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At least 721 records · Page 40

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DOE OSTI2025

Enhancing nanoscale charged colloid crystallization near a metastable liquid binodal

Achieving predictive control over crystallization using non-classical nucleation while avoiding kinetic traps would be a step towards designing materials with new functionalities. We address these challenges by inducing the bottom-up assembly of nanocrystals into ordered arrays, or superlattices. Using electrostatics—rather than density—to tune the interactions between particles, we watch self-assembly proceed through a metastable liquid phase. Here, we systematically investigate the phase behaviour as a function of quench conditions in situ and in real time using small-angle X-ray scattering. By fitting to colloid, liquid and superlattice models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the predictive power of the phase diagram, we establish control of the self-assembly rate over three orders of magnitude, and we identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. The presence of the metastable liquid increases the superlattice formation rate relative to the equivalent one-step pathway, and the superlattice order increases with the rate, revealing a generalizable kinetic strategy for promoting and enhancing ordered assembly.

Tanner, Christian P. N. [University of California,↗

DOE OSTI2026

Field testing and validation of a low-cost MPC for demand flexibility for grid-interactive K-12 schools

K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.

Ham, Sang Woo↗

DOE OSTI2022

Charge-Based Droop Control Addressing Control Saturation for Low-Inertia Converters

With bulky dc-link capacitors/inductors, traditional voltage-source converters (VSCs) and current-source converters (CSCs) feature a large inertia to facilitate the converter control under large transients. To achieve high power density with reduced cost, low-inertia converters (LICs) featuring significantly reduced dc-link capacitors/inductors have attracted growing attention. However, without bulky dc-link energy buffer, LICs are prone to control saturation under large transients, resulting in undesired oscillation and instability. This issue cannot be managed by traditional proportional-integral based control due to the low inertia. And it will deteriorate when several LICs are connected in series. To address this challenge in LICs, a model-predictive control (MPC) with a computation-inexpensive feed-forward compensation method has been proposed to provide fast dynamic responses. But it would suffer control saturation under larger transients that degrades the control performance. In this article, a charge-based droop control (CDC) is proposed to address this remaining challenge. This paper firstly analyzes the control saturation challenge in LICs by using a tri-port soft-switching solid-state transformer as an example. Next, the operating principle of the proposed CDC are introduced. Two different implementation approaches are discussed in detail. Lastly, the proposed scheme is validated in simulation with a high-fidelity model of hardware prototype. As a result, the proposed CDC eliminated the 2 kHz oscillation and reduced the dc-link ripple and overshoot due to control saturation by 75% and 50%, respectively.

30 DIRECT ENERGY CONVERSION↗

NASA NTRS2007

Pseudo-Waypoint Guidance for Proximity Spacecraft Maneuvers

A paper describes algorithms for guidance and control (G&C) of a spacecraft maneuvering near a planet, moon, asteroid, comet, or other small astronomical body. The algorithms were developed following a model-predictive-control approach along with a convexification of the governing dynamical equations, control constraints, and trajectory and state constraints.

Acikmese, Ahmet↗

NASA NTRS1971

SSS-A attitude control prelaunch analysis and operations plan

A description of the attitude control support being supplied by the Mission and Data Operations Directorate is presented. Descriptions of the computer programs being used to support the mission for attitude determination, prediction, control, and definitive attitude processing are included. In addition, descriptions of the operating procedures which will be used to accomplish mission objectives are provided.

Werking, R. D.↗

NASA NTRS1984

Control system testing

A three stage process of ground testing of the Space Telescope Pointing Control System is used for verification prior to on-orbit operation. First, development tests are conducted in a laboratory environment using flight/engineering model control sensor and actuators configured with an engineering model of the flight computer and data management system breadboards. These development tests validate the results of computer simulations predicting control system performance. Integration tests bring together flight system elements and software interfaced to a software simulation of vehicle dynamics to confirm closed loop performance. The final ground test phase, flight systems testing, is conducted on the fully assembled Space Telescope, verifies interfaces with the Fine Guidance Sensors and includes a thermal vacuum testing period. During the final test phase, the Point Control System is exercised with the dynamics simulator running in real time.

Whittler, W. H.↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2019

Tank Test of an Active Power Rectification in Wave-to-Wire Energy Conversion

Maximum power control algorithms in wave energy converter (WEC) are mainly based on the fluid-structure dynamics with impedance matching from power take-off (PTO) damping or model predictive control through the buoy dynamics. However, the conventional buoy characteristics study cannot satisfy estimation of maximum output power in a practical wave-to-wire (W2W) system due to significant losses on mechanical and electrical components from PTO to power converter in comparison with the losses on the buoy. To analysis the non-ideal effects from mechanical transmission and energy conversion characteristics of WEC with a power converter output, a W2W simulation and tank testing is performed. The 1:20 scale WEC system with 1.17m buoy diameter includes a single body heaving buoy, mechanical-motion-rectifier based power-take-off, and power converter that store its output energy to a battery load. From the tank testing results, a circuit model is built and is used to estimate the system performance. The non-ideal effects such as viscous damping between buoy and power take-off, gearbox loss, generator loss, and power converter loss in the system are included in the circuit model with efficiencies from 60 - 80%. The maximum extracted power results under various wave conditions are dominated by these non-ideal effect.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗

DOE OSTI2020

Agent-Based Coordination Scheme for PV Integration (ABC4PV)

Renewables and especially photovoltaics (PV) have benefitted significantly from a host of incentives and policies targeted toward enhanced integration and adoption of specific energy technologies. However, with the push to move forward into a subsidy-free market framework, behind-the-meter residential PV applications have generally struggled to retain their value (unlike utility scale and commercial projects) [1]. This project focused on developing control-theoretic solutions aimed at improving the integration and interaction of behind-the-meter residential PV with other distribution system assets (controllable and non-controllable) to enhance the integrated value of residential PV. To this end, a suite of decentralized control methodologies have been developed to enable effective coordination and control of behind-the-meter residential load customers’ PV, battery storage systems (BSS), controllable loads and other similar assets within a distribution feeder. This interaction aims at procuring energy savings and, thus, energy bill savings. The main source of savings is drawn from reducing the effect of demand charge pricing and is realized at the feeder level, assuming community level interaction and management among the aforementioned assets. Optimal control of the assets is implemented with a distributed optimization methodology, leveraging consensus-based algorithms. The results gathered from the optimal control simulations demonstrates that the savings can be duly achieved and the algorithm decision times (to dynamically control asset set points, for example) are fast. As for the overall efficiency of PV+BSS systems, to procure energy savings from curtailment of the demand charge pricing effects, the optimal control is set up so as to minimize the variance of the load for all customers, throughout a feeder and throughout time in a rolling horizon scheduling with model predictive control. The control takes into account inter-temporal electrochemical storage (battery) degradation costs: specifically, we have developed a long-term lifetime model for the BSS that weighs in the effect of the degradation factor in the dispatch formulations, thus, a considerable operating cost that affects energy decision making. The levelized cost of energy (LCOE – redefined for the purpose of quantifying asset integration effectiveness through the customers’ energy cost) is shown to be below the threshold set for the combined PV+BSS topology of $ 0.14/kWh for multiple cases of PV penetration all the way up to 50%, provided that a policy of shared ownership of and savings is in place. Further, the LCOE calculated for the case before the deployment PV+BSS systems is also achievable, i.e. the deployment of PV+BSS, if planned and scheduled optimally. will have no effect on customers’ energy costs. From the control methodology viewpoint, the developed consensus-based algorithms are shown to converge for a wide range of problem cases (spanning normal operating scenarios and contingencies), guaranteeing dispatch solutions under forecasting errors, communication break-downs and cyber-security attacks. The proposed control solutions are scalable and real-time implementable, with dispatch computations and device set-point updates converging in less than 2s in most practical instances of the above events.

14 SOLAR ENERGY↗

DOE OSTI2025

Transforming Aeration Energy in Water Resource Recovery Facilities (WRRFs) through Suboxic Nitrogen Removal (Final Report)

The objective of this project was to advance two key technological components—aeration control strategies and process design methodologies—to support the development and broader adoption of suboxic biological nitrogen removal (SBNR). The project focused on achieving the following three goals: • Enhance Model Predictive Control (MPC) Technology: Advance the DO/Nmaster MPC platform from its initial 2018 pilot deployment at the Chico Water Resource Recovery Facility in California to full-scale integration. This included partnering with a blower technology commercialization partner and incorporating machine learning (ML) capabilities to enable nationwide deployment. • Bridge Knowledge Gaps in SBNR Process Design: Address fundamental gaps in SBNR process understanding through controlled pilot-scale testing at a dedicated pilot facility. These efforts supported the development of robust kinetic models to inform reliable SBNR control, operational strategies, and design frameworks. • Demonstrate Full-Scale Implementation of Low DO/SBNR with ML: Transition low dissolved oxygen (DO)/SBNR coupled with ML from pilot-scale trials to full-scale demonstration in flow-through biological nutrient removal (BNR) systems, with the goal of enabling scalable, nationwide adoption in activated sludge treatment processes. The project included demonstration of SBNR at the pilot scale as performed by Hampton Roads Sanitation District (HRSD) and at the full-scale as performed by the Los Angeles County Sanitation Districts' (LACSD) Pomona Water Reclamation Plant (POWRP).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

ActiveBAS: A Low-cost, Scalable Control Solution for Grid-Interactive Small and Medium Sized Commercial Buildings

This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Buffered Coordination Modulation as a Means of Controlling Crystal Morphology and Molecular Diffusion in an Anisotropic Metal-Organic Framework

We know significant advances have been made in the synthesis of chemically selective environments within metal-organic frameworks, yet materials development and industrial implementation have been hindered by the inability to predictively control crystallite size and shape. One common strategy to control crystal growth is the inclusion of coordination modulators, which are molecular species designed to compete with the linker for metal coordination during synthesis. However, these modulators can simultaneously alter the pH of the reaction solution, an effect that can also significantly influence crystal morphology. Herein, noncoordinating buffers are used to independently control reaction pH during metal-organic framework synthesis, enabling direct interrogation of the role of the coordinating species on crystal growth. We demonstrate the efficacy of this strategy in the synthesis of low-dispersity single-crystals of the framework Co 2 (dobdc) (dobdc 4- = 2,5-dioxido-1,4-benzenedicarboxylate) in a pH 7-buffered solution using cobalt(II) acetate as the metal source. Density functional theory calculations reveal that acetate competitively binds to Co during crystallization, and by using a series of cobalt(II) salts with carboxylate anions of varying coordination strength, it is possible to control crystal growth along the c -direction. Finally, we use zero length column chromatography to show that crystal morphology has a direct impact on guest diffusional path length for the industrially important hydrocarbon m -xylene. Together, these results provide molecular-level insight into the use of modulators in governing crystallite morphology and a powerful strategy for the control of molecular diffusion rates within metal-organic frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Resilient Operation of Power Distribution Systems Using MPC-Based Critical Service Restoration

Power distribution systems are more prone to disruptions and cause most power system outages. We propose a service restoration technique to recover the system service (electricity delivery) following an extreme event-triggered substation outage. The proposed technique considers the problem of controlling distributed energy resources (DERs) of a distribution system with the objective of achieving maximum load pick up while satisfying network flow and voltage constraints. The problem is formulated as a model predictive control (MPC), where a linearized optimal power flow (OPF) model is employed to describe the network. The formulation is augmented with a ramping (up) reserve product for the DERs to ensure an upward monotonic load restoration as time evolves. We perform simulations considering the IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage devices. We demonstrate the efficacy of the devised technique in restoring the system loads monotonically, without shedding previously restored loads. We also show the benefit of co-optimization of power and reserve products for DERs on service restoration. In addition, the capability of the technique in regulating nodal voltages and reducing renewable power curtailment is demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1990

Demand Assignment in the ACTS LBR System

On the Advanced Communications Technology Satellite (ACTS) being developed at NASA Lewis, low-burst-rate (LBR) traffic stations will access the ACTS multibeam package via two hopping beams that can be directed at certain areas in the continental U.S. An onboard baseband processor (BBP) demodulates uplink traffic, switches it between uplink and downlink beams at baseband, and then remodulates it for retransmission at 20 GHz. This study describes the demand-assigned operation of the ACTS LBR system, where the onboard switch is remote from both traffic stations and master control station (MCS). Network control uses inbound and outbound orderwire channels and a BBP control channel, allowing the MCS to coordinate assignment of individual 64-kb/s spacecraft channels. Models are developed to simulate the dynamics of the assignment process and verify the call blocking behavior, to predict control channel loads, and to evaluate algorithms for burst time plan rearrangement.

White, Lawrence W.↗

DOE OSTI2011

Supervisory control of a pilot-scale cooling loop

We combine a previously developed strategy for Fault Detection and Identification (FDI) with a supervisory controller in closed loop. The combined method is applied to a model of a pilot-scale cooling loop of a nuclear plant, which includes Kalman filters and a model-based predictive controller as part of normal operation. The system has two valves available for flow control meaning that some redundancy is available. The FDI method is based on likelihood ratios for different fault scenarios which in turn are derived from the application of the Kalman filter. A previously introduced extension of the FDI method is used here to enable detection and identification of non-linear faults like stuck valve problems and proper accounting of the time of fault introduction. The supervisory control system is designed so to take different kinds of actions depending on the status of the fault diagnosis task and on the type of identified fault once diagnosis is complete. Some faults, like sensor bias and drift, are parametric in nature and can be adjusted without need for reconfiguration of the regulatory control system. Other faults, like a stuck valve problem, require reconfiguration of the regulatory control system. The whole strategy is demonstrated for several scenarios.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Resilient Operation of Power Distribution Systems Using MPC-Based Critical Service Restoration: Preprint

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Resilient Operation of Power Distribution Systems Using MPC-Based Critical Service Restoration

61 RADIATION PROTECTION AND DOSIMETRY↗