Customer Selection for Residential Demand Response with Thermostatically Controlled Loads

Smart cities will have better managed smart grids thanks to demand response (DR) programs using thermostatically controlled loads (TCL). Selecting suitable customers is an essential part of a successful residential DR program. The current recruitment methodologies aim to enroll as many participants as possible. This method is costly as there is a cost associated with each recruitment such as providing the customers with remotely controllable air-conditioning systems. DR operators can reduce their cost by recruiting the customers that are more likely to be effective in the DR programs. In this paper, the customers' thermal sensitivity (CTS) is obtained from a modified breakpoint model to identify the customers with high demand reduction potential in a TLC-DR program. Specifically, the linear regression break-point model is extended by taking into account the impact of time to identify CTS using the noisy smart meter data of individual customers. The effectiveness of this customer selection methodology is tested with the real DR data of fifty-two residential customers in a DR trial conducted in Australia. The results show that customers with higher CTS contribute about four times more to the TCL-DR program compared to the customers with low CTS.