Analysis of decision-making for air conditioning users based on the discrete choice model. (October 2021) - Wang, Jingjie; Wu, Hongbin; Yang, Shihai; Bi, Rui; Lu, Junhua
Highlights: Established the mathematical model of users' cooling load based on principle of heat transfer. Analyzed the uncertainty of the users responses considering the differences in the economic and comfort preferences. Constructed users' differential response probability model through the multi-objective optimization method. Analyzed the users' decision behavior characteristic with the proposed discrete choice model. Abstract: Because of the thermal inertia of air conditioning load, electric power companies can guide users to manage it effectively and reduce the peak-valley difference of power grid through the electricity price mechanism on the premise of less affecting the comfort of users. Although the load adjustable potential of a single air conditioner user is small, its aggregation potential cannot be ignored due to a large number of residential air conditioners. In order to accurately describe users' differential response decision behavior, an analysis method of air conditioning users' response decision behavior considering uncertainty and demand diversity response behavior is proposed in this paper. Firstly, according to the change in the indoor and outdoor environmental factors of air conditioning, the cooling load characteristics of residential buildings were modeled, the difference in the comfort temperature of the users was considered, the cooling load characteristics of various types of residential users were identified, and the daily load curves of the cooling demand of the users were obtained. Secondly, the uncertainty of the user response under the electricity price mechanism was analyzed, and the user preference for economy and comfort was comprehensively considered. Finally, the decision-making behavior of the users was analyzed with the discrete choice model, and the probability model of the differential responses of the users was simulated by the multi-objective optimization method. An example is given to analyze the actual response data of users in a residential district in Changzhou City, Jiangsu Province, and get the response of users to air conditioning load under the time-of-use electricity price and different incentive degrees. It provides more comprehensive user response decision information for the grid side, and enables the power company to improve the implementation effect of air conditioning load participating in demand response.