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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗
DOE OSTI2022

Load Shifting with Space Conditioning Heat Pumps During Heating Season in the Pacific Northwest

Ductless mini- and multi-split heat pumps are commonly deployed in Pacific Northwest residences, which historically have inefficient space heating and no space cooling. Heat pumps provide these residences with more energy-efficient space heating in the winter and space cooling for increasingly warm summer periods. For many of these applications, heat pumps add a new electrical load at the residence, compelling regional utilities to seek to understand their impacts on the electrical grid and opportunities for load management. In coordination with regional utilities, this study examined the winter load-shifting potential of residential multi-split heat pumps with variable capacity technology. The study included nine residential sites with a total of 20 ductless indoor units, which underwent simulated smart grid control. Grid control functionality was provided through the CTA-2045 communication protocol and included offsetting temperature setpoints for individual indoor units in the study. Field data collection included whole-house and heating, ventilation, and air conditioning electrical power consumption; indoor zone temperatures; occupant surveys; and CTA-2045 curtailment data for both baseline and simulated load-shifting events by site. This paper provides an overview of the load-shifting strategies and aggregated results from winter data collection.

ASHRAE, Heat pump, energy effciency, residential b↗
DOE OSTI2025

Short-Term Electric Load Forecasting for a Residential Household in Alaska

Accurate short-term load forecasting at a fine scale is essential for demand response programs, peak shaving, and load-shedding strategies [1]. While traditionally, only aggregate short-term consumption data was available, advanced metering infrastructure (AMI) now provides data at the individual consumer level [1]. There is increasing interest in utilizing this data for short-term load forecasting (from an hour to a few days) to optimize grid operations. Electricity consumption in individual households is highly influenced by residents’ personal behaviors [2]. As a result, unlike aggregate loads, electrical power usage in single households often shows significant volatility, making meter-level load forecasting for individual users particularly challenging [3], [4]. Deep learning methods, with their strong ability to model nonlinear data, have become popular for improving the accuracy of household electricity consumption forecasting [4]. Notably, the Long ShortTerm Memory (LSTM) has attracted significant attention [5], [6].

42 ENGINEERING↗
DOE OSTI2026

Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints: Preprint

Timely and effective load shedding in power systems is critical for maintaining supply-demand balance and preventing cascading blackouts. To eliminate load shedding bias against specific regions in the system, optimization-based methods are uniquely positioned to help balance between economic and fairness considerations. However, the resulting optimization problem involves complex constraints, which can be time-consuming to solve and thus cannot meet the real-time requirements of load shedding. To tackle this challenge, in this paper we present an efficient machine learning algorithm to enable millisecond-level computation for the optimization-based load shedding problem. Numerical studies on both a 3-bus toy example and a realistic RTS-GMLC system have demonstrated the validity and efficiency of the proposed algorithm for delivering fairness-aware and real-time load shedding decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Plug Load Management System Field Study

In 2018, NREL performed a demonstration of the IBIS InteliNetwork(TM) plug load management system, which includes: intelligent socket devices, which plug into existing electrical outlets and collect energy usage information; a gateway, which manages communication between the intelligent sockets and the PLM cloud service; and a PLM network, which is a cloud-based measurement and control network for the entire system. Two retail stores served as the field study sites. The PLM system was found to be effective at reducing plug load energy use, with a projected annual plug load reduction of 11% and 18% at the test locations. Retail staff reported that the hardware was easy to use, but no one used the online data management system, intended to analyze and control the hardware. This PLM solution shows promise as an effective means to monitor, control, and reduce plug load energy use, but the economics of the system need to be improved.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A parallel p ‐adaptive discontinuous Galerkin method for the Euler equations with dynamic load‐balancing on tetrahedral grids

Abstract A novel p ‐adaptive discontinuous Galerkin (DG) method has been developed to solve the Euler equations on three‐dimensional tetrahedral grids. Hierarchical orthogonal basis functions are adopted for the DG spatial discretization while a third order TVD Runge‐Kutta method is used for the time integration. A vertex‐based limiter is applied to the numerical solution in order to eliminate oscillations in the high order method. An error indicator constructed from the solution of order and is used to adapt degrees of freedom in each computational element, which remarkably reduces the computational cost while still maintaining an accurate solution. The developed method is implemented with under the Charm++ parallel computing framework. Charm++ is a parallel computing framework that includes various load‐balancing strategies. Implementing the numerical solver under Charm++ system provides us with access to a suite of dynamic load balancing strategies. This can be efficiently used to alleviate the load imbalances created by p ‐adaptation. A number of numerical experiments are performed to demonstrate both the numerical accuracy and parallel performance of the developed p ‐adaptive DG method. It is observed that the unbalanced load distribution caused by the parallel p ‐adaptive DG method can be alleviated by the dynamic load balancing from Charm++ system. Due to this, high performance gain can be achieved. For the testcases studied in the current work, the parallel performance gain ranged from 1.5× to 3.7×. Therefore, the developed p ‐adaptive DG method can significantly reduce the total simulation time in comparison to the standard DG method without p ‐adaptation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Analytic analysis of load alignment for coning extreme‐scale rotors

Abstract Extreme‐scale wind turbines (rated powers greater than 10 MW) with large rotor diameters and conventional upwind designs must resist extreme downwind and gravity loads. This can lead to significant structural design challenges and high blade masses that can impede the reduction of levelized cost of wind energy. Herein, the theoretical basis for downwind load alignment is developed. This alignment can be addressed with active downwind coning to reduce/eliminate flapwise bending loads by balancing the transverse components of thrust, centrifugal, and gravitational force. Equations are developed herein that estimates the optimal coning angle that reduces flapwise loads by a specified amount. This analysis is then applied to a 13.2‐MW scale with 100‐m‐level wind turbine blades, where it is found that a load alignment coning schedule can substantially reduce the root flapwise bending moments. This moment reduction in this example can allow the rotor mass to be decreased significantly when compared with a conventional upwind three‐bladed rotor while maintaining structural performance and annual energy output.

Noyes, Carlos↗
DOE OSTI2019

3D pore analysis of gasoline particulate filters using X-ray tomography: impact of coating and ash loading

Since particulate emissions control technologies are dependent on filtration technologies, development of porous materials with optimized pore structures is crucial to improve filtration efficiency and pressure drop across filters. Despite increasing attention to 3D measurements of porous materials, there are few reports of pore structure investigations of diesel/gasoline particulate filters (D/GPFs) using 3D visualization techniques. Here, GPF 3D pore structures were examined using X-ray tomography (XRT) to identify the impacts of catalyst coating or ash loading. Voxel resolution of 2.93 mu m made it impossible to distinguish coating or ash materials from the cordierite substrate or to recognize smaller pores than the voxel resolution. However, pores up to 200 mu m, which are responsible for the most pore volume, were successfully analyzed. Coating and ash loading resulted in lowering average pore diameter, total porosity, and open porosity, as the peak density of pore diameter at 60 mu m decreased, while pores below 20 mu m increased. Also, the visualized closed pores, which were homogeneously distributed throughout the bare filter, tended to get larger from inlet to outlet sides and more likely to be on the surface inlet due to coating and ash loading, indicating gas pathways originally existing in open pores were blocked due to coating and ash loading, leading to increased pressure drop. The ash impact was found to be more noticeable on mid and back positions than on front position. In addition, the investigation of areal porosity along the direction of gas flow suggested that while ash penetration could reach the outlet side as noted from increased closed pore population, most ash particles would be contained up to 150 mu m. Lastly, the 2D crosscut microscopic analysis that requires destructive procedures with the limited examination area could provide underestimation of ash penetration, whereas the 3D XRT analysis seems to provide more accurate information of pore structure changes due to ash loading at different locations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Fatigue of Composite Materials Subjected to Variable Loadings

A model describing the fatigue behavior of composite materials under variable loadings is presented. The approach is based on a two-parameter model fulfilling the principal responses’ features of composite materials subjected to constant amplitude cyclic loadings. It is shown that when coupled with the equivalent residual strength assumption the model can describe the strength degradation of composite materials subjected to variable amplitude loadings without simplifications or substantial reductions of the loading spectrum. Compared to the linear Miner’s rule, the results highlight the importance of the sequence of loads and the approach discriminates the ascending, descending and random patterns. It is shown that for the case under study the ordered descending (high to low) sequence showed the worst scenario in terms of damage accumulation extent, while the random spectrum resulted in an intermediate response between ordered ascending and descending sequences.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Real-Time In Situ Neutron Diffraction Investigation of Phase-Specific Load Sharing in a Cold-Rolled TRIP Sheet Steel

Real-time in situ neutron diffraction was used to investigate the loading-direction dependence of phase-specific load sharing in a cold-rolled TRIP 780 sheet steel under tension at room temperature. A slightly lower yield stress and relatively faster initial strain hardening were observed for the tension along the rolling direction (RD), than those along the transverse direction (TD) and the diagonal direction (DD). In all three loading directions, the redistribution of inter-phase and inter-granular load sharing occurs upon the yields of the BCC matrix and the FCC phase. The FCC phase is found to yield earlier along the RD than those along the TD and DD. Based on the Hooke’s law and the rule of mixture, it is found that the lattice strains of BCC-(211)/FCC-(311) reflections via single peak fit are better representative of phase elastic strains than the strains calculated via the lattice parameters by Rietveld refinement. The quantitative evolution of phase-specific stress indicates a rapid strain hardening in the FCC phase after yielding during the RD tension. Possible mechanisms of the direction-dependent phase-specific load sharing are discussed in association with the different initial textures in the RD, DD and TD.

Yu, Dunji↗
DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

A phase model approach for thermostatically controlled load demand response

A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we present an ab initio phase model for general TCLs, and use the concept to develop a continuous oscillator model of a TCL and compute its phase response to changes in temperature and applied power. This yields a simple control system model that can be used to evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. We demonstrate this concept by comparing simulations of ensembles of heterogeneous loads using the continuous state model and an established hybrid state model. The developed phase model approach is a novel means of evaluating DR provision using TCLs, and is instrumental in estimating the capacity of ancillary services or DR on different time scales. We further propose a novel phase response based open-loop control policy that effectively modulates the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. This is demonstrated by low-error tracking of a regulation signal by filtering it into frequency bands and using TCL sub-ensembles with duty cycles in corresponding ranges. As a result, control policies that can maintain a uniform distribution of power consumption by aggregated heterogeneous loads will enable distribution system management (DSM) approaches that maintain stability as well as power quality, and further allow more integration of renewable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Cystathionine gamma lyase-H2S contributes to osteoclastogenesis during bone remodeling induced by mechanical loading

Highlights: • This study investigated the role of H{sub 2}S and osteoclastogenesis in bone remodeling induced by mechanical loading in mice. • CSE-H{sub 2}S was a dominant H{sub 2}S generating system in osteoclasts, while CBS did not generate H{sub 2}S. • In CSE-deficient mice, osteoclastogenesis and tooth movement were inhibited, which could be rescued by a H{sub 2}S donor. Hydrogen sulfide (H{sub 2}S), a gaseous signaling molecule produced by cystathionine γ-lyase (CSE) and cystathionine β-synthase (CBS), influences bone remodeling in many ways. Osteoclasts play an important role in bone remodeling during tooth movement induced by mechanical loading, which increases the rate of tooth movement. Recently, we found that osteoclasts could produce H{sub 2}S. However, whether H{sub 2}S modulates bone remodeling by affecting osteoclasts remains unclear. In this study, we found that CSE-H{sub 2}S was a dominant H{sub 2}S generating system in osteoclasts, while CBS did not generate H{sub 2}S. A significant increase in CSE mRNA expression and H{sub 2}S production was observed in periodontal ligament (PDL) tissues of wild-type (WT) mice after 3 days of mechanical loading. CSE gene knockout led to a significant reduction in the number of maxillary osteoclasts and in the amount of tooth movement. The number of RANKL-induced TRAP-positive osteoclasts and the mRNA expression of osteoclast markers were downregulated after 6 days of incubation in monocytes extracted from CSE{sup −/−} mice. The expression of IL-1, IL-6 and TNF-α, which can stimulate osteoclastogenesis in periodontal tissue and serum samples, was lower in CSE{sup −/−} mice after mechanical loading. Application of the H{sub 2}S donor GYY4137 increased the number of RANKL-induced osteoclasts, the number of osteoclasts in periodontal tissues and tooth movement distance in CSE{sup −/−} mice. The results suggested that endogenous H{sub 2}S and CSE play vital roles in the osteoclastogenesis and alveolar bone resorption induced by mechanical loading.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Effect of Carbon and Binder on High Sulfur Loading Electrode for Li-S Battery Technology

For the Lithium-Sulfur (Li-S) battery to be competitive in commercialization, it is requested that the sulfur electrode must have deliverable areal capacity > 8 mAh cm -2 , which corresponds to a sulfur loading > 6 mg cm -2 . At this relatively high sulfur loading, we evaluated the impact of binder and carbon type on the mechanical integrity and the electrochemical properties of sulfur electrodes. We identified hydroxypropyl cellulose (HPC) as a new binder for the sulfur electrode because it offers better adhesion between the electrode and the aluminum current collector than the commonly used polyvinylidene fluoride (PVDF) binder. In combination with the binder study, multiple types of carbon with high specific surface area were evaluated as sulfur hosts for high loading sulfur electrodes. A commercial microporous carbon derived from wood with high pore volume showed the best performance. An electrode with sulfur loading up to 10 mg cm -2 was achieved with the optimized recipe. Based on systematic electrochemical studies, the soluble polysulfide to insoluble Li 2 S 2 /Li 2 S conversion was identified to be the major barrier for high loading sulfur electrodes to achieve high sulfur utilization.

25 ENERGY STORAGE↗
DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗
DOE OSTI2023

Intensive and extensive margins of the peak load: Measuring adaptation with mixed frequency panel data

In this work we investigate the response of daily electricity peak load to daily maximum temperatures across states in Europe and India. We propose a method that decomposes short- from medium/long-run effects, retains the high frequency nature of the load-weather covariation and treats economic growth as a modulating factor. By simultaneously exploiting variation in unexpected daily weather anomalies and decade-long climatic changes in each location we decompose transitory - intensive margin - adjustments from permanent - extensive margin - adjustments. We find that the shocks over the long-run differ substantially from the short-run dynamics. Furthermore, we find evidence that per capita income modulates the adjustments over the short- and long-run. We project that in response to climate change around 2050 the peak load may increase by up to 20%-30% in Southern Europe and in several states in India, depending on the degree of warming and the evolution of socio-economic conditions. Even with a limited scope to two world regions, we identify that the structure of the economy and differences in future income growth matter in shaping the adaptation to climate change. Our decomposition allows to identify how future weather anomalies can further amplify the relative increase associated to the shift in the climate norm. Assuming that the interannual variability of maximum temperatures follows the distribution observed in the past, we find a doubling of the impacts of climate change during the summer in Europe. Uncertainty around the distribution of future weather anomalies may lead to further unexpected peak load amplifications. Our results have important policy implications for power systems’ generation capacity, transmission and storage, as we show that the challenges to accommodate the peak load in days with extreme temperatures may substantially increase already around mid-century.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗