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At least 865 records · Page 48

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid “close-in”, and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.

47 OTHER INSTRUMENTATION↗

Considerations for Distributed Edge Data Centers and Use of Building Loads to Support Large Interconnections

The rapid expansion of artificial intelligence (AI) and machine learning is driving unprecedented electricity demand from data centers. It is predicted that by 2030, 90% of AI workloads will be inference-based, requiring interconnection of multiple low-latency edge data centers (<20 MW) sited closer to end users - often on already constrained distribution feeders. Although individually small, these loads can aggregate to large loads per feeder, straining infrastructure, creating multi-year interconnection delays, and driving up customer costs. This paper proposes a data center-focused grid-integration framework that combines feeder hosting capacity analysis with building energy efficiency, building load flexibility, and waste heat reuse to expand effective feeder and substation headroom. Such approaches can reduce interconnection delays, lower costs for ratepayers, and accelerate AI-ready infrastructure deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Managing Sustainable Data Infrastructures: The Gestalt of EOSDIS

EOSDIS epitomizes a System of Systems, whose many varied and distributed parts are integrated into a single, highly functional organized science data system. A distributed architecture was adopted to ensure discipline-specific support for the science data, while also leveraging standards and establishing policies and tools to enable interdisciplinary research, and analysis across multiple scientific instruments. The EOSDIS is composed of system elements such as geographically distributed archive centers used to manage the stewardship of data. The infrastructure consists of underlying capabilities connections that enable the primary system elements to function together. For example, one key infrastructure component is the common metadata repository, which enables discovery of all data within the EOSDIS system. EOSDIS employs processes and standards to ensure partners can work together effectively, and provide coherent services to users.

remote sensing↗

Advancing the Representation of Human Actions in Large‐Scale Hydrological Models: Challenges and Future Research Directions

Characterizing the impact of human actions on terrestrial water fluxes and storages at multi-basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human-water interactions in large-scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (~10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper-resolution” hydrological models (~0.1–1 km), multi-sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human-water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper-resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort.

catchment hydrology↗

7th Annual Earth System Grid Federation Face-to-Face Conference Report

The Seventh Annual Earth System Grid Federation (ESGF) Face-to-Face (F2F) Conference held December 4–8, 2017, in San Francisco, California, USA, assembled together a collection of independently funded national and international projects comprised of government agencies, institutions, and companies dedicated to the creation, management, analysis, and distribution of extreme-scale scientific data. The purpose of the conference was to discuss sustaining and enhancing the resilient ESGF data infrastructure with friendlier tools for the expanding global scientific community—this year’s emphasis was placed on the preparedness of the Coupled Model Intercomparison Project, phase 6 (CMIP6). It also focused on new tools that fulfill important and strategic capability gaps in scientific data archiving, access, analysis, and knowledge discovery. As the Executive Committee Chair of ESGF, I would like to personally thank each of conference attendees and those who could not attend but contributed to and/or supported the development of the ESGF software stack. It is an exciting time for the ESGF consortium as we continue to grow and adjust, remaining always adaptable, motivated, and responsive to our growing base of community projects. As we move forward, our ESGF organization is confronting and addressing many changes during a time of larger national and international commitment with fewer community resources. That said, our commitment to our sponsors and the community remains strong as we continue to meet the challenges before us and bring inspired developers and the scientific community together through forums like this conference, ensuring our ESGF organization remains robust and at the cutting edge of technology.

58 GEOSCIENCES↗

Extending Shared Socioeconomic Pathways to Future Water Supply In-frastructure Scenarios: A Case Study of San Antonio, TX

Datasets supporting findings and visualization behind Okoye and McManamay (2025) Extending Shared Socioeconomic Pathways to Future Water Supply Infrastructure Scenarios: A Case Study of San Antonio, TX. Environmental Research Communications, DOI: 10.57931/2563186 These datasets contains the results of a site selection analysis for municipal water supply planning across multiple Shared Socioeconomic Pathways (SSPs 1–5) and hard scenario classification of water systems in San Antonio, TX. It includes data at the resolution of individual surface water supply sources (COMIDs) and integrates a wide range of hydrologic, socioeconomic, infrastructural, and scenario-based planning variables. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions: Dataset_SSP1_SSP4.xlsx - Contains data used for site selection optimization under SSP1 to SSP4. This dataset was generated based on multi-indicator computations (e.g., WAI, WQI, ERI, WTC, WIC), scenario demand projections, and resource and spatial constraints, excluding new reservoir values. Dataset_SSP5.xlsx - Used for site optimization under SSP5. Unlike Dataset_SSP1_SSP4, this dataset includes new reservoir features with updated calculations of WAI, WTC, and WIC to reflect the added infrastructure and supply potential. hard_classification.xlsx - Provides the scenario classification output for each site. Includes both the initial scenario classification based on Euclidean Distance and adjusted classifications based on 30% change reduction BAU.zip - Zipped folder of .shp files showing spatially optimized water supply sites per SSP under the Business-As-Usual (BAU) water demand strategy LowGW.zip - Zipped folder of .shp files showing optimized site selections under the Low Groundwater strategy

geospatial↗

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗

Modeling Hydrogen Refueling Infrastructure to Support Passenger Vehicles

The year 2014 marked hydrogen fuel cell electric vehicles (FCEVs) first becoming commercially available in California, where significant investments are being made to promote the adoption of alternative transportation fuels. A refueling infrastructure network that guarantees adequate coverage and expands in line with vehicle sales is required for FCEVs to be successfully adopted by private customers. In this article, we provide an overview of modelling methodologies used to project hydrogen refueling infrastructure requirements to support FCEV adoption, and we describe, in detail, the National Renewable Energy Laboratory's scenario evaluation and regionalization analysis (SERA) model. As an example, we use SERA to explore two alternative scenarios of FCEV adoption: one in which FCEV deployment is limited to California and several major cities in the United States; and one in which FCEVs reach widespread adoption, becoming a major option as passenger vehicles across the entire country. Such scenarios can provide guidance and insights for efforts required to deploy the infrastructure supporting transition toward different levels of hydrogen use as a transportation fuel for passenger vehicles in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Viability of Reclaiming Municipal Wastewater for Potential Microalgae-Based Biofuel Production in the U.S.

Reclaimed municipal wastewater is a crucial component in biofuel production, especially in regions experiencing increasing freshwater scarcity. However, accurately estimating the potential for fuel production is challenging because of the uneven distribution of biofuel feedstock regions and wastewater treatment plants (WWTPs). This study assesses the viability of using reclaimed municipal water for algal biomass production in pond systems co-located with WWTPs under scenarios driven by biomass production and based on water transport logistics. We performed state- and county-level analysis of reclaimed water resources throughout the United States based on WWTP facility data. We overlaid these data onto estimated algae facility sites and examined the temporal resource availability to address seasonal variations in cultivation demand. Our findings reveal that 2694 billion liters per year of reclaimed water could potentially be used to produce 42.2 million metric tons (ash-free dry weight) of algal biomass, equivalent to 29.2 billion liters of renewable diesel equivalent (RDe). The use of reclaimed water would double current national water reuse and expand such reuse significantly in 455 counties across the United States. However, when we limit the construction of algae facilities to counties that can fully meet their water demand in order to minimize water transport burdens, the available supply decreases by 80%, to 512 billion liters, resulting in annual production of 12.2 billion liters of RDe, which still doubles current biodiesel production. Our analysis highlights the degree to which the location and flow of WWTPs and water transport affect the deployment of algae biofuel facilities and tradeoffs. These findings underscore the importance of improving the current WWTP infrastructure for reclaimed water reuse, especially in southern states.

09 BIOMASS FUELS↗

Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing

Scientific user facilities—particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more—operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.

Bethel, E. Wes↗

Quantifying Load Uncertainty Using Real Smart Meter Data

As we get closer to customers in distribution systems, load stochasticity increases. In the past, due to lack of real-time data, the comprehensive knowledge of load behavior was limited, and simplistic assumptions had to be made for distribution system modeling and analysis, especially in the processes of network design and expansion. With the deployment of Advanced Metering Infrastructure (AMI), ample real-time smart meter data has become available to utilities. In this paper, using real hourly smart meter data, we have quantified load uncertainty in terms of average, maximum and maximum noncoincident demands on a daily basis, as well as load factor and diversity factor. These uncertainty metrics are examined for individual residential, commercial and industrial customers, as well as distribution transformers serving residential customers. This paper provides a benchmark on load uncertainty quantification for practicing engineers and researchers.

Bu, Fankun↗

COMMON – Communications System Monitor v. 1.5

COMMON is a tool for collecting, storing, and displaying critical information to facilitate analysis on Microsoft Windows based IT systems. COMMON was developed to provide insight into the IT infrastructure for operational radiation detection systems, but is applicable to most Windows-based operating systems and TCP/IP network components. The COMMON tool aggregates a host of important Windows Management Instrumentation (WMI) information and as well as network ping responses to provide valuable insight into the performance of the IT system. A unique feature is the creation of daily output files (JSON) from the data collected for off site evaluation by subject matter experts.

Aragon, MonaL↗

Microgrid Design Toolkit (MDT) User Guide Software (V.1.1)

The Microgrid Design Toolkit (MDT) most readily supports decision analysis for new ("greenfield") microgrid designs, but can also be used to evaluate microgrids with existing infrastructure under some limitations. The current version of MDT includes two main capabilities. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new microgrid in the early stages of the design process. MSC is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on refining a microgrid design for operation in islanded mode. This second capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM).

24 POWER TRANSMISSION AND DISTRIBUTION↗

IMT-2000 Satellite Standards with Applications to Mobile Air Traffic Communications Networks

The International Mobile Telecommunications - 2000 (IMT-2000) standard and more specifically the Satellite component of it, is investigated as a potential alternative for communications to aircraft mobile users en-route and in terminal area. Its application to Air Traffic Management (ATM) communication needs is considered. A summary of the specifications of IMT-2000 satellite standards are outlined. It is shown via a system research analysis that it is possible to support most air traffic communication needs via an IMT-2000 infrastructure. This technology can compliment existing, or future digital aeronautical communications technologies such as VDL2, VDL3, Mode S, and UAT.

Shamma, Mohammed A.↗

California Statewide PEV Infrastructure Assessment; NREL (National Renewable Energy Laboratory)

This presentation discusses how the California Statewide Plug-In Electric Vehicle (PEV) Infrastructure Assessment provides a framework for understanding the potential energy (kWh) and demand (MW) impacts of PEV market growth; how PEV travel simulations can inform the role of public infrastructure in future market growth; and how ongoing assessment updates and Alternative Fuels Data Center outreach can help coordinate stakeholder planning and decision making and reduce uncertainties.

33 ADVANCED PROPULSION SYSTEMS↗

Management, Analysis, and Visualization of Experimental and Observational Data -- The Convergence of Data and Computing

Scientific user facilities---particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more---operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.

Bethel, E. Wes↗