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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How Today's Hydropower Impacts Tomorrow's Grid: Counterfactual Scenarios Showing Grid Impacts if Hydropower Goes Away

Amidst ongoing discussions about hydropower removals, retirements, and reduced availability due to drought and other environmental considerations, it is important to understand the long-term effects of reduced hydropower resources on the U.S. electric grid. This analysis uses the Regional Energy Deployment System (ReEDS™) grid planning model to compare several representative scenarios of retiring hydropower and pumped storage hydropower (PSH) capacity over time and explore the overall implications on the U.S. grid from present day through 2050. Reduced hydropower capacity and generation is replaced by a mix of both fossil and non-fossil resources, including natural gas, wind, solar, and battery technologies. Additional natural gas usage leads to an increase in cumulative national electric sector carbon dioxide and criteria pollutant operating emissions of less than 1% in many scenarios but up to 4-5.3% in some cases. Total electric sector costs also increase by <1% in many scenarios but up to 3.6% in the most extreme scenarios where nearly all the hydropower and PSH fleet retires, equating to $\$$340 billion in undiscounted costs. National results indicate that absent additional interventions, retiring hydropower and PSH capacity could increase electric sector emissions and direct capital and operating costs. However, more focused analysis is required to evaluate specific asset-level, local, and regional implications, and a broader scope is necessary to weigh these electric sector impacts alongside economic, ecological, water management, and other cross-sectoral effects that could be either negative or positive.

14 SOLAR ENERGY↗
DOE OSTI2025

Hydropower and environmental flow management: System-level trade-offs at Glen Canyon Dam

The research focuses on the Colorado River Basin, specifically examining the Glen Canyon Dam (GCD) and its influence on surrounding aquatic ecosystems. This area is crucial due to its role in hydropower production and its impact on downstream environments, including the Grand Canyon National Park. This study explores the integration of environmental factors into hydro dispatch modeling at GCD to tackle ecological challenges posed by the invasive smallmouth bass (SMB). Utilizing the GTMax SL and SERM models, the research assesses the effects of SMB control experiments on hydropower generation, economic value, and grid stability. The study examines the financial and economic impacts of bypass flows designed to release colder water to prevent SMB spawning, which can significantly reduce hydropower output and increase costs. The research identifies that declining reservoir levels and rising water temperatures in Lake Powell have facilitated SMB spawning, posing a threat to native fish populations like the endangered humpback chub. The findings highlight the importance of adaptive management strategies to balance ecological preservation with hydropower generation amid long-term weather-related challenges. The study underscores the need for comprehensive assessments of flow options to prevent SMB establishment below GCD, considering the broader implications for sediment dynamics and ecological interactions.

Ecological impact assessment↗
DOE OSTI2025

A high resolution, gridded product for vapor pressure deficit using Daymet

Vapor pressure deficit (VPD) is a critical variable in assessing drought conditions and evaluating plant water stress. Gridded products of global and regional VPD are not freely available from satellite remote sensing, model reanalysis, or ground observation datasets. We present two versions of the first gridded VPD product for the Continental US and parts of Northern Mexico and Southern Canada (CONUS+) at a 1 km spatial resolution and daily time step. We derived VPD from Daymet maximum daily temperature and average daily vapor pressure and scale the estimates based on (1) climate determined by the Köppen-Geiger classifications and (2) land cover determined by the International Geosphere-Biosphere Programme. Ground-based VPD data from 253 AmeriFlux sites representing different climate and land cover classifications were used to improve the Daymet-derived VPD estimates for every pixel in the CONUS+ grid to produce the final datasets. We evaluated the Daymet-derived VPD against independent observations and reanalysis data. The CONUS+ VPD datasets will aid in investigating disturbances including drought and wildfire, and informing land management strategies.

54 ENVIRONMENTAL SCIENCES↗
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↗
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↗