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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High-Resolution Synthetic Solar Irradiance Sequence Generation: An LSTM-Based Generative Adversarial Network

The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources' primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy Laboratory. The results of the case studies demonstrate the proposed approach's capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.

dynamic scheduling↗
DOE OSTI2020

Development of Hydrogen Mitigation for the Nevada Solar One Power Plant

Acciona Solar Power and NREL agreed to a collaborative effort (CRADA) to develop and implement a method to reduce hydrogen levels in the circulating HTF at the NSO power plant. The project was planned as a three-year effort, and work started in June 2015. The CRADA's goal was to solve the long-standing lost efficiency problem caused by the buildup of hydrogen in the HTF, which significantly impacts the efficiency, electricity generation, and revenue from parabolic trough power plants.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Fundamentals of Resource Adequacy for Modern Power Systems

This webinar covers and introduction to power system resource adequacy, examining definitions, metrics, an overview of probabilistic methods, incentives and capacity credits, storage modeling, and evolving practices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DE‐SC0022708 RESEARCH PERFORMANCE FINAL REPORT

GismoPower’s Final Research Report documents the outcomes of a DOE SBIR Phase II project focused on advancing the MEGA® (Mobile Electricity Generating Appliance), a trailerable, plug-in solar canopy appliance designed to deliver appliance-class electricity generation for homes, small businesses and renters. The project’s core objective was to remove the technical and regulatory barriers that have historically prevented plug-in solar systems from being safely certified, permitted, and interconnected in the United States.

14 SOLAR ENERGY↗
DOE OSTI2023

A Stochastic Approach to Reconstruct Gamma-Ray-burst Light Curves

Gamma-ray bursts (GRBs), as they are observed at high redshift ( z = 9.4), are vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a stochastic reconstruction, wherein we fit two preexisting models (the Willingale model; W07; and a broken power law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases by 33.3%, 35.03%, and 43.32% on average for the W07, and by 33.3%, 30.78%, 43.9% for the BPL, respectively. The uncertainty of the slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models, and for inferring the redshift of GRBs with future machine-learning analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1979

A 15kWe (nominal) solar thermal electric power conversion concept definition study: Steam Rankine reheat reciprocator system

An evaluation was made of the potential of a steam Rankine reheat reciprocator engine to operate at high efficiency in a point-focusing distributed receiver solar thermal-electric power system. The scope of the study included the engine system and electric generator; not included was the solar collector/mirror or the steam generator/receiver. A parametric analysis of steam conditions was completed leading to the selection of 973 K 12.1 MPa as the steam temperature/pressure for a conceptual design. A conceptual design was completed for a two cylinder/ opposed engine operating at 1800 rpm directly coupled to a commercially available induction generator. A unique part of the expander design is the use of carbon/graphite piston rings to eliminate the need for using oil as an upper cylinder lubricant. The evaluation included a system weight estimate of 230 kg at the mirror focal point with the condenser mounted separately on the ground. The estimated cost of the overall system is $1932 or $90/kW for the maximum 26 kW output.

Fuller, H.↗
DOE OSTI2015

R&D100: 6.5kV Enhancement-Mode Silicon Carbide JFET Switch

Researchers at Sandia National Laboratories have partnered with United Silicon Carbide, Inc. to combine advanced materials with novel manufacturing ideas to build a new product for significantly more efficient power conversion. Harnessing the unique features of silicon carbide, this first of its kind device allows higher voltage switching, and reductions in switching losses to significantly boost the efficiency and reliability of power generation and power conversion.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2023

Housing Energy Connection Project (Final Technical Report)

This project expanded existing medium-voltage distribution to 33 households, a daycare, a mechanical shop, irrigation pumps, and a sewer lift station, all of which previously received electric service from the local electric utility. As part of the project, automatic meter reading (AMR) and advanced metering infrastructure (AMI) was installed to allow the Tribe to set up its own electric metering like a utility. The primary objective of this project was to expand the Tribe-owned electrical distribution to provide highly reliable electrical service to these existing loads. The objective was successfully met after project completion. The project reduced high costs for electric energy previously supplied by the utility, by supplanting utility-supplied electric power with Tribe-generated electric power. The project gave the Tribe greater independence from traditional power sources and provided jobs for the community.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

17 WIND ENERGY↗
DOE OSTI2015

Geographic Visualization of Power-Grid Dynamics

The visualization enables the simulation analyst to see changes in the frequency through time and space. With this technology, the analyst has a bird's eye view of the frequency at loads and generators as the simulated power system responds to the loss of a generator, spikes in load, and other contingencies. The significance of a contingency to the operation of an electrical power system depends critically on how the resulting tansients evolve in time and space. Consequently, these dynamic events can only be understood when seen in their proper geographic context. this understanding is indispensable to engineers working on the next generation of distributed sensing and control systems for the smart grid. By making possible a natural and intuitive presentation of dynamic behavior, our new visualization technology is a situational-awareness tool for power-system engineers.

Sukumar, SreenivasR.↗
DOE OSTI2015

Geographic Visualization of Power-Grid Dynamics

The visualization enables the simulation analyst to see changes in the frequency through time and space. With this technology, the analyst has a bird's eye view of the frequency at loads and generators as the simulated power system responds to the loss of a generator, spikes in load, and other contingencies. The significance of a contingency to the operation of an electrical power system depends critically on how the resulting tansients evolve in time and space. Consequently, these dynamic events can only be understood when seen in their proper geographic context. this understanding is indispensable to engineers working on the next generation of distributed sensing and control systems for the smart grid. By making possible a natural and intuitive presentation of dynamic behavior, our new visualization technology is a situational-awareness tool for power-system engineers.

Sukumar, Sreenivas R.↗
DOE OSTI2022

Seeking Repeating Anthropogenic Seismic Sources: Implications for Seismic Velocity Monitoring at Fault Zones

Abstract Seismic velocities in rocks are highly sensitive to changes in permanent deformation and fluid content. The temporal variation of seismic velocity during the preparation phase of earthquakes has been well documented in laboratories but rarely observed in nature. It has been recently found that some anthropogenic, high‐frequency (>1 Hz) seismic sources are powerful enough to generate body waves that travel down to a few kilometers and can be used to monitor fault zones at seismogenic depth. Anthropogenic seismic sources typically have fixed spatial distribution and provide new perspectives for velocity monitoring. In this work, we propose a systematic workflow to seek such powerful seismic sources in a rapid and straightforward manner. We tackle the problem from a statistical point of view, considering that persistent, powerful seismic sources yield highly coherent correlation functions (CFs) between pairs of seismic sensors. The algorithm is tested in California and Japan. Multiple sites close to fault zones show high‐frequency CFs stable for an extended period of time. These findings have great potential for monitoring fault zones, including the San Jacinto Fault and the Ridgecrest area in Southern California, Napa in Northern California, and faults in central Japan. However, extra steps, such as beamforming or polarization analysis, are required to determine the dominant seismic sources and study the source characteristics, which are crucial to interpreting the velocity monitoring results. Train tremors identified by the present approach have been successfully used for seismic velocity monitoring of the San Jacinto Fault in previous studies.

58 GEOSCIENCES↗
DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration: Preprint

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions, i.e., the reserve requirement, renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained off-line using historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine and battery. Case studies demonstrated that the proposed method outperforms other operating reserve determination methods.

distribution system↗
DOE OSTI2018

General Requirements for Designing and Implementing a Cryptography Module for Distributed Energy Resource (DER) Systems

Penetration of distributed energy resources (DERs) is rapidly increasing in the bulk power system (BPS); they are growing to be a significant portion of generation. As such, grid-support capabilities are being developed and implemented. However, as their presence increases, the impact of DERs on the BPS also increases. Therefore, if a disturbance occurs in the DER system, its effects could propagate throughout the BPS. These disturbances could range from equipment malfunctions to resource variability to cyber attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Changes in European wind energy generation potential within a 1.5 °C warmer world

Global climate model simulations from the 'Half a degree Additional warming, Prognosis and Projected Impacts ' (HAPPI) project were used to assess how wind power generation over Europe would change in a future world where global temperatures reach 1.5 °C above pre-industrial levels. Comparing recent historical (2006–2015) and future 1.5 °C forcing experiments highlights that the climate models demonstrate a northward shift in the Atlantic jet, leading to a significant (p < 0.01) increase in surface winds over the UK and Northern Europe and a significant (p < 0.05) reduction over Southern Europe. We use a wind turbine power model to transform daily near-surface (10 m) wind speeds into daily wind power output, accounting for sub-daily variability, the height of the turbine, and power losses due to transmission and distribution of electricity. To reduce regional model biases we use bias-corrected 10 m wind speeds. We see an increase in power generation potential over much of Europe, with the greatest increase in load factor over the UK of around four percentage points. Increases in variability are seen over much of central and northern Europe with the largest seasonal change in summer. Focusing on the UK, we find that wind energy production during spring and autumn under 1.5 °C forcing would become as productive as it is currently during the peak winter season. Similarly, summer winds would increase driving up wind generation to resemble levels currently seen in spring and autumn. We conclude that the potential for wind energy in Northern Europe may be greater than has been previously assumed, with likely increases even in a 1.5 °C warmer world. While there is the potential for Southern Europe to see a reduction in their wind resource, these decreases are likely to be negligible.

54 ENVIRONMENTAL SCIENCES↗