Search NASA⌕ Search

SEARCH · Search NASA

Results for “Resilient control”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An Alternating, Current-Induced Electromagnetic Field for Membrane Fouling and Scaling Control during Desalination of Secondary Effluent from Municipal Wastewater

Membrane treatment of secondary effluent for reuse applications is a promising approach to expand water supplies and provide flexibility to water resources management. However, effective control of membrane fouling and scaling is crucial for cost-effective treatment and system resilience. This study compared the performance of antiscalants to an alternating, current-induced electromagnetic field (EMF) as an alternative pretreatment method to reverse osmosis. Compared to the no-EMF control experiments, the EMF device resulted in 13% higher water recovery and 366% lower flux decline at 60% of water recovery, along with 2–8 times lower precipitation of fouling and scaling, as evidenced by scanning electron microscope, energy dispersive X-ray spectroscopy, and chemical extraction analysis. The combination of the EMF with antiscalant was more effective for reducing membrane fouling and scaling, increasing water recoveries up to 89.3%, as compared to the EMF (67.5%) and antiscalant-only (73.6%) configurations. This is the first study to demonstrate synergistic effects of using an EMF in combination with antiscalants and could lead to lower pretreatment costs. Additional research is required to quantify the economics of this approach and to fully understand the fundamental mechanisms governing fouling and scaling control by an EMF.

42 ENGINEERING↗
DOE OSTI2023

Solarize Fairbanks BRITE: Facilitating Efficient, Resilient Homes in Cold Climates

Solarize Fairbanks began an annual Solarize campaign in the Interior Alaska city in 2020, located in IECC climate zone 8, with a goal to increase the number of solar PV panels in the community. The campaign provides peer support, education, bulk purchase discounts, and simplified installation of solar PV technologies for homes, businesses, and nonprofits. In 2021, the campaign began offering energy audits to building and homeowners with a bulk discount; however, building owners were responsible for pursuing next steps on their own. In 2022, a diverse team of local, state, and federal partners formed a team to create a process to further facilitate energy efficiency improvements alongside solar PV technology. The resulting project, Solarize Fairbanks - Building Resilience for the Interior (BRITE) aims to build out an efficiency component over 3 years. If implemented, it will be the first efficiency add-on to a solarize campaign in Alaska. In year one, the team conducted energy audits of four nonprofits located in the cold climate of Interior Alaska and is providing technical assistance and fundraising for the nonprofits to pursue the recommended retrofits. These audits provided insights on the types of retrofits that could be expected to increase efficiency, comfort, and resiliency of buildings, including LED lighting retrofits, increased envelope insulation, improved building controls, and air source heat pump technology. A pre- and post-retrofit analysis will provide further insight on the energy savings and other benefits of the retrofits. It will also inform the offerings in the following years of the BRITE add-on to Solarize campaigns. In this presentation, program implementers will review the past campaigns of Solarize Fairbanks, summarize the energy efficiency and resiliency analyses of the nonprofit buildings, cover future plans for Solarize Fairbanks BRITE, and provide recommendations for other communities pursuing similar programs.

Alaska↗
DOE OSTI2023

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.

Artificial Intelligence (AI), reinforcement learni↗
DOE OSTI2024

Solid State Power Substation DC Node Optimization and Controller Hardware-In-The-Loop Demonstration

A solid state power substation (SSPS) node is a microgrid that integrates distributed energy resources and loads and injects/absorbs power to/from the SSPS distribution network. It is an essential building block of a futuristic distribution grid network. This paper presents the development and demonstration of optimization use cases of a SSPS DC node. By adopting multi-layer hierarchical control architecture and developing automatic device identification and dynamic optimization formulation algorithms, the SSPS DC node can perform plug-and-play resource integration and seamless transition of the optimized node operation under on and off grid condition without sophisticated algorithms, control mode changes, and user interactions. Four optimization use cases including economic dispatches with price signal changes, a sudden PV power drop, and a single directional meter and its associated costs with sending power back to the grid, and resiliency under a grid inverter trip condition were demonstrated through the real-time controller hardware-in-the-loop simulation.

Kim, Namwon↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2023

Control optimization for parametric Hamiltonians by pulse reconstruction

Optimal control techniques provide a means to tailor the control pulses required to generate customized quantum gates, which helps to improve the resilience of quantum simulations to gate errors and device noise. However, the significant amount of (classical) computation required to generate customized gates can quickly undermine the effectiveness of this approach, especially when pulse optimization needs to be iterated. We propose a method to reduce the computational time required to generate the control pulse for a Hamiltonian that is parametrically dependent on a time-varying quantity. We use simple interpolation schemes to accurately reconstruct the control pulses from a set of pulses obtained in advance for a discrete set of predetermined parameter values. We obtain a reconstruction with very high fidelity and a significant reduction in computational effort. We report the results of the application of the proposed method to device-level quantum simulations of the unitary (real) time evolution of two interacting neutrons based on superconducting qubits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗
DOE OSTI2021

Scalable, Physical Effects Measureable Microgridfor Cyber Resilience Analysis

SPEMMCRA provides a low-cost simulated method to provide for testing of control theory and cybersecurity approaches to microgrid implementations. Specifically, this physics-based approach provides realistic responses to compare control strategies in simulation, but also to demonstrate actions that can or cannot be taken in taking cybersecurity actions . As communications interfaces or other actions are taken that affect the flow of data, SPEMMCRA can show whether the microgrid operation is adversely affected by what is intended to mitigate a cybersecurity event.

Rieger, CraigG↗
DOE OSTI2023

A Framework to Design Consumer-Centric Operational Strategies for Resilience Enhancement

Extreme temperature-related events like heat waves or cold snaps can significantly stress the power distribution grid as electricity demand spikes leading to brownouts or blackouts if not managed properly. In addition, such extreme events can exacerbate inequity, with vulnerable populations (for example, houses with poor insulation, located in non-critical zones, and lack of local resources) at greater risk. There is a growing deployment of distribution systems automation technologies such as advanced metering infrastructure, sensors, and automated control systems to enhance the visibility of the entire distribution grid while meeting resilience objectives. Here, this paper proposes a framework for investigating how automation can improve the distribution system's resilience and ensure customers' health and safety during such extremes. The proposed framework's efficacy will be demonstrated for the Electricity Reliability Council of Texas (ERCOT) during winter storm Uri with varying levels of distribution automation technologies such as feeder isolation using smart switches, remote outage signals using advanced meters, and comfort-aware outage using advanced analytics and communication. Simulation results show that an outage strategy involving advanced grid technology and communication can reduce the occupant exposure to severe cold by 93% and, simultaneously, reduce expected energy not served by 73.2% when compared against feeder isolation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Resilient Consensus State Observer for Nonlinear Systems and Against Attacks

Herein, we investigate the design of a resilient consensus observer that yields correct and continuous state estimates of a nonlinear control system whose state observations are subject to attacks. The observations are assumed to be obtained from multiple sensors and/or communication channels employed to provide redundancy. It is shown that, in an attack-free setting, individual globally convergent observers can be designed for each sensor/channel using the corresponding state-dependent Jacobian system. To counter attacks, a resilient consensus observer is then designed by merging the observers estimates to produce a smooth consensus estimate. Resilient convergence of the consensus estimate to the true state is ensured under the condition that the sparsity of the attacks being less than a half of the number of redundant channels. As an illustrative example, the proposed scheme is successfully applied to the chaotic circuit synchronization problem in which their synchronization has been attacked.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Extreme Problem Solving II: How Can 4 Astronauts do the Jobs of 80 Experts?

On past and present space missions, resilience is largely dependent on the problem-solving expertise of flight controllers at Mission Control on the ground. Missions to Mars will instead experience long communication delays and blackouts that require a small crew to detect, diagnose, and respond to critical events with only intermittent and limited real-time ground support. Our 2021 SpaceCHI paper “Extreme Problem Solving: The New Challenges of Deep Space Exploration” introduced the paradigm shift of increasingly Earth-independent mission operations and identified the capabilities that are not currently available on-board but will be needed to enable safe exploration beyond low-Earth orbit [1]. This paper investigates how the ground team achieves resilience today to inform what will be needed to achieve human-systems resilience on future long duration exploration missions – how can a crew of four generalists achieve the same outcomes as a team of 80+ experts? An actual ISS anomaly is analyzed and then reimagined under Mars transit conditions, to reveal critical decision-points and the onboard capabilities that will be needed for successful resolution. This paper also presents criteria for what makes urgent, unanticipated events so challenging to resolve.

human-systems integration architecture↗
DOE OSTI2022

Autonomous Microgrid Restoration Using Grid-Forming Inverters and Smart Circuit Breakers: Preprint

The proliferation of distributed inverter-based resources (IBRs) raises the questions if these IBRs can be used to blackstart microgrids and distribution feeders after major outages. In this paper, we propose and evaluate an autonomous microgrid restoration concept using grid-forming (GFM) IBRs and smart circuit creakers (SCBs). The concept is first explored in simulation platform and then a hardware testbed containing actual GFM inverters is developed to demonstrate these functionalities. A combination of dispatchable virtual oscillator control (dVOC) and droop-based control schemes have been designed for GFM inverter controls in the software simulations and hardware testbed. Subsequently, operation of SCBs using two distinct principles have been demonstrated that can restore or connect portions of the network.

grid reconfiguration↗
DOE OSTI2022

RLC4CLR (Reinforcement Learning Controller for Critical Load Restoration Problems)

RLC4CLR demonstrates using a reinforcement learning controller (RLC) to solve a critical load restoration (CLR) problem, which improves the grid resilience after a substation outage event. RLC4CLR consists of two parts. (1) RL environment: This environment encapsulates the CLR problem to be solved and provides interfacing functions to follow the standard OpenAI Gym format. A power system simulator, i.e., OpenDSS, is included to provide the power flow solution. Controller inputs and outputs (RL state and action) as well as the reward are defined in this environment as well. In summary, the RL environment is the problem formulation from which the RL agent can learn. (2) RL training script: The training script enables the RL agent to learn its control policy by interacting with the RL environment. For RL training, an open-sourced RL library, i.e., RLlib, is leveraged which is based on a distributed computing framework (Ray). The training script is designed to be able to be run on both local machine or the NREL HPC system. Other components of RLC4CLR include input data, e.g., grid model (standard IEEE test feeders), and other files used for results analysis.

Zhang, Xiangyu↗
DOE OSTI2020

An Operational Resilience Metric for Modern Power Distribution Systems

The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics alone do not provide adequate energy security. As a result, researchers have focused their efforts on a new set of metrics based on the concept of resilience to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose a real-time resilience metric for modern power distribution systems. The metric is an aggregation of the controllable assets adaptive capacity, or their temporal flexibility in real and reactive power. This metric gives information to the magnitude and duration of a disturbance which the system can respond to without having to drop loads or have stability issues in voltage or frequency. We demonstrate the impact to resilience in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages.

42 ENGINEERING↗
DOE OSTI2024

Long-term drought promotes invasive species by reducing wildfire severity

Anthropogenic climate change has increased the frequency of drought, wildfire, and invasions of non-native species. Although high-severity fires linked to drought can inhibit recovery of native vegetation in forested ecosystems, it remains unclear how drought impacts the recovery of other plant communities following wildfire. We leveraged an existing rainfall manipulation experiment to test the hypothesis that reduced precipitation, fuel load, and fire severity convert plant community composition from native shrubs to invasive grasses in a Southern California coastal sage scrub system. We measured community composition before and after the 2020 Silverado wildfire in plots with three rainfall treatments. Drought reduced fuel load and vegetation cover, which reduced fire severity. Native shrubs had greater prefire cover in added water plots compared to reduced water plots. Native cover was lower and invasive cover was higher in postfire reduced water plots compared to postfire added and ambient water plots. Our results demonstrate the importance of fuel load on fire severity and plant community composition on an ecosystem scale. Management should focus on reducing fire frequency and removing invasive species to maintain the resilience of coastal sage scrub communities facing drought. In these communities, controlled burns are not recommended as they promote invasive plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations

Understanding and quantifying the inertia of power systems with the integration of converter-interfaced generation (CIG) plays an essential role in the safe transition to a future low-inertia scenario. Here we provide a comprehensive summary of inertia definitions for both synchronous generators and CIGs as well as their corresponding estimation methods. In particular, the estimation methods are categorized as model-based and measurement-based approaches considering both small and large disturbances. The advantages and disadvantages of different methods are carefully discussed. This paper also offers for the first time a framework to quantify the virtual inertia of CIGs at the component and aggregation levels, an open problem in the literature. Finally, future directions for inertia estimation are identified and discussed. This significantly benefits the design of appropriate control and protection schemes in achieving a more reliable, secure, and resilient power system.

42 ENGINEERING↗
DOE OSTI2023

A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching

To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level (bond type, non-uniform chain length, entanglement density, etc.) to macroscopic quantities that govern damage and failure must be established. Many of the most successful constitutive models for elastomers have been rooted in statistical mechanical treatments of polymer chains. Typically, such constitutive models have used variants of the freely jointed chain model with rigid links. However, since the free energy state of a polymer chain is dominated by enthalpic bond distortion effects as the chain approaches its rupture point, bond extensibility ought to be accounted for if the model is intended to capture chain rupture. To that end, a new bond potential is supplemented to the freely jointed chain model (as derived in the u FJC framework of Buche and Silberstein (2021) and Buche et al. (2022)), which we have extended to yield a tractable, closed-form model of single chain behavior that should be amenable to continuum-level constitutive model development. Inspired by the asymptotically matched u FJC model response in both the low/intermediate chain force and high chain force regimes, a simple, quasi-polynomial bond potential energy function is derived. This bond potential exhibits harmonic behavior near the equilibrium state and anharmonic behavior for large bond stretches tending to a characteristic energy plateau (akin to the Lennard-Jones and Morse bond potentials). Using this bond potential, approximate yet highly-accurate analytical functions for bond stretch and chain force dependent upon chain stretch are established. Then, using this polymer chain model, a stochastic thermal fluctuation-driven chain rupture framework is developed. This framework is based upon a force-modified tilted bond potential that accounts for distortional bond potential energy, allowing for the derivation and subsequent calculation of the dissipated chain scission energy. Here, the cases of rate-dependent and rate-independent scission are accounted for throughout the rupture framework. The impact of Kuhn segment number on chain rupture behavior is also investigated. The model is fit to single chain mechanical response data collected from atomic force microscopy tensile tests for validation and to glean deeper insight into the molecular physics taking place. Due to their analytical nature, this polymer chain model and the associated rupture framework can in the future be implemented in finite element models accounting for fracture and fatigue in polydisperse elastomer networks.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The dual role of iron in alkaline water electrolysis: A friend or a foe?

Trace iron (Fe) impurities pose a challenge in alkaline water electrolysis (AWE) and can significantly impact long-term performance, yet their comprehensive system-level effects remain poorly understood. Here, we present the mechanistic roles of Fe in AWE, using combined rotation disk electrode studies, zero-gap cell measurements, and durability tests up to 1,000 h. Fundamental electrochemistry shows that Fe deposition proceeds through a solution-mediated pathway, with dendritic growth triggered under specific mass-transport and potential conditions. In single-cell operation, Fe incorporation substantially enhances nickel (Ni)-based anode activity by forming highly active Ni-Fe oxyhydroxides, while cathodic Fe forms either thin films or dendrites, depending on potential, surface morphology, and electrolyte distribution. Although dendrites do not degrade hydrogen evolution performance up to 1,000 h, they reveal partial intrusion into the separator, indicating a potential durability risk. We identify five governing factors controlling Fe dendrite formation and provide design principles for impurity-resilient AWE systems.

Lyu, Xiang [ORNL] (ORCID:0000000208673248)↗