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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The generation of gridded emissions data for CMIP6

Abstract. Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded datasets produced for use by the modeling community, particularly for the Coupled Model Intercomparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios was coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-methane volatile organic compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Spaceflight Operations Services Grid (SOSG) Prototype Implementation and Feasibility Study

Science Operations Services Grid is focusing on building a prototype grid-based environment that incorporates existing and new spaceflight services to enable current and future NASA programs with cost savings and new and evolvable methods to conduct science in a distributed environment. The Science Operations Services Grid (SOSG) will provide a distributed environment for widely disparate organizations to conduct their systems and processes in a more efficient and cost effective manner. These organizations include those that: 1) engage in space-based science and operations, 2) develop space-based systems and processes, and 3) conduct scientific research, bringing together disparate scientific disciplines like geology and oceanography to create new information. In addition educational outreach will be significantly enhanced by providing to schools the same tools used by NASA with the ability of the schools to actively participate on many levels in the science generated by NASA from space and on the ground. The services range from voice, video and telemetry processing and display to data mining, high level processing and visualization tools all accessible from a single portal. In this environment, users would not require high end systems or processes at their home locations to use these services. Also, the user would need to know minimal details about the applications in order to utilize the services. In addition, security at all levels is an underlying goal of the project. The Science Operations Services Grid will focus on four tools that are currently used by the ISS Payload community along with nine more that are new to the community. Under the prototype four Grid virtual organizations PO) will be developed to represent four types of users. They are a Payload (experimenters) VO, a Flight Controllers VO, an Engineering and Science Collaborators VO and an Education and Public Outreach VO. The User-based services will be implemented to replicate the operational voice, video, telemetry and commanding systems. Once the User-based services are in place, they will be analyzed to establish feasibility for Grid enabling. If feasible then each User-based service will be Grid enabled. The remaining non-Grid services if not already Web enabled will be so enabled. In the end, four portals will be developed one for each VO. Each portal will contain the appropriate User-based services required for that VO to operate.

Bradford, Robert N.↗
DOE OSTI2023

Blockchain cybersecurity audit platform

A cybersecurity distributed ledger is provided herein for managing, tracking, auditing, and securing assets in a power infrastructure. The cybersecurity distributed ledger may include a blockchain, and may combine with smart contract or smart negotiation technology, such as in a permissioned proof of authority blockchain. The cybersecurity distributed ledger may manage the complete life cycle of a grid asset, from asset requirement and specification, through production, testing, deployment, maintenance, and retirement. The cybersecurity distributed ledger may create an immutable record of the grid asset, which may be audited for regulatory compliance or build or development compliance. Further, the cybersecurity distributed ledger may store unique identifying information for a grid asset, which may be used to detect a security breach or other tampering with a grid asset. The cybersecurity distributed ledger may also track control or ownership of the grid asset, as well as changes or updates to the grid asset.

Mylrea, Michael E.↗
DOE OSTI2024

I/O performance studies of analysis workloads on production and dedicated resources at CERN

The recent evolutions of the analysis frameworks and physics data formats of the LHC experiments provide the opportunity of using central analysis facilities with a strong focus on interactivity and short turnaround times, to complement the more common distributed analysis on the Grid. In order to plan for such facilities, it is essential to know in detail the performance of the combination of a given analysis framework, of a specific analysis and of the installed computing and storage resources. This contribution describes performance studies performed at CERN, using the EOS disk-based storage, either directly or through an XCache instance, from both batch resources and highperformance compute nodes which could be used to build an analysis facility. A variety of benchmarks, both synthetic and based on real-world physics analyses and their corresponding input datasets, are utilized. In particular, the RNTuple format from the ROOT project is put to the test and compared to the latest version of the TTree format, and the impact of caches is assessed. In addition, we assessed the difference in performance between the use of storage system specific protocols, like XRootd, and FUSE. The results of this study are intended to be a valuable input in the design of analysis facilities, at CERN and elsewhere.

Sciabà, Andrea↗
DOE OSTI2020

DC Link Voltage Balancing of the Active Front-End for the Extreme Fast Charging Stations

One of the most important challenges of the extreme fast charging (XFC) stations is to balance the DC link voltages of the active front-end (AFE). The multilevel cascaded H-bridge (CHB) converter is a great topology for the AFE applications due to the high power density, low total harmonic distortion (THD), and a low number of active and passive components. In this paper, a technique is proposed for balancing DC-link capacitor voltages of a three-phase CHB converter, when a phase shiftPWM modulation technique is used on all cells of the converter. To reach this goal, the redundant states of the CHB converters are used for generating different AC voltage levels by sorting the voltages of the converters. The proposed voltage balancing technique uses the principles that are employed for the DC link voltage balancing of the low-frequency modulation techniques to balance the DC link voltages of the CHB converter. As shown in this paper, the DC link voltages of the CHB converter can accurately be balanced very fast without using PI controllers, which are used in the conventional DC link voltage balancing techniques. A controller also is proposed which will be used to control the dq currents of the CHB converter. To verify the advantages of the proposed technique, the simulation results of the proposed technique will be carried out in a three-phase 7-cell CHB AFE.

Grid-tied converters, cascaded H-bridge, DC link v↗
DOE OSTI2022

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗
DOE OSTI2022

Employing Interacting Qubits for Distributed Microgrid Control

To empower flexible and scalable operations, distributed control of multi-inverter microgrids, based on classical communication networks among distributed energy resources, has attracted considerable attention as it can guarantee synchronization and provide suitable remedies to the problem of improper power sharing. Notwithstanding this, resilience of the current schemes on classical communication makes microgrids vulnerable to cyber attacks. Inspired by recent revolutionary breakthroughs in quantum communication, in this paper, we devise a novel synchronization mechanism. We extend the synchronization framework utilized in distributed control algorithms to networks of quantum systems by generating pinning terms and coupling mechanism for the new synchronization rule via exploiting proper quantum jump operators and observables, and show that the quantum system will converge to a time-variant target state. Our devised quantum distributed controller (QDC) gives rise to a novel quantum communication scheme for distributed control of microgrids and enables microgrids to exploit the state-of-the-art quantum communication frameworks as communication infrastructure. Finally, test results on two representative AC and DC networked microgrids validate the efficacy and universality of the quantum distributed control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to addresses this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4) numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.

batteries↗
DOE OSTI2023

Unified Communication Optimization Strategies for Sparse Triangular Solver on CPU and GPU Clusters

This paper presents a unified communication optimization framework for sparse triangular solve (SpTRSV) algorithms on CPU and GPU clusters. The framework builds upon a 3D communication-avoiding (CA) layout of Px × Py × Pz processes that divides a sparse matrix into Pz submatrices, each handled by a Px × Py 2D grid with block-cyclic distribution. We propose three communication optimization strategies: First, a new 3D SpTRSV algorithm is developed, which trades the inter-grid communication and synchronization with replicated computation. This design requires only one inter-grid synchronization, and the inter-grid communication is efficiently implemented with sparse allreduce operations. Second, broadcast and reduction communication trees are used to reduce message latency of the intra-grid 2D communication on CPU clusters. Finally, we leverage GPU-initiated one-sided communication to implement the communication trees on GPU clusters. With these nested inter- and intra-grid communication optimization strategies, the proposed 3D SpTRSV algorithm can attain up to 3.45x speedups compared to the baseline 3D SpTRSV algorithm using up to 2048 Cori Haswell CPU cores. In addition, the proposed GPU 3D SpTRSV algorithm can achieve up to 6.5x speedups compared to the proposed CPU 3D SpTRSV algorithm with Pz up to 64. Finally it is remarkable that the proposed GPU 3D SpTRSV can scale to 256 GPUs using the Perlmutter system while the existing 2D SpTRSV algorithm can only scale up to 4 GPUs.

Sao, Piyush↗
DOE OSTI2024

InfraSys (Infrastructure Systems) [SWR-24-42]

This package implements a data store for components and time series in support of Python-based modeling packages. While it is designed to support teams modeling transmission and distribution systems for electrical grids, it can be used by any package that needs to store components (e.g., generators and buses) that have quantities (e.g., power and voltage) which may vary over time. The package was inspired by InfrastructureSystems.jl (https://github.com/NREL-Sienna/InfrastructureSystems.jl).

Thom, Daniel↗
DOE OSTI2021

An Analysis Framework for Distribution Network DER Integration Analysis in India: Distributed Solar in Tamil Nadu

This report is part of a two-part series that represents a year-long collaboration with the Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO) on power sector planning. The first report in this series, the Pathways for Tamil Nadu’s Electric Power Sector 2017-2030 report outlines NREL’s work with TANGEDCO's electricity sector planning department to develop a model of the State’s power system and evaluate multiple scenarios of system growth given resource constraints, costs of technologies, and power sector policies. This second report in this series focuses on the rapidly transforming distribution network in the State. The report outlines a framework developed by NREL with TANGEDCO's distribution utility to quickly and accurately analyze the impacts of integrating renewable energy, specifically rooftop solar PV onto Tamil Nadu's distribution system. Together these studies help to prepare Tamil Nadu for a rapidly transforming power system.

14 SOLAR ENERGY↗
DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Improving Solar and Solar+Storage Screening Techniques to Reduce Utility Interconnection Time and Costs (Final Technical Report)

Residential PV installations have increased rapidly over the last decade, and the increased application volume has caused permitting delays and lower overall adoption rates. In this project, we developed and evaluated whether data-driven secondary modeling and screening techniques can help utilities assess customer applications more accurately than traditional screening shortcuts. Secondary topologies are predicted using decision trees and commonly available information, such as service transformer, customer, and street locations. Conductors were predicted using a logistic regression method based on real world object (RWO) types, service transformer ratings, conductor length, and distance to transformer. After developing the combined primary and secondary distribution network model, hosting capacity results were used to train a random forest model to predict the pass/fail likelihood of a customer application. Powerflow based models with predicted secondaries and data-driven methods both increased the screening success rate, relative to common utility heuristics, by as much as 55 percentage points. Data-driven screening techniques were described by one utility as a "right-sized" approach for residential customers given the low-risk of small errors and the high-cost of accurate modeling.

14 SOLAR ENERGY↗
DOE OSTI2025

DistOPF: Advanced Solutions for Distribution Optimal Power Flow Analysis - DistOPF v0.2 Documentation

To achieve an affordable and reliable energy system, research on power distribution system is often focused on integration of distributed generators, energy storage solution, EV charging, smart meters, and other advanced assets that may benefit from or require more advanced control and optimization techniques. Despite this focus on advanced distribution system topics, early researchers and grid scientists often start from scratch when developing optimization programs for power distribution systems. This report introduces DistOPF, a Python package that consolidates years of research into a versatile and modular tool. DistOPF provides researchers with essential capabilities to solve distribution system Optimal Power Flow (OPF) problems using standard network models. Additionally, it offers a platform to benchmark both new and existing algorithms against established test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗