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Summary As an important method for demand‐side management, the real‐time pricing can not only adjust the power balance between the supply and demand‐sides, but also effectively promote the internal energy dispatching behavior of the microgrid. In this article, a bilevel programming model is proposed based on the demand response to coordinate the master‐slave hierarchical relationship in the electricity market. At the upper level, the main grid determines the prices and generation for the supplier's maximum profit. At the lower level, we consider multiple microgrids, which operate in isolated‐island or grid‐connected mode, with photovoltaic generation system, energy storage system, and loads. Then, the optimal energy strategy is emerged for the user's maximum welfare according to the dynamic prices. Furthermore, we solve the upper and lower models separately to avoid user privacy being violated. The upper problem uses the genetic algorithm (GA), while the lower problem is handled by the branch and bound algorithm (BBA). A novel distributed algorithm named GA‐BBA is developed accordingly. Finally, taking an example of a smart grid system with three microgrids, the model and algorithm are simulated and compared. The results show that the peak‐to‐average ratio under the real‐time pricing is reduced from 1.6547 (fixed electricity price) and 1.4513 (time‐of‐use) to 1.2545. The peak‐to‐valley difference is also decreased by approximately 14.33%. In addition, the total social welfare of the system is increased by 145.53 kCNY (Chinese Yuan) through comparison with the existing model. The conclusions verify that the proposed bilevel programming model not only effectively reduces peak load but also increases the social welfare, which is more reasonable and realistic. Novelty Statement A novel bilevel optimization energy management system is proposed to optimize the hierarchical market structure between the main grid and multi‐microgrids (MMGs). A RTP mechanism based on demand response is formulated to balance the benefits between the main grid and MMGs. Moreover, the optimal energy strategy is obtained along with the dynamic price. Besides the operating cost, users' utility in microgrids are taken to our model. A hybrid distributed GA‐BBA is developed to protect the user's utility privacy.