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.

Existing System

Electric Water Heaters in Smart Grids.

Proposed System

This paper describes the results from a study that used the Smart EWH Controllers (SECs) is used to measure household demand profiles for both electricity and water, creating a record for further analysis in a central database. These SECs were installed in households across South Africa with Mkhondo and the Western Cape forming the bulk of the installations. This paper provides an early investigation into the demand profiles of these two regions and presents some findings based on the different regions as well as day of the week between the two. The system with its relatively new infrastructure provided a good means to obtain the resulting data for mining. The minutely resolution of the data provides a high level of resolution at minimal data cost which adds to the feasibility of the solution. Looking at the data, the two regions, Mkhondo and the Western Cape, provided a great first impression of the knowledge that can be gained from the data. Mkhondo is observed to be a more rural setting from the usage patterns and having a more lagging power demand. This lead to low efficiency EWH usage, especially in the evenings. Contrasting with the Western Cape, a typically more urban setting, which showed a marginal increase in energy demand but a significant increase in hot water demand, managed to utilise effective scheduled control to provide a great improvement to EWH efficiency both in the mornings and evenings. The resolution of the data furthermore provided insight into EWH usage over weekends, providing the interesting depiction that usage profiles do not differ much from weekdays, albeit with lower demand overall. As the technology matures and becomes part of the daily routine of all users, large economic benefits may result as users take control of their usage due to the awareness. This will also provide great insight in the long term to seasonality changes in user demand profiles.

Architecture

Hardware Setup