Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

Demand response (DR) of thermostatically controlled load (TCL) represented by residential consumers' air conditioning (AC) is playing an increasingly important role in enhancing the operational flexibility and sustainability of the power grid. The evaluation of the DR potential of AC is of great significance in the power grid dispatching and the selection of DR target consumer groups. However, due to the large number of AC units, the large difference in parameters, and the randomness and uncertainty of consumers' electricity consumption behavior and DR willingness, it is difficult to accurately evaluate the achievable potential of the AC. In order for power system operator (PSO) or load aggregator (LA) to better mine and manage the AC resources, this paper proposes a systematic method suitable for evaluating the achievable DR potential of a single residential AC and aggregate residential ACs the next day. Firstly, based on the parameter identification of equivalent thermal parameter (ETP) model, a method for estimating the theoretical time-varying potential of the AC is proposed. Then considering the influence of the randomness of residents' electricity consumption behavior on the DR potential, an AC state prediction model is constructed based on the deep learning network. On this basis, social psychology and fuzzy systems are used to quantify the impact of residential consumers' DR willingness on the DR potential, and an achievable potential evaluation model is constructed. Finally, based on the actual load data of the Muller project in Austin, the effectiveness and accuracy of the proposed DR potential evaluation method are further demonstrated.