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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development of Solid Synchronous Reluctance Rotors With Multi-Material Additive Manufacturing

Synchronous reluctance (SynR) machines are promising rare-earth material-free alternatives to permanent magnet machines. However, structural challenges limit their operating speed and power density. This paper proposes and investigates multi-material additive manufacturing (MMAM) as a key-enabler to realize power-dense and high-speed SynR machines. It does so by proposing designs that guide magnetic flux through solid rotors realized by selective placement of magnetic and non-magnetic materials. To explore this concept, first, material samples are additively manufactured and experimentally characterized to assess the structural and magnetic properties that can be expected for the proposed rotors. Second, the design space of each rotor type is explored using these measured properties within finite element analysis. The results reveal that MMAM can enable fabrication of SynR motors with power density levels that are at the leading edge of all conventional electric machine topologies. It is shown that tip speeds in excess of 300 m/s can be achieved, resulting in 3-4x improvement in power density over conventional SynR motors. A solid SynR rotor is printed in an experimental MMAM laser powder bed fusion system. The rotor is paired with an existing stator to create a functional SynR motor with a saliency ratio of 2.59 and torque rating of 4.15 Nm. This is the first publication of a SynR rotor prototype constructed via MMAM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards an intuitive application of WEC control co-design

A simple co-design example in a reduced parameter space is presented for an oscillating flap device. Initially, the WEC geometry and mass properties are considered along with drivetrain gear ratio, inertia, motor constant and stiffness under both PI and optimal control. This parameter space is reduced to those to which performance is most sensitive for a fixed geometry. The gear ratio, drivetrain stiffness, and flap mass are found to be the most impactful design criteria as they can create orders of magnitude variations in power performance. The performance of the optimized system is compared with several sub-optimal variants in terms of electrical and mechanical power capture, transmission coefficients, and transducer power gain. Notably, though substantial power capture improvements are demonstrated when an optimal controller is employed, this power capture remains sensitive to appropriate selections of drivetrain and flap design parameters, implying that control co-design procedures remain necessary for high-performing WECs. Furthermore, a number of practical caveats and extensions to the presented co-design methodology are suggested, including the characterization of system static friction, especially in the presence of high gear ratios.

Control co-design↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Balancing Charging Station Utilization and Throughput in Electric Vehicle Charging Stations with Queuing Theory

The rapid increase in electric vehicle (EV) adoption demands enhancements in the efficiency and adaptability of EV supply equipment (EVSE). Traditional EVSE systems often fail to optimize power delivery to meet the variable acceptance rates of EV batteries, resulting in significant energy wastage and reduced operational efficiency. This research addresses these challenges by integrating queuing theory with modular EVSE architectures, offering a dual strategy to optimize the operation of EV charging stations. A simulation model was developed to assess various configurations of charger capacities and outlet numbers. This model aimed to identify the optimal setup that maximizes station utilization while minimizing charging times and maximizing throughput. The model focused on charger capacities ranging from 50 to 250 kW and analyzed the different capacities’ effects on charging times and the number of vehicles served. The results indicate that a charger capacity of 125 kW is optimal, striking a balance between the charging time and the number of EVs served per hour, thus achieving the highest station utilization rate. This capacity allows for servicing a significant number of EVs with moderate increases in charging times. Lower capacities, although capable of serving more vehicles, lead to longer charging times and decreased throughput efficiency. The study underscores the effectiveness of combining queuing theory with flexible, modular charging systems that can dynamically adjust to EV charging demands.

Kumar, Praveen↗
DOE OSTI2021

Distributed ADMM Using Private Blockchain for Power Flow Optimization in Distribution Network With Coupled and Mixed-Integer Constraints

The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.

25 ENERGY STORAGE↗
DOE OSTI2021

Peak Load Management in Distribution Systems Using Legacy Utility Equipment and Distributed Energy Resources: Preprint

The ability to perform peak load management in distribution systems has several benefits for utilities, including reduced demand charges and improved reliability, efficiency, and utilization of the network infrastructure. This paper demonstrates the coordinated operation of an advanced distribution management system (ADMS) and a distributed energy resource management system (DERMS) to achieve peak load management using a realistic laboratory test bed. A commercial ADMS reduces the peak demand by reducing system voltages using a dynamic voltage regulation (DVR) application. A prototype DERMS—based on real-time optimal power flow—controls distributed battery energy storage systems to further reduce the feeder power. Results from the experiments conducted using a model of a real distribution feeder show that the coordinated operation of the ADMS and DERMS is effective in accomplishing peak load management.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

25 ENERGY STORAGE↗
DOE OSTI2022

Assessing the performance of global thermostat adjustment in commercial buildings for load shifting demand response

Abstract Efficiently leveraging new sources of flexibility is critical to mitigating load balancing challenges posed by variable renewable resources. The thermal inertia of commercial buildings allows us to shift their power consumption on minute to hourly timescales to provide demand response to the grid while maintaining occupant comfort. Global thermostat adjustment (GTA) provides a readily available and scalable approach for implementing load shifting demand response using commercial heating, ventilation, and air conditioning (HVAC) systems, since it leverages the inherent sophistication of modern building automation systems. However, there is an incomplete understanding of GTA’s performance for this purpose and its impact on building systems and occupant comfort. In this paper, we explore the performance of GTA by analyzing results from nearly nine hundred experiments on eight university campus buildings in Michigan and North Carolina. Using GTA, we manipulate each building’s thermostat setpoints causing the building to shift its power consumption with respect to its baseline. We quantify the magnitude of HVAC power response, energy use of HVAC subsystems, and impact on occupant comfort. Finally, we connect our experimental results with power system operation using an optimization model that coordinates GTA actions across a large collection of grid-interactive efficient buildings (GEBs) to reduce high ramp rates on the grid and mitigate renewable energy curtailment. Overall, our work finds that the impacts on HVAC subsystems are often complex, and may result in additional energy being consumed by fans and terminal reheat. These effects must be considered when using GTA for load shifting. Additionally, we demonstrate that occupant comfort, as assessed by indoor temperature and humidity, can be maintained during GTA events. From a societal perspective, our modeling work finds that the additional renewable energy that can be integrated through the use of GTA strategies eclipses any additional energy consumed by buildings.

Keskar, Aditya (ORCID:0000000244617980)↗
DOE OSTI2023

An Online Joint Optimization–Estimation Architecture for Distribution Networks

Here in this article, we propose an optimal joint optimization-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the OPF decisions and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization and estimation usually operate at distinct layers and different time scales. Hence, it enables a computationally affordable, efficient, and robust online operational framework for distribution networks under time-varying settings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Value of Information and Data Management Platform Promoting Modern Data Analysis Utilization. STTR Phase I, Final Technical Report

The immense growth in weather and river sensor data availability along with increases in computational power and algorithmic sophistication have enabled the widespread application of physical, machine learning, and artificial intelligence algorithms to river forecasting. The forecasts produced by widely varying model approaches are inherently limited in different ways, such as by the number and quality of training observations, producing biased predictions that achieve optimal skill only over specific ranges of weather and river conditions. Thus, despite the growing availability and new sources of forecasts, hydropower owners cannot fully leverage the information because they lack an effective mechanism to determine the “best” forecast for given conditions or objectives. Tools are needed to consolidate forecast information, demonstrate skill, and extract optimal forecast guidance from the range of possibilities. Such tools will increase uptake and beneficial use of the growing wealth of forecast information, and lead to improved operating decisions, optimal power generation, and enhanced system value. This research proposes to improve the value of forecasts by developing a broadly accessible verification framework and applying machine learning techniques to identify the ‘best’ forecast from an ensemble of sources given the current weather, river, and operating conditions.

13 HYDRO ENERGY↗
DOE OSTI2025

Dynamic Model Development of a Wind Power Plant Using Neural Net Method to Forecast Wind Power Output (CRADA Final Report)

This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).

17 WIND ENERGY↗
DOE OSTI2021

OPFLearnData: Dataset for Learning AC Optimal Power Flow

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

SolarExPert: Large-Scale OPF, DSSE and HIL

This presentation, part of the ADMS Test Bed Webinar Series, discusses how to accelerate industry adoption of ADMS to improve normal operations with high levels of distributed energy resources (DERs) and improve resilience and reliability.

ADMS↗