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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

High Brightness Superconducting RF Gun Cavity

The Phase I goals were to numerically optimize a TM020/TM010 SRF gun with a demountable back wall, using SLAC’s state-of-the-art massive parallel ACE3P suite of 3D Finite Element Analysis codes available at the National Energy Research Scientific Computing Center (NERSC) , which includes: a) TM020 cell shape, including impact of RF focusing on beam dynamics; b) optimum number of TM010-cells to achieve low emittances; c) multipacting simulations back wall opening; d) dark current investigations; e) studies on LOM and HOM damping with waveguides/port on TM020-cells using wakefield solver can be conducted as well but are not required stringently; f) conceptual design of a power input coupler; g) multiphysics simulations to optimize the gun thermal management and cooling requirements for a superconducting joint; h) multivariate beam dynamical optimization in conjunction with the gun optimization; and i) finalize an engineering design for vertical high power tests at JLab.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2019

Collaboration for the Management of the Bruce Power Major Component Replacement (MCR) Nuclear Waste - 19630

Bruce Power's Major Component Replacement (MCR) Program will generate large volume waste via steam generators (SGs) and intermediate-level nuclear waste, which will require specialized packaging and handling to safely receive and store it at Ontario Power Generation's (OPG) Western Waste Management Facility (WWMF). The refurbishment of a Candu reactor involves, in part, the removal and replacement of all of a reactor core's fuel channel assemblies. Bruce Power and OPG collaborated to develop nuclear waste management solutions to optimize the use of OPG's existing Steam Generator Storage Building (SGSB) and Retube Component Storage Building (RCSB). By designing specialized Retube Waste Containers (RWCs) and optimizing configurations for the waste to be stored in the buildings, we will reduce the interim nuclear waste storage needs at the OPG WWMF, while improving Bruce Power MCR process efficiencies and reducing the overall costs of the MCR Program (Units 3 through 8). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Precursor Report of Data Needs and Recommended Practices for PV Plant Availability Operations and Maintenance Reporting.

Characterizing the factors that affect reliability of a photovoltaic (PV) power plant is an important aspect of optimal asset management. This document describes the many factors that affect operation and maintenance (O&M) of a PV plant, identifies the data necessary to quantify those factors, and describes how data might be used by O&M service providers and others in the PV industry. This document lays out data needs from perspectives of reliability, availability, and key performance indicators and is intended to be a precursor for standardizing terminology and data reporting, which will improve data sharing, analysis, and ultimately PV plant performance.

14 SOLAR ENERGY↗
NASA NTRS2006

Development of Efficient Mid-IR Interband Cascade Lasers for Chemical Sensing

Significant progress has been made: Above room temperature (up to 350K) pulsed operation has been demonstrated. CW operation temperature has been raised up to 237 K. DFB IC lasers have been applied for the detection of trace gases such as CH4, HCl, and H2CO. Devices have been operated continuously over several hundred hours without degradation. Main challenge remains for many potential applications of ICLs. CW operation at room temperature and above with significant output powers. There is still significant room for improvement: Laser design and material quality - many parameters have not been optimized. Device fabrication and thermal management (passivation, better mounting, etc.). Significantly higher output power can be achieved with laser arrays.

diode lasers↗
DOE OSTI2018

A Strawman for an HPC PowerStack

The landscape of High-Performance Computing (HPC) is changing as we enter the exascale era and power and energy management are key design points for any next generation of supercomputers. Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints is challenging due to several reasons including dynamic phase behavior, processor manufacturing variability and increasing heterogeneity of node-level components. Extending the scope from the node- and application-level up to the system-level introduces further challenges on top of power-unaware job scheduling, which is known to be NP-hard on its own. While there exists several individual efforts across the community to research automatic techniques for managing power and energy better, the majority of these techniques have been specialized to meet the needs of a specific HPC center or specific optimization goals and provide little support to connect them to each other. Some projects, most notably the PowerAPI efforts [1], discuss interfaces that form a good starting point for full stack integration, but these interfaces still need to be hooked up to the wide range of software components offered by academic partners, developers or vendors. Furthermore, a recent survey conducted by the EE HPC WG concluded that the majority of such techniques have lacked the application-awareness required to achieve the best system performance and throughput. Other observations were that each technique tended to target management of power and energy for a different subset of the site or system hardware and that the different techniques tended to perform management at different (and often conflicting) granularities. Unfortunately, the existing techniques have not been designed to coexist simultaneously on one site and cooperate on management in an integrated manner [2]. The lack of application-awareness, lack of coordinated management across different granularities, and the lack of widely accepted interfaces and consequent limited connectivity between modules, can result in substantially underutilized Watts and FLOPS. To address these gaps, the HPC community needs a holistic stack for power and energy management and none currently exists. In our view, a holistic stack is one that is extensible enough to support the present and anticipated future needs of various different HPC centers, one that achieves best system performance and throughput through application-awareness, one that is designed to coordinate management at different granularities, and one that enables the seamless integration of software components from different developers and vendors. In this seminar, our goal is bring together experts from academia, research laboratories, and industry in order to take stock of existing approaches, design points and power management concepts and requirements at the various participant’s institutions, and to design concepts for a holistic power and energy management stack, which we refer to as the HPC PowerStack. Further, we hope to take steps toward defining the interfaces necessary for providing a complete prototype shared among many groups. The intention is to align development and research efforts across the community so that we may share development resources, avoid duplicating effort, agree on common interfaces and reap the rewards together as a community. The intended final outcome of this collaboration will be a holistic, flexible and extensible concept of a software stack ecosystem that allows us to combine product-grade open-source software components to enable runtime optimization of system power, energy, and performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); soft open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Final Report for ARPA-E NODES "Real-Time Optimization and Control of Next-Generation Distribution Infrastructure" Project

The project examined next-generation power distribution systems with high levels of power- electronics-interfaced distributed energy resources (DERs) that include: renewable energy systems (RESs), energy-storage devices, electric vehicles, fuel cells, small-scale diesel generators, and controllable loads. The project developed a comprehensive distribution- network management framework that unifies real-time voltage and frequency control with network-wide energy management under an integrated framework. The aim of the project was to: i) systematically optimize network-wide operation and congruently regulate voltages and frequency in the face of high volatility induced by rapidly varying RES and uncontrolled loads; ii) facilitate high renewable integration beyond 50% on an energy basis; iii) meet power-quality requirements and operational constraints; and, iv) respect response, ramp- time, and hold-time requirements for synthetic frequency and regulating reserves via fast- and longer-term dynamic energy management.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Smart Grid and Smart Cities

We hope the proceedings will offer the readers a good opportunity to explore the state-of-the art developments and future directions of researches on smart grid and smart cities. These conferences covered the recent trends and progresses made in the field of smart grid and smart cities. For the past few years, smart grids have been one of the main topics in research and development at academic, industry, business and government level. The smart grid is envisaged to be the next generation electric grid for Smart Cities. It enables the smart integration of conventional power generation, renewable generation, distributed generation, energy storage, transmission, distribution and demand management. The benefits of smart grid include the enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality. However, all these prospected transformations also bring with them numerous challenges and opportunities. The conference program covers keynotes, invited, oral and poster presentations. The keynotes and invited talks are focused on the potential application of smart grid and smart cities in various fields of application. Among 105 full length articles submitted to the conference, 59 manuscripts were accepted after a thorough peer reviews and were recommended for presentations and publication in this proceedings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Laboratory Evaluation of Federated, Hierarchical Controls for Distribution Power System Management: Preprint

The connection of more loads and distributed energy resources (DERs) to the distribution power system brings both challenges and opportunities to system operators. There are opportunities to aggregate flexible loads and DERs to provide transmission grid services, but the coordinated actions of DERs being managed by independent, third-party DER aggregators to support transmission system operations can present challenges. We developed a federated DER management architecture and control framework that aims to manage heterogeneous DERs to deliver reliable transmission grid services while respecting distribution system constraints. The controls include stochastic day-ahead optimization, model predictive control, and a simple real-time management scheme. We present simulation results obtained from a realistic laboratory test bed of federated controls managing DERs within a substation service area to make the substation net power follow the optimal net power determined by the day-ahead optimization based on cost and limiting reverse power flow.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Real-time hybrid controls of energy storage and load shedding for integrated power and energy systems of ships

This paper presents an original energy management methodology to enhance the resilience of ship power systems. The integration of various energy storage systems (ESS), including battery energy storage systems (BESS) and super-capacitor energy storage systems (SCESS), in modern ship power systems poses challenges in designing an efficient energy management system (EMS). The EMS proposed in this paper aims to achieve multiple objectives. The primary objective is to minimize shed loads, while the secondary objective is to effectively manage different types of ESS. Considering the diverse ramp-rate characteristics of generators, SCESS, and BESS, the proposed EMS exploits these differences to determine an optimal long-term schedule for minimizing shed loads. Furthermore, the proposed EMS balances the state-of-charge (SoC) of ESS and prioritizes the SCESS’s SoC levels to ensure the efficient operation of BESS and SCESS. For better computational efficiency, we introduce the receding horizon optimization method, enabling real-time EMS implementation. Further, a comparison with the fixed horizon optimization (FHO) validates its effectiveness. Simulation studies and results demonstrate that the proposed EMS efficiently manages generators, BESS, and SCESS, ensuring system resilience under generation shortages. Additionally, the proposed methodology significantly reduces the computational burden compared to the FHO technique while maintaining acceptable resilience performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Harnessing Virtual Power Plants Reliably: Enabling tools for increased observability, controllability, operation, and aggregation of distributed energy resources

Harnessing virtual power plants enhances the integration of distributed energy resources into utility grids for a sustainable energy future. Virtual power plants (VPPs) aggregate DERs to enhance resource adequacy and reduce emissions. U.S. utilities are exploring various technologies to manage DERs effectively. FERC Order 2222 allows DERs to participate in both wholesale and retail markets. Enhancing observability and controllability of behind-the-meter (BTM) DERs is essential for reliable grid operations. A hierarchical control architecture can improve coordination among residential energy resources. Field tests showed nearly 20% energy savings and 30% peak power reduction during grid events. Effective management of DERs requires enhanced situational awareness to prevent grid congestion. Integrating DER management systems (DERMS) with existing planning tools can improve operational security. Near-real-time grid models can validate optimal resource set points against resource uncertainty. Traditional uninterruptible power supplies (UPS) can be upgraded to support grid services and become part of VPPs. Upgrading UPS systems can reduce costs by 75% and unlock significant battery capacity. New battery management systems and grid-aware controllers are essential for optimizing UPS performance. Continued research and development are necessary to address challenges in integrating DERs into utility grids. Encouraging customer participation in pilot programs is vital for the evolution of VPPs. Here, the shift towards price-responsive DERs and VPPs is expected to enhance energy distribution efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimization-Based Deconfliction of Distribution System Applications

Power distribution systems are transitioning towards a future with large numbers of controllable grid-edge devices and distributed generation, as well as more complex operational objectives. This evolution requires a new distribution management approach, and modular applications can play a significant role. Thus, new strategies to coordinate independent applications with diverging operational goals are necessary. In this paper, we propose a novel optimization-based deconflictor to facilitate coordination in future advanced distribution management systems.

application deconfliction, power system optimizati↗
DOE OSTI2022

Multi-service battery energy storage system optimization and control

Battery energy storage systems (BESS) have become fundamental part of modern power systems due to their capability to provide multiple grid services. As the renewable penetration increases, BESS procurement is also expected to increase where it is envisioned to play a systematic and strategical role in power systems planning and operation. Hence, in this paper we present a multiple grid service procurement and operation for BESS - ranging from energy arbitrage, reserve/regulation services, power factor correction, and demand management. The proposed framework considers an optimal multi-temporal dimension, designed to be operable for both planning and real-time operation. Moreover, non-linearity inherent to BESS services and uncertainty associated to market forecasts variables are addressed using techniques such as polyhedral norms and robust optimization approaches. Here, the developed model is tested using a utility-scaled BESS and the obtained results show the effectiveness of the systematic BESS multi-service planning and operation approach.

25 ENERGY STORAGE↗
DOE OSTI2023

A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗