Given the high proportion of renewable energy participating in market competition, the fluctuation of electricity price will be more severe. In order to predict the range of electricity price, a dynamic Bayesian network (DBN) interval prediction method is proposed. In this method, the predicted data of wind power generation, total power generation and total electricity consumption, and the real value of electricity price, are taken as input data. The network structure of DBN is determined by a greedy search algorithm, and the network parameters of DBN are estimated by maximum likelihood estimated (MLE). The DBN model is established. Then, with the predicted value of wind power generation, total power generation and total electricity consumption as the reasoning evidence, the discrete value and a posteriori probability of electricity price prediction are obtained using union tree reasoning, and the interval prediction of electricity price is realized. Finally, the proposed method is compared with the real value of electricity price and the comparison method to verify the effectiveness of the proposed method. The proposed method can not only get the predicted range of electricity price, but also give the corresponding probability. This has guiding significance for increasing the income of market members and avoiding price risk. This work is supported by the Natural Science Foundation of Fujian Province (No. 2019J01845).