Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “GRIDS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Parallel grid population

Parallel population of a grid with a plurality of objects using a plurality of processors. One example embodiment is a method for parallel population of a grid with a plurality of objects using a plurality of processors. The method includes a first act of dividing a grid into n distinct grid portions, where n is the number of processors available for populating the grid. The method also includes acts of dividing a plurality of objects into n distinct sets of objects, assigning a distinct set of objects to each processor such that each processor determines by which distinct grid portion(s) each object in its distinct set of objects is at least partially bounded, and assigning a distinct grid portion to each processor such that each processor populates its distinct grid portion with any objects that were previously determined to be at least partially bounded by its distinct grid portion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Cost benefit analysis for smart grid projects

The U.S. is unusual in that a definition of the term “smart grid” was written into legislation, appearing in the Energy Independence and Security Act (2007). When the recession called for stimulus spending and the American Recovery and Reinvestment Act (ARRA, 2009) was passed, a framework already existed for identification of smart grid projects. About $4.5B of the U.S. Department of Energy’s (U.S. DOE’s) $37B allocation from ARRA was directed to smart grid projects of two types, investment grants and demonstrations. Matching funds from other sources more than doubled the total value of ARRA-funded smart grid projects. The Smart Grid Investment Grant Program (SGIG) consumed all but $620M of the ARRA funds, which was available for the 32 projects in the Smart Grid Demonstration Program (SGDP, or demonstrations). Given the economic potential of these projects and the substantial investments required, there was keen interest in estimating the benefits of the projects (i.e., quantifying and monetizing the performance of smart grid technologies). Common method development and application, data collection, and analysis to calculate and publicize the benefits were central objectives of the program. For this purpose standard methods and a software tool, the Smart Grid Computational Tool (SGCT), were developed by U.S. DOE and a spreadsheet model was made freely available to grantees and other analysts. The methodology was intended to define smart grid technologies or assets, the mechanisms by which they generate functions, their impacts and, ultimately, their benefits. The SGCT and its application to the Demonstration Projects are described, and actual projects in Southern California and in China are selected to test and illustrate the tool. The usefulness of the methodology and tool for international analyses is then assessed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Decentralized control of units in smart grids for the support of renewable energy supply

Due to the significant environmental impact of power production from fossil fuels and nuclear fission, future energy systems will increasingly rely on distributed and renewable energy sources (RES). The electrical feed-in from photovoltaic (PV) systems and wind energy converters (WEC) varies greatly both over short and long time periods (from minutes to seasons), and (not only) by this effect the supply of electrical power from RES and the demand for electrical power are not per se matching. In addition, with a growing share of generation capacity especially in distribution grids, the top-down paradigm of electricity distribution is gradually replaced by a bottom-up power supply. This altogether leads to new problems regarding the safe and reliable operation of power grids. In order to address these challenges, the notion of Smart Grids has been introduced. The inherent flexibilities, i.e. the set of feasible power schedules, of distributed power units have to be controlled in order to support demand–supply matching as well as stable grid operation. Controllable power units are e.g. combined heat and power plants, power storage systems such as batteries, and flexible power consumers such as heat pumps. By controlling the flexibilities of these units we are particularly able to optimize the local utilization of RES feed-in in a given power grid by integrating both supply and demand management measures with special respect to the electrical infrastructure. In this context, decentralized systems, autonomous agents and the concept of self-organizing systems will become key elements of the ICT based control of power units. In this contribution, we first show how a decentralized load management system for battery charging/discharging of electrical vehicles (EVs) can increase the locally used share of supply from PV systems in a low voltage grid. For a reliable demand side management of large sets of appliances, dynamic clustering of these appliances into uniformly controlled appliance sets is necessary. We introduce a method for self-organized clustering for this purpose and show how control of such clusters can affect load peaks in distribution grids. Subsequently, we give a short overview on how we are going to expand the idea of self-organized clusters of units into creating a virtual control center for dynamic virtual power plants (DVPP) offering products at a power market. For an efficient organization of DVPPs, the flexibilities of units have to be represented in a compact and easy to use manner. We give an introduction how the problem of representing a set of possibly 10{sup 100} feasible schedules can be solved by a machine-learning approach. In summary, this article provides an overall impression how we use agent based control techniques and methods of self-organization to support the further integration of distributed and renewable energy sources into power grids and energy markets. - Highlights: • Distributed load management for electrical vehicles supports local supply from PV. • Appliances can self-organize into so called virtual appliances for load control. • Dynamic VPPs can be controlled by extensively decentralized control centers. • Flexibilities of units can efficiently be represented by support-vector descriptions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Q-Learning-Based Impact Assessment of Propagating Extreme Weather on Distribution Grids

The increasing number of power outage events due to extreme weather conditions is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical. It can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantity the weather severity and its effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability, and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by extreme weather events.

distribution system↗
DOE OSTI2021

Artificial Intelligence Techniques in Smart Grid: A Survey

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

energy systems↗
NASA NTRS2017

JIGSAW-GEO (1.0): Locally Orthogonal Staggered Unstructured Grid Generation for General Circulation Modelling on the Sphere

An algorithm for the generation of non-uniform, locally orthogonal staggered unstructured spheroidal grids is described. This technique is designed to generate very high-quality staggered VoronoiDelaunay meshes appropriate for general circulation modelling on the sphere, including applications to atmospheric simulation, ocean-modelling and numerical weather prediction. Using a recently developed Frontal-Delaunay refinement technique, a method for the construction of high-quality unstructured spheroidal Delaunay triangulations is introduced. A locally orthogonal polygonal grid, derived from the associated Voronoi diagram, is computed as the staggered dual. It is shown that use of the Frontal-Delaunay refinement technique allows for the generation of very high-quality unstructured triangulations, satisfying a priori bounds on element size and shape. Grid quality is further improved through the application of hill-climbing-type optimisation techniques. Overall, the algorithm is shown to produce grids with very high element quality and smooth grading characteristics, while imposing relatively low computational expense. A selection of uniform and non-uniform spheroidal grids appropriate for high-resolution, multi-scale general circulation modelling are presented. These grids are shown to satisfy the geometric constraints associated with contemporary unstructured C-grid-type finite-volume models, including the Model for Prediction Across Scales (MPAS-O). The use of user-defined mesh-spacing functions to generate smoothly graded, non-uniform grids for multi-resolution-type studies is discussed in detail.

geophysical fluid dynamics↗
DOE OSTI2010

Vehicle to Micro-Grid: Leveraging Existing Assets for Reliable Energy Management (Poster)

Fort Carson, a United States Army installation located south of Colorado Springs, Colorado, is seeking to be a net-zero energy facility. As part of this initiative, the base will be constructing a micro-grid that ties to various forms of renewable energy. To reduce petroleum consumption, Fort Carson is considering grid-connected vehicles (GCVs) such as pure electric trucks to replace some of itson-base truck fleet. As the availability and affordability of distributed renewable energy generation options increase, so will the GCV options (currently, three all-electric trucks are available on the GSA schedule). The presence of GCVs on-base opens up the possibility to utilize these vehicles to provide stability to the base micro-grid. This poster summarizes work to estimate the potentialimpacts of three electric vehicle grid interactions between the electric truck fleet and the Fort Carson micro-grid: 1) full-power charging without management, 2) full-power charging capability controlled by the local grid authority, and 3) full-power charge and discharge capability controlled by the local grid authority. We found that even at relatively small adoption rates, the control ofelectric vehicle charging at Fort Carson will aid in regulation of variable renewable generation loads and help stabilize the micro-grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Q-Learning Based Impact Assessment of Propagating Extreme Weather on Distribution Grids: Preprint

Increasing number of power outage events due to extreme weather condition is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical and can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantify weather severity and it’s effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by the extreme weather events.

distribution system↗
DOE OSTI2023

Geophysical Constraints on Decarbonized Systems—Building Spatio-Temporal Uncertainties into Future Electricity Grid Planning

Future electricity grids will be characterized by the high penetration of renewables to support the decarbonization process. Yet, this transition will further expose grids to a broad spectrum of geophysical forces, such as weather and climate, land and space, minerals and materials. Understanding their role is thus important to support the grid decarbonization process. Here, we synthesize the current body of knowledge on the relationship between geophysical constraints and electricity grid planning. Additionally, we show that there have been promising advances in the data, methods, and modelling tools needed to incorporate the effect of geophysical constraints on demand, resource availability, and grid operations. There are, however, multiple research avenues that deserve further attention. More system-specific and finer-scale analyses are necessary to better understand how spatio-temporal variability---and associated uncertainty---in geophysical forces affect grid planning. Moreover, we need a broader focus on the multi-sectoral implications of grid decarbonization efforts, so as to limit the risk of unintended consequences. Importantly, all these efforts are challenged by the computational requirements of existing power system models, which often limit our ability to characterize uncertainty, scale analyses across larger domains, or study the multi-sector implications of grid expansion decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimisation model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernisation of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state-of-the-art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Control system design and stability analysis for a three phase SiC-based Filter-less grid-connected PV inverter

In this paper, a control system of a filter-less grid-connected SiC inverter is designed. Stability analysis has been researched with a focus on grid disturbance rejection. The analysis shows that the conventional control method with instantaneous grid voltage feedforward (IGVF) will significantly limit the bandwidth or stability margin of a filter-less grid-connected inverter, thus make the inverter sensitive to grid disturbance. Two proposed grid voltage feedforward control methods, which require little additional computation resources, are presented to suppress the grid voltage disturbance. The design methods of control parameters are derived. The effects of the proposed feedforward methods are also compared with that of conventional IGVF. Lastly, the grid-connected experimental results of a three phase SiC based filter-less PV inverter are provided to verify and compare the proposed control methods.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Enhancing Distribution Grid Resilience Through Model Predictive Controller Enabled Prioritized Load Restoration Strategy

Effective resilience improvement strategies enable the power grid to cope with disruptive extreme events. Most power grid outages are caused by disruptions in distribution grids. Motivated by the urgent need for power system resilience research, this paper proposes a priority-weighted optimal load restoration technique to enhance the resilience of distribution grids against extreme events. The proposed technique is based on smart distribution technology and framed as sequential multi-step decision process (MDP) and mixed integer linear program (MILP). It is formulated as optimal control problem with a model predictive control (MPC) approach. We applied the devised MILP-MPC-based load restoration technique to a simplified single-bus version of the IEEE 13-bus distribution system with integrated distributed energy resources (DERs) such as wind turbine, photovoitaic array, microturbine, and energy storage device. The technique executes a reducing and rolling horizon optimization in each control step in real-time using the forecasted information of the renewables, the fuel status of the microturbine and the state of charge of the energy storage device. We consider an extreme event which triggered outage of the upstream utility grid and caused islanded operation of the distribution grid. We demonstrated the effectiveness of the proposed MPC approach in restoring the distribution grid loads based on their priority during the main grid outage-caused islanded operation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid

We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors - such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals - such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machine-learning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Economic Analysis of an Electric Thermal Energy Storage System Using Solid Particles for Grid Electricity Storage

As renewable power generation becomes the mainstream new-built energy source, energy storage will become an indispensable need to complement the uncertainty of renewable resources to firm the power supply. When phasing out fossil-fuel power plants to meet the carbon neutral utility target in the midcentury around the world, large capacity of energy storage will be needed to provide reliable grid power. The renewable power integration with storage can support future carbon-free utility and has several significant impacts including increasing the value of renewable generation to the grid, improving the peak-load response, and balancing the electricity supply and demand. Long-duration energy storage (10–100 hours duration) can potentially complement the reduction of fossil-fuel baseload generation that otherwise would risk grid security when a large portion of grid power comes from variable renewable sources. Current energy storage methods based on pumped storage hydropower or batteries have many limitations. Thermal energy storage (TES) has unique advantages in scale and siting flexibility to provide grid-scale storage capacity. A particle-based TES system has promising cost and performance for the future growing energy storage needs. This paper introduces the system and components required for the particle TES to be technically and economically competitive. A technoeconomic analysis based on preliminary component designs and performance shows that the particle TES integrated with an efficient air-Brayton combined cycle power system can provide power for several days by low-cost, high-performance storage cycles. It addresses grid storage needs by enabling large-scale grid integration of intermittent renewables like wind and solar, thereby increasing their grid value. The design specifications and cost estimations of major components in a commercial scale system are presented in this paper. The cost model provides insights for further development and cost comparison with competing technologies.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2016

SU-F-T-508: A Collimator-Based 3-Dimensional Grid Therapy Technique in a Small Animal Radiation Research Platform

Purpose: Three dimensional (3D) Grid Therapy using MLC-based inverse-planning has been proposed to achieve the features of both conformal radiotherapy and spatially fractionated radiotherapy, which may deliver very high dose in a single fraction to portions of a large tumor with relatively low normal tissue dose. However, the technique requires relatively long delivery time. This study aims to develop a collimator-based 3D grid therapy technique. Here we report the development of the technique in a small animal radiation research platform. Methods: Similar as in the MLC-based technique, 9 non-coplanar beams in special channeling directions were used for the 3D grid therapy technique. Two specially designed grid collimators were fabricated, and one of them was selectively used to match the corresponding gantry/couch angles so that the grid opening of all 9 beams are met in the 3D space in the target. A stack of EBT3 films were used as 3D dosimetry to demonstrate the 3D grid-like dose distribution in the target. Three 1-mm beams were delivered to the stack of films in the area outside the target for alignment when all the films were scanned to reconstruct the 3D dosimtric image. Results: 3D film dosimetry showed a lattice-like dose distribution in the 3D target as well as in the axial, sagittal and coronal planes. The dose outside the target also showed a grid like dose distribution, and the average dose gradually decreased with the distance to the target. The peak to valley ratio was approximately 5:1. The delivery time was 7 minutes for 18 Gy peak dose, comparing to 6 minutes to deliver a 18-Gy 3D conformal plan. Conclusion: We have demonstrated the feasibility of the collimator-based 3D grid therapy technique which can significantly reduce delivery time comparing to MLC-based inverse planning technique.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-628: An Evaluation of Grid Size in Eclipse AcurosXB Dose Calculation Algorithm for SBRT Lung

Purpose: purpose of this study is to evaluate the effect of grid size in Eclipse AcurosXB dose calculation algorithm for SBRT lung. Methods: Five cases of SBRT lung previously treated have been chosen for present study. Four of the plans were 5 fields conventional IMRT and one was Rapid Arc plan. All five cases have been calculated with five grid sizes (1, 1.5, 2, 2.5 and 3mm) available for AXB algorithm with same plan normalization. Dosimetric indices relevant to SBRT along with MUs and time have been recorded for different grid sizes. The maximum difference was calculated as a percentage of mean of all five values. All the plans were IMRT QAed with portal dosimetry. Results: The maximum difference of MUs was within 2%. The time increased was as high as 7 times from highest 3mm to lowest 1mm grid size. The largest difference of PTV minimum, maximum and mean dose were 7.7%, 1.5% and 1.6% respectively. The highest D2-Max difference was 6.1%. The highest difference in ipsilateral lung mean, V5Gy, V10Gy and V20Gy were 2.6%, 2.4%, 1.9% and 3.8% respectively. The maximum difference of heart, cord and esophagus dose were 6.5%, 7.8% and 4.02% respectively. The IMRT Gamma passing rate at 2%/2mm remains within 1.5% with at least 98% points passing with all grid sizes. Conclusion: This work indicates the lowest grid size of 1mm available in AXB is not necessarily required for accurate dose calculation. The IMRT passing rate was insignificant or not observed with the reduction of grid size less than 2mm. Although the maximum percentage difference of some of the dosimetric indices appear large, most of them are clinically insignificant in absolute dose values. So we conclude that 2mm grid size calculation is best compromise in light of dose calculation accuracy and time it takes to calculate dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

SCADA↗
DOE OSTI2019

Physics–Dynamics Coupling with Element-Based High-Order Galerkin Methods: Quasi-Equal-Area Physics Grid

Atmospheric modeling with element-based high-order Galerkin methods presents a unique challenge to the conventional physics–dynamics coupling paradigm, due to the highly irregular distribution of nodes within an element and the distinct numerical characteristics of the Galerkin method. The conventional coupling procedure is to evaluate the physical parameterizations ( physics) on the dynamical core grid. Evaluating the physics at the nodal points exacerbates numerical noise from the Galerkin method, enabling and amplifying local extrema at element boundaries. Grid imprinting may be substantially reduced through the introduction of an entirely separate, approximately isotropic finite-volume grid for evaluating the physics forcing. Integration of the spectral basis over the control volumes provides an area-average state to the physics, which is more representative of the state in the vicinity of the nodal points rather than the nodal point itself and is more consistent with the notion of a “large-scale state” required by conventional physics packages. This study documents the implementation of a quasi-equal-area physics grid into NCAR’s Community Atmosphere Model Spectral Element and is shown to be effective at mitigating grid imprinting in the solution. The physics grid is also appropriate for coupling to other components within the Community Earth System Model, since the coupler requires component fluxes to be defined on a finite-volume grid, and one can be certain that the fluxes on the physics grid are, indeed, volume averaged.

54 ENVIRONMENTAL SCIENCES↗