Under the background of carbon peak carbon neutrality, energy sharing among multiple entities in active distribution network (ADN) helps to absorb wind and light. However, the growing wind and photovoltaic capacity within microgrids has resulted in more pronounced daily fluctuations in net load. This has rendered traditional time-of-use electricity pricing less effective in mitigating peak demand and filling valleys in microgrids. To address this issue, a scheduling strategy considering the shared energy among multiple entities in the context of an active distribution network was proposed in this paper. The objective was to maximize the net income by deducting the electricity purchase cost from the electricity sales revenue generated within the ADN and distributed to sub-level microgrids. Furthermore, the strategy takes into consideration the scheduling autonomy of the sub-level microgrids, which aim to minimize their own operating costs under the pricing policies of the electricity grid. To achieve this optimization, the sub-levels' goal of minimizing operating costs was transformed into a constraint on the super-level objective function by employing Karush-Kuhn-Tucker (KKT) multipliers, as done in the study. The KKT multipliers were employed along with the big M method to linearize the nonlinear constraints, thereby improving the computational efficiency of the model. Additionally, the continuous coupled variables between the upper and lower levels were discretized in the MATLAB Gurobi environment. The effectiveness of the proposed scheduling strategy was evaluated through a case study carried out on an active distribution network implemented in the IEEE 33-node system. This analysis demonstrates the practical feasibility and benefits of the model for optimizing energy distribution within the ADN framework.