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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessing the Performance and Impact of PV Technologies on Storage in Hybrid Renewable Systems

Traditional monofacial photovoltaic (mPV) systems are commonly adopted and well-documented because of their lower upfront costs in comparison to bifacial photovoltaic (bPV) systems. This study investigates how PV technologies impact energy storage in grid-scale hybrid renewable systems, focusing on optimizing and assessing the performance of mPV and bPV technologies integrated with pumped storage hydropower. Using Ludington City, Michigan as a case study and analyzing real-world data such as solar irradiance, ambient temperature, and utility-scale load profiles, the research highlights the operational and economic benefits of bPV systems. The results reveal that bPV systems can pump approximately 10.38% more water annually to the upper reservoir while achieving a lower levelized cost of energy ($0.0578/kWh for bPV vs. $0.0672/kWh for mPV). This study underscores the outstanding potential of bPV systems in enhancing energy storage and management strategies, contributing to a more sustainable and resilient renewable energy future.

13 HYDRO ENERGY↗
DOE OSTI2020

Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience

Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

42 ENGINEERING↗
DOE OSTI2022

Epistemology of voltage control in DER-rich power system

Despite the recent development of several scalable, robust, and resilient control approaches with superior convergence properties considering an increasing penetration of distributed energy resources (DERs), cognitive oversights often simplify several aspects of the cyber–physical power system in the controller development. Here, following the identification of the limitations of classical controller definitions, we justify alternative definitions of voltage control approaches classifiers considering three inter-disciplinary domains: (i) power system, (ii) optimization and decision-making, and (iii) networking and cyber-security, to develop a taxonomy for helping in real-world comparative performance analysis and deployability of these controllers. We observe that classical and introduced domain-based definitions together can better classify the control algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Adaptive primal–dual control for distributed energy resource management

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal–dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal–dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. Here we evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Performance Analysis and Optimal Node-aware Communication for Enlarged Conjugate Gradient Methods

Krylov methods are a key way of solving large sparse linear systems of equations but suffer from poor strong scalability on distributed memory machines. Furthermore, this is due to high synchronization costs from large numbers of collective communication calls alongside a low computational workload. Enlarged Krylov methods address this issue by decreasing the total iterations to convergence, an artifact of splitting the initial residual and resulting in operations on block vectors. In this article, we present a performance study of an enlarged Krylov method, Enlarged Conjugate Gradients (ECG), noting the impact of block vectors on parallel performance at scale. Most notably, we observe the increased overhead of point-to-point communication as a result of denser messages in the sparse matrix-block vector multiplication kernel. Additionally, we present models to analyze expected performance of ECG, as well as motivate design decisions. Most importantly, we introduce a new point-to-point communication approach based on node-aware communication techniques that increases efficiency of the method at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Validation of a Co-Optimized, Smart Hybrid Heat Pump Control

This study examines a residential dual fuel (DF) heating system that integrates heat pump and gas furnace technology with a home’s forced-air distribution ducts. Specifically, it explores a retrofit where an “add-on” heat pump replaces the air conditioning (A/C) system and is combined with the home’s existing furnace and air handler. The research team developed and validated an innovative control scheme for this integrated DF system that selected the more economical heating source (furnace or heat pump) while maintaining comfort in the home. The project’s field study and subsequent modeling analysis show that this control scheme can decrease energy use, cost, and emissions compared to a furnace-only system. As homes in mixed and cold climates transition to more electric space heating, furnace-to-dual fuel system retrofits offer a solution that can alleviate the retrofit costs, energy cost concerns, and peak load impacts of full electrification.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Summary: Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

The Clean Energy Investment Accelerator conducted a case study analysis of battery energy storage system (BESS) feasibility for an industrial park in Vietnam using the National Renewable Energy Laboratory's (NREL's) REopt platform (a distributed energy modeling and optimization tool) to evaluate how BESS may reduce electricity costs, increase utilization of onsite renewable energy generation, and improve resilience to utility grid outages. This presentation summarizes the analysis and key takeaways.

batteries↗
DOE OSTI2024

H2Integrate [SWR-23-31]

H2Integrate(H2I) is an open-source Python package for hybrid systems engineering design and technoeconomic analysis. It models and optimizes hybrid energy plants that produce electricity, hydrogen, ammonia, steel, and other products. H2Integrate is designed to be flexible and extensible, allowing users to create their own components and models for various energy systems. The tool currently includes distributed energy generation (wind, solar, wave, tidal), battery storage, hydrogen, ammonia, methanol, and steel technologies. Other elements such as desalination systems, pipelines, compressors, and storage systems can also be included as developed by users. Some modeling capabilities in H2Integrate are provided by integrating existing tools, such as HOPP, PySAM, ORBIT, and ProFAST. The H2Integrate tool is built on top of NASA's OpenMDAO framework, which provides a powerful and flexible environment for modeling and optimization.

King, Jennifer [National Renewable Energy Laborato↗
DOE OSTI2020

LDMS-GPU: Lightweight Distributed Metric Service (LDMS) for NVIDIA GPGPUs

GPUs are now a fundamental accelerator for many high-performance computing applications. They are viewed by many as a technology facilitator for the surge in fields like machine learning and Convolutional Neural Networks. To deliver the best performance on a GPU, we need to create monitoring tools to ensure that we optimize the code to get the most performance and efficiency out of a GPU. Since NVIDIA GPUs are currently the most commonly implemented in HPC applications and systems, NVIDIA tools are the solution for performance monitoring. The Light-Weight Distributed Metric System (LDMS) at Sandia is an infrastructure widely adopted for large-scale systems and application monitoring. Sandia has developed CPU application monitoring capability within LDMS. Therefore, we chose to develop a GPU monitoring capability within the same framework. In this report, we discuss the current limitations in the NVIDIA monitoring tools, how we overcame such limitations, and present an overview of the tool we built to monitor GPU performance in LDMS and its capabilities. Also, we discuss our current validation results. Most of the performance counter results are the same in both vendor tools and our tool when using LDMS to collect these results. Furthermore, our tool provides these statistics during the entire runtime of the tool as a time series and not just aggregate statistics at the end of the application run. This allows the user to see the progress of the behavior of the applications during their lifetime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Power system restoration incorporating diverse distributed energy resources

An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

Ding, Fei↗
DOE OSTI2024

Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method

Abstract. The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and the data gaps are filled by a simple optimal interpolation method using Gaussian covariance with a constant e-folding length scale. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The simple optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. The North Atlantic and subpolar North Pacific are relatively well sampled, and the simple optimal interpolation is capable of reconstructing more than 80 % of the oxygen trend in the non-eddying CMIP models. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. The application of the simple optimal interpolation method to the historical dataset estimated the global oxygen loss to be 1.5 % over the past 50 years. However, the ratio of the global oxygen trend between the sub-sampled and full model output has increased the estimated loss rate in the range of 1.7 % to 3.1 % over the past 50 years, which partially overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-filling methods to estimate oxygen content trends and their uncertainties due to sampling sparseness.

Ito, Takamitsu (ORCID:000000019873099X)↗
DOE OSTI2024

The Monolithic Heat Pipe Microreactor Reference Plant Model

This work introduces a reference plant model for a generic monolithic heat-pipe-cooled microreactor. The model will serve as a springboard to develop future evaluation models in the licensing process of similar microreactor designs at the U.S. Nuclear Regulatory Commission. This model has been developed with the Comprehensive Reactor Analysis Bundle and its specifications are based on open literature publications for the eVinci TM design. BlueCRAB is the U.S. Nu- clear Regulatory Commission non-light-water reactor analysis system based on the Multiphysics Object-Oriented Simulation Environment framework, which can couple the Griffin, BISON, and Sockeye applications to resolve the various physics that are essential for the safety analysis of this type of reactor system. The core specifications includes tristructural isotropic fuel, graphite monolith, graphite reflectors, and drums composed of graphite and B 4 C. No moderator or burnable poison pins are used in the design. The fuel enrichment is reduced to control excess reactivity in the core. This core design is not optimized and only serves for testing purposes, since the primary objective of this work is to exercise the multiphysics coupling for this type of reactor system. A three dimensional (3D) core heterogeneous Griffin discrete ordinates (SN) transport model allows the precise calculation of the flux distribution and pin powers. Griffin transfers the power density distribution and obtains a temperature distribution to and from BISON. The BISON model com- putes the 3D core temperature distribution and is coupled to 876 Sockeye subapplications running a heat pipe model. This 3D conduction model is coupled to the various heat pipes via heat flux boundary conditions. The model includes a small gap between the heat pipe and the monolith. Convective heat transfer boundaries with either ambient temperature or condenser temperature as heat sinks are imposed at the model boundaries. The 2D Sockeye heat pipe model uses a vapor- only methodology, which provides the needed resolution for transient calculations and allows the determination of various heat pipe limits. This approach is superior to the superconductor model traditionally used in steady-state calculations. BlueCRAB computes steady-state power and temperature distributions that serve as the initial condition for a loss-of-heat-sink transient simulation. The steady-state results show significant peaking due to the position of the control drum, but this is a characteristic of the particular design used, which is not optimized at this stage. The transient results show the reactor power slowly stabilizing towards a 3% power level after the partial loss of secondary heat removal. Several recriticalities are observed due to cooling through the secondary system but the reactor is self-stabilizing and behaves as expected.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Optimization of Distribution Feeder Topology: A Differential Programming Learning Approach

This paper presents a gradient based method for optimizing distribution feeder network topology under load un- certainty. We recast the optimal network reconfiguration problem as a learning problem where edge weights of a graph are learned to produce an optimized spanning tree for a distribution network. Using recent methods published on differentiable programming, we provide a data driven method for learning these weights. We test our method on 100 variations of an IEEE 15-bus test system. Our results show that our method outperforms more traditional mathematical programming-based approaches.

differentiable programming↗
DOE OSTI2022

Network Reconfiguration for Enhanced Operational Resilience Using Reinforcement Learning

This paper proposes a reinforcement learning-based approach for distribution network reconfiguration(DNR) to enhance the resilience of the electric power supply. Resilience enhancements usually require solving large-scale stochastic optimization problems that are computationally expensive and sometimes infeasible. The exceptional performance of reinforcement learning techniques has encouraged their adoption in various power system control studies, specifically resilience-based real-time applications. In this paper, a single agent framework is developed using an Actor-Critic algorithm (ACA) to determine statuses of tie-switches in a distribution feeder impacted by an extreme weather event. The proposed approach provides a fast-acting control algorithm that reconfigures the feeder topology to reduce or even avoid load shedding. The problem is formulated as a discrete Markov decision process in such a way that a system state captures the system topology and its operational characteristics. An action is made to open or close a specific set of tie-switches after which a reward is calculated to evaluate the practicality and advantage of that action. The iterative Markov process is used to train the proposed ACA under diverse failure scenarios and is demonstrated on the 33-node distribution feeder system. Results show the capability of the proposed ACA to determine proper switching action of tie-switches with accuracy exceeding 93%.

actor critic↗
DOE OSTI2025

Resilient Operation of Networked Community Microgrids with High Solar Penetration

This project, funded by the US Department of Energy’s Solar Energy Technologies Office (SETO), focused on the operation of microgrids as a coordinated network. The primary objective, which was successfully achieved, was to develop both control strategies and hardware solutions to support the resilient and efficient operation of networked microgrids with high solar penetration. The work was structured around the following four main tasks: • Development of distributed and scalable optimization algorithms for AC-coupled networked microgrids. • Design and implementation of a novel DC interconnection hardware to enable precise power exchange between microgrids. • Laboratory operational validation of the developed technologies using 480 V testbeds and commercially available hardware. • Field operational validation of the complete solution in Adjuntas, Puerto Rico, interconnecting two kW-scale, split-phase microgrids of Casa Pueblo’s microgrids. This project addressed multiple technical challenges across the domains of optimization, control, hardware interconnection, and protection. One of its key contributions was delivering tangible, real-world solutions for networking microgrids. In contrast to purely theoretical or simulation-based work, this project included full-scale hardware operational validation both in the lab and in the field. The work conducted as part of this project—in collaboration with the University of Puerto Rico; the University of Tennessee, Knoxville; the University of Central Florida; and Casa Pueblo—has advanced the state of the art in networked microgrids. Key contributions include the development of distributed control strategies, practical solutions for real-world implementation challenges, and the introduction of a novel DC interlink approach for microgrid interconnection. The project featured both laboratory and field validation using commercial off-the-shelf components. The field deployment successfully validated that a group of microgrids can operate in a coordinated manner, enabling precise power flow between systems and mutual support during extreme events. This project resulted in 15 journal publications and 15 conference papers; 5 graduate students and 15 undergraduate students were supported. The codes of distributed optimization and forecasting were made open-source through OSTI.gov for distributed optimization and forecasting. All the publications are available in the ORNL-hosted project landing page. The DC interlink with state-of-charge balancing control was operationally validated in Adjuntas by interconnecting two real-world, 240 V split-phase microgrids. To the best knowledge of the team, this represents the first operational validation of AC microgrids interconnected via DC-interlinks. As a culmination of this project, a follow-on grant was awarded to support the technology transfer of the distributed optimization framework to a commercial microgrid controller, Stellar Edge, developed by the California-based company New Sun Road.

14 SOLAR ENERGY↗
DOE OSTI2022

Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate the advantages of networked microgrids over independent microgrids in terms of reducing total operating cost and improving the resilience of power supply.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Cybersecurity Assessment in DER-rich Distribution Operations: Criticality Levels and Impact Analysis

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control op- erations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. This paper utilizes real-time feeder models, which are instrumental in developing cybersecurity testbeds tailored for hardware-in-loop (HIL) systems. These models enable users to simulate cyber attacks in a real-world environment and analyze the power distribution operations during vulnerabilities. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.

Maharjan, Manisha↗