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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Real-Time Testbed for Studying Cyberattacks and Defense in DER-integrated Smart Inverter Systems

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a gridtied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man in the middle attacker. The Man-in-the-Middle (MITM) attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from a DNP3 controller that exploit smart inverter grid support functions. We choose DNP3 and implement grid support functions according to the IEEE Std. 1547-2018 mandated for the interconnection and interoperability of DER power systems with associated power components. Furthermore, we develop a protocol payload agnostic attack detection framework that leverages the round-trip time (RTT) anomalies between DNP3 requests and responses and can detect the presence of attacks without having to analyze the payload’s contents, while balancing trade-offs between false alarm counts, missed detections, and time to detection. To facilitate further research, we publicly release benign and attack network traffic exchanged between various sensors, controllers, and actuators in our grid-tied inverter testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗
DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Measuring Flexibility through Reduction Potential

As electrical loads, electric vehicles (EVs) are distinguished in many cases by their substantial flexibility. Harnessing this flexibility, however, requires a precise characterization of its timing and degree. Current approaches to flexibility characterization tend to be limited either by the complexity of their representation or their inability to capture aggregate flexibility across multiple vehicles. In this paper, we present the reduction potential matrix, a novel metric capturing fleet-level EV load flexibility while being both straightforward to calculate and intuitive to interpret. We use the metric to quantify flexibility for two operationally distinct commercial groups: freight vehicles and transit buses. While we find that both vehicle groups have substantial flexibility, its timing, magnitude, and duration varies across the groups. We describe how this variability translates into variation in the each group's role as a grid resource, and conclude with a discussion of how system planners, fleet operators, and other stakeholders can use the novel metric to cultivate symbiosis between vehicles and the grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

EVs@Scale FUSE Project Review

This project will analyze and demonstrate SCM and VGI approaches to reduce grid impacts from EVs@Scale as a result of the charging needs of the LD, MD, and HD on-road electrified fleet.

33 ADVANCED PROPULSION SYSTEMS↗
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

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2025

Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro- Waveform Measurements

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. Here, the outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements.

High-fidelity synchro-waveform measurements↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Resident Tolerance to Transitional Temperature Deviation in Smart Communities

Choosing the right HVAC system or the right algorithm of implementing demand response may create significant energy and environmental gains while maintaining resident comfort. But these questions are closely related to the concept of user comfort, which in turn requires a reasonable fit between user preferences and temperature setpoints. While setting the temperature right is a well-researched question, systems in transition from one setpoint to another are currently not thoroughly addressed in research. But how tolerant the residents really are if a system spends a large share of time outside of the comfort setpoint. This study gives some early insights on how the deviation of temperature from the setpoint affect perceived resident comfort. We use two weeks of data for a smart neighborhood located in Atlanta, GA. We find that the system spends 20% - 50% of time deviating from the setpoint by more than 1℉. However, we do not find that increasing deviations cause resident complaints or increasing overrides.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

EVs@Scale: NextGen Profiles EVSE Characterization 2025

As part of the U.S. DOE EVs@Scale consortium, the Next-Generation Profiles (NextGen Profiles [NGP]) project presents analysis and results from the characterization of high-power conductive and wireless charging infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. Electric Vehicle Service Equipment (EVSE) characterization involves testing over a wide range of DC charging currents and voltages during nominal and off-nominal conditions. This testing allows for a better understanding of the impact that high-power charging will have on the electric grid. A common set of standard test plans, procedures, and data requirements were applied to the characterization in this document with minor updates and improvements. This report covers all conductive characterization activities performed between October 2024 and September 2025 on the Delta Electronics 350KW Electric Vehicle Charging System, consisting of power cabinet model EIDN-U350KTA01 and dispenser model EIDD-U350SSUUAEG-350.Key Findings include: Output regulation, Efficiency and power factor, Load management, Grid Resilience, Smart Charge Management (SCM) performance, Thermal control system performance, Multi-port simultaneous charging performance, and Selected performance comparisons with other EVSEs characterized in the NextGen Profiles project. Hot and cold temperature testing was not conducted on the Delta 350KW due to laboratory limitations. Future research could include continued testing the Delta hardware under off-nominal temperature conditions including multi-port/multi-session simultaneous charge testing, in addition to collecting data on other high-power conductive chargers to augment.

25 ENERGY STORAGE↗
NASA NTRS2011

Modeling Humans as Reinforcement Learners: How to Predict Human Behavior in Multi-Stage Games

This paper introduces a novel framework for modeling interacting humans in a multi-stage game environment by combining concepts from game theory and reinforcement learning. The proposed model has the following desirable characteristics: (1) Bounded rational players, (2) strategic (i.e., players account for one anothers reward functions), and (3) is computationally feasible even on moderately large real-world systems. To do this we extend level-K reasoning to policy space to, for the first time, be able to handle multiple time steps. This allows us to decompose the problem into a series of smaller ones where we can apply standard reinforcement learning algorithms. We investigate these ideas in a cyber-battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.

Lee, Ritchie↗
DOE OSTI2024

EVs@Scale FUSE Fall '24 Deep Dive

Electric vehicle (EV) adoption will expand beyond light-duty vehicles charging at lower-power public locations and family residences to include medium- and heavy-duty applications with more powerful charging at public stations and private depots. The Vehicle Grid Integration (VGI) and Smart Charge Management (SCM) pillar in EVs@Scale supports multiple projects. The FUSE project is developing and evaluating enabling technologies, charging solutions, and infrastructure design approaches to investigate the intricacies and potential benefits of vehicle grid integration and the application of smart charge management to charging of electric vehicles at scale. This presentation delves into the technical findings of this research in 2024.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗
DOE OSTI2026

Scaled Implementation of Smart Charge Management for Electric Vehicles

Smart charge management (SCM) has become a critical strategy for mitigating potential grid impacts and reducing electricity costs for all customers. This study will evaluate the economics of implementing light-duty EV SCM at scale across the United States. This work will enhance distribution system analysis by estimating SCM implementation costs, exploring viable business cases, and developing a framework for national-level applications. The primary methodology involves leveraging detailed grid modeling from a specific service territory and using spatial extrapolation techniques to generalize findings to other regions. The analysis will develop key metrics to quantify the costs and benefits of SCM, including implementation costs relative to strategy and scale, the cost of distribution system upgrades with and without SCM, and the percentage of peak-load reduction. The objective is to produce a comprehensive report and a parameterized framework that enables utilities to self-assess the value of SCM in their own service territories. This will support the development of cost-effective charging strategies, accelerate the energization of new EV chargers, and facilitate the seamless integration of EVs into the nation's power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Spatially Resolved Domicile Charging Demands for Light-, Medium-, and Heavy-Duty Electric Vehicles in Virginia

The use of plug-in electric vehicles (PEVs) and resulting grid impacts are likely to grow rapidly, and evaluation of optimal smart charge management and grid integration strategies is warranted now. Evaluating distribution grid impacts requires fine-grained models of PEV operations to estimate charging loads across diverse vehicles at high spatial resolution. We propose such a model and consider a high-electrification scenario in Richmond and Newport News, Virginia. Our framework considers four categories of vehicle that are amenable to early aggressive electrification: light-duty passenger vehicles (LDV), trucks and vans with a focus on delivery or other local operations, school buses, and transit buses. These vehicles have a relatively consistent domicile, reducing the need for public charging infrastructure rollout to electrify. We apply a recent LDV model and propose new models for each vocation of medium- and heavy-duty vehicle, leveraging telematics data. We demonstrate our framework in Virginia and find energy demands in the region may total 15 GWh day, with most consumed by LDV. However, considering power demand at high spatial resolution reveals a different trend: LDVs have relatively small peak loads at specific sites (peak site demand around 800 kW) compared to average and high demand medium- and heavy-duty vehicle charging sites (peak site demand around 6,000 kW at a transit bus depot, 1,500 kW at a local freight hub, and 1,000 kW at a school). Our framework yields insights on the relative impacts of each vocation and enables future work to tailor grid integration strategies to each vehicle category.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
DOE OSTI2026

Voltage Service Limits Smart Contract Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Generator

Modern electrical grids face growing stability risks from customer-owned generators, especially at points of common couplings (PCCs). Disruptive behavior from power-electronic sources can cause protective relays to isolate problematic generators, making measurement integrity critical. This article presents a distributed ledger technology (DLT) approach that uses smart contracts to evaluate PCC voltage measurements and trigger backup breaker operations. The approach is framed as a verifiable, multi-organization attestation and audit layer, not as a real-time control security mechanism. In the proposed architecture, voltage measurements from a hardware protective relay are anchored on a DLT through the Cyber Grid Guard (CGG) system for attestation by both the grid utility and customer-owned generator. A Voltage Service Limits (VSLs) smart contract evaluates the on-chain measurements against allowable phase-voltage limits derived from the ANSI C84.1 standard. The framework is validated in a hardware-relay-in-the-loop test bed under sustained-undervoltage, sustained-overvoltage, and transient line-to-line fault scenarios. The results show that the VSL smart contract can process these measurements and issue backup breaker actions consistent with the defined service-limit criteria, demonstrating the DLT potential as a verifiable audit layer at the PCC that complements primary protection.

cyber security↗