Electric Water Heaters Research Articles

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.

Understanding price functions to control domestic electric water heaters for demand response

A well-known mechanism for demand response is sending price signals to customers a day ahead. Customers then postpone or advance their usage of electricity to minimize cost. Setting up price functions that adapt the customers’ load to availability is a big challenge. This paper investigates the feasibility of finding day-ahead price functions to induce a desired load profile of domestic electric water heaters (DEWHs) minimizing their electricity cost for demand response. Bilevel optimization is applied for a single DEWH using a simplified linear model and full knowledge. This leads to a solvable bilevel problem and allows understanding optimality of price functions and resulting heating profiles. It is shown that with the resulting price functions the DEWH may select many significantly different heating profiles leading to the same cost. Thus the price does not uniquely induce the desired heating profile. The acquired knowledge forms the basis for a procedure to create price functions for controlling the load profile of many DEWHs.

Energy, exergy and economic viability of a heat storage system used for domestic hot water supply in urban and isolated households

The endeavor of the paper is the study the feasibility of using a locally made daily solar storage system, SSS, for domestic hot water supply especially in urban and isolated households. In order to evaluate the system efficiency during the charging and discharging periods an experimental and a numeric investigation were simultaneously achieved. Results showed that the daily energy efficiency of the energy stored in the HSU ranged from 30 to 49.5% whereas the daily exergy efficiency ranged from 19 to 27%. The results show also that for wet sand the quantity of daily destocked energy varies in the array of 1.75–0.8kWh. The results showed that 75% of the total DHW requirements can be achieved from the heat storage system during the night. The results of the economic analysis shows that the SSS, the annual energy kept (YES) and the payback period (PBP) were about 1317kWh/year and 4years based on electric water heater and about 460m3 gas saving and 3.5years based on gas water heater, respectively.

Scheduling of demand-side resources for a building energy management system

In this paper, an algorithm for scheduling of demand-side resources is presented for residential loads in dynamic pricing environment. The main objective of this study is to minimize the operational cost of energy consuming devices in an entire building over a day without violating set of consumer comfort preferences. The residential end-use consumer loads considered in the proposed work are heat ventilation and air conditioning system, plug-in hybrid electric vehicle, electric water pump, and electric water heater. The optimal control operation of the loads under a real-time pricing scheme is analyzed using particle swarm optimization. The results show that the proposed scheme gives a significant reduction in the building energy cost as compared to the normal ON/OFF based control operation. Two case studies of 2 typical buildings consisting of 3 and 100 houses are taken to evaluate the proposed optimal control scheme. The comparison between the proposed and normal ON/OFF methods shows that global optimization gives the significant energy as well as cost saving.

IGreen: green scheduling for peak demand minimization

Home owners are typically charged differently when they consume power at different periods within a day. Specifically, they are charged more during peak periods. Thus, in this paper, we explore how scheduling algorithms can be designed to minimize the peak energy consumption of a group of homes served by the same substation. We assume that a set of demand/response switches are deployed at a group of homes to control the activities of different appliances such as air conditioners or electric water heaters in these homes. Given a set of appliances, each appliance is associated with its instantaneous power consumption and duration, our objective is to decide when to activate different appliances in order to reduce the peak power consumption. This scheduling problem is shown to be NP-Hard. To tackle this problem, we propose a set of appliance scheduling algorithms under both offline and online settings. For the offline setting, we propose a constant ratio approximation algorithm (with approximation ratio \(\frac{1+\sqrt{5}}{2}+1\)). For the online setting, we adopt a greedy algorithm whose competitive ratio is also bounded. We conduct extensive simulations using real-life appliance energy consumption data trace to evaluate the performance of our algorithms. Extensive evaluations show that our schedulers significantly reduce the peak demand when compared with several existing heuristics.

Demand response implementation in smart households

Home energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for end-users without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its’ effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%–31.5% by using the proposed optimization-based algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.

Energetic, exergetic and economic analysis of an innovative Solar CombiSystem (SCS) producing thermal and electric energies: Application in residential and tertiary households

The endeavor of this paper is to study of the potential offered by the expenditure of an innovative Solar CombiSystem, SCS, used for the space heating load, the domestic hot water supply and the electric energy production. The investigation achieved in this work was based on an experimental and a simulation studies. A TRNSYS simulation program was achieved in order to evaluate the SCS monthly/annual thermal and electric performances. It was found that the proposed SCS covered between 20 and 45% of the SH energy needs by considering only solar energy. The result shows also that the SCS provided from 40 to 70% of the total DHW needs. It was also found that the SCS electric production ranged between 32 and 225MJ/m2 with a gain factor varying between 49 and 125%. An economic appraisal was also achieved to appraise the SCS feasibility. The results of the economic analysis show that the annual energy saved (ARE) and the payback period (Pb) based on electric water heater were respectively equal to 7618.3kWh/year and 10.2years. It was found that ARE and Pb based on gas/gas town were about 5825m3 and 8.7years, respectively. The results of the economic analysis shows that the adoption of the SCS saves about 48% of electric energy and about 46% of gas/gas town kept back by the conventional system.

Investigation of solar water heater by using flat plate collector and evacuated tubes

Collection, storage and utilization of solar energy by solar water heating by flat plate collector and evacuated tube is cheapest and effective renewable energy technologies. This solar water heater design uses a nonconventional source of energy which can be used for house hold applications. The cost of production will be comparatively low in cost and high in capacity. The G.I. sheet collector boxes are replaced by copper tube, stainless steel water tank, thick costly PUF insulations and toughened glass etc. Pebbles are used as a medium of heat storage this increases the capacity of solar water heater by using this method natural source of energy can be used instead of depending on electric water heaters.

Optimal operation scheduling of electric water heaters under dynamic pricing

This paper presents an optimal operation scheduling algorithm and evaluates its performance under a day-ahead real-time pricing (DA-RTP) tariff as well as under a combination of a DA-RTP and a time varying bound on power consumption (demand limit). The scheduling algorithm is based on Dijkstra’s algorithm and can be applied to continuously controlled loads (e.g., electric water heaters – EWHs, heating, ventilation and air conditioning systems – HVACs), which belong to the category of thermostatically controlled devices (TCAs). In this paper, the algorithm deals with the operation scheduling of EWHs, which are devices with a lot of flexibility as they possess high nominal power ratings and can be used as thermal energy buffers. User’s preferences regarding the preferred water temperature, the tolerable temperature range and the acceptable deviation from the minimum energy cost are mapped to the relative weights of energy and comfort cost of the objective function, which is strived to be minimized by the scheduling algorithm, in order to optimize energy cost as well as user’s comfort. Simulation results that verify the performance of the scheduling algorithm are presented under various realistic scenarios which study the effect of the upper temperature set point and the rated power on both the energy cost and the perceived comfort level. Any deviations between the real-time and the predicted hot water demand due to forecasting errors trigger a real-time adjustment of the operation scheduling. Regarding the implementation issue, the algorithm may be used for the control of smart EWHs by optimally adjusting the temperature set point at each time slot as well as for the direct ON/OFF control of the heating element of legacy EWHs.

Demand-Side Management of Domestic Electric Water Heaters Using Approximate Dynamic Programming

In this paper, two techniques based on ${Q}$ -learning and action dependent heuristic dynamic programming (ADHDP) are demonstrated for the demand-side management of domestic electric water heaters (DEWHs). The problem is modeled as a dynamic programming problem, with the state space defined by the temperature of output water, the instantaneous hot water consumption rate, and the estimated grid load. According to simulation, ${Q}$ -learning and ADHDP reduce the cost of energy consumed by DEWHs by approximately 26% and 21%, respectively. The simulation results also indicate that these techniques will minimize the energy consumed during load peak periods. As a result, the customers saved about $466 and $367 annually by using ${Q}$ -learning and ADHDP techniques to control their DEWHs (100 gallons tank size) operation, which is better than the cost reduction that resulted from using the state-of-the-art ($246) control technique under the same simulation parameters. To the best of the authors’ knowledge, this is the first work that uses the approximate dynamic programming techniques to solve the DEWH’s load management problem.

Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice

Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of electric water heaters, makes it challenging to harness their flexibility. Driven by this challenge, this paper formulates the underlying sequential decision-making problem as a Markov decision process and uses techniques from reinforcement learning. Specifically, we apply an auto-encoder network to find a compact feature representation of the sensor measurements, which helps to mitigate the curse of dimensionality. A well-known batch reinforcement learning technique, fitted ${Q}$ -iteration, is used to find a control policy, given this feature representation. In a simulation-based experiment using an electric water heater with 50 temperature sensors, the proposed method was able to achieve good policies much faster than when using the full state information. In a laboratory experiment, we apply fitted ${Q}$ -iteration to an electric water heater with eight temperature sensors. Further reducing the state vector did not improve the results of fitted ${Q}$ -iteration. The results of the laboratory experiment, spanning 40 days, indicate that compared to a thermostat controller, the presented approach was able to reduce the total cost of energy consumption of the electric water heater by 15%.

An economic assessment of distributed solar PV generation in Sweden from a consumer perspective – The impact of demand response

We present an economic assessment of the impacts of Demand Response (DR) and pricing schemes on the conditions for distributed solar photovoltaics, with the focus on individual households. An optimization model has been developed that minimizes the electricity cost for individual households with the option of dispatching DR loads. DR of appliances and hydronic heating (electrical water heating for both space heating and hot water) are investigated, as well as the effects of applying a monthly, hourly or net metering pricing scheme for selling excess generated electricity and a tax reduction scheme for electricity sold to the grid.We show that for Swedish conditions a monthly net metering pricing scheme would result in the largest PV installations per household (median rated capacity of 4.2 kWp/household). The use of the tax reduction scheme reduces the installation per household (2.1 kWp/household), but with an installation being profitable for a larger fraction of the households. Furthermore, the tax reduction scheme retains an incentive for engaging in DR. The use of hydronic DR can support the same installations sizes as the tax reduction scheme, whilst Appliance DR is shown to have only a low impact on the profitability of a PV installation.

Equivalent electricity storage capacity of domestic thermostatically controlled loads

A method to quantify the equivalent storage capacity inherent the operation of thermostatically controlled loads (TCLs) is developed. Equivalent storage capacity is defined as the amount of power and electricity consumption which can be deferred or anticipated in time with respect to the baseline consumption (i.e. when no demand side event occurs) without violating temperature limits. The analysis is carried out for 4 common domestic TCLs: an electric space heating system, freezer, fridge, and electric water heater. They are simulated by applying grey-box thermal models identified from measurements. They describe the heat transfer of the considered TCLs as a function of the electric power consumption and environment conditions. To represent typical TCLs operating conditions, Monte Carlo simulations are developed, where models inputs and parameters are sampled from relevant statistical distributions. The analysis provides a way to compare flexible demand against competitive storage technologies. It is intended as a tool for system planners to assess the TCLs potential to support electrical grid operation. In the paper, a comparison of the storage capacity per unit of capital investment cost is performed considering the selected TCLs and two grid-connected battery storage systems (a 720 kVA/500 kWh lithium-ion unit and 15 kVA/120 kWh Vanadium flow redox) is performed.

Smart house-based optimal operation of thermal unit commitment for a smart grid considering transmission constraints

ABSTRACTThis paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.

Generation following with thermostatically controlled loads via alternating direction method of multipliers sharing algorithm

A fundamental requirement of the electric power system is to maintain a continuous and instantaneous balance between generation and load. The intermittency and uncertainty introduced by renewable energy generation requires expanded ancillary services to maintain this balance. In this paper, we examine the potential of thermostatically controlled loads (TCLs), such as refrigerators and electric water heaters, to provide generation following services in real-time energy markets (1–5min). Previous research in this area has primarily focused on the development of centralized control schemes with an aggregate TCL model. An objective of our approach is to enable each TCL to model and control its dynamics independently and to use distributed convex optimization techniques to allow a central aggregator to influence, but not directly control, the behavior of the population. To control the non-linear dynamics of hysteretic dead-band systems in a manner suitable for convex optimization, we introduce an alternative control trajectory representation of the TCLs and their discrete input signals. This approach allows us to approximate the control of a TCL as a convex program and to produce a solution that can be interpreted stochastically for implementation. To perform distributed optimization across large populations of TCLs, we apply a variation of the alternating direction method of multipliers (ADMM) algorithm. The objective of the distributed optimization algorithm is to enable an aggregator to coordinate with a population of TCLs and to increase or decrease the total power demand according to a control signal. We include experimental results in which different populations of TCLs with varying levels heterogeneity are optimized to provide 5-min ahead generation following services. We numerically demonstrate the algorithm's potential for controlling a TCL population's power demand within a definable error tolerance.

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.

Life cycle assessment of domestic heat pump hot water systems in Australia

Water heating accounts for 23% of residential energy consumption in Australia, and, as over half is provided by electric water heaters, is a significant source of greenhouse gas emissions. Due to inclusion in rebate schemes heat pump water heating systems are becoming increasingly popular, but do they result in lower greenhouse gas emissions? This study follows on from a previous life cycle assessment study of domestic hot water systems to include heat pump systems. The streamlined life cycle assessment approach used focused on the use phase of the life cycle, which was found in the previous study to be where the majority of global warming potential (GWP) impacts occurred. Data was collected from an Australian heat pump manufacturer and was modelled assuming installation within Australian climate zone 3 (AS/NZS 4234:2011). Several scenarios were investigated for the heat pumps including different sources of electricity (grid, photovoltaic solar modules, and batteries) and the use of solar thermal panels. It was found that due to their higher efficiency heat pump hot water systems can result in significantly lower GWP than electric storage hot water systems. Further, solar thermal heat pump systems can have lower GWP than solar electric hot water systems that use conventional electric boosting. Additionally, the contributions of HFC refrigerants to GWP can be significant so the use of alternative refrigerants is recommended. Heat pumps combined with PV and battery technology can achieve the lowest GWP of all domestic hot water systems.

American Solar Network, Ltd. (A)

A.C. Rich has invented and patented a new form of solar water heater that he claims will significantly reduce home-heating costs and is aesthetically pleasing. The case asks students to evaluate Rich's design in terms of cost, performance, and marketability, and also in relation to a set of ethical and environmental criteria presented in Note on Moral Reasoning (E-0092) and Note on Five Traditional Theories of Environmental Sustainability (E-0094). Students also must decide whether Rich should move to a state that provides subsidies. Excerpt UVA-E-0097 Rev. Aug. 9, 2013 AMERICAN SOLAR NETWORK, LTD. (A) Albert C. Rich, an inventor and entrepreneur, had come up with a design for a new solar water heater. He explained its benefits: The average home electric water heater emits over 5.8 tons of carbon dioxide into the atmosphere each year (when the electricity is produced by coal), which is more than the average automobile emits! These systems also produce over 91 pounds of sulfur dioxide and 850 pounds of nitrogen oxides (which contributes to acid rain and smog). A solar water heater can prevent over 4,000 pounds of these pollutants from being emitted. Rich also claimed, “If 50% of the homes in the United States had a solar collector, it would eliminate 12 large nuclear-, coal-, and oil-generating plants.” . . .

Effct of Full Implementation of Domestic Solar Water Heaters on the Electricity Peak Load in Libya

Electricity plays an important role in contemporary life, and it has become indispensable nowadays. Reducing the peak electricity load and increasing the load factor have been considered as one of the main tasks that have to be accomplished by both electricity generation-side and demand-side management. The residential sector of Libya consumes over 31% of the total sold electricity, and 29.8% of that is delivered to the electric water heating load. This is an inefficient way of electricity utilization. Usually, the electricitysupplier in Libya is used to increase the local generation capacity or import electricity from neighboring countries. Both solutions did not resolve the problem. This work attempts to investigate the effect of replacing electric water heaters in the residential sector of Libya with solar water heaters on reducing the electricity peak load and increasing the load factor. Th results show that on average 3% of the peak load demand can be saved.This is equivalent to 149.5 MW of reduced power. The study also revealed that the annual amount of energy saved is up to 2.55TWh, and the load factor is improved by 2% (i.e. from 65% to 67%). This saved energy is equivalent to a power plant with a nominal capacity of 448 MW considering a load factor of 0.65

Development of Control Strategies and Implementation to Electrical Water Heaters for Energy Conservation

Abstract Optimum design for storage type Electric Water Heaters (EWHs) was analyzed for energy conservation and maximum hot water output. Single and dual tank EWHs were compared with each other. For each comparison, data were gathered for tank volumes and power ratings ranging 100-400land 1-4 k Wrespectively. Different dual tank EWHs were designed with different control mechanisms to find which one had the optimum outcome. For these comparisons, dual tank EWHs in which the upper part of the tanks had 25%of the total volume and 75%of the total power rating were used. Afive day usage period of an EWHinatypical household was simulated on SIMULINK. To minimize this energy loss, transient analysis on FLUENTwas performed to see how long it took for the outlet water temperature. In these simulations, single and dual tank EWHs with different control mechanisms were compared with each other to find the design with minimum energy consumption and maximum hot water output. From these results the most efficient control system cost analysis was determined for minimum operating cost and minimum carbon footprint through optimized control strategies.