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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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Distribution Grid Optimal Power Flow (D-OPF): Modeling, Analysis, and Benchmarking
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A Novel Interior-Exterior Approach for the TSO-DSO Based Bilevel Optimal Power Flow
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Distributed Optimal Power Flow Algorithm for Radial Networks, I: Balanced Single Phase Case
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Stochastic Optimal Power Flow Under Forecast Errors and Failures in Communication
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Training Data Regarding Optimal Power Flow For Efficient Machine Learning
The code is intended to take the data outputted by the MATPOWER OPF solver tools and restructures the data in a way that is best suited for machine learning. The variables are read and the desired values are taken and added to an array in the correct format. This array is then converted into a python array for future use. The code also utilizes MATPOWER's ability to construct different power flow scenarios and repeat them a chosen number of times. Each iteration will add a new line to the formatted array so that the final output is a matrix has a height equal to the number of repetition used.
Chance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty
When uncontrollable resources fluctuate, Optimum Power Flow (OPF), routinely used by the electric power industry to re-dispatch hourly controllable generation (coal, gas and hydro plants) over control areas of transmission networks, can result in grid instability, and, potentially, cascading outages. This risk arises be cause OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under the OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by simulations of real grids, would likely resulting in automatic line tripping to protect lines from thermal stress, a risky and undesirable outcome which compromises stability. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our Chance Constrained (CC) OPF corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, our CC-OPF satisfies all the constraints with high probability while simultaneously minimizing the cost of economic re-dispatch. CC-OPF allows efficient implementation, e.g. solving a typical instance over the 2746-bus Polish network in 20s on a standard laptop.
Linearized three-phase optimal power flow models for distribution grids [Slides]
Abstract not provided.
Transformation of the Distribution System: Distributed and Resilient Optimal Power Flow
This grant funded a Market and Technology Assessment project which included 1) an analysis of the total addressable market (TAM) for the RT-OPF technology; 2) a segmented assessment of North American utility business models and barriers to adoption of the RT-OPF technology for those models; 3) a validation of DER customer needs and barriers to participation in utility programs; and 4) a forward-looking analysis of utility regulatory innovation and changes that may impact utility business models and ability for utilities to adopt this technology.
Optimizing Power System Stability Margins After Wide-Area Emergencies.
Abstract not provided.
Analysis and Testing of Optimal Power Control Strategy for NASA Moon Base Interconnected DC Microgrid System
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Analysis and Testing of Optimal Power Control Strategy for NASA Moon Base Interconnected DC Microgrid System
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Solar electric interplanetary trajectory and performance data for sub-optimal powered spacecraft
Trajectory and spacecraft design data for unmanned interplanetary missions using suboptimal powered solar electric propulsion
Optimizing Power Density and Efficiency of a Double-Halbach Array Permanent-Magnet Ironless Axial-Flux Motor
NASA Glenn Research Center is investigating hybrid electric and turboelectric propulsion concepts for future aircraft to reduce fuel burn, emissions, and noise. Systems studies show that the weight and efficiency of the electric system components need to be improved for this concept to be feasible. This effort aims to identify design parameters that affect power density and efficiency for a double-Halbach array permanent-magnet ironless axial flux motor configuration. These parameters include both geometrical and higher-order parameters, including pole count, rotor speed, current density, and geometries of the magnets, windings, and air gap.
Optimizing Power Density and Efficiency of a Double-Halbach Array Permanent-Magnet Ironless Axial-Flux Motor
NASA Glenn Research Center is investigating hybrid electric and turboelectric propulsion concepts for future aircraft to reduce fuel burn, emissions, and noise. Systems studies show that the weight and efficiency of the electric system components need to be improved for this concept to be feasible. This effort aims to identify design parameters that affect power density and efficiency for a double-Halbach array permanent-magnet ironless axial flux motor configuration. These parameters include both geometrical and higher-order parameters, including pole count, rotor speed, current density, and geometries of the magnets, windings, and air gap.
Rethinking Propellant-Optimal Powered Descent Guidance
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Model-Based, Memory-Centric Performance and Power Optimization on NUMA Multiprocessors
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