•Construct a two-level decision model of monthly electricity trade.•Consider the interaction between monthly and day-ahead markets and risk.•Optimize total monthly electricity trade and hourly decomposition simultaneously. With the development of wind power generation, energy storage and demand side management technology, the schedule of monthly electricity purchase is more uncertain and risky. To enhance the executability and economy of the monthly market and reserve enough space to accommodate wind power, a bi-level decision model, which integrates demand side responses, energy storage, wind power accommodation and day-ahead electricity purchase schedules, is proposed in this paper. In the first layer, the monthly purchase volume and its hourly decomposition schedule are optimized. In the second layer, factors of uncertain day-ahead prices and economic risk are examined, and the corresponding dispatching schedule of energy storage system and thermal power in the day-ahead markets are further optimized to promote the monthly market benefit. The proposed methodology is illustrated using a realistic case study and related affecting factors are analyzed in detail.