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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Scalable Truck Charging Demand Simulation for Cost-Optimized Infrastructure Planning

This project developed a scalable, high-resolution model to simulate medium- and heavy-duty (MHD) electric truck charging demand and assess its impact on grid infrastructure. Using generative modeling, simulation, and cost optimization, the project delivered an end-to-end software pipeline and a library of 96 real-world scenarios for the Dallas–Houston megaregion. We demonstrated a modular architecture for transportation and grid modeling, implemented cost-optimized infrastructure planning methods, and quantified grid capital, operational, and environmental costs across a wide range of truck electrification scenarios. The results have been adopted by major utility stakeholders and contributed to regional planning efforts.

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 OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grids

This paper investigates the impact of Sustainable Aviation Fuel production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aims to meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies in power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO 2 -to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aim to meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO 2 -to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

capacity expansion model↗
DOE OSTI2024

Searching for Grid-Forming Technologies That Do Not Impact Protection Systems: A promising technology

The design of legacy-line protection elements has been guided by the behavior of synchronous machines during faults. Because of the significant field-winding inductance and rotating mass, the magnitude, angular frequency, and phase angle of the back-electromotive force (EMF) voltage waveforms of synchronous machines remain practically constant for several hundreds of milliseconds after a fault occurs. Furthermore this has facilitated the engineering of the memory-polarization technique in mho distance elements, which has been effective for machine-dominant power grids. However, this assumption is no longer held for inverter-based resources (IBRs) because of the lack of field winding and moment of inertia in power electronics devices. Notably, the negative-sequence directional overcurrent protection and the quadrilateral distance elements have been impacted by early IBRs with grid-following (GFL) controls because they did not inject negative-sequence currents during asymmetrical faults.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Impacts and emerging research opportunities in Vehicle-Grid Integration for transportation: A review

This review provides a comprehensive examination of Vehicle-Grid Integration (VGI) technologies and their impacts on transportation systems, with a particular emphasis on the transportation-energy nexus. It systematically explores how VGI affects key transportation applications such as charging infrastructure planning, electric vehicle (EV) routing, smart charging coordination, shared mobility, and dynamic pricing. By synthesizing recent literature from both transportation and energy systems perspectives, this study highlights how advanced methodologies, such as reinforcement learning, game theory, and optimization techniques, are used to model the complex interactions between EVs, mobility patterns, and distributed energy systems. Furthermore, the review also identifies critical challenges, including behavioral factors, data limitations, and system scalability. Drawing on these insights, the paper outlines emerging research opportunities to support the design of integrated, resilient, and user-centric VGI solutions that advance sustainable mobility and energy system efficiency.

Charging coordination↗
DOE OSTI2026

Flexible AI Models for Grid Resilience

The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Impact of Nuclear Power Plant Energy Storage on Grid Reliability and Generation Costs with Variable Power Generation

This report examines the impact of combining integrated energy systems (IES), specifically hydrogen-based energy-storage systems, with nuclear power plants (NPPs) on grid reliability and generation flexibility in scenarios with substantial variable-power generation from solar energy. The analysis focuses on the challenges and solutions associated with high-levels of solar energy penetration, highlighting the role of advanced energy-storage solutions and flexible-generation technologies in enhancing grid stability and reliability.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2024

High-Fidelity Dataset Generation for Sensor Anomalies in Power Grids using Hardware-in-the-Loop Testbed

Sensor anomalies in power grids can have significant impacts on the operation of the grid due to the increased reliance of the grid operation on data-driven applications. However, there is a lack of datasets that accurately capture these anomalies as many of the anomalies go undetected using the current bad data detectors. High-fidelity labeled datasets are essential for developing robust applications that can detect and mitigate the impacts of anomalies. In this paper, we propose a hardware-in-the-loop testbed model that can emulate the grid behavior with high-fidelity. This testbed is used to inject anomalies at various levels in the grid architecture and generate labeled datasets. These high-fidelity datasets can be used for development and validation of data-driven applications for detection and mitigation of anomalies in grids and other cyber-physical systems.

Hyder, Burhan↗
DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid: Preprint

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aimto meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Intelligently Partitioned Phasor-EMT Hybrid Simulations of Large-Scale, High-IBR Power Systems

As the penetration level of power electronics-interfaced renewables such as photovoltaics (PV) and wind has surged in modern electric grids, new operational risks caused by the dynamics of those inverter-based resources (IBRs) are emerging in parallel. Lessons learned from various grid events include that the impact of IBRs on system-level grid stability will become prominent along with the increase of renewables and that the short-timescale dynamic impacts of IBRs on grid stability are not fully captured by current commercial dynamic simulation tools [1] [2]. For example, IBRs can be controlled to mitigate those destabilizing interactions, but conventional phasor-domain tools (e.g. PSS/E, PSLF) often cannot capture that; likewise, the existing electromagnetic transient (EMT) simulation tools (e.g. PSCAD, EMTP) can simulate detailed IBR controls, but for large power systems with many IBRs, slow simulation speeds severely impede the ability to study dynamic events [3] [4]. Massively paralleling simulations using high-performance computing (HPC) can help address this, especially now that cloud-based HPC capability is widely available, but today s EMT tools are not HPC-compatible, and parallelization of dynamic simulation solvers is not trivial because each region can dynamically affect the others. Thus, dynamic simulation of grids with very large numbers of IBRs potentially poses a barrier to the ongoing energy transition.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Microgrid design and multi-year dispatch optimization under climate-informed load and renewable resource uncertainty

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Grid-Forming Storage Networks: Analytical Characterization of Damping and Design Insights

Grid-forming storage resources are critical to the operation of power grids with high renewable penetration. Over the years, there has been considerable emphasis on understanding the benefits these inverter-based storage resources bring towards managing volatility and enhancing grid reliability but little is studied about the supplementary advantages latent in their operation. This paper investigates one such application in which we explore the impact of grid-forming distributed storage in damping low-frequency inter-area oscillations. We present a detailed analysis characterizing the impact of inverter-droop and storage size on the slower eigenvalues, highlighting potential design considerations for enhancing system stability.

Chatterjee, Kaustav [BATTELLE (PACIFIC NW LAB)] (O↗
DOE OSTI2024

Survey and Gap Prioritization of U.S. Electric Vehicle Charge Management Deployments

The goal of this study was to survey and characterize the scope of current technical and programmatic knowledge pertaining to EV charge management technologies and practices in the US and relevant international jurisdictions. This characterization of existing field demonstrations and knowledge derived were used to determine gaps in the SCM demonstration landscape. Addressing these gaps through research and demonstration could increase confidence in the U.S. that load management and EV charge control could achieve overarching societal benefits. A survey of charge management deployments and input from stakeholders was completed to determine the state-of-the-art of smart charge management (SCM) where SCM is defined as controlling the amount of power exchanged between chargers and EVs to meet customers' charging needs while also responding to external power demand or pricing signals to provide load management, resilience, or other benefits to the customer and electric grid. The survey was the basis of the gap analysis in this report and determines which areas are well understood, with high confidence, and which areas need further investigation. Existing examples of EV charge management are characterized here to determine aspects that are ready for widespread deployment and have been demonstrated in the field. These include demonstration studies, pilots, programs, and EV-specific tariffs. In all, 110 examples of charge management were characterized. The data sources were public literature and utility filings as well as targeted interviews. In addition, 43 interviews with stakeholders were conducted with a consistent set of questions used in each interview. This study prioritized gaps in demonstrated SCM capabilities based on 1) Urgency of the particular use-case to offset traditional grid assets, 2) Impact, extensibility, and scaling of results across the entire spectrum of 3000+ utility service territories including projected technical and market potential for a given grid service, and 3) Value of federal funding in addressing the gap, including potential to leverage and/or add scope to existing field demonstrations funded by other non-federal funding mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Impact of Inverter-Based Resources on Grid Protection: A Review of Negative-Sequence Current Generation

The increasing integration of inverter-based resources (IBRs) in power grids poses challenges to traditional protection systems, primarily due to their different fault current signatures compared to conventional synchronous generators. Unlike synchronous generators whose fault response is dictated by their physical design, IBRs exhibit a wide range of fault characteristics due to manufacturer-specific control algorithms and settings. This dependence on proprietary control schemes makes modeling IBR behavior during faults significantly more complex, especially considering the rapid evolution of inverter technology and the diverse control strategies employed. While much research has focused on the positive-sequence current injections of IBRs during symmetrical faults, the understanding of negative-sequence current generation during non-symmetrical faults remains limited. This report provides an overview of current research on IBRs' negative-sequence current generation during unbalanced faults and its impact on protection schemes based on negative-sequence components. It covers both type III wind turbines and full-size converter-based IBRs. Additionally, this report reviews strategies for grid-forming controlled inverters to generate negative-sequence current during unbalanced faults, in addition to grid-following controlled ones.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Impact of Glen Canyon Generation Loss

Colorado River Basin hydropower generation has faced challenges due to droughts and ecological and social water requirements. Specifically, Glen Canyon hydropower generation fluctuates substantially with recent extreme weather trends. Further, the western grid evolves with higher wind and solar share, and Glen Canyon hydropower's contribution to grid flexibility services is essential. The Western Area Power Administration (WAPA) markets and schedules electricity production at GCD, and the loss of this power could have significant financial consequences for the WAPA Colorado River Storage Project's (CRSP) Office because it may need to purchase relatively large amounts of energy to serve its firm electrical obligations. In addition, GCD provides grid reliability services for the WAPA Colorado-Missouri (WACM) balancing authority (BA). Both WAPA and DOE's Water & Power Technology Office (WPTO) are interested in researching how these drier hydrological conditions will impact federal electrical energy production, the Western Electricity Coordinating Council (WECC) power grid, and the value of hydropower in the face of lower production. We study multiple hydrologic and power grid scenarios to understand the grid impacts of the loss of Glen Canyon generation. The study uses a production cost model, water resources planning models, water-centric grid models, and various data analytic techniques. The study progress presentation discusses the selection of probable CRSP' hydropower scenarios and power grid scenarios to understand the impacts of Glen Canyon generation, which includes technologies that compensate the Glen Canyon energy and ancillary services contributions, transmission availability, and energy local marginal prices at interested grid locations of WAPA operation.

droughts↗