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

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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration

Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multiagent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

distribution system↗
DOE OSTI2018

Distributed Load Sharing Under False Data Injection Attack in an Inverter-Based Microgrid

In microgrids, distributed load sharing plays an important role in maintaining the supply-demand balance of power. Because false data injection (FDI) is one of the crucial threats faced by future microgrids, the study of the impact of FDI on distributed load sharing is both of theoretical merit and practical value. Here, we consider the distributed load sharing problem of the microgrids operating in autonomous mode under FDI. Each bus is assumed to be equipped with an agent. Under a well-developed distributed load sharing protocol based on multiagent systems, we first construct an FDI attack model, where the attacker is capable of injecting false data into the bus agents. Finally, a utilization level is introduced for coordinating generators, and its variation is evaluated in the presence of FDI attacks with given injection strategies. The stable region of the microgrid is defined, and conditions are given to determine stability. In conclusion, theoretical results are validated on the Canadian urban distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

True Load Balancing for Matricized Tensor Times Khatri-Rao Product

MTTKRP is the bottleneck operation in algorithms used to compute the CP tensor decomposition. For sparse tensors, utilizing the compressed sparse fibers (CSF) storage format and the CSF-oriented MTTKRP algorithms is important for both memory and computational efficiency on distributed-memory architectures. Existing intelligent tensor partitioning models assume the computational cost of MTTKRP to be proportional to the total number of nonzeros in the tensor. However, this is not the case for the CSF-oriented MTTKRP on distributed-memory architectures. We outline two deficiencies of nonzero-based intelligent partitioning models when CSF-oriented MTTKRP operations are performed locally: failure to encode processors' computational loads and increase in total computation due to fiber fragmentation. We focus on existing fine-grain hypergraph model and propose a novel vertex weighting scheme that enables this model encode correct computational loads of processors. We also propose to augment the fine-grain model by fiber nets for reducing the increase in total computational load via minimizing fiber fragmentation. In this way, the proposed model encodes minimizing the load of the bottleneck processor. In conclusion, parallel experiments with real-world sparse tensors on up to 1024 processors prove the validity of the outlined deficiencies and demonstrate the merit of our proposed improvements in terms of parallel runtimes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Topology Control for Load Shed Recovery

This paper introduces load shed recovery actions for transmission networks by presenting the dc optimal load shed recovery with transmission switching model (DCOLSR-TS). The model seeks to reduce the amount of load shed, which may result due to transmission line and/or generator contingencies, by modifying the bulk power system topology. Since solving DCOLSR-TS is computationally difficult, the current work also develops a heuristic (MIP-H), which improves the system topology while specifying the required sequence of switching operations. Experimental results on a list of N-1 and N-2 critical contingencies of the IEEE 118-bus test case demonstrate the advantages of utilizing MIP-H for both on-line load shed recovery and recurring contingency-response analysis. This is reinforced by the introduction of a parallelized version of the heuristic (Par-MIP-H), which solves the list of critical contingencies close to 5x faster than MIP-H with 8 cores and up to 14x faster with increased computational resources. The current work also tests MIP-H on a real-life, large-scale network in order to measure the computational performance of this tool on a real-world implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A robust dynamic state estimation approach against model errors caused by load changes

Dynamic state estimation (DSE) plays an important role in power system security monitoring and online control. In practice, there are two approaches to implementing DSE. The first approach is distributed DSE, which is based on the assumption that the terminal bus of each generator can be measured by PMUs (phasor measurement units). The assumption cannot be satisfied currently, however, because PMUs usually are installed at important high-voltage buses such as 500-kV buses installed in portions of the grid overseen by the Western Electricity Coordinating Council. Another issue of this approach is that performance of DSE is vulnerable to bad measurement data. The reason for this vulnerability is that DSE is performed separately through measurements at each terminal bus, and measurements at terminal buses are the only measurement upon which DSE can rely. Therefore, important redundant measurements are not included in this approach. The second approach is centralized DSE. This approach does not have the requirement for PMU location, and redundant measurements can be considered fully. However, load changes and grid topology changes impact centralized DSE. In this paper, we propose a new approach for handling the impact of load changes on DSE. We have developed a new algorithm that includes two sequential steps. In the first step, errors caused by load changes are detected by analyzing the difference between prediction results and measured results. In the second step, once model error is detected, a model optimization procedure is run to correct the error so the state estimation error can be mitigated. Simulation results from the IEEE 68 bus system show that the proposed approach can effectively handle model errors caused by load changes.

robust dynamic state estimation, load change, powe↗
DOE OSTI2020

Distribution Feeder-Scale Fast Frequency Response via Optimal Coordination of Net-load Resources Part I: Solution Design

This work is the first of a two-part series that develops and experimentally demonstrates a first-of-its-kind hierarchical control solution for optimally dispatching thousands of deferrable loads and distributed energy resources (DERs) across a distribution feeder to provide fast frequency response (FFR) within 500 ms to the bulk power system. This approach rapidly coordinates resources online after a frequency event occurs, allowing fast-changing, behind-the-meter (BTM) resources to be incorporated and aggregate FFR power set points to be achieved more quickly and accurately than existing approaches. We also present a solution for determining the optimal amount of headroom to operate solar inverters with to minimize opportunity cost while ensuring the FFR response viability of a building with the inverter and deferrable loads. In Part I, we develop practical algorithms for fast, cost-based optimal dispatch at multiple aggregation scales (single building, multiple buildings, and full distribution feeder), establish their optimality, and demonstrate via simulation that they are faster than state-of-the-art, coordinated frequency response approaches. In Part II, the entire platform is implemented and experimentally verified using a unique power hardware-in-the-loop demonstration, including more than 100 powered loads and DERs connected to a real-world distribution network model and over 10,000 net-load resources dispatched.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Restoration Strategy for Active Distribution Systems Considering Endogenous Uncertainty in Cold Load Pickup

Cold load pickup (CLPU) phenomenon is identified as the persistent power inrush upon a sudden load pickup after an outage. Under the active distribution system (ADS) paradigm, where distributed energy resources (DERs) are extensively installed, the decreased outage duration can induce a strong interdependence between CLPU pattern and load pickup decisions. In this paper, we propose a novel modelling technique to tractably capture the decision-dependent uncertainty (DDU) inherent in the CLPU process. Subsequently, a two-stage stochastic decision-dependent service restoration (SDDSR) model is constructed, where first stage searches for the optimal switching sequences to decide step-wise network topology, and the second stage optimizes the detailed generation schedule of DERs as well as the energization of switchable loads. Further, to tackle the computational burdens introduced by mixed-integer recourse, the progressive hedging algorithm (PHA) is utilized to decompose the original model into scenario-wise subproblems that can be solved in parallel. The numerical test on modified IEEE 123-node test feeders has verified the efficiency of our proposed SDDSR model and provided fresh insights into the monetary and secure values of DDU quantification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Bucket Loading Trajectory Optimization for the Automated Wheel Loader

The bucket motion of a wheel loader during the loading process is investigated in this study. The optimal bucket loading trajectory can be obtained by solving an optimal control problem with the performance criteria of the fill factor , fuel efficiency and operation cycle time . A control-oriented model for the bucket loading operation is developed. Based on the control-oriented model, the bucket loading process is formulated as an optimal control problem with physical constraints. An open-source optimization solver, OpenOCL, is used to solve this problem. The simulation results show that the proposed method is effective in generating the optimal trajectory with significantly increased energy efficiency and productivity. Furthermore, this optimal trajectory can be employed as a reference for an automated wheel loader or as a driver advisory for a human operated wheel loader.

42 ENGINEERING↗
DOE OSTI2016

Development of a Stiffness-Based Chemistry Load Balancing Scheme, and Optimization of Input/Output and Communication, to Enable Massively Parallel High-Fidelity Internal Combustion Engine Simulations

A closed-cycle gasoline compression ignition engine simulation near top dead center (TDC) was used to profile the performance of a parallel commercial engine computational fluid dynamics code, as it was scaled on up to 4096 cores of an IBM Blue Gene/Q supercomputer. The test case has 9 million cells near TDC, with a fixed mesh size of 0.15 mm, and was run on configurations ranging from 128 to 4096 cores. Profiling was done for a small duration of 0.11 crank angle degrees near TDC during ignition. Optimization of input/output performance resulted in a significant speedup in reading restart files, and in an over 100-times speedup in writing restart files and files for post-processing. Improvements to communication resulted in a 1400-times speedup in the mesh load balancing operation during initialization, on 4096 cores. An improved, “stiffness-based” algorithm for load balancing chemical kinetics calculations was developed, which results in an over 3-times faster run-time near ignition on 4096 cores relative to the original load balancing scheme. With this improvement to load balancing, the code achieves over 78% scaling efficiency on 2048 cores, and over 65% scaling efficiency on 4096 cores, relative to 256 cores.

CFD, Mira, Supercomputing, High Performance Comput↗
DOE OSTI2019

Substructure Flexibility and Member-Level Load Capabilities for Floating Offshore Wind Turbines in OpenFAST

OpenFAST is an open-source, physics-based engineering tool applicable to the load analysis of land-based and offshore wind turbines, including floating offshore wind turbines. The substructure for a floating wind turbine has historically been modeled in OpenFAST as a rigid body with hydrodynamic loads lumped at a point, which enabled the tool to predict the global response of the floating substructure but not the structural loads within its individual members. This limitation is an impediment to designing floating substructures - especially newer designs that are more streamlined, flexible, and cost-effective. This paper presents the development plan of new capabilities in OpenFAST to model floating substructure flexibility and member-level loads, including the functional requirements and modeling approaches needed to understand and apply them correctly.

17 WIND ENERGY↗
DOE OSTI2019

Extreme Load Computational Fluid Dynamics Analysis and Verification for a Multibody Wave Energy Converter

A wave energy converter (WEC) must be designed to survive the extreme sea states that it will be subject to throughout its lifetime. Although there are many analysis methods and codes available to accomplish this, there are currently several engineering challenges to WEC survival design. Foremost, the computational design approach will typically involve a trade-off between accuracy and computational efficiency. Additionally, most computational fluid dynamics (CFD) codes are not ideally suited to modeling extreme events for WECs with multibody dynamics, power-take-off systems, and mooring systems. Finally, although WEC design standards and CFD guidelines are emerging, with the current immaturity of the WEC industry, they are not yet well established. In this study, loads on a 1:35-scale, moored, multibody WEC are evaluated with CFD. The CFD results are compared with results obtained from a computationally efficient, midfidelity model based on linearized potential flow hydrodynamics. For these model verification comparisons, both operational and survival configurations are considered. The extreme load results obtained, using both codes, indicate that the survival configuration successfully sheds loads during extreme sea states. It is also found that WEC-Sim, when appropriately applied, can provide reasonable load results, at a fraction of the computational expense of CFD. However, for the more extreme sea states, and for higher-order effects not included in the WEC-Sim model, the linear-based results have significant errors in comparison to the CFD-based results, and should be used judiciously.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Dislocation model of nucleation and development of slip bands and their effect on service life of structural materials subject to cyclic loading

Most of the destructions of machine parts are of fatigue character. Under cyclic loading, the surface layer, in which hardening–softening processes rapidly occur, is formed almost at once after its beginning. The interaction of plastic-deformation traces with each other and with other structural elements, such as grains, results in the formation of a characteristic microstructure of the machine-part surface subject to cyclic loadings. The character of accumulation of slip bands and their shape (narrow, wide, twisting, and broken) depends on the conditions under which (under what factors) the cyclic loading occurs. The fatigue-resistance index expressed in terms of the slope of left portion of the fatigue curve linearized in logarithmic coordinates also depends on the set of relevant factors. The dependence of the surface damageability on the fatigue resistance index makes it possible to implement the method of predicting the fatigue curve by the description of the factors acting on a detail or construction. The position of the inflection point on the curve in the highcycle fatigue region (the endurance limit and the number of loading cycles, the ordinate and abscissa of the inflection point on the fatigue curve, respectively) also depends on the set of relevant factors. In combination with the previously obtained value of the slope of the left portion of the curve in the high-cycle fatigue region, this makes it possible to construct an a priori fatigue curve, thus reducing the scope of required fatigue tests and, hence, high expenses because of their long duration and high cost. The scope of tests upon using the developed method of prediction may be reduced to a minimum of one or two samples at the predicted level of the endurance limit.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Cross-Categorical Transfer Learning based Composite Load Protection Modeling

Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's dynamic performance and stability after a fault occurs. Induction motors are usually equipped with several types of protection with different operation mechanisms, making it challenging to develop adequate yet not overly complex protection models and determine their parameters for aggregate induction motor models. This paper proposes a novel cross-categorical transfer learning based framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Introducing a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a combination of feature extraction using transferable layers, followed by affine operations through non-transferable layers applied on the previous extracted features. Numerical results are provided to illustrate the application of the framework.

transfer learning, Stacked Autoencoder, Composite ↗
DOE OSTI2016

Safety envelope for load tolerance of structural element design based on multi-stage testing

Structural elements, such as stiffened panels and lap joints, are basic components of aircraft structures. For aircraft structural design, designers select predesigned elements satisfying the design load requirement based on their load-carrying capabilities. Therefore, estimation of safety envelope of structural elements for load tolerances would be a good investment for design purpose. In this article, a method of estimating safety envelope is presented using probabilistic classification, which can estimate a specific level of failure probability under both aleatory and epistemic uncertainties. An important contribution of this article is that the calculation uncertainty is reflected in building a safety envelope using Gaussian process, and the effect of element test data on reducing the calculation uncertainty is incorporated by updating the Gaussian process model with the element test data. It is shown that even one element test can significantly reduce the calculation uncertainty due to lacking knowledge of actual physics, so that conservativeness in a safety envelope is significantly reduced. The proposed approach was demonstrated with a cantilever beam example, which represents a structural element. The example shows that calculation uncertainty provides about 93% conservativeness against the uncertainty due to a few element tests. As a result, it is shown that even a single element test can increase the load tolerance modeled with the safety envelope by 20%.

42 ENGINEERING↗
DOE OSTI2022

Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves to absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Field Trial of a Low-Cost, Distributed Plug Load Monitoring System

Researchers have struggled to inventory and characterize the energy use profiles of the ever-growing category of so-called miscellaneous electric loads (MELs) because plug-load monitoring is cost-prohibitive to the researcher and intrusive to the homeowner. However, these data represent a crucial missing link to our understanding of how homes use energy, and we cannot control what we do not understand. Detailed energy use profiles would enable the nascent automated home energy management (AHEM) industry to develop effective control algorithms that target consumer electronics and other plug loads. If utility and other efficiency programs are to incent AHEM devices, they need large-scale datasets that provide statistically meaningful justification of their investments by quantifying the aggregate energy savings achievable. To address this need, we have investigated a variety of plug-load measuring devices available commercially and tested them in the laboratory to identify the most promising candidates for field applications. The scope of this report centers around the lessons learned from a field validation of one proof-of-concept system, called Smartenit (formerly SimpleHomeNet). The system was evaluated based on the rate of successful data queries, reliability over a period of days to weeks, and accuracy. This system offers good overall performance when deployed with up to ten end nodes in a residential environment, although deployment with more nodes and in a commercial environment is much less robust. We conclude that the current system is useful in selected field research projects, with the recommendation that system behavior is observed over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Field Trial of a Low-Cost, Distributed Plug Load Monitoring System

Researchers have struggled to inventory and characterize the energy use profiles of the ever-growing category of so-called miscellaneous electric loads (MELs) because plug-load monitoring is cost-prohibitive to the researcher and intrusive to the homeowner. However, these data represent a crucial missing link to understanding how homes use energy. Detailed energy use profiles would enable the nascent automated home energy management (AHEM) industry to develop effective control algorithms that target consumer electronics and other plug loads. If utility and other efficiency programs are to incent AHEM devices, they need large-scale datasets that provide statistically meaningful justification of their investments by quantifying the aggregate energy savings achievable. To address this need, NREL researchers investigated a variety of plug-load measuring devices available commercially and tested them in the laboratory to identify the most promising candidates for field applications. This report centers around the lessons learned from a field validation of one proof-of-concept system, called Smartenit (formerly SimpleHomeNet). The system was evaluated based on the rate of successful data queries, reliability over a period of days to weeks, and accuracy. This system offers good overall performance when deployed with up to 10 end nodes in a residential environment, although deployment with more nodes and in a commercial environment is much less robust. NREL concludes that the current system is useful in selected field research projects, with the recommendation that system behavior is observed over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Implementation of Substructure Flexibility and Member-Level Load Capabilities for Floating Offshore Wind Turbines in OpenFAST

OpenFAST is an open-source, physics-based engineering tool applicable to the load analysis of land-based and offshore wind turbines, including floating offshore wind turbines. The substructure for a floating wind turbine has historically been modeled in OpenFAST as a rigid body with hydrodynamic loads lumped at a point, which enabled the tool to predict the global response of the floating substructure but not the structural loads within its individual members. This limitation is an impediment to designing floating substructures - especially newer designs that are more streamlined, flexible, and cost-effective. This paper presents the development of new functionality in OpenFAST to model floating substructure flexibility and member-level loads, as well as the concepts and mathematical background needed to understand and apply it correctly.

17 WIND ENERGY↗