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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Anomaly Detection and Mitigation for Dynamic Frequency Regulation in Hydropower-Battery Systems

Hydropower operators and energy storage providers are increasingly interested in participating in frequency regulation services, driven by the incentives offered by independent system operators, such as the PJM Interconnection. This transition, however, unfolds against the backdrop of a modernizing and rapidly digitizing power grid, exposing the integrated legacy infrastructure to a multitude of cybersecurity threats. This work presents an approach for developing an anomaly detection and mitigation system to address cybersecurity challenges during the participation of a hydropower-integrated battery energy storage system (BESS) in a frequency regulation market. The applied anomaly detector utilizes machine learning algorithms to provide detailed classification of cyber-physical events. Later, the applied mitigation system triggers predefined corrective actions to minimize the impact of data integrity attacks on the regulation market and system stability. We evaluated the proposed approach on a hydropower-integrated BESS topology, specifically analyzing the slow regulation signal (Reg A) coming from the PJM market. Our simulation results demonstrate that the proposed approach performs well in detecting data integrity attacks within the allocated time frame and also minimizes the system's transient instability during the participation of hydropower and BESS in the regulation market.

battery energy storage system↗
DOE OSTI2024

A Measurement-Based Adaptive Voltage Regulation Method Considering Topology Changes

This paper proposes an online adaptive data-driven distributed energy resource (DER) dispatch optimization method for voltage control considering topology changes. By using a local sensitivity factor (LSF)-enabled voltage control, traditional DER control can be reformulated into a linear programming (LP) problem, leading to faster computation speeds. Power injection alteration and topology changes are two common operational changes in the distribution network that can affect the LSF and voltage control performance. To address this issue, a robust estimation method is developed to adjust the sensitivity matrix at each time step for the time-varying power injection changes. When topology changes occur, only the allocated predominant LSF submatrices are updated based on measurement data, allowing for a fast adaptation to the system reconfiguration. Results obtained from a real distribution feeder in Southern California demonstrate its robustness as compared to traditional volt-var control and constant LSF matrix dispatch control methods.

DERs↗
DOE OSTI2025

Model-Free Control of Grid-Interactive Efficient Buildings Under Communication Time Delays

Grid-interactive efficient buildings (GEBs) have recently been used to enhance the reliability and stability of the electric grid through demand response (DR) programs. However, most existing DR control strategies require accurate modeling of the various building thermostatically controlled loads (TCLs) and are computationally expensive. To address these challenges, a model-free control (MFC)-based strategy has recently been introduced for coordinating and controlling GEBs. MFC is a data-enabled control strategy that is computationally efficient and does not require the analytical models of the various building equipment. In this paper, we numerically investigate the impact of communication time delays on the performance of MFC in maintaining the TCLs' temperatures within the desired comfort levels while meeting the assigned power allocation constraint.

Telsang, Bhagyashri [University of Tennessee, Knox↗
DOE OSTI2025

A Novel Secondary Frequency Regulation with Optimal Priority Selection of AGC Contributions

Automatic generation control (AGC) is used to maintain acceptable frequencies during operation owing to fluctuations in load and variable resources. In conventional industry applications, the AGC signal is allocated to each generator according to the predispatched frequency regulation capacity or the order of economic efficiency. However, with the increasing integration of inverter-based resources (IBRs), the retirement of conventional synchronous generators (SGs) has posed new challenges to frequency control schemes because fewer of them are optional for AGC regulation. In this paper, we propose a novel model predictive control (MPC)-based frequency regulation model to reduce control cost and ensure stability, by considering different critical dynamic factors when optimally selecting the AGC units. The proposed control model – developed in a general form – comprehensively embeds characteristics such as generator ramping rates, reserve capacity, and operation cost. The model predictive control–based two-timescale AGC scheme enhances the capability of immunizing the power disturbance from types of resources by coordinating the control signals between faster IBRs and slower SGs. The case study’s proposed model is verified to be effective in synergistically enforcing different dynamic properties of AGC units into the frequency regulation scheme.

Jiang, Sufan [The University of North Carolina at ↗
DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗
DOE OSTI2025

A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem

Solving problems related to planning and operations of large-scale power systems is challenging on classical computers due to their inherent nature as mixed-integer and nonlinear problems. Quantum computing provides new avenues to approach these problems. We develop a hybrid quantum-classical algorithm for the Unit Commitment (UC) problem in power systems which aims at minimizing the total cost while optimally allocating generating units to meet the hourly demand of the power loads. The hybrid algorithm combines a variational quantum algorithm (VQA) with a classical Benders-type heuristic. The resulting algorithm computes approximate solutions to UC in three stages: i) a collection of UC vectors capable meeting the power demand with lowest possible operating costs is generated based on VQA; ii) a classical sequential least squares programming (SLSQP) routine is leveraged to find the optimal power level corresponding to a predetermined number of candidate vectors; iii) in the last stage, the approximate solution of UC along with generating units power level combination is given. To demonstrate the effectiveness of the presented method, three different systems with 3 generating units, 10 generating units, and 26 generating units were tested for different time periods. In addition, convergence of the hybrid quantum-classical algorithm for select time periods is proven out on IonQ's Forte system.

Aboumrad, Willie [IonQ, Inc]↗
DOE OSTI2024

Utility-Scale Shared Energy Storage Deployment: Challenges, Research Gaps, and Opportunities

Although community energy storage (CES) and behind-the-meter (BTM) energy storage systems have been widely used to offer homeowners and communities a variety of localized benefits, their scalability and grid support functionalities are limited. On the other hand, utility-scale shared energy storage (USES) systems may offer a number of benefits for grid integration, scalability, and economic viability. When compared to BTM and CES alternatives, these large-scale systems provide more storage capacity, more efficient operations, and more economically viable options. The deployment of USES presents opportunities for optimizing grid performance, integrating renewable energy resources, and improving energy security at the community level. However, significant research gaps exist in optimizing the integration and operation of these systems, especially to allocate energy for consumer use, grid services, and enhancing energy resilience. This paper reviews the literature in this regard, focusing on the opportunities, research gaps, and challenges associated with USES deployment. Firstly, the paper provides an overview of USES systems and emphasizes their benefits. Secondly, the key challenges are identified, and research gaps associated with the operation and integration of these systems are highlighted. Lastly, some potential solutions and opportunities that can be adopted to facilitate the rapid deployment and management of USES are presented. Technological, economic, regulatory, and environmental aspects are also discussed in this paper, providing an overview of the current state and future prospects of this technology.

Gautam, Mukesh [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
DOE OSTI2024

A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale

We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if" scenarios, system optimizations, and virtual prototyping of future systems. Using Frontier as a case study, we demonstrate the framework's capabilities by replaying six months of system telemetry for systematic verification and validation. Such a comprehensive analysis of a liquid-cooled exascale supercomputer is the first of its kind. ExaDigiT elucidates complex transient cooling system dynamics, runs synthetic or real workloads, and predicts energy losses due to rectification and voltage conversion. Throughout our paper, we present lessons learned to benefit HPC practitioners developing similar digital twins. We envision the digital twin will be a key enabler for sustainable, energy-efficient supercomputing.

Brewer, Wes↗
DOE OSTI2024

Enabling HPC Scientific Workflows for Serverless

The convergence of edge computing, big data analytics, and AI with traditional scientific calculations is increasingly being adopted in HPC workflows. Workflow management systems are crucial for managing and orchestrating these complex computational tasks. However, it is difficult to identify patterns within the growing population of HPC workflows. Serverless has emerged as a novel computing paradigm, offering dynamic resource allocation, quick response time, fine-grained resource management and auto-scaling. In this paper, we propose a framework to enable HPC scientific workflows on serverless. Our approach integrates a widely used traditional HPC workflow generator with an HPC serverless workflow management system to create benchmark suites of scientific workflows with diverse characteristics. These workflows can be executed on different serverless platforms. We comprehensively compare executing workflows on traditional local containers and serverless computing platforms. Our results show that serverless can reduce CPU and memory usage respectively by 78.11% and 73.92% without compromising performance.

Andrei da silva, Anderson↗
DOE OSTI2024

Improving transition to IPv6-only via RFC8925 and IPv4 DNS Interventions

Nine years have passed since the American Registry for Internet Numbers exhausted its allocation of Internet Protocol version 4 (IPv4) addresses, and four years have passed since the United States Government mandated federal agencies to complete the transition to Internet Protocol version 6 (IPv6). Despite the IPv4 address shortage and IPv6 mandate, Federally Funded Research and Development Centers (FFRDCs) are still struggling to sunset IPv4. As demonstrated on SC23’s SC23v6 wireless network, newer tooling such as RFC8925 allows clients to disable their IPv4 protocol stack while retaining legacy IP connectivity via the RFC6145 translation algorithm. However, SC23v6 wireless clients without RFC8925 support or a disabled IPv6 stack would continue to receive internet access via legacy IPv4. This paper introduces a method of using poisoned IPv4 Domain Name System (DNS) records to gracefully inform IPv4-only clients at SC24’s SC24v6 wireless network about their inability to use the current version of internet protocol, with a goal of minimal impact to RFC8925 and dual-stack clients. When implemented as designed, this method may improve supportability and user experience of IPv6-only deployments at FFRDCs.

Costello, Thomas M↗
DOE OSTI2024

Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets

Pricing and settlement mechanisms are crucial for efficient resource allocation, investment incentives, market competition, and regulatory oversight. In the United States, Regional Transmission Operators (RTOs) adopts a uniform pricing scheme that hinges on the marginal costs of supplying additional electricity. This study investigates the pricing and settlement impacts of alternative reserve constraint modeling, highlighting how even slight variations in the modeling of constraints can drastically alter market clearing prices, reserve quantities, and revenue outcomes. Focusing on the diverse market designs and assumptions in ancillary services by U.S. RTOs, particularly in relation to capacity sharing and reserve substitutions, the research examines four distinct models that combine these elements based on a large-scale synthetic power system test data. Our study provides a critical insight into the economic implications and the underlying factors of these alternative reserve constraints through market simulations and data analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Real-Time Multiregional Market-to-Market Congestion Management Through Exchange of Relief Cost Curve

This paper introduces a novel method for multiregional market-to-market (M2M) coordinated congestion management. It identifies shortcomings in existing M2M approaches, where Regional Transmission Organizations (RTOs) exchange shadow prices and relief requests to optimize congestion relief allocations across interconnected regions. Two methods are proposed to enhance flow and price convergence. The first method proposes that both Regional Transmission Organizations (RTOs) use state-estimator flows directly to determine relief requirements, eliminating delays and potential oscillations caused by using market flows calculated from the prior period under existing M2M approach. The second method involves exchanging transmission relief cost curves, enabling each RTOs to integrate other RTOs' relief costs curve into its real-time security-constrained economic dispatch (SCED). This method can effectively extend the coordination to multiple transmission lines and across more than two RTOs. The alternating direction method of multipliers (ADMM) is also applied to the M2M coordination problem and compared with the proposed methods. Case studies on small and large-scale systems demonstrate the effectiveness of these approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗
DOE OSTI2026

Robust Design Under Uncertainty in Quantum Error Mitigation

Error mitigation techniques are crucial to achieving near-term quantum advantage. Classical postprocessing of quantum computation outcomes is a popular approach for error mitigation, which includes methods, such as zero noise extrapolation, virtual distillation, and learning-based error mitigation. However, these techniques have limitations due to the propagation of uncertainty resulting from the finite shot number of a quantum measurement. In this work, we introduce general and unbiased methods for quantifying the uncertainty and error of error-mitigated observables based on the strategic sampling of error mitigation outcomes. We then extend our approach to demonstrate the optimization of performance and robustness of error mitigation under uncertainty. To illustrate our methods, we apply them to zero noise extrapolation and Clifford date regression in the ground state of the XY model simulated using depolarizing and International Business Machines Corporation (IBM) Toronto noise models, respectively. In particular, we optimize the choice of noise levels and the allocation of shots for zero noise extrapolation and the distribution of the training circuits for Clifford data regression. While our methods are readily applicable to any postprocessing-based error mitigation approach, in practice they must not be prohibitively expensive—even though they perform optimizations of the error mitigation hyperparameters requiring sampling of a statistical distribution of error mitigation outcomes. By leveraging surrogate-based optimization, we show that our methods can efficiently perform optimal design for a zero noise extrapolation implementation. We then further demonstrate the transferability of learned zero noise extrapolation hyperparameters to other similar circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Exploring Enhanced Dominant Resource Fairness Using Linear Programming Calculated Weights

Maintaining resource fairness while achieving optimization for various performance metrics such as resource utilization, turnaround time and job latency is a well-known resource scheduling challenge in cloud computing. Despite the significant progress made with the introduction of dominant resource fairness by Ghodsi et al., which ensures major allocation properties such as sharing incentive, strategy-proofness, envy-freeness and Pareto efficiency to be achieved

Yan, Bo [Binghamton University]↗
DOE OSTI2026

Operational Simulation of Multi-Functional Charging Station for Sustainable Transportation

As transportation systems move toward electrification and decarbonization, multifunctional charging stations (MFCS) are emerging as key infrastructure for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper presents a simulation model of an MFCS that integrates solar photovoltaic (PV), wind power, battery storage, hydrogen (\mathrm{H}_{2}) production, dual-pressure \mathrm{H}_{2} storage, fuel cells, and dynamic grid interactions. The model simulates daily operations using 5 -minute resolution data to capture realtime variability in renewable energy (RE), demand, and electricity prices. A flexible dispatch algorithm dynamically allocates energy for EV charging, \mathrm{H}_{2} production, storage, and grid transactions while respecting system constraints. Results show that the MFCS effectively prioritizes RE usage, minimizes waste, meets diverse energy demands, and achieves net operational profit. The model serves as a valuable decision-support tool for designing and optimizing integrated clean energy hubs for zero-emission transportation.

Gbadegoye, Jeremiah [University of Tennessee, Knox↗