are deployed to monitor physical processes in critical cyber-enabled services like power generation. However, CPS ecosystems are typically designed without robust security. While it is important to ensure optimal performance of the Operational Technology (OT) environments, security cannot be overlooked. To modernize traditional OT services, 5G technology is being integrated. 5G technology offers low latency and high availability, making it a suitable infrastructure for managing and monitoring physical processes. How-ever, integrating 5G mechanisms into large-scale OT networks introduces new implementation and performance challenges. Therefore, this paper presents a 5G-enabled CPS architecture (5G-CPS) that describes the necessary components, services, and communication protocols and conducts feasibility study to integrate 5G technology in industrial control system networks to understand the performance merits. The 5G-CPS architecture aims to minimize implementation and operational challenges associated with integrating 5G technology into constrained OT.

Aguayo, Jared M.↗
DOE OSTI2025

Effect of Gamma Radiation on TaOₓ ECRAM

Electrochemical random access memory (ECRAM) is an emerging three-terminal nonvolatile memory (NVM) with highly controllable channel conductance which is promising for use as an analog memory (or synapse) in analog in-memory computing (IMC) systems. Energy-efficient analog IMC computing is particularly desirable for power-constrained, high-radiation environments such as satellites. However, little is known about the suitability of ECRAM for use in a total ionizing dose (TID) environment. Here, this work investigates the effect of Co-60 gamma radiation on the channel conductance and noise—two properties critical for analog IMC systems—of a TaO x -based ECRAM up to 17.3 Mrad(SiO 2 ) for both low- and high-channel-conductance state devices. A transient increase in conductance is observed in response to radiation which consists of two elements: an immediate increase in conductivity due to photocurrent and a secondary increase in conductivity, which has a slower rise and saturation and can persist for hours after exposure. This secondary, persistent photoconductivity is attributed to charging caused by hole trapping. These transient effects would not likely occur in a space environment due to the low dose rate compared with this experiment. No permanent change is found in the low conductance state (LCS) following exposure and the minor shift in the high conductance change would be less significant than the regular retention decay in this state. A permanent increase in the random telegraph noise is observed, possibly due to increased traps created in the channel. This work demonstrates that TaO x -based ECRAM is suitable for use in spaceborne analog IMC systems that are subject to significant TID.

ECRAM↗
DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗
DOE OSTI2022

Comparison of animal and human blood for in vitro dynamic thrombogenicity testing of biomaterials

Abstract Background To determine suitable alternatives to human blood for in vitro dynamic thrombogenicity testing of biomaterials, four different animal blood sources (ovine, bovine, and porcine blood from live donors, and abattoir porcine blood) were compared to fresh human blood. Methods To account for blood coagulability differences between individual donors and species, each blood pool was heparinized to a donor‐specific concentration immediately before testing in a dynamic flow loop system. The target heparin level was established using a static thrombosis pre‐test. For dynamic testing, whole blood was recirculated at room temperature for 1 h at 200 ml/min through a flow loop containing a single test material. Four materials with varying thrombotic potentials were investigated: latex (positive control), polytetrafluoroethylene (PTFE) (negative control), silicone (intermediate thrombotic potential), and high‐density polyethylene (HDPE) (historically thromboresistant). Thrombus weight and surface area coverage on the test materials were quantified, along with platelet count reduction in the blood. Results While donor‐specific heparin levels varied substantially from 0.6 U/ml to 7.0 U/ml among the different blood sources, each source was able to differentiate between the thrombogenic latex and the thromboresistant PTFE and HDPE materials ( p < 0.05). However, only donor ovine and bovine blood were sensitive enough to differentiate an increased response for the intermediate thrombotic silicone material compared to PTFE and HDPE. Conclusions These results demonstrated that multiple animal blood sources (particularly donor ovine and bovine blood) may be suitable alternatives to fresh human blood for dynamic thrombogenicity testing when appropriate control materials and donor‐specific anticoagulation levels are used.

Engineering↗
DOE OSTI2020

Critical habitat identification of peripheral Sage Thrashers under climate change

Abstract The Sage Thrasher ( Oreoscoptes montanus ) has been assessed as “Endangered” in Canada since 1992. Like other species with a geographic range that barely extends into Canada, Sage Thrashers are rare. Thirty‐one percent of Canada's bird species listed for recovery under the Canadian Species at Risk Act (SARA) are at the periphery of their range. A listing of “endangered” under SARA requires identification of critical habitat for the species. With anticipated climate change, recovery of species requires a more proactive intervention than relying on historical occurrence to locate suitable habitat. We synthesized 19 years of Sage Thrasher occurrence and related habitat data across the species' northern range in British Columbia (BC) and Washington (WA) to define critical habitat characteristics. We found Sage Thrashers selected less leaf litter and less grass cover in flat or low‐slope regions farther from anthropogenic or natural habitat breaks; habitat sensitive to the expected climate change impacts of fire, changes in precipitation, and invasive species establishment. By augmenting the BC data collected in the species' peripheral range with data from their core distribution in WA, we identified key habitat elements of an otherwise data‐poor species that do not breed in sufficient numbers in Canada to reliably characterize their habitat. These methods improve the identification of “critical habitat” for peripheral species like Sage Thrashers in preparation for climate‐induced range expansion northward. The framework developed demonstrates a useful template for conservation strategies for data‐limited peripheral populations in other regions. Focusing on the landscape‐level variables that indicate good habitat, and not the locations of habitat, can identify suitable future areas for conservation.

Millikin, Rhonda L.↗
DOE OSTI2021

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (R S ), the soil-to-atmosphere CO 2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T soil ) and water content (SWC). Regional to global-scale R S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T air ) and monthly precipitation (P m ) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. In this report we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T air as a surrogate for T soil , and data from 507 sites to examine the suitability of P m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T soil and T air are highly correlated and explain similar R S variability. In contrast, P m is not a good surrogate for SWC, even though P m explains a similar amount of R S variability to SWC. The wide variability in the site-specific relationships between R S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T air in continental-to-global scale R S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Redefining marginal land for bioenergy crop production

Abstract Marginal land has received wide attention for its potential to produce bioenergy feedstocks while minimizing diversion of productive agricultural land from food crop production. However, there has been no consensus in the literature on how to define or identify land that is marginal for food crops and beneficial for bioenergy crops. Studies have used different definitions to quantify the amount of such land available; these have largely been based on assumed biophysical thresholds for soil quality and productivity that are unchanging over space and time. We discuss the limitations of these definitions and the rationale for considering economic returns and environmental outcomes in classifying land as marginal. We then propose the concept of “socially” marginal which is defined as land that is earning close to zero returns after accounting for the monetized costs of environmental externalities generated. We discuss a broad set of criteria for classifying land as socially marginal for food crops and suitable for bioenergy crops; with these criteria, this classification depends on spatially varying and time‐varying factors, such as climate and market conditions and policy incentives. While there are challenges related to identifying this marginal land, satellite and other large‐scale datasets increasingly enable such analysis at a fine spatial resolution. We also discuss reasons why landowners might choose not to convert bioenergy‐suitable land to bioenergy crops, and thus the need for policy incentives to support conversion of land that is socially beneficial for bioenergy crop production.

09 BIOMASS FUELS↗
DOE OSTI2022

Calibrating constitutive models with full‐field data via physics informed neural networks

Abstract The calibration of solid constitutive models with full‐field experimental data is a long‐standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics‐informed deep‐learning framework for the discovery of hyperelastic constitutive model parameterizations given full‐field surface displacement data and global force‐displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non‐linear elastic behaviour, elastomer behaviour with finite strain lock‐up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full‐field experimental data are utilized to calibrate constitutive models under finite deformations.

Hamel, Craig M.↗
DOE OSTI2020

Using Concentrating Solar Power to Create a Geological Thermal Energy Reservoir for Seasonal Storage and Flexible Power Plant Operation

Abstract We propose a hybrid renewable energy system—a geothermal energy storage system (GeoTES) with solar—to provide low-cost dispatchable power at various timescales from daily, to weekly, to seasonally. GeoTES with solar uses a concentrating solar power collector field to produce hot water that is injected into a sedimentary basin to create a synthetic geothermal resource. The stored geothermal heat can then be dispatched when required by the electrical grid. GeoTES is particularly valuable for a grid with a high penetration of non-flexible renewable technologies such as photovoltaic and wind power. In this work, a sophisticated hybrid model is developed to assess the technical and economic potential of GeoTES by combining IPSEpro, which is a power-cycle simulation tool, and SAM, an economic analysis tool by National Renewable Energy Laboratory (NREL). The analysis shows with proper initial charging period that the heat loss in storage is almost negligible and is a suitable technology for long-term energy storage. Various power-cycle options are evaluated, and the most suitable power cycle is selected for further study. Annual calculations of the GeoTES system indicate that a levelized cost of storage (LCOS) of 12.4 ¢/kWh e can be achieved for seasonal storage of 4000 h; this value is much lower than the existing long-term storage. The LCOS of GeoTES is insensitive to the storage duration above 8 h, unlike battery and molten-salt thermal storage systems. This result demonstrates that GeoTES can be a competitive seasonal storage technology in the future electricity market. The levelized cost of electricity of the GeoTES system is also carefully analyzed and can vary between 10.0 and 16.4 ¢/kWh e , depending on solar-collector prices.

25 ENERGY STORAGE↗
DOE OSTI2021

Performance of a Natural Gas Solid Oxide Fuel Cell System With and Without Carbon Capture

The fuel cell program at the United States Department of Energy (DOE) National Energy Technology Laboratory (NETL) is focused on the development of low-cost, highly efficient, and reliable fossil-fuel-based solid oxide fuel cell (SOFC) power systems that can generate environmentally-friendly electric power with at least 90 percent carbon capture. NETL’s SOFC technology development roadmap is aligned with near-term market opportunities in the distributed generation sector to validate and advance the technology while paving the way for utility-scale natural gas (NG)- and coal-derived synthesis gas-fueled applications via progressively larger system demonstrations. The present study represents a part of a series of system evaluations being carried out at NETL to aid in prioritizing technological advances along research pathways to the realization of utility-scale SOFC systems, a transformational goal of the fuel cell program. In particular, the system performance of utility-scale NG fuel cell (NGFC) systems with and without carbon dioxide (CO2) capture is presented. The NGFC system analyzed features an external auto-thermal reformer (ATR) feeding the fuel to the SOFC system consisting of planar anode-supported SOFC with separated anode and cathode off-gas streams. In systems with CO2 capture, an air separation unit (ASU) is used to provide the oxygen for the ATR and for the combustion of unutilized fuel in the SOFC anode exhaust along with a CO2 purification unit to provide a nearly pure CO2 stream suitable for transport for usage in enhanced oil recovery operations or for storage in underground saline formations. Remaining thermal energy in the exhaust gases is recovered in a bottoming steam Rankine cycle while supplying any process heat requirements. A reduced order model (ROM) developed at the Pacific Northwest National Laboratory (PNNL) is used to predict the SOFC performance. The ROM, while being computationally effective for system studies, provides other detailed information about the state of the stack, such as the internal temperature gradient, generally not available from simple performance models often used to represent the SOFC. Such additional information can be important in system optimization studies to preclude operation under off-design conditions that can adversely impact overall system reliability. The NGFC system performance was analyzed by varying salient system parameters, including the percent of internal (to the SOFC module) NG reformation—ranging from 0 to 100 percent—fuel utilization, and current density. The impact of advances in underlying SOFC technology on electrical performance was also explored.

solid oxide fuel cell (SOFC), natural gas fuel cel↗