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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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NASA Capabilities that Could Impact Terrestrial Smart Grids of the Future
Incremental steps to steadily build, test, refine, and qualify capabilities that lead to affordable flight elements and a deep space capability. Potential Deep Space Vehicle Power system characteristics: power 10 kilowatts average; two independent power channels with multi-level cross-strapping; solar array power 24 plus kilowatts; multi-junction arrays; lithium Ion battery storage 200 plus ampere-hours; sized for deep space or low lunar orbit operation; distribution120 volts secondary (SAE AS 5698); 2 kilowatt power transfer between vehicles.
Recent Progress in the Development of a Multifunctional Smart Coating for Autonomous Corrosion Detection and Protection
No abstract available
Shadow Mode Assessment Using Realistic Technologies for the National Airspace (SMART NAS)
Develop a simulation and modeling capability that includes: (a) Assessment of multiple parallel universes, (b) Accepts data feeds, (c) Allows for live virtual constructive distribute environment, (d) Enables integrated examinations of concepts, algorithms, technologies and National Airspace System (NAS) architectures.
Smart Sensors System Development and Application for Harsh Environments
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Compact Receivers and Smart-Pixel Chips for Optical Interconnects and Signal Processing
A continuous-time shift-invariant cellular neural network (CNN) with hardware annealing
Self-Security for Grid-Interactive Smart Inverters
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Final Scientific/Technical Report: A Hierarchical, Secure, Wireless Communications Network Architecture for the Smart Electric Grid, Applicable to Digital Twin
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Best Practices for Smart Grid-Interactive Efficient Building Ready Performance Contracts
Grid-interactive efficient building (GEB) measures reduce costs and optimize energy use for additional grid services by coordinating building energy loads and providing continuous demand management. Incorporating GEB energy conservation measures (ECMs) in performance contracts is reliant upon multiple factors. These factors include site selection with utility tariffs and incentives favorable to GEB, the identification of GEB as a priority in the initial stages of the contracting process, integration of GEB within comprehensive performance contracts with multiple other ECMs, and careful consideration of GEB measurement and verification (M&V) for energy savings performance contracts (ESPCs) and performance assurance for utility energy service contracts (UESCs).
Smart Charge Management
This session will discuss "on-board" electric vehicle (EV) technology, its availability, and benefits. Learn about how these tools can empower agencies to further adopt EVs and aid in fleet electrification efforts. We will explore the evolving advancements of managed charging, available telematics, on-board technology and how to incorporate them into your overall EV adoption strategy. Furthermore, we'll discuss what to expect in the near future; and how to future proof your investments to make the technology work for you.
Estimating Flexibility Envelopes for Residential Customers From Utility Smart Meter Data: Preprint
Demand response from residential customers has significant potential to support power system operations, but accurate flexibility estimation is challenging due to the limited resolution of advanced metering infrastructure (AMI) data. Most utility AMI measurements are recorded at hourly intervals, with only a small portion at higher resolutions, and even fewer households have appliance-level energy usage data. To address this issue, this paper proposes a two-stage long short-term memory (LSTM) framework for estimating household flexibility envelopes from low-resolution AMI data. In the first stage, the heating, ventilating, and air-conditioning (HVAC) load and non-HVAC loads are estimated by using a model trained on a small set of households with appliance-level profiles. These estimated data are then used to compute the upper- and lower-flexibility bounds, which are subsequently down-sampled to lower-resolution data. In the second stage, these flexibility bounds serve as training inputs for another LSTM model, enabling direct prediction of flexibility envelopes for households with only hourly AMI data. This method is validated using Pecan Street data from two different areas, and the results demonstrate its applicability and effectiveness.
Federated Learning with Frequency Estimation for Smart Meter Systems
Federated learning (FL) is a powerful framework that enables multiple distributed clients to collaborate without the need to transfer their data to a central server. However, FL does not inherently guarantee the level of privacy that clients often require. In our review of recent studies on privacy-enhancing techniques in FL, we found that frequency estimation (FE) methods remain underexplored. To address this gap, we developed and integrated FE techniques on the client side, further examining the effects of incorporating an adaptive range and a shuffled model. We also analyzed the impact of varying hyper-parameters on privacy preservation. Our results provide clear guidance on the algorithms and configurations that are most effective for enhancing privacy in FL, particularly when using long short-term memory (LSTM) architectures.
Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters
Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.
Formal threat analysis of machine learning-based control systems: A study on smart healthcare systems
Not Available
Exploring the effects of interdependencies on energy systems in smart communities: A multi-domain modeling and quasi-Monte Carlo sensitivity analysis
Not provided.