Optimal schedule and temperature control of stratified water heaters

Abstract

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.

Practically-Achievable Energy Savings with the Optimal Control of Stratified Water Heaters with Predicted Usage

“…These results indicate the effect of predicting hot water usages when determining the optimal plan for an EWH. The simulation results for TM, EM and EML obtained in [42] for the optimal planning produced for a two-node EWH model with perfect foreknowledge of the water usages are compared with those obtained in Figure 6 for the optimal planning of predicted water usages.…”

“…The work presented in this paper uses components from our previous papers, [41,42], and makes the following contributions:…”

“…In our previous paper, Engelbrecht et al [42], we investigated how much energy can theoretically be saved with optimal control for electric water heaters compared to traditional thermostat control, when perfect foreknowledge of the hot water usage profile is available. The probabilistic hot water usage model and the hot water usage predictor were therefore not used in the paper, but perfect foreknowledge of the hot water usage profile was used instead.…”

Centrally Adapted Optimal Control of Multiple Electric Water Heaters

“…The optimal heating control sequence and corresponding optimal temperature plan are determined by the optimal schedule planner. This component uses an A* search algorithm (developed in [38]) to solve the optimal control problem for a one-node EWH. This component requires the optimisation constraints and heating control strategy to be specified.…”

“…More information on optimal control theory can be found in [40]. Various approaches to solving the optimal control problem are available, specifically, search-based (Dijkstra [41], A* search [38], dynamic programming [32]) and optimisation-based (sequential quadratic programming [42]) methods.…”