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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance Evaluation of Drain Water Heat Recovery Exchangers for Heat Pump Water Heaters

Water heating constitutes approximately 18% of energy consumption and is the second largest energy expenses in United States homes. Heat pump water heaters (HPWHs) are energy efficient technologies with lower carbon footprints as compared to conventional water heating technologies, such as gas and electrical resistance heaters. The performance of water-source HPWHs can be improved by recovering heat from blackwater using drain heat recovery heat exchangers. Depending on water draw patterns of single family and multifamily residences, the operation and heat transfer performance of these heat exchangers are highly transient. This paper examines the thermo-hydraulic performance of a drain heat recovery heat exchanger during its transient and steady-state operations. The heat recovery performance of the exchanger was evaluated at different inlet temperatures and flow rates ranging from 9°C to 36°C and 0.03 kg/s to 0.28 kg/s, respectively. The obtained effectiveness varies from 45%–85% depending on the operating conditions. The test facility, steady-state and transient experimental procedures are reported in detail. Further, the effect of operating conditions on the effectiveness is discussed. The experimental approach and results of the study will provide insights into the transient operation of drain recovery heat exchangers as well as, guide sizing for various steady state conditions.

Krishnan, Easwaran↗
DOE OSTI2021

ENERGY STAR Residential Water Heater Specification and Test Method for Connected Residential Water Heaters

Electric water heaters have long been used as a tool for demand response due to their inherent thermal storage and large electric load. Traditionally, electric water heaters have been controlled by external load control switches, but that type of control does not work well with more efficient heat pump water heaters and cannot ensure that consumers have adequate hot water. Water heater manufacturers are adding more sophisticated controls that allow utilities or consumers to control their water heaters in new ways that can provide demand response without impacting consumer comfort. Further, the electric utility grid has seen a rapid increase the availability of renewable energy, which, depending on the type of renewable energy, can provide large amounts of energy for a portion of the day, but reduced or no energy at other times. In response to these advances in controls, availability of periodic renewable energy, and in an effort to have a standard which can be applied at a national level, ENERGY STAR and the Department of Energy have developed a Product Specification and Test Method for Connected Residential Water Heaters. In this session, we will discuss the new specification and test method, as well as show some initial results from running the new test method with two different HPWHs at NREL.

connected water heater↗
DOE OSTI2021

Double Deep Q-Networks for Optimizing Electricity Cost of a Water Heater

Electric water heaters represent 14% of the electricity consumption in the residential buildings and the cost associated with domestic water heating account for a good portion of the household expenses in the United States. In this context, intelligent control of water heaters gained a lot of research attention. In recent years, a significant number of intelligent water heater controllers, with various methods and intended uses, have been proposed. However, existing studies are mostly model-based approaches that require an accurate modelling of the water heater. Towards addressing this research gap, this paper presents a model-free reinforcement learning-based controller for a day-ahead price market. The controller aims to minimize the cost of domestic water heating while maintaining the user comfort. The results showed that the developed controller can help save energy cost while maintaining the temperatures within the desired comfort band.

Amasyali, Kadir↗
DOE OSTI2023

Experimental Evidence on Latency in a Fleet of Controllable Water Heaters

Demand response is an important emerging part of smart grids with wide coverage in theoretical and modeling research. However, experimental evidence on the real-life behavior of controllable loads is still limited. We present observations regarding latency and communication aspects of the operation on a fleet of residential water heaters in a smart neighborhood in Atlanta, GA. Our analysis shows that latency in water heaters is not constant and does not follow a Gaussian distribution. We also find that there is a systematic relationship between latency and hour of the day. Latency was found to increase during morning and evening hours compared to the afternoon. These findings could help better plan deployment of control for demand response programs. Understanding delays associated with controlling smart devices is crucial for proper design and algorithm development for optimization, frequency of dispatch, and override detection.

communication delay↗
DOE OSTI2023

The Empirical Effect of Fleet Optimization on Synchronization and Rebound Effects in Heat Pump Water Heaters

Demand response is a growing concept in light of the internet of things and an increasing need for grid flexibility. Water heaters are one of the preferred devices for providing demand response for grid services and peak management due to their capability to store energy. The efficient use of water heaters for demand response requires consideration of the associated load effects such as synchronization of device schedules and rebound effect. These effects present a significant challenge. Despite the importance of the mentioned effects for water heater queuing and scheduling, there has been no effort to quantify and empirically validate their impact. This study attempts to address this gap by offering two methods - Ward clustering and Euclidean K-means - to evaluate the extent of synchronization in a fleet of 42 water heaters in Atlanta, GA. Using the aforementioned methods on the measured data, we find evidence of convergence of water heater loads as a result of optimization compared to an idle period and analyzed their impact.

demand response↗
DOE OSTI2023

Using Synchronization as an Indicator of Controllability in a Fleet of Water Heaters

Peak reduction is an important concern that can help reduce the growing stress on distribution grid and allow to defer investments in new capacity. However, the growing concern for customer privacy and comfort may impact the performance of load control for residential devices. Water heaters represent a convenient way of reducing peak due to their ability to store thermal energy for future use. In this paper, we developed a methodology to help utilities gain more insight with respect to the impact of load control efforts for shaving peak with no necessary information about the water heaters except the device status (on/off). To this end, we use a fleet of water heaters in a controlled residential neighborhood in Atlanta, GA. Our findings show that convergence in device status can serve as a proxy for peak shifting during hours of the evening peak.

demand response↗
DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗
DOE OSTI2023

Peak Reduction Using Mode Adjustment of Heat Pump Water Heaters in a Residential Neighborhood

Building electrification is putting pressure on distribution grid worldwide. Peak reduction is an important concern that can help reduce the growing stress and allow to defer investments in new capacity. Water heaters represent a convenient way of reducing peak because they are less dependent on weather, and their storage volume allows for asynchronous water heating and hot water use. Previous empirical studies investigated the ability of water heaters to reduce peak through the adjustment of the temperature setpoint. However, not all equipment vendors offer this option. This study aims at understanding the feasibility of peak reduction with an alternative configuration available in the market - by adjusting the device mode rather than temperature setpoint. The peak reduction methodology is tested in an occupied 46-townhome neighborhood located in Atlanta, GA. We find that peak shifting is possible with the adjustable mode approach, with the change in the peak load by 30-60%.

demand response↗
NASA NTRS1995

Preheating Water In The Covers Of Solar Water Heaters

Solar water heaters that include glass covers over absorber plates redesigned to increase efficiencies according to proposal. Redesign includes modification of single-layer glass cover into double-layer glass cover and addition of plumbing so cool water to be heated made to flow between layers of cover before entering absorber plate.

Bhandari, Pradeep↗
DOE OSTI2017

Development and validation of quasi-steady-state heat pump water heater model having stratified water tank and wrapped-tank condenser

Heat pump water heater systems (HPWH) introduce new challenges for design and modeling tools, because they require vapor compression system balanced with a water storage tank. In addition, a wrapped-tank condenser coil has strong coupling with a stratified water tank, which leads HPWH simulation to a transient process. To tackle these challenges and deliver an effective, hardware-based HPWH equipment design tool, a quasi-steady-state HPWH model was developed based on the DOE/ORNL Heat Pump Design Model (HPDM). Two new component models were added via this study. One is a one-dimensional stratified water tank model, an improvement to the open-source EnergyPlus water tank model, by introducing a calibration factor to account for bulk mixing effect due to water draws, circulations, etc. The other is a wrapped-tank condenser coil model, using a segment-to-segment modeling approach. In conclusion, the HPWH system model was validated against available experimental data. After that, the model was used for parametric simulations to determine the effects of various design factors.

42 ENGINEERING↗
DOE OSTI2021

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Simple Retrofit High-Efficiency Natural Gas Water Heater Field Test

High performance water heaters are typically more time consuming and costly to install in retrofit applications, making high performance water heaters difficult to justify economically. However, recent advancements in high performance water heaters have targeted the retrofit market, simplifying installations and reducing costs. Four high efficiency natural gas water heaters designed specifically for retrofit applications were installed in single-family homes along with detailed monitoring systems to characterize their savings potential, their installed efficiencies, and their ability to meet household demands. The water heaters tested for this project were designed to improve the cost-effectiveness and increase market penetration of high efficiency water heaters in the residential retrofit market. The retrofit high efficiency water heaters achieved their goal of reducing costs, maintaining savings potential and installed efficiency of other high efficiency water heaters, and meeting the necessary capacity in order to improve cost-effectiveness. However, the improvements were not sufficient to achieve simple paybacks of less than ten years for the incremental cost compared to a minimum efficiency heater. Significant changes would be necessary to reduce the simple payback to six years or less. Annual energy savings in the range of $200 would also reduce paybacks to less than six years. These energy savings would require either significantly higher fuel costs (greater than $1.50 per therm) or very high usage (around 120 gallons per day). For current incremental costs, the water heater efficiency would need to be similar to that of a heat pump water heater to deliver a six year payback.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗
DOE OSTI2024

Decarbonization Technology Snapshot: Heat Pump Water Heaters

Heat pump water heaters use electricity to move heat from one place to another instead of generating heat directly. In many cases they replace gas-fired water heaters and are significantly more energy efficient, reducing on-site Scope 1 emissions and often resulting in reduced total emissions.

building decarbonization↗