Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “APPLIANCES”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Evaluating the Impact of Price-Responsive Load on Power Systems Using Integrated T&D Simulation

This paper explores the differences between simulating price-responsive load (PRL) interactions with power systems using integrated transmission and distribution (T&D) models and transmission-only (T-only) models. This analysis uses the Integrated Grid Modeling System (IGMS) software to capture 'ISO-to-appliance' simulations using a synthetic T&D model built on the PJM 5-Bus transmission network with multiple full-scale taxonomy feeders that include physics-based models of thousands of customers and PRLs. The results show important differences in the impacts of PRLs between integrated T&D and T-only models. Experiments with the synthetic integrated T&D dataset demonstrated that integrated T&D simulation revealed notably larger differences between the PRL and no-PRL cases for load, and prices compared to T-only simulation. Similarly, differences are observed between the price response of individual buildings and distribution feeders and the corresponding transmission bus in the integrated T&D simulations, which are difficult to capture in traditional T-only simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

High-Frequency, Multiclass Nonintrusive Load Monitoring for Grid-Interactive Residential Buildings

Smart buildings with net-load metering and control capabilities can provide valuable flexibility to grid operators. This article develops a novel approach for high-frequency, multiclass nonintrusive load monitoring (NILM) that enables effective net-load monitoring capabilities with minimal additional equipment and cost. Relative to existing NILM work, the proposed solution operates at a faster timescale, providing accurate multiclass state predictions for each 60-Hz ac cycle without relying on event-detection techniques. The approach is validated using a test bed with residential appliances and shown to have high accuracy, good generalization properties, and sufficient response time to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2012

Semantic Information Modeling for Emerging Applications in Smart Grid

Smart Grid modernizes power grid by integrating digital and information technologies. Millions of smart meters, intelligent appliances and communication infrastructures are under deployment allowing advanced IT applications to be developed to secure and manage power grid operations. Demand response (DR) is one such emerging application to optimize electricity demand by curtailing/shifting power load when peak load occurs. Existing DR approaches are mostly based on static plans such as pricing policies and load shedding schedules. However, improvements to power management applications rely on data emanating from existing and new information sources with the growth of Smart Grid information space. In particular, dynamic DR algorithms depend on information from smart meters that report interval-based power consumption measurement, HVAC systems that monitor buildings heat and humidity, and even weather forecast services. In order for emerging Smart Grid applications to take advantage of the diverse data influx, extensible information integration is required. In this paper, we develop an integrated Smart Grid information model using Semantic Web techniques and present case studies of using semantic information for dynamic DR. We show the semantic model facilitates information integration and knowledge representation for developing the next generation Smart Grid applications.

complex event processing↗
DOE OSTI2023

AutoTG: Reinforcement Learning-Based Symbolic Optimization for AI-Assisted Power Converter Design

Power converters are pervasive in modern electronic component design. They can be found in all electronic devices from household appliances and cellphone chargers to vehicles. Currently, designing new circuit topologies is hard because it requires human expertise based on experience and is difficult to automate. However, artificial-intelligence-assisted design can significantly facilitate the development of new power converters and/or improve the final result. Intelligently designed highly efficient power converters can have a significant effect on many important attributes, such as power efficiency, layout size, cost, heat dissemination, energy requirements, etc. We propose Autonomous Topology Generator (AutoTG), a reinforcement-learning-based framework that generates power converter topology candidates based on user specifications, optimized for user preferences. By modeling power converter design as a symbolic optimization problem, we sequentially sample components in an autoregressive manner until new topologies are formed, providing both the topology specification and the sizing (magnitude of each component parameter) of the proposed power converter. Here, we provide an empirical evaluation and show that AutoTG is able to generate varied high-efficiency topologies within component restrictions based on user input and show that previously unknown topologies can be found for further evaluation.

(AI)-based design↗
DOE OSTI2018

Data-Enabled Building Energy Savings (D-E BES)

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building's physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. Herein this paper illustrates that creating an affinity between a building resident's thermal preferences and a building apartment's unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents' thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building's physical characteristics as well as the variation in residents' thermal preferences. The work further demonstrates that both the unregulated temperature and the resident's preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment's geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Unobtrusive Sensor-Based Occupancy Facing Direction Detection and Tracking Using Advanced Machine Learning Algorithms

Facing direction detection plays a critical role in human-computer interaction and has a wide application in surveillance systems, driving awareness recognition, smart home appliances, computer games, and so on. Current detection methods are mainly focused on extracting specific patterns from user's optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in a dark environment. To address these concerns, here this paper proposes an activity recognition system guided by an unobtrusive sensor (ARGUS). By using a low pixel infrared thermopile array sensor, ARGUS is capable of identifying five facing directions (left 45°/90°, right 45°/90°, and front) through the support vector machine classifier. Also two feature extraction methods are compared. One is manually-defined and the other is based on a pre-trained convolutional neural network (CNN) model. The facing direction detection accuracy resulting from manually-defined features reaches 85.3%, 90.6%, and 85.2% at detection distance of 0.6, 1.2, and 1.8 m, respectively. The level of accuracy resulted from using pre-trained CNN features demonstrates a much more reliable performance (89.1%, 95.3%, and 95.1% at distance of 0.6, 1.2, and 1.8 m, respectively). In addition, ARGUS has been successfully applied for occupancy tracking with a root mean square error of 0.19 m.

42 ENGINEERING↗
DOE OSTI2025

Online Dynamic Mode Decomposition Based System Identification of Multi-Zone Building HVAC Systems

Many works have recently been conducted to reduce the electricity consumption of smart buildings and allow them to support various grid services. Most of these works require accurate system models for the various appliances in the building including heating, ventilation, and air conditioning (HVAC) units. In this paper, we investigate a recursive data-driven system identification strategy to construct the thermal model for a time-varying building with a multi-zone HVAC unit. The online dynamic mode decomposition (DMD)-based strategy is employed to identify the multi-zone thermal building dynamics, where a simple information update (rank-1) is selected to avoid computational complexity. The DMD-based identification strategy is validated using a real gymnasium building equipped with a 4-zone HVAC unit, and its performance is compared with that of the traditional nuclear-norm subspace identification (N2SID) strategy.

Wu, Tumin [University of Tennessee, Knoxville (UTK↗
DOE OSTI2016

Enabling Smart Grid Cosimulation Studies: Rapid Design and Development of the Technologies and Controls

The 21st century electric power grid is transforming with an unprecedented increase in demand and increase in new technologies. In the United States Energy Independence and Security Act of 2007, Title XIII sets the tenets for modernizing the electricity grid through what is known as the 'Smart Grid Initiative.' This initiative calls for increased design, deployment, and integration of distributed energy resources, smart technologies and appliances, and advanced storage devices. The deployment of these new technologies requires rethinking and re-engineering the traditional boundaries between different electric power system domains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Welcome to the Special Issue on Grid-Edge Computing With Behind-the-Meter Resources [Guest Editorial]

The integration of distributed energy resources (DERs), such as solar photovoltaic systems, as well as other synergistic assets, including electric vehicles, energy storage, and smart appliances, in electric power systems has been dramatically increasing in the past few years. These assets have the capability to provide much-needed flexibility to electric power systems for improved grid reliability, resilience, and economic efficiency; however, most of these resources are located behind the meter (BTM) on customer premises, and their flexibility is not fully used in current grid operations. Grid-edge computing plays an important role in unlocking the great benefits and potential that BTM resources could provide to electric power systems by enhancing visibility and controllability at the grid edge.

behind-the-meter resources↗
DOE OSTI2011

The Potential and Limits of Residential Demand Response Control Strategies

Abstract- Demand response is playing an increasingly important role in smart grid research today. Technologies that control building equipment and appliances using signals such as real-time prices are making their way into our lives. But the behavior of load both affects and is affected by load control strategies that are designed to support the electric grid. This paper explores the natural behavior of electric load, how it is affected by various load control strategies and what the implications are for concepts such as using load control to support the integration of renewable energy resources.

Chassin, David P.↗
DOE OSTI2018

Fast Primary Frequency Response using Coordinated DER and Flexible Loads: Framework and Residential-scale Demonstration

Methods for supporting grid frequency stabilization using widespread flexible load and DER are becoming essential as the portion of synchronous generation decreases. This article develops a framework for providing fast primary frequency response that leverages deferrable loads and DER coordinated using high-speed measurements in real time to ensure that local objectives are met. The framework is demonstrated in a residential-scale experiment with four household appliances and an inverter. The effectiveness of an example load management algorithm is demonstrated using three underfrequency anomaly test cases. The experimental data corroborates that in all cases, the entire coordinated response, from detection and measurement of a frequency anomaly to completed coordinated net-load response, is completed within 143 ms (~8.5 cycles).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Hardware-in-the-Loop (HIL) Simulations for Smart Grid Impact Studies

Hardware-in-the-loop (HIL) simulations are increasingly employed in power engineering as more inverter-based generation and smart appliances are connected to the electric grid. HIL techniques allow for the co-simulation of analytical models with actual devices whose complex behavior is computationally inefficient or difficult to model. They also allow for testing the behavior of these devices under adverse conditions that rarely occur in the field but are important to evaluate. This paper provides an overview of how HIL simulations have been used to date and proposes that HIL simulations should play an important role in evaluating new control strategies, especially at the distribution level, that are being proposed to allow for continued affordable and reliable operation of the electric grid. We present a new capability that was developed to evaluate the interactions between residential loads and the smart grid: smart home hardware-in-the-loop. The paper includes results from an HIL experiment that incorporates multiple technologies and controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Stochastic Model Predictive Control for Demand Response in a Home Energy Management System

This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Design Considerations of a Coordinative Demand Charge Mitigation Strategy

This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable loads, and conservation voltage reduction. All resources are directly controlled by load serving entities. A mixed integer linear programming based energy management algorithm is developed to optimally coordinate of DCM resources considering the load payback effect. To better capture system peak periods, two different kinds of load forecast are used: the day-ahead load forecast and the peak-hour probability forecast. Five DCM strategies are compared for reconciling the discrepancy between the two forecasting results. The DCM strategies are tested using actual utility data. Simulation results show that the proposed algorithm can effectively mitigate the demand charge while preventing the system peak from being shifted to the payback hours. We also identify the diminishing return effect, which can help load serving entities optimize the size of their DCM resources.

Hu, Rongxing↗
DOE OSTI2016

Experiences Integrating Transmission and Distribution Simulations for DERs with the Integrated Grid Modeling System (IGMS)

This paper discusses the development of, approaches for, experiences with, and some results from a large-scale, high-performance-computer-based (HPC-based) co-simulation of electric power transmission and distribution systems using the Integrated Grid Modeling System (IGMS). IGMS was developed at the National Renewable Energy Laboratory (NREL) as a novel Independent System Operator (ISO)-to-appliance scale electric power system modeling platform that combines off-the-shelf tools to simultaneously model 100s to 1000s of distribution systems in co-simulation with detailed ISO markets, transmission power flows, and AGC-level reserve deployment. Lessons learned from the co-simulation architecture development are shared, along with a case study that explores the reactive power impacts of PV inverter voltage support on the bulk power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2011

Data Intensive Architecture for Scalable Cyber Analytics

Cyber analysts are tasked with the identification and mitigation of network exploits and threats. These compromises are difficult to identify due to the characteristics of cyber communication, the volume of traffic, and the duration of possible attack. In this paper, we describe a prototype implementation designed to provide cyber analysts an environment where they can interactively explore a month’s worth of cyber security data. This prototype utilized On-Line Analytical Processing (OLAP) techniques to present a data cube to the analysts. The cube provides a summary of the data, allowing trends to be easily identified as well as the ability to easily pull up the original records comprising an event of interest. The cube was built using SQL Server Analysis Services (SSAS), with the interface to the cube provided by Tableau. This software infrastructure was supported by a novel hardware architecture comprising a Netezza TwinFin® for the underlying data warehouse and a cube server with a FusionIO drive hosting the data cube. We evaluated this environment on a month’s worth of artificial, but realistic, data using multiple queries provided by our cyber analysts. As our results indicate, OLAP technology has progressed to the point where it is in a unique position to provide novel insights to cyber analysts, as long as it is supported by an appropriate data intensive architecture.

Olsen, Bryan K.↗