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Results for “distributed control and optimization”

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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optimal Energy Scheduling and Sensitivity Analysis for Integrated Power-Water-Heat Systems

The conventionally independent power, water, and heating networks are becoming more tightly connected, which motivates their joint optimal energy scheduling to improve the overall efficiency of an integrated energy system. However, such a joint optimization is known as a challenging problem with complex network constraints and couplings of electric, hydraulic, and thermal models that are nonlinear and nonconvex. We formulate an optimal power-water-heat flow (OPWHF) problem and develop a computationally efficient heuristic to solve it. The proposed heuristic decomposes OPWHF into subproblems, which are iteratively solved via convex relaxation and convex-concave procedure. Simulation results validate that the proposed framework can improve operational flexibility and social welfare of the integrated system, wherein the water and heating networks respond as virtual energy storage to time-varying energy prices and solar photovoltaic generation. Moreover, we perform sensitivity analysis to compare two modes of heating network control: by flow rate and by temperature. Our results reveal that the latter is more effective for heating networks with a wider space of pipeline parameters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The Heuristic Dynamic Programming Approach in Boost Converters

In this study, a heuristic dynamic programming controller is proposed to control a boost converter. Conventional controllers such as proportional-integral-derivative (PID) or proportional-integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during the start-up, the load change, or the input voltage variation is not optimal since the system model changes by varying the operating point. The heuristic dynamic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the HDP is that the neural network-based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. An HDP with a well-trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the HDP boost converter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Coastal Community Resiliency Enhancement Using Marine Hydrokinetic (MHK) Resources and Networked Microgrids

Enormous challenges are inherent in the operation of modern distribution grids. In particular, distribution feeders can be severely compromised under extreme conditions such as large-scale weather events (e.g., hurricanes, floods, and other types of natural disasters). To combat this problem, they turn to local energy support from distributed energy resources, such as solar and wind, marine hydrolinetic (MHK), and other types of energy. In this report, we investigate the actual characteristics of MHK resources to enhance coastal community resiliency. Two typical types of MHK resources (i.e., wave and tidal resources) illustrate the coastal resilience enhancement framework. By identifying their operational characteristics, such as target time-scales and potential power generation variations, we detail a holistic coastal community resiliency enhancement framework that integrates both optimization and control efforts. The optimization problem is formulated to significantly minimize the power outage time at the critical infrastructures in coastal communities by implementing multiple autonomous and dynamic microgrids around MHK resources. Therefore, during severe power outages caused by natural disasters (e.g., floods, hurricanes), the coastal community load profiles can be satisfied using local MHK resources in each microgrid with limited or no power support from upstream power grids.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning

The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Multiscale Modeling Framework for Lithium Nucleation in 3D Porous Carbon Anodes

Porous carbon scaffolds offer a promising route for mitigating non-uniform lithium (Li) plating to enhance the safety and longevity of Li metal batteries. However, the influence of microstructural morphology on Li nucleation is not well understood. Here, we present a multiscale modeling framework to investigate how the porous microstructure of carbon materials affects Li nucleation behavior. Ab initio molecular dynamics simulations quantify the nucleation energy barriers of Li on graphene as a function of Li content, surface curvature, and applied potential, providing key parameters for a classical nucleation theory (CNT) model. From macroscale half-cell simulations, we obtained Li concentration and electrical potential profiles to define boundary conditions for mesoscopic simulations. At the mesoscale, three distinct synthetic 3D microstructures with different porosities and characteristic feature sizes are generated to resolve local distributions of Li flux, current density, and mechanical stress. These outputs are integrated into the CNT model to map spatial variation in nucleation rates. Our findings reveal trade-offs between suppressing nucleation rates and achieving spatial uniformity, offering design guidelines for optimizing porous carbon anodes to balance nucleation control and mechanical integrity.

Materials science↗
DOE OSTI2020

Southwest Wind Power R&D Test Site Development at the National Wind Technology Center (CRADA CRD-12-00472 Final Report)

Southwest Windpower, Inc. (SWWP) has been designing and distributing small wind turbines for more than 22 years and is the recognized global leader in the design, manufacturing and distribution of small wind systems (400-3000 watts). The company has been a pioneer in the development of wind technology and has built and shipped more than 170,000 wind turbines to over 120 countries worldwide. Headquartered in Flagstaff AZ, SWWP has sales representatives in over 88 countries. Applications for SWWP systems include residential homes, commercial properties, micro grids, remote cabins, telecom transmitters, offshore platforms, water pumping and sailboats. In addition, U.S. Department of Defense uses SWWP’s products in Forward Operating Bases (FOB’s), USGS uses them for remote monitoring of glacier movements and the CIA uses them to provide power seismic monitoring. Today, SWWP plans development of new technologies to address distributed energy market needs and become even more aggressive in international markets. This strategy includes extensive Research, Development, Demonstration & Deployment (RDD&D) activities to improve existing production line into more simple, reliable, and cost-effective wind turbine systems. Important part of such activities is the field testing of the new systems, which should provide: (i) validation of the reliable operation of hardware and software; (ii) validation of numerical models of the system and its components; (iii) field comparison of various control and optimization strategies; (iv) customized reliability testing; (v) customized monitoring and analysis testing; and (vi) IEC certification testing (vii) filed demonstration of system performance and reliability. Such field testing facility should encompass multiple towers and appropriate data acquisition systems. SWWP is exploring to move part of the engineering department to Colorado, and execute its RDD&D strategies in this new location. Partnership with NREL will provide great insights into SWWP’s RDD&D processes and more efficient commercialization by utilizing NWTC as the field testing site, and working close with NREL personnel. NWTC is ideally suited to strain the design. NREL is very experienced with customized and IEC testing. During this project, Southwest Windpower aims to improve the value proposition of distributed renewable energy, and make it a competitive choice in the energy markets. As of today, more then 170,000 small wind turbines, manufactured in the U.S., are installed in more then 180 countries. With the improved value proposition, Southwest Windpower wants to create and address new market needs, substantially increase its sales domestically and internationally, which should have positive impact on the creation of American jobs, competitiveness of U.S. economy, export or U.S. products, and the use of renewable energy.

17 WIND ENERGY↗
DOE OSTI2023

Robust Optimized Pulse Schemes for Atomic Fountain Interferometry

The robustness of an atomic fountain interferometer with respect to variations in the initial velocity of the atoms and deviations from the optimal pulse amplitude is examined. We numerically simulate the dynamics of an interferometer in momentum space with a maximum separation of and map out the expected signal contrast depending on the variance of the initial velocity distribution and the value of the laser field amplitude. We show that an excitation scheme based on rapid adiabatic passage significantly enhances the expected signal contrast, compared to the commonly used scheme consisting of a series of and pulses. We demonstrate further substantial increase of the robustness by using optimal control theory to identify splitting and swapping pulses that perform well on an ensemble average of pulse amplitudes and velocities. Our results demonstrate the ability of optimal control to significantly enhance future implementations of atomic fountain interferometry.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 1: ATT Foundations and Dynamic Line Ratings (DLRs)

The INL TADA GETs Cohort Session 1, held on November 4, 2025, convened experts to address the integration of advanced transmission technologies, including Grid-Enhancing Technologies (GETs) and High Performance Conductors (HPCs), with a focus on digital assurance challenges. The session highlighted the growing importance of cybersecurity, supply chain transparency, reliability, and business risk management in deploying GETs, especially Dynamic Line Ratings (DLRs). Participants examined how expanded attack surfaces, limited vendor pools, and new regulatory requirements—such as FERC Orders 881, 2023, and 1920—are influencing utilities and technology providers. The workshop underscored the need for cyber-informed engineering, secure-by-design principles, and practical risk management strategies, while fostering collaboration and knowledge sharing among industry peers. Technical discussions covered the evolution from static to dynamic line ratings, complexities of cloud-based architectures, and NERC CIP compliance challenges. The session concluded with a collaborative risk exercise and a preview of future workshops on advanced power flow control and transmission topology optimization, reinforcing the cohort’s commitment to advancing digital assurance in the energy sector.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Control Co-Design Framework for Joint Optimization of PID Governor and Hydraulic Dynamics for a Hydropower Plant

This paper presents a control co-design (CCD) framework tailored to the hydraulic and mechanical subsystems of hydropower systems, aimed at enhancing their dynamic performance and responsiveness to grid demands. Conventional sequential design approaches often fall short in capturing the coupled interactions between plant dynamics and control objectives. The proposed CCD methodology enables simultaneous optimization of key hydro plant parameters - such as governor settings, penstock characteristics, and valve control - with the control system architecture. Results indicate that both CCD and sequential design approaches achieve comparable transient and steady-state performance, with minor discrepancies arising from differences in their optimized parameters. However, CCD demonstrates a distinct advantage by tripling the water time constant (Tw) while maintaining similar dynamic performance to the sequential approach. This increase in Tw enables optimization of penstock dimensions, as it is directly influenced by penstock design, thereby contributing to overall cost optimization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Simulating quantum-classical interfaces via the Lindblad master equation

In hybrid quantum systems, the interface between quantum and classical domains is essential for the generation, control, and measurement of quantum states. Quantum-classical interfaces (QCIs) are ubiquitous in devices such as optical modulators, quantum sensors, and signal processors, where classical signals influence quantum dynamics. In this paper, we employ the Lindblad master equation to simulate the evolution of a quantum system interacting with a classical control system. Our model captures both linear and nonlinear interactions by incorporating first- and second-order susceptibilities, and it quantifies the influence of externally applied control parameters on decoherence and state evolution. As an illustrative example, we analyze an optical modulator and demonstrate how variations in material response and drive conditions affect photon statistics, coherence, and phase-space distributions. In conclusion, the findings offer a path to an all-encompassing model for understanding and optimizing QCIs, with wide-ranging implications for the performance, design, and robustness of next-generation quantum devices.

Quantum engineering↗
DOE OSTI2020

Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multilevel implementation of the primal-dual gradient algorithm to solve the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verify that the proposed algorithm can significantly improve the computational speed without compromising any optimality.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Exploring the physics of cesium lead halide perovskite quantum dots via Bayesian inference of the photoluminescence spectra in automated experiment

The unique optoelectronic properties of metal halide perovskite quantum dots (QDs) make them promising candidates for applications in light-emitting diodes (LEDs), scintillators, and other photonic devices. The automated micropipetting synthesis platform equipped with an optical reader enables the opportunity for high throughput synthesis and photoluminescent (PL) characterization of metal halide perovskite QDs for the first time. Here, we explore the compositional dependence of the PL behavior and stability of the combinatorial library of cesium lead halide (CsPbX 3 ) perovskites QDs via the automated platform. To study the stability of synthesized QDs in the binary and ternary configurations, we study the time-dependent PL properties using previously developed machine learning analysis. Additionally, to systematically explore the PL behavior in the ternary CsPbX 3 QDs system, we introduce the Bayesian inference framework that allows the probabilistic fit of multiple models to the PL data and establishes both optimal model and model parameter robustly. Furthermore, these behaviors can be used as a control parameter for the navigation of the multidimensional compositional spaces in automated synthesis. Furthermore, this analysis shows the nonuniformity of the PL peak behavior in the ternary CsPbX 3 QDs system. Further, the analysis confirms narrow size distribution and good quality of CsPbBr 3 QDs alloyed with low concentrations of iodide and chloride. We note that Bayesian Inference fit parameters can be further used as a control signal for navigation of the chemical spaces in automated synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-Level Optimal Power Flow Solver in Large Distribution Networks: Preprint

Solving optimal power flow (OPF) problem for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution networks of tree topology with deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multi-level implementation of the primal-dual gradient algorithm for solving the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verifies that the proposed algorithm can significantly improve computational speed without compromising any optimality.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. Further, we develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.

24 POWER TRANSMISSION AND DISTRIBUTION↗