Abstract: With the development of smart grid,interaction between consumers and grid becomes more frequently,Under the condition of satisfying their own economy,users can gain additional economic benefits when they participate in grid demand response. Therefore,multi-time home energy management optimization strategies and optimal scheduling algorithms of considering demand response mechanisms are proposed. Established the model of excitation demand response under power constraint and demand response constraint and the household electricity model with distributed power supply,energy storage and electric vehicle. Based on the multi-scale energy management of the prediction model,the two-layer objective function for real-time optimization adjustment strategy to minimize the user’s own electricity cost and power purchase fluctuations. By adjusting the charge and discharge of storage battery and electric vehicle in real time,the user can purchase electricity to meet the corresponding requirements. Finally,the improved particle swarm optimization（IPSO）algorithm was used to solve the multi-time scale target function and compared with the original PSO,the results show that the proposed algorithm can significantly reduce the power cost and power fluctuation