Design of a direct load control program for residential electric water heaters

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University of New Brunswick


Domestic electric water heaters (DEWH) hold a large share of residential load in North America. The aggregated load profile of electric water heaters follows a similar pattern to the total household load profile, which means that changing the profile of DEWH load can significantly change the shape of the aggregated load profile. The research contribution is a new methodology for aggregated load control such that a prescribed load profile is followed by controlling the power consumption of the individual electric water heaters. This is achieved by: – a novel methodology to model an individual DEWH and estimate its hot water usage based on measurements of its power consumption, – a forecasting algorithm that considers the payback effect of the control actions and adjusts the load forecasts accordingly, – a novel predictive controller that provides a forecast on ramp-up or ramp-down reserve capacity to follow a desired load profile. There are two main benefits of using this controller: i) it provides an estimate of reserve capacity in future, which is important for scheduling ancillary services power load balancing, peak shaving and load shifting, and ii) it is a more green, less expensive alternate method compared to using fossil fuel generators for balancing the power generation and consumption. One of the main significance of this research is that the methodology for aggregated control was deployed on a pilot project called PowerShift Atlantic (PSA), led by the provincial utility, NB Power. The objective of the PSA was to provide up to 20MW of load following ancillary services to balance the intermittency of renewable generation by directly controlling the power consumption of various load classes, such as electric water heaters, electric thermal storage units and HVACs. The new methodology was deployed as a load aggregator in the PSA project to model and control 300 residential water heaters, and to assess performance in real-time operation.