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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Voltage regulation in distribution grids: A survey

Environmental and sustainability concerns have caused a recent surge in the penetration of distributed energy resources into the power grid. This may lead to voltage violations in the distribution systems making voltage regulation more relevant than ever. Owing to this and rapid advancements in sensing, communication, and computation technologies, the literature on voltage control techniques is growing at a rapid pace in distribution networks. In particular, there is a paradigm shift from traditional offline centralized approaches to distributed ones leveraging increased and varied types of actuators, real-time sensing, fast and efficient computations, and an overall distributed situational awareness. This paper reviews state-of-the-art voltage control algorithms, summarizes the underlying methods, and classifies their coordination mechanisms into local, centralized, distributed, and decentralized. The underlying solution methodologies are further classified into two categories, open-loop and feedback-based. Two specific example workflows are provided to illustrate these solutions for voltage regulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. Finally, it is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLPD-OPF model for the same distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
DOE OSTI2024

Diaspora: Resilience-enabling services for science from HPC to edge

Scientific applications of interest to DOE must increasingly engage distributed resources (e.g., instruments, remote computers, data stores, edge devices) and deliver more stringent levels of service (e.g., uninterrupted processing of experiment data streams). In such systems, state is distributed and components can fail in many ways, often silently, making application resilience a major concern. Addressing the resilience needs of such applications requires methods for gaining knowledge of resources and applications and for translating that knowledge into action. We are working on addressing these needs in the context of multi-messenger astronomy, where detecting and responding to unusual transient events in multiple cosmic messengers (gravitational wave, electromagnetic, high- energy particles) from different instruments leads to a federated learning problem.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Cyber Infrastructure for the Smart Electric Grid

As electric power systems undergo a transformative upgrade with the integration of advanced technologies to enable the smarter electric grid, professionals who work in the area require a new understanding of the evolving complexity of the grid. Cyber Infrastructure for the Smart Electric Grid delivers a comprehensive overview of the fundamental principles of smart grid operation and control, smart grid technologies, including sensors, communication networks, computation, data management, and cyber security, and the interdependencies between the component technologies on which a smart grid's security depends. The book offers readers the opportunity to critically analyze the smart grid infrastructure needed to sense, communicate, compute, and control in a secure way.

communication networks↗
DOE OSTI2021

Oscillatory Spreading and Inertia in Power Grids

The increase in variable renewable generators (VRGs) in power systems has altered the dynamics from a historical experience. VRGs introduce new sources of power oscillations, and the stabilizing response provided by synchronous generators (SGs, e.g., natural gas, coal, etc.), which help avoid some power fluctuations, will lessen as VRGs replace SGs. These changes have led to the need for new methods and metrics to quickly assess the likely oscillatory behavior for a particular network without performing computationally expensive simulations. This work studies the impact of a critical dynamical parameter - the inertia value - on the rest of a power system's oscillatory response to representative VRG perturbations. We use a known localization metric in a novel way to quantify the number of nodes responding to a perturbation and the magnitude of those responses. This metric allows us to relate the spread and severity of a system's power oscillations with inertia. We find that as inertia increases, the system response to node perturbations transitions from localized (only a few close nodes respond) to delocalized (many nodes across the network respond). We introduce a heuristic computed from the network Laplacian to relate this oscillatory transition to the network structure. We show that our heuristic accurately describes the spread of oscillations for a realistic power-system test case. Using a heuristic to determine the likely oscillatory behavior of a system given a set of parameters has wide applicability in power systems, and it could decrease the computational workload of planning and operation.

dynamical systems↗
DOE OSTI2022

A Framework for Neural Network Inference on FPGA-Centric SmartNICs

FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication, thereby mitigating the von Neumann bottleneck. Existing work, however, has been generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs, but most control tasks are left to the CPUs. This leads to frequent waiting, inferior performance, and scaling challenges. In this work, we propose a new distributive data-centric computing framework, named FCsN, for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture, FCsN allows the complete detaching of kernel execution, control logic, system scheduling, and network communication to the SmarNICs. This boosts performance by: (i) avoiding the control dependency with CPUs and (ii) supporting streaming kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network applications including graph neural networks; as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with the support of FCsN can achieve, on average, 144 speedups over the MPI-based standard CPU baselines.

Guo, Anqi↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2022

Frequency Domain Detection and Precoding for Massive MIMO With Single Carrier Modulation

Single carrier modulation (SCM) schemes are attractive for uplink (UL) transmissions due to improved power efficiency at the user equipment (UE) transmitter compared with multi-carrier modulation schemes. In a massive MIMO scenario with SCM, the UL detection must mitigate the effects of inter-symbol interference and multiuser interference. This processing is effectively performed in the frequency domain (FD) using a minimum mean squared error (MMSE) detector when the transmission is framed with a cyclic prefix. This paper presents a MMSEbased detector that is compatible with distributed processing in a time-division duplex (TDD) system. The matrix inverses computed for the UL detection are then reused to perform multi-user precoding for the downlink (DL). We find that this scheme yields a tremendous savings in computational complexity compared to commonly used zero-forcing (ZF) precoding without sacrificing any performance. Since MMSE processing introduces a bias to the estimates, we then derive the scalar coefficients necessary to cancel the MMSE bias. The impact of channel estimation errors are analyzed for both the UL and DL cases in conjunction with a power-efficient approach to SCM channel estimation. Moreover, extensive simulations are performed to confirm our theoretical findings.

42 ENGINEERING↗
DOE OSTI2025

torc (Torc Workflow Management System) [SWR-24-127]

This software package orchestrates execution of a workflow of jobs on distributed computing resources. It is optimized for use on HPCs with Slurm, but also can be used in the cloud and on local computers. Please refer to the documentation at https://nrel.github.io/torc

Thom, Daniel [National Renewable Energy Laboratory↗
DOE OSTI2026

Microgrid Design Toolkit [Slides]

This presentation provides an Introduction to the Microgrid Design Toolkit and how users can employ it.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

REopt: Energy Decision Support [Slides]

The REopt presentation, developed for the Energy Technology Innovation Partnership Project, provides an overview of the REopt tool. It covers tool's capabilities, how to use the tool, resources, and applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗