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At least 145 records · Page 8

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Commercialization of Distribution System Load Modeling Tool for Improved DER Interconnection Studies

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

14 SOLAR ENERGY↗

How Many Trip Requests Could We Support? An Activity-Travel Based Vehicle Scheduling Approach

In a world of ever-changing travel behavior and ever-increasing modal options, is vital to have integrated models that could capture the interactions between supply and demand layers of travel. Addressing this need, we propose three different versions of network representation and mathematical models for the activity-based vehicle routing problem to connect activity-travel graphs of passengers (demand layer) to spatio-temporal networks of vehicles (supply layer). Versions I and II are arc-based, while version III is path-based. In version I, we introduce the concept of activity-travel graphs for passengers. For vehicles, we construct space–time networks and add a new dimension, called “under-service state”, to track the execution status of trip requests at any location and time. In version II, we reduce the complexity of the network structure by eliminating the state dimension and some other modifications in the structure of the passengers’ and vehicles’ network. Although both versions can capture various behavioral constraints of the activity-based vehicle routing problem (e.g., mandatory and optimal activities, duration of activities, chain of activities, preferred starting and ending times of activities), due to the high level of complexity of the network structure, both versions can only solve small-sized problems. To tackle the computational complexity, we propose a path-based network representation in version III, and to make a balance between the disutility of passengers and vehicles, we present a tolled user equilibrium problem. Mathematical models are coded in C and GAMS and implemented on real-world Phoenix regional transportation network with more than 39 million trip requests, which demonstrate the effectiveness of the proposed solution for the original and restricted master problems.

33 ADVANCED PROPULSION SYSTEMS↗

Use of Shared Automated Vehicles for First-Mile Last-Mile Service: Micro-Simulation of Rail-Transit Connections in Austin, Texas

Shared fleets of fully automated vehicles (SAVs) coupled with real-time ride-sharing to and from transit stations are of interest to cities and nations in delivering more sustainable transportation systems. By providing first-mile last-mile (FMLM) connections to key transit stations, SAVs can replace walk-to-transit, drive-to-transit, and drive-only trips. Using the SUMO (Simulation of Urban MObility) toolkit, this paper examines mode splits, wait times, and other system features by micro-simulating two fleets of SAVs providing an FMLM ride-sharing service to 10% of central Austin’s trip-makers near five light-rail transit stations. These trips either start or end within two geofenced areas (called automated mobility districts [AMDs]), and travel time and wait time feedbacks affect mode choices. With rail service headways of 15?min, and 15 SAVs serving FMLM connections to and from each AMD, simulations predict that 3.7% of the person-trip-making will shift from driving alone to transit use in a 3?mi?×?6?mi central Austin area. During a 3-h morning peak, 30 SAVs serve about 10 person-trips each (to or from the stations), with 3.4?min average wait time for SAVs, and an average vehicle occupancy of 0.74 persons (per SAV mile-traveled), as a result of empty SAV driving between riders. Sensitivity analysis of transit headways (from 5 to 20?min) and fleet sizes (from 5 to 20 vehicles in each AMD) shows an increase in FMLM mode share with more frequent transit service and larger fleet size, but total travel time served as the biggest determinant in trip-makers’ mode share.

47 OTHER INSTRUMENTATION↗

Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven stochastic decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) consider complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematically integrate zonal occupancy patterns to better identify short-term (and time-varying) opportunities for grid service participation. We evaluate the performance of the proposed framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone commercial office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend we demonstrate a way for the building operators to monitor and change in real-time the available flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.

Jain, Milan↗

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗

A systematic review of building energy sufficiency towards energy and climate targets

Among the sufficiency, efficiency, and renewable frameworks for reducing energy use and energy-related carbon emissions, Building Energy Sufficiency (BES) is gaining attention from policy makers and engineers. Despite the significant role of the building sector in the success of national energy and climate plans, there is a lack of research on the drivers, technologies, and effective policy instruments required to achieve BES in the building operational phase. To fill this gap, this study presents a systematic review of the definition and paradigm of BES and concludes that BES should address both occupant demand and energy or emissions requirements simultaneously. The characteristics of occupant demand in building services are divided into four dimensions: time and space, quality and quantity, control and adjustment, and flexibility. Technical options regarding the building architecture, the envelope system, and the building energy system are reviewed. Finally, policy implications and recommendations are discussed. As a result, the multiple benefits and multidisciplinary nature of BES justify further research and accelerated policy implementation in developed and developing countries.

54 ENVIRONMENTAL SCIENCES↗

Network Slicing for Federated Learning in Operational Technology Environment

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

Delgado, Brian G. Rodiles [University of Texas at ↗

A new method for measuring refractory corrosion of ceramics in glass

Abstract Nuclear waste glass vitrification furnaces are lined with refractory ceramic blocks to contain the molten glass. The refractory liner is susceptible to corrosion and has a finite service lifetime. For this reason, predicting the refractory corrosion in contact with molten glass is integral to estimating melter service lifetime. Standardized laboratory tests varying time and temperature are commonly performed to estimate refractory material loss as a function of glass composition. These data are time and resource‐intensive to collect and are susceptible to considerable measurement error. In order to accelerate glass formulation and design for nuclear waste vitrification, methods are needed to increase laboratory‐scale throughput while maintaining data quality. In this work, a method to remove the residual glass from a corroded coupon using hydrofluoric acid is presented that accelerates the throughput of sample analysis while simultaneously facilitating more accurate measurements.

Amoroso, Jake W. [Savannah River National Laborato↗

Antimicrobial resistance of enteric pathogens in the Military Health System, 2009 – 2019

Abstract Background Acute diarrhea (AD) can have significant impacts on military troop readiness. Medical providers must understand current trends of enteropathogen antimicrobial resistance (AMR) in service members (SMs) to inform proper, timely treatment options. However, little is known of enteric pathogen profiles across the Military Health System (MHS). The primary objectives of this study were to identify gaps in enteric pathogen surveillance within the MHS, describe the epidemiology of AMR in enteric pathogens, and identify trends across the MHS both within the Continental United States (CONUS) and outside of the Continental United States (OCONUS). Methods Health Level 7 (HL7)-formatted laboratory data were queried for all specimens where Salmonella , Shigella , and Campylobacter species, as well as Shiga toxin-producing Escherichia coli ( E. coli ) (STEC) were isolated and certified between 1 January 2009 - 31 December 2019. Antibiotic susceptibility testing (AST) results were queried and summarized where available. Descriptive statistics were calculated for each organism by specimen source, year, and susceptibility testing availability. Results Among a total of 13,852 enteric bacterial isolates, 11,877 (86%) were submitted from CONUS locations. Out of 1479 Shigella spp. and 6755 Salmonella spp. isolates, 1221 (83%) and 5019 (74%), respectively, reported any susceptibility results through the MHS. Overall, only 15% of STEC and 4% of Campylobacter spp. specimens had AST results available. Comparing AST reporting at CONUS versus OCONUS locations, AST was reported for 1175 (83%) and 46 (78%) of Shigella isolates at CONUS and OCONUS locations, respectively, and for 4591 (76%) and 428 (63%) of Salmonella isolates at CONUS and OCONUS locations, respectively. Conclusions This study revealed inconsistent enteropathogen AST conducted across the MHS, with differing trends between CONUS and OCONUS locations. Additional work is needed to assess pathogen-specific gaps in testing and reporting to develop optimal surveillance that supports the health of the force.

Ashbaugh, Hayley↗

Optimal Electric Vehicle Charging and Discharging Strategies Under DER Compensation Programs: Preprint

The adoption of electric vehicles (EVs) is becoming increasingly popular because of environmental concerns, the greater availability of models, and increased cost-competitiveness with gas vehicles. Because EVs have both charging and discharging capabilities, they provide great potential to help electric utilities with grid operation. When the grid demand is high, EVs can discharge to the grid to reduce the peak load, and vice versa; therefore, electric utilities have designed different policies to encourage EV charging station operators to charge or discharge at certain time periods. The New York State Public Service Commission established the Value of Distributed Energy Resources (VDER), or the Value Stack, to compensate for energy created by distributed energy resources, including EVs. This paper presents an optimization-based approach to identify the "golden hours" and "golden spots," i.e., the effective time periods and geographic locations for EV charging station operators to charge or discharge under the VDER program that can provide them the highest benefit. The proposed methodology can be applied to other compensation mechanisms and distribution systems as well. By working with industry partner NineDot Energy, realistic charging station information is used in this study, and the proposed approach is tested on a distribution feeder. The results from this study can help electric utilities and EV charging station operators determine the ideal charging/discharging time and the ideal locations for the charging station(s) in their distribution systems to achieve maximized benefit.

electric vehicle↗

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗

Data for Autonomous Transportation Awareness: Data Exchange Use Cases, Standards, and Barriers

This report examines the critical data exchanges between automated vehicle (AV) service providers and the cities and municipalities they serve. It assists municipal authorities in navigating the often complex and real-time digital data exchanges needed to support AV mobility services, with emphasis in three areas: (1) critical safety data for broad-area situational awareness of hazards typically associated emergency dispatch or roadway work zones; (2) performance metrics of AV services that inform the quantity, quality, spatial extents, and impact on the roadway network; and (3) regulatory and policy information, particularly dynamic information that governs how AV services interact with the roadway network, with emphasis on curb space. The report reviews existing practices and emerging protocols and standards and identifies key gaps to address moving forward.

33 ADVANCED PROPULSION SYSTEMS↗

A systematic solution to quantify economic values of vehicle grid integration

We report Vehicle-Grid-Integration (VGI) supplies one of the potential benefit extensions for electric vehicles (EVs) to make use of their parking time, which enables the EVs to provide grid services while still meeting consumer driving needs. However, the costs, benefits and risks of VGI still remain unclear, which limits the development of the VGI to promote the interaction between the EV and grid. In this study, we propose an integrated framework to quantify and utilize the aggregate flexibility of the EVs to supply the grid services in electricity markets. The integrated solution includes five sub-modules that cover end-to-end functionalities from individual EV energy consumption estimation to final monetary values calculation of providing grid services. Both wholesale market and local level charging management are formulated in the optimization module. A predictive control algorithm is proposed to allocate power to individual vehicles in real time, considering uncertainties from dispatch signal and travel behavior. Simulation results from 10,000 EVs indicate that the proposed optimization methods can significantly reduce the system cost in both wholesale market and retail market. Local tariff optimization reduces the electricity cost by 24.4% compared to uncontrolled charging. Wholesale market optimization results show that $\$$691 and $\$$255 revenues can be captured by each EV in ERCOT and CAISO markets per year, although with a conservative assumption on battery throughput cost at 0.16$\$$/kWh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Vis-SAGA: Visual Analytics for Situational Awareness of Grid Anomalies: Preprint

We describe supporting near real-time situational awareness of the electric distribution system by visualizing novel data from voltage sensors deployed on existing broadband cable television network equipment. Our scalable web-based visual analytics platform supports interactive geospatial exploration, time-series analysis, and summarization of grid behavior during potentially anomalous events. The broadband cable television sensor network provides observability of the electrical distribution system at a higher local spatial resolution than is typically available to most utilities, revealing the operational state of the network and aiding in the detection of abnormal behaviors or deviations from expected patterns, particularly across electric utility service areas. We outline the design and development of interactive geospatial and time-series visualization components and the scalable data services that supply metadata, historical, and real-time streams of sensor data across the network. We evaluate our platform during periods of extreme weather, demonstrating its ability to assist in detecting patterns of operation that affect power availability, quality, resiliency, and service restoration.

cable television↗

Meeting in the Middle: Pooled Rideshare as Compelling Transportation Offering for Elders

Pooled ridesharing is an innovative new transportation offering with the potential to positively impact traffic and emissions. Through pooling, rideshare can aggregate demand, decreasing the number of vehicles required on the road, but also offering options to those without access to reliable means of transportation. In this study pooled ridesharing is explored in the context of its impacts on seniors. Through analysis of income, location, accessibility, and mode shares, pooled rideshare is demonstrated to offer a compelling middle ground for seniors looking for more affordable transportation than personal vehicles, but who are time sensitive or may not have access to public transportation. An efficient rideshare assignment strategy, which caters to discounts for riders' specific trips, is compared to a more basic heuristic. Results indicate that more riders utilize the service with catered discounts, despite increased wait and travel times over solo rideshare. Additionally, results indicate that although public transportation usage decreases when rideshare service offerings improve, there is not necessarily a direct shift in user base, as even pooled rideshare is still expensive enough to prevent seniors from the lowest household income quartiles from using the service.

Paul, Joseph↗

Modeling Value Flows in Utility Rate Structures

As the increased adoption of distributed energy resources continues to challenge flat utility rate structures, time-varying rates and more dynamic mechanisms like transactive energy systems can better leverage customer-sited distributed energy resources to provide grid services. However, adopting new utility policies can be a timely process and requires a high level of transparency into the energy system. A wide range of stakeholders must understand who may be affected by policy changes and how. This work employs the valuation methodology developed under Pacific Northwest National Laboratory’s Transactive Systems Program to outline the functional differences in value flow under a series of conventional rate structures and a transactive energy system. The resulting value model illustrates the nuances that arise and highlights future avenues of work that will be necessary as utilities across the country continue to develop new rate structures and market mechanisms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Stochastic Continuous-time Flexibility Scheduling and Pricing in Wholesale Electricity Markets

Large-scale integration of intermittent renewable energy sources (RES) is calling for additional flexibility resources as well as more advanced modeling and optimization techniques to account for the increasing uncertainty and variability in power systems operation. As the RES integration gains momentum, the magnitude and frequency of their variations increase, which may trigger ramping scarcity events in real-time power systems operation. This necessitates revisiting the present definition of power systems flexibility and reserve services to reflect their robustness and adequacy towards sub-interval variations of the load and RES, as well as adjusting the operation models to accommodate the new reserve services. This project took a fundamental approach and aimed at developing continuous-time scheduling and pricing model that accurately models the continuous-time variations of load and RES and efficiently deploys the ramping capability of flexible resources to compensate the sources of variability and uncertainty in the market. In this regard, this project pursued the following goals: Developing stochastic multi-fidelity continuous-time optimization models for scheduling of energy storage (ES) systems and flexible loads in wholesale energy markets; Developing the theory and practices of continuous-time locational marginal pricing for valuating energy storage systems and flexible loads in wholesale energy markets; Developing function space solution approach to convert the proposed stochastic multi-fidelity continuous-time optimization models into tractable mixed-integer linear optimization models; and Defining flexibility reserve as a new type of reserve in markets that would enable ultimate participation of energy storage devices in provision of services to compensate the variability and uncertainty of RES in electricity markets. This project successfully completed all five major tasks defined in the SOPO, and produced 8 high-impact journal papers, 6 conference papers, 3 published U.S. patents, and one web-based software for continuous-time operation optimization of power systems. The application of the proposed flexibility reserve and the stochastic multi-fidelity continuous-time operation scheduling models would modify the forward commitment and schedule of generating units, ES devices and flexible loads, and would line up the resources in such a way that the composition of available resources is better prepared to respond to the sub-hourly variations of the load and renewable resources in real-time operation. Therefore, this project paves the way to sustainable, reliable, and economic integration of renewable energy resources in power system, supporting the progress towards reaching the national targets on energy independence. Even if the proposed models offers a radically different point of view as compared to existing models, it does not alter fundamentally the architecture of power systems operations, nor the complexity of the scheduling problem, so the integration of this project in power systems is extremely practical.

24 POWER TRANSMISSION AND DISTRIBUTION↗