Abstract: Accurately predicting spot market electricity prices is of significant importance for safeguarding the interests of participants in the electricity market.Currently, a large amount of renewable energy participates in spot market trading, making it extremely challenging to predict spot market electricity prices.To address this, this paper proposes a mixed electricity price prediction model based on decomposition-optimization-integration.Firstly, the original electricity price time series is decomposed using the complete ensemble empirical mode decomposition method.Then, a combination of Cat chaotic mapping strategy, stagnation detection strategy, and Gaussian-Levy perturbation strategy is employed to overcome the problem of the grey wolf optimization algorithm falling into local optima, thereby enhancing population diversity.Next, the improved grey wolf optimization algorithm is used to optimize the hidden layer parameters of the deep extreme learning machine and construct the spot electricity price prediction model.Finally, the proposed method is analyzed and validated by simulation experiments.The results indicate that the proposed approach effectively enhances the accuracy of the prediction model.