The energy requirements for an Electric Water Heater (EWH) accounts for 40% of a household's total energy consumption and 30% of greenhouse gases emissions. The flexibility of the device to store thermal energy for long periods highlights how the intensity of the grid demand can be alleviated by implementing demand-side management (DSM) strategies. In this paper, we evaluate energy savings that can be achieved by modelling the EWH as a variable number of multiple nodes and providing it with optimal control with perfect foreknowledge of the hot water usage profile. We simulated 77 household's for all four seasons and determined that an average daily energy saving of 6.2% for temperature-matching and 16.3% for energy-matching can be achieved for a 20-node EWH. We also evaluated how increasing the number of nodes of the EWH when determining the optimal planning affects energy savings. It was concluded that using more than four nodes produced diminishing returns.
Water heating is a major component of domestic electrical energy usage, in some countries contributing to 25% of the residential sector energy consumption. Demand response strategies can reduce the time-of-use costs and overall electrical energy consumption. We present a method to reduce the electrical energy usage itself. Our novel heating schedule control minimises the electric water heater's energy usage without compromising user convenience. We achieve optimal control, while taking into account the natural temperature stratification of the water in the tank, using the A* search algorithm. Since previous research assumes a one-node thermal model, we also assess the effect of excluding stratification. We match three optimal control strategies to a baseline: the standard "always on'" thermostat control. The first two strategies respectively match the temperature and the energy of the hot water supplied by the water heater. The third, a variation on the second, includes a method of preventing the growth of Legionella bacteria. We tested 77 water heaters over four weeks, a week for each season, and all three strategies saved energy. The median savings were 6.3% for temperature-matching, 21.9% for energy-matching and 16.2% for energy-matching with Legionella prevention. Taking stratification into account increased these savings by 1.2%, 5.4% and 5.5% respectively.
Water heating contributes up to 40% of a household's total electricity usage and places a substantial burden on the electricity grid due to high power ratings and users' largely simultaneous hot water usage. The main determinants of its electricity draw are physical properties such as set temperature, insulation, and plumbing configuration; environmental conditions such as ambient temperature and inlet temperature; and the hot water usage profiles. These profiles include the usage volumes, the times of usage and the outlet temperatures. The efficacy of energy management techniques that model water heaters and the accuracy of their simulation results therefore rely on representative hot water usage profiles. Existing models for household hot water usage neglect differences between users, and temporal variations such as the season and the day of the week, and are not fully autonomous. We propose a probabilistic data-driven model for modelling individualised hot water profiles and an accompanying hot water usage simulator that includes all these factors. We gathered data from 77 residential households over a period of one year to train and evaluate the model for all four seasons. The results show that the simulated hot water usage profiles match the statistical properties of the measured data. Moreover, the individual hot water usage modelling and the resulting aggregated energy load on the grid closely match the measured data, improving on the existing hot water usage by halving the modelling error.
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