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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamic Matrix Completion Based State Estimation in Distribution Grids

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this paper, we propose two dynamic sparsity-based state estimation approaches for distribution systems: (1) locally weighted matrix completion (LW-MC) and (2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Finally, results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

42 ENGINEERING↗
DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science

Digital wireless communication has become a foundational technology for the nation. The U.S. Department of Energy’s Office of Science (DOE-SC) is the Nation’s largest supporter of basic research in the physical sciences discovering new materials, designing advanced microelectronics, and understanding the physics of radio frequency signaling. The expanding national rollout of a new fifth-generation (5G) mobile network, coupled with the torrent of scientific data generated by next-generation devices such as battery-powered Internet of Things (IoT) sensors, has created an urgent need to enhance cutting-edge wireless technology. Breakthroughs in the deployment, integration, security, and operational range of wireless networking can provide new scientific capabilities for the next decade - from autonomous mobile instruments for scientific user facilities to intelligent sensors networks distributed over thousands of kilometers to study environmental processes. To realize this promise, however, we must continue to drive innovations in computing, artificial intelligence (AI), advanced materials, high-speed networking, and microelectronics. In March 2020, the DOE-SC convened a workshop to identify the potential opportunities and explore the scientific challenges of advanced wireless technologies.

42 ENGINEERING↗
DOE OSTI2026

U.S. Department of Energy Solar District Cup Collegiate Design Competition

The Solar District Cup is a multidisciplinary collegiate competition that challenges student teams to design and model standalone solar and solar-plus-storage systems for multiple buildings on a local electrical distribution network, such as a college campus, across a development, or in a district. Students learn cross-cutting skills that prepare them to become part of the next generation of the distributed energy workforce. Student teams act as renewable energy developers proposing to solar and solar-plus-storage system requests. Students, faculty, and prospective employers can benefit from knowing about this program and what students learn--either immediately as participants, mentors, or judges, or as potential employers of Solar District Cup alumni. RE+ is also one of our competition partners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Characterizing short-term stability for Boolean networks over any distribution of transfer functions

Here we present a characterization of short-term stability of random Boolean networks under arbitrary distributions of transfer functions. Given any distribution of transfer functions for a random Boolean network, we present a formula that decides whether short-term chaos (damage spreading) will happen. We provide a formal proof for this formula, and empirically show that its predictions are accurate. Previous work only works for special cases of balanced families. Finally, it has been observed that these characterizations fail for unbalanced families, yet such families are widespread in real biological networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Real-Time Feedback-Based Optimization of Distribution Grids: A Unified Approach

This paper develops an algorithmic framework for real-time optimization of distribution-level distributed energy resources (DERs). The framework optimizes the operation of both DERs that are individually controllable and groups of DERs (i.e., aggregations) that are jointly controlled at an electrical point of connection. From an electrical standpoint, wye and delta single- and multi-phase connections are accounted for. The algorithm enables (groups of) DERs to pursue given performance objectives, while adjusting their (aggregate) powers to respond to services requested by grid operators and to maintain electrical quantities within engineering limits. The design of the algorithm leverages a time-varying bi-level problem formulation capturing various performance objectives and engineering constraints, and an online implementation of primal-dual projected-gradient methods. The gradient steps are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. Stability and convergence claims are established in terms of tracking of the solution of the time-varying optimization problem. Finally, the method is tested in a realistic distribution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Enhanced INL Power Grid Test Bed Infrastructure – Phase I

Idaho National Laboratory (INL), a Department of Energy (DOE) laboratory, owns, operates, and maintains transmission and distribution power grid infrastructure to support the INL multi program mission. Sections of this power infrastructure, known as the INL Power Grid Test Bed, have been and are being used by government and industry to develop, demonstrate, and validate technologies for the modern grid, including smart grid, on a full scale utility test bed. INL’s power grid includes 61 miles of 140 MW, 138 kV rated electrical power transmission supplying seven main substations, each feeding a separate facility complex (or ‘city’) within the INL’s 890 square mile Site. This power grid is fed by three commercial utilities into the INL’s main control substation, but is operated independently from the commercial utility through its primary substation and command and control center. Within the INL complex, one of the seven complexes, the Critical Infrastructure Test Range Complex (CITRC), has been designated as the INL complex for supporting critical infrastructure research and testing. This complex includes its own substation and 13.8kV distribution network, all configurable and controlled by the INL research and development programs. Through investment partnership with the DOE Office of Electricity Delivery and Energy Reliability (DOE OE), INL is enhancing its existing distribution infrastructure to expand the types of testing that can be conducted and increase flexibility for testing configurations. The enhancement of the INL Power Grid Test Bed will enable development and full scale testing of smart-grid-related technologies and smart devices including testing interoperability, operational performance, reliability, and resiliency contribution at multiple distribution voltage classes, specifically 15kV, 25kV, and 35kV. The expected time frame for completion of the Phase I portion of the enhancement would be 4th quarter fiscal year (FY) 2015.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Distribution Feeder-Scale Fast Frequency Response via Optimal Coordination of Net-load Resources Part I: Solution Design

This work is the first of a two-part series that develops and experimentally demonstrates a first-of-its-kind hierarchical control solution for optimally dispatching thousands of deferrable loads and distributed energy resources (DERs) across a distribution feeder to provide fast frequency response (FFR) within 500 ms to the bulk power system. This approach rapidly coordinates resources online after a frequency event occurs, allowing fast-changing, behind-the-meter (BTM) resources to be incorporated and aggregate FFR power set points to be achieved more quickly and accurately than existing approaches. We also present a solution for determining the optimal amount of headroom to operate solar inverters with to minimize opportunity cost while ensuring the FFR response viability of a building with the inverter and deferrable loads. In Part I, we develop practical algorithms for fast, cost-based optimal dispatch at multiple aggregation scales (single building, multiple buildings, and full distribution feeder), establish their optimality, and demonstrate via simulation that they are faster than state-of-the-art, coordinated frequency response approaches. In Part II, the entire platform is implemented and experimentally verified using a unique power hardware-in-the-loop demonstration, including more than 100 powered loads and DERs connected to a real-world distribution network model and over 10,000 net-load resources dispatched.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

GPS Data Analysis for Earth Orientation at the Jet Propulsion Laboratory

Beginning June 1992 and continuing indefinitely as part of our contribution to FLINN (Fiducial Laboratories for an International Natural Science Network), DOSE (NASA's Dynamics of the Solid Earth Program), and the IGS (International GPS Geodynamics Service), analysts at the Jet Propulsion Laboratory (JPL) have routinely been reducing data from a globally-distributed network of Rogue Global Positioning System (GPS) receivers.

GPS satellite ephemerides polar motion↗
NASA NTRS2006

Distributed Computing Framework for Synthetic Radar Application

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python based software framework, that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.

synthetic aperture radar (SAR)↗
DOE OSTI2021

Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems

Solving nonlinear optimal power flow (OPF) problem is computationally expensive, and poses scalability challenges for power distribution networks. An alternative to solving the original nonlinear OPF is the linear approximated OPF models. Although, these linear approximated OPF models are fast, the resulting solutions may result in significant optimality gap. Lately, the application of machine learning (ML) methods in successfully solving the nonlinear OPF has been reported. These methods learn and estimate the nonlinear control policies using a purely data-driven approach. In this paper, we propose an approach to complements the ML based approach to solving OPF using solutions from known linearized OPF model. Specifically, we use supervised learning to map the solutions of linear OPF to nonlinear control variables. Unlike, the traditional ML based methods for OPF that approximate the full distribution feeder model using function approximation, our approach uses a two-node approximation of radial networks. The proposed approach is validated using IEEE 123 bus test system for OPF solutions obtained using the nonlinear OPF models.

optimal power flow, power distribution systems, su↗
DOE OSTI2018

Coordinated Optimal Control of Constrained DERs

In this paper, a coordinated optimal control is designed to simultaneously satisfy multiple power system objectives at the distribution network level. With high penetration of renewables, traditional generation and control may not be enough to compensate for intermittent changes in net load. Dynamic coordination of battery storage devices and demand response at aggregated distributed energy resource (DER) sites becomes necessary to maintain power system operation, however, each dynamic power component operates at different time scales. Frequency stabilization can be achieved by designing optimal controls for traditional generation, storage devices and demand response. The proposed optimal controls naturally render frequency separation of these load balancing mechanisms. Given a 24-hour net load forecast, optimal tracking control inputs are designed subject to constraints of selected system elements to ensure optimal performance of the power system. Simulation results are presented and discussed.

Harvey, Roland↗
DOE OSTI2020

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2013

Sustaining the National Geothermal Data System: Considerations for a System Wide Approach and Node Maintenance, Geothermal Resources Council 37th Annual Meeting, Las Vegas, Nevada, September 29-October 2, 2013

Since the 2009 American Recovery and Reinvestment Act the U.S. Department of Energy’s Geothermal Technologies Office has funded $33.7 million for multiple data digitization and aggregation projects focused on making vast amounts of geothermal relevant data available to industry for advancing geothermal exploration. These projects are collectively part of the National Geothermal Data System (NGDS), a distributed, networked system for maintaining, sharing, and accessing data in an effort to lower the levelized cost of electricity (LCOE). Determining “who owns” and “who maintains” the NGDS and its data nodes (repositories in the distributed system) is yet to be determined. However, the invest- ment in building and populating the NGDS has been substantial, both in terms of dollars and time; it is critical that this investment be protected by ensuring sustainability of the data, the software and systems, and the accessibility of the data. Only then, will the benefits be fully realized. To keep this operational system sustainable will require four core elements: continued serving of data and applications; maintenance of system operations; a governance structure; and an effective business model. Each of these presents a number of challenges. Data being added to the NGDS are not strictly geothermal but data considered relevant to geothermal exploration and develop- ment, including vast amounts of oil and gas and groundwater wells, among other data. These are relevant to a broader base of users. By diversifying the client base to other users and other fields, the cost of maintaining core infrastructure can be spread across an array of stakeholders and clients. It is presumed that NGDS will continue to provide free and open access to its data resources. The next-phase NGDS operation should be structured to eventually pursue revenue streams to help off-set sustainability expenses as necessary and appropriate, potentially including income from: grants and contracts (agencies, foundations, pri- vate sector), membership, fees for services (consulting, training, customization, ‘app’ development), repository services (data, services, apps, models, documents, multimedia), advertisements, fees for premier services or applications, subscriptions to value added services, licenses, contributions and donations, endow- ments, and sponsorships.

15 GEOTHERMAL ENERGY↗
NASA NTRS1973

Analysis and synthesis of distributed-lumped-active networks by digital computer

The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.

Source record↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2023

US 2023/0182605 A1 Network constraint energy management system for electric vehicle depot charging and scheduling

Network constraint energy management system for electric vehicle (EV) depot charging and scheduling. In an embodiment, a power schedule is received from an economic dispatch application for a charging depot comprising EV charging station(s) and distributed energy resource(s). The power schedule may be simulated on a distribution network model of the charging depot, according to load flow analysis, to determine whether any grid-code violations occur. In response to the detection of violation(s), a constraint may be generated for each violating node, and the economic dispatch application may be re-executed with the constraint(s) to produce a new power schedule, until no violations are detected. When not all load demand can be satisfied by the power schedule, a charging schedule may be adjusted to ensure that critical energy requirements are satisfied. The final power and charging schedules may be used to schedule and control power generation and charging in the charging depot.

Hafiz, Faeza↗
DOE OSTI2014

Impact of Electric Vehicle Infrastructure on the City of Chatsworth Distribution System

With the growing penetration of the electric vehicles to our daily life owing to their economic and environmental benefits, there will be both opportunities and challenges to the utilities when adopting plug-in electric vehicles (PEV) to the distribution network. In this paper, a thorough analysis based on real-world project is conducted to evaluate the impact of electric vehicles infrastructure on the grid relating to system load flow, load factor, and voltage stability. Chatsworth distribution system was selected and tested along with different case scenarios utilizing the electrical distribution design (EDD) software to find out the potential impacts to the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗