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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Battery charging control methods, electrical vehicle charging methods, battery charging control apparatus, and electrical vehicles

Battery charging control methods, electrical vehicle charging methods, battery charging control apparatus, and electrical vehicles are described. In one arrangement, battery charging control methods include accessing price information for electrical energy supplied by an electrical power distribution system and controlling an adjustment of an amount of the electrical energy from the electrical power distribution system used to charge a rechargeable battery at different moments in time using the price information. Other arrangements are described.

25 ENERGY STORAGE↗
DOE OSTI2021

Thermal energy storage apparatus, controllers and thermal energy storage control methods

Thermal energy storage apparatus, controllers and thermal energy storage control methods are described. According to one aspect, a thermal energy storage apparatus controller includes processing circuitry configured to access first information which is indicative of surpluses and deficiencies of electrical energy upon an electrical power system at a plurality of moments in time, access second information which is indicative of temperature of a thermal energy storage medium at a plurality of moments in time, and use the first and second information to control an amount of electrical energy which is utilized by a heating element to heat the thermal energy storage medium at a plurality of moments in time.

Hammerstrom, Donald J.↗
DOE OSTI2024

Practical Insights on Applying Simulation-Based Control Methods in Experimental Studies

Advanced nuclear reactors are crucial to the future of energy both in the United States and around the globe. In contrast to the current operating fleet, they are characterized as being deployable in remote locations and able to operate in semi-autonomous or autonomous fashion. This leap forward necessitates a new reactor control paradigm. Because advanced nuclear reactors are still under development in the United States, the creation of new control methods to achieve autonomous operations has been based on systems modeling and simulation. However, an important factor in successfully deploying these new control methods is the ability to seamlessly transition from simulation environments to real-world settings. Control methods tested in both simulation and experimental settings need to be investigated in the context of advanced reactor applications. This work developed a series of simple controllers for Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor operating in load-following scenarios. These controllers were tested in both simulation and experimental settings, and a comparative performance analysis was performed. The simulation tests leveraged the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software developed in a previous stage of the current effort, along with the MARVEL Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The experimental tests leveraged the COMMAND software, MARVEL models, and the U.S. Department of Energy Microreactor Program’s Microreactor Automated Control System (MACS). MACS was developed to serve as a control method testbed. It was customized to mirror the MARVEL microreactor, and COMMAND enabled MACS to emulate the physics of MARVEL. The load-following controller was developed using the simulation platform, with efforts to emulate real systems by introducing actuator saturation and noise. These factors were incrementally accounted for in the controller design. After finalizing the controller design, it was implemented with the experimental setup. The experimental conditions tested included an initial test under conditions similar to the final simulation test, and two additional scenarios. The first scenario introduced additional actuator saturation to account for equipment aging over time, which was unknown to the controller. The second scenario introduced sensor delay, a phenomenon anticipated with the use of remote operations or wireless communication in advanced reactors. These tests revealed several notable differences. While the controller performed well in simulation, it exhibited several limitations when transitioning to hardware. The main challenges involved maintaining the steady-state target power, as evidenced by larger error values between the true reactor power and setpoint power, as well as persistent oscillations in controlled reactor power. These issues could lead to unacceptable transient conditions in real reactor testing. Introducing actuator aging and stochastic delays in the experimental setup significantly impacted controller performance, resulting in increased overshoot and undershoot, and exacerbated error and oscillations previously mentioned. These findings underscore the importance of experimental testbeds for testing and validating control methods, as controllers developed using only theory and/or simulation may perform unexpectedly when applied to actual hardware. This research emphasizes the need for an experimental testbed for achieving such validation.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Internal combustion engine control method

A method for controlling an internal combustion engine having a plurality of cylinders including a first cylinder and one or more remaining cylinders includes selecting a desired auto-ignition dwell for a first combustion cycle for the first cylinder of the plurality of cylinders. A first fuel mass is provided to the first cylinder which is combusted during the first combustion cycle. An actual auto-ignition dwell for the first combustion cycle which results from the first fuel mass is determined and a dwell error is calculated. The dwell error is used to determine a second fuel mass which provided to the first cylinder and which is combusted during the second combustion cycle.

02 PETROLEUM↗
DOE OSTI2023

Control methods for mitigating flow oscillations in a supercritical CO 2 recompression closed Brayton cycle

A dynamic model of a 10 MWe supercritical CO 2 (sCO 2 ) recompression closed Brayton power generation cycle is used to investigate control methods for mitigating oscillations in flow conditions and power demand load, especially as the load ramp rate is increased and the load setpoint is more closely tracked. The focus is on control of the main compressor inlet temperature (MCIT) since oscillations are prompted by the strong nonlinearities in sCO 2 fluid properties near the critical point and by changes in system inventory for efficient load management. In this study, the oscillatory behavior during load ramping is first shown and then two potential control solutions for substantially reducing oscillations are given: 1) the use of the main compressor inlet guide vanes (IGV) and 2) bypassing a portion of the sCO 2 around the cooler to maintain the MCIT. These control methods reduce the effect of cycle feedback from interactions between the MCIT and load. When implementing either control method, a load setpoint ramp of 7.5%/min was maintainable while achieving closer load setpoint tracking with limited oscillations. Finally, the impact of sensor noise on the control was examined since derivative action was used in the IGV and cooler sCO 2 bypass control. Finally, while some effect was evident, signal noise was not problematic to the control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Comparative and Cost Analysis of a Novel Predictive Power Ramp Rate Control Method: A Case Study in a PV Power Plant in Puerto Rico

One of the most important aspects that need to be addressed to increase solar energy penetration is the power ramp-rate control. In weak grids such as the one found in Puerto Rico, it is important to smooth power fluctuations caused by the intermittence of passing clouds. In this work, a novel power ramp-rate control strategy is proposed. Additionally, a comparison with some of the most common power ramp-rate control methods is performed using a proposed model and real solar radiation data from the Coto Laurel photovoltaic power plant located in Ponce, Puerto Rico. The proposed model was validated using one-year real data from Coto Laurel. The power ramp-rate control methods were compared in real-time simulations using the OP5700 from Opal-RT Technologies considering power ramp rate fluctuations, power ramp-rate violations, fluctuations in the state-of-charge, among other indicators. Moreover, the proposed power ramp-rate control strategy, called predictive dynamic smoothing was explained and compared. Results indicate that the predictive dynamic smoothing produced a considerably reduced Levelized Cost of Storage compared to other power ramp-rate control methods and provided a higher lifetime expectancy for lithium batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An Inner-Loop Control Method for the Filter-less, Voltage Sensor-less, and PLL-less Grid-Following Inverter-Based Resource

This paper presents a novel inner-loop control method for the inverter-based resource (IBR). The innovative concepts include removing the voltage sensors at the point of common coupling (PCC), removing the inverter interface inductance, and removing the traditional phase-locked loop (PLL) circuits. Simulation is conducted to verify the feasibility of the method. Furthermore, the virtual impedance is applied to the control loop to improve the current THD and dynamics. Compared to the traditional control, the proposed one helps offload the system by reducing the bulky inductors and voltage sensors without compromising the control performance.

grid-forming inverter, inner-loop control, filterl↗
DOE OSTI2022

Integration of Control Methods and Digital Twins for Advanced Nuclear Reactors

Advanced nuclear reactors offer a new set of features to energy generation, due to their ability to adapt to variable energy demand, operate autonomously, be deployed in rural locations and monitored remotely, afford compact size and lower power ratings, and rely on novel technologies to achieve safer operations. Thus, a requirement for the success of these reactors is the use of intelligent forms of control to track changing power demands, make autonomous decisions, and reduce the need for human involvement. Regulatory requirements pertaining to control of nuclear reactors could be met via historical means of control; however, these are not expected to enable the level of highly autonomous operations desired in advanced nuclear reactors. Historical control methods rely on both logical and high-performance (HP) control. These two types of control are usually used separately, with a human element being introduced whenever decisions are cascaded from one science to another. AI/ML control, on the other hand, can replace the human element in the current U.S. fleet of nuclear power plants (NPPs) by acting as a supervisory optimizer that understands the plant internal/external variables in order to make control decisions, and can easily handle non-linear and multi-input/multi out (MIMO) decisions—another requirement for advanced nuclear reactors that could be difficult to handle via logical and HP control. Because of the harsh operating environments produced in advanced reactors, resulting in the frequent failure of sensors and other types of equipment, and considering the lack of operating history for advanced nuclear reactors, control of advanced nuclear reactors would necessitate relying on a model that can track and adapt to the actual process (i.e., a digital twin). This digital twin can make approximations when knowledge and data are unavailable and would evolve as more knowledge is gained. The reactor control must also be risk-informed to account for the high-consequence nature of advanced reactors. This report introduces a high-level (i.e., not method- or process-specific) integration of the three different control and digital twinning methods able to meet the requirements for advanced nuclear reactors. These methods could be applied during both the operational and design stages of these reactors. The aim is to demonstrate how each method interfaces with and highlights enabling solutions necessitated by the unique features of advanced nuclear reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems

Active distribution networks are being challenged by frequent and rapid voltage violations due to renewable energy integration. Conventional model-based voltage control methods rely on accurate parameters of the distribution networks, which are difficult to achieve in practice. This paper proposes a novel physical-model-free two-timescale voltage control framework for active distribution systems. To achieve fast control of PV inverters, the whole network is first partitioned into several subnetworks using voltage-reactive power sensitivity. Then, the scheduling of PV inverters in the multiple sub-networks is formulated as Markov games and solved by a multi-agent soft actor-critic (MASAC) algorithm, where each subnetwork is modeled as an intelligent agent. All agents are trained in a centralized manner to learn a coordinated strategy while being executed based on only local information for fast response. For the slower time-scale control, OLTCs and switched capacitors are coordinated by a single agent-based SAC algorithm using the global information with considering control behaviors of the inverters. Particularly, the two-level agents are trained concurrently with information exchange according to the reward signal calculated from the data-driven surrogate model. Comparative tests with different benchmark methods on IEEE 33-and 123-bus systems and 342-node low voltage distribution system demonstrate that the proposed method can effectively mitigate the fast voltage violations and achieve systematical coordination of different voltage regulation assets without the knowledge of accurate system model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Power conversion devices and control methods therefor

Power conversion devices with control algorithms that can solve the double frequency harmonic problem are provided, as are techniques for controlling power converters when the instantaneous values of input and output power are not the same.

Amirabadi, Mahshid↗
DOE OSTI2024

A tutorial review of machine learning-based model predictive control methods

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

Wu, Zhe [Department of Chemical and Biomolecular E↗
DOE OSTI2024

Enabling a Physical Twin for Control Methods Evaluation

Advanced nuclear reactors play an important role in the energy future of the United States and the rest of the world. They are designed and operated based on a different model than that of the current operating fleet, thus enabling deployment in remote locations and allowing for safe semi-autonomous or autonomous operations. Such characteristics require the development of a new reactor control paradigm. A significant factor in the development of control technologies and methods is integration of the various technologies and methods with each other and with hardware (both reactor system hardware and control hardware). A recent workshop on control of advanced reactors identified the lack of a flexible, expandable software/hardware infrastructure to enable such integration as a key gap. A previous phase of the current effort involved developing and demonstrating the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) platform, which is capable of integrating autonomous-control-enabling technologies and methods, without the constraints imposed by existing software solutions. This platform will enable advanced reactor developers to deploy and test any developed technologies and methods by employing a common framework, and to couple them with their own models and hardware. The present phase of this effort entails using the Microreactor Automated Control System (MACS) platform, which was developed by the U.S. Department of Energy (DOE) Microreactor Program, to serve as a control method testbed. MACS can be used by advanced reactor developers to integrate their control related research activities with any reactor system. For the present effort, MACS was customized to mirror Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and COMMAND was leveraged to enable MACS to emulate the physics of MARVEL, thus positioning MACS as a physical twin of MARVEL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Novel Magnetron Operation and Control Methods for Superconducting RF Accelerators

High power magnetrons designed and optimized for industrial heating, being injection-locked, have been suggested to power superconducting RF cavities for accelerators due to lower cost and higher efficiency. However, standard operation methods do not provide high efficiency with wideband control suppressing microphonics. We have developed and experimentally verified novel methods of operating and controlling the magnetron that provide stable RF generation with higher efficiency and lower noise than other RF sources. By our method the magnetrons operate with the anode voltage notably lower than the self-excitation threshold improving its performance. This is also a promising way to increase tube reliability and longevity. A magnetron operating with the anode voltage lower than the self-excitation threshold, in so-called stimulated coherent generation mode has special advantage for pulse operation with a gated injection-locking signal. This eliminates the need for expensive pulsed HV modulators and additionally increases the magnetron RF source efficiency due to absence of losses in HV modulators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

A Data-driven Control Method for Operating the Commercial HVAC Load as a Virtual Battery

Building loads have a great potential to provide ancillary services because of its large power consumption. In order to quantify their flexibility, methods of modeling building loads as virtual battery (VB) have been proposed. The VB model provides an effective way to coordinate building loads and energy storage systems together when providing service to grid. However, after grid send a power signal to VB, the control strategy has not been fully studied to let building consumes this desired amount of power yet. This paper introduces a method to control commercial heating, ventilation, and air conditioning (HVAC) system. First, the thermal model of commercial building is introduced. We then used several data-driven models to approximate this thermal dynamic and the operation of different HVAC components. Base on these data-driven models, we developed a centralized control algorithm to utilize HVAC system consuming the desired amount of power. Finally, we modeled a realistic commercial building in EnergyPlus to test the performance of this proposed control algorithm

Wang, Jiyu↗
DOE OSTI2023

Reactive Power Control Methods for Islanded Operation

As feeder-level microgrid controls begin to incorporate multiple GFM DER and voltage regulation equipment, island reactive power dispatch strategies can be improved. Although Volt-Var Optimization (VVO) control is typical in an Advanced Distribution Management System (ADMS), most microgrid controllers do not apply VVO during island operations. This report investigates reactive power control strategies for feeder-level microgrid operations. It also proposes evaluation metrics that can be applied to analyze the performance improvements of the new microgrid control strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗