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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Extending Grid Computing to Remote Locations

NASA Glenn Research Center, in cooperation with NASA Ames and geologists from the University of Cincinnati and Bowling Green State University has extended the computational capabilities of the information Power Grid to remote research sites. The combination of satellite (EOS) data acquisition and the IPG processing provides geologists with the ability to identify the key mineralogical features at the research site. The underlying connectivity for this research environment is provided by the Numerical Research and Education Network (NREN) using a combination of terrestrial and mobile satellite-based networking solutions. Our approach not only speeds the process of scientific discovery, but also serves as a simple demonstration of NASA's capacity for geological classification and exploration of remote sites such as the Martian surface. The NASA Glenn Demonstrations combines satellite (EOS) data acquisition and the computational capabilities or the Information Power Grid (IPG) to provide geologists with the ability to identify key mineralogical features in near real-time of an area in study. This capability could someday allow geological classification of rocks and minerals of remote sites such as the Martian surface.

Griffin, Robert↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
DOE OSTI2025

Consensus-Based Control and Optimization of Power System Inertia

The integration of distributed energy resources (DERs), such as solar, wind, and energy storage systems, into power grids through inverter-based resources reduces power system inertia, leading to faster frequency dynamics and potential grid instability. To address this challenge, this paper proposes a distributed, consensus-based approach for the real-time control and optimization of inertia sources (synchronous generators and/or DERs) during system disturbances, enhancing both system stability and economic performance. The distributed control and optimization approach assumes each inertia source exchanges information solely with its neighbor ones, making it easily scalable to large power grid networks. The impacts of the communication connectivity among the inertia sources as well as their generation capacity limits on the distributed approach are investigated. We also demonstrate the approach’s robustness in scenarios involving communication time delays and packet losses, validating its effectiveness through numerical simulations on a 4-bus test system and a two-area 8-bus test system.

Power system inertia↗
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow: Preprint

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. They demonstrate that these are viable computational alternatives to interior point methods. This is done by using Julia and the packages PowerModels.jl and Manopt.jl.

manifold optimization↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗
DOE OSTI2024

Bilevel Nodal Behind-the-meter Solar Disaggregation Under Unexpected Extreme Weather Conditions

As the power grid undergoes significant paradigm shift due to the increasing penetration of renewable generation, the ever-growing installation of behind-the-meter (BTM) solar generation in the power grid also has a significant impact on nodal loads, posing challenges on transmission operators. Furthermore, increasing frequent and severe extreme weather events intertwine with ubiquitous BTM solar generations and have amplified the challenges of accurately model nodal load profiles, especially under the lack of ground-truth information for verification. To tackle these challenges, this paper introduces a bilevel model that utilizes year-long data (e.g., proxy solar, zonal load, and individual node load profiles) to disaggregate metered profiles into actual demand and BTM solar generation at each transmission node. The proxy solar not only scales the BTM solar generation of individual nodes but also create a compensation term for enhancing performance on days with unexpected extreme weather events. The proposed algorithm is validated with real-world PJM Interconnection data during unexpected events like the recent Winter Storm Elliott. For quantitative evaluations, a novel Score error is introduced, which is based on mean percentages and load scales and offers a universal assessment method suitable for all nodes and different data formats (e.g., normalized or raw values).

behind-the-meter solar, load disaggregations, load↗
NASA NTRS2001

Latency Hiding in Dynamic Partitioning and Load Balancing of Grid Computing Applications

The Information Power Grid (IPG) concept developed by NASA is aimed to provide a metacomputing platform for large-scale distributed computations, by hiding the intricacies of highly heterogeneous environment and yet maintaining adequate security. In this paper, we propose a latency-tolerant partitioning scheme that dynamically balances processor workloads on the.IPG, and minimizes data movement and runtime communication. By simulating an unsteady adaptive mesh application on a wide area network, we study the performance of our load balancer under the Globus environment. The number of IPG nodes, the number of processors per node, and the interconnected speeds are parameterized to derive conditions under which the IPG would be suitable for parallel distributed processing of such applications. Experimental results demonstrate that effective solution are achieved when the IPG nodes are connected by a high-speed asynchronous interconnection network.

Das, Sajal K.↗
DOE OSTI2024

5G Energy FRAME Report on 5G for Grid Use Case (Year 3 Final Report)

This report provides an extensive overview of the interrelationships among energy, communication, and computing—especially in the context of decarbonization goals, challenges, and opportunities. Technical examples enabled by 5G technologies and their performance are presented, discussed, based on the experiment performed at Pacific Northwest National Laboratory. This is the first use case focused on using 5G for the U.S. power grid and will be referred to as the 5G for Grid Use Case from here on. Specifically, this use case looks at the workflow, which integrates the performance data of a real-world 5G communication testbed and a grid transmission and distribution co-simulation platform. The cross-domain information flow and logic design are illustrated with a combination of power grid contingencies and events. Lastly, a summary of the project achievement and outcome is provided, along with a technology roadmap envisioned by the project team.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Efficient Simulation of Cascading Outages Using an Energy Function-Embedded Quasi-Steady-State Model

Here, this paper proposed an energy function-embedded quasi-steady-state model for efficient simulation of cascading outages on a power grid while addressing transient stability concerns. Compared to quasi-steady-state models, the proposed model incorporates short-term dynamic simulation and an energy function method to efficiently evaluate the transient stability of a power grid together with outage propagation without transient stability simulation. Cascading outage simulation using the proposed model conducts three steps for each disturbance such as a line outage. First, it performs time-domain simulation for a short term to obtain a post-disturbance trajectory. Second, along the trajectory, the system state with the local maximum potential energy is found and used as the initial point to search for a relevant unstable equilibrium by Newton's method. Third, the transient energy margin is estimated based on this unstable equilibrium to predict an out-of-step condition with generators. The proposed energy function-embedded quasi-steady-state model is tested in terms of its accuracy and time performance on an NPCC 140-bus power system and compared to a quasi-steady-state model embedding transient stability simulation.

Guo, Zhenping [Univ. of Tennessee, Knoxville, TN (↗
DOE OSTI2024

Modelling and analysis of nuclear reactor system coupled with a liquid metal battery

Traditionally, nuclear power plants in the U.S. provide baseload power to the power grid because they have less flexibility for ramping their output power than natural gas peaking plants. However, achieving climate goals to reduce the consumption of fossil‐based natural gas places pressure on nuclear power plants and other power generators to ramp up their power output to balance grid generation with demand. This paper presents the modelling and performance analysis of a nuclear reactor system (NRS) coupled to a liquid‐metal battery (LMB) to improve its dynamic response and enable its black start capability. The NRS and LMB thermal behaviour are modelled in Dymola, while the electrical dynamics of the LMB and power grid are modelled in RTDS‐RSCAD. Both simulation platforms are coupled and share their thermal and electrical data using a Transmission Control Protocol/Internet Protocol (TCP/IP) communication protocol. The dynamic performance of the NRS‐LMB integration is tested on the IEEE 9 bus, which demonstrates its ability to respond and provide frequency and voltage regulation. The black start capability of the NRS‐LMB is also evaluated by simulating a grid outage and using the LMB to supply the auxiliary loads required to bring the NRS back online as soon as possible. The results show that coupling an NRS to an LMB improves the system dynamic performance and enables it to black start after being disconnected from the grid for several days.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Lunar Microgrid Trade Studies to Define Interface Converter Requirements

The National Aeronautics and Space Administration (NASA) is interested in developing an incremental lunar power grid to support continuous human/robotic operations under the Artemis missions and can scale to global power utilization and industrial power levels. The initial lunar surface power system will be composed primarily of assets that contain their own power generation and energy storage. These assets can be connected to form a grid, which allows power to be shared between the system’s distributed energy resources. This grid can provide backup power to existing assets as well as primary power to future assets. A trade study was conducted for an Artemis scale grid, which has identified 3 kVAC as an ideal transmission voltage. This paper documents the analysis and findings from these trade studies. These studies were used to define the operating voltage and develop a common interface for connecting lunar surface assets to the power grid. The Universal Modular Interface Converter (UMIC) connects lunar surface assets to the high voltage grid. Products from this grid definition and UMIC development work will enable highly reliable and available power for Artemis and future planetary surface missions, which is key to achieving NASA’s long-term vision of space exploration.

Planetary surface power↗
DOE OSTI2024

Dynamic Temporal Graph Sequence Data for Resilience-Oriented Distribution Network Reconfiguration

This dataset comprises temporal dynamic graph sequences generated from power grid simulations focused on grid reconfiguration to enhance resilience. The simulations model failure propagation under varying conditions, with nodes assigned distinct failure probabilities. For each time step, the dataset captures the evolution of node states (functional or failed) and features critical to grid operations, such as pv_output, load_profile, load_dispatch, dg_output, loss, and voltage. Node types include sources, normal loads, and nodes with specific equipment like PVs, micro turbines, or shunt capacitors. The dataset is structured to support the training of dynamic graph neural networks, facilitating research on node feature prediction and edge dynamics under failure scenarios. Three distinct configurations are included, providing a robust foundation for modeling power grid resilience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies

Hydroelectric power generation in Western Canada significantly contributes to power grid operations of the North American Western Interconnection through substantial generation, some of which is exported to the United States (U.S.). However, the lack of publicly available hydropower generation datasets poses challenges for future market projections and resource adequacy evaluations. We present a simulation-based monthly power system model-ready hydropower generation dataset for 110 facilities in British Columbia and Alberta from 1981 to 2019. These monthly hydropower generation estimates are developed from integrated hydrologic model simulations of runoff and reservoir-operated streamflow, followed by scaling that considers diversion inflow constraints based on hydropower water license information. To address the lack of comparable hydropower generation records, we conduct step-by-step evaluations for simulated runoff, regulated streamflow, and hydropower generation using available observations or estimates. The presented hydropower dataset aims to enhance the representation of hydropower resources in Western Canada, supporting power grid system studies for the Western Interconnection of the U.S. and Canada.

13 HYDRO ENERGY↗
DOE OSTI2025

Center for Alternate Synchronization and Timing (CAST) PTP Network Monitoring Report

The Oak Ridge National Laboratory Center for Alternative Synchronization and Timing (CAST) performs research, development, testing, and evaluation of alternative terrestrial-based timing and synchronization infrastructure for the US power grid and other critical infrastructures. Alternative timing options reduce reliance on GPS and enhance the overall resilience of critical infrastructures. CAST infrastructure uses Precision Time Protocol (PTP) as the primary conduit for delivery of synchronization packets. CAST deploys PTP over long terrestrial links to synchronize a multitude of remote boundary clocks and downstream power grid components with the authoritative grand master clocks. Network traffic issues can severely degrade PTP accuracy. This report focuses on examining network traffic anomalies and their effects on PTP operation as well as the potential implications to CAST’s high-precision remote synchronization operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2017

Propulsion Powertrain Real-Time Simulation Using Hardware-in-the-Loop (HIL) for Aircraft Electric Propulsion System

It is essential to design a propulsion powertrain real-time simulator using the hardware-in-the-loop (HIL) system that emulates an electrified aircraft propulsion (EAP) systems power grid. This simulator would enable us to facilitate in-depth understanding of the system principles, to validate system model analysis and performance prediction, and to demonstrate the proof-of-concept of the EAP electrical system. This paper describes how subscale electrical machines with their controllers can mimic the power components in an EAP powertrain. In particular, three powertrain emulations are presented to mimic 1) a gas turbo-=shaft engine driving a generator, consisting of two permanent magnet (PM) motors with brushless motor drives, coupled by a shaft, 2) a motor driving a propulsive fan, and 3) a turbo-shaft engine driven fan (turbofan engine) operation. As a first step towards the demonstration, experimental dynamic characterization of the two motor drive systems, coupled by a mechanical shaft, were performed. The previously developed analytical motor models1 were then replaced with the experimental motor models to perform the real-time demonstration in the predefined flight path profiles. This technique can convert the plain motor system into a unique EAP power grid emulator that enables rapid analysis and real-time simulation performance using hardware-in-the-loop (HIL).

turbo-electric propulsion↗
NASA NTRSDate not supplied

Analysis of Electrical Grid Capacity in Major U.S. Metropolitan Areas for Urban Air Mobility

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles (EVs) over time. Under the worst-case assumption that each U.S. Metropolitan Statistical Area (MSA) can only access and utilize the electricity generated by power plants within its boundaries, it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the 25 most populous MSAs will decrease by about 61% from about 129,000 in the year 2021 to about 51,000 in the year 2050, due to the projected increase in ground EVs. Also, it is estimated that the number of these MSAs that may lack any available electrical grid power capacity for UAM operations will increase from zero MSAs in 2021 to eleven MSAs in 2050—including three MSAs that prospective UAM operators have announced as target markets (Los Angeles, Miami, and Orlando)—unless electricity demand for other purposes is reduced.

electrical grid↗
NASA NTRSDate not supplied

Analysis of Electrical Grid Capacity in Major U.S. Metropolitan Areas for Urban Air Mobility

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles (EVs) over time. Under the worst-case assumption that each U.S. Metropolitan Statistical Area (MSA) can only access and utilize the electricity generated by power plants within its boundaries, it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the 25 most populous MSAs will decrease by about 61% from about 129,000 in the year 2021 to about 51,000 in the year 2050, due to the projected increase in ground EVs. Also, it is estimated that the number of these MSAs that may lack any available electrical grid power capacity for UAM operations will increase from zero MSAs in 2021 to eleven MSAs in 2050—including three MSAs that prospective UAM operators have announced as target markets (Los Angeles, Miami, and Orlando)—unless electricity demand for other purposes is reduced.

electrical grid↗
DOE OSTI2025

Investigating the Vulnerabilities of the Direct Transfer Trip Scheme for Network Protector Units in the Secondary Networks of Electric Power Distribution Grids

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection.

direct transfer trip↗