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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins

Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Secondary Control Framework for Microgrid Interoperability With Vendor-Agnostic Grid-Forming Units: Design, Implementation, and Demonstration via Large-Scale Hardware Setup

The reliable operation of islanded microgrids increasingly depends on secondary controls that restore voltage and frequency to nominal values and ensure accurate active and reactive power sharing. Centralized secondary control architectures achieve high accuracy through global coordination at the cost of single-point failures and limited scalability compared with decentralized/distributed approaches. But a critical gap remains in addressing the interoperability and vendor-agnostic operation of secondary controls in real-world microgrids where heterogeneous diesel generator(s) and grid-forming (GFM) inverter(s) from multiple manufacturers always coexist. Practical and vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids with multiple GFM units have not yet been developed; therefore, this paper proposes an interoperable and vendor-agnostic secondary control framework that operates seamlessly across GFM units from different vendors without relying on proprietary controls and protocols, hardware, or lock-ins. The framework leverages existing communication infrastructures (e.g., Modbus TCP/IP) to enable cost-effective deployment while addressing practical challenges, such as packet loss and quantization errors. Mitigation strategies-including data averaging, situational event-triggered control, and finite-iteration execution-are introduced to enhance reliability under real-world conditions. A generalized modeling and design framework is also presented, supported by robustness analysis to demonstrate independence from vendor-specific implementations. The proposed framework is validated through a large-scale hardware demonstration using a 3-$\phi$, 480-V, 60-Hz, 713-kVA laboratory hardware microgrid involving a heterogeneous diesel generator and multiple GFM inverters, showcasing its effectiveness in achieving stable voltage and frequency restoration and accurate power sharing under practical constraints. The results highlight the framework's potential as a scalable and practical solution for next-generation microgrids requiring openness, standard framework, and interoperability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Synchrophasors-based Master State Awareness Estimator for Cybersecurity in Power Grid: Testbed Implementation & Field Demonstration

The integration of distributed energy resources(DERs) and expansion of complex network in the distribution grid requires an advanced distributed state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper proposes a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the proposed MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs),deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement the proposed methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the “valley of death” and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex(CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time

42 ENGINEERING↗
DOE OSTI2025

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗
DOE OSTI2024

Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

14 SOLAR ENERGY↗
DOE OSTI2024

Sensor Data Analytics and Data Quality Assessment Software

The proposed framework derives a set of quality metrics to provide critical insights into and tracking of grid operations, sensor performance, sensor longevity, and event statistics. Power grid engineers can utilize this information to identify problems with existing sensor locations and problematic power grid assets including generators, transmission lines, load centers, and substations. This information can also be used to identify unexpected/abnormal behavior of power grid components, improve power grid observability, and operational monitoring, and thus enhance real-time decision-making support system. Power grid planners can utilize this information to augment existing sensing architecture with new sensors and improve the observability of the network.

Mahapatra, Kaveri↗
DOE OSTI2026

Demonstrating the data center as a flexible grid asset using a C-HIL setup

Increasing data center demand is outpacing grid infrastructure development. Artificial intelligence workloads and hyperscale cloud growth are creating unprecedented demand for power, while traditional grid expansion faces multiyear development timelines. Verrus is developing an innovative datacenter solution for this challenge, data centers that act as active grid-supportive assets rather than passive loads. Our approach integrates a novel grid-aware power flow management system with battery energy storage systems(BESS) into a microgrid-controlled, medium-voltage power distribution architecture that delivers critical capabilities, such as: * Fast response to grid disturbances such over/ under voltage or over/ under frequency * Demand flexibility that can service requests from the utility within 10 s * Uninterrupted transition to islanded operation during grid outages * Continuous uptime assurance for compute loads while maintaining all customer service level agreements. Through Verrus' strategic partnership with the National Renewable Energy Laboratory (NREL), these capabilities were validated using NREL's Advanced Research on Integrated Energy Systems (ARIES) virtual emulation environment to model a 70-MW grid-interactive data center. This paper outlines the design, methodology, and results of this emulated deployment, demonstrating that data centers can provide both critical load resilience and ancillary grid support without compromising uptime requirements. Specifically, we present a digital real time simulation of a 70 MW data center integrated with a physical microgrid controller, and demonstrate the data center response in the event of a grid voltage and frequency event, utility demand response request and utility outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Commercial Building Planning and Retrofitting Strategy for Grid Services

The increasing integration of distributed energy resources (DERs) plays an important role in improving energy consumption efficiency. In September 2020, the Federal Energy Regulatory Commission (FERC) approved Order 2222 which opens wholesale electricity markets to small capacity DERs. The benefit of this new FERC Order 2222 is that DERs, such as rooftop solar panels and batteries, will be able to participate in regional electricity markets and provide grid services. Meanwhile, the planning and operation strategies of DERs are facing new challenges to account for the impact of the wholesale market with numerous uncertainty factors. Therefore, in this paper, we propose a new planning and retrofitting model for long-term commercial buildings that considers both DER investment and market participation. Specifically, we explore the capability of implementing DERs for grid services. The effectiveness of the proposed model is validated using real-world data. Simulation results also validate that participating in grid services can significantly increase revenues through appropriate building energy management and shorten the payback period of DER investments.

building energy management↗
DOE OSTI2023

EVs@Scale Next-Gen Profiles - Fleet Utilization 2023

As U.S. fleet operators begin transitioning to electric vehicles (EVs), critical questions arise regarding how to manage this shift without disrupting fleet operations or placing undue stress on the electric grid. A major challenge for fleets is maintaining effective operational schedules while accommodating charging requirements, particularly with high-power charging (HPC) infrastructure, which presents grid stability concerns for utilities. Proposed solutions such as charging substations, megawatt charging systems (MCS), and smart charge management systems (SCMS) offer potential pathways forward, but their effectiveness depends on alignment with real-world fleet behavior and operational constraints. This report investigates the charging and utilization behavior of EV and EVSE fleets actively employing HPC technologies by conducting detailed case study analyses based on telematics data. A suite of predefined metrics—covering charging, routing, and other operational behaviors—is developed to evaluate the impact of fleet activities on grid infrastructure and identify opportunities for optimization. Results highlight variations in charging behavior across fleets, such as weekday versus weekend usage, diurnal charging trends, and the role of operational predictability in enabling SCMS effectiveness. While SCMS can help lower costs and improve energy efficiency for fleets with stable schedules, they may be insufficient for fleets with highly variable or long-haul operations, which may require more robust solutions like MCS. Visualization of aggregated hourly energy metrics reveals that while fleet behaviors are diverse, there are common temporal patterns that could inform infrastructure planning and energy management. These insights emphasize the need for fleet-specific charging strategies that minimize grid impact while supporting reliable fleet operations. Additionally, the report underscores the broader economic stakes of electrification, particularly in high-value markets such as freight, where misaligned transitions could stall EV adoption. By examining current EV and EVSE fleet deployments using predetermined standardized metrics, this study offers a foundation for developing technologies and operational frameworks that support scalable, grid-compatible electrification across a variety of fleet types while establishing a baseline understanding of operational behaviors. In doing so, we aim to ensure that future charging solutions reflect actual fleet needs and grid constraints—an essential step toward maintaining operational continuity and achieving a successful transition to electric fleet operations.

Charging↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Effectiveness of Privacy Techniques in Smart Metering Systems

Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also susceptible to privacy attacks within the energy company and from outside hackers. We implemented four different privacy models: k-anonymous, l-diversity, t-closeness, and ε-differential privacy. We demonstrate the models’ effectiveness using a real-world dataset composed of 15 different residential households with energy consumption data spanning over a year.

Peralta-Peterson, Martin↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Risk-informed Hierarchical Control of Behind-the-Meter DERs with AMI Data Integration (Final Technical Report)

This project addresses several key barriers to implement the next generation demand response applications and provides a clear understanding of implementing hierarchical and standalone control using AMI data. Through this program, Eaton has developed and tested a meter-as-a-controller prototype with the help of other partners--- National Renewable Energy Laboratory (NREL), Electric Power Research Institute (EPRI), Pecan St Inc. (PSI), and Delaware Electric Cooperative (DEC). The controller can utilize residential controllable loads such as heating, ventilation, and air conditioner (HVAC), electric water heater and distributed energy resources like solar PV and battery energy storage systems for off-setting the demand that is required from the grid, thus providing reliable grid-services for demand reduction or peak shaving. The controller is also capable of coordinating the resources of the premises for better management and energy efficiency while meeting the comfort bound of the premises owner as quality-of-service. The development has been demonstrated in a three virtual-home setup at system performance lab of NREL with real appliances (HVAC, electric water heater, solar PV, and battery). The technology has also been proved through laboratory and field demonstration with successful interconnectivity (e.g., end-to-end communication and data exchange) between the residential appliances and utility through the RF network at Delaware Electric Co-op (DEC) in Delaware.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

High Performance Adaptive Deep-Reinforcement-Learning-based Real-time Emergency Control (HADREC) to Enhance Power Grid Resilience in Stochastic Environment (Final Report)

The HADREC technology developed in this project features real-time, validated, and predictive emergency control capabilities needed to effectively safeguard the grid against costly disturbances from extreme weather and other disruptive events. It has the potential to revolutionize the emergency control by providing faster and more effective solution for power grid emergency control. The technology can reduce the grid emergency control action time from minutes to seconds and mitigate the impact of outages and reduce system recovery time. The evaluation results using the models and data from PacifiCorp system have shown ~20% improvement in responding time and 70% improved in tripped generator output, comparing against today’s industrial application. This performance can help reduce preventive measurements and improve network utilization and market efficiency, and help lead to a more reliable and resilient power grid with renewable integration. The project has generated significant interest from industry, academic and commercial companies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Toward the Instrumentation and Data Acquisition of a Tidal Turbine in Real Site Conditions

The National Renewable Energy Laboratory manufactured, instrumented, and deployed thermoplastic composite blades and a data acquisition system (NDAQ) on one of Verdant Power’s Gen5d 5 m diameter tidal turbines in New York’s East River. The thermoplastic blades had internal strain gages, and the NDAQ was a stand-alone system for monitoring and recording the strain and angular position of the blades. The turbine with thermoplastic blades operated and produced power successfully for 3 months, contributing energy to the New York City electric grid. The NDAQ hardware, instrumentation, and structure all survived the deployment and were still functional upon retrieval of the system, but no data were collected. Even though the data retrieval was not a success, data acquisition for deployed subsea marine renewable structures is a new undertaking, and it is critical to share lessons learned from national laboratory experiences. The successful deployment of thermoplastic composite blades marks a significant advancement toward improved materials for subsea components, as well as an advancement in recyclable composite materials. This article outlines the methodology and lessons learned for the instrumentation and data acquisition system.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗
DOE OSTI2021

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗