With a large number of distributed energy storage accessed by virtual power plants, the market trading model based on traditional two-stage stochastic planning faces problems such as large computational volume and single decision result. Therefore, considering the different decision risk preferences of different decision makers, this paper introduces the decentralized trading theory, combines the nonlinear planning theory, and optimizes it through the mixed integer optimization model, thus forming a set of market trading models applicable to complex situations. Taking 8MW distributed wind turbines as the research object for example analysis, the results show that in the middle and late stages of the transaction, as the transaction continues, the total social welfare and the number of transactions under the decentralized trading model based on nonlinear programming gradually reach the optimal value under the ideal state. Compared with other trading models, the value of the increase in the total social welfare of this paper’s model in the peak hour, the normal hour, and the valley hour is more than 15%. This shows that the model in this paper can reduce the cost of electricity and improve the comprehensive social benefits.
