Use of smart grid technology to compare regions and days of the week in household water heating

Abstract

Water heating is a leading cause of household energy consumption and, given its capacitive nature, has been the focus of research on demand side management and grid peak load management. Despite all the existing literature on energy for water heating, very little is known about an inextricably linked key determinant of it -demand for hot water and consumption patterns thereof. Moreover, even though water heating energy demand profiles have been investigated in the past, little is known about the different energy profiles for the days of the week, and regional variance of such profiles. This paper measures and reports actual hot water demand acquired through a novel smart metering solution. The different profiles for the days of the week are evaluated, in addition to weekdays and weekend days. Finally, differences between units in peri-rural Mkhondo and the urban Western Cape are compared in terms of water demand, energy demand, and efficiency (energy in vs. energy out). The results show a striking similarity to previous work, with the exception that scheduling has led to energy demand leading water consumption. The results also show that daily routines vary significantly, and also between regions. Surprisingly, the efficiencies and consumption patterns between the regions are also stark, with the urban Western Cape using 20 % more water on an average day, and with 70.2 % efficiency vs. 45.8 % in Mkhondo.Index Terms-electric water heating, hot water, smart grid, smart metering, Domestic energy consumption, domestic water consumption, demand forecasting, demand-side management, energy management, load modelling, load profile.

How much energy can optimal control of domestic water heating save?

“…If the residual warm water in the piping is at a lower temperature than the TC set point, the net losses will be significantly less: the average volume used per user was 142 L/day with an average of 7.5 events/day, which means as much as 10 % of usage would have resulted in usage losses. As an example of unintended use, we have anecdotal evidence of users of a smart EWH controller who became aware that their casual workers were unnecessarily washing cars or cleaning floors with hot water -behaviour that was quickly remedied (Roux and Booysen, 2017;Booysen et al, 2019). It may be argued that this control is essentially the same as setting the thermostat to a lower temperature, but the EM control does better than just supplying a lower temperature on occasion; it ensures that hot water is delivered when needed.…”

“…We compare the strategies by using field-measured hot water usage patterns that were sampled every minute from 30 water heaters over a period of 20 days using a simulated thermal model. The water heaters were part of a research project in which users from South Africa's Western Cape, Gauteng, and Mpumalanga volunteered to trial smart water heater controllers Reported energy savings (%) Fanney and Dougherty (1996) 4-6 Goh and Apt (2004) 5-6 Booysen et al 201314-17 Kepplinger et al (2014) 5-13 Kepplinger et al (2015) 10-12 Kepplinger et al (2016) 12 Booysen and Cloete (2016) 29-34 Gholizadeh and Aravinthan (2016) 6 Cloete (2016) 6 Nel et al (2018) 9-18 Matos et al 201939…”

“…However, the results of both of these studies were from simulations that did not take into account the outlet temperature and useful energy used, and only with predicted (non-real) consumption patterns. Kepplinger et al (2014) proposed an optimisation method that uses dynamic programming to optimise scheduling for cost and/or energy usage. The method involved an hourly optimisation and implemented an hourly control of water heaters.…”

Comfort, peak load and energy: Centralised control of water heaters for demand-driven prioritisation

“…Of note is that the energy measured in the natural experiment falls between the 60°C and 50°C levels, indicating that consumers are effectively operating their EWHs at those temperatures. An improvement to the approach presented in this paper is to limit the heating period to a shorter time before the consumption events, which will reduce the energy consumption further, and move the grid load to lead the water consumption profile, similar to that seen in [11] and [7].…”

“…Since costsensitive consumers in developing countries (such as South Africa) pay for energy consumed, and are not penalised for the time of use, many consumers resort to applying scheduled heating to only heat water in preparation for hot water usage [10]. The energy used for water heating can be reduced by as much as 29% if intelligent scheduling is employed to only heat water as and when needed, but, the impact of this self-serving scheduling has an adverse impact on the grid [7,11,10]. .…”