The integration of high-level renewable energy, increased load demand, and the aging of the transmission network have all exacerbated the congestion of the transmission system. This requires transmission service providers to make full use of the existing transmission infrastructure by using cost-effective new transmission technologies, thereby taking full advantage of the inherent flexibility of the system. Dynamic thermal rating (DTR) is one of several technologies that can help address challenges with transmission operation, especially congestion management. Optimal transmission switching (OTS) and DTR technologies together provide flexible methods to enhance the performance of the power system and maximize the use of existing transmission system. In this study, a probabilistic multi-objective based congestion management procedure is proposed using OTS strategies considering the maximization of system reliability and minimization of the total generation cost. Additionally, the prevention of islanding is considered to ensure the feasibility of transmission switching status. The uncertainties associated with load demand and wind power generation are handled in this study using the points estimation method (PEM). The formulated optimization problem is solved using the multi-objective teacher learning based optimization (MOTLBO) algorithm. As the studied problem is a nonlinear constrained multi-objective optimization problem with conflicting objectives, the Pareto-optimality is used to find the set of optimal solutions. The best compromise solution among the Pareto set is chosen by a fuzzy decision-making approach. Several case studies are simulated on the modified IEEE RTS-96 system. The simulation results show that transmission system reconfiguration while considering the DTR leads to the enhancement of the system performance as compared to static line rating (SLR). For example, the generation cost has dropped by 6.78% when utilizing the DTR as compared to the SLR.

高水平可再生能源的并网、负荷需求的增加、输电网络的老化，都加剧了输电系统的拥堵。这就要求传输服务提供商通过使用具有成本效益的新传输技术，充分利用现有传输基础设施，从而充分利用系统固有的灵活性。动态热额定值 (DTR) 是可以帮助解决输电运营挑战的几种技术之一，尤其是拥塞管理。最优传输切换 (OTS) 和 DTR 技术共同提供了增强电力系统性能和最大限度利用现有传输系统的灵活方法。在这项研究中，考虑到系统可靠性的最大化和总发电成本的最小化，使用OTS策略提出了一种基于概率多目标的拥塞管理程序。此外，还考虑防止孤岛，以确保传输切换状态的可行性。本研究使用点估计法 (PEM) 处理与负载需求和风力发电相关的不确定性。使用基于多目标教师学习的优化 (MOTLBO) 算法来解决公式化的优化问题。由于所研究的问题是具有冲突目标的非线性约束多目标优化问题，因此使用帕累托最优来寻找最优解集。通过模糊决策方法选择帕累托集合中的最佳折衷解决方案。在修改后的 IEEE RTS-96 系统上模拟了几个案例研究。仿真结果表明，与静态线路额定值 (SLR) 相比，考虑 DTR 的传输系统重新配置可提高系统性能。例如，与 SLR 相比，使用 DTR 时的发电成本下降了 6.78%。