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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
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 OSTI2025

Enhancing Smart Home Privacy: A Tutorial on Local Differential Privacy Techniques for Frequency and Mean Estimation

The ubiquity of Internet of Things (IoT) systems has seamlessly integrated into our daily lives, particularly in smart homes where devices continuously monitor and optimize our living environments. These systems significantly contribute to home automation, energy efficiency, and overall comfort. However, this widespread connectivity poses inherent risks linked to the streaming of sensitive household data, necessitating robust privacy preservation mechanisms. This tutorial systematically examines privacy preservation through local differential privacy (LDP), with a particular focus on frequency and mean estimation techniques for smart home applications. Here, we present a comprehensive taxonomy of smart home data formats and provide detailed implementation guidance for event-based and w-event LDP mechanisms. Through practical examples using smart thermostats and HVAC systems, we demonstrate how these techniques can be effectively deployed in real-world scenarios. The tutorial concludes by examining emerging research directions, including adaptive privacy budgets and federated learning approaches, establishing a foundation for privacy-preserving smart home deployments.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2015

A Distributed Cooperative Power Allocation Method for Campus Buildings

We propose a coordination algorithm for cooperative power allocation among a collection of commercial buildings within a campus. We introduced thermal and power models of a typical commercial building Heating, Ventilation, and Air Conditioning (HVAC) system, and utilize model predictive control to characterize their power flexibility. The power allocation problem is formulated as a cooperative game using the Nash Bargaining Solution (NBS) concept, in which buildings collectively maximize the product of their utilities subject to their local flexibility constraints and a total power limit set by the campus coordinator. To solve the optimal allocation problem, a distributed protocol is designed using dual decomposition of the Nash bargaining problem. Numerical simulations are performed to demonstrate the efficacy of our proposed allocation method

Hao, He↗
DOE OSTI2020

Stochastic Home Energy Management Systems with Varying Controllable Resources

This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2014

Aggregated Residential Load Modeling Using Dynamic Bayesian Networks

Abstract—It is already obvious that the future power grid will have to address higher demand for power and energy, and to incorporate renewable resources of different energy generation patterns. Demand response (DR) schemes could successfully be used to manage and balance power supply and demand under operating conditions of the future power grid. To achieve that, more advanced tools for DR management of operations and planning are necessary that can estimate the available capacity from DR resources. In this research, a Dynamic Bayesian Network (DBN) is derived, trained, and tested that can model aggregated load of Heating, Ventilation, and Air Conditioning (HVAC) systems. DBNs can provide flexible and powerful tools for both operations and planing, due to their unique analytical capabilities. The DBN model accuracy and flexibility of use is demonstrated by testing the model under different operational scenarios.

Bayesian↗
DOE OSTI2014

Co-Simulation of Detailed Whole Building with the Power System to Study Smart Grid Applications

Modernization of the power system in a way that ensures a sustainable energy system is arguably one of the most pressing concerns of our time. Buildings are important components in the power system. First, they are the main consumers of electricity and secondly, they do not have constant energy demand. Conventionally, electricity has been difficult to store and should be consumed as it is generated. Therefore, maintaining the demand and supply is critical in the power system. However, to reduce the complexity of power models, buildings (i.e., end-use loads) are traditionally modeled and represented as aggregated “dumb” nodes in the power system. This means we lack effective detailed whole building energy models that can support requirements and emerging technologies of the smart power grid. To gain greater insight into the relationship between building energy demand and power system performance, it is important to constitute a co-simulation framework to support detailed building energy modeling and simulation within the power system to study capabilities promised by the modern power grid. This paper discusses ongoing work at Pacific Northwest National Laboratory and presents underlying tools and framework needed to enable co-simulation of building, building energy systems and their control in the power system to study applications such as demand response, grid-based HVAC control, and deployment of buildings for ancillary services. The optimal goal is to develop an integrated modeling and simulation platform that is flexible, reusable, and scalable. Results of this work will contribute to future building and power system studies, especially those related to the integrated ‘smart grid’. Results are also expected to advance power resiliency and local (micro) scale grid studies where several building and renewable energy systems transact energy directly. This paper also reviews some applications that can be supported and studied using the framework introduced to understand their implications before they can be successfully implemented in the power system.

Makhmalbaf, Atefe↗
DOE OSTI2022

Resilience Metrics for Building-Level Electrical Distribution Systems with Energy Storage

The energy system infrastructure that delivers power to a building's loads needs to be resilient to withstand and recover from extreme outages (e.g., grid faults that leave millions of people without power during severe weather events). Building-level electrical distribution systems (BEDSs) distribute power from a building's energy sources - including the grid, solar photovoltaic (PV) panels, and batteries - to its loads, including lighting, HVAC, and plug loads. BEDS with storage can provide resilience by distributing local electricity supply to critical loads during an outage. Quantitative metrics are needed to assess the resilience improvements associated with new BEDS and storage system technologies. In this paper, we apply an existing metric, the probability of outage survival curve (POSC), to BEDS with storage and propose novel metrics that improve upon POSC. Through a simulation-based case study, we demonstrate how these metrics are impacted by the BEDS design and how they can be used to design a resilient system.

buildings↗
DOE OSTI2018

PhaseChange Material Melting in an Energy Storage Module for a Micro Environmental ControlSystem

An experimentally validated finite element model (FEM) was developed to analyze the design parameters of a latent heat storage device (LHSD) for a micro environmental control system ( μ X). The μ X provides local cooling to an office worker in a room whose thermostat setpoint has been elevated from 23.9 °C (75 °F) to 26.1 °C (79 °F) in order to reduce heating, ventilation, and air conditioning (HVAC) energy consumption. For this application, the LHSD is designed to provide ≥50 W of cooling for a full, 8.5 h workday to restore thermal comfort in the warm, 26.1 °C room. The LHSD comprises several parallel slabs of encased phase change material (PCM) with interposed airflow channels. The airflow rate is selected to obtain ≥50 W of cooling at the end of the 8.5 h operation. The LHSD exhibits a decreasing cooling rate over the 8.5 h period when a constant airflow is passed through it, indicating that more cooling is supplied during the day than the minimum 50 W required for thermal comfort. The parametric analysis explores the effects of PCM thermal conductivity, slab thickness, air channel width, and number of slabs on LHSD performance. Parametric cases are compared against each other on the basis of their required PCM mass and energy consumption.

Engineering↗
DOE OSTI2020

Experimental Analysis of Salt Hydrate Latent Heat Thermal Energy Storage System With Porous Aluminum Fabric and Salt Hydrate as Phase Change Material With Enhanced Stability and Supercooling

Phase change materials (PCMs), especially salt hydrates possess high volumetric energy storage capacity in their transition temperature range. These materials are used in applications where it is necessary to store thermal energy due to temporary load shift between demand and availability. Thus, possible applications are HVAC, recovery of waste heat, and defense thermal management. Despite salt hydrates potential, the practical feasibility of latent heat storage with salt hydrates is limited due to low power rating, supercooling, phase segregation, and long-term stability. Its low power rating and long-term stability limits its application in most applications. This work experimentally validates the stability and thermal performance of a compact heat exchanger charged with salt hydrate during melting and freezing. The compact heat exchanger was designed with fins on both the heat transfer fluid (HTF) and salt hydrate PCM side. The thermal performance of the latent heat thermal energy storage system (LHTESS) was evaluated for various operating conditions. The results show that LHTESS could achieve an average heat transfer coefficient of 124 and 87 W/(m2 K) during melting and solidification, respectively. The stability of the system in suppressing supercooling was validated over 800 cycles with nucleating agent and active homogenous nucleation techniques. The supercooling was reduced to 3 °C with zinc hydroxyl nitrate as nucleating agent and less than 1 °C with the active homogenous nucleation technique. The LHTESS showed less than 6% degradation in energy storage capacity over 800 cycles.

42 ENGINEERING↗
DOE OSTI2020

Analysis of Energy Savings and CO2 Emission Reduction Contribution for Industrial Facilities in USA

Abstract Energy audits can identify energy consumptions, energy costs of the facility and evolve to develop measures to maximize efficiency, optimize supply energy, and eliminate waste. This paper investigates the potential energy savings at 20 different industrial sectors with 152 assessments for various facilities in Wisconsin, USA. On average, eight energy recommendations were suggested and applied in each facility. This paper provides a detailed guideline for each industry in terms of eight different energy categories: heating, ventilation, and air conditioning (HVAC) systems, heat recovery systems, electrical demand management, and utility bills, motors, compressors, waste management, and productivity enhancement, lighting, besides building envelope. In total, the energy savings were as follows: 98 million kWh in the shape of electricity, 561 billion British thermal units (BTUs) gas savings, 44 million gallons water savings, and 2-million-pound solid waste savings. Based on these savings, a 100-thousand-ton reduction in carbon dioxide emissions was obtained.

Energy & Fuels↗
DOE OSTI2021

Potential of Utilizing Thermal Energy Storage Integrated Ground Source Heat Pump System to Reshape Electricity Demand in the United States

Each year, more than 20% of electricity generated in the United States is consumed for meeting the thermal demands (e.g., space cooling, space heating, and water heating) in residential and commercial buildings. Integrating thermal energy storage (TES) with building’s HVAC systems has the potential to reshape the electric load profile of the building and mitigate the mismatch between the renewable generation and the demand of buildings. A novel ground source heat pump (GSHP) system integrated with underground thermal energy storage (UTES) has been proposed to level the electric demand of buildings while still satisfying their thermal demands. This study assessed the potential impacts of the proposed system with a bottom-up approach. The impacts on the electricity demand in various electricity markets were quantified. The results show that, within the capacity of the existing electric grids, the maximum penetration rate of the proposed system in different wholesale markets could range from 51% to 100%. Altogether, about 46 million single-family detached houses can be retrofitted into the proposed system without increasing the annual peak demand of the corresponding markets. By implementing the proposed system at its maximum penetration rate, the grid-level summer peak demand can be reduced by 9.1% to 18.2%. Meanwhile, at the grid level, the annual electricity consumption would change by –12% to 2%. The nationwide total electricity consumption would be reduced by 9%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Energy Impact of Radiative Cooling Paints in Warehouses Under Various United States Climates

Although radiative cooling research is widely found in the literature, no comprehensive study has yet been conducted on the impact of novel radiant cooling (>0.91 reflectance) on the energy efficiency of warehouses. Here, in this work, we develop three building models based on a Department of Energy prototype warehouse model using trnsys, representing a typical warehouse with a black roof, a typical warehouse with a white roof, and a warehouse with novel radiative cooling (RC) paint on its roof. These models are run for 15 different cities, each representative of a different ASHRAE climate zone, to better understand the impact of RC in many different climates. It was found that an RC-coated roof in a warehouse could reduce the building's annual heating, ventilation, and air conditioning (HVAC) loads by up to 14.11 kWh/m 2 of the roof area compared to a black roof, resulting in a maximum reduction in energy costs of 0.55 $\$$/m 2 or $\$$2646/year for a large 4835 m 2 warehouse. Similarly, replacing the typical white roof coating with an RC coating could reduce the warehouse's energy consumption by up to 8.17 kWh/ m 2 of roof area, thus reducing energy costs by as much as 0.29 $\$$/m 2 or $\$$1386/year for a 4835 m 2 warehouse. In addition, applying RC paint to an unconditioned warehouse could reduce the building's ASHRAE Standard 55 indoor temperature exceedance by up to 1330 h/year compared to a black roof and up to 532 h/year compared to a white roof.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

UNDERSTANDING FLOW OF ENERGY IN BUILDINGS USING MODAL ANALYSIS METHODOLOGY

It is widely understood that energy storage is the key to integrating variable generators into the grid. It has been proposed that the thermal mass of buildings could be used as a distributed energy storage solution and several researchers are making headway in this problem. However, the inability to easily determine the magnitude of the building’s effective thermal mass, and how the heating ventilation and air conditioning (HVAC) system exchanges thermal energy with it, is a significant challenge to designing systems which utilize this storage mechanism. In this paper we adapt modal analysis methods used in mechanical structures to identify the primary modes of energy transfer among thermal masses in a building. The paper describes the technique using data from an idealized building model. The approach is successfully applied to actual temperature data from a commercial building in downtown Boise, Idaho.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

UrbanScaping: Community Spatial Data Visualization & Analytics

Evaluating the electrification potential of buildings through retrofitting is crucial for reducing carbon emissions and the carbon footprint of built environments. This study leverages the Automatic Building Energy Modeling (AutoBEM) software, integrating the Model America database to create an urban context-based spatial analysis platform for community engagement and development. We selected Camp Hill Borough, PA, as a case study to analyze building-specific energy performance and evaluate the electrification potential of each building by switching to different Heating, Ventilation, and Air Conditioning (HVAC) systems and measurement components. The simulation results generated by the workflow provide retrofitting suggestions to help mitigate the carbon footprint as well as energy saving statistics of buildings. Additionally, the developed web-based interface serves as a community engagement platform, allowing residents to provide feedback and further develop interactive communication protocols. The outcomes of this project offer a baseline for community electrification planning and contribute to the design of low-carbon communities.

Chowdhury, Shovan [ORNL]↗
DOE OSTI2022

EXPERIMENTAL AND MODELLING ANALYSIS OF A LARGE-SCALE TWO-PHASE LOOP THERMOSYPHON

Liquid pumping requires large quantities of electrical energy, including about 7% of the energy of building heating, ventilation, and air conditioning (HVAC) systems. To replace pumped condenser-cooling tower water loops with a passive alternative system, we implemented a commercial-scale two-phase loop thermosyphon (TPLT). The unit consists of a 13 m riser integrated with a commercially available cooling tower and circulation water heater that simulates heat loads up to 25 kW. In addition to providing passive cooling capabilities, the cooling tower unit is also maintenance free, reliable, and can operate in both dry and wet modes. This study characterizes the performance (temperature difference between the evaporator and condenser) and the two-phase flow behavior of the loop under various refrigerant charges. Sight glasses installed throughout the loop are used to identify the operating flow regimes in the riser and downcomer. Over the range of operating conditions, we identified that there is an optimal refrigerant charge range for a specific heat load at which near-isothermal operation can be achieved. We further developed a model to predict the operating flow rate and gravitational height in the downcomer and compared it with the experimental data. The results show that the model agrees with the experimental data, in particular the threshold gravitational head height at which there will be subcooled liquid at the condenser exit, indicating that it can be used as a predictive tool for identifying the optimal loop charge for a given heat load.

Loop thermosyphon, passive two phase↗
DOE OSTI2023

A NUMERICAL STUDY OF REFRIGERANT LEAKAGE FROM A PROPANE-BASED REFRIGERATION SYSTEM

Low Global Warming Potential (GWP) refrigerants, e.g. Propane (R290) (GWP = 3) and R1234yf (GWP = 4), are becoming a popular choice among refrigeration and HVAC systems, such as vending machines and air conditioning. However, most of Low GWP refrigerants are flammable (ASHRAE class A2 or A3), so safety is a very important consideration when designing and deploying low GWP refrigerants-based equipment in buildings. In the event of a refrigerant leak, the flammability of low GWP refrigerants depends on the local concentration of the refrigerant within the vicinity of the leak. In addition, the low GWP refrigerants concentration is affected by the indoor air environment, such as air flow rate and temperature. In the present study, a computational fluid dynamics (CFD) model is developed to model the flow and temperature of air surrounding a propane-based refrigeration system, as well as the concentration of leaked low GWP refrigerants surrounding the refrigeration system. The model results reveal the concentration distribution of low GWP refrigerants within the building, and as a result, the flammable regions within the building can be identified. Moreover, different ventilation layouts will be tested using the model to improve the design of ventilation. The numerical model can assist in the design of ventilation systems to minimize flammable regions within buildings due to leakage of flammable refrigerants.

Zhang, Mingkan↗