With the development of smart devices and information technology, it is possible for users to optimize their usage of electrical equipment through the home energy management system (HEMS). To solve the problems of daily optimal scheduling and emergency demand response (DR) in an uncertain environment, this paper provides an opportunity constraint programming model for the random variables contained in the constraint conditions. Considering the probability distribution of the random variables, a home energy management method for DR based on chance-constrained programming is proposed. Different confidence levels are set to reflect the influence mechanism of random variables on constraint conditions. An improved particle swarm optimization algorithm is used to solve the problem. Finally, the demand response characteristics in daily and emergency situations are analyzed by simulation examples, and the effectiveness of the method is verified.
Thermostatically controlled loads (TCLs) have become a major tool for the demand response (DR) program when air conditioners cause peak loads in a day during the winter or summer. To solve the problem of a direct load control with TCL usually affecting user comfort and hardly considering responsiveness, a power retailer airconditioning load aggregation operation control and demand response method was proposed in this research. From the perspective of a power retailer, a compensation mechanism for TCL was constructed, which was composed of a basic incentive program and an additional incentive program. The basic incentive program aimed to encourage users with a low response degree to increase the response capacity in order to participate in DR. An auxiliary service market control strategy based on a new compensation mechanism of the electricity retailer was detailed, which fully considered the enthusiasm of the user in mobilizing the response and reducing the load reduction fluctuation when using the state-queuing (SQ) model. Case studies were provided to verify the effectiveness of the proposed method. Compared with other compensation schemes, the simulation results showed that the compensation mechanism provided in this research was more reasonable, and it could smooth the load and reduce fluctuations. The compensation distribution among the user groups could effectively control the uniform distribution in the user groups in the temperature range, and it could mobilize users at different temperatures to participate in DR.
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