在大规模配电网中，负荷转供可以有效缓解单个分区供电紧张的问题。然而，由于网格规模过大，传统的数学优化方法难以解决这一问题。首先提出了一种基于网络化简的分区可用转供能力量化评估模型，以选取互济运行的分区规模。其次，提出了一种基于深度强化学习的配电网分区间互济运行优化方法以快速寻找到分区间互济运行方案。该方法改进了传统的奖励函数以提升智能体对不同负荷场景的适应能力与泛化特性。然后，采用对决双深度Q网络（dueling double deep Q network，D3QN）算法进行策略学习，以应对大规模系统的复杂性。最后，通过对IEEE 33节点系统和实际445节点系统进行仿真验证，验证了所提方法的有效性。

In large-scale distribution networks, load transfer can effectively alleviate power supply shortages in individual partitions. However, due to the excessive network size, traditional mathematical optimization methods struggle to address this issue. First, a quantitative assessment model for partition transfer capability based on network simplification is proposed to select the scale of partitions for inter-sub-district operation. Second, an optimization method for inter-sub-district operation between distribution network partitions using deep reinforcement learning is introduced to quickly identify inter-sub-district operation support schemes. This approach improves the traditional reward function to enhance the agent's adaptability and generalization chracteristic across different load scenarios. Then, the dueling double deep Q-network(D3QN)algorithm is employed for policy learning to tackle the complexity of large-scale systems. Finally, simulations on the IEEE 33-node system and a practical 445-node system are conducted to validate the effectiveness of the proposed method.