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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Error field detection and correction studies towards ITER operation

In magnetic fusion devices, error field (EF) sources, spurious magnetic field perturbations, need to be identified and corrected for safe and stable (disruption-free) tokamak operation. Within Work Package Tokamak Exploitation RT04, a series of studies have been carried out to test the portability of the novel non-disruptive method, designed and tested in DIII-D (Paz-Soldan et al 2022 Nucl. Fusion62 126007), and to perform an assessment of model-based EF control strategies towards their applicability in ITER. In this paper, the lessons learned, the physical mechanism behind the magnetic island healing, which relies on enhanced viscous torque that acts against the static electro-magnetic torque, and the main control achievements are reported, together with the first design of the asynchronous EF correction current/density controller for ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Theoretical Development of Controller Transfer applied to Dynamical Systems

Data-driven control strategies suffer from the necessity of training and its associated computational time and cost. This motivates the continuous increase of popularity of transfer learning based methods. In this paper, we have investigated transfer learning in the context of controller for dynamical systems. First, we have derived conditions under which controller can be transferred between two linear dynamical systems. Furthermore, we have introduced a novel algorithm to design the transferable and non-transferable control components for linear systems. Secondly, we have derived conditions under which a stabilizing controller can be transferred between two different nonlinear dynamical systems. Furthermore, we have used the notion of zero dynamics to transform the nonlinear dynamics into global normal form, to derive the transferability conditions on the controller. Finally, we have numerically evaluated the performance of our transfer learning based methods for two sets of example linear and nonlinear dynamical systems.

Chakraborty, Indrasis↗
DOE OSTI2023

Co-Simulation Model for Optimal Wind-Hydro Coordination Using Wind Farm Control Dynamics

The growing share of Variable Renewable Energy sources (VRES) in power systems presents challenges for regula- tors, grid operators and energy producers. The VRES’ operation has limited flexibility in their operations, as they are highly dependent on ambient environments. To address these challenges, decision-makers must consider multiple objectives, among these are revenue, power system services and mechanical load on wind turbines. Coordinated operation of power plants and different wind farm control strategies are examples of measures that can benefit these objectives. This study proposes a Multi-Objective Linear Programming (MOLP) model to simulate the optimum operation of wind and hydropower plants that share limited transmission capacity. Further, wind farm control dynamics are included to obtain realistic output power and accumulated damage. From this, a case study based on a relevant location in Norway is presented to analyze the improved effect of wind- hydro coordination and wind farm control in achieving the objectives of accumulated wind turbine damage and total revenue of the hybrid power system. In addition, the study considers the potential advantages of adding a variable-speed pump to the hydropower plant. The results demonstrate that by considering multiple objectives in the optimization, one may achieve better overall performance of the objectives. By utilizing the flexibility of hydro storage, the decision maker may adjust to obtain the most desired outcome. Moreover, the added flexibility of utilizing a pump for hydro storage shows great improvements for the combined revenue of the power plants and reduced curtailment of the wind farm. However, less impact is observed from using a variable speed pump compared to a fixed speed pump.

13 HYDRO ENERGY↗
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2026

Biconjugate Impedance Matching Control of the HERO WEC: Preprint

Effective control strategies are essential for maximizing the power output of wave energy converters (WECs). In this study, we explore the methodology of port-based modelling which can be used for bi-conjugate impedance matching, a type of reactive control that involves maximizing power transfer by setting the load (PTO) impedance to be the complex conjugate of the source impedance (WEC). Despite its foundational role in electrical engineering, this method has seen limited application in wave energy and has not yet been demonstrated in wave-powered desalination systems. We represent our complex multidisciplinary system as an equivalent circuit to explore frequency-based relations. The HERO WEC, a small-scale, modular point absorber (PA) designed for reverse osmosis desalination is used as a case study to demonstrate port-based modelling for control applications. Improving the performance of wave-powered water desalination systems could play a critical role in addressing freshwater scarcity for coastal communities, especially those in remote or disaster-prone regions.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

An Investigation of Strategies for Sensing Window Attachment Position: Survey of Candidate Technologies, Evaluation, and Deployment Recommendations

While windows are desirable feature in buildings for views, daylighting, and aesthetics, it is important to recognize that windows are a major contributor to a building’s energy load. Interior window coverings, such as shades and blinds, can help improve the energy performance of windows without sacrificing the benefits; however, while appropriate use of window coverings provide the potential for energy savings, there is little consensus regarding how occupants typically use these window attachments, which sometimes puts into question how much energy they actually save in an occupied home. Automated shading control strategies can reduce the uncertainty of energy savings; however, users can overwrite pre-defined schedules or write their own shade control programs to reach their personal comfort and privacy goals. Because it is difficult to identify how occupants interact with window shades, it is difficult to identify the energy savings potential of blinds. To address this problem, this study was developed to identify a candidate sensor that can be deployed to sense blind position. The intended application for this sensor is deployment in energy efficiency field studies so that shade usage can be measured to better understand results and identify the energy impact of specific window attachment technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Grid Integration of Small-Scale Photovoltaic Systems in Secondary Distribution Network—A Review

The relative share of renewable energy, specifically the solar photovoltaic (PV), is increasing exponentially in the world electric energy sector. This is a cumulative result of reduction in the cost of solar panels, improvement in the panel efficiency, and advancement in the associated power electronics. Among different types of PV plants, installation of small-scale rooftop PV is growing rapidly due to direct end-user benefits and lucrative governmental incentives. There are various standards developed in regards to grid integration of PVs and other distributed generations (DGs). Different power converter topologies are developed to interface the PV panel with the utility grid. To keep up with the stringent regulations imposed by the standards, various control strategies and grid synchronization methods have been developed. This review article amalgamates and summarizes all of the aforementioned aspects of a grid-integrated PV system including various standards, power stage architectures, grid synchronization methods, operation under extreme events, and control methodologies, pertaining to small-scale PV plants. This article will help freshman researchers to gain some familiarity with the topic and introduce them to some of the key issues encountered in this field.

14 SOLAR ENERGY↗
DOE OSTI2021

Segmented Energy Routing for a Modular AC/DC Hybrid System

This article presents a modular ac/dc system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge (CHB)-organized medium-voltage (MV) dc links, with fully distributed low-voltage (LV) dc power ports. Copies of modules share the centralized load bus and interact with an MV ac grid in parallel. Hybrid power port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this article, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed to realize fully autonomous energy routing involving MV ac grid, distributed PV generations, distributed battery storages, and LV load. The system is proven to be stable using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

Wang, Z↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2020

Learning reference governor for cycle-to-cycle combustion control with misfire avoidance in spark-ignition engines at high exhaust gas recirculation–diluted conditions

Cycle-to-cycle feedback control is employed to achieve optimal combustion phasing while maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the exhaust gas recirculation valve position. The control development is based on a control-oriented model that captures the effects of throttle position, exhaust gas recirculation valve position, and spark timing on the combustion phasing. Under the assumption that in-cylinder pressure information is available, an adaptive extended Kalman filter approach is used to estimate the exhaust gas recirculation rate into the intake manifold based on combustion phasing measurements. The estimation algorithm is adaptive since the cycle-to-cycle combustion variability (output covariance) is not known a priori and changes with operating conditions. A linear quadratic regulator controller is designed to maintain optimal combustion phasing while maximizing exhaust gas recirculation levels during load transients coming from throttle tip-in and tip-out commands from the driver. During throttle tip-outs, however, a combination of a high exhaust gas recirculation rate and an overly advanced spark, product of the dynamic response of the system, generates a sequence of misfire events. In this work, an explicit reference governor is used as an add-on scheme to the closed-loop system in order to avoid the violation of the misfire limit. The reference governor is enhanced with model-free learning which enables it to avoid misfires after a learning phase. Experimental results are reported which illustrate the potential of the proposed control strategy for achieving an optimal combustion process during highly diluted conditions for improving fuel efficiency.

42 ENGINEERING↗
DOE OSTI2021

Enhancing the Operational Resilience of Advanced Reactors with Digital Twins by Recurrent Neural Networks

Because of a lack of operational data and uncertainty in evaluation model for abnormal and accident scenarios, the established operating procedures can be biased in characterizing the reactor states and ensuring operational resilience. To reduce uncertainty associated with actual plant conditions, digital twin (DT) technology is suggested to support operator’s decision-making by effectively extracting and using knowledge of the current and future plant states from the knowledge base. This study first builds a knowledge base based on the characterization of issue space and the simulation tool. Next, this study discusses diagnosis and prognosis DTs for enhancing operational resilience by recovering the complete states of reactors and by predicting the future reactor behaviors. Finally, the decision-making module of the control system can determine the optimal control strategy that meets operational goals during loss-of-flow scenarios. To demonstrate and evaluate the DTs capability for supporting the operations of nuclear reactors, this study develops and assesses both the diagnosis and prognosis DTs in a nearly autonomous management and control system for an Experimental Breeder Reactor-II simulator during different loss-of-flow scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Real-time avoidance of the L-mode and H-mode density limit via machine-learned stability metrics

Reliable operation of burning plasma tokamaks will require robust control strategies to avoid macroscopic instability limits such as the L-mode and H-mode density limits (LDL, HDL). In this work, we explore closed-loop avoidance of these phenomena at DIII-D using machine-learned risk metrics. Feedback control is implemented via the ‘DL Supervisor’ scheme, which regulates the chosen risk metric by reducing the density target or increasing NBI heating in real-time. Using the LDL 25 risk metric, the LDL is reproducibly suppressed. We also introduce an HDL risk metric in this study, HDL 25 , which reduces the False Positive Rate by 2x compared to the Greenwald fraction. Applying this scaling to a plasma current ramp-down, we successfully avoid an HDL-driven H/L back-transition. These experiments constitute the first demonstration of real-time DL avoidance using machine-learned risk metrics. These instability metrics outline a path to safer high-density operation, more reliable ramp-down scenarios, and improved off-normal control for next-step devices such as ITER and SPARC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Control development and sizing analysis for a 5th generation district heating and cooling network using Modelica

5th generation district heating and cooling system (5GDHC) are a relatively new concept. They use a single district loop near ambient temperature to provide heating and cooling. This paper improves on the modelling and control of a 5GDHC system called the reservoir network. The study updates the sewage heat exchanger plant model to more realistically represent seasonal changes, uses refined pump models with variable efficiency, introduces a ground coupled district pipe model to consider the inertia of the district network and implements a new control strategy for geothermal storage and sewage heat exchanger. The new approach reduced operating costs, mainly due to pumping cost for storage, sewage heat exchanger plant and distribution pump, while increasing the overall robustness of the approach in different sizing conditions. Thanks to the new controller, the pumping consumption was reduced by 21% with respect to the original baseline. Furthermore, the new control makes the system take better advantage of design changes, when reducing borehole field size and increasing the sewage heat exchanger size, the pumping energy savings become 29% with respect to the original baseline. Lastly, borehole field temperature stability was analyzed through 40 years of simulation.

Zanetti, Ettore↗
DOE OSTI2021

Review of wake management techniques for wind turbines

Summary The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.

17 WIND ENERGY↗
DOE OSTI2023

Stabilizing the Grid and Reducing Utility Bills Through Price-Responsive Controls for Heat Pump Water Heaters

The electricity grid is facing increasing challenges in cost-effectively balancing supply and demand. These challenges are exacerbated by increased penetration of photovoltaics, which causes mid-day overproduction, and electrification of gas appliances, which increases peak-period electricity demand. Decarbonization requires shifting building loads from fossil-intensive high-cost times to renewable-intensive low-cost times while maintaining quality of service to occupants. Utilities and ISO’s are investigating new ways of incentivizing this load shifting. One promising method is the use of Highly Dynamic Prices (HDPs). HDPs feature continuously changing prices that reflect real-time grid generation and distribution costs and capacity constraints, and thus incentivize consumers to shift their loads. California’s CPUC CalFUSE proposal and Hawaii’s recent changes demonstrate that electricity tariffs are moving towards this model. For this to work however, loads must have the capability to respond to these prices. Heat pump water heaters (HPWHs) are an ideal device for this purpose because their storage tanks decouple delivery of domestic hot water from electricity consumption. The storage enables control strategies that consume midday solar power to increase the energy stored in the tank, then provide evening peak domestic hot water services using the stored energy. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls for many flexible loads - including HPWHs - in response to HDP. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of the on-board controller in a residential, integrated HPWH . The price-responsive controls a) shift load in ways that consume additional midday solar power to help stabilize the grid and reduce overall emissions, b) ensure that occupants receive equal or better hot water delivery service, and c) minimize the operating cost for each home in the fleet. On the grid level, the resulting shift will reduce utility operating costs and emissions, and can avoid expensive system capacity expansions. The control approach is customized to each home based on typical hot water consumption patterns. HPWH controllers, whether on the device or remotely, will receive a schedule of CTA-2045-B signals or set temperature adjustments customized to the current HDP price schedule and home. Simulation results for a fleet of 148 HPWHs on a summer day in Berkeley, California show cost savings of 29% and high price electricity consumption reductions of 80%, while maintaining full quality of service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A State-Space Model of an Inverter-Based Microgrid for Multivariable Feedback Control Analysis and Design

In this work, a synchronous model for grid-connected and islanded microgrids is presented. The grid-connected model is based on the premise that the reference frame is synchronized with the AC bus. The quadrature component of the AC bus voltage can be cancelled, which allows to express output power as a linear equation for nominal values in the AC bus amplitude voltage. The model for the islanded microgrid is developed by integrating all the inverter dynamics using a state-space model for the load currents. This model is presented in a comprehensive way such that it could be scalable to any number of inverter-based generators using inductor–capacitor–inductor (LCL) output filters. The use of these models allows designers to assess microgrid stability and robustness using modern control methods such as eigenvalue analysis and singular value diagrams. Both models were tested and validated in an experimental setup to demonstrate their accuracy in describing microgrid dynamics. In addition, three scenarios are presented: non-controlled model, Linear-Quadratic Integrator (LQI) power control, and Power-Voltage (PQ/Vdq) droop–boost controller. Experimental results demonstrate the effectiveness of the control strategies and the accuracy of the models to describe microgrid dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Antibody dynamics to SARS‐CoV‐2 in asymptomatic COVID‐19 infections

Abstract Background The missing asymptomatic COVID‐19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic. Measure Utilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty‐three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS‐CoV‐2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset. Results A combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS‐CoV‐2. Different from strong and persistent N‐specific antibodies, S1‐specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months. Conclusion Our findings might have important implications for the definition of asymptomatic COVID‐19 infections, diagnosis, serological survey, public health, and immunization strategies.

Lei, Qing↗