Impact of node count on energy-optimal control of stratified vertical water heaters in smart grid applications

Abstract

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.

Which Strategy Saves the Most Energy for Stratified Water Heaters?

“…None of these studies include the energy savings that can be achieved by implementing modern optimal control strategies that have been proven to produce substantially positive results. These studies also did not implement a multi-node EWH model, which has been proven to show more accurate results when modelling the energy flow inside the tank, to obtain the results [31].…”

“…The median energy savings were 8% and 18% with the temperature-and energy-matched strategies. Ritchie et al [31] expanded further on their research and used an A* search algorithm to determine the impact on node count on optimal energy savings of a multi-node EWH. Their results determined that four nodes were sufficient to accurately model the EWH thermodynamics, and the median savings with the temperature-and energy-matched strategy were 4.2% and 14.9%.…”

“…Thermal energy is lost to the surrounding environment due to standing losses. The rate of standing losses for each node is determined by the thermal resistance of the tank wall that encapsulates each node, R T , and the temperature difference between the ambient temperature, T amb , and the temperature of the water in node n. Further information describing the thermodynamics of this model can be found in [31].…”

“…With these strategies, heating only occurs right before each water usage, and the time required for the heating element to turn on is predetermined so that the desired outlet temperature is reached just before the start of the usage. The optimisation algorithm that obtains the optimal heating plan is elaborated on in [31].…”