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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec↗
DOE OSTI2012

Volt-VAR Optimization on American Electric Power Feeders in Northeast Columbus

In 2007 American Electric Power launched the gridSMART® initiative with the goals of increasing efficiency of the electricity delivery system and improving service to the end-use customers. As part of the initiative, a coordinated Volt-VAR system was deployed on eleven distribution feeders at five substations in the Northeast Columbus Ohio Area. The goal of the coordinated Volt-VAR system was to decrease the amount of energy necessary to provide end-use customers with the same quality of service. The evaluation of the Volt-VAR system performance was conducted in two stages. The first stage was composed of simulation, analysis, and estimation, while the second stage was composed of analyzing collected field data. This panel paper will examine the analysis conducted in both stages and present the estimated improvements in system efficiency.

Schneider, Kevin P.↗
DOE OSTI2022

CALORIE: A Constraint Language and Optimizing Runtime for Exascale Power Management (Final Report)

This final technical report summarizes the key accomplishments on the CALORIE project, a DOE Early Career award received by PI Henry Hoffmann at the University of Chicago. CALORIE’s main goal was to create principled methodologies, tools, and practices to help scientists and high-performance computing (HPC) operators maximize the performance and insights obtained from scientific computing applications in the face of exascale power constraints. The project accomplished these goals by completing the following objectives: • Designing a language for describing application goals (including constraints and objectives) and system capabilities. The application goals will include things like which simulations or simulations plus in situ analysis will be run together, what the power constraints are, and what requirements there are for in situ analysis (for example, a desired frame rate for visualization). The system capabilities include all components that can be adjusted to tradeoff power and performance. • Designing a runtime system that takes specified goals and capabilities and dynamically determines what capabilities to use to meet the goals. This runtime adapts to changes in goals, application behavior, or available capabilities to automatically maintain the goals despite unexpected disturbances. • Developing a foundational understanding of how power constraints affect the problem of scheduling applications in large-scale systems. This report provides an overview of the accomplishments related to each of these key objectives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An Iterative Approach for Solving the SCOPF Problem Applying LP, SOCP, and NLP Subproblems

We propose to develop efficient algorithms and software for the SCOPF problem. We will employ an iterative approach that will: a) use linear subproblems and other active set filtering techniques to identify the most important contingencies and drastically reduce the SCOPF model size; b) solve SOCP relaxations of the reduced SCOPF to converge to the neighborhood of the global optimal solution and establish a lower bound on the solution, and; c) use a non-convex, nonlinear interior-point solver, Artelys Knitro, to converge quickly to the optimal solution. To identify the most effective approach, we will experiment with several techniques to identify the tradeoffs between contingency subproblem complexity and fast solvability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1976

A technique for optimizing the design of power semiconductor devices

A technique is described that provides a basis for predicting whether any device design change will improve or degrade the unavoidable trade-off that must be made between the conduction loss and the turn-off speed of fast-switching high-power thyristors. The technique makes use of a previously reported method by which, for a given design, this trade-off was determined for a wide range of carrier lifetimes. It is shown that by extending this technique, one can predict how other design variables affect this trade-off. The results show that for relatively slow devices the design can be changed to decrease the current gains to improve the turn-off time without significantly degrading the losses. On the other hand, for devices having fast turn-off times design changes can be made to increase the current gain to decrease the losses without a proportionate increase in the turn-off time. Physical explanations for these results are proposed.

Schlegel, E. S.↗
NASA NTRS1979

Structural cost optimization of photovoltaic central power station modules and support structure

The results of a comprehensive study of photovoltaic module structural support concepts for photovoltaic central power stations and their associated costs are presented. The objective of the study has been the identification of structural cost drivers. Parametric structural design and cost analyses of complete array systems consisting of modules, primary support structures, and foundations were performed. Area related module cost was found to be constant with design, size, and loading. A curved glass module concept was evaluated and found to have the potential to significantly reduce panel structural costs. Conclusions of the study are: array costs do not vary greatly among the designs evaluated; panel and array costs are strongly dependent on design loading; and the best support configuration is load dependent

Sutton, P. D.↗
NASA NTRS2019

Using Power Diagrams to Build Optimal Unstructured Meshes for C-Grid Models

The Model for Prediction Across Scales (MPAS) for Ocean (-O), Sea-Ice (-SI) and Land-Ice (-LI), in addition to the Coastal Ocean Marine Prediction Across Scales (COMPAS) are two novel general circulation models designed to resolve coupled ocean-ice dynamics over variable spatial scales using non-uniform unstructured grids. Both models are based on a conservative mimetic finite-difference/volume formulation (TRiSK), in which staggered momentum, vorticity and mass-based degrees- of-freedom are distributed over an orthogonal 'primal-dual' mesh.

Engwirda, Darren↗
NASA NTRSDate not supplied

Onboard Autonomous Trajectory Planning for Mars Power Descent

In recent years, there has been an increasing interest in space-qualified processors such as multi-core central processing units and graphics processing units that can withstand the adverse effects of space radiation. These processors can allow parallel programming to perform tasks that typically demand high computational power. One can study guidance schemes that can take advantage of these currently developing processors and provide more robust guidance. Software for Multi-model Autonomous Real-time Trajectories (SMART) guidance can identify robust trajectories by running an onboard Monte Carlo analysis. SMART guidance can take advantage of knowledge updates obtained from the onboard sensors, allowing it to consider the off-nominal cases that it would not typically encounter during the offline trajectory analysis. This work uses the SMART guidance for the powered divert at Mars simulation in Program to Optimize and Simulated Trajectories- II.

Autonomous Planning↗