Day-ahead and intraday optimization models for demand response of the aggregators of thermostatically controlled loads

In order to solve the problem of wind curtailment, the heat storage electric boiler is used to promote clean heating in the heating season in northern China. When the room temperature of the heating room changes in a certain range, it will not cause user discomfort, so there is a certain adjustment space for the electric heating equipment. Through aggregator, it can benefit both sides by aggregating several heat storage electric boilers to participate in demand response. In this paper, based on the chance constrained programming, considering the influence of uncertain factors such as weather and user randomness, day-ahead and intraday optimization models for demand response of the thermostatically controlled loads aggregator are established. The objective function of the day-ahead optimization is to maximize the benefits of aggregators, and the objective function of the intraday optimization is to smooth the fluctuation of renewable energy. Finally, the feasibility of the model is verified by an example.