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Residential air conditionings (RACs) can be seen as one of the important flexible demand resources controlled to provide balance services for the power system due to their spatial thermal storage feature. However, such control on RACs will bring out the negative impact on the comfort level of residents inevitably, affecting their proactivity in demand response (DR). So, a novel two-stage control model of large-scale RACs is proposed to participate in DR considering different comfort sensitivity of various residents. The comfort sensitivity index is developed in terms of daily operation time and daily power consumption per area of RACs, classified by K-means clustering. Besides, the DR potential of RACs is established with the adjusted temperature setpoint, where the temperature setpoint is determined by the comfort sensitivity index. Moreover, a two-stage control model of large-scale RACs is proposed comprised of load response and recovery, where the corresponding control strategies at each stage are proposed. Simulations of the proposed method have been performed on residential 20,000 RACs to validate its effectiveness in Changzhou, China. The outcome reveals that the strategies could maximize the DR potential of RACs and ensure the stable recovery of RACs under the premise of meeting the comfort demand of differentiated population.

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population