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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems

Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, reliability, and sustainability while addressing challenges associated with variability. The integration of Small Modular Reactors (SMRs) in NIES offers significant benefits over traditional nuclear facilities, although transferring involves overcoming legal and operational barriers, particularly in economic dispatch. This study proposes a novel off-policy Reinforcement Learning (RL) approach with an ensemble reward system to optimize economic dispatch for nuclear-powered generation companies equipped with an SMR, demonstrating superior accuracy and efficiency when compared to conventional methods and emphasizing RL’s potential to improve NIES profitability and sustainability. Finally, the research attempts to demonstrate the viability of implementing the proposed integrated RL approach in spot energy markets to maximize profits for nuclear-driven generation companies, establishing NIES’ profitability over competitors that rely on fossil fuel-based generation units to meet baseload requirements.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Current status and grand challenges for small wind turbine technology

While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines (SWTs) is fostering energy transition and smart grid development. Small machines have traditionally not received the same level of aerodynamic refinement as their larger counterparts, resulting in lower efficiency, lower capacity factors, and therefore a higher cost of energy. In an effort to reduce this gap, research programs are developing worldwide. With this background, the scope of the present study is 2-fold. In the first part of this paper, an overview of the current status of the technology is presented in terms of technical maturity, diffusion, and cost. The second part of the study proposes five grand challenges that are thought to be key to fostering the development of small wind turbine technology in the near future, i.e. (1) improving energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind; (2) better predicting long-term turbine performance with limited resource measurements and proving reliability; (3) improving the economic viability of small wind energy; (4) facilitating the contribution of SWTs to the energy demand and electrical system integration; (5) fostering engagement, social acceptance, and deployment for global distributed wind markets. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Based on them, improvement areas are suggested, for which 10 key enabling actions are finally proposed.

17 WIND ENERGY↗
DOE OSTI2026

Science Uses Deployment Operations-Advanced Wireless: Exploring Open Radio Access Network Technologies for Energy Science

Open Radio Access Network is emerging as a solution to the increasing demand for more flexible, cost-effective, and advanced mobile network infrastructures. This evolution is driven by advancements in wireless technologies and the growing complexity of deploying and managing these networks. O-RAN represents a significant shift in wireless technology, building upon the 3rd Generation Partnership Project framework to foster openness, flexibility, and interoperability. By decoupling hardware and software components, Open Radio Access Network enables a multi-vendor ecosystem that encourages innovation and diverse solutions. Open Radio Access Network's potential extends beyond traditional wireless applications, with growing interest in its role in advancing energy systems, particularly in the context of smart grids, microgrids, and the integration of renewable energy sources. While the role of open-wireless technologies in driving energy transformation is increasingly recognized, further exploration is needed. Vendors and utilities are investigating how Open Radio Access Network technologies can optimize energy use cases and improve the performance of 5G and beyond applications. This report outlines efforts under the Science Uses Deployment Operations Advance Wireless project, a collaboration between the National Laboratory of the Rockies' Cybersecurity Research Center, Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the Department of Energy's Energy Science Network research and operations staff. The focus of this project is on due diligence, through testing and evaluation, preparing for the deployment of advanced wireless infrastructure for scientific use cases, with an emphasis on Open Radio Access Network technology, its components, integrations, and its ability to support vertical stack application across the energy sector. Additionally, the report highlights the value cases for utilities, underscoring how adopting open wireless standards can accelerate the evolution of energy systems, foster innovation, and improve the integration of critical energy technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Integrated Energy Systems White Paper

This analysis compares how the existing critical infrastructure continues to be vulnerable and with smart grid technologies being implemented exposes IES, power systems, the grid, and renewable energy in new ways. Continued work in cybersecurity will thwart efforts against adversaries having an opportunity to penetrate these systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Dynamic Contingency Analysis Visualization Tool

We are developing a web-based visualization that shows the results of running a contingency analysis on a power grid system. Power grid analysists will be able to use this tool to visualize power event simulations and be better prepared for contingencies that may arise. The tool consists of a map, showing the power grid and its current state, and tables and charts showing the current status of various elements on the grid. The user can iterate over a number of cascading contingencies to visualize how the power grid will change under various scenarios.

Contingency analysis, power grid, smart grid, visu↗
DOE OSTI2022

Real-Time Hardware-in-the-Loop Testbed to Evaluate FLISR Implemented with OpenFMB

With the increasing complexity of the distribution smart grid architecture, algorithms such as the fault location, isolation, and service restoration (FLISR) scheme rely on robust communications that are resilient to natural and man-made adverse conditions and exhibit robustness. Existing communications infrastructure for information exchange are centralized at the distribution management system, with very little autonomy or intelligence at the grid-edge. As a first step towards achieving grid-edge self-healing, this paper aims to bridge this shortcoming by implementing a centrally coordinated rules-based FLISR scheme and integrating it with Open Field Message Bus (OpenFMB), which is a flexible publish-subscribe architecture with the potential to enable point-to-multipoint communications and is more robust and resilient to natural and man-made adverse conditions. A proof of concept is developed to validate the centrally coordinated FLISR and OpenFMB mounted on an SEL-3360 computer that interacts with a simple feeder network of five SEL-651R relays, an SEL-3530 RTAC, and a hardware-in-the-loop testbed. Results demonstrate the efficacy of this approach in enabling direct, low-latency information exchange. OpenFMB's publish-subscribe data model also opens new ways to enable grid-edge interoperability among devices of different vendors interacting with different protocols.

Sundararajan, Aditya↗
DOE OSTI2022

Lessons Learned for Rapid Decarbonization Of Power Sectors: Key Messages for Energy Ministers

This report covers key lessons learned for the rapid decarbonization of power systems, emphasizing best practices in planning, building, and operating power systems. This report is the result of a collaborative effort among various Clean Energy Ministerial (CEM) workstreams and partner initiatives: 21st Century Power Partnership (21CPP); Carbon Capture, Utilization and Storage (CCUS); Global Power System Transformation Consortium (G-PST), a partner to CEM; Industrial Deep Decarbonization Initiative (IDDI); International Energy Agency Digital Demand-Driven Electricity Networks Initiative (IEA 3DEN), a partner to CEM; International Smart Grid Action Network (ISGAN); Long Term Scenarios for the Energy Transition (LTES); Nuclear Innovation: Clean Energy Future (NICE Future); Super-Efficient Equipment and Appliance Deployment (SEAD). The contents are not intended to be comprehensive of all power sector topics, and there may be overlap between content in each section due to the nature of this first-of-its-kind collaborative effort to deliver unified messaging on power sector decarbonization to energy ministers. This work is intended to complement other work at the Clean Energy Ministerial and offers options for consideration, not specific policy recommendations.

21CPP↗
DOE OSTI2023

Grand Challenges for Small Wind: Results of the European Academy Study

While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines (SWTs) is fostering energy transition and smart grid development. Small machines have traditionally not received the same level of aerodynamic refinement as their larger counterparts, resulting in lower efficiency, lower capacity factors, and therefore a higher cost of energy. In an effort to reduce this gap, research programs are developing worldwide. With this background, the scope of the present study is 2-fold. In the first part of this paper, an overview of the current status of the technology is presented in terms of technical maturity, diffusion, and cost. The second part of the study proposes five grand challenges that are thought to be key to fostering the development of small wind turbine technology in the near future, i.e. (1) improving energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind; (2) better predicting long-term turbine performance with limited resource measurements and proving reliability; (3) improving the economic viability of small wind energy; (4) facilitating the contribution of SWTs to the energy demand and electrical system integration; (5) fostering engagement, social acceptance, and deployment for global distributed wind markets. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Based on them, improvement areas are suggested, for which 10 key enabling actions are finally proposed.

ENGINEERING,WIND ENERGY↗
DOE OSTI2023

Uncertainty Error Modeling for Non-Linear State Estimation With Unsynchronized SCADA and µPMU Measurements

Distribution systems of the future smart grid require enhancements to the reliability of distribution system state estimation (DSSE) in the face of low measurement redundancy, unsynchronized measurements, and dynamic load profiles. Micro phasor measurement units (µPMUs) facilitate co-synchronized measurements with high granularity, albeit at an often prohibitively expensive installation cost. Supervisory control and data acquisition (SCADA) measurements can supplement µPMU data, although they are received at a slower sampling rate. Further complicating matters is the uncertainty associated with load dynamics and unsynchronized measurements–not only are the SCADA and µPMU measurements not synchronized with each other, but the SCADA measurements themselves are received at different time intervals with respect to one another. This paper proposes a non-linear state estimation framework which models dynamic load uncertainty error by updating the variances of the unsynchronized measurements, leading to a time-varying system of weights in the weighted least squares state estimator. Case studies are performed on the 33-Bus Distribution System in MATPOWER, using Ornstein–Uhlenbeck stochastic processes to simulate dynamic load conditions.

Cooper, Austin↗
DOE OSTI2021

DISTRIBUTION TRANSFORMER ASSET MONITORING ON THE GRID EDGE USING SMART SENSOR DATA

As new loads such as rooftop photovoltaics, electric vehicles and other distributed energy resources become commonplace on the distribution grid, the stress on already aging assets begins to escalate. This increased loading and changing dynamics can exacerbate failure rates. While traditional monitoring efforts focus on transmission and generation assets, utilities are now beginning to pay close attention to distribution assets in order to increase reliability indices and reduce cost from unexpected outages. This research develops low-cost and scalable methods to monitoring the health of a critical distribution grid asset: the service transformer. Existing methods in literature are either invasive and thus difficult to implement or require the device to be tested offline in an expensive lab setting. Data from the ubiquitous smart meter as well as a novel Bluetooth based transformer monitor are leveraged to automatically notify the utility of deteriorating or damaged transformers. Voltage, temperature, and vibration are some of the signals measured and analyzed by the proposed algorithms to predict transformer failures. Furthermore, these algorithms are designed to keep the implementation and processing costs low by taking advantage of edge computing where possible.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level. Using charging data from over 4,600 EV drivers, the study analyzed SCM’s impact on the distribution systems of BGE and Pepco, which consists of over 2000 feeders. Unlike prior research that focused on system-wide trends or synthetic feeders, this analysis offers granular, feeder-level insights based on real-world operational data. It highlights how transformer density, load profiles, and infrastructure constraints influence smart charging performance. Results show feeder-level conditions play a crucial role in SCM effectiveness, with most feeders benefiting more from LB, while TOU-based SCM may be sufficient for others. By 2035, LB reduced peak charging loads by 27% on average, compared to 23% under TOU-based SCM, though some feeders saw reductions exceeding 35%, while others experienced minimal impact. Feeders with higher transformer utilization and limited capacity benefited more from LB, which more effectively distributed charging demand during off-peak hours. Beyond reducing grid constraints, SCM offers long-term operational and financial benefits. By shifting EV charging demand strategically, utilities can optimize asset utilization, delay infrastructure investments, and enhance grid performance. In terms of infrastructure upgrade deferrals, at the feeder level, LB consistently reduced peak charging loads and resulting infrastructure upgrade costs, particularly in high EV enrollment areas, decreasing the number of overloaded transformers by up to 35%, while TOU-based SCM achieved 20-30% reductions depending on feeder characteristics. At the system level, LB has the potential to defer total upgrade costs by $\$$186 million for BGE, compared to $\$$159 million under TOU-based SCM. For Pepco, TOU-based SCM performed slightly better, deferring upgrade costs by $\$$30 million, compared to $\$$29 million under LB. Section 4.5 reviews some of the system differences between BGE and Pepco. However, as EV adoption scales, TOU-based SCM will introduce secondary peak charging loads, reinforcing the need for more advanced, adaptive SCM approaches to prevent new grid challenges. As EV adoption continues to grow, feeder-level managed charging strategies will be essential for mitigating grid stress, improving infrastructure efficiency, and maintaining energy affordability for consumers. This report provides critical insights for utilities, Public Utility Commissions (PUCs), and state agencies on the role of feeder-specific smart charging in infrastructure planning, policy development, and grid modernization. The findings underscore the importance of tailored, data-driven SCM solutions that align with local grid conditions, ensuring a resilient, cost-effective transition to increasing EV adoption while safeguarding distribution system performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

PHEV Distribution Grid Integration and Smart Systems Testing: Cooperative Research and Development Final Report

To continue NREL's mission, which includes preparing technologies and markets with "speed and scale," NREL researchers will collaborate with Toyota on plug-in electric vehicle technology. Toyota will provide a collection of plug-in hybrid electric vehicles ("PHEVs" [and ultimately including PEVs], collectively, the "Vehicles") to conduct infrastructure interaction testing at NREL facilities. NREL will use available resources in the Vehicle Testing and Integration Facility (VTIF), its staff parking garage with numerous commercial Electric Vehicle Supply Equipment ("EVSEs"), and its Energy Systems Integration Facility (ESIF) to safely execute research experiments that highlight potential distribution system power quality challenges related to plug-in electric vehicles. The expected outcome of this project is a better understanding of the correlation of individual vehicle power quality attributes to a system of vehicles on a distribution network. The data collected and the simulations conducted will be used to guide future experiments and project development that aid in the deployment of plug-in electric vehicles ("PEV").

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Applications of Digital Twin System in a Smart City System with Multi-Energy

With the increasing number of electric vehicles (EV), the smart transportation system is becoming more closely related to the smart energy system in a smart city. However, because of the high complexity, dynamics, and large-scale of these two systems, it is challenging to study their operation problems systematically, especially in severe conditions, e.g., the 2021 winter Texas power traffic crisis. With artificial intelligence, federated learning, edge computing, and automatic control, a digital twin based smart city is proposed in this paper and it focuses on two applications: smart transportation and smart energy grid. Both systems are presented in a structured design manner with several components in the digital twin system, which also contains many real-implementations of different scenarios. The proposed digital twin of a smart city provides a potential clue for the problems mentioned above.

artificial intelligence↗
DOE OSTI2020

A Stabilizer based Predictive Control Scheme for Smart Inverters in Weak Grid

This paper presents a self-stabilization mechanism based on finite-set model predictive control (FCS-MPC) framework for smart inverters operating in weak grid conditions. As weak grid’s large parasitic impedance and low short-circuitratio (SCR) challenge the stable operation of grid-connected inverters. Specifically, the inverter may experience frequencies that might excite the LCL filter resonance phenomenon. The inverter stability collapses if this LCL resonance is triggered. To address this issue, a robust predictive controller is proposed that features a self-stabilization mechanism for smart inverters interacting with a weak grid. The proposed methodology utilizes the idea that in stiff grid conditions the grid current feedback (GCF) is stable and in weak grid conditions the inverter current feedback (ICF) is stable. Therefore, the proposed FCS-MPC toggles between GCF and ICF to achieve inherent LCL filter resonance damping. The toggling action between GCF and ICF is leveraged by comparing the moving RMS grid current with threshold current as a constrained in the proposed FCS-MPC cost function. The theoretical analyses are verified by several case studies for a single-phase grid-connected inverter. The analysis and results demonstrated that the proposed FCS-MPC operates well under weak, ultra-weak and stiff grid conditions.

Umar, Muhammad Farooq↗
DOE OSTI2025

Plug-in Electric Vehicle Charging Response Characterization for Grid Integration: Implications for Smart Charge Management

The rapid expansion of plug-in electric vehicles (PEVs) has created a unique challenge for electrical grids due to their significant power demand. At the same time, PEVs also create a unique opportunity to ease their own burden on the power grid, as they create a growing fleet of distributed energy resources capable of providing grid services such as demand response, frequency regulation, and renewable balancing. For aggregators and grid operators to effectively integrate PEVs into grid management, it is essential to first understand and characterize how they would respond in such situations. This study examines 25 models of PEVs across 25 makes, spanning model years from 2013 to 2025, to characterize their responses to the basic controls used in vehicle-grid integration (VGI): stopping, starting, and modulating the charge rate. Each vehicle was tested in a controlled laboratory setting to evaluate its performance in response to varying the maximum allowable current via the SAE J1772 control pilot signal, as well as its response to wake-up commands outlined in SAE J1772. Results show measurable differences across vehicle makes in the accuracy, latency, and precision with which PEVs respond to changes in ampacity, as well as varying sleep and wake-up behavior. The test results show that all vehicles respond to changes in ampacity, though with varying accuracy, precision, latency, and resolution. Wake-up behavior also differs across makes and models. These findings indicate that effective grid integration strategies must account for these differences. The results provide a foundation for understanding current vehicle behavior and advancing smart-charging methods while also highlighting the need for further testing, broader standardization, and manufacturer collaboration to ensure the successful integration of PEVs into the electrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗