This work is a combined study of the economics-based customer survey and demand response (DR) optimization. We present a methodology to capture the customers’ behavior and thermal parameter differences within the decision-making of the voluntary air-conditioning DR program for critical peak reduction. Firstly, we developed a face-to-face survey based on the contingent valuation method (CVM) to investigate the demand-side bid preferences of residential customers, while the households’ willingness-to-accept (WTA) distribution on the particular AC interruptions is evaluated by an expenditure difference model (EDM). Based on the survey, we decoupled the inner relationships between the customers’ bidding behavior versus consumption properties through an aggregate thermal state analysis of the customers’ entity. Subsequently, the above data are fitted into an optimization framework with DR modeling to maximize the system’s benefit, optimize the DR control, and find the optimal reward price on saving the system’s generation investment with critical peak reduction. In the DR modeling, we presented a group thermal state model, in which the customers are divided into several groups and respond to the DR signal sequentially to mitigate the behavior-lead cooling-rebound. Besides, we considered the responding convenience of the customers, which is based on their real-time stay-at-home rate and thermal state evaluation during the summer peak. The proposed methodology could strongly support the system’s decision-making and is finally validated within the city of Xi’an, China.
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