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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Closed-Form Solution for Synchrophasor Data Correction under GPS Spoofing Attack

Phasor Measurement Units (PMUs) are increasingly being deployed to augment monitoring, protection and control applications in the power grid. PMUs rely on the Global Positioning System (GPS) for generation of time-synchronized and accurate measurements. However, civilian GPS signals, being unencrypted, are susceptible to GPS spoofing attacks (GSA). Hence ensuring the integrity of GPS-timing dependent synchrophasor data has become critical. In this paper, a closed-form analytical solution for estimating the GSA phase shift has been proposed and integrated to the spoofing detection & correction framework. Extensive simulations have been performed in order to verify the accuracy of the solution in determining the location and the phase shift of the compromised PMU. Time domain dynamic simulations presented in this paper demonstrate the applicability of the proposed solution for near-real-time detection of GSA, and this will enable faster detection and correction of phase angles of compromised synchrophasor data.

Synchrophasor, PMUs, GPS, Spoofing, Data Correctio↗
DOE OSTI2017

A Testbed Environment for Buildings-to-Grid Cyber Resilience Research and Development

The Smart Grid is characterized by the proliferation of advanced digital controllers at all levels of its operational hierarchy from generation to end consumption. Such controllers within modern residential and commercial buildings enable grid operators to exercise fine-grained control over energy consumption through several emerging Buildings-to-Grid (B2G) applications. Though this capability promises significant benefits in terms of operational economics and improved reliability, cybersecurity weaknesses in the supporting infrastructure could be exploited to cause a detrimental effect and this necessitates focused research efforts on two fronts. First, the understanding of how cyber attacks in the B2G space could impact grid reliability and to what extent. Second, the development and validation of cyber-physical application-specific countermeasures that are complementary to traditional infrastructure cybersecurity mechanisms for enhanced cyber attack detection and mitigation. The PNNL B2G testbed is currently being developed to address these core research needs. Specifically, the B2G testbed combines high-fidelity buildings+grid simulators, industry-grade building automation and Supervisory Control and Data Acquisition (SCADA) systems in an integrated, realistic, and reconfigurable environment capable of supporting attack-impact-detection-mitigation experimentation. In this paper, we articulate the need for research testbeds to model various B2G applications broadly by looking at the end-to-end operational hierarchy of the Smart Grid. Finally, the paper not only describes the architecture of the B2G testbed in detail, but also addresses the broad spectrum of B2G resilience research it is capable of supporting based on the smart grid operational hierarchy identified earlier.

Sridhar, Siddharth↗
DOE OSTI2017

Investigating Time-Varying Drivers of Grid Project Emissions Impacts

The emissions consequences of smart grid technologies depend heavily on their context and vary not only by geographical location, but by time of year. The same technology operated to meet the same objective may increase the emissions associated with energy generation for part of the year and decrease emissions during other times. The Grid Project Impact Quantification (GridPIQ) tool provides the ability to estimate these seasonal variations and garner insight into the time-varying drivers of grid project emissions impacts. This work leverages GridPIQ to examine the emissions implications across years and seasons of adding energy storage technology to reduce daily peak demand in California and New York.

Emissions↗
DOE OSTI2017

The Emissions Impacts of Varied Energy Storage Operational Objectives Across Regions

The emissions consequences of smart grid technologies can be significant but are not always intuitive. This is particularly true in the implementation of energy storage (ES) systems that are being increasingly adopted to integrate more intermittent renewable generation, to reduce peak demand, and to participate in energy markets. Both the location of the ES system within the grid and the way it is operated will dictate its resulting impacts. The Grid Project Impact Quantification tool can provide insight into some of the emissions implications of hypothetical ES systems for a variety of operational objectives in diverse locations within the United States.

Barrett, Emily L.↗
DOE OSTI2017

Estimating the Impacts of Direct Load Control Programs Using GridPIQ, a Web-Based Screening Tool

In direct load control (DLC) programs, utilities can curtail the demand of participating loads to contractually agreed-upon levels during periods of critical peak load, thereby reducing stress on the system, generation cost, and required transmission and generation capacity. Participating customers receive financial incentives. The impacts of implementing DLC programs extend well beyond peak shaving. There may be a shift of load proportional to the interrupted load to the times before or after a DLC event, and different load shifts have different consequences. Tools that can quantify the impacts of such programs on load curves, peak demand, emissions, and fossil fuel costs are currently lacking. The Grid Project Impact Quantification (GridPIQ) screening tool includes a Direct Load Control module, which takes into account project-specific inputs as well as the larger system context in order to quantify the impacts of a given DLC program. This allows users (utilities, researchers, etc.) to test and compare different program specifications and their impacts.

Demand Reponse↗
DOE OSTI2025

Large-signal Stability Analysis of Grid-forming Inverters with Equivalent-circuit Models

Here, this paper proposes an energy function-based direct method for large-signal stability assessment of grid-forming (GFM) inverters leveraging an equivalent-circuit representation of all involved control- and physical-layer dynamics. Three different primary controls, a standard inner-current outer-voltage cascaded-control architecture, output LCL filter, and reference-current saturation limiting are featured in the modeling and analysis framework. A composite energy function for the GFM inverter is obtained by summing up individual energy contributions gleaned from the circuit representation. The approach can readily be generalized to different primary controls, output-filter arrangements, and current limiters since it is based on a circuit-theoretic foundation. Numerical simulations validate the efficacy of the approach in estimating the critical clearing time following a large-signal disturbance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Real-World Subsynchronous Oscillation Events in Power Grids with High Penetrations of Inverter-Based Resources

This paper presents a survey of real-world sub-synchronous oscillation events associated with inverter-based resources (IBR) over the past decade. The focus is on those oscillations in the subsynchronous frequency range known to be influenced by power grid characteristics, e.g., series compensation or low system strength. A brief overview of the historical events is presented followed by detailed descriptions of a series of events. This paper also examines causation mechanisms and proposes future research directions to meet grid needs worldwide.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A Fixed-Flexible BESS Allocation Scheme for Transmission Networks Considering Uncertainties

Battery energy storage systems (BESSs) can play a key role in mitigating the intermittency and uncertainty associated with adding large amounts of renewable energy to the bulk power system (BPS). Two BESS technologies that have gained prominence in this regard are Lithium-ion (LI) BESS and Vanadium redox flow (VRF) BESS. Here, this paper proposes a fixed-flexible BESS allocation scheme that exploits the complementary characteristics of LI and VRF BESSs to attain optimal techno-economic benefits in a wind-integrated BPS. Studies carried out on relatively large transmission networks demonstrate that benefits such as reduction in system operation cost, wind spillage, voltage fluctuations, and discounted payback period, can be realized by using the proposed scheme.

42 ENGINEERING↗
DOE OSTI2015

Functional and structural diversity in GH62 α-L-arabinofuranosidases from the thermophilic fungus Scytalidium thermophilum

The genome of the thermophilic fungus Scytalidium thermophilum (strain CBS 625.91) harbours a wide range of genes involved in carbohydrate degradation, including three genes, abf62A , abf62B and abf62C , predicted to encode glycoside hydrolase family 62 (GH62) enzymes. Transcriptome analysis showed that only abf62A and abf62C are actively expressed during growth on diverse substrates including straws from barley, alfalfa, triticale and canola. The abf62A and abf62C genes were expressed in Escherichia coli and the resulting recombinant proteins were characterized. Calcium-free crystal structures of Abf62C in apo and xylotriose bound forms were determined to 1.23 and 1.48 Å resolution respectively. Site-directed mutagenesis confirmed Asp55, Asp171 and Glu230 as catalytic triad residues, and revealed the critical role of non-catalytic residues Asp194, Trp229 and Tyr338 in positioning the scissile α-L-arabinofuranoside bond at the catalytic site. Further, the +2R substrate-binding site residues Tyr168 and Asn339, as well as the +2NR residue Tyr226, are involved in accommodating long-chain xylan polymers. Overall, our structural and functional analysis highlights characteristic differences between Abf62A and Abf62C, which represent divergent subgroups in the GH62 family.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Multidimensional Numerical Simulations of Knocking Combustion in a Cooperative Fuel Research Engine

A numerical approach was developed based on multidimensional computational fluid dynamics (CFD) to predict knocking combustion in a cooperative fuel research (CFR) engine. G-equation model was employed to track the turbulent flame front and a multizone model was used to capture auto-ignition in the end-gas. Furthermore, a novel methodology was developed wherein a lookup table generated from a chemical kinetic mechanism could be employed to provide laminar flame speed as an input to the Gequation model, instead of using empirical correlations. To account for fuel chemistry effects accurately and lower the computational cost, a compact 121-species primary reference fuel (PRF) skeletal mechanism was developed from a detailed gasoline surrogate mechanism using the directed relation graph (DRG) assisted sensitivity analysis (DRGASA) reduction technique. Extensive validation of the skeletal mechanism was performed against experimental data available from the literature on both homogeneous ignition delay and laminar flame speed. The skeletal mechanism was used to generate lookup tables for laminar flame speed as a function of pressure, temperature, and equivalence ratio. The numerical model incorporating the skeletal mechanism was employed to perform simulations under research octane number (RON) and motor octane number (MON) conditions for two different PRFs. Parametric tests were conducted at different compression ratios (CR) and the predicted values of critical CR, delineating the boundary between “no knock” and “knock,” were found to be in good agreement with available experimental data. The virtual CFR engine model was, therefore, demonstrated to be capable of adequately capturing the sensitivity of knock propensity to fuel chemistry.

CFD↗
DOE OSTI2019

Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators

This work evaluates different optimization algorithms for computational fluid dynamics (CFD) simulations of engine combustion. Due to the computational expense of CFD simulations, emulators built with machine learning algorithms were used as surrogates for the optimizers. Two types of emulators were used: a Gaussian process (GP) and a weighted variety of machine learning methods called SuperLearner (SL). The emulators were trained using a dataset of 2048 CFD simulations that were run concurrently on a supercomputer. The design of experiments (DOE) for the CFD runs was obtained by perturbing nine input parameters using a Monte-Carlo method. The CFD simulations were of a heavy duty engine running with a low octane gasoline-like fuel at a partially premixed compression ignition mode. Ten optimization algorithms were tested, including types typically used in research applications. Each optimizer was allowed 800 function evaluations and was randomly tested 100 times. The optimizers were evaluated for the median, minimum, and maximum merits obtained in the 100 attempts. Some optimizers required more sequential evaluations, thereby resulting in longer wall clock times to reach an optimum. The best performing optimization methods were particle swarm optimization (PSO), differential evolution (DE), GENOUD (an evolutionary algorithm), and micro-genetic algorithm (GA). These methods found a high median optimum as well as a reasonable minimum optimum of the 100 trials. Moreover, all of these methods were able to operate with less than 100 successive iterations, which reduced the wall clock time required in practice. Two methods were found to be effective but required a much larger number of successive iterations: the DIRECT and MALSCHAINS algorithms. A random search method that completed in a single iteration performed poorly in finding optimum designs but was included to illustrate the limitation of highly concurrent search methods. The last three methods, Nelder–Mead, bound optimization by quadratic approximation (BOBYQA), and constrained optimization by linear approximation (COBYLA), did not perform as well.

Probst, Daniel M.↗
DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗