标题： 一种多目标传输切换的深度强化学习方法 显示英文标题

标题： A Deep Reinforcement Learning Method for Multi-objective Transmission Switching

摘要： 传输切换是一种广泛采用的方法，主要用于通过战略性的网络重新配置来降低运行成本。 然而，仅专注于成本降低可能会损害系统可靠性。 虽然多目标传输切换可以在节省成本与提高可靠性之间取得平衡，但由于涉及固有的非线性和高计算需求，随着系统规模的增长，可行解变得极其难以获得。 本文提出了一种用于多目标传输切换的深度强化学习（DRL）方法。 该方法结合了基于双路的演员-评论家框架，以评估动作空间中每条线路切换决策的相对影响，这提高了决策质量，并增强了系统可靠性和成本效率。 在IEEE 118节点系统上的数值研究验证了所提方法相对于两种基准DRL算法的有效性和效率。 显示英文摘要

摘要： Transmission switching is a well-established approach primarily applied to minimize operational costs through strategic network reconfiguration. However, exclusive focus on cost reduction can compromise system reliability. While multi-objective transmission switching can balance cost savings with reliability improvements, feasible solutions become exceedingly difficult to obtain as system scale grows, due to the inherent nonlinearity and high computational demands involved. This paper proposes a deep reinforcement learning (DRL) method for multi-objective transmission switching. The method incorporates a dueling-based actor-critic framework to evaluate the relative impact of each line switching decision within the action space, which improves decision quality and enhances both system reliability and cost efficiency. Numerical studies on the IEEE 118-bus system verify the effectiveness and efficiency of the proposed approach compared to two benchmark DRL algorithms.