Recent advances in smart grid technology enable new approaches to address the problem of load control for domestic water heating. Since water heaters store energy, they are well-suited to load management. However, existing approaches have focused on the electrical supply side, ignoring the obvious link between the user and the grid: individual hot water consumption patterns. This paper proposes a load spreading approach in which water heaters compete for access to the heating medium. The proposed smart grid solution takes grid load limits, real-time temperature measurements, water usage patterns, individual user comfort, and heater meta-data into consideration. The scheduler only turns on the heaters with the highest level of need, but limits the number of on heaters to ensure that the grid load stays below a set limit for a set time. The method is evaluated by simulation against various heater set temperature levels, and for various load limits, and compared with ripple control and actual consumption measured in a field trial of 34 water heaters. The proposed algorithm reduces the load from 62kW to 20, 30, 40, and 50kW (vs. 106kW for full ripple control). The resulting number of unwanted cold events is fewer than for ripple control, and only slightly more than no control, while reducing the total energy by 14% from a user-optimised natural experiment.
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.
The resource-constraint energy sector faces an insatiable demand for energy, which necessitates improvements in efficiency. One key sector that has potential for savings is residential water heating, which makes up 32% of household energy. Previous studies have proven that with effective scheduling up to 29% savings can be achieved for a nominal consumption pattern. The model that was used to estimate the savings, calculates the energy usage for a given hot water consumption pattern and given heating schedule for a horizontally mounted water heater. This two-node model is used to aid user-informed scheduling and autoscheduling, but was developed as a black-box model, validating the energy and not the internal temperatures, which could be misleading. This paper evaluates the accuracy of the model by performing temperature measurements inside the horizontal electric water heater. Moreover, two aspects neglected by the model are investigated: The node state transfer usage threshold, and the inter-nodal thermal resistance. The results show that the model significantly underestimates the stratification that occurs naturally. This underestimation also severely affects the modelled energy consumption and hides limitations of the model, preferring a lower threshold and higher inter-nodal resistance. The results also show that Legionella growth in the EWH could be a concern despite a high set point.
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