Search NASA⌕ Search

SEARCH · Search NASA

Results for “energy management and control”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Energy Management Control Systems: Tools for Energy Savings and Environmental Protection

The change in the price of energy has encouraged the increase of energy efficiency. This report will discuss a tool to promote energy efficiency in intelligent buildings, energy management control systems (EMCS). In addition to the online control of energy production, supply, and consumption, the function of the EMCS is to support short- and long-term planning of the system operation as well as to collect, store, and regularly evaluate operation data. The strategies behind planning and implementing the EMCS as well as the manipulating the resulting data are discussed in this report.

Zsebik, Albin↗
DOE OSTI2021

Packetized energy management control systems and methods of using the same

Aspects of the present disclosure include anonymous, asynchronous, and randomized control schemes for distributed energy resources (DERs). Such control schemes may include packetized energy management (PEM) control schemes for managing DERs that may provide near-optimal tracking performance under imperfect information and consumer quality of service (QoS) constraints.

Frolik, Jeffrey↗
DOE OSTI2022

Reinforcement Learning for Intelligent Building Energy Management System Control *

A building energy management system (BEMS) is a computer-based system designed to monitor and control a building's energy needs. Modern BEMS rely on the sensing and connectivity capabilities of Internet of Things (IoT) technology to intelligently adjust the energy consumption to reduce cost while respecting the consumers' preferences. Increasingly, control decisions are made based on predictions by models trained using supervised machine learning methods, which still requires control policies to be formulated in a rule-based fashion. When using reinforcement learning (RL) instead, control policies are learned by observing the utility in terms of cost and comfort associated with actions such as a change in the heating system's setpoint. The resulting RL-based controllers can capture not only the dynamics of the building and the associated electrical devices, but also fluctuations in electricity prices and user demand, avoiding the need to combine multiple predictive models with tailored control policies. This chapter will provide an overview of RL-based approaches for BEMS. After sketching the taxonomy of general RL methods, we discuss the implications of relying on the individual methods in a BEMS context. Existing work applying RL is presented along the key devices controlled by BEMS systems. Finally, we summarize the state-of-the-art and sketch limitations and open research directions.

Kotevska, Olivera↗
NASA NTRSDate not supplied

Hybrid-Electric Aero-Propulsion Controls Testbed Results

NASA is supporting the development of Electrified Aircraft Propulsion (EAP) technology due to its potential to reduce aircraft fuel burn, emissions, and noise as well as improving safety and performance. One focus of this research is the electrification of conventional turbomachinery propulsion systems, which offers ways to improve the performance and operability of turbine-engine powered aircraft through the addition of electro-mechanical systems. These hybrid-electric turbine engines provide additional actuation and energy management control opportunities for improving stability and transient response behavior. This paper summarizes the results of a Hardware-in-the-Loop (HIL) test performed at the NASA Electric Aircraft Testbed (NEAT) during the summer of 2022. The test demonstrates the feasibility and performance of an advanced energy management control strategy by integrating a simulated turbofan engine with scaled electro-mechanical hardware. A full-scale real-time reference model of a geared turbofan was run alongside a scaled electro-mechanical system representing the electrified turbofan components operating at a megawatt-scale power level. The model was interfaced with the hardware through a novel closed-loop control and scaling algorithm that emulated the dynamic speed and torque response of the turbofan shafts. The control strategy was implemented on the electrical machines connected to the emulated turbomachinery shafts. The results from the testbed are compared against simulations that predict the testbed and geared turbofan model operation. The energy management control strategy successfully changed the operating point of the engine model and improved its stability during throttle transients. These results also demonstrate the success of the novel closed loop control and scaling approach for emulating turbomachinery and elevate the Technology Readiness Level (TRL) of the energy management control strategy.

Aeronautics↗
NASA NTRS2023

Hybrid-Electric Aero-Propulsion Controls Testbed Results

NASA is supporting the development of Electrified Aircraft Propulsion (EAP) technology due to its potential to reduce aircraft fuel burn, emissions, and noise as well as improving safety and performance. One focus of this research is the electrification of conventional turbomachinery propulsion systems, which offers ways to improve the performance and operability of turbine-engine powered aircraft through the addition of electro-mechanical systems. These hybrid-electric turbine engines provide additional actuation and energy management control opportunities for improving stability and transient response behavior. This paper summarizes the results of a Hardware-in-the-Loop (HIL) test performed at the NASA Electric Aircraft Testbed (NEAT) during the summer of 2022. The test demonstrates the feasibility and performance of an advanced energy management control strategy by integrating a simulated turbofan engine with scaled electro-mechanical hardware. A full-scale real-time reference model of a geared turbofan was run alongside a scaled electro-mechanical system representing the electrified turbofan components operating at a megawatt-scale power level. The model was interfaced with the hardware through a novel closed-loop control and scaling algorithm that emulated the dynamic speed and torque response of the turbofan shafts. The control strategy was implemented on the electrical machines connected to the emulated turbomachinery shafts. The results from the testbed are compared against simulations that predict the testbed and geared turbofan model operation. The energy management control strategy successfully changed the operating point of the engine model and improved its stability during throttle transients. These results also demonstrate the success of the novel closed loop control and scaling approach for emulating turbomachinery and elevate the Technology Readiness Level (TRL) of the energy management control strategy.

Aeronautics↗
NASA NTRSDate not supplied

Hybrid-Electric Aero-Propulsion Controls Testbed Results with Energy Storage

Electrified aircraft propulsion (EAP) research is a priority of the National Aeronautics and Space Administration (NASA) for its potential to increase propulsion system efficiency, performance, and operability at the subsystem and vehicle levels while decreasing emissions. These EAP systems demand more advanced control algorithms due to increased complexity. NASA has developed a reconfigurable, hardware-in-the-loop rig to verify control algorithm performance using a sub-scale electro-mechanical system. A novel capability of this rig is the ability to test full scale EAP control algorithms on a sub-scale representation of the electro-mechanical system without turbomachinery/rotors. A novel feature is the use of a physical energy storage device within the electro-mechanical system. A dual spool, parallel hybrid-electric turbofan architecture and energy management control system is tested with the goal of verifying the ability to obtain turbomachinery model operability benefits while controlling sub-scale electro-mechanical hardware. Pre-test predictions of the turbofan model, control, and rig performance were obtained through simulation using a software model of the rig. Theoretical results showing the true performance of the turbofan model were obtained through a software simulation using full-scale mechanical shaft models. The paper compares theoretical, predicted, and actual test results from the turbofan model, energy management control and rig perspectives. The results show that the presence of sub-scale electro-mechanical hardware did not inhibit the energy management algorithm from achieving turbomachinery operability benefits.

hybrid↗
NASA NTRSDate not supplied

Hybrid-Electric Aero-Propulsion Controls Testbed Results with Energy Storage

Electrified aircraft propulsion (EAP) research is a priority of the National Aeronautics and Space Administration (NASA) for its potential to increase propulsion system efficiency, performance, and operability at the subsystem and vehicle levels while decreasing emissions. These EAP systems demand more advanced control algorithms due to increased complexity. NASA has developed a reconfigurable, hardware-in-the-loop rig to verify control algorithm performance using a sub-scale electro-mechanical system. A novel capability of this rig is the ability to test full scale EAP control algorithms on a sub-scale representation of the electro-mechanical system without turbomachinery/rotors. A novel feature is the use of a physical energy storage device within the electro-mechanical system. A dual spool, parallel hybrid-electric turbofan architecture and energy management control system is tested with the goal of verifying the ability to obtain turbomachinery model operability benefits while controlling sub-scale electro-mechanical hardware. Pre-test predictions of the turbofan model, control, and rig performance were obtained through simulation using a software model of the rig. Theoretical results showing the true performance of the turbofan model were obtained through a software simulation using full-scale mechanical shaft models. The paper compares theoretical, predicted, and actual test results from the turbofan model, energy management control and rig perspectives. The results show that the presence of sub-scale electro-mechanical hardware did not inhibit the energy management algorithm from achieving turbomachinery operability benefits.

hybrid↗
DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Inverse reinforcement learning control for building energy management

Reinforcement learning (RL) based control is widely considered a promising approach in building automation and control as it has demonstrated the potential to deal with complex objectives in adjacent domains like robotics, autonomous vehicles, gaming applications, and advertisement recommendations. When applied to any environment, model-free RL learns to improve its control performance over time without requiring a control model, by receiving and then analyzing feedback from the building environment after each control action. Operational objectives are becoming increasingly complex through the simultaneous consideration of thermal comfort, carbon emissions, grid services, and indoor air quality. In this context, conventional rule-based control approaches are proving sub-optimal, mostly heuristic, and inadequate. The model-free and self learning nature of RL appears promising and attractive as it may address the scalability issues associated with advanced control approaches. However, it suffers from long training times and unstable control behavior during the early stages of its learning process, which makes it unsuitable to be applied directly to buildings. This paper addresses these issues using an inverse reinforcement learning approach (IRL), a technique utilized to learn the objective of a controller agent which is considered an expert in its respective domain. Here, we consider a rule-based control as the expert demonstrator. IRL is different from a direct imitation (i.e., direct mapping of states to actions) of control actions as it tries to find the underlying intent of an expert's policy, providing the controller with a better-generalized policy for unseen states or environments with slightly different dynamics. This approach propels the RL controller's policy to levels similar to or better than that of a rule-based policy before it starts learning by interacting with the building. This makes RL for building energy management applications more practical as it prevents the erratic and exploratory behavior in the initial training period, simultaneously speeding up the learning process when compared to applying an untrained RL agent directly to a building environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1997

Retrofitting Laboratory Fume Hoods With Face Velocity Monitors at NASA Lewis Research Center

Extensive use and reliance on laboratory fume hoods exist at LeRC for the control of chemical hazards (nearly 175 fume hoods). Flow-measuring devices are necessary to continually monitor hood performance. The flow-measuring device should he tied into an energy management control system to detect problems at a central location without relying on the users to convey information of a problem. Compatibility concerns and limitations should always be considered when choosing the most effective flow-measuring device for a particular situation. Good practice on initial hood design and placement will provide a system for which a flow-measuring device may be used to its full potential and effectiveness.

Wagner, Ingrid E.↗
NASA NTRS1978

Installation package for integrated programmable electronic controller and hydronic subsystem - solar heating and cooling

A description is given of the Installation, Operation, and Maintenance Manual and information on the power panel and programmable microprocessor, a hydronic solar pump system and a hydronic heating hot water pumping system. These systems are integrated into various configurations for usages in solar energy management, control and monitoring, lighting control, data logging and other solar related applications.

Source record↗
DOE OSTI2021

Modelling of Wastewater Heat Recovery Heat Pump Systems

Wastewater heat recovery is currently an underutilized technology that could be part of solving the climate crisis. A large portion of the heat that leaves a building in the form of wastewater is potentially recoverable for pre-heating domestic hot water or other service water systems. While there are several different approaches to wastewater heat recovery, this project focused on creating detailed, integrated building models for wastewater heat recovery heat pump systems. EnergyPlus models were developed featuring inputs and assumptions corresponding to manufacturers’ specifications, performance lab test data and feedback from engineering consultants. EnergyPlus’s supervisory control Energy Management System objects were heavily relied upon to overcome modelling challenges. The developed EnergyPlus model was integrated into U.S. Department of Energy New Construction Reference Building models for various climate zones and building types to assess potential energy use, energy cost and greenhouse gas emission reductions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Identification and Testing of Electric Vehicle Fast Charger Cybersecurity Mitigations

Fast-charging infrastructure for electric vehicles (EVs) is needed to enable and achieve the national goals of transitioning the vehicle fleet toward more electrification. Idaho National Laboratory, Oak Ridge National Laboratory, and the National Renewable Energy Laboratory (NREL) have jointly worked to identify, evaluate, and mitigate potential cyber-related consequences associated with fast charger systems. NREL contributed by considering cyberattack scenarios and consequences associated with integrating distributed energy resources (DERs) at fast-charging stations. The dynamic nature of fast-charger load profiles would encourage site operators to incorporate solar for energy cost reduction and energy storage for peak demand cost management at future charging facilities with multiple fast chargers at a site. These energy resources would be monitored and coordinated via a site energy management controller with data exchange between devices and local power metering infrastructure; thus, networking between devices and the design of the system becomes important in the overall cybersecurity posture. In addition, component vendors and system operators might have remote interfaces to any of these systems. It is therefore important to understand the breadth of the cyberattack surface and potential strategies to mitigate impacts. This project has focused on components and protocols expected to be found within a local charging site that includes multiple chargers and DER resources. Our methods and results are summarized in this final report.

42 ENGINEERING↗
NASA NTRS1984

The potential impact of new power system technology on the design of a manned space station

Larger, more complex spacecraft of the future such as a manned Space Station will require electric power systems of 100 kW and more, orders of magnitude greater than the present state of the art. Power systems at this level will have a significant impact on the spacecraft design. Historically, long-lived spacecraft have relied on silicon solar cell arrays, a nickel-cadmium storage battery and operation at 28 V dc. These technologies lead to large array areas and heavy batteries for a Space Station application. This, in turn, presents orbit altitude maintenance, attitude control, energy management and launch weight and volume constraints. Size (area) and weight of such a power system can be reduced if new higher efficiency conversion and lighter weight storage technologies are used. Several promising technology options including concentrator solar photovoltaic arrays, solar thermal dynamic and ultimately nuclear dynamic systems to reduce area are discussed. Also, higher energy storage systems such as nickel-hydrogen and the regenerative fuel cell (RFC) and higher voltage power distribution which add system flexibility, simplicity and reduce weight are examined. Emphasis is placed on the attributes and development status of emerging technologies that are sufficiently developed so that they could be available for flight use in the early to mid 1990's.

Fordyce, J. S.↗