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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Review of Power System Support Functions for Inverter-Based Distributed Energy Resources - Standards, Control Algorithms, and Trends

Penetration of renewable energy in power systems has been increasing in the past decades in response to increased global electricity demand and concerns for the environment. Distributed energy resources (DERs) based on renewables have experienced rapid growth thanks to the incentive programs and broad-based participation. With the growing prevalence of DERs, the risk of grid instability and vulnerability increases due to the intermittent nature of renewable energy. At the same time, the voltage and frequency deviation problems emerge more often when the reverse power flow occurs under supply-demand imbalance in distributed power systems. Standards and grid codes have been issued for DER inverters to interconnect with the distribution grid. The updated standard and grid codes expect DERs to provide a variety of power system support functions in order to incorporate higher DER penetration and to maximize DER value to the grid. This paper provides an overview of the power system support functions from renewable DER inverters, which are categorized as: voltage regulation by active/reactive power control, frequency regulation by active power control, voltage ride-through, and frequency ride-through. The benefits and drawbacks of each algorithm are presented and compared with its predecessor, manifesting the logic in the evolution of the algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Fitting of Failure Rate Data to a Closed Form Solution using Gamma-Poisson Method of Moments

Due to the responsibility and gravity of estimation of nuclear power plant failure rates, it is required that there be consistent and accurate methodology for describing the likelihood of an event occurring. However, the methodology currently employed can vary dependent on the data, as well as subjective conjecture from the expert performing the analysis. The current implementation of the empirical Bayes method to a Gamma-Poisson (GaP) distribution utilizes algorithms to solve for the parameters that do not provide consistent answers. Additionally, to achieve a distribution of the likelihood, the variance of each parameter of the GaP must be determined. There does not exist an exact solution to one of the parameters variance and is typically estimated using techniques like the Kass-Steffey adjustment. Thus a new approach to the problem is proposed, built upon the method of moments for a negative binomial, based upon the work of Bradlow. Using the method of moments approach, we are able to achieve a closed form estimation of the mean and variance for each parameter in the negative binomial distribution. Due to the relationship between the negative binominal and GaP distribution, results can be made between the distributions. The hyper-priors defined assume beta prime distribution which is appropriately informed; the results of this application translate back to the Gamma distribution for easy utilization in SAPHIRE. Additionally several cases are explored using publically available NRC data that considers zero inflated, over dispersed, and Poisson data.

Failure Rate↗
DOE OSTI2016

Chance-Constrained System of Systems Based Operation of Power Systems

In this paper, a chance-constrained system of systems (SoS) based decision-making approach is presented for stochastic scheduling of power systems encompassing active distribution grids. Based on the concept of SoS, the independent system operator (ISO) and distribution companies (DISCOs) are modeled as self-governing systems. These systems collaborate with each other to run the entire power system in a secure and economic manner. Each self-governing system accounts for its local reserve requirements and line flow constraints with respect to the uncertainties of load and renewable energy resources. A set of chance constraints are formulated to model the interactions between the ISO and DISCOs. The proposed model is solved by using analytical target cascading (ATC) method, a distributed optimization algorithm in which only a limited amount of information is exchanged between collaborative ISO and DISCOs. In this paper, a 6-bus and a modified IEEE 118-bus power systems are studied to show the effectiveness of the proposed algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Pele: An Exascale-Ready Suite of Combustion Codes

High fidelity simulations of realistic combustion devices are extremely demanding computationally because of the requirements to capture complex fuel chemical decomposition, its intricate interactions with turbulent, often multiphase, flows, and the wide separation of space and time scales between the thin flame and the device boundaries. Software required to carry out such computations tends to be extremely complex, particularly when designed to exploit hardware accelerators, and can be difficult to port and maintain. We present Pele, a performance portable suite of tools for the simulation of combustion systems, including codes to evolve reactive multiphase configurations in the low Mach number and compressible flow regimes, along with a set of inter-compatible post processing and in situ analysis tools. The Pele suite of tools is built on top of the AMReX framework for block-structured adaptive mesh refinement, which provides efficient data structures and algorithms that enable the development of a wide variety of efficient mesh and particle based PDE integration schemes. A hierarchical MPI+X parallelism scheme supports CPU-only and accelerated architectures, where X can be OpenMP, CUDA, and HIP based approaches for intra-node computational work distribution. The algorithms and data structures underlying the Pele simulation and analysis tools are highly scalable and performant across a wide variety of high-performance computing platforms, including DOEs newest exascale-class machines, Frontier and Aurora. The simulation and analysis tools are fully documented and freely distributed as open source via GitHub. We present key algorithmic and software challenges, solution strategies, performance and resulting set of capabilities.

AMReX↗
DOE OSTI2021

Peer-to-Peer Energy Management System for Distributed Microgrid Coordination [SWR-21-92]

Resiliency is one of the key challenges in today's power system. Natural disasters and cyber-attacks both can limit communications between microgrids and the central management system. Thus, having a Distributed Microgrid Coordination (DMC) algorithm can improve the system resiliency, which enables the microgrids to operate without communication with the central management system. Peer-to-Peer Energy Management System for Distributed Microgrid Coordination adopts a primal-dual approach, where each microgrid controller keeps a local estimate of the dual variables. The estimate is updated with local measurements and peer-to-peer communication, leading to a fully distributed algorithm. While the DMC is developed for microgrid coordination, it can be used for general distributed control purpose.

Li, Yashen↗
DOE OSTI2019

Beyond Hosting Capacity: Using Shortest-Path Methods to Minimize Upgrade Cost Pathways

This paper presents a graph-based forward looking algorithm applied to distribution planning in the context of distributed photovoltaic penetration. We study the target hosting capacity problem where the objective is to find the least-cost sequence of system upgrades to reach a predefined hosting capacity target value. We show that commonly used short-term cost minimization approaches often lead to suboptimal long-term solutions. By comparing our method against such myopic techniques on real distribution systems, we show that our algorithm is able to reduce the overall integration costs by looking at future decisions. Because hosting capacity is hard to compute, this problem requires efficient methods to search the space. We demonstrate that heuristics using domain-specific knowledge can be efficiently used to improve the algorithm performance, such that real distribution systems can be studied.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Beyond Hosting Capacity: Using Shortest Path Methods to Minimize Upgrade Cost Pathways: Preprint

We present in this paper a graph based forwardlooking algorithm applied to distribution planning in the context of distributed PV penetration. We study the target hosting capacity (THC) problem where the objective is to find the cheapest sequence of system upgrades to reach a predefined hosting capacity target value. We show in this paper that commonly used short-term cost minimization approaches lead most of the time to suboptimal solutions. By comparing our method against such myopic techniques on real distribution systems, we show that our algorithm is able to reduce the overall integration costs by looking at future decisions. Because hosting capacity is hard to compute, this problem requires efficient methods to search the space. We demonstrate here that heuristics using domain specific knowledge can be efficiently used to improve the algorithm performance such that real distribution systems can be studied.

14 SOLAR ENERGY↗
DOE OSTI2018

Resilient Distributed Estimation: Sensor Attacks

Here, this paper studies multi-agent distributed estimation under sensor attacks. Individual agents make sensor measurements of an unknown parameter belonging to a compact set, and, at every time step, a fraction of the agents’ sensor measurements may fall under attack and take arbitrary values. We present the Saturated Innovation Update (SIU) algorithm for distributed estimation resilient to sensor attacks. Under the iterative SIU algorithm, if less than one half of the agent sensors fall under attack, then, all of the agents’ estimates converge at a polynomial rate (with respect to the number of iterations) to the true parameter. The resilience of SIU to sensor attacks does not depend on the topology of the inter-agent communication network, as long as it remains connected. We demonstrate the performance of SIU with numerical examples.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg$^{2}$ over 5 yr to constrain the cosmic expansion history through precise measurements of baryon acoustic oscillations (BAO). The scientific program for DESI was evaluated during a 5 month survey validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar Milky Way Survey (MWS), Bright Galaxy Survey (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the 5 yr program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a One-Percent Survey conducted at the conclusion of SV covering 140 deg$^{2}$ using the final target selection algorithms with exposures of a depth typical of the main survey. The SV indicates that DESI will be able to complete the full 14,000 deg$^{2}$ program with spectroscopically confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval z < 1.1, 0.39% over the redshift interval 1.1 < z < 1.9, and 0.46% over the redshift interval 1.9 < z < 3.5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Fault-Tolerant Decentralized Control for Large-Scale Inverter-Based Resources for Active Power Tracking

Integration of inverter-based resources (IBRs) which lack the intrinsic characteristics such as the inertial response of the traditional synchronous-generator (SG)-based sources presents a new challenge in the form of analyzing the grid stability under their presence. While the dynamic composition of IBRs differs from that of the SGs, the control objective remains similar in terms of tracking the desired active power. This letter presents a decentralized primal-dual-based fault-tolerant control framework for the power allocation in IBRs. Overall, a hierarchical control algorithm is developed with a lower level addressing the current control and the parameter estimation for the IBRs and the higher level acting as the reference power generator to the low level based on the desired active power profile. The decentralized network-based algorithm adaptively splits the desired power between the IBRs taking into consideration the health of the IBRs transmission lines. The proposed framework is tested through a simulation on the network of IBRs and the high-level controller performance is compared against the existing framework in the literature. The proposed algorithm shows significant performance improvement in the magnitude of power deviation and settling time to the nominal value under faulty conditions as compared to the algorithm in the literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Finally, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Hierarchical Management of Distributed Energy Resources Using Chance-Constrained OPF and Extremum Seeking Control: Preprint

Distributed energy resources (DERs) are becoming an important part of distribution systems because of their economic and environmental benefits. Although their inherent intermittency and volatility introduce uncertainties into the system, they have potential to provide controllability to the system under proper coordination. In this paper, we propose a hierarchical control algorithm for distribution systems with DERs so that they have controllability similar to that of a generator bus. The upper level scheduler solves a chance-constrained optimal power flow (OPF) problem to plan the operation of the DERs based on forecasts, and the lower level distributed DER controllers leverage the extremum seeking approach to deliver the planned power at the feeder head. The proposed algorithm is tested on a modified IEEE 13-node feeder, demonstrating its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Hierarchical Management of Distributed Energy Resources Using Chance-Constrained OPF and Extremum Seeking Control

Distributed energy resources (DERs) are becoming an important part of distribution systems, because of their economic and environmental benefits. Although their inherent intermittency and volatility introduce uncertainties into the electric power system, they have the potential to provide controllability to the system under proper coordination. In this paper, we propose a hierarchical control algorithm for distribution systems with DERs so that they have controllability similar to a generator bus. The upper level scheduler solves a chance-constrained optimal power flow (OPF) problem to plan the operation of the DERs based on forecasts, and the lower level distributed DER controllers leverage the extremum seeking approach to deliver the planned power at the feeder head. The proposed algorithm is tested on a modified IEEE 13-node feeder, demonstrating its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Theoretical limit of spatial resolution in diffuse optical tomography using a perturbation model

We have assessed the limit of spatial resolution of timedomain diffuse optical tomography (DOT) based on a perturbation reconstruction model. From the viewpoint of the structure reconstruction accuracy, three different approaches to solving the inverse DOT problem are compared. The first approach involves reconstruction of diffuse tomograms from straight lines, the second – from average curvilinear trajectories of photons and the third – from total banana-shaped distributions of photon trajectories. In order to obtain estimates of resolution, we have derived analytical expressions for the point spread function and modulation transfer function, as well as have performed a numerical experiment on reconstruction of rectangular scattering objects with circular absorbing inhomogeneities. It is shown that in passing from reconstruction from straight lines to reconstruction using distributions of photon trajectories we can improve resolution by almost an order of magnitude and exceed the accuracy of reconstruction of multi-step algorithms used in DOT. (optical tomography)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Fast and Accurate Support Vector Machines on Large Scale Systems

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

Vishnu, Abhinav↗
DOE OSTI2018

Hierarchical Clock Synchronization in MPI

We present a new distributed community detection algorithm for large graphs based on the Louvain method. We exploit a distributed delegate partitioning to ensure the workload and communication balancing among processors. In addition, we design a new heuristic strategy to carefully coordinate the community constitution in a distributed environment, and ensure the convergence of the distributed clustering algorithm. Our intensive experimental study has demonstrated the scalability and the correctness of our algorithm with various large-scale real-world and synthetic graph datasets using up to 32,768 processors

Computer Science↗
DOE OSTI2014

SU-E-T-520: Four-Dimensional Dose Calculation Algorithm Considering Variations in Dose Distribution Induced by Sinusoidal One-Dimensional Motion Patterns

Purpose: To investigate quantitatively the variations in dose-distributions induced by motion by measurements and modeling. A four-dimensional (4D) motion model of dose distributions that accounts for different motion parameters was developed. Methods: Variations in dose distributions induced by sinusoidal phantom motion were measured using a multiple-diode-array-detector (MapCheck2). MapCheck2 was mounted on a mobile platform that moves with adjustable calibrated motion patterns in the superior-inferior direction. Various plans including open and intensity-modulated fields were used to irradiate MapCheck2. A motion model was developed to predict spatial and temporal variations in the dose-distributions and dependence on the motion parameters using pencil-beam spread-out superposition function. This model used the superposition of pencil-beams weighted with a probability function extracted from the motion trajectory. The model was verified with measured dose-distributions obtained from MapCheck2. Results: Dose-distribution varied considerably with motion where in the regions between isocenter and 50% isodose-line, dose decreased with increase of the motion amplitude. Dose levels increased with increase in the motion amplitude in the region beyond 50% isodose-line. When the range of motion (ROM=twice amplitude) was smaller than the field length both central axis dose and the 50% isodose-line did not change with variation of motion amplitude and remained equal to the dose of stationary phantom. As ROM became larger than the field length, the dose level decreased at central axis dose and 50% isodose-line. Motion frequency and phase did not affect the dose distributions which were delivered over an extended time longer than few motion cycles, however, they played an important role for doses delivered with high-dose-rates within one motion cycle . Conclusion: A 4D-dose motion model was developed to predict and correct variations in dose distributions induced by one-dimensional sinusoidal motion. This model was verified with measured dose-distributions and can be expanded to consider complicated patient motion patterns.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Genetic-algorithm retrieval of the molecular alignment distribution with high-order harmonics generated from transiently aligned CO 2 molecules

Using genetic algorithm, we propose a method to retrieve the alignment distribution of transiently aligned CO 2 molecules from the high-order harmonic generation (HHG) spectra. The retrieval method is based on the quantitative rescattering (QRS) model where averaged photorecombination transition dipole can be factored out from the parallel (or perpendicular) harmonic spectra after the propagation of the harmonic fields in the gas medium. We examine how the retrieved alignment distributions are affected by uncertainty in alignment dependent ionization probability and on multiple orbital contribution to the HHG. We further confirm that alignment distribution is more accurately retrieved by using the minima in the HHG spectra driven by a long-wavelength laser. In addition, we show that earlier experimental data on the ratios between the perpendicular and the parallel HHG components of aligned CO 2 molecules are in better agreement with the QRS model if the macroscopic propagation and multiple orbital interference are included in the theoretical calculation.

74 ATOMIC AND MOLECULAR PHYSICS↗