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Due to the increasing microgrid group and shared energy storage integration into active distribution network (ADN), it is necessary to effectively coordinate these complexity energy elements. Therefore, a master-slave game schedule strategy is constructed for ADN based on microgrid group and shared energy storage. The time-of-use electricity price is decided by the ADN as the main body, so the microgrid group and shared energy storage should respond to the electricity price as the subordinate body, which may consider the safe operation and the peak shaving schedule. Moreover, the two-stage power interaction strategy between the microgrid group and shared energy storage is developed by the time-of-use electricity price. In the first stage, the energy storage leasing demand of microgrid group can be calculated through multi-objective optimization algorithms. Then, the charging and discharging strategy is formulated for the shared energy storage which can meet the power demand of the microgrid group and respond to distribution network schedule by the remaining capacity. In the second stage, a schedule strategy is formulated for the cooperative alliance considering power interaction among microgrids and a mechanism of benefit allocation. Furthermore, the equilibrium solution of the master-slave game may be solved through the Quantum Particle Swarm Optimization (QPSO) algorithm nested with the cplex solver. At last, the effectiveness and rationality of the proposed strategy can be verified by the improved IEEE33 bus system. • A master-slave game schedule strategy is proposed for ADN based on microgrid group and SES to solve the problem of pricing and optimization in multi-entity game. • The innovative multi-objective energy storage leasing model for microgrids is constructed by introducing the net load mean square deviation and the surplus/shortage power into the optimization objective function. • A multi-stage distributed iterative algorithm is proposed to effectively solve the leasing model and game equilibrium solution, which can avoid nested iterations in the solving process. • The benefit allocation mechanisms for MGCO is developed based on the power interaction ratio among microgrids to improve the efficiency and rationality of distribution. [ABSTRACT FROM AUTHOR]