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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require an elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.

42 ENGINEERING↗
DOE OSTI2019

A Supervised Machine Learning Approach to Control Energy Storage Devices

This paper introduces a supervised machine learning approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/Battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control (EMPC) approach is used as the benchmark. The residential load and PV data used in the paper are 1-minute data downloaded from the the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four machine learning algorithms (i.e. neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the machine learning approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based Machine Learning engine. Furthermore, the work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.

25 ENERGY STORAGE↗
DOE OSTI2018

Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Control Co-Design of Wind Turbines

Wind energy is recognized worldwide as cost-effective and environmentally friendly, and it is among the fastest-growing sources of electrical energy. To further decrease the cost of wind energy, wind turbines are being designed at ever-larger scales. To expand the deployment of wind energy, wind turbines are also being designed on floating platforms for placement in deep-water locations offshore. Both larger-scale and floating wind turbines pose challenges because of their greater structural loads and deflections. Complex, large-scale systems such as modern wind turbines increasingly require a control co-design approach, whereby the system design and control design are performed in a more integrated fashion. This article reviews recent developments in control co-design of wind turbines. We provide an overview of wind turbine design objectives and constraints, issues in the design of key wind turbine components, modeling of the wind turbine and environment, and controller coupling issues. Wind turbine control functions and the integration of control design in co-design are detailed with a focus on co-design compatible control approaches.

17 WIND ENERGY↗
DOE OSTI2026

Submodule-Level Decentralized Control for Cascaded Quadruple Active Bridge-Based Medium-Voltage Converters: Preprint

High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Model-free control for frequency response support in microgrids utilizing wind turbines

Islanded microgrids integrated with renewable energy resources bring additional challenges to the primary frequency control due to their power-electronic interfaced generations. Developing an efficient control strategy for inertia emulation and frequency response support of microgrids is becoming an important issue. Wind turbine dynamics, however, will degrade the emulated inertial and primary responses with most traditional control strategies. On the other hand, compensation methods often require the knowledge of system models at the design stage and state estimation for feedback once deployed online. To overcome these challenges, this paper explores a model-free control (MFC) strategy to emulate the desired inertia and support the frequency response of a diesel-wind microgrid system. Additionally, the proposed MFC strategy utilizes a model reference concept to obtain guaranteed inertial response and improve the frequency response of microgrids. This work considers different control strategies with different operating points. The proposed controller is implemented and verified using the modified 33-bus full nonlinear model of the three-phase diesel-wind system in Simulink. Simulation results demonstrate the improved frequency response by precisely emulating the desired inertia using the MFC strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Feedback control of stored energy and rotation with variable beam energy and perveance on DIII-D

Design and testing of a new method for real-time control on DIII-D using the new voltage and perveance variation capability of DIII-D's neutral beam injection system is presented. The approach enables control of power, torque, stored energy, and rotation without requiring pulse-width modulation of the beams. Such modulations are perturbative to experiments, can cause beam reliability problems, and could potentially damage or fatigue accelerator grids, especially looking forward to active control of long-pulse devices and reactors. Here, the control algorithm accounts for beam line loss mechanisms and includes approaches that address the complexity of having three manipulated variables for each of the eight beams and compensate for the rate and magnitude limits affecting the voltage and perveance actuators. Results are presented that demonstrate the performance of the approach in the first feedback control experiments to use the expanded controllability of the DIII-D beams.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Predictive Coordinated and Cooperative Voltage Control for Systems With High Penetration of PV

In this paper, we propose a predictive coordinated and cooperative voltage control method in a power distribution system with high penetration of photovoltaic (PV) units. First, an integrated coordinated voltage control of voltage regulators (VRs) tap positions and cooperative distributed control of the reactive power output from PV inverters are used to maintain system voltages within an appropriate bandwidth. Next, solar power forecasting is applied to predict voltage changes, which are used to set the VR tap positions and capacitor switch status to prevent large voltage fluctuations. The fine tuning of voltage adjustment is then achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on a modified IEEE 123-node test feeder with high penetration of PVs using real measurement data and compared with the base case. Simulation results demonstrate the effectiveness of the integrated voltage control, as well as the enhancement from the predictive control through solar power forecasting-enabled voltage change estimates. Comparison to previous work in the literature shows significant improvement in terms of voltage deviation and reduction in excessive tap changes.

14 SOLAR ENERGY↗
DOE OSTI2021

Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Commercial Building Sensors and Controls Systems: Barriers, Drivers, and Costs

Building sensors and controls systems, including building automation systems, consists of the sensor-based devices installed in buildings and the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 8% of small commercial buildings, however, have installed sensors and controls systems. This is largely due to cost barriers. This work seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 20 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. The qualitative interview data was analyzed using grounded theory to identify overarching concepts, such as barriers, drivers, and future directions on the field. From this analysis, primary barriers were found to be complexity, a lack of knowledge, and money. Primary drivers were found to be the knowledge of data, savings, and remote access. The qualitative cost data was collected in the form of invoices during the interviews. The cost values were used to develop a percentage-based cost stack which identifies the average fraction of the total cost attributed to each category (hardware, software, labor, fees, and taxes). This greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.

advanced building controls↗
DOE OSTI2016

Economic implications of incorporating emission controls to mitigate air pollutants emitted from a modeled hydrocarbon-fuel biorefinery in the United States

The implementation of the US Renewable Fuel Standard is expected to increase the construction and operation of new biofuel facilities. Allowing this industry to grow without adversely affecting air quality is an important sustainability goal sought by multiple stakeholders. However, little is known about how the emission controls potentially required to comply with air quality regulations might impact biorefinery cost and deployment strategies such as siting and sizing. In this study, we use a baseline design for a lignocellulosic hydrocarbon biofuel production process to assess how the integration of emission controls impacts the minimum fuel selling price (MFSP) of the biofuel produced. We evaluate the change in MFSP for two cases as compared to the baseline design by incorporating (i) emission controls that ensure compliance with applicable federal air regulations and (ii) advanced control options that could be used to achieve potential best available control technology (BACT) emission limits. Our results indicate that compliance with federal air regulations can be achieved with minimal impact on biofuel cost (~$0.02 per gasoline gallon equivalent (GGE) higher than the baseline price of $5.10 GGE -1 ). However, if air emissions must be further reduced to meet potential BACT emission limits, the cost could increase nontrivially. For example, the MFSP could increase to $5.50 GGE -1 by adopting advanced emission controls to meet potential boiler BACT limits. Finally, given tradeoffs among emission control costs, permitting requirements, and economies of scale, these results could help inform decisions about biorefinery siting and sizing and mitigate risks associated with air permitting.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems

The goal of this paper is to solve a class of stochastic optimal control problems numerically, in which the state process is governed by an Itô type stochastic differential equation with control process entering both in the drift and the diffusion, and is observed partially. The optimal control of feedback form is determined based on the available observational data. In this work, we call this type of control problems the data driven feedback control. The computational framework that we introduce to solve such type of problems aims to find the best estimate for the optimal control as a conditional expectation given the observational information. To make our method feasible in providing timely feedback to the controlled system from data, we develop an efficient stochastic optimization algorithm to implement our computational framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Infrared triggered dwell and active cooling thermal control effects on microstructural uniformity in DED

Directed Energy Deposition (DED) offers rapid large scale fabrication, but difficulty in delivering consistent microstructures and properties hinders the use of DED fabricated components in safety or performance critical applications. Variability stems from the complex thermal cycles generated by the toolpath used to print the required geometry. Several practical methods have become established in DED to regulate overheating, such as active cooling of the baseplate structure or the use of an infrared camera to inject interlayer pauses to ensure the top layer of the component cools to a set temperature, which have been shown to affect microstructure. However, no critical assessment has been performed as to how effective these controls are in promoting microstructural uniformity in the context of complex layer timing commonly generated by non-prismatic geometries. Here we show how controls influence the thermal field, phase transformations, and dynamic annealing of a low-temperature transformation steel using infrared imaging and operando neutron diffraction. Counterintuitively, common thermal homogenization process controls can reduce microstructural uniformity because these approaches stabilize peak temperature while overlooking temperatures near the solid-state phase transformation fronts. Instead, the cyclic reheating induces spatially-variant dynamically annealed regions which can be modulated via control parameters. We show that these controls have spatially linked effects centimeters away from the active weld, which implies that microstructure control must co-optimize thermal input across many subsequent layers. In conclusion, our results demonstrate the pressing need for higher order controls that integrate predictive elements of simulation data to stabilize printed properties for future qualification of DED components.

Directed energy deposition↗
DOE OSTI2016

A preliminary implementation of metabolic-based pH control to reduce CO 2 usage in outdoor flat-panel photobioreactor cultivation of Nannochloropsis oceanica microalgae

A crucial challenge associated with high-density, commercial-scale, outdoor microalgal cultivation is maintaining pH stability without excessive use of CO 2 buffering. This includes a media-dependent, intracellular metabolic proton imbalance leading to the alkalization or acidification of growth media that results when algae consume, respectively, nitrate or ammonium ions. Feeding these two nitrogen sources can theoretically achieve balanced proton metabolism as well as pH control that is economically more favorable as compared to CO 2 buffering or acid/base addition. To accomplish this, a fed-batch nutrient feeding strategy must be adopted as a component of a model-based pH control system, particularly in the case of ammonium preference. This work represents a preliminary study of the challenges of implementing a nitrogen metabolism based open-loop pH control strategy in the challenging environment of an outdoor photobioreactor at the DOE-ATP 3 testbed facility in Mesa, Arizona during the high solar insolation period of summer 2015. The approach was limited to twice daily fed-batch addition while accounting for ammonium-N preference in a background of nitrate-based algae growth media. Despite these limitations, growth achieved for a photobioreactor operated based on predicted metabolic nitrogen demand (PND) was comparable to ‘CO 2 -on-demand’ (CoD) for pH control. PND reduced CO 2 usage to b10% of CoD control, where the reduced buffering resulted in much greater pH fluctuations due to daily variations in light availability, and a much lower and more consistent media alkalinity (5.73–5.79 mEq/L) as compared to the CoD control (6.51–8.49 mEq/L). While this effort illustrates the utility of feed-forward model-based control, it further illustrates the need for far more sophisticated ‘real time’ monitoring and modeling to accommodate the dynamic outdoor conditions. In conclusion, a need for improved analytics for accurate closure of nitrogen mass balance is also indicated.

09 BIOMASS FUELS↗
DOE OSTI2020

Preliminary design of control rods in the single-fluid double-zone thorium molten salt reactor (SD-TMSR)

Recent studies on Molten Salt Reactors (MSRs) showed that the excess reactivity at the beginning of the operation is large for many fueling strategies and must be compensated by a reactivity control system. The current work introduces a reliable safety system based on control rods in addition to the online feed system reactivity control in the Single-fluid Double-zone Thorium-based Molten Salt Reactor (SD-TMSR). Three different initial fissile loadings are considered: 233 U, reactor-grade Pu, and transuranic (TRU) elements as a startup fuel. We applied six different absorbing materials to investigate the main operational and safety parameters in the SD-TMSR: natural B 4 C, enriched B 4 C with 90% 10 B, HfB 2 , HfH 1.62 , Eu 2 O 3 , and Gd 2 O 3 . The present work focuses on control rod design, integral and differential control rod worth, shutdown margin, and shadowing effects at steady-state. We employed the SERPENT-2 Monte-Carlo code to calculate the reactivity worth and analyze the performance of the reactivity control system. We showed that 233 U and reactor-grade Pu startup cores maintain adequate shutdown margin with all considered absorbers. Finally, this report proposes a design of control rod clusters that compensate the excess reactivity of the SD-TMSR loaded with different initial fissile material.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks

The increasing penetration of distributed generators (DGs) exacerbates the risk of voltage violations in active distribution networks (ADNs). The var capacity provided by DG inverters is a potential solution for voltage regulation. The conventional centralized control strategies limited by computation and communication burdens are difficult to meet the requirement of rapid voltage control. However, the local control strategies based on real-time measurements have a fast response to the frequent fluctuations of DG outputs. The performance of these local controllers depends on the tuning of the control parameters. This paper proposes a centralized-based method to determine the local voltage control strategies for DGs. A centralized parameter tuning model of control curves is built, in which and curves of DG inverters are mathematically formulated based on piecewise linearization. The original mixed-integer nonlinear programming (MINLP) model is converted into an effectively solved mixed-integer second-order cone programming (MISOCP) model using convex relaxation. The potential benefits of DG inverters are explored to regulate both reactive and curtailed active power, based on local voltage measurements. Case studies on a modified PG&E 69-node distribution system are carried out to verify the effectiveness of the proposed method. Results show that power losses of ADNs are significantly reduced and voltage profiles are also improved.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗