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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The Statistical Spread of Transmission Outages on a Fast Protection Time Scale Based on Utility Data

When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outage data, determine some key statistics of these patterns, and then show how to generate new patterns consistent with these statistics. The generated patterns provide a new and easily feasible way to model the overall effect of the protection system at the scale of a large transmission system. This new data-driven generative modeling of protection is expected to contribute to simulations of disturbances in large grids so that they can better quantify the risk of blackouts. Analysis of the pattern sizes suggests an index that describes how much outages spread in the transmission network at the fast timescale.

Transmission↗
DOE OSTI2017

Resilient off-grid microgrids: Capacity planning and N-1 security

Over the past century the electric power industry has evolved to support the delivery of power over long distances with highly interconnected transmission systems. Despite this evolution, some remote communities are not connected to these systems. These communities rely on small, disconnected distribution systems, i.e., microgrids to deliver power. However, as microgrids often are not held to the same reliability standards as transmission grids, remote communities can be at risk for extended blackouts. To address this issue, we develop an optimization model and an algorithm for capacity planning and operations of microgrids that include N-1 security and other practical modeling features like AC power flow physics, component efficiencies and thermal limits. Lastly, we demonstrate the computational effectiveness of our approach on two test systems; a modified version of the IEEE 13 node test feeder and a model of a distribution system in a remote community in Alaska.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Enhancing Active Distribution Systems Resilience by Fully Distributed Self-Healing Strategy

Distributed restoration can exploit smart grid technologies to enhance the resilience of active distribution networks toward a self-healing smart grid. However, the large number of decision variables, especially the binary ones for reconfiguration, bring challenges to developing scalable distributed distribution service restoration (DDSR) strategies. This paper proposes a fully distributed solution procedure based on the alternating direction method of multipliers (ADMM) for mixed-integer programming problems and applies to develop the DDSR framework. The method consists of relax-drive-polish phases, 1) relaxing binary variables, and applying the convex ADMM as a warm start; 2) driving the solutions toward Boolean values through a proximal operator; 3) fixing the obtained binding binary variables and solving the rest of the problem to polish results and achieve a high-quality suboptimal solution. Then, an autonomous clustering strategy and consensus ADMM are integrated with the proposed method to realize the fully distributed cluster-based framework of DDSR. This framework can first determine DER scheduling and switch status for reconfiguration to energize the out-of-service areas from local faults, and then provide the load restoration solution in a distributed manner for total blackouts in large-scale distribution networks. Furthermore, the effectiveness and scalability of the proposed DDSR framework are demonstrated through testing on the IEEE 123-node, IEEE 8500-node, and synthetic 100k-node test feeders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Blackstart and Fault Ride-Through Capability of DFIG-Based Wind Turbines

Grid-forming wind turbines must be able to re-energize transmission branches, motor loads, unbalanced loads, and power electronics loads following a blackout. Further, they must ride through faults that can arise during restoration. To tackle these challenges, this paper sets forth a grid-forming control strategy for wind turbines using doubly-fed induction generators (DFIGs). In particular, a special set of feedforward control loops are engineered to mitigate the impacts of restoration transients as well as symmetrical and asymmetrical faults on the regulation of DFIG rotor currents and stator voltages. This paper also leverages two-axis anti-windup regulators to bound two-axis voltage and current commands within ratings. As a result, the controller performance is demonstrated via a high-fidelity simulation model of a notional wind-dominant grid.

17 WIND ENERGY↗
DOE OSTI2023

Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here in this study as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Electron depletion via cathode spot dispersion of dielectric powder into an overhead plasma

The effectiveness of cathode spot delivered dielectric particles for the purpose of plasma depletion is investigated. Here, cathode spot flows kinetically entrain and accelerate dielectric particles originally at rest into a background plasma. The time variation of the background plasma density is tracked using a cylindrical Langmuir probe biased approximately at electron saturation. As inferred from changes in the electron saturation current, depletion fractions of up to 95% are observed. This method could be exploited as a means of communications blackout mitigation for manned and unmanned reentering spacecraft as well as any high speed vehicle enveloped by a dense plasma layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Frequency Response Tool

According to the North American Electric Reliability Corporation (NERC) definition: "Frequency response is a measure of an Interconnection's ability to stabilize frequency immediately following the sudden loss of generation or load, and is a critical component of the reliable operation of the Bulk-Power System, particularly during disturbances and recoveries. Failure to maintain frequency can disrupt the operation of equipment and initiate disconnection of power plant equipment to prevent it from being damaged, which could lead to wide-spread blackouts." Frequency Response Tool automates the power system frequency response analysis process. The tool performs initial estimation of the system frequency parameters (initial frequency, minimum frequency, settling point). User can visually inspect and adjust these parameters. The tool also calculates the frequency response performance metrics of the system, archives the historic events and baselines the system performance. Frequency response performance characteristics of the system are calculated using phasor measurement unit (PMU) information. Methodology of the frequency response performance assessment implemented in the tool complies with the NERC Frequency response standard.

Etingov, Pavel↗
DOE OSTI2022

SolarPlus-Optimizer v0.1

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this software package, we present the "Solar+ Optimizer" (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

Prakash, AnandKrishnan↗
DOE OSTI2023

Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations

Abstract The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.

17 WIND ENERGY↗
DOE OSTI2014

Pyroelectric Energy Scavenging Techniques for Self-Powered Nuclear Reactor Wireless Sensor Networks

Recent advances in technologies for harvesting waste thermal energy from ambient environments present an opportunity to implement truly wireless sensor nodes in nuclear power plants. These sensors could continue to operate during extended station blackouts and during periods when operation of the plant s internal power distribution system has been disrupted. The energy required to power the wireless sensors must be generated using energy harvesting techniques from locally available energy sources, and the energy consumption within the sensor circuitry must therefore be low to minimize power and hence the size requirements of the energy harvester. Harvesting electrical energy from thermal energy sources can be achieved using pyroelectric or thermoelectric conversion techniques. Recent modeling and experimental studies have shown that pyroelectric techniques can be cost competitive with thermoelectrics in self powered wireless sensor applications and, using new temperature cycling techniques, has the potential to be several times as efficient as thermoelectrics under comparable operating conditions. The development of a new thermal energy harvester concept, based on temperature cycled pyroelectric thermal-to-electrical energy conversion, is outlined. This paper outlines the modeling of cantilever and pyroelectric structures and single element devices that demonstrate the potential of this technology for the development of high efficiency thermal-to-electrical energy conversion devices.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Topological vulnerability of power grids to disasters: Bounds, adversarial attacks and reinforcement

Natural disasters like hurricanes, floods or earthquakes can damage power grid devices and create cascading blackouts and islands. The nature of failure propagation and extent of damage, among other factors, is dependent on the structural features of the grid, that are distinct from that of random networks. This paper analyzes the structural vulnerability of real power grids to impending disasters and presents intuitive graphical metrics to quantify the extent of topological damage. We develop two improved graph eigen-value based bounds on the grid vulnerability. Further we study adversarial attacks aimed at weakening the grid’s structural robustness and present three combinatorial algorithms to determine the optimal topological attack. Simulations on power grid networks and comparison with existing work show the improvements of the proposed measures and attack schemes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Alternative Approaches for Incentivizing the Frequency Responsive Reserve Ancillary Service

Frequency responsive reserve is the autonomous response of generators and demand response to deviations of system frequency, usually as a result of the instantaneous outage of a large supplier. Frequency responsive reserve arrests the frequency decline resulting in the stabilization of system frequency, and avoids the triggering of under-frequency load-shedding or the reaching of unstablefrequencies that could ultimately lead to system blackouts. It is a crucial service required to maintain a reliable and secure power system. Regions with restructured electricity markets have historically had a lack of incentives for frequency responsive reserve because generators inherently provided the response and on large interconnected systems, more than sufficient response has beenavailable. This may not be the case in future systems due to new technologies and declining response. This paper discusses the issues that can occur without proper incentives and even disincentives, and proposes alternatives to introduce incentives for resources to provide frequency responsive reserve to ensure an efficient and reliable power system.

17 WIND ENERGY↗
DOE OSTI2012

NREL's Energy-Saving Technology for Air Conditioning Cuts Peak Power Loads Without Using Harmful Refrigerants (Fact Sheet)

This fact sheet describes how the DEVAP air conditioner was invented, explains how the technology works, and why it won an R&D 100 Award. Desiccant-enhanced evaporative (DEVAP) air-conditioning will provide superior comfort for commercial buildings in any climate at a small fraction of the electricity costs of conventional air-conditioning equipment, releasing far less carbon dioxide and cutting costly peak electrical demand by an estimated 80%. Air conditioning currently consumes about 15% of the electricity generated in the United States and is a major contributor to peak electrical demand on hot summer days, which can lead to escalating power costs, brownouts, and rolling blackouts. DEVAP employs an innovative combination of air-cooling technologies to reduce energy use by up to 81%. DEVAP also shifts most of the energy needs to thermal energy sources, reducing annual electricity use by up to 90%. In doing so, DEVAP is estimated to cut peak electrical demand by nearly 80% in all climates. Widespread use of this cooling cycle would dramatically cut peak electrical loads throughout the country, saving billions of dollars in investments and operating costs for our nation's electrical utilities. Water is already used as a refrigerant in evaporative coolers, a common and widely used energy-saving technology for arid regions. The technology cools incoming hot, dry air by evaporating water into it. The energy absorbed by the water as it evaporates, known as the latent heat of vaporization, cools the air while humidifying it. However, evaporative coolers only function when the air is dry, and they deliver humid air that can lower the comfort level for building occupants. And even many dry climates like Phoenix, Arizona, have a humid season when evaporative cooling won't work well. DEVAP extends the applicability of evaporative cooling by first using a liquid desiccant-a water-absorbing material-to dry the air. The dry air is then passed to an indirect evaporative cooling stage, in which the incoming air is in thermal contact with a moistened surface that evaporates the water into a separate air stream. As the evaporation cools the moistened surface, it draws heat from the incoming air without adding humidity to it. A number of cooling cycles have been developed that employ indirect evaporative cooling, but DEVAP achieves a superior efficiency relative to its technological siblings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

SPAR Model Structural Efficiencies

The Nuclear Regulatory Commission (NRC) and the Electric Power Research Institute (EPRI) are supporting initiatives aimed at improving the quality of probabilistic risk assessments (PRAs). Included in these initiatives are the resolution of key technical issues that are have been judged to have the most significant influence on the baseline core damage frequency of the NRC’s Standardized Plant Analysis Risk (SPAR) models and licensee PRA models. Previous work addressed issues associated with support system initiating event analysis and loss of off-site power/station blackout analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Resilience Measurement Index: An Indicator of Critical Infrastructure Resilience

The world faces numerous threats from both natural and man-made sources. Since the beginning of the 21 st century, in the United States alone, we have experienced several devastating events. Incidents such as the attacks on the World Trade Center in 2001, the Northeast blackout in 2003, Hurricane Katrina in 2005, and Superstorm Sandy in 2012 have had far-reaching impacts that have directly affected our society’s well-being. Although current efforts that have focused on preventing or mitigating the impact of incidents have achieved admirable results, a more comprehensive approach is needed to improve the nation’s overall resilience. An all-hazards methodology that emphasizes not only protection but also preparedness, mitigation, response, and recovery programs and capabilities is desired. Presidential Policy Directive 21 defines 16 critical infrastructure sectors that are essential to the nation’s security, public health and safety, economic vitality, and general quality of life (White House, 2013). If the operations of these critical infrastructure sectors are essential, their protection and resilience is paramount. As stated by President Obama, “Our goal is to ensure a more resilient Nation—one in which individuals, communities, and our economy can adapt to changing conditions as well as withstand and rapidly recover from disruption due to emergencies” (Department of Homeland Security, 2010a). Enhancing the resilience of critical infrastructure requires its owners/operators to determine the ability of the system to withstand specific threats, minimize or mitigate potential impacts, and to return to normal operations if degradation occurs. Thus, a resilience methodology requires the comprehensive assessment of critical infrastructure systems/assets—from threat to consequence. The methodology needs to support decision-making for risk management, disaster response, and business continuity. Considering these issues, the Infrastructure Assurance Center at Argonne National Laboratory, in partnership with the Protective Security Coordination Division of the U.S. Department of Homeland Security (DHS), has developed an index, the Resilience Measurement Index (RMI), to characterize the resilience of critical infrastructure.

Petit, F. D. P.↗
DOE OSTI2013

DAKOTA reliability methods applied to RAVEN/RELAP-7.

This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR).

Swiler, Laura Painton↗
DOE OSTI2013

Quantifiably secure power grid operation, management, and evolution :

This report summarizes findings and results of the Quantifiably Secure Power Grid Operation, Management, and Evolution LDRD. The focus of the LDRD was to develop decisionsupport technologies to enable rational and quantifiable risk management for two key grid operational timescales: scheduling (day-ahead) and planning (month-to-year-ahead). Risk or resiliency metrics are foundational in this effort. The 2003 Northeast Blackout investigative report stressed the criticality of enforceable metrics for system resiliency the grid s ability to satisfy demands subject to perturbation. However, we neither have well-defined risk metrics for addressing the pervasive uncertainties in a renewable energy era, nor decision-support tools for their enforcement, which severely impacts efforts to rationally improve grid security. For day-ahead unit commitment, decision-support tools must account for topological security constraints, loss-of-load (economic) costs, and supply and demand variability especially given high renewables penetration. For long-term planning, transmission and generation expansion must ensure realized demand is satisfied for various projected technological, climate, and growth scenarios. The decision-support tools investigated in this project paid particular attention to tailoriented risk metrics for explicitly addressing high-consequence events. Historically, decisionsupport tools for the grid consider expected cost minimization, largely ignoring risk and instead penalizing loss-of-load through artificial parameters. The technical focus of this work was the development of scalable solvers for enforcing risk metrics. Advanced stochastic programming solvers were developed to address generation and transmission expansion and unit commitment, minimizing cost subject to pre-specified risk thresholds. Particular attention was paid to renewables where security critically depends on production and demand prediction accuracy. To address this concern, powerful filtering techniques for spatio-temporal measurement assimilation were used to develop short-term predictive stochastic models. To achieve uncertaintytolerant solutions, very large numbers of scenarios must be simultaneously considered. One focus of this work was investigating ways of reasonably reducing this number.

Gray, Genetha Anne.↗