Residential Electric Water Heaters Research Articles

Adversarial Dynamic Load-Altering Cyberattacks Against Peak Shaving Using Residential Electric Water Heaters

Adversarial Dynamic Load-Altering Cyberattacks Against Peak Shaving Using Residential Electric Water Heaters

Peer-to-peer energy trading for demand response of residential smart electric storage water heaters

Environmental concerns and emission reduction targets are driving a transition from fossil fuel to renewable-based electricity generation. However, intermittent and distributed renewable generation brings challenges for the grid operation, as the low voltage distribution grid increasingly becomes constrained during high residential solar PV generation. The electrification of water heating presents a large opportunity to address this challenge by intelligently responding to electrical network conditions. Smart water heaters with thermal storage can soak up excess PV as a thermal battery, which facilitates maximum renewable generation and solves local grid problems. However, the centralized operation of electricity markets tends to impede the benefits of the localized distributed energy resources (DER). In such a scenario, peer-to-peer (P2P) energy trading allows neighbouring prosumers to trade energy between themselves with minimum interference from electricity grid operators. This research develops a P2P energy trading framework, using advanced, multi-zone electric storage water heaters with autonomous and aggregated control. The financial benefits identified in this research are forthcoming due to new participation in the electricity market ancillary services, demand management and P2P energy trading. Simulated results have identified that up to 92% of household water heating energy can originate from their own rooftop PV. At the electricity grid level, P2P energy trading demonstrates an increase in the aggregated PV self-consumption from 39% to 83%, importantly allowing the remaining 17% to target grid support in periods of supply shortfall. Average retail consumer energy savings of AUD$369/annum are identified, which include 17% of savings that are attributed to P2P energy transactions, delivering a capital payback of <3 years.

Design and implementation of a data-driven intelligent water heating system for an island community: A case study

Water heating accounts for approximately 25% of household energy use in developed countries. Therefore, the optimal control of water heating through the deployment of intelligent residential Electric Water Heaters (EWH) brings significant benefits. This paper presents an innovative design and implementation of an easy-to-use device for intelligent residential water heating. The device relied upon machine learning techniques to forecast a consumer’s hot water demand and optimize the operation of an EWH using a novel data collection process that relied on non-intrusive vibration data alone. The device was deployed in a six-month pilot project on the island of São Miguel, Portugal. The major difficulties were the novel use of vibration data to forecast the volume of hot water used and the uncertain behavior of the consumers. The challenges of only using vibration data were solved by careful data collection and artificial intelligence methods. To tackle the issue of uncertain consumer behavior, an innovative ‘heat now’ function was incorporated into the device to override the novel control framework. Results show that the device could accurately forecast hot water demand and optimally operate the EWH to meet this demand. The results showed an average reduction of 1.33 kWh/day per consumer, which equates to an average decrease of 35.5% in water heating costs. Calculations show that these devices can reduce the total energy used by 2832 kWh daily or 0.21% of total electricity generated. Furthermore, a fleet of these devices could reduce thermal generation by 0.37%, reducing emissions by 693.31 tons of CO2 per year. The results from the consumer survey show that the device did not affect the consumer’s comfort, validating the benefits and efficacy of the proposed device. Hence, the paper shows that a simple-to-use, novel device using an innovative forecasting algorithm based on non-intrusive vibration data brings numerous quantifiable benefits to actual consumers and electrical utilities.

Recursive training based physics-inspired neural network for electric water heater modeling

Aggregating flexibility from residential electric water heaters (EWHs) is fast gaining commercial interest. Flexibility modeling of an EWH involves highly precise and quick simulation of EWH water temperature using the EWH thermal dynamics model for various flexibility control actions. Since EWH tank water temperature data is usually unavailable or costly to obtain, developing an accurate and computationally inexpensive EWH thermal dynamics model with limited sensor data is essential for devising advanced control strategies for EWH flexibility aggregation. In this paper, we present a novel recursive training-based unsupervised physics-informed neural network (PINN) model for predicting tank water temperature which requires only historical EWH power consumption data to train the model. PINN models enable the integration of domain knowledge from traditional physical processes and methods into neural network (NN) models. Single-zone thermal grey-box differential equation model (DEM) is used as the basis to develop and demonstrate proof-of-concept of the proposed approach. Physics from the single-zone model is encoded into the PINN loss function to incorporate domain knowledge and the PINN architecture is structured to mimic the single-zone DEM. The recursive training approach enables the use of previous-step water temperature as an input to the simulation model. Two separate models for EWH ON- and OFF-states are developed and trained with real-world EWH power consumption data. Water temperature prediction results indicate that the proposed approach has similar performance as the traditional single-zone DEM model, thereby demonstrating the ability of the proposed model to learn the underlying physics behind the single-zone model without water temperature data. The proposed model has high accuracy and performs well outside the control set point temperatures indicating its suitability for simulating load shifting and other DR events. Additionally, EWH simulation results for two different scenarios with different water demand compositions are presented to study the effects of propagation errors on temperature prediction. The proposed approach paves the way for developing advanced EWH flexibility modeling tools for the aggregator to precisely control a large portfolio of EWHs considering user comfort and rebound effects.

Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption.

Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters

The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution’s efficacy in detecting load-altering attacks with high precision and low false alarm and latency.

Resource Study of Large-Scale Electric Water Heater Aggregation

Residential-scale distributed energy assets, like residential electric water heaters, individually present a negligible load to the power grid. When aggregated, however, these assets can impart significant effects within a balancing area; they may be dispatched en masse to provide grid services. An aggregation of water heaters may be controlled to assume generator-like functions with the ability to effectively “decrement power” through dispatch of load. This resource study examines the capabilities of a 10,000 unit water heater aggregation by subjecting the aggregate to dispatch requests of various size and duration, then analyzing how the aggregate responds to and recovers from these requests. Results show that a large-scale aggregation of electric water heaters may effectively decrement power on the scale of megawatts when the dispatch request size and duration are appropriately considered.

Developing and Testing a Unit-Commitment-Based Controller of Bus-Split Aggregated Residential Electric Water Heaters

This article develops and tests a controller for residential electric water heaters (EWHs). The developed controller is operated to maximize the energy stored in EWHs during off-peak-demand times, in order to reduce their power demands during peak-demand times. Desired control actions aim to adjust the minimum temperature settings of EWHs using the unit commitment (UC). In order to eliminate the need for a direct measurement of EWH power demands, the bus-split (BS) aggregation method is employed. The BS method is employed due to its ability to extract the power demands of an EWH from household power meter readings. The UC is formulated using the energy stored in a EWH as a cost function, which is to be maximized during the off-peak-demand time. The solution of the UC problem is obtained using the Lagrange relaxation method that can offer fast convergence and reduced computations. The BS-UC controller is implemented for performance testing using power meter readings that are collected from 150 residential households during the fall, winter, spring, and summer seasons. Test results demonstrate the ability of the BS-UC controller to provide accurate and effective control of EWHs, which are complimented by a minor sensitivity to the number controlled EWHs, hot water consumption, and seasonal variations in residential load power demands.

Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering

Previous studies have shown that electric water heaters (EWHs) have strong potential in demand-side management applications more precisely because they offer energy storage capability, so, can be employed as shift loads. However, the challenge of EWH curtailment strategies is to minimize the impact on the hot water availability while shaving the peak of consumption during critical periods. The success of such strategies depends highly on the knowledge of the consumption behavior of each user. Thus, appropriated modeling and consumption analysis could yield better management strategies. This study proposes an electric water heater control strategy based on the dynamic programming and power consumption profile classification. An adaptive clustering process allows recognizing the clients who contribute to the highest power consumption during the peak periods. The analysis and simulation indicate that an appropriate control on the group of users could be implemented to reduce the peak demand and to meet the hot water demand. A k-means clustering algorithm has been used for cluster analysis. The silhouette method has been applied to estimate the appropriate number of clusters.

Direct load control of thermostatically controlled loads based on sparse observations using deep reinforcement learning

This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment. Extracting a relevant set of features from these observations is a challenging task and may require substantial domain knowledge. One way to tackle this problem is to store sequences of past observations and actions in the state vector, making it high dimensional, and apply techniques from deep learning. This paper investigates the capabilities of different deep learning techniques, such as convolutional neural networks and recurrent neural networks, to extract relevant features for finding near-optimal policies for a residential heating system and electric water heater that are hindered by sparse observations. Our simulation results indicate that in this specific scenario, feeding sequences of time-series to an LSTM network, which is a specific type of recurrent neural network, achieved a higher performance than stacking these time-series in the input of a convolutional neural network or deep neural network.

Power Factor Correction in Feeders With Distributed Photovoltaics Using Residential Appliances as Virtual Batteries

Thermostatically controlled residential appliances have built-in thermodynamic storage that, even within a narrow temperature band that does not degrade the comfort level of occupants, can be used to provide a variety of value streams to power system operators and customers. In this paper, residential air conditioners and electric water heaters are used to improve feeder power factor in a distribution system where photovoltaic systems cause the feeder power factor to dip daily, increasing losses. Improving distribution feeder power factor improves the efficiency of the transmission and bulk generation systems. A daily optimal dispatch regime is used to maximize the daily minimum feeder power factor. Following this regime, electric water heaters cool off in preparation for a low-feeder-power-factor event, turn on to improve the power factor during the event and return to a neutral condition after the event. Air conditioners, which have a power factor lower than the feeder overall, are optimally dispatched in the inverse pattern. Using a model of a real commercial and residential distribution feeder in the western United States, the optimal dispatch of virtual batteries is shown to be capable of improving the daily minimum power of that feeder by as much as 0.026. The power factor correction and optimal dispatch techniques are based on a robust virtual battery framework, making them portable to other applications, such as volt-var optimization and transactive energy systems.

Aggregating the Demand Flexibility Related to Electric Hot Water Heating Under Endogenous Day-Ahead Prices

Residential sector has a large underutilized potential for demand response in its electricity consumption optimization. In this study residential electric hot water heaters (EHWH) are considered as distributed energy storage providing demand response in electricity markets. Heating optimization is conducted with a dynamic optimization model describing the water heating characteristics in Finland. Cost and CO2 emissions minimizing optimal heating controls are solved for an individual household and for a pool of EHWHs controlled by an aggregator. Water heating costs under fully dynamic and static electricity pricing contracts are compared. Our main contribution is to provide important insights of complex market dynamics related to the demand response resource aggregation, different electricity pricing contracts and increasing wind power generation. There are three main results. First, households benefit from adopting dynamic electricity pricing and allowing load control in terms of heating cost savings. Secondly, the optimal number of controlled EHWHs depend on the viewpoint. Lastly, cost minimization yields relatively good solution from environmental perspective when compared with the emissions minimization target.

Imperfection sensitivity of externally-pressurized, thin-walled, torispherical-head buckling

The bottom head in the residential electric water heater tank is an externally-pressurized, thin-walled, torispherical structure. In this paper, the buckling behavior of the bottom head was studied by experimental and finite element analysis (FEA) methods. The shapes of four randomly selected water heater tanks were measured by Computer Aided Inspection (CAI), and hydrostatic tests and strain measurements were performed. The buckling simulations of the tested tanks were carried out by finite element method, and the obtained results were in good agreement with the experimental data. Two parameters, contact imperfection ratio, Rimc, and geometry imperfection ratio, Rimg, were used to quantitatively characterize the magnitudes of the main imperfections in the water heater tank manufacturing process. FEA models, including both contact and geometry imperfections, were used to investigate the buckling pressure of the bottom head. The results show that the geometry imperfection, within the range of ASME Code Ⅷ-2 requirements, has a greater effect on the buckling pressure than the contact imperfection. The obtained curves of buckling pressure versus imperfections can provide guidance for water heater tank design and manufacture.

Buckling of thin-walled torispherical heads in water heater tanks

The buckling strength of a thin-walled torispherical head in a residential electric water heater tank was investigated by both experimental and finite element analyses. Three water heater tanks pressurized with water were tested, and the strains on the heads and the pressure variations were measured and recorded. Finite element analysis was used to predict the buckling of the torispherical head. The effect of the imperfection induced by the contact nonuniformity between the torispherical head and the shell on the buckling of the structure is included. Good agreement between the test and finite element results shows that finite element models used in this paper are viable to predict the buckling pressure of a thin-walled torispherical head in a water heater tank. The results also show that the contact between the bottom head and the shell has a reinforcing effect on the buckling strength of the head. The contact imperfection will produce a dent adjacent to the knuckle region when the head buckles. The buckling pressure of the head perfectly contacting with the shell is 6.88% higher than that without contacting with the shell. The obtained results provide reference for the design and manufacture of water heater tanks.

Increase of Acceptable Wind Power Capacity by Power Consumption Control on Residential Electric Water Heaters

Increase of Acceptable Wind Power Capacity by Power Consumption Control on Residential Electric Water Heaters

Smith (A.O.) Corp

AbstractElectrical (MIC: SIC: 3621 NAIC: 335312)A.O. Smith is engaged as a manufacturer of water heating equipment and electric motors, serving a range of residential, commercial and industrial end markets principally in the U.S. As of Dec 31 2007, Co. conducted its businesses through two operating segments. In detail, Co.'s Water Products business manufactures and markets a variety of residential gas and electric water heaters, standard and specialty commercial water heating equipment, copper‐tube boilers, and water systems tanks. In addition, Co.'s Electrical Products business manufactures and markets a range of hermetic motors, fractional horsepower alternating current and direct current motors.

Smith (A.O.) Corp

AbstractElectrical (MIC: SIC: 3621 NAIC: 335312) A. O. Smith is engaged as a manufacturer of water heating equipment and electric motors, serving a range of residential, commercial and industrial end markets principally in the U.S. As of Dec 31 2007, Co. conducted its businesses through two operating segments. In detail, Co.'s Water Products business manufactures and markets a variety of residential gas and electric water heaters, standard and specialty commercial water heating equipment, copper‐tube boilers, and water systems tanks. In addition, Co.'s Electrical Products business manufactures and markets a range of hermetic motors, fractional horsepower alternating current and direct current motors.

Smith (A.O.) Corp

AbstractElectrical (MIC: 11.14 SIC: 3621 NAIC: 335312)A. O. Smith is engaged as a manufacturer of water heating equipment and electric motors, serving a range of residential, commercial and industrial end markets principally in the U.S. As of Dec 31 2007, Co. conducted its businesses through two operating segments. Co.'s Water Products business manufactures and markets an array of residential gas and electric water heaters, standard and specialty commercial water heating equipment, high‐efficiency copper‐tube boilers, and water systems tanks. Co.'s Electrical Products business manufactures and markets a range of hermetic motors, fractional horsepower alternating current and direct current motors.

Smith (A.O.) Corp

AbstractElectrical (MIC: 11.14 SIC: 3621 NAIC: 335312)A. O. Smith is a manufacturer of water heating equipment and electric motors, serving a diverse mix of residential, commercial and industrial end markets principally in the U.S. Co. is comprised of two operating segments: Water Products and Electrical Products. Co.'s Water Products business manufactures and markets a line of residential gas and electric water heaters, standard and specialty commercial water heating equipment, copper‐tube boilers, and water systems tanks. Co.'s Electrical Products business manufactures and markets a line of hermetic motors, fractional horsepower alternating current and direct current motors, and integral horsepower motors.

Smith (A.O.) Corp.

AbstractElectrical (MIC: 11.14 SIC: 3621 NAIC: 335312)A. O. Smith is a manufacturer of water heating equipment and electric motors, serving a diverse mix of residential, commercial and industrial end markets principally in the U.S. Co. is comprised of two operating segments: Water Products and Electrical Products. Co.'s Water Products business manufactures and markets a line of residential gas and electric water heaters, standard and specialty commercial water heating equipment, copper‐tube boilers, and water systems tanks. Co.'s Electrical Products business manufactures and markets a line of hermetic motors, fractional horsepower alternating current and direct current motors, and integral horsepower motors.