The assessment of customer electricity demand potential is a critical part of further stimulating electricity demand growth and promoting utility development. The main aim of this paper is to investigate how electricity demand response potential can be assessed under uncertainty. The decision variables are set through a two-stage stochastic planning model, focusing on fuzzy decision-making through linear programming in interval planning, combining with consequent opportunity constraint planning and fuzzy opportunity constraint planning to realize artificial regulation of unfavorable factors, and finally completing the treatment of uncertain factors. Combining a Gaussian mixture model to construct a model for measuring electricity demand response potential. It has been found that different electricity pricing policies lead to other potential demand response values for peak electricity load. The optimal electricity pricing and revenue of each user will be greatly affected by the participation of different users in the electricity demand response. The optimal power pricing decreases from 3-4 when the number of users is 4 to 2.5 when the number of users is 20 because of the generation of scale rewards, and the benefits to the users increase substantially when the number of users participating in electricity demand response is 20. Evaluating customer electricity demand in an uncertain environment can help drive electricity system reform to some extent.