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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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Vu-graphs are presented that show that applications are a viable distributed renewable power generation option. Quality energy can be produced in the form of electricity and high temperature heat. Modular systems are described that can be distributed to new or existing plants and that are mass producible with the associated economies of production.
Traditionally, it is assumed that microgrids transition seamlessly from grid‐connected operation to islanded mode in the event of sudden main grid outages. In reality, the islanding process, especially unintentional islanding, is rarely seamless. Instead, it is subject to voltage and frequency fluctuations caused by the instantaneous disconnection of the point of common coupling (PCC) switch, variations in loads and renewable generation output and even the protection tripping of distributed energy resources (DERs). To mitigate these fluctuations and facilitate a smooth islanding process, we propose a stochastic microgrid scheduling model that incorporates chance‐constrained resilience measures. Specifically, the resilience measure is defined as the probability of successful islanding (PSI), that is, the probability that a microgrid can mitigate the generation‐demand imbalance caused by the disconnection of the PCC switch, variations in load and renewable generation and DER tripping. This measure is modelled using chance constraints. Unlike existing reliability and resilience indices, which typically neglect the possibility of microgrid/DER failure under extreme events and assume their survival while primarily focussing on reducing impact duration or magnitude, the proposed PSI‐based framework explicitly addresses microgrid and DER survival during the islanding transition. The formulated nonlinear chance constraints are approximated using a multiinterval approach and equivalently represented as a mixed‐integer linear programming (MILP) formulation. Case study results validate the proposed method, showing that the PSI estimation error is reduced to less than 8%, compared to approximately 28% with existing methods. Various sensitivity analyses on the DER tripping rate and PSI settings were performed to validate the robustness of the proposed method. In particular, the necessity of accounting for DER tripping in the PSI calculation was demonstrated.
The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.
The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.
The growing interest in onsite solar photovoltaic and energy storage systems is partially motivated by customer concerns regarding grid reliability. However, accurately assessing the effectiveness of PVESS in mitigating these interruptions requires a comprehensive understanding of location-specific outage patterns and the ability to simulate realistic scenarios. To address the gap, we introduce the Power Reliability Event Simulation TOol (PRESTO), the first publicly available tool that simulates location-specific power interruptions at the county level. PRESTO allows for a more realistic assessment of system reliability by considering the unpredictability and location-specific patterns of power interruptions. We applied PRESTO in a case study of a single-family home across three U.S. counties, examining the performance of a solar photovoltaic system with 10kWh of battery storage during short-duration power interruptions. Our findings show that this system reliably met 93% of energy demand for essential non-heating and cooling loads, fully serving these loads in 84% of events, despite the constraints of daily time-of-use bill management which limits the battery's state-of-charge reserve. However, when heating and cooling loads were included, system performance decreased significantly, with only 70% of demand met and full service in 43% of events. These results highlight the challenges of using solar photovoltaic and energy storage systems for short-duration outages, emphasizing the need to consider factors like battery size and grid charging strategies to improve reliability. Our study demonstrates the practical applications of PRESTO, providing valuable insights into potential mitigation strategies including grid charging and optimizing battery size.
Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.
Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.
Virtual Power Plants (VPPs) aggregate distributed energy resources (DERs) to provide grid services traditionally delivered by centralized power plants. This article reviews the current state of VPP deployment, highlighting business models, compensation mechanisms, and global pilot projects. While VPPs offer benefits such as grid flexibility, resilience, and cost savings, challenges remain in communication infrastructure, regulatory frameworks, market access, and customer engagement. To address these, we propose a scalable, privacy-preserving hierarchical VPP architecture that coordinates with distribution utilities and preserves customer data. We also present the Integrated T&D Control Room of the Future as a key test bed for validating and accelerating VPP adoption. These innovations can help transition VPPs from pilot programs to integral components of a modern, reliable power grid.
This Final Technical Report describes the successful completion of all project tasks under award DE-EE0008272, plus three additional deliverables, all completed within the original budget. Our objective was to position CHP as a key driver of American competitiveness, the strategic use of domestic energy resources, and enhanced energy resilience. This vision was pursued through sustained collaboration with a broad range of public and private stakeholders, including manufacturers, commercial and institutional facilities, state and federal agencies, and associations of end-users, among others.
Possible improvements to present aircraft electrical power systems for use in future advanced types of aircraft have been investigated. The conventional power system is examined, the characteristics of electric loads are reviewed, and various methods of power generation and distribution are appraised. It is shown that a hybrid system, with variable-frequency generation and high-voltage dc distribution, could overcome some of the limitations of the conventional system.
NASA and NASA-funded contractor contributions to the field of composite materials are surveyed. Existing and potential non-aerospace applications of the newer composite materials are emphasized. Economic factors for selection of a composite for a particular application are weight savings, performance (high strength, high elastic modulus, low coefficient of expansion, heat resistance, corrosion resistance,), longer service life, and reduced maintenance. Applications for composites in agriculture, chemical and petrochemical industries, construction, consumer goods, machinery, power generation and distribution, transportation, biomedicine, and safety are presented. With the continuing trend toward further cost reductions, composites warrant consideration in a wide range of non-aerospace applications. Composite materials discussed include filamentary reinforced materials, laminates, multiphase alloys, solid multiphase lubricants, and multiphase ceramics. New processes developed to aid in fabrication of composites are given.
The design and characteristics of a space tug are discussed. The primary objective of the study is to verify the performance capability of a baseline design to deliver and retrieve payloads between 100 nautical miles, 28.5 degrees inclination, and geosynchronous. The space tug is ground based, reusable for 20 mission cycles, and is shuttled to and from low earth orbit by an earth orbital shuttle (EOS) with a 65,000 pound payload capability. It is shown that the baseline concept can meet the target performance goals. The design analysis encompassed: (1) definition of the vehicle primary structure, (2) thermal control, (3) meteoroid protection, (4) propulsion and mechanical subsystems, and (5) avionics including power generation and distribution.
HEAO 1 spectral observations of 12 active galaxies in the 12-165 keV and 2-50 keV ranges are reported. The spectra of these galaxies in the 2-165 keV range are well represented by a single power law model; within experimental uncertainties a narrow dispersion in power law index attributable to the individual galaxies is observed, while the 2-165 keV luminosities of these galaxies ranged from 3 x 10 to the 43rd to 3 x 10 to the 45th ergs/s. An apparent universality of the spectral form is found which can be interpreted as due to a common electron distribution with a temperature of tens of keV in the Compton scattering region or as a common nonthermal power-law distribution generating the observed flux through synchrotron-Compton processes. The data indicate that relativistic particles are likely to be responsible for the X-rays from cores of active galaxies through synchroton-Compton processes. In addition, it is noted that only weak number evolution, if any at all, is present in active galaxies.
An example application for cost-benefit methodology is presented for the use of wind generators. The approach adopted for the example application consisted of the following activities: (1) surveying of the available wind data and wind power system information, (2) developing models which quantitatively described wind distributions, wind power systems, and cost-benefit differences between conventional systems and wind power systems, and (3) applying the cost-benefit methodology to compare a conventional electrical energy generation system with systems which included wind power generators. Wind speed distribution data were obtained from sites throughout the contiguous United States and were used to compute plant factor contours shown on an annual and seasonal basis. Plant factor values (ratio of average output power to rated power) are found to be as high as 0.6 (on an annual average basis) in portions of the central U. S. and in sections of the New England coastal area. Two types of wind power systems were selected for the application of the cost-benefit methodology. A cost-benefit model was designed and implemented on a computer to establish a practical tool for studying the relative costs and benefits of wind power systems under a variety of conditions and to efficiently and effectively perform associated sensitivity analyses.
Beam injection systems in hadron colliders require kickers generating ±50 kV peak voltages into a 50 Ω impedance, with peak currents of 1000 A and sub-10 ns rise and fall times. This paper presents a novel high-voltage pulse power generator utilizing a distributed pulser architecture. It combines gallium nitride (GaN) transistors in a Marx topology with an inductive adder, achieving nanosecond-scale switching speeds and high-power efficiency. Compared to other solutions such as based on MOSFETs or fast ionization dynistors, our development offers superior peak and average power performance, reduced system complexity, and enhanced reliability, marking a significant step forward in high-voltage pulse generation for accelerator applications.
Here we present the results of a study focused on the feasibility of using Medium Voltage DC (MVDC) power distribution from wind power generation to electrolyzers for hydrogen production. This approach, using hybrid energy generation in a MVDC microgrid, offers many advantages. These include possible improvements in efficiency, reliability and installation cost compared to a more typical state-of-the-art AC distribution configuration. It also eliminates the need for transformers, which have recently been subject to price volatility and availability concerns. This study highlights the practical feasibility of MVDC distribution networks for integrating various energy sources, offering improved efficiency and reduced system complexity compared to conventional AC-based solutions. Future work will focus on enhancing fault protection strategies, scaling the system to larger renewable installations, and conducting hardware implementation at the National Renewable Energy Laboratory's (NREL) Flatirons Campus (FC). In the sections that follow we show that a DC Collection and Distribution System (DC CDS) reduces the losses associated with the electrical conversion / distribution process relative to a state-of-the-art AC approach, improving overall efficiency by 5%. On the qualitative side, reducing the number of conversion stages is likely to improve reliability, reduce capital investment cost, and enable simpler control algorithms to be used, and reduced risk of instabilities and malfunctions.
Equipment was designed to receive power from a radioisotope thermoelectric generator source, condition, distribute, and control this power for the spacecraft loads. The TOPS mission, aimed at a representative tour of the outer planets, would operate for an estimated 12 year period. Unique design characteristics required for the power conditioning equipment results from the long mission time and the need for autonomous on-board operations due to large communications distances and the associated time delays of ground initiated actions. The salient features of the selected power subsystem configuration are: (1) The PCE regulates the power from the radioisotope thermoelectric generator power source at 30 vdc by means of a quad-redundant shunt regulator; (2) 30 vdc power is used by certain loads, but is more generally inverted and distributed as square-wave ac power; (3) a protected bus is used to assure that power is always available to the control computer subsystem to permit corrective action to be initiated in response to fault conditions; and (4) various levels of redundancy are employed to provide high subsystem reliability.
Consideration it is given to the performance to the characteristics of an AC magnetohydrodynamic power generator, A rotating magnetic field is imposed on the vortex flow of an electrically conducting fluid, which is injected tangentially into an annulus formed by two nonconducting concentric cylinders and two nonconducting end plates. A perturbation technique is used to determine the two dimensional velocity and three dimensional electromagnetic field and current distributions. Finally, the generated power, the ohmic losses, the effective power and the electrical efficiency of the converter system are calculated.