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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

rabpro: global watershed boundaries, river elevation profiles, and catchment statistics

River and Basin Profiler (rabpro) is a Python package to delineate watersheds, extract river flowlines and elevation profiles, and compute watershed statistics for any location on the Earth’s surface. As fundamental hydrologically-relevant units of surface area, watersheds are areas of land that drain via aboveground pathways to the same location, or outlet. Delineations of watershed boundaries are typically performed on digital elevation models (DEMs) that represent surface elevations as gridded rasters. Depending on the resolution of the DEM and the size of the watershed, delineation may be very computationally expensive. With this in mind, we designed rabpro to provide user-friendly workflows to manage the complexity and computational expense of watershed calculations given an arbitrary coordinate pair. In addition to basic watershed delineation, rabpro will extract the elevation profile for a watershed’s mainchannel flowline. This enables the computation of river slope, which is a critical parameter in many hydrologic and geomorphologic models. Finally, rabpro provides a user-friendly wrapper around Google Earth Engine’s (GEE) Python API to enable cloud-computing of zonal watershed statistics and/or time-varying forcing data from hundreds of available datasets. Altogether, rabpro provides the ability to automate or semi-automate complex watershed analysis workflows across broad spatial extents.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Role of Innovation in the Electric Utility Sector

Innovation is essential for future power systems to be safe and secure, clean and sustainable, affordable and equitable, and reliable and resilient, according to a recent National Academies report. But state regulatory reforms are needed to encourage adoption of new technologies to support evolution of the nation’s power systems.1 Berkeley Lab's report, The Role of Innovation in the Electric Utility Sector, provides consumer, labor, utility, third-party provider, and clean technology consultant perspectives on this theme. To achieve state targets for clean energy and greenhouse gas emissions, some state regulatory utility commissions are exploring new approaches to spur innovation: -For utilities, regulatory and marketing flexibility, increased funding for demonstration projects, and performance-based ratemaking including multi-year rate plans -For third parties, ways to provide utility customers with innovative products and services directly Among the questions the report addresses: 1. How are consumer advocate views evolving with respect to innovative regulatory and ratemaking approaches? 2. How can utility decarbonization and grid modernization initiatives provide opportunities for local communities and workers to receive tangible benefits and facilitate community support for siting electricity infrastructure? 3. How are electric utilities partnering with technology companies to provide innovative energy management services and sustainable energy solutions for utility customers? 4. What regulatory innovations are public utility commissions exploring to enable third-party providers to participate in the transition to a modern electric system? 5. What regulatory changes are needed to enable innovative solutions from utilities and third parties at the necessary speed and scale to meet state decarbonization goals? The report is the 13th in the Future Electric Utility Regulation series, which taps leading thinkers to tackle complex regulatory issues for electricity. 1 National Academies of Sciences, Engineering, and Medicine. 2021. The Future of Electric Power in the United States. The National Academies Press.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2012

MAGIC: A Tool for Combining, Interpolating, and Processing Magnetograms

Transients in the solar coronal magnetic field are ultimately the source of space weather. Models which seek to track the evolution of the coronal field require magnetogram images to be used as boundary conditions. These magnetograms are obtained by numerous instruments with different cadences and resolutions. A tool is required which allows modelers to fmd all available data and use them to craft accurate and physically consistent boundary conditions for their models. We have developed a software tool, MAGIC (MAGnetogram Interpolation and Composition), to perform exactly this function. MAGIC can manage the acquisition of magneto gram data, cast it into a source-independent format, and then perform the necessary spatial and temporal interpolation to provide magnetic field values as requested onto model-defined grids. MAGIC has the ability to patch magneto grams from different sources together providing a more complete picture of the Sun's field than is possible from single magneto grams. In doing this, care must be taken so as not to introduce nonphysical current densities along the seam between magnetograms. We have designed a method which minimizes these spurious current densities. MAGIC also includes a number of post-processing tools which can provide additional information to models. For example, MAGIC includes an interface to the DA VE4VM tool which derives surface flow velocities from the time evolution of surface magnetic field. MAGIC has been developed as an application of the KAMELEON data formatting toolkit which has been developed by the CCMC.

Allred, Joel↗
DOE OSTI2021

Porting the Nonlinear Optimization Library HiOp to Accelerator-Based Hardware Architectures

While interior point method has been the centerpiece of nonlinear programming tools used in science and engineering, its reliance on linear solvers that can tackle sparse symmetric indefinite and highly ill-conditioned problems made it difficult to implement it effectively on hardware accelerators. HiOp optimization package attempts to provide an implementation of the interior point method suitable for hardware accelerators by compressing the original sparse problem to produce an underlying linear problem that is dense and of manageable size. Implementations of dense linear solvers are more mature and utilize hardware accelerators better than their sparse counterparts. There is a number of important domain problems, such as optimal power flow analysis for power grids, where the sparse problem can be effectively compressed and deploying dense linear solver within the interior point method can improve performance. Here we describe a portable implementation of HiOp optimization engine, which uses a linear solver from Magma library and runs entirely on hardware accelerators. To compress the problem, HiOp uses customized mixed dense-sparse linear algebra. All HiOp kernels are implemented using Umpire and RAJA portability libraries. We describe details of the implementation and discuss trade-offs between performance, portability and development cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids: Preprint

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILPPS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to specified time durations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2022

Prediction of Power Measurements Using Adaptive Filters

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyberattack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System 9EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be complex. In signal processing, time-series solutions do not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

Hamad, Khaled↗
DOE OSTI2021

Defining a Use Case for the ADMS Test Bed: Fault Location, Isolation, and Service Restoration with Distributed Energy Resources

Advanced distribution management systems (ADMS) integrate multiple enterprise-level functions into a single platform and offer advanced applications such as fault location, isolation, and service restoration (FLISR), to utilities to meet their operational needs for a modernized grid. These applications need to operate reliably even as distributed energy resource (DER) penetration increases on distribution systems. The ADMS test bed at the National Renewable Energy Laboratory offers utilities and vendors the opportunity to evaluate the performance of such advanced applications on distribution feeders of the future and to understand their potential benefits for a specific utility. This is done through defining use cases that address specific questions. This paper presents the definition of a use case on the performance of a commercially-available FLISR application on a feeder of an electric cooperative with DERs. After experiments are completed, results from this use case will be disseminated to the electric utility and research community to improve understanding of the challenges and benefits that DERs present to ADMS applications and the operation of a modernized grid.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Prescient

Prescient is a software package for simulating electricity grid (power systems) operations, under both nominal and extreme event conditions. Core functionality includes discrete event simulation, commitment and dispatch optimization, probabilistic scenario construction, and reporting/visualization. SAND2020-0169 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Watson, Jean Paul↗
DOE OSTI2024

Platform Of Optimal Experiment Management

The platform of optimal experiment management, POEM, powered with automated machine learning to accelerate the discovery of optimal solutions, and automatically guide the design of experiments to be evaluated. POEM currently supports 1) random model explorations for experiment design, 2) sparse grid model explorations with Gaussian Polynomial Chaos surrogate model to accelerate experiment design ,3) time-dependent model sensitivity and uncertainty analysis to identify the importance features for experiment design, 4) model calibrations via Bayesian inference to integrate experiments to improve model performance, and 5) Bayesian optimization for optimal experimental design. In addition, POEM aims to simplify the process of experimental design for users, enabling them to analyze the data with minimal human intervention, and improving the technological output from research activities.

Wang, Congjian [Idaho National Laboratory (INL), I↗
DOE OSTI2022

Use of Operating Agreements and Energy Storage to Reduce Photovoltaic Interconnection Costs: Technical and Economic Analysis

This report presents an analytical methodology to identify alternative options to manage interconnection costs and streamline interconnection timelines for distribution system-connected photovoltaic (PV) systems not co-located with load. Specifically, the report details the methodology and results of an analysis that: 1. Identifies potential grid violations that would be induced by a PV system requesting interconnection to a distribution circuit; 2. Identifies multiple technically viable options for mitigating the potential violations, including infrastructure upgrades, downsizing the PV system size, curtailment of PV, and addition of battery energy storage; 3. Defines the required technical operating parameters of the system in order to mitigate all potential violations (the "Operating Envelope"); 4. Compares the economics of each option, from the PV developer's perspective. The analysis supports the ongoing evolution of interconnection standards for combined PV and energy storage systems by providing a methodology to define allowable maximum export limits during each hour. The methodology presented could be used by utilities as part of the interconnection study process, to inform negotiations with DER developers and the drafting of mutually acceptable interconnection agreements. The resulting technical operating parameters could be included as part of the interconnection service agreement (ISA) between the system owner and the utility company. The companion report to this analysis, Gill et al. (2022), details the envisioned framework for the implementation of the technical operating parameters.

14 SOLAR ENERGY↗
DOE OSTI2021

Defining a Use Case for the ADMS Test Bed: Fault Location, Isolation, and Service Restoration with Distributed Energy Resources: Preprint

Advanced distribution management systems (ADMS) integrate multiple enterprise-level functions into a single platform and offer advanced applications such as fault location, isolation, and service restoration (FLISR), to utilities to meet their operational needs for a modernized grid. These applications need to operate reliably even as distributed energy resource (DER) penetration increases on distribution systems. The ADMS test bed at the National Renewable Energy Laboratory offers utilities and vendors the opportunity to evaluate the performance of such advanced applications on distribution feeders of the future and to understand their potential benefits for a specific utility. This is done through defining use cases that address specific questions. This paper presents the definition of a use case on the performance of a commercially-available FLISR application on a feeder of an electric cooperative with DERs. Results from this use case will be disseminated to the electric utility and research community to improve understanding of the challenges and benefits that DERs present to ADMS applications and the operation of a modernized grid.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation inland-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine)rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover

58 GEOSCIENCES↗
NASA NTRS2017

Global and Regional Sea Level Rise Scenarios for the United States

The Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force, jointly convened by the U.S. Global Change Research Program (USGCRP) and the National Ocean Council (NOC), began its work in August 2015. The Task Force has focused its efforts on three primary tasks: 1) updating scenarios of global mean sea level (GMSL) rise, 2) integrating the global scenarios with regional factors contributing to sea level change for the entire U.S. coastline, and 3) incorporating these regionally appropriate scenarios within coastal risk management tools and capabilities deployed by individual agencies in support of the needs of specific stakeholder groups and user communities. This technical report focuses on the first two of these tasks and reports on the production of gridded relative sea level (RSL, which includes both ocean-level change and vertical land motion) projections for the United States associated with an updated set of GMSL scenarios. In addition to supporting the longer-term Task Force effort, this new product will be an important input into the USGCRP Sustained Assessment process and upcoming Fourth National Climate Assessment (NCA4) due in 2018. This report also serves as a key technical input into the in-progress USGCRP Climate Science Special Report (CSSR).

Sweet, William V.↗
NASA NTRSDate not supplied

Combinatorial Auction-Based Strategic Deconfliction of Federated UTM Airspace

Unmanned Aerial Vehicles (UAVs) have become commonly used to perform a wide range of commercial activities such as cinematography and medical supply delivery. Consequently, regulators have become interested in designing UAV Traffic Management systems (UTMs) to coordinate UAV traffic among a collection of UAV operators. One framework which has been recently proposed for a UTM system is a combinatorial auction. In this framework, airspace is modelled as a 4D grid of space-time cells. UAV operators bid on cells which collectively form paths for their UAVs. Ideally, an airspace auction should reveal information about the current price of flight paths to bidders, allowing bidders to identify and bid on a select number of paths instead of placing as many bids as possible in the hopes of stumbling on a cheap path. Revealing too much information, however, can allow bad actors to place bids which are intended not to win but to raise the price that a rival bidder must pay. We address these twin challenges with a new information revelation framework which provides bidders with wide-ranging pricing information while suppressing bad actors. We evaluate our framework on scenarios based on a Japan Aerospace Exploration Agency (JAXA) case study and find that it can scale to thousands of bids.

Christopher J C Leet↗