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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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This conceptual design report is for the DOESC Closure Leak Test Assembly, which satisfies a portion of the milestone "Preliminary Design Report for Remote Helium Leak Test System". This CDR is part of the Preliminary Design Report/Package.
This paper develops an efficient methodology for protection screening of large-scale transmission systems as part of the planning studies for the integration of offshore wind power plants into the power grid. This methodology avails to determine whether any upgrades are required to the protection system. The uncertainty is considered in form of variability of the power generation by offshore wind power plant. This paper uses the integration of a 1000 MW offshore wind power plant operating in Lake Erie into the FirstEnergy/PJM service territory as a case study. This study uses a realistic model of a 63,000-bus test system that represents the U.S. Eastern Interconnection.
The purpose of these Guidelines is to provide direction for conducting performance acceptance testing for large power tower solar systems that can yield results of a high level of accuracy consistent with good engineering knowledge and practice. The recommendations have been developed under a National Renewable Energy Laboratory (NREL) subcontract and reviewed by stakeholders representing concerned organizations and interests throughout the concentrating solar power (CSP) community. An earlier NREL report provided similar guidelines for parabolic trough systems. These Guidelines recommend certain methods, instrumentation, equipment operating requirements, and calculation methods. When tests are run in accordance with these Guidelines, we expect that the test results will yield a valid indication of the actual performance of the tested equipment. But these are only recommendations--to be carefully considered by the contractual parties involved in the Acceptance Tests--and we expect that modifications may be required to fit the particular characteristics of a specific project.
Distribution grid, in general, is constructionally loopy but a radially operated network. Opening and closing of suitable tie-switches allows transition from one radial topology to another. Tie-switches ensure reliability and provide restoration paths when faults (outages) occur. The estimation of the system's current operational topology is a precursor to implementing any optimal control actions (during normal operation) or restorative actions (during outage condition). The existing literature in topology detection suffers with several limitations including: (1) computationally intensive nonlinear formulations that are not scalable for large distribution systems, (2) methods requiring exhaustive search for the correct topology by enumerating the complete topological search space, and (3) approaches requiring large volumes of time-series measurements and are not applicable for estimating continuously changing operational topology. Furthermore, the existing literature on distribution grid topology estimation cannot be extended to estimate the operational topology during outage conditions. For operational topology estimation during outages, our objective is to estimate both energized radial feeder and the outaged sections. Starting with a radial topology, these methods identify outaged feeder sections and hence, are inapplicable when the current radial operational topology is not known. In this work, we present a mathematical programming approach to estimate the operational topology of a three-phase unbalanced power distribution system under both outage and normal operating conditions. The aforementioned limitations of existing literature were countered by modeling the problem as a mixed-integer linear programming (MILP) problem which minimizes the errors in available measurements while satisfying the power flow and structural constraints of the system. Specically, a minimum weighted least absolute value estimator is proposed that uses the instantaneous line flow measurements, historical/ forecasted load data, and ping measurements from a limited set of smart meters and solves an MILP to identify the operational topology based on a linearized three-phase unbalanced power flow model. The proposed approach is tested on IEEE 123-bus feeder and 1069 bus multi-feeder test system and the numerical results prove the applicability of the proposed method to large three phase unbalanced grids with high errors in measurements. It is observed that the approach performs well even at high percentages of measurement errors.
Variability in power generation at multiple consumer owned renewable energy sources can cause random voltage fluctuations, which may result in voltage violations at multiple nodes in a power distribution system. Traditional methods of voltage control are ineffective to address fast voltage variations. New dynamic methods of voltage control using end user devices can significantly benefit from the knowledge of the dominant influencer of voltage fluctuation (DIVF). This paper introduces information theoretic metrics to identify DIVF for every node in the power distribution system. A node can be DIVF due to its location in the distribution system and its capacity to generate and change its power. Use of traditional power flow algorithms to identify the DIVF is computationally complex which limits their use in real-time applications. In this work, information theoretic indicators of the DIVF are computed analytically in a computationally efficient manner, and their effectiveness to identify the DIVF is tested using the IEEE 69 node test system. From simulations, it is illustrated that differential entropy, Kullback-Leibler distance and Frechet distance are effective indicators of the DIVF and can successfully identify the most influential nodes providing the foundation for intelligent voltage control.
A modular liquid argon (LAr) Time Projection Chamber (TPC) will be part of the near-detector facility for the Deep Underground Neutrino Experiment (DUNE). Such a TPC is being developed by the ArgonCube collaboration. To demonstrate the feasibility of the LAr DUNE Near Detector, a medium size prototype has been built: the ArgonCube 2x2 Demonstrator. The detector will be placed in the MINOS-ND hall, upstream of the current MINERνA/MINOS-ND locations, at Fermilab. Currently two modules are being tested at the Liquid Argon Test Facility (LArTF), before being place on-axis in the NuMI neutrino beam. The present report will be focused on the work done within the 2x2 Demonstrator group: the first part of the project is focused on the Low Noise AC Power distribution at LArTF, whereas the second is related to the 2x2 demonstrator light readout system tests.
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In this paper, we propose a novel multi-time scale emergency frequency response model by unlocking the flexibility of High Voltage Direct Current (HVDC) systems. Unlike assigning power ramping rates for FACTS to regulate frequency in traditional methods, this paper designs a step-change electromagnetic power frequency response (EPFR) scheme, by leveraging the temporal over/under DC voltage capability of HVDC. Wherein the Kullback-Leibler Divergence is adopted to convexify the modified swing equation after integrating the step-change power. Further, to avoid the complicated differential equations, we equivalently reformulate the duration limits of DC voltage deviation into the HVDC decreasing power ramping rates, which participate in the system primary frequency response. Finally, the new steady-state operation level of HVDC is involved with the secondary frequency response. As a result, an improved three-level algorithm is developed to solve the model, wherein the instant step-change, primary, and secondary frequency response are coordinated together. After applying the proposed frequency response scheme on the test system, the EPFR is validated to effectively provide the most instantaneous supports when faced with bulk power loss due to extreme contingencies, and the resilience is ensured within acceptable expenditures.
The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi timescale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. Here, the proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.
Abstract Infrared spectroscopy is a widely used tool for studying microplastics and identifying microparticles. Researchers rely on spectral libraries to differentiate between synthetic and natural materials. Unfortunately, spectral library matching is not perfect, and best practices require researchers to use time consuming, manual peak matching to assess spectral matches. Moving toward automated matching requires increased confidence in the matching process. Using spectra matching software may increase the efficiency of particle identification, however some matching strategies may confuse natural materials such as cotton, silk, and plant matter with common classes of synthetics such as polyesters and polyamides. In this experiment, we prepared 22 pristine sample materials from natural and synthetic sources and measured micro-Fourier transform infrared (µFTIR) spectra in transmission mode for each sample using a Thermo Nicolet iN10 MX instrument. The collected spectra were then input into two spectral library matching systems (Omnic Picta and Open Specy), using a total of five identification routines. Next, we placed a subset of four pristine microplastic materials in a biologically active river system for two weeks to simulate environmental samples. These simulated environmental samples were processed using 10% hydrogen peroxide for 24 h to remove organic contamination and then identified using the strongest performing library. We found that libraries with fewer sample spectra produced lower correlation matches and that using derivative correction greatly reduced the number of inaccuracies in identifying materials as either natural or synthetic. We also found that environmental fouling reduced the correlation value of library matches when compared to pristine particles, however the effect was not consistent across the four materials tested. Overall, we found that the accuracy of automated library matching in the tested systems and processing routines varied from 64.1 to 98.0% for distinguishing between natural and synthetic materials, and that a high Hit Quality Index (HQI) did not always correlate with accuracy. These results are important for the microplastic field, demonstrating a need to rigorously test spectral libraries and processing routines with known materials to ensure identification accuracy.
Oceanographic measurement buoys are power constrained by the capacity of their batteries and the available solar power. Many buoys are in locations with abundant wave energy, but wave energy converters are not being used to power them. One reason wave energy converters may not have not been developed for wave measurement buoys is that many buoys measure waves by precisely following the wave motion. Wave energy converters may inhibit this wave-following ability by altering a buoy's response to incoming waves. In this report, we present the results of field tests that measured the extent to which buoy motion was changed by modifications to the buoy for wave energy harvesting. The motion of the buoy was significantly modified by the addition of the hardware components necessary for harvesting wave energy. The ratio of the mass of the wave energy converter to the mass of the buoy was high for the system tested, which strongly affected buoy motion. A lower value for this ratio would decrease the effect of the wave energy converter on the motion of the buoy, thereby decreasing the impact on the buoy's ability to measure waves but also decreasing the power production capabilities. Therefore, the use of wave energy converters to power wave measurement buoys may be feasible for large buoys that measure low-frequency waves and have low power requirements.
In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.
Extreme natural hazards, such as hurricanes or earthquakes, have a high probability of threatening energy supply security and causing high-order contingencies to island city-integrated energy systems (IC-IESs). To better evaluate and enhance resilience, a novel approach is proposed in this work for IC-IESs. The resilience of an IC-IES is analyzed from both the system level and the component level. At the system level, the impacts of extreme natural disasters are quantified. At the component level, the importance of individual components is analyzed through pre-failure and post-failure indices. The pre-failure index identifies the system’s weak links before an energy interruption, and the post-failure index determines the optimal repair strategy to restore the service. The proposed indices are solved by the impact increment method (IIM), which significantly improves computational efficiency without much affecting result accuracy. Numerical simulation studies are conducted on the modified Barry Island IES and IES E123-G48-H32 test systems. Furthermore, the results validate the effectiveness of the proposed approach.
With the increasing connectedness and interdependence of systems that are stochastic in nature, the issue of how to manage and coordinate them for safe operation has evidently become more important. In many networked system architectures, the system-wide output has to be delicately managed; often within a prescribed set of bounds. In this paper, a novel control framework is proposed where the bounds on the outputs are translated into independent bounds on the controllable inputs of each subsystem. The main benefit of this framework is that respecting the individual control bounds suffices to guarantee that the system-wide outputs will remain within safe boundaries. Since the systems are assumed to be stochastic, the bounds on the output are introduced as probabilistic chance constraints. The benefits of this framework are demonstrated by applying it to the control of distributed energy resources in a distribution networks where main goal is to keep the voltage magnitudes with their prescribed bounds. The control bounds are evaluated using real data on an IEEE test system.
Understanding of the failure mechanisms of submarine dynamic power cables (SDPC) is critical for innovative design to meet the 2035 cost reduction target of U.S. DOE Floating Offshore Wind Shot. This is important because the current design suffers a significant failure rate in the field. This project explored a multiphysics approach and conducted systematic electro-thermo-mechanical (ETM) experimental study on the power cores extracted from a 15 kV power cable. The power cable had three cores of copper conductor and ethylene propylene rubber (EPR) insulation. An ETM testing system with transverse compression capability was developed. Mechanical loading induced dielectric breakdown. Increasing temperature from room temperature (RT, 22 degrees Celsius) to 90 degrees Celsius resulted in the 67% decrease in the failure mechanical load as defined by the dielectric breakdown. With the same temperature rise, the creep mechanical load was reduced by 70% at a given dielectric breakdown time. The post-test measurement revealed an impressive recovery of electrical resistance, although the dielectric breakdown occurred in the ETM test that would not be captured without the use of ETM testing.
Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.
With the increasing connectedness and interdependence of systems that are stochastic in nature, the issue of how to manage and coordinate them for safe operation has evidently become more important. In many networked system architectures, the system-wide output must be delicately managed, often within a prescribed set of bounds. In this paper, a novel control framework is proposed where the bounds on the outputs are translated into independent bounds on the controllable inputs of each subsystem. The main benefit of this framework is that respecting the individual control bounds suffices to guarantee that the system-wide outputs will remain within safe boundaries. Because the systems are assumed to be stochastic, the bounds on the output are introduced as probabilistic chance constraints. The benefits of this framework are demonstrated by applying it to the control of distributed energy resources in a distribution network where the main goal is to keep the voltage magnitudes within their prescribed bounds. The control bounds are evaluated using real data on an IEEE test system.
Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.