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DOE OSTI2026

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗

DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗

DOE OSTI2024

Frequency Nadir Constrained Unit Commitment for High Renewable Penetration Island Power Systems

The process of energy decarbonization in island power systems is accelerated due to the swift integration of inverter-based renewable energy resources (IBRs). The unique features of such systems, including rapid frequency changes resulting from potential generation outages or imbalances due to the unpredictability of renewable power, pose a significant challenge in maintaining the frequency nadir without external support. This paper presents a unit commitment (UC) model with data-driven frequency nadir constraints, including either frequency nadir or minimum inertia requirements, helping to limit frequency deviations after significant generator outages. The constraints are formulated using a linear regression model that takes advantage of real-world, year-long generation scheduling and dynamic simulation data. The efficacy of the proposed UC model is verified through a year-long simulation in an actual island power system using historical weather data. The alternative minimum inertia constraint, derived from actual system operation assumptions, is also evaluated. Findings demonstrate that the proposed frequency nadir constraint notably improves the system's frequency nadir under high photovoltaic (PV) penetration levels, albeit with a slight increase in generation costs, when compared to the alternative minimum inertia constraint.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Dynamic Provisioning and Execution of HPC Workflows Using Python

High-performance computing (HPC) workflows over the last several decades have proven to assist in the understanding of scientific phenomena and the production of better products, more quickly, and at reduced cost. However, HPC workflows are difficult to implement and use for a variety of reasons. In this paper, we describe the development of the Python-based cumulus, which addresses many of these barriers. cumulus is a platform for the dynamic provisioning and execution of HPC workflows. cumulus provides the infrastructure needed to build applications that leverage traditional or Cloud-based HPC resources in their workflows. Lastly, we demonstrate the use of cumulus in both web and desktop simulation applications, as well as in an Apache Spark-based analysis application.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. Furthermore, these training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Enabling the High Level Synthesis of Data Analytics Accelerators

Conventional High Level Synthesis (HLS) tools mainly tar- get compute intensive kernels typical of digital signal pro- cessing applications. We are developing techniques and ar- chitectural templates to enable HLS of data analytics appli- cations. These applications are memory intensive, present fine-grained, unpredictable data accesses, and irregular, dy- namic task parallelism. We discuss an architectural tem- plate based around a distributed controller to efficiently ex- ploit thread level parallelism. We present a memory in- terface that supports parallel memory subsystems and en- ables implementing atomic memory operations. We intro- duce a dynamic task scheduling approach to efficiently ex- ecute heavily unbalanced workload. The templates are val- idated by synthesizing queries from the Lehigh University Benchmark (LUBM), a well know SPARQL benchmark.

Data Analytics↗

DOE OSTI2021

A population data-driven workflow for COVID-19 modeling and learning

CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces, and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of protective behaviors of individuals on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county, and state stakeholders in response to evolving decision-making priorities, while incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible, and performant analytical platform for planning and crisis response.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Automation Technologies Impact on the Work Process of Nuclear Power Plants

The current cost associated with the work process of nuclear power plants necessities modernizing the work process by augmenting it with automation technologies. This work was performed to assess various automation technologies and the means by which they assimilate into the work process in order to make gains in cost savings in modern nuclear power plants. The goal was to identify key informational items that would be useful to members of industry who are undertaking the large effort to reduce costs through automation by identifying technology maturity levels, recent research, and an analysis of cost impacts of the work process. The technologies cost saving was categorized into low, medium, and high cost savings. Through this effort, a substantive literature review and analysis of recent technological and market trends was performed. The key areas of cost impact were identified through communication with users in industry from operations and maintenance organizations. The results were assembled into a list of work process steps and technologies that are ready be implemented or researched for the automating aspects of that step. This effort aims to present to utilities opportunities of cost saving that are achievable in the short term, and gaps for research organizations and vendors to consider for further research and development. The results of this effort generated a list of technologies that fit each of the three cost saving categories. Electronic work packages remain a high cost saving technology, but data mining and integration, smart scheduling, and dynamic risk models are also indicated to yield a high cost savings. Video applications, spatial mapping, and image processing and identification technologies appear to provide solid cost savings in the medium category, and rounding out the low cost saving areas are deployable technologies such as bar codes, communication tools, and electronic tags.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Fracture Network Prediction Using Physics-based Machine Learning Algorithms

In recent years, systematic CO2 injection into geological reservoirs across the U.S. has gained traction as a strategy to mitigate greenhouse gas emissions. This approach necessitates precise monitoring to ensure secure containment, minimize risks, and optimize storage management. Our study leverages machine learning (ML) techniques to advance the understanding of CO2 injection processes, focusing on the Illinois Basin. Over a three-year injection period, we analyzed microseismic data, identifying 19 temporal intervals with significant bottom-hole pressure changes. By partitioning microseismic events into these intervals and estimating b-values, we revealed over 100 clusters of events related to fracture initiation or reactivation. Advanced spatial analysis highlighted horizontally-oriented fractures along the NNW-SSE axis. This quantification of fracture networks informs dynamic injection scheduling, work-over strategies, and risk assessments, enhancing carbon capture, utilization, and storage (CCUS) operations. Additionally, our methodology offers valuable insights for oil and gas operations and geothermal development, supporting fracture-based monitoring and risk mitigation.

Kumar, Abhash↗

DOE OSTI2026

Frequency-Nadir-Constrained Unit Commitment for Low-Inertia, High-IBR Island Power Systems [Slides]

14 SOLAR ENERGY↗

DOE OSTI2020

Nationwide Energy Saving Potential Evaluation for Office Buildings with Occupant-Based Building Controls

The heating ventilation and air-conditioning (HVAC) system is a major contributor to building energy consumption. This paper aims to quantify the nationwide energy savings potential of implementing the occupancy-based control (OBC) for the HVAC system in typical medium-sized office buildings using a whole building simulation program (i.e., EnergyPlus). First, the medium-sized office from the DOE Commercial Prototype Building Models was modified to have detailed layout and space functions. Then, a statistic-algorithm-based tool was used to generate the dynamic occupancy schedules for various rooms. Next, both a baseline case and two OBC cases were created for the comparison based on the updated ASHRAE Standard 90.1 and ASHRAE Guideline 36. The HVAC operation in the baseline case follows a static occupancy schedule and fixed setpoint schedules for both room air temperatures and minimal system outside air flowrates, while the proposed OBC cases use the room temperature reset, system minimum outdoor airflow reset, and zone minimal supply airflow rate reset. These cases were extended to five climate zones in the United States to further quantify the energy savings potential associated with the OBC on a nationwide scale. The preliminary result of this case study shows that there could be significant energy savings potential from the proposed OBC strategies in medium-sized office buildings in the United States.

Pang, Zhihong↗

DOE OSTI2016

Severe Accident Modeling for Cyber Scenarios

A generic severe accident model of a PWR is developed to support analyses of cyber-fault scenarios that may lead to extensive core damage. For this purpose, a MELCOR 2.1 model of a generic PWR is created that is capable of simulating primary and secondary thermal-hydraulics, core heatup and fuel degradation, RPV failure, containment response, and radionuclide release and transport to the environment. The size and detail of the severe accident model are intended to facilitate scoping analyses of various cyber-based scenarios in an automated, dynamic framework. A dynamic job scheduler such as ADAPT will be used to evaluate many accident scenarios in an automated fashion utilizing parallel computing. Individual test calculations are first conducted of a basic unmitigated scenario (i.e. no significant operator intervention) with a cyber-based accident initiator. This preliminary assessment is used to gauge modelling capabilities for further, expanded cyber accident scenarios in conjunction with ADAPT. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Impact of Hydrological Data on Power System Operational Studies: Preprint

Hydropower is expected to play an important role in maintaining grid reliability and flexibility as the share of of variable renewable energy increases. While the current hydropower operational models have been studied and used widely, they haven't been updated for decades to meet new performance standards. For example, current steady state and dynamic models often neglect hydrological conditions, which may lead to unrealistic expectations when relying on hydropower for energy and ancillary services. To study this impact, a multi-timescale hydrological model was created by leveraging the National Renewable Energy Laboratory-developed Multi-timescale Integrated Dynamics and Scheduling (MIDAS) tool. Using MIDAS, we compare the impact of considering hydrological conditions in a day-ahead unit commitment (DAUC) schedule on the reduced 240-bus Western Interconnect (WI) test system under winter and summer case studies. We show that neglecting current hydrological conditions of hydropower plants in power system models can lead to an overestimation of hydropower capabilities, which could lead to power balancing issues. For example, power system DAUC simulation results of our reduced test system show that in the case where hydrological conditions are not considered in the model, an approximate 31% overestimation of hydropower capabilities occurs in the summer case and approximately 60% occurs in the winter case compared to what is available. Additionally, results show an underestimation of WI day-ahead power system generation costs by approximately $54M - $80M in the weekly summer scenario and $116M - $126M in the weekly winter scenario. This analysis helps to underscore the importance of considering hydrological data in power system operational studies.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗

DOE OSTI2013

Using cross-layer adaptations for dynamic data management in large scale coupled scientific workflows, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

As system scales and application complexity grow, managing and processing simulation data has become a significant challenge. While recent approaches based on data staging and in-situ/in-transit data processing are promising, dynamic data volumes and distributions, such as those occurring in AMR-based simulations, make the efficient use of these techniques challenging. In this paper we propose cross-layer adaptations that address these challenges and respond at runtime to dynamic data management requirements. Specifically we explore (1) adaptations of the spatial resolution at which the data is processed, (2) dynamic placement and scheduling of data processing kernels, and (3) dynamic allocation of in-transit resources. We also exploit coordinated approaches that dynamically combine these adaptations at the different layers. We evaluate the performance of our adaptive cross-layer management approach on the Intrepid IBM-BlueGene/P and Titan Cray-XK7 systems using Chombo-based AMR applications, and demonstrate its effectiveness in improving overall time-to-solution and increasing resource efficiency.

Computer Science↗

DOE OSTI2017

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed this issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing

This paper proposes a dynamic control algorithm to enable an energy-aware single machine scheduling under the time-varying electricity pricing policy, in which price rates remain fixed day-to-day over the season. The key issue is to assign a set of jobs to available time periods where different electricity prices are assigned, while considering requested due dates of jobs so as to minimize total penalty costs for earliness and tardiness of jobs and total energy consumption costs, simultaneously. As the first contribution of this study, we develop a new mixed integer nonlinear programming (MINLP) model that aims at determining job arrival times and resulting earliness and tardiness of jobs and energy consumption costs for machine idle and normal processing. Second, an efficient heuristic approach based on continuous-time variable control models and algorithm is developed. The proposed heuristic adaptively changes job arrival times and due dates, which finally determine production sequence over the time periods of different electricity prices, machine turn-off, and machine idle with minimum energy consumption costs and just-in-time (JIT) penalty. Energy and JIT performance of the proposed approach is examined using real energy and machining parameters of a HAAS machine and compared to those of the metaheuristic approach. For relatively large size data groups, the proposed approach incurs about higher energy consumption costs on average, which are offset by up to lower JIT costs, resulting in lower total costs on average compared to the metaheuristic approach. Further, the proposed time-scaled heuristic algorithm yields extremely short computational time, which enables production managers to flexibly select proper production strategies and to implement them for different production environments.

42 ENGINEERING↗

DOE OSTI2019

Sky Survey Scheduling Using Reinforcement Learning

Modern cosmic sky surveys (e.g., CMB S4, DES, LSST) collect a complex diversity of astronomical objects. Each of class objects presents different requirements for observation time and sensitivity. For determining the best sequence of exposures for mapping the sky systematically, conventional scheduling methods do not optimize the use of survey time and resources. Dynamic Sky survey scheduling is an np-hard problem that has been therefore treated primarily with heuristic methods. We present an alternative scheduling method based on reinforcement learning (RL) that aims to optimize the use of telescope resources for scheduling sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗